From 71a59ecb7986dffc21932041889dd913bfe52066 Mon Sep 17 00:00:00 2001 From: Pradeep Dasigi Date: Thu, 25 Sep 2025 23:01:27 -0700 Subject: [PATCH 001/166] [Olmo 3] olmo3 tool parser and tests Signed-off-by: Pradeep Dasigi --- .../tool_parsers/test_olmo3_tool_parser.py | 199 +++++++++++ .../openai/tool_parsers/__init__.py | 2 + .../openai/tool_parsers/olmo3_tool_parser.py | 338 ++++++++++++++++++ 3 files changed, 539 insertions(+) create mode 100644 tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py create mode 100644 vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py diff --git a/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py b/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py new file mode 100644 index 000000000000..56394ace5488 --- /dev/null +++ b/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py @@ -0,0 +1,199 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +from unittest.mock import MagicMock, patch + +import pytest + +from tests.entrypoints.openai.tool_parsers.utils import ( + run_tool_extraction, run_tool_extraction_streaming) +from vllm.entrypoints.openai.protocol import FunctionCall +from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager + +# https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/text_prompt_format.md#model-response-format-1 +SIMPLE_FUNCTION_OUTPUT = "get_weather(city='San Francisco', metric='celsius')" +SIMPLE_FUNCTION_CALL = FunctionCall( + name="get_weather", + arguments='{"city": "San Francisco", "metric": "celsius"}', +) +MORE_TYPES_FUNCTION_OUTPUT = ( + "register_user(name='John Doe', " + "age=37, " + "address={'city': 'San Francisco', 'state': 'CA'}, " + "role=None, " + "passed_test=True, " + "aliases=['John', 'Johnny'])") +MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS = ( + "register_user(name='John Doe', " + "age=37, " + "address={'city': 'San Francisco', 'state': 'CA'}, " + "role=null, " + "passed_test=true, " + "aliases=['John', 'Johnny'])") +MORE_TYPES_FUNCTION_CALL = FunctionCall( + name="register_user", + arguments='{"name": "John Doe", ' + '"age": 37, ' + '"address": {"city": "San Francisco", "state": "CA"}, ' + '"role": null, ' + '"passed_test": true, ' + '"aliases": ["John", "Johnny"]}', +) +PARAMETERLESS_FUNCTION_OUTPUT = "get_weather()" +PARAMETERLESS_FUNCTION_CALL = FunctionCall( + name="get_weather", + arguments='{}', +) +EMPTY_DICT_FUNCTION_OUTPUT = "do_something_cool(additional_data={})" +EMPTY_DICT_FUNCTION_CALL = FunctionCall( + name="do_something_cool", + arguments='{"additional_data": {}}', +) +EMPTY_LIST_FUNCTION_OUTPUT = "do_something_cool(steps=[])" +EMPTY_LIST_FUNCTION_CALL = FunctionCall( + name="do_something_cool", + arguments='{"steps": []}', +) +ESCAPED_STRING_FUNCTION_OUTPUT = ( + r"get_weather(city='Martha\'s Vineyard', metric='\"cool units\"')") +ESCAPED_STRING_FUNCTION_CALL = FunctionCall( + name="get_weather", + arguments='{"city": "Martha\'s Vineyard", "metric": "\\"cool units\\""}', +) + + +@pytest.mark.parametrize("streaming", [True, False]) +def test_no_tool_call(streaming: bool): + mock_tokenizer = MagicMock() + tool_parser: ToolParser = ToolParserManager.get_tool_parser("olmo3")( + mock_tokenizer) + model_output = "How can I help you today?" + + content, tool_calls = run_tool_extraction(tool_parser, + model_output, + streaming=streaming) + + assert content == model_output + assert len(tool_calls) == 0 + + +TEST_CASES = [ + pytest.param(True, + f"{SIMPLE_FUNCTION_OUTPUT}", [SIMPLE_FUNCTION_CALL], + id="simple_streaming"), + pytest.param(False, + f"{SIMPLE_FUNCTION_OUTPUT}", [SIMPLE_FUNCTION_CALL], + id="simple_nonstreaming"), + pytest.param(True, + f"{MORE_TYPES_FUNCTION_OUTPUT}", [MORE_TYPES_FUNCTION_CALL], + id="more_types_streaming"), + pytest.param(False, + f"{MORE_TYPES_FUNCTION_OUTPUT}", [MORE_TYPES_FUNCTION_CALL], + id="more_types_nonstreaming"), + pytest.param(True, + f"{MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS}", [MORE_TYPES_FUNCTION_CALL], + id="more_types_streaming_json_literals"), + pytest.param(False, + f"{MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS}", [MORE_TYPES_FUNCTION_CALL], + id="more_types_nonstreaming_json_literals"), + pytest.param(True, + f"{PARAMETERLESS_FUNCTION_OUTPUT}", + [PARAMETERLESS_FUNCTION_CALL], + id="parameterless_streaming"), + pytest.param(False, + f"{PARAMETERLESS_FUNCTION_OUTPUT}", + [PARAMETERLESS_FUNCTION_CALL], + id="parameterless_nonstreaming"), + pytest.param(True, + f"{EMPTY_DICT_FUNCTION_OUTPUT}", [EMPTY_DICT_FUNCTION_CALL], + id="empty_dict_streaming"), + pytest.param(False, + f"{EMPTY_DICT_FUNCTION_OUTPUT}", [EMPTY_DICT_FUNCTION_CALL], + id="empty_dict_nonstreaming"), + pytest.param(True, + f"{EMPTY_LIST_FUNCTION_OUTPUT}", [EMPTY_LIST_FUNCTION_CALL], + id="empty_list_streaming"), + pytest.param(False, + f"{EMPTY_LIST_FUNCTION_OUTPUT}", [EMPTY_LIST_FUNCTION_CALL], + id="empty_list_nonstreaming"), + pytest.param(True, + f"{ESCAPED_STRING_FUNCTION_OUTPUT}", + [ESCAPED_STRING_FUNCTION_CALL], + id="escaped_string_streaming"), + pytest.param(False, + f"{ESCAPED_STRING_FUNCTION_OUTPUT}", + [ESCAPED_STRING_FUNCTION_CALL], + id="escaped_string_nonstreaming"), + pytest.param(True, + f"{SIMPLE_FUNCTION_OUTPUT}\n{MORE_TYPES_FUNCTION_OUTPUT}", + [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL], + id="parallel_calls_streaming"), + pytest.param(False, + f"{SIMPLE_FUNCTION_OUTPUT}\n{MORE_TYPES_FUNCTION_OUTPUT}", + [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL], + id="parallel_calls_nonstreaming"), +] + + +@pytest.mark.parametrize("streaming, model_output, expected_tool_calls", + TEST_CASES) +def test_tool_call(streaming: bool, model_output: str, + expected_tool_calls: list[FunctionCall]): + mock_tokenizer = MagicMock() + tool_parser: ToolParser = ToolParserManager.get_tool_parser("olmo3")( + mock_tokenizer) + + content, tool_calls = run_tool_extraction(tool_parser, + model_output, + streaming=streaming) + + assert content is None + assert len(tool_calls) == len(expected_tool_calls) + for actual, expected in zip(tool_calls, expected_tool_calls): + assert actual.type == "function" + assert actual.function == expected + + +def test_streaming_tool_call_with_large_steps(): + mock_tokenizer = MagicMock() + tool_parser: ToolParser = ToolParserManager.get_tool_parser("olmo3")( + mock_tokenizer) + model_output_deltas = [ + "get_weather(city='San", + " Francisco', metric='celsius')\n" + f"{PARAMETERLESS_FUNCTION_OUTPUT}\n" + f"{EMPTY_LIST_FUNCTION_OUTPUT}", + ] + + reconstructor = run_tool_extraction_streaming( + tool_parser, model_output_deltas, assert_one_tool_per_delta=False) + + assert reconstructor.other_content == "" + assert len(reconstructor.tool_calls) == 3 + assert reconstructor.tool_calls[0].function == SIMPLE_FUNCTION_CALL + assert reconstructor.tool_calls[1].function == PARAMETERLESS_FUNCTION_CALL + assert reconstructor.tool_calls[2].function == EMPTY_LIST_FUNCTION_CALL + + +@pytest.mark.parametrize("streaming", [False]) +def test_regex_timeout_handling(streaming: bool): + """test regex timeout is handled gracefully""" + mock_tokenizer = MagicMock() + tool_parser: ToolParser = ToolParserManager.get_tool_parser( + "olmo3")(mock_tokenizer) + + fake_problematic_input = "hello world[A(A=" + "\t)A(A=,\t" * 2 + + # create a mock regex that raises TimeoutError + mock_regex = MagicMock() + mock_regex.match.side_effect = TimeoutError("Regex timeout") + + with patch.object(tool_parser, 'TOOL_CALL_REGEX', mock_regex): + content, tool_calls = run_tool_extraction(tool_parser, + fake_problematic_input, + streaming=streaming) + + # should treat as regular text when regex times out + assert content == fake_problematic_input + assert len(tool_calls) == 0 + mock_regex.match.assert_called_once() diff --git a/vllm/entrypoints/openai/tool_parsers/__init__.py b/vllm/entrypoints/openai/tool_parsers/__init__.py index 5e77c406b8d9..1d02ab0144d3 100644 --- a/vllm/entrypoints/openai/tool_parsers/__init__.py +++ b/vllm/entrypoints/openai/tool_parsers/__init__.py @@ -16,6 +16,7 @@ from .llama_tool_parser import Llama3JsonToolParser from .minimax_tool_parser import MinimaxToolParser from .mistral_tool_parser import MistralToolParser +from .olmo3_tool_parser import Olmo3PythonicToolParser from .openai_tool_parser import OpenAIToolParser from .phi4mini_tool_parser import Phi4MiniJsonToolParser from .pythonic_tool_parser import PythonicToolParser @@ -50,4 +51,5 @@ "SeedOssToolParser", "Step3ToolParser", "OpenAIToolParser", + "Olmo3PythonicToolParser", ] diff --git a/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py new file mode 100644 index 000000000000..41ee7ef5cf7a --- /dev/null +++ b/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py @@ -0,0 +1,338 @@ +import ast +import json +from collections.abc import Sequence +from typing import Any, Union + +import regex as re +from transformers import PreTrainedTokenizerBase + +import vllm.envs as envs +from vllm.entrypoints.openai.protocol import (ChatCompletionRequest, + DeltaFunctionCall, DeltaMessage, + DeltaToolCall, + ExtractedToolCallInformation, + FunctionCall, ToolCall) +from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import ( + ToolParser, ToolParserManager) +from vllm.entrypoints.openai.tool_parsers.pythonic_tool_parser import PythonicToolParser +from vllm.logger import init_logger + +logger = init_logger(__name__) + + +class _UnexpectedAstError(Exception): + pass + + +@ToolParserManager.register_module("olmo3") +class Olmo3PythonicToolParser(ToolParser): + """ + Tool call parser for Olmo 3 models that produce tool calls as newline-separated pythonic strings. + Used when --enable-auto-tool-choice --tool-call-parser pythonic are all set + Code copied from vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py and updated to + - support newline separated pythonic tool calls. + - handle the argument values being null/true/false instead of Pythonic literals. + """ + # TODO(mdepinet): Possible future improvements: + # 1. Support text + tools separated by either <|python_tag|> or \n\n + # 2. Support tools outside of a list (or separated by a semicolon). + # This depends on item 1 for consistent streaming. + # Neither of these are necessary for e.g. ToolACE, but both would help make + # Llama3.2 models more reliable. + + TOOL_CALL_REGEX = re.compile( + r"\[([a-zA-Z]+\w*\(([a-zA-Z]+\w*=.*,\s*)*([a-zA-Z]+\w*=.*\s)?\),\s*)*([a-zA-Z]+\w*\(([a-zA-Z]+\w*=.*,\s*)*([a-zA-Z]+\w*=.*\s*)?\)\s*)+\]", + re.DOTALL) + + def __init__(self, tokenizer: PreTrainedTokenizerBase): + super().__init__(tokenizer) + + # Rename for readability. This is NOT a tool id. + @property + def current_tool_index(self) -> int: + return self.current_tool_id + + @current_tool_index.setter + def current_tool_index(self, value: int) -> None: + self.current_tool_id = value + + def extract_tool_calls( + self, model_output: str, + request: ChatCompletionRequest) -> ExtractedToolCallInformation: + """ + Extract the tool calls from a complete model response. + """ + original_model_output = model_output + # Remove xml tags. + model_output = ( + model_output.replace("", "").replace("", "").strip() + ) + # Make the newline separated function calls into a list. + model_output = ", ".join( + [line.strip() for line in model_output.splitlines() if line.strip()] + ) + model_output = f"[{model_output}]" + + is_tool_call_pattern = False + try: + is_tool_call_pattern = self.TOOL_CALL_REGEX.match( + model_output, + timeout=envs.VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS) is not None + except TimeoutError: + logger.warning( + "Regex timeout occurred when matching tool call pattern.") + logger.debug("Regex timeout occurred when matching user input: %s", + model_output) + + if not is_tool_call_pattern: + return ExtractedToolCallInformation(tools_called=False, + tool_calls=[], + content=original_model_output) + + try: + module = ast.parse(model_output) + parsed = getattr(module.body[0], "value", None) + if isinstance(parsed, ast.List) and all( + isinstance(e, ast.Call) for e in parsed.elts): + return ExtractedToolCallInformation( + tools_called=True, + tool_calls=[ + _handle_single_tool(e) # type: ignore + for e in parsed.elts + ], + content=None) + else: + raise _UnexpectedAstError( + "Tool output must be a list of function calls") + except Exception: + logger.exception("Error in extracting tool call from response.") + # Treat as regular text + return ExtractedToolCallInformation(tools_called=False, + tool_calls=[], + content=original_model_output) + + def extract_tool_calls_streaming( + self, + previous_text: str, + current_text: str, + delta_text: str, + previous_token_ids: Sequence[int], + current_token_ids: Sequence[int], + delta_token_ids: Sequence[int], + request: ChatCompletionRequest, + ) -> Union[DeltaMessage, None]: + # All function calls start with the tag. But since this is a streaming parser, we may have seen only part of the tag. + if not current_text.startswith("<"): + return DeltaMessage(content=delta_text) + + try: + # Remove xml tags. + if current_text.startswith(""): + current_text = current_text[len(""):] + if current_text.endswith(""): + current_text = current_text[:current_text. + rfind("")] + + valid_and_added_text = _make_valid_python(current_text) + if valid_and_added_text is None: + return None + valid_text, added_text = valid_and_added_text + + # Make the newline separated function calls into a list. + valid_text = ", ".join( + [line.strip() for line in valid_text.splitlines() if line.strip()] + ) + valid_text = f"[{valid_text}]" + module = ast.parse(valid_text) + parsed = getattr(module.body[0], "value", None) + if not isinstance(parsed, ast.List) or not all( + isinstance(e, ast.Call) for e in parsed.elts): + raise _UnexpectedAstError( + "Tool output must be a sequence of newline-separated function calls") + tool_calls = [ + _handle_single_tool(e) # type: ignore + for e in parsed.elts + ] + + tool_deltas = [] + for index, new_call in enumerate(tool_calls): + if index < self.current_tool_index: + continue + + self.current_tool_index = index + if len(self.streamed_args_for_tool) == index: + self.streamed_args_for_tool.append("") + + new_call_complete = index < len( + tool_calls) - 1 or ")" not in added_text + if new_call_complete: + self.current_tool_index += 1 + + withheld_suffix = (added_text[:-1] + if not new_call_complete else "") + if not new_call_complete and added_text[-1] == ")": + # Function call is incomplete. Withhold the closing bracket. + withheld_suffix = withheld_suffix + "}" + # Strings get single quotes in the model-produced string. + # JSON requires double quotes. + withheld_suffix = withheld_suffix.replace("'", '"') + delta = _compute_tool_delta(self.streamed_args_for_tool[index], + new_call, index, withheld_suffix) + + if delta is not None: + tool_deltas.append(delta) + if (delta.function is not None + and delta.function.arguments is not None): + self.streamed_args_for_tool[ + index] += delta.function.arguments + + # HACK: serving_chat.py inspects the internal state of tool parsers + # when determining its final streaming delta, automatically + # adding autocompleted JSON. + # These two lines avoid that nonsense while ensuring finish_reason + # is set to tool_calls when at least one tool is called. + if tool_deltas and not self.prev_tool_call_arr: + self.prev_tool_call_arr = [{"arguments": {}}] + + if tool_deltas: + return DeltaMessage(tool_calls=tool_deltas) + elif not added_text and self.current_tool_id > 0: + # Return an empty DeltaMessage once the tool calls are all done + # so that finish_reason gets set. + return DeltaMessage(content='') + else: + return None + except Exception: + logger.exception("Error trying to handle streaming tool call.") + logger.debug( + "Skipping chunk as a result of tool streaming extraction " + "error") + return None + + +def _get_parameter_value(val: ast.expr) -> Any: + if isinstance(val, ast.Constant): + return val.value + elif isinstance(val, ast.Dict): + if not all(isinstance(k, ast.Constant) for k in val.keys): + raise _UnexpectedAstError( + "Dict tool call arguments must have literal keys") + return { + k.value: _get_parameter_value(v) # type: ignore + for k, v in zip(val.keys, val.values) + } + elif isinstance(val, ast.List): + return [_get_parameter_value(v) for v in val.elts] + # The model may return function calls where the values are null/true/false because the system prompt has API description in json. + elif isinstance(val, ast.Name) and val.id in ["null", "true", "false"]: + if val.id == "null": + return None + elif val.id == "true": + return True + elif val.id == "false": + return False + else: + raise _UnexpectedAstError("Tool call arguments must be literals") + + +def _handle_single_tool(call: ast.Call) -> ToolCall: + if not isinstance(call.func, ast.Name): + raise _UnexpectedAstError("Invalid tool call name") + function_name = call.func.id + arguments = {} + for keyword in call.keywords: + arguments[keyword.arg] = _get_parameter_value(keyword.value) + return ToolCall( + type="function", + function=FunctionCall(name=function_name, + arguments=json.dumps(arguments, + ensure_ascii=False)), + ) + + +def _make_valid_python(text: str) -> Union[tuple[str, str], None]: + bracket_stack = [] + for index, char in enumerate(text): + if char in {"[", "(", "{"}: + bracket_stack.append(char) + elif char == "]": + if not bracket_stack or bracket_stack.pop() != "[": + raise _UnexpectedAstError("Mismatched square brackets") + elif char == ")": + if not bracket_stack or bracket_stack.pop() != "(": + raise _UnexpectedAstError("Mismatched parentheses") + elif char == "}": + if not bracket_stack or bracket_stack.pop() != "{": + raise _UnexpectedAstError("Mismatched curly braces") + elif char in {"'", '"'}: + if bracket_stack and bracket_stack[-1] == char: + if index > 0 and text[index - 1] == "\\": + # Treat an escaped quote as a regular character + pass + else: + bracket_stack.pop() + elif bracket_stack and bracket_stack[-1] in {"'", '"'}: + # Double quote within a single quote string or vice versa. + pass + else: + bracket_stack.append(char) + + text = text.rstrip() + if text.endswith("=") or text.endswith(":"): + # Since we have no type information for this property/parameter value, + # we can't fill in a valid value. + return None + if bracket_stack and bracket_stack[-1] == "{": + trailing_dict_text = text[:text.rfind("{")] + num_keys = trailing_dict_text.count(":") + num_values = trailing_dict_text.count(",") + if num_keys <= num_values: + return None # Incomplete property name within parameter value + if bracket_stack and bracket_stack[-1] == "(": + trailing_params_text = text[:text.rfind("(")] + num_full_param_names = trailing_params_text.count("=") + num_full_param_values = trailing_params_text.count(",") + if num_full_param_names <= num_full_param_values: + return None # Incomplete parameter name + if text.endswith(","): + text = text[:-1] + if bracket_stack and bracket_stack[-1] == "[" and not text.endswith( + "[") and not text.endswith(")"): + return None # Incomplete function name + + added_text = "" + for char in reversed(bracket_stack): + if char == "[": + added_text += "]" + elif char == "(": + added_text += ")" + elif char == "{": + added_text += "}" + elif char == "'": + added_text += "'" + elif char == '"': + added_text += '"' + + return text + added_text, added_text + + +def _compute_tool_delta(previously_sent_args: str, new_call: ToolCall, + index: int, + withheld_suffix: str) -> Union[DeltaToolCall, None]: + new_call_args = new_call.function.arguments + if withheld_suffix: + assert new_call_args.endswith(withheld_suffix) + new_call_args = new_call_args[:-len(withheld_suffix)] + if not previously_sent_args: + return DeltaToolCall(id=new_call.id, + type="function", + index=index, + function=DeltaFunctionCall( + name=new_call.function.name, + arguments=new_call_args, + )) + + arg_diff = new_call_args[len(previously_sent_args):] + return DeltaToolCall( + id=None, index=index, function=DeltaFunctionCall( + arguments=arg_diff)) if arg_diff else None From e046165a596c1ef7c82a62aa3116faed50aad006 Mon Sep 17 00:00:00 2001 From: yitingdc <59356937+yitingdc@users.noreply.github.com> Date: Fri, 26 Sep 2025 16:18:02 +0800 Subject: [PATCH 002/166] [CI/Build] fix doc build warning: Failed to get 'name: description' pair (#25733) Signed-off-by: yiting.jiang --- vllm/v1/spec_decode/ngram_proposer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vllm/v1/spec_decode/ngram_proposer.py b/vllm/v1/spec_decode/ngram_proposer.py index fd8e0a6fd1d2..aed050a3540c 100644 --- a/vllm/v1/spec_decode/ngram_proposer.py +++ b/vllm/v1/spec_decode/ngram_proposer.py @@ -71,8 +71,8 @@ def batch_propose( Args: valid_ngram_requests: Set of indices of requests that need ngram proposals. - num_tokens_no_spec: - Numpy array of shape (batch_size,) representing the number + num_tokens_no_spec: + Numpy array of shape (batch_size,) representing the number of tokens without speculative tokens for each request. token_ids_cpu: Numpy array of shape (batch_size, max_model_len) From 64107450c238b6fd189397630543c297e9788586 Mon Sep 17 00:00:00 2001 From: Andrew Sansom Date: Fri, 26 Sep 2025 03:18:05 -0500 Subject: [PATCH 003/166] fix: revert cast to cpu in `MsgpackEncoder._encode_tensor` to avoid hidden performance regressions (#25738) Signed-off-by: Andrew Sansom --- vllm/inputs/preprocess.py | 5 +++++ vllm/v1/serial_utils.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/vllm/inputs/preprocess.py b/vllm/inputs/preprocess.py index a24307b79d95..7518cd8fc897 100644 --- a/vllm/inputs/preprocess.py +++ b/vllm/inputs/preprocess.py @@ -278,6 +278,11 @@ def _process_embeds( raise ValueError( "prompt_embeds must be of shape (seq_len, hidden_size).") + # Tensors must be on CPU for serialization between processes + # in the MsgpackEncoder. Casting to CPU here ensures that there is no + # hidden device transfer in the critical path of generation. + prompt_embeds = prompt_embeds.cpu() + return embeds_inputs(prompt_embeds=prompt_embeds, cache_salt=parsed_content.get("cache_salt")) diff --git a/vllm/v1/serial_utils.py b/vllm/v1/serial_utils.py index c812a2ec6427..876838084b9a 100644 --- a/vllm/v1/serial_utils.py +++ b/vllm/v1/serial_utils.py @@ -208,7 +208,7 @@ def _encode_tensor( ) -> tuple[str, tuple[int, ...], Union[int, memoryview]]: assert self.aux_buffers is not None # view the tensor as a contiguous 1D array of bytes - arr = obj.flatten().contiguous().cpu().view(torch.uint8).numpy() + arr = obj.flatten().contiguous().view(torch.uint8).numpy() if obj.nbytes < self.size_threshold: # Smaller tensors are encoded inline, just like ndarrays. data = msgpack.Ext(CUSTOM_TYPE_RAW_VIEW, arr.data) From 8bafbfe56206327a096cd64289ba05accfa1f807 Mon Sep 17 00:00:00 2001 From: Andrew Sansom Date: Fri, 26 Sep 2025 03:18:09 -0500 Subject: [PATCH 004/166] perf: Avoid copying inputs_embeds tensors to GPU unless prompt_embeds is enabled (#25739) Signed-off-by: Andrew Sansom --- vllm/v1/worker/gpu_model_runner.py | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index dca6feded12e..a1969463cbfb 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -836,8 +836,9 @@ def _prepare_input_ids(self, total_num_scheduled_tokens: int, if self.input_batch.prev_sampled_token_ids is None: # Normal scheduling case self.input_ids.copy_to_gpu(total_num_scheduled_tokens) - self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens) - self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens) + if self.enable_prompt_embeds: + self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens) + self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens) return # Async scheduling case, where some decode requests from the previous @@ -863,8 +864,9 @@ def _prepare_input_ids(self, total_num_scheduled_tokens: int, # If not all requests are decodes from the last iteration, # We need to copy the input_ids_cpu to the GPU first. self.input_ids.copy_to_gpu(total_num_scheduled_tokens) - self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens) - self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens) + if self.enable_prompt_embeds: + self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens) + self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens) if num_commmon_tokens == 0: # No requests in common with the previous iteration # So input_ids_cpu will have all the input ids. @@ -878,7 +880,8 @@ def _prepare_input_ids(self, total_num_scheduled_tokens: int, self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0], non_blocking=True) - self.is_token_ids.gpu[:num_commmon_tokens] = True + if self.enable_prompt_embeds: + self.is_token_ids.gpu[:num_commmon_tokens] = True return # Upload the index tensors asynchronously # so the scatter can be non-blocking. @@ -978,12 +981,13 @@ def _prepare_inputs( 0, token_indices_tensor, out=self.input_ids.cpu[:total_num_scheduled_tokens]) - is_token_ids = self.input_batch.is_token_ids.flatten() - torch.index_select( - is_token_ids, - 0, - token_indices_tensor, - out=self.is_token_ids.cpu[:total_num_scheduled_tokens]) + if self.enable_prompt_embeds: + is_token_ids = self.input_batch.is_token_ids.flatten() + torch.index_select( + is_token_ids, + 0, + token_indices_tensor, + out=self.is_token_ids.cpu[:total_num_scheduled_tokens]) # Because we did not pre-allocate a massive prompt_embeds CPU tensor on # the InputBatch, we need to fill in the prompt embeds into the expected From c6f880fcc6d2b93c578ed4b60b52084777662030 Mon Sep 17 00:00:00 2001 From: xaguilar-amd Date: Fri, 26 Sep 2025 10:18:20 +0200 Subject: [PATCH 005/166] [Harware][AMD][Model] Triton MoE tuning configs for GLM-4.5 for MI300X (#25703) Signed-off-by: xaguilar --- ...me=AMD_Instinct_MI300X,dtype=fp8_w8a8.json | 164 ++++++++++++++++++ 1 file changed, 164 insertions(+) create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=160,N=384,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8.json diff --git a/vllm/model_executor/layers/fused_moe/configs/E=160,N=384,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8.json b/vllm/model_executor/layers/fused_moe/configs/E=160,N=384,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8.json new file mode 100644 index 000000000000..8239492d8f4f --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=160,N=384,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8.json @@ -0,0 +1,164 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 2, + "waves_per_eu": 0 + }, + "2": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 16, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "4": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2, + "waves_per_eu": 0 + }, + "8": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + "num_warps": 4, + "num_stages": 2, + "waves_per_eu": 0 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 4, + "num_warps": 1, + "num_stages": 2, + "waves_per_eu": 0 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 2, + "waves_per_eu": 0 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "128": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "256": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "512": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "1024": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 8, + "num_warps": 4, + "num_stages": 2, + "waves_per_eu": 0 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + "num_warps": 8, + "num_stages": 2, + "waves_per_eu": 0 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + "num_warps": 8, + "num_stages": 2, + "waves_per_eu": 0 + }, + "3072": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 4, + "num_warps": 2, + "num_stages": 2, + "waves_per_eu": 0 + }, + "4096": { + "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 8, + "num_stages": 2, + "waves_per_eu": 0 + } +} From 95ecee9a7b0e5ce09bef43a56e3dfb0e9f40c075 Mon Sep 17 00:00:00 2001 From: Iceber Gu Date: Fri, 26 Sep 2025 16:18:24 +0800 Subject: [PATCH 006/166] fix: print outputt offline_inference/base/chat.py example (#25744) Signed-off-by: Iceber Gu --- examples/offline_inference/basic/chat.py | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/offline_inference/basic/chat.py b/examples/offline_inference/basic/chat.py index d078c517d00e..9e7036fea613 100644 --- a/examples/offline_inference/basic/chat.py +++ b/examples/offline_inference/basic/chat.py @@ -87,6 +87,7 @@ def print_outputs(outputs): use_tqdm=False, chat_template=chat_template, ) + print_outputs(outputs) if __name__ == "__main__": From 4c31bdefd398cc452ada8406b8dd064a28964331 Mon Sep 17 00:00:00 2001 From: Tao He Date: Fri, 26 Sep 2025 16:18:58 +0800 Subject: [PATCH 007/166] [Qwen3-Next][GDN] fixes cuda graph capturing bug in GDN metadata and a stride bug in causal_conv_1d. (#25743) Signed-off-by: Tao He --- .../layers/mamba/ops/causal_conv1d.py | 11 +++- vllm/v1/attention/backends/gdn_attn.py | 61 ++++++++----------- vllm/v1/worker/gpu_model_runner.py | 23 ++++--- 3 files changed, 50 insertions(+), 45 deletions(-) diff --git a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py index 010fcdda156c..5e5011fa2ac5 100644 --- a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py +++ b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py @@ -41,6 +41,7 @@ def _causal_conv1d_fwd_kernel( # continuous batching stride_istate_seq: tl.constexpr, stride_istate_dim: tl.constexpr, stride_istate_token: tl.constexpr, + stride_cache_indices: tl.constexpr, stride_o_seq: tl.constexpr, stride_o_dim: tl.constexpr, stride_o_token: tl.constexpr, @@ -69,7 +70,7 @@ def _causal_conv1d_fwd_kernel( # continuous batching # rather than mixing sequences - to make updating initial_states across sequences efficiently # single-sequence id - idx_seq = tl.load(batch_ptr + tl.program_id(0)) + idx_seq = tl.load(batch_ptr + tl.program_id(0)).to(tl.int64) chunk_offset = tl.load(token_chunk_offset_ptr + tl.program_id(0)) # BLOCK_N elements along the feature-dimension (channel) @@ -91,8 +92,9 @@ def _causal_conv1d_fwd_kernel( # continuous batching if IS_CONTINUOUS_BATCHING: # cache_idx - conv_state_batch_coord = tl.load(conv_state_indices_ptr + idx_seq).to( - tl.int64) + conv_state_batch_coord = tl.load(conv_state_indices_ptr + + idx_seq * stride_cache_indices).to( + tl.int64) else: # cache_idx conv_state_batch_coord = idx_seq @@ -480,6 +482,8 @@ def causal_conv1d_fn( stride_o_seq = out.stride(0) stride_o_dim = out.stride(1) stride_o_token = out.stride(2) + stride_cache_indices = cache_indices.stride( + 0) if cache_indices is not None else 0 if validate_data: assert x.dim() == 2 @@ -595,6 +599,7 @@ def grid(META): stride_istate_seq, stride_istate_dim, stride_istate_token, + stride_cache_indices, stride_o_seq, stride_o_dim, stride_o_token, diff --git a/vllm/v1/attention/backends/gdn_attn.py b/vllm/v1/attention/backends/gdn_attn.py index 843958bc79de..11f165d6cfc6 100644 --- a/vllm/v1/attention/backends/gdn_attn.py +++ b/vllm/v1/attention/backends/gdn_attn.py @@ -125,7 +125,7 @@ def build( # type: ignore[override] common_prefix_len: int, common_attn_metadata: CommonAttentionMetadata, num_accepted_tokens: Optional[torch.Tensor] = None, - num_draft_tokens: Optional[torch.Tensor] = None, + num_decode_draft_tokens_cpu: Optional[torch.Tensor] = None, fast_build: bool = False, ) -> GDNAttentionMetadata: m = common_attn_metadata @@ -133,23 +133,25 @@ def build( # type: ignore[override] query_start_loc = m.query_start_loc context_lens = m.num_computed_tokens_cpu context_lens_tensor = context_lens.to(query_start_loc.device) - seq_lens_tensor = m.seq_lens nums_dict, batch_ptr, token_chunk_offset_ptr = None, None, None - if (not self.use_spec_decode or num_draft_tokens is None - or num_draft_tokens.sum().item() == 0): + if (not self.use_spec_decode or num_decode_draft_tokens_cpu is None + or num_decode_draft_tokens_cpu[num_decode_draft_tokens_cpu >= + 0].sum().item() == 0): spec_sequence_masks = None + num_spec_decodes = 0 else: - spec_sequence_masks = (num_draft_tokens > 0) & ( - context_lens_tensor + - (num_draft_tokens + 1) == seq_lens_tensor) - if spec_sequence_masks.sum().item() == 0: + spec_sequence_masks = num_decode_draft_tokens_cpu >= 0 + num_spec_decodes = spec_sequence_masks.sum().item() + if num_spec_decodes == 0: spec_sequence_masks = None + else: + spec_sequence_masks = spec_sequence_masks.to( + query_start_loc.device, non_blocking=True) if spec_sequence_masks is None: num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = ( split_decodes_and_prefills(m, decode_threshold=1)) - num_spec_decodes = 0 num_spec_decode_tokens = 0 spec_token_masks = None spec_state_indices_tensor = None @@ -158,7 +160,6 @@ def build( # type: ignore[override] non_spec_query_start_loc = query_start_loc num_accepted_tokens = None else: - num_spec_decodes = spec_sequence_masks.sum().item() query_lens = query_start_loc[1:] - query_start_loc[:-1] non_spec_query_lens = query_lens[~spec_sequence_masks] @@ -314,28 +315,18 @@ def build_for_cudagraph_capture( """ m = common_attn_metadata - assert (m.num_reqs * (self.num_spec + 1) <= m.num_actual_tokens - and ((m.num_reqs + 1) * (self.num_spec + 1) - >= m.num_actual_tokens)), \ - "GDN only supports decode-only full CUDAGraph capture. " \ - "Make sure all cudagraph capture sizes <= max_num_seq." - - num_accepted_tokens = torch.full((m.num_reqs, ), - m.max_query_len, - dtype=torch.int32, - device=m.query_start_loc.device) - num_drafted_tokens = torch.full((m.num_reqs, ), - self.num_spec, - dtype=torch.int32, - device=m.query_start_loc.device) - - # Fixes query-start loc for spec-sequence-indices. - m.query_start_loc = torch.arange(0, - m.num_actual_tokens + 1, - step=m.max_query_len, - device=m.query_start_loc.device, - dtype=torch.int32) - m.num_computed_tokens_cpu = (m.seq_lens_cpu - torch.full( - (m.num_reqs, ), m.max_query_len, dtype=torch.int32, device='cpu')) - - return self.build(0, m, num_accepted_tokens, num_drafted_tokens) + assert ( + m.num_reqs <= self.decode_cudagraph_max_bs + and m.num_actual_tokens <= self.decode_cudagraph_max_bs), ( + f"GDN only supports decode-only full CUDAGraph capture. " + f"Make sure batch size ({m.num_reqs}) <= " + f"cudagraph capture sizes ({self.decode_cudagraph_max_bs}), " + f"and number of tokens ({m.num_actual_tokens}) <= " + f"cudagraph capture sizes ({self.decode_cudagraph_max_bs}).") + + num_accepted_tokens = torch.diff(m.query_start_loc) + num_decode_draft_tokens_cpu = (num_accepted_tokens - 1).cpu() + m.num_computed_tokens_cpu = m.seq_lens_cpu - num_accepted_tokens.cpu() + + return self.build(0, m, num_accepted_tokens, + num_decode_draft_tokens_cpu) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index a1969463cbfb..cbf439aa697b 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -360,8 +360,8 @@ def __init__( dtype=torch.int64) self.num_discarded_requests = 0 - self.num_draft_tokens = self._make_buffer(self.max_num_reqs, - dtype=torch.int32) + self.num_decode_draft_tokens = self._make_buffer(self.max_num_reqs, + dtype=torch.int32) self.num_accepted_tokens = self._make_buffer(self.max_num_reqs, dtype=torch.int64) @@ -1103,17 +1103,25 @@ def _prepare_inputs( # Iterate over the dictionary rather than all requests since not all # requests have draft tokens. num_draft_tokens = np.zeros(num_reqs, dtype=np.int32) + # For chunked prefills, use -1 as mask rather than 0, as guided + # decoding may rollback speculative tokens. + num_decode_draft_tokens = np.full(num_reqs, -1, dtype=np.int32) for req_id, draft_token_ids in ( scheduler_output.scheduled_spec_decode_tokens.items()): req_idx = self.input_batch.req_id_to_index[req_id] num_draft_tokens[req_idx] = len(draft_token_ids) - + num_decode_draft_tokens[req_idx] = (len(draft_token_ids) if ( + self.input_batch.num_computed_tokens_cpu[req_idx] + >= self.input_batch.num_prompt_tokens[req_idx]) else -1) spec_decode_metadata = self._calc_spec_decode_metadata( num_draft_tokens, cu_num_tokens) logits_indices = spec_decode_metadata.logits_indices - self.num_draft_tokens.np[:num_reqs] = num_draft_tokens - self.num_draft_tokens.np[num_reqs:].fill(0) - self.num_draft_tokens.copy_to_gpu() + + # For DECODE only cuda graph of some attention backends (e.g., GDN). + self.num_decode_draft_tokens.np[: + num_reqs] = num_decode_draft_tokens + self.num_decode_draft_tokens.np[num_reqs:].fill(-1) + self.num_decode_draft_tokens.copy_to_gpu() logits_indices_padded = None if self.cache_config.kv_sharing_fast_prefill: @@ -1217,7 +1225,8 @@ def _prepare_inputs( extra_attn_metadata_args = dict( num_accepted_tokens=self.num_accepted_tokens. gpu[:num_reqs], - num_draft_tokens=self.num_draft_tokens.gpu[:num_reqs], + num_decode_draft_tokens_cpu=self. + num_decode_draft_tokens.cpu[:num_reqs], ) if ubatch_slices is not None: From e1b68e0287e277eb6a696493c7eb41bf4d8ea211 Mon Sep 17 00:00:00 2001 From: Icey <1790571317@qq.com> Date: Fri, 26 Sep 2025 16:19:20 +0800 Subject: [PATCH 008/166] Remove cuda hard-code in compute_causal_conv1d_metadata (#25555) Signed-off-by: Icey <1790571317@qq.com> --- vllm/v1/attention/backends/utils.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/vllm/v1/attention/backends/utils.py b/vllm/v1/attention/backends/utils.py index 0c6e0dfefd8a..f37a829f401c 100644 --- a/vllm/v1/attention/backends/utils.py +++ b/vllm/v1/attention/backends/utils.py @@ -947,6 +947,7 @@ def compute_causal_conv1d_metadata(query_start_loc_p: torch.Tensor): nums_dict = {} # type: ignore batch_ptr = None token_chunk_offset_ptr = None + device = query_start_loc_p.device for BLOCK_M in [8]: # cover all BLOCK_M values nums = -(-seqlens // BLOCK_M) nums_dict[BLOCK_M] = {} @@ -968,11 +969,11 @@ def compute_causal_conv1d_metadata(query_start_loc_p: torch.Tensor): batch_ptr = torch.full((MAX_NUM_PROGRAMS, ), PAD_SLOT_ID, dtype=torch.int32, - device='cuda') + device=device) token_chunk_offset_ptr = torch.full((MAX_NUM_PROGRAMS, ), PAD_SLOT_ID, dtype=torch.int32, - device='cuda') + device=device) else: if batch_ptr.nelement() < MAX_NUM_PROGRAMS: batch_ptr.resize_(MAX_NUM_PROGRAMS).fill_(PAD_SLOT_ID) From 6784078cca852c479d6f240e802fc17a2868e07c Mon Sep 17 00:00:00 2001 From: yyzxw <34639446+yyzxw@users.noreply.github.com> Date: Fri, 26 Sep 2025 16:22:06 +0800 Subject: [PATCH 009/166] [misc] refactor speculative config (#25657) Signed-off-by: zxw <1020938856@qq.com> --- vllm/config/speculative.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index 04e2e7d800a1..2e4b3d3a6b20 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -209,12 +209,9 @@ def __post_init__(self): if self.model is None and self.num_speculative_tokens is not None: # TODO(Shangming): Refactor mtp configuration logic when supporting - # mtp acceleration for more models besides deepseek_v3 - if self.target_model_config and \ - (self.target_model_config.hf_text_config.model_type \ - == "deepseek_v3" or - self.target_model_config.hf_text_config.model_type in - ("mimo","ernie4_5_moe", "qwen3_next")): + if (self.target_model_config + and self.target_model_config.hf_text_config.model_type + in ("deepseek_v3", "mimo", "ernie4_5_moe", "qwen3_next")): # use the draft model from the same model: self.model = self.target_model_config.model # Align the quantization of draft model for cases such as @@ -224,8 +221,9 @@ def __post_init__(self): elif self.method in ("ngram", "[ngram]"): self.model = "ngram" else: - raise ValueError("num_speculative_tokens was provided without " - "speculative model.") + raise ValueError( + "num_speculative_tokens was provided but without " + "speculative model.") # Automatically configure the method for ngram when "model" is used # instead of "method" From 9d1dbba5a8318f90e95cbd131abb3336778c9750 Mon Sep 17 00:00:00 2001 From: Sage Moore Date: Fri, 26 Sep 2025 01:25:28 -0700 Subject: [PATCH 010/166] [Bugfix] Fix Shared Expert/Zero expert code in FusedMoE.process_chunk (#25698) Signed-off-by: Sage Moore Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com> --- vllm/model_executor/layers/fused_moe/layer.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py index 92241f50db07..eccae8b2a7af 100644 --- a/vllm/model_executor/layers/fused_moe/layer.py +++ b/vllm/model_executor/layers/fused_moe/layer.py @@ -1925,7 +1925,9 @@ def process_chunk(chunk_start, chunk_end, skip_result_store=False): assert self.shared_experts is None or isinstance( final_hidden_states, tuple) - if isinstance(final_hidden_states, tuple): + if self.zero_expert_num is not None and self.zero_expert_num > 0: + assert isinstance(final_hidden_states, tuple) + assert self.shared_experts is None final_hidden_states, zero_expert_result = final_hidden_states if zero_expert_result is not None: final_hidden_states += zero_expert_result From d35759832a7dc2c94ae2ae9a98abc41919d31d30 Mon Sep 17 00:00:00 2001 From: Xu Wenqing <121550081+Xu-Wenqing@users.noreply.github.com> Date: Fri, 26 Sep 2025 17:25:39 +0800 Subject: [PATCH 011/166] Support LongCat-Flash-Chat tool call (#24083) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 许文卿 --- docs/features/tool_calling.md | 9 +++++ .../openai/tool_parsers/__init__.py | 2 + .../tool_parsers/longcat_tool_parser.py | 39 +++++++++++++++++++ 3 files changed, 50 insertions(+) create mode 100644 vllm/entrypoints/openai/tool_parsers/longcat_tool_parser.py diff --git a/docs/features/tool_calling.md b/docs/features/tool_calling.md index 291c313cd57a..16693db7255c 100644 --- a/docs/features/tool_calling.md +++ b/docs/features/tool_calling.md @@ -310,6 +310,15 @@ Flags: * For non-reasoning: `--tool-call-parser hunyuan_a13b` * For reasoning: `--tool-call-parser hunyuan_a13b --reasoning-parser hunyuan_a13b --enable_reasoning` +### LongCat-Flash-Chat Models (`longcat`) + +Supported models: + +* `meituan-longcat/LongCat-Flash-Chat` +* `meituan-longcat/LongCat-Flash-Chat-FP8` + +Flags: `--tool-call-parser longcat` + ### GLM-4.5 Models (`glm45`) Supported models: diff --git a/vllm/entrypoints/openai/tool_parsers/__init__.py b/vllm/entrypoints/openai/tool_parsers/__init__.py index 1d02ab0144d3..9730d176323d 100644 --- a/vllm/entrypoints/openai/tool_parsers/__init__.py +++ b/vllm/entrypoints/openai/tool_parsers/__init__.py @@ -14,6 +14,7 @@ from .kimi_k2_tool_parser import KimiK2ToolParser from .llama4_pythonic_tool_parser import Llama4PythonicToolParser from .llama_tool_parser import Llama3JsonToolParser +from .longcat_tool_parser import LongcatFlashToolParser from .minimax_tool_parser import MinimaxToolParser from .mistral_tool_parser import MistralToolParser from .olmo3_tool_parser import Olmo3PythonicToolParser @@ -37,6 +38,7 @@ "Llama3JsonToolParser", "JambaToolParser", "Llama4PythonicToolParser", + "LongcatFlashToolParser", "PythonicToolParser", "Phi4MiniJsonToolParser", "DeepSeekV3ToolParser", diff --git a/vllm/entrypoints/openai/tool_parsers/longcat_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/longcat_tool_parser.py new file mode 100644 index 000000000000..87a3fdc44397 --- /dev/null +++ b/vllm/entrypoints/openai/tool_parsers/longcat_tool_parser.py @@ -0,0 +1,39 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import regex as re + +from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import ( + ToolParserManager) +from vllm.entrypoints.openai.tool_parsers.hermes_tool_parser import ( + Hermes2ProToolParser) +from vllm.transformers_utils.tokenizer import AnyTokenizer + + +@ToolParserManager.register_module("longcat") +class LongcatFlashToolParser(Hermes2ProToolParser): + + def __init__(self, tokenizer: AnyTokenizer): + super().__init__(tokenizer) + + self.tool_call_start_token: str = "" + self.tool_call_end_token: str = "" + + self.tool_call_regex = re.compile( + r"(.*?)|(.*)", + re.DOTALL) + + self.tool_call_start_token_ids = self.model_tokenizer.encode( + self.tool_call_start_token, add_special_tokens=False) + self.tool_call_end_token_ids = self.model_tokenizer.encode( + self.tool_call_end_token, add_special_tokens=False) + + self.tool_call_start_token_array = [ + self.model_tokenizer.decode([token_id]) + for token_id in self.tool_call_start_token_ids + ] + + self.tool_call_end_token_array = [ + self.model_tokenizer.decode([token_id]) + for token_id in self.tool_call_end_token_ids + ] From b75c0a34659cec477a2a4447466b80a238a3800b Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Fri, 26 Sep 2025 17:37:40 +0800 Subject: [PATCH 012/166] [Doc] Update Batch-level DP docs (#25757) Signed-off-by: DarkLight1337 --- docs/configuration/optimization.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/docs/configuration/optimization.md b/docs/configuration/optimization.md index 5564d8a81d93..5c74610ebd29 100644 --- a/docs/configuration/optimization.md +++ b/docs/configuration/optimization.md @@ -139,9 +139,9 @@ there is relatively little gain from TP. On the other hand, TP incurs significan overhead because of all-reduce being performed after every layer. Given this, it may be advantageous to instead shard the batched input data using TP, essentially -performing batch-level DP. This has been shown to improve the throughput by around 10% for +performing batch-level DP. This has been shown to improve the throughput and TTFT by around 10% for `tensor_parallel_size=8`. For vision encoders that use hardware-unoptimized Conv3D operations, -batch-level DP can provide another 40% increase to throughput compared to regular TP. +batch-level DP can provide another 40% improvement compared to regular TP. Nevertheless, since the weights of the multi-modal encoder are replicated across each TP rank, there will be a minor increase in memory consumption and may cause OOM if you can barely fit the model already. @@ -172,14 +172,15 @@ Batch-level DP needs to be implemented on a per-model basis, and enabled by setting `supports_encoder_tp_data = True` in the model class. Regardless, you need to set `mm_encoder_tp_mode="data"` in engine arguments to use this feature. -Known supported models: +Known supported models (with corresponding benchmarks): -- GLM-4.5V GLM-4.1V () +- dots_ocr () +- GLM-4.1V or above () - InternVL () - Kimi-VL () - Llama4 () - MiniCPM-V-2.5 or above (, ) -- Qwen2.5-VL () +- Qwen2-VL or above (, , ) - Step3 () ## Input Processing From 38712fef15c512eedfb0dbdaeb050d149f41c3b7 Mon Sep 17 00:00:00 2001 From: Chih-Chieh Yang <7364402+cyang49@users.noreply.github.com> Date: Fri, 26 Sep 2025 07:31:14 -0400 Subject: [PATCH 013/166] [Model] Mamba2 varlen refactor (#21467) Signed-off-by: Chih-Chieh-Yang <7364402+cyang49@users.noreply.github.com> Co-authored-by: RishiAstra <40644327+RishiAstra@users.noreply.github.com> --- tests/kernels/mamba/test_mamba_ssm_ssd.py | 125 ++--- .../layers/mamba/mamba_mixer2.py | 23 +- .../layers/mamba/ops/causal_conv1d.py | 2 +- .../layers/mamba/ops/ssd_bmm.py | 158 +++---- .../layers/mamba/ops/ssd_chunk_scan.py | 395 +++++++--------- .../layers/mamba/ops/ssd_chunk_state.py | 430 ++++++++---------- .../layers/mamba/ops/ssd_combined.py | 156 +++---- .../layers/mamba/ops/ssd_state_passing.py | 268 +++++------ vllm/model_executor/models/plamo2.py | 22 +- vllm/v1/attention/backends/mamba2_attn.py | 9 +- 10 files changed, 723 insertions(+), 865 deletions(-) diff --git a/tests/kernels/mamba/test_mamba_ssm_ssd.py b/tests/kernels/mamba/test_mamba_ssm_ssd.py index fc60d5ac82b2..927af32588e6 100644 --- a/tests/kernels/mamba/test_mamba_ssm_ssd.py +++ b/tests/kernels/mamba/test_mamba_ssm_ssd.py @@ -7,7 +7,7 @@ from einops import rearrange, repeat from vllm.model_executor.layers.mamba.ops.ssd_combined import ( - mamba_chunk_scan_combined) + mamba_chunk_scan_combined_varlen) from vllm.platforms import current_platform from vllm.v1.attention.backends.mamba2_attn import ( _query_start_loc_to_chunk_indices_offsets) @@ -185,9 +185,14 @@ def end_boundary(n: int): IND_S = [x % full_length for x in IND_E] IND_E = [end_boundary(x + y) for x, y in zip(IND_S, spec)] + # varlen has implicit batch=1 + dt2 = dt2.squeeze(0) + X2 = X2.squeeze(0) + B2 = B2.squeeze(0) + C2 = C2.squeeze(0) yield ([Y_min[s, IND_S[s]:IND_E[s]] for s in range(num_examples)] if return_naive_ref else None, - cu_seqlens, seq_idx.unsqueeze(0), (A, dt2, X2, B2, C2)) + cu_seqlens, seq_idx, (A, dt2, X2, B2, C2)) @pytest.mark.parametrize("itype", @@ -198,7 +203,7 @@ def end_boundary(n: int): def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, itype): - # this tests the kernels on a single example (no batching) + # this tests the kernels on a single example (bs=1) # TODO: the bfloat16 case requires higher thresholds. To be investigated @@ -219,23 +224,40 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, Y_min, final_state_min = ssd_minimal_discrete(X * dt.unsqueeze(-1), A * dt, B, C, chunk_size) + + cu_seqlens = torch.tensor((0, seqlen), device='cuda').cumsum(dim=0) + seq_idx = torch.zeros(seqlen, dtype=torch.int32, device=cu_seqlens.device) + + chunk_indices, chunk_offsets = \ + _query_start_loc_to_chunk_indices_offsets( + cu_seqlens, chunk_size, cu_seqlens[-1]) + + # varlen has implicit batch=1 + X = X.squeeze(0) + dt = dt.squeeze(0) + A = A.squeeze(0) + B = B.squeeze(0) + C = C.squeeze(0) Y = torch.empty_like(X) - final_state = mamba_chunk_scan_combined(X, - dt, - A, - B, - C, - chunk_size, - D=None, - return_final_states=True, - out=Y) + final_state = mamba_chunk_scan_combined_varlen(X, + dt, + A, + B, + C, + chunk_size, + D=None, + cu_seqlens=cu_seqlens, + seq_idx=seq_idx, + chunk_indices=chunk_indices, + chunk_offsets=chunk_offsets, + out=Y) # just test the last in sequence - torch.testing.assert_close(Y[:, -1], Y_min[:, -1], atol=atol, rtol=rtol) + torch.testing.assert_close(Y[-1], Y_min[0, -1], atol=atol, rtol=rtol) # just test the last head # NOTE, in the kernel we always cast states to fp32 - torch.testing.assert_close(final_state[:, -1], + torch.testing.assert_close(final_state[:, -1].to(torch.float32), final_state_min[:, -1].to(torch.float32), atol=atol, rtol=rtol) @@ -300,7 +322,7 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases, cu_seqlens, chunk_size, cu_seqlens[-1]) Y = torch.empty_like(X) - new_states = mamba_chunk_scan_combined( + new_states = mamba_chunk_scan_combined_varlen( X, dt, A, @@ -312,7 +334,6 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases, seq_idx=seq_idx, chunk_indices=chunk_indices, chunk_offsets=chunk_offsets, - return_varlen_states=True, initial_states=states, out=Y, ) @@ -321,7 +342,7 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases, for i in range(num_examples): # just test one dim and dstate - Y_eg = Y[0, cu_seqlens[i]:cu_seqlens[i + 1], 0, 0] + Y_eg = Y[cu_seqlens[i]:cu_seqlens[i + 1], 0, 0] Y_min_eg = Y_min[i][:, 0, 0] torch.testing.assert_close(Y_eg, Y_min_eg, atol=atol, rtol=rtol) @@ -386,7 +407,7 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): _query_start_loc_to_chunk_indices_offsets( cu_seqlens, chunk_size, cu_seqlens[-1]) Y_ref = torch.empty_like(X) - state_ref = mamba_chunk_scan_combined( + state_ref = mamba_chunk_scan_combined_varlen( X, dt, A, @@ -398,7 +419,6 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): seq_idx=seq_idx, chunk_indices=chunk_indices, chunk_offsets=chunk_offsets, - return_varlen_states=True, initial_states=None, out=Y_ref, ) @@ -414,27 +434,27 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): chunked_seq_idx = torch.repeat_interleave( torch.arange(len(chunked_seqlens), device=device), chunked_seqlens, - output_size=chunked_cu_seqlens[-1]).unsqueeze(0).to(torch.int32) + output_size=chunked_cu_seqlens[-1]).to(torch.int32) chunked_input_seq_len = chunked_cu_seqlens[-1] - X_chunked = torch.zeros_like(X)[:, :chunked_input_seq_len, ...] - dt_chunked = torch.zeros_like(dt)[:, :chunked_input_seq_len, ...] - B_chunked = torch.zeros_like(B)[:, :chunked_input_seq_len, ...] - C_chunked = torch.zeros_like(C)[:, :chunked_input_seq_len, ...] + X_chunked = torch.zeros_like(X)[:chunked_input_seq_len, ...] + dt_chunked = torch.zeros_like(dt)[:chunked_input_seq_len, ...] + B_chunked = torch.zeros_like(B)[:chunked_input_seq_len, ...] + C_chunked = torch.zeros_like(C)[:chunked_input_seq_len, ...] for i in range(num_sequences): # fmt: off - chunk_f = lambda x, i: x[:, cu_seqlens[i]:cu_seqlens[i] + chunked_seqlens[i], ...] # noqa: E501 + chunk_f = lambda x, i: x[cu_seqlens[i]:cu_seqlens[i] + chunked_seqlens[i], ...] # noqa: E501 - X_chunked[:, chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(X, i) # noqa: E501 - dt_chunked[:, chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(dt, i) # noqa: E501 - B_chunked[:, chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(B, i) # noqa: E501 - C_chunked[:, chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(C, i) # noqa: E501 + X_chunked[chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(X, i) # noqa: E501 + dt_chunked[chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(dt, i) # noqa: E501 + B_chunked[chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(B, i) # noqa: E501 + C_chunked[chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(C, i) # noqa: E501 # fmt: on chunk_indices, chunk_offsets = \ _query_start_loc_to_chunk_indices_offsets( chunked_cu_seqlens, chunk_size, chunked_cu_seqlens[-1]) Y_partial = torch.empty_like(X_chunked) - partial_state = mamba_chunk_scan_combined( + partial_state = mamba_chunk_scan_combined_varlen( X_chunked, dt_chunked, A, @@ -446,7 +466,6 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): seq_idx=chunked_seq_idx, chunk_indices=chunk_indices, chunk_offsets=chunk_offsets, - return_varlen_states=True, initial_states=None, out=Y_partial, ) @@ -461,29 +480,28 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): remaining_chunked_seq_idx = torch.repeat_interleave( torch.arange(len(remaining_chunked_seqlens), device=device), remaining_chunked_seqlens, - output_size=remaining_chunked_cu_seqlens[-1]).unsqueeze(0).to( - torch.int32) + output_size=remaining_chunked_cu_seqlens[-1]).to(torch.int32) remaining_chunked_input_seq_len = remaining_chunked_cu_seqlens[-1] # fmt: off - remaining_X_chunked = torch.zeros_like(X)[:, :remaining_chunked_input_seq_len, ...] # noqa: E501 - remaining_dt_chunked = torch.zeros_like(dt)[:, :remaining_chunked_input_seq_len, ...] # noqa: E501 - remaining_B_chunked = torch.zeros_like(B)[:, :remaining_chunked_input_seq_len, ...] # noqa: E501 - remaining_C_chunked = torch.zeros_like(C)[:, :remaining_chunked_input_seq_len, ...] # noqa: E501 + remaining_X_chunked = torch.zeros_like(X)[:remaining_chunked_input_seq_len, ...] # noqa: E501 + remaining_dt_chunked = torch.zeros_like(dt)[:remaining_chunked_input_seq_len, ...] # noqa: E501 + remaining_B_chunked = torch.zeros_like(B)[:remaining_chunked_input_seq_len, ...] # noqa: E501 + remaining_C_chunked = torch.zeros_like(C)[:remaining_chunked_input_seq_len, ...] # noqa: E501 for i in range(num_sequences): - remaining_chunk_f = lambda x, i: x[:, cu_seqlens[i] + chunked_seqlens[i]:cu_seqlens[i+1], ...] # noqa: E501 + remaining_chunk_f = lambda x, i: x[cu_seqlens[i] + chunked_seqlens[i]:cu_seqlens[i+1], ...] # noqa: E501 - remaining_X_chunked[:, remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(X, i) # noqa: E501 - remaining_dt_chunked[:, remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(dt, i) # noqa: E501 - remaining_B_chunked[:, remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(B, i) # noqa: E501 - remaining_C_chunked[:, remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(C, i) # noqa: E501 + remaining_X_chunked[remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(X, i) # noqa: E501 + remaining_dt_chunked[remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(dt, i) # noqa: E501 + remaining_B_chunked[remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(B, i) # noqa: E501 + remaining_C_chunked[remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1], ...] = remaining_chunk_f(C, i) # noqa: E501 # assert input chunking is correct concat_chunk_f = lambda pt1, pt2, i: torch.cat([ - pt1[:,chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1],...], - pt2[:,remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1],...], + pt1[chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1],...], + pt2[remaining_chunked_cu_seqlens[i]:remaining_chunked_cu_seqlens[i+1],...], ], - dim=1) - concat_batch_f = lambda pt1, pt2: torch.cat([concat_chunk_f(pt1, pt2, i) for i in range(num_sequences)], dim=1) # noqa: E501 + dim=0) + concat_batch_f = lambda pt1, pt2: torch.cat([concat_chunk_f(pt1, pt2, i) for i in range(num_sequences)], dim=0) # noqa: E501 # fmt: on assert concat_batch_f(X_chunked, remaining_X_chunked).equal(X) @@ -498,7 +516,7 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): remaining_chunked_cu_seqlens[-1]) Y_chunked = torch.empty_like(remaining_X_chunked) - state_chunked = mamba_chunk_scan_combined( + state_chunked = mamba_chunk_scan_combined_varlen( remaining_X_chunked, remaining_dt_chunked, A, @@ -510,7 +528,6 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): seq_idx=remaining_chunked_seq_idx, chunk_indices=chunk_indices, chunk_offsets=chunk_offsets, - return_varlen_states=True, initial_states=partial_state, out=Y_chunked, ) @@ -518,17 +535,17 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): # kernel chunked is same as kernel overall for i in range(num_sequences): - Y_seq = Y[:, cu_seqlens[i]:cu_seqlens[i + 1], ...] - Y_ref_seq = Y_ref[:, cu_seqlens[i]:cu_seqlens[i + 1], ...] + Y_seq = Y[cu_seqlens[i]:cu_seqlens[i + 1], ...] + Y_ref_seq = Y_ref[cu_seqlens[i]:cu_seqlens[i + 1], ...] torch.testing.assert_close( - Y_seq[:, :chunked_seqlens[i], ...], - Y_ref_seq[:, :chunked_seqlens[i], ...], + Y_seq[:chunked_seqlens[i], ...], + Y_ref_seq[:chunked_seqlens[i], ...], atol=atol, rtol=rtol, msg=lambda x: f"seq{i} output part1 " + x) # noqa: B023 torch.testing.assert_close( - Y_seq[:, chunked_seqlens[i]:, ...], - Y_ref_seq[:, chunked_seqlens[i]:, ...], + Y_seq[chunked_seqlens[i]:, ...], + Y_ref_seq[chunked_seqlens[i]:, ...], atol=atol, rtol=rtol, msg=lambda x: f"seq{i} output part2 " + x) # noqa: B023 diff --git a/vllm/model_executor/layers/mamba/mamba_mixer2.py b/vllm/model_executor/layers/mamba/mamba_mixer2.py index 908ea6e0025f..6dd09fad7a90 100644 --- a/vllm/model_executor/layers/mamba/mamba_mixer2.py +++ b/vllm/model_executor/layers/mamba/mamba_mixer2.py @@ -29,7 +29,7 @@ from vllm.model_executor.layers.mamba.ops.mamba_ssm import ( selective_state_update) from vllm.model_executor.layers.mamba.ops.ssd_combined import ( - mamba_chunk_scan_combined) + mamba_chunk_scan_combined_varlen) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.model_loader.weight_utils import ( LoaderFunction, composed_weight_loader, sharded_weight_loader) @@ -504,6 +504,7 @@ def forward_cuda( seq_idx_p = attn_metadata.seq_idx_p chunk_indices_p = attn_metadata.chunk_indices_p chunk_offsets_p = attn_metadata.chunk_offsets_p + query_start_loc_p = attn_metadata.query_start_loc_p # 1. Gated MLP's linear projection projected_states, _ = self.in_proj(hidden_states) @@ -545,6 +546,7 @@ def forward_cuda( out, _ = self.out_proj(hidden_states) return out + # NOTE: V0 put prefill before decode, v1 puts decode before prefill num_prefills = attn_metadata.num_prefills # request count num_decodes = attn_metadata.num_decode_tokens # token count (=request) num_prefill_tokens = attn_metadata.num_prefill_tokens # token count @@ -570,9 +572,6 @@ def forward_cuda( [num_decodes, num_prefills], dim=0, ) - query_start_loc_p = ( - attn_metadata.query_start_loc[-num_prefills - 1:] - - num_decodes if has_prefill else None) # Preallocate output tensor to avoid memcpy cost for merging prefill # and decode outputs @@ -620,15 +619,15 @@ def forward_cuda( ssm_state[state_indices_tensor_p], 0) # NOTE: final output is an in-place update of out tensor - varlen_state = mamba_chunk_scan_combined( - hidden_states_p.view(1, num_prefill_tokens, + varlen_states = mamba_chunk_scan_combined_varlen( + hidden_states_p.view(num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim), - dt_p.unsqueeze(0), + dt_p, self.A, - B_p.view(1, num_prefill_tokens, self.n_groups // self.tp_size, + B_p.view(num_prefill_tokens, self.n_groups // self.tp_size, -1), - C_p.view(1, num_prefill_tokens, self.n_groups // self.tp_size, + C_p.view(num_prefill_tokens, self.n_groups // self.tp_size, -1), chunk_size=chunk_size, D=self.D, @@ -639,17 +638,15 @@ def forward_cuda( chunk_offsets=chunk_offsets_p, cu_seqlens=query_start_loc_p, initial_states=initial_states, - return_varlen_states=True, - return_final_states=False, dt_softplus=True, dt_limit=(0.0, float("inf")), - out=preallocated_ssm_out_p.view(1, num_prefill_tokens, -1, + out=preallocated_ssm_out_p.view(num_prefill_tokens, -1, self.head_dim), state_dtype=ssm_state.dtype) # update ssm states # - varlen state is a (num_prefills, nheads, headdim, dstate) tensor - ssm_state[state_indices_tensor_p] = varlen_state + ssm_state[state_indices_tensor_p] = varlen_states # Process decode requests if has_decode: diff --git a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py index 5e5011fa2ac5..c4102c4753c7 100644 --- a/vllm/model_executor/layers/mamba/ops/causal_conv1d.py +++ b/vllm/model_executor/layers/mamba/ops/causal_conv1d.py @@ -427,7 +427,7 @@ def causal_conv1d_fn( batch_ptr = metadata.batch_ptr token_chunk_offset_ptr = metadata.token_chunk_offset_ptr else: - seqlens = np.diff(query_start_loc.to('cpu')) + seqlens = query_start_loc.diff().to('cpu') args = seqlens MAX_NUM_PROGRAMS = 1024 diff --git a/vllm/model_executor/layers/mamba/ops/ssd_bmm.py b/vllm/model_executor/layers/mamba/ops/ssd_bmm.py index 11ca1255ebfb..601b71ab2a51 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_bmm.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_bmm.py @@ -99,34 +99,28 @@ def _bmm_chunk_fwd_kernel( seq_idx_ptr, # Matrix dimensions seqlen, - chunk_size, - K, - ngroups, - stride_a_batch, - stride_a_seqlen, - stride_a_head, - stride_ak, - stride_b_batch, - stride_b_seqlen, - stride_b_head, - stride_bk, - stride_out_batch, - stride_out_chunk, - stride_out_head, - stride_outm, - stride_outn, - stride_seq_idx_batch, - stride_seq_idx_seqlen, + chunk_size: tl.constexpr, + K: tl.constexpr, + ngroups: tl.constexpr, + stride_a_seqlen: tl.int64, + stride_a_head: tl.int64, + stride_ak: tl.constexpr, + stride_b_seqlen: tl.int64, + stride_b_head: tl.int64, + stride_bk: tl.constexpr, + stride_out_chunk: tl.int64, + stride_out_head: tl.int64, + stride_outm: tl.int64, + stride_outn: tl.constexpr, + stride_seq_idx_seqlen: tl.constexpr, # Meta-parameters IS_CAUSAL: tl.constexpr, dot_dtype: tl.constexpr, - HAS_SEQ_IDX: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, ): - pid_b = tl.program_id(axis=1) - pid_ch = tl.program_id(axis=2).to(tl.int64) + pid_ch = tl.program_id(axis=1).to(tl.int64) pid_c = pid_ch // ngroups pid_h = pid_ch - pid_c * ngroups num_pid_n = tl.cdiv(chunk_size, BLOCK_SIZE_N) @@ -135,10 +129,10 @@ def _bmm_chunk_fwd_kernel( if IS_CAUSAL: if pid_n * BLOCK_SIZE_N >= (pid_m + 1) * BLOCK_SIZE_M: return - a_ptr += pid_b * stride_a_batch + pid_c * chunk_size * stride_a_seqlen + pid_h * stride_a_head - b_ptr += pid_b * stride_b_batch + pid_c * chunk_size * stride_b_seqlen + pid_h * stride_b_head - if HAS_SEQ_IDX: - seq_idx_ptr += pid_b * stride_seq_idx_batch + pid_c * chunk_size * stride_seq_idx_seqlen + a_ptr += pid_c * chunk_size * stride_a_seqlen + pid_h * stride_a_head + b_ptr += pid_c * chunk_size * stride_b_seqlen + pid_h * stride_b_head + + seq_idx_ptr += pid_c * chunk_size * stride_seq_idx_seqlen offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) @@ -150,6 +144,8 @@ def _bmm_chunk_fwd_kernel( chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + + # compute a * b.T for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): a = tl.load(a_ptrs, mask=(offs_m[:, None] < chunk_size_limit) & @@ -165,18 +161,19 @@ def _bmm_chunk_fwd_kernel( offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - if HAS_SEQ_IDX: - chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) - seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen, - mask=offs_m < chunk_size_limit, - other=-1) - seq_idx_n = tl.load(seq_idx_ptr + offs_n * stride_seq_idx_seqlen, - mask=offs_n < chunk_size_limit, - other=-2) - acc = tl.where(seq_idx_m[:, None] == seq_idx_n[None, :], acc, 0.0) - out = acc.to(out_ptr.dtype.element_ty) - out_ptr += pid_b * stride_out_batch + pid_c * stride_out_chunk + pid_h * stride_out_head + # Zero out the results that are not from the same request + # in the varlen batch + seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen, + mask=offs_m < chunk_size_limit, + other=-1) + seq_idx_n = tl.load(seq_idx_ptr + offs_n * stride_seq_idx_seqlen, + mask=offs_n < chunk_size_limit, + other=-2) + acc = tl.where(seq_idx_m[:, None] == seq_idx_n[None, :], acc, 0.0) + + out = acc.to(out_ptr.dtype.element_ty) + out_ptr += pid_c * stride_out_chunk + pid_h * stride_out_head out_ptrs = out_ptr + (stride_outm * offs_m[:, None] + offs_n[None, :] * stride_outn) tl.store(out_ptrs, @@ -185,78 +182,61 @@ def _bmm_chunk_fwd_kernel( (offs_n[None, :] < chunk_size)) -def _bmm_chunk_fwd(a, - b, - chunk_size, - seq_idx=None, - causal=False, - output_dtype=None): +def _bmm_chunk_fwd(a, b, chunk_size, seq_idx, causal=False, output_dtype=None): """ Argument: - a: (batch, seqlen, k) or (batch, seqlen, ngroups, k) - b: (batch, seqlen, k) or (batch, seqlen, ngroups, k) - seq_idx: (batch, seqlen) or None. out[i, j] for seq_idx[i] != seq_idx[j] will be zeroed out. + a: (seqlen, ngroups, k) + b: (seqlen, ngroups, k) + seq_idx: (seqlen,). out[i, j] for seq_idx[i] != seq_idx[j] will be zeroed out. causal: if True, then out[i, j] for i > j will be arbitrary, only out[i, j] for i <= j are guaranteed to be correct. Return: - out: (batch, nchunks, chunk_size, chunk_size) or (batch, nchunks, ngroups, chunk_size, chunk_size) + out: (nchunks, ngroups, chunk_size, chunk_size) """ - # Check constraints. - has_groups = a.dim() == 4 - if not has_groups: - batch, seqlen, k = a.shape - else: - batch, seqlen, ngroups, k = a.shape + seqlen, ngroups, k = a.shape assert b.shape == a.shape - if seq_idx is not None: - assert seq_idx.shape == (batch, seqlen) - if a.stride(-1) != 1 and a.stride(1) != 1: + assert seq_idx is not None + assert seq_idx.shape == (seqlen, ) + if a.stride(-1) != 1 and a.stride(0) != 1: a = a.contiguous() - if b.stride(-1) != 1 and b.stride(1) != 1: + if b.stride(-1) != 1 and b.stride(0) != 1: b = b.contiguous() + nchunks = math.ceil(seqlen / chunk_size) # Allocates output. out_dtype = a.dtype if output_dtype is None else output_dtype - out = torch.empty( - (batch, nchunks, chunk_size, chunk_size) if not has_groups else - (batch, nchunks, ngroups, chunk_size, chunk_size), - device=a.device, - dtype=out_dtype) + out = torch.empty((nchunks, ngroups, chunk_size, chunk_size), + device=a.device, + dtype=out_dtype) dot_dtype = (tl.bfloat16 if a.dtype == torch.bfloat16 or b.dtype == torch.bfloat16 else (tl.float16 if a.dtype == torch.float16 or b.dtype == torch.float16 else tl.float32)) grid = lambda META: (triton.cdiv( chunk_size, META['BLOCK_SIZE_M']) * triton.cdiv( - chunk_size, META['BLOCK_SIZE_N']), batch, nchunks - if not has_groups else nchunks * ngroups) + chunk_size, META['BLOCK_SIZE_N']), nchunks * ngroups) with torch.cuda.device(a.device.index): _bmm_chunk_fwd_kernel[grid]( - a, - b, - out, - seq_idx, - seqlen, - chunk_size, - k, - ngroups if has_groups else 1, - a.stride(0), - a.stride(1), - 0 if not has_groups else a.stride(2), - a.stride(-1), - b.stride(0), - b.stride(1), - 0 if not has_groups else b.stride(2), - b.stride(-1), - out.stride(0), - out.stride(1), - 0 if not has_groups else out.stride(2), - out.stride(-2), - out.stride(-1), - *((seq_idx.stride(0), - seq_idx.stride(1)) if seq_idx is not None else (0, 0)), - causal, - dot_dtype, - HAS_SEQ_IDX=seq_idx is not None, + a_ptr=a, + b_ptr=b, + out_ptr=out, + seq_idx_ptr=seq_idx, + seqlen=seqlen, + chunk_size=chunk_size, + K=k, + ngroups=ngroups, + stride_a_seqlen=a.stride(0), + stride_a_head=a.stride(1), + stride_ak=a.stride(2), + stride_b_seqlen=b.stride(0), + stride_b_head=b.stride(1), + stride_bk=b.stride(2), + stride_out_chunk=out.stride(0), + stride_out_head=out.stride(1), + stride_outm=out.stride(-2), + stride_outn=out.stride(-1), + stride_seq_idx_seqlen=seq_idx.stride(0), + IS_CAUSAL=causal, + dot_dtype=dot_dtype, ) return out diff --git a/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py b/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py index fb8350e191c9..add72617fcea 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py @@ -6,7 +6,6 @@ # ruff: noqa: E501,SIM102 -import torch from packaging import version from vllm.triton_utils import tl, triton @@ -114,7 +113,6 @@ def _chunk_scan_fwd_kernel( x_ptr, z_ptr, out_ptr, - out_x_ptr, dt_ptr, dA_cumsum_ptr, seq_idx_ptr, @@ -126,60 +124,49 @@ def _chunk_scan_fwd_kernel( chunk_offsets_ptr, chunk_meta_num, # Matrix dimensions - chunk_size, - hdim, - dstate, - batch, + chunk_size: tl.constexpr, + hdim: tl.constexpr, + dstate: tl.constexpr, seqlen, - nheads_ngroups_ratio, + nheads_ngroups_ratio: tl.constexpr, # Strides - stride_cb_batch, - stride_cb_chunk, - stride_cb_head, - stride_cb_csize_m, - stride_cb_csize_k, - stride_x_batch, - stride_x_seqlen, - stride_x_head, - stride_x_hdim, - stride_z_batch, - stride_z_seqlen, - stride_z_head, - stride_z_hdim, - stride_out_batch, - stride_out_seqlen, - stride_out_head, - stride_out_hdim, - stride_dt_batch, - stride_dt_chunk, - stride_dt_head, - stride_dt_csize, - stride_dA_cs_batch, - stride_dA_cs_chunk, - stride_dA_cs_head, - stride_dA_cs_csize, - stride_seq_idx_batch, - stride_seq_idx_seqlen, - stride_C_batch, - stride_C_seqlen, - stride_C_head, - stride_C_dstate, - stride_states_batch, - stride_states_chunk, - stride_states_head, - stride_states_hdim, - stride_states_dstate, - stride_init_states_batch, - stride_init_states_head, - stride_init_states_hdim, - stride_init_states_dstate, - stride_D_head, + stride_cb_chunk: tl.int64, + stride_cb_head: tl.int64, + stride_cb_csize_m: tl.int64, + stride_cb_csize_k: tl.constexpr, + stride_x_seqlen: tl.int64, + stride_x_head: tl.int64, + stride_x_hdim: tl.constexpr, + stride_z_seqlen: tl.int64, + stride_z_head: tl.int64, + stride_z_hdim: tl.constexpr, + stride_out_seqlen: tl.int64, + stride_out_head: tl.int64, + stride_out_hdim: tl.constexpr, + stride_dt_chunk: tl.int64, + stride_dt_head: tl.int64, + stride_dt_csize: tl.constexpr, + stride_dA_cs_chunk: tl.int64, + stride_dA_cs_head: tl.int64, + stride_dA_cs_csize: tl.constexpr, + stride_seq_idx_seqlen: tl.constexpr, + stride_C_seqlen: tl.int64, + stride_C_head: tl.int64, + stride_C_dstate: tl.constexpr, + stride_states_chunk: tl.int64, + stride_states_head: tl.int64, + stride_states_hdim: tl.int64, + stride_states_dstate: tl.constexpr, + stride_init_states_batch: tl.int64, + stride_init_states_head: tl.int64, + stride_init_states_hdim: tl.int64, + stride_init_states_dstate: tl.constexpr, + stride_D_head: tl.constexpr, # Meta-parameters IS_CAUSAL: tl.constexpr, HAS_D: tl.constexpr, D_HAS_HDIM: tl.constexpr, HAS_Z: tl.constexpr, - HAS_SEQ_IDX: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, @@ -187,9 +174,7 @@ def _chunk_scan_fwd_kernel( IS_TRITON_22: tl.constexpr, HAS_INITSTATES: tl.constexpr, ): - pid_bc = tl.program_id(axis=1).to(tl.int64) - pid_c = pid_bc // batch - pid_b = pid_bc - pid_c * batch + pid_c = tl.program_id(axis=1).to(tl.int64) if not HAS_INITSTATES: c_idx = pid_c c_off = 0 @@ -201,53 +186,51 @@ def _chunk_scan_fwd_kernel( num_pid_n = tl.cdiv(hdim, BLOCK_SIZE_N) pid_m = tl.program_id(axis=0) // num_pid_n pid_n = tl.program_id(axis=0) % num_pid_n - cb_ptr += pid_b * stride_cb_batch + c_idx * stride_cb_chunk + ( - pid_h // nheads_ngroups_ratio) * stride_cb_head - x_ptr += pid_b * stride_x_batch + c_idx * chunk_size * stride_x_seqlen + pid_h * stride_x_head - dt_ptr += pid_b * stride_dt_batch + c_idx * stride_dt_chunk + pid_h * stride_dt_head - dA_cumsum_ptr += pid_b * stride_dA_cs_batch + c_idx * stride_dA_cs_chunk + pid_h * stride_dA_cs_head - C_ptr += pid_b * stride_C_batch + c_idx * chunk_size * stride_C_seqlen + ( + cb_ptr += c_idx * stride_cb_chunk + (pid_h // + nheads_ngroups_ratio) * stride_cb_head + x_ptr += c_idx * chunk_size * stride_x_seqlen + pid_h * stride_x_head + dt_ptr += c_idx * stride_dt_chunk + pid_h * stride_dt_head + dA_cumsum_ptr += c_idx * stride_dA_cs_chunk + pid_h * stride_dA_cs_head + C_ptr += c_idx * chunk_size * stride_C_seqlen + ( pid_h // nheads_ngroups_ratio) * stride_C_head # M-block offsets and prev states # - logic in next block may override these if there is an active offset offs_m = pid_m * BLOCK_SIZE_M + c_off + tl.arange(0, BLOCK_SIZE_M) - prev_states_ptr = states_ptr + pid_b * stride_states_batch + c_idx * stride_states_chunk + pid_h * stride_states_head + prev_states_ptr = states_ptr + c_idx * stride_states_chunk + pid_h * stride_states_head prev_states_hdim = stride_states_hdim prev_states_dstate = stride_states_dstate chunk_size_limit = min(chunk_size, seqlen - c_idx * chunk_size) - if HAS_SEQ_IDX: - seq_idx_ptr += pid_b * stride_seq_idx_batch + c_idx * chunk_size * stride_seq_idx_seqlen - - # - we only need seq_idx_prev to be aligned to chunk boundary - seq_idx_prev = tl.load(seq_idx_ptr - stride_seq_idx_seqlen, - mask=c_idx >= 1, - other=0) - - if HAS_INITSTATES: - # if there are init states, we only need seq_idx_m to point - # what is the current seq_idx - - # get current seq idx - if (pid_m * BLOCK_SIZE_M + c_off) < chunk_size_limit: - seq_idx_m = tl.load( - seq_idx_ptr + - (pid_m * BLOCK_SIZE_M + c_off) * stride_seq_idx_seqlen, ) - - # - recall that in ssd_state_passing, for the case c_off == 0 - # i.e., the very first sequence, we made states_ptr hold its initial state - # so this edge case is taken care of - if ((c_off == 0) and - (seq_idx_prev != seq_idx_m - ) # if a seq is changed exactly on boundary - or (c_off > 0) # implies a new example (pseudo chunk) - ): - - # - replace prev_states_ptr with init_states - prev_states_ptr = initstates_ptr + seq_idx_m * stride_init_states_batch + pid_h * stride_init_states_head - prev_states_hdim = stride_init_states_hdim # override strides - prev_states_dstate = stride_init_states_dstate + + seq_idx_ptr += c_idx * chunk_size * stride_seq_idx_seqlen + # - we only need seq_idx_prev to be aligned to chunk boundary + seq_idx_prev = tl.load(seq_idx_ptr - stride_seq_idx_seqlen, + mask=c_idx >= 1, + other=0) + + if HAS_INITSTATES: + # if there are init states, we only need seq_idx_m to point + # what is the current seq_idx + + # get current seq idx + if (pid_m * BLOCK_SIZE_M + c_off) < chunk_size_limit: + seq_idx_m = tl.load( + seq_idx_ptr + + (pid_m * BLOCK_SIZE_M + c_off) * stride_seq_idx_seqlen, ) + + # - recall that in ssd_state_passing, for the case c_off == 0 + # i.e., the very first sequence, we made states_ptr hold its initial state + # so this edge case is taken care of + if ((c_off == 0) and (seq_idx_prev != seq_idx_m + ) # if a seq is changed exactly on boundary + or (c_off > 0) # implies a new example (pseudo chunk) + ): + + # - replace prev_states_ptr with init_states + prev_states_ptr = initstates_ptr + seq_idx_m * stride_init_states_batch + pid_h * stride_init_states_head + prev_states_hdim = stride_init_states_hdim # override strides + prev_states_dstate = stride_init_states_dstate offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) dA_cs_m = tl.load(dA_cumsum_ptr + offs_m * stride_dA_cs_csize, @@ -256,7 +239,6 @@ def _chunk_scan_fwd_kernel( # - handle chunk state limit if HAS_INITSTATES: - # have to split this if otherwise compilation will have problems dA_cs_m_boundary = 0.0 @@ -296,13 +278,11 @@ def _chunk_scan_fwd_kernel( dA_cumsum_ptr + (c_off - 1) * stride_dA_cs_csize, mask=(((c_off - 1) > -1) and ((c_off) < chunk_size)), other=0.0).to(tl.float32) - - if HAS_SEQ_IDX: + else: # - handle seq idx when HAS_INITSTATES==False - if not HAS_INITSTATES: - seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen, - mask=offs_m < chunk_size_limit, - other=-1) + seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen, + mask=offs_m < chunk_size_limit, + other=-1) acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) @@ -319,18 +299,15 @@ def _chunk_scan_fwd_kernel( prev_states_ptrs = prev_states_ptr + ( offs_n[None, :] * prev_states_hdim + offs_k_dstate[:, None] * prev_states_dstate) - if HAS_SEQ_IDX: - - if not HAS_INITSTATES: - # - this is for continuous batching where there is no init states - scale_m = tl.where(seq_idx_m == seq_idx_prev, tl.exp(dA_cs_m), - 0.0) - else: - # - if there is initstates, we will rely on prev_states, no zeroing - # required. - scale_m = tl.exp(dA_cs_m - dA_cs_m_boundary) + + if not HAS_INITSTATES: + # - this is for continuous batching where there is no init states + scale_m = tl.where(seq_idx_m == seq_idx_prev, tl.exp(dA_cs_m), 0.0) else: - scale_m = tl.exp(dA_cs_m) + # - if there is initstates, we will rely on prev_states, no zeroing + # required. + scale_m = tl.exp(dA_cs_m - dA_cs_m_boundary) + if BLOCK_SIZE_DSTATE <= 128: C = tl.load(C_ptrs, mask=(offs_m[:, None] < chunk_size_limit) & @@ -416,15 +393,7 @@ def _chunk_scan_fwd_kernel( acc += x_residual * D if HAS_Z: - out_x_ptr += pid_b * stride_out_batch + c_idx * chunk_size * stride_out_seqlen + pid_h * stride_out_head - out_x_ptrs = out_x_ptr + (stride_out_seqlen * offs_out_m[:, None] + - offs_out_n[None, :]) - tl.store(out_x_ptrs, - acc, - mask=(offs_out_m[:, None] < chunk_size_limit) & - (offs_out_n[None, :] < hdim)) - - z_ptr += pid_b * stride_z_batch + c_idx * chunk_size * stride_z_seqlen + pid_h * stride_z_head + z_ptr += c_idx * chunk_size * stride_z_seqlen + pid_h * stride_z_head z_ptrs = z_ptr + (stride_z_seqlen * offs_out_m[:, None] + stride_z_hdim * offs_out_n[None, :]) z = tl.load(z_ptrs, @@ -433,7 +402,7 @@ def _chunk_scan_fwd_kernel( other=0.0).to(tl.float32) acc *= z * tl.sigmoid(z) - out_ptr += pid_b * stride_out_batch + c_idx * chunk_size * stride_out_seqlen + pid_h * stride_out_head + out_ptr += c_idx * chunk_size * stride_out_seqlen + pid_h * stride_out_head out_ptrs = out_ptr + (stride_out_seqlen * offs_out_m[:, None] + offs_out_n[None, :] * stride_out_hdim) tl.store(out_ptrs, @@ -449,126 +418,110 @@ def _chunk_scan_fwd( dA_cumsum, C, states, + out, + seq_idx, D=None, z=None, - seq_idx=None, chunk_indices=None, chunk_offsets=None, initial_states=None, - out=None, ): - batch, seqlen, nheads, headdim = x.shape - _, _, nchunks, chunk_size = dt.shape - _, _, ngroups, dstate = C.shape + assert seq_idx is not None, "this implementation requires seq_idx" + + seqlen, nheads, headdim = x.shape + _, nchunks, chunk_size = dt.shape + _, ngroups, dstate = C.shape assert nheads % ngroups == 0 - assert C.shape == (batch, seqlen, ngroups, dstate) - assert cb.shape == (batch, nchunks, ngroups, chunk_size, chunk_size) - if z is not None: - assert z.shape == x.shape + assert C.shape == (seqlen, ngroups, dstate) + assert cb.shape == (nchunks, ngroups, chunk_size, chunk_size) if D is not None: assert D.shape == (nheads, headdim) or D.shape == (nheads, ) - assert dt.shape == (batch, nheads, nchunks, chunk_size) - assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size) - assert states.shape == (batch, nchunks, nheads, headdim, dstate) - - if seq_idx is not None: - assert seq_idx.shape == (batch, seqlen) - - if initial_states is not None: - # with initial states, we need to take care of how - # seq_idx crosses the boundaries - assert batch == 1, "chunk scan only supports initial states with batch 1" - assert chunk_indices is not None and chunk_offsets is not None, \ - "chunk_indices and chunk_offsets should have been set" - else: - chunk_indices, chunk_offsets = None, None - else: - chunk_indices, chunk_offsets = None, None - - assert out.shape == x.shape - if z is not None: - out_x = torch.empty_like(x) - assert out_x.stride() == out.stride() + assert z.shape == x.shape + assert dt.shape == (nheads, nchunks, chunk_size) + assert dA_cumsum.shape == (nheads, nchunks, chunk_size) + assert states.shape == (nchunks, nheads, headdim, dstate) + assert seq_idx.shape == (seqlen, ) + + if initial_states is not None: + # with initial states, we need to take care of how + # seq_idx crosses the boundaries + assert chunk_indices is not None and chunk_offsets is not None, \ + "chunk_indices and chunk_offsets should have been set" else: - out_x = None + chunk_indices, chunk_offsets = None, None grid = lambda META: ( triton.cdiv(chunk_size, META['BLOCK_SIZE_M']) * triton.cdiv( - headdim, META['BLOCK_SIZE_N']), batch * nchunks + headdim, META['BLOCK_SIZE_N']), nchunks if chunk_offsets is None else len(chunk_offsets), nheads) - z_strides = ((z.stride(0), z.stride(1), z.stride(2), - z.stride(3)) if z is not None else (0, 0, 0, 0)) + + z_strides = ((z.stride(0), z.stride(1), z.stride(2)) if z is not None else + (0, 0, 0)) + initial_states_strides = ((initial_states.stride(0), + initial_states.stride(1), + initial_states.stride(2), + initial_states.stride(3)) + if initial_states is not None else (0, 0, 0, 0)) + _chunk_scan_fwd_kernel[grid]( - cb, - x, - z, - out, - out_x, - dt, - dA_cumsum, - seq_idx, - C, - states, - D, - initial_states, - chunk_indices, - chunk_offsets, - len(chunk_indices) if chunk_indices is not None else 0, - chunk_size, - headdim, - dstate, - batch, - seqlen, - nheads // ngroups, - cb.stride(0), - cb.stride(1), - cb.stride(2), - cb.stride(3), - cb.stride(4), - x.stride(0), - x.stride(1), - x.stride(2), - x.stride(3), - z_strides[0], - z_strides[1], - z_strides[2], - z_strides[3], - out.stride(0), - out.stride(1), - out.stride(2), - out.stride(3), - dt.stride(0), - dt.stride(2), - dt.stride(1), - dt.stride(3), - dA_cumsum.stride(0), - dA_cumsum.stride(2), - dA_cumsum.stride(1), - dA_cumsum.stride(3), - *((seq_idx.stride(0), seq_idx.stride(1)) if seq_idx is not None else - (0, 0)), - C.stride(0), - C.stride(1), - C.stride(2), - C.stride(3), - states.stride(0), - states.stride(1), - states.stride(2), - states.stride(3), - states.stride(4), - *((initial_states.stride(0), initial_states.stride(1), - initial_states.stride(2), - initial_states.stride(3)) if initial_states is not None else - (0, 0, 0, 0)), - D.stride(0) if D is not None else 0, - True, - D is not None, - D.dim() == 2 if D is not None else True, - BLOCK_SIZE_DSTATE=max(triton.next_power_of_2(dstate), 16), + cb_ptr=cb, + x_ptr=x, + z_ptr=z, + out_ptr=out, + dt_ptr=dt, + dA_cumsum_ptr=dA_cumsum, + seq_idx_ptr=seq_idx, + C_ptr=C, + states_ptr=states, + D_ptr=D, + initstates_ptr=initial_states, + chunk_indices_ptr=chunk_indices, + chunk_offsets_ptr=chunk_offsets, + chunk_meta_num=len(chunk_indices) if chunk_indices is not None else 0, + chunk_size=chunk_size, + hdim=headdim, + dstate=dstate, + seqlen=seqlen, + nheads_ngroups_ratio=nheads // ngroups, + stride_cb_chunk=cb.stride(0), + stride_cb_head=cb.stride(1), + stride_cb_csize_m=cb.stride(2), + stride_cb_csize_k=cb.stride(3), + stride_x_seqlen=x.stride(0), + stride_x_head=x.stride(1), + stride_x_hdim=x.stride(2), + stride_z_seqlen=z_strides[0], + stride_z_head=z_strides[1], + stride_z_hdim=z_strides[2], + stride_out_seqlen=out.stride(0), + stride_out_head=out.stride(1), + stride_out_hdim=out.stride(2), + stride_dt_chunk=dt.stride(1), + stride_dt_head=dt.stride(0), + stride_dt_csize=dt.stride(2), + stride_dA_cs_chunk=dA_cumsum.stride(1), + stride_dA_cs_head=dA_cumsum.stride(0), + stride_dA_cs_csize=dA_cumsum.stride(2), + stride_seq_idx_seqlen=seq_idx.stride(0), + stride_C_seqlen=C.stride(0), + stride_C_head=C.stride(1), + stride_C_dstate=C.stride(2), + stride_states_chunk=states.stride(0), + stride_states_head=states.stride(1), + stride_states_hdim=states.stride(2), + stride_states_dstate=states.stride(3), + stride_init_states_batch=initial_states_strides[0], + stride_init_states_head=initial_states_strides[1], + stride_init_states_hdim=initial_states_strides[2], + stride_init_states_dstate=initial_states_strides[3], + stride_D_head=D.stride(0) if D is not None else 0, + IS_CAUSAL=True, + HAS_D=D is not None, + D_HAS_HDIM=D.dim() == 2 if D is not None else True, HAS_Z=z is not None, - HAS_SEQ_IDX=seq_idx is not None, + BLOCK_SIZE_DSTATE=max(triton.next_power_of_2(dstate), 16), IS_TRITON_22=TRITON_22, HAS_INITSTATES=initial_states is not None, ) - return out_x + return diff --git a/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py b/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py index 2e657426143b..8ee41f2cbc1b 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py @@ -35,41 +35,35 @@ def _chunk_cumsum_fwd_kernel( dt_out_ptr, dA_cumsum_ptr, # Matrix dimension - batch, seqlen, - nheads, - chunk_size, - dt_min, - dt_max, + nheads: tl.constexpr, + chunk_size: tl.constexpr, + dt_min: tl.constexpr, + dt_max: tl.constexpr, # Strides - stride_dt_batch, - stride_dt_seqlen, - stride_dt_head, - stride_A_head, - stride_dt_bias_head, - stride_dt_out_batch, - stride_dt_out_chunk, - stride_dt_out_head, - stride_dt_out_csize, - stride_dA_cs_batch, - stride_dA_cs_chunk, - stride_dA_cs_head, - stride_dA_cs_csize, + stride_dt_seqlen: tl.int64, + stride_dt_head: tl.constexpr, + stride_A_head: tl.constexpr, + stride_dt_bias_head: tl.constexpr, + stride_dt_out_head: tl.int64, + stride_dt_out_chunk: tl.int64, + stride_dt_out_csize: tl.constexpr, + stride_dA_cs_head: tl.int64, + stride_dA_cs_chunk: tl.int64, + stride_dA_cs_csize: tl.constexpr, # Meta-parameters DT_SOFTPLUS: tl.constexpr, HAS_DT_BIAS: tl.constexpr, BLOCK_SIZE_H: tl.constexpr, BLOCK_SIZE_CHUNK: tl.constexpr, ): - pid_b = tl.program_id(axis=0) - # if dt is long, may cause problems, so use 64 bit # https://github.com/triton-lang/triton/issues/1058 - pid_c = tl.program_id(axis=1).to(tl.int64) - pid_h = tl.program_id(axis=2) - dt_ptr += pid_b * stride_dt_batch + pid_c * chunk_size * stride_dt_seqlen - dt_out_ptr += pid_b * stride_dt_out_batch + pid_c * stride_dt_out_chunk - dA_cumsum_ptr += pid_b * stride_dA_cs_batch + pid_c * stride_dA_cs_chunk + pid_c = tl.program_id(axis=0).to(tl.int64) + pid_h = tl.program_id(axis=1) + dt_ptr += pid_c * chunk_size * stride_dt_seqlen + dt_out_ptr += pid_c * stride_dt_out_chunk + dA_cumsum_ptr += pid_c * stride_dA_cs_chunk offs_h = pid_h * BLOCK_SIZE_H + tl.arange(0, BLOCK_SIZE_H) offs_c = tl.arange(0, BLOCK_SIZE_CHUNK) @@ -93,9 +87,8 @@ def _chunk_cumsum_fwd_kernel( dt += dt_bias[:, None] if DT_SOFTPLUS: dt = tl.where(dt <= 20.0, softplus(dt), dt) - # As of Triton 2.2.0, tl.clamp is not available yet - # dt = tl.clamp(dt, dt_min, dt_max) - dt = tl.minimum(tl.maximum(dt, dt_min), dt_max) + + dt = tl.clamp(dt, dt_min, dt_max) dt = tl.where( (offs_h[:, None] < nheads) & (offs_c[None, :] < chunk_size_limit), dt, 0.0) @@ -197,56 +190,46 @@ def _chunk_state_fwd_kernel( dA_cumsum_ptr, seq_idx_ptr, # Matrix dimensions - hdim, - dstate, - chunk_size, - batch, + hdim: tl.constexpr, + dstate: tl.constexpr, + chunk_size: tl.constexpr, seqlen, - nheads_ngroups_ratio, + nheads_ngroups_ratio: tl.constexpr, # Strides - stride_x_batch, - stride_x_seqlen, - stride_x_head, - stride_x_hdim, - stride_b_batch, - stride_b_seqlen, - stride_b_head, - stride_b_dstate, - stride_states_batch, - stride_states_chunk, - stride_states_head, - stride_states_hdim, - stride_states_dstate, - stride_dt_batch, - stride_dt_chunk, - stride_dt_head, - stride_dt_csize, - stride_dA_cs_batch, - stride_dA_cs_chunk, - stride_dA_cs_head, - stride_dA_cs_csize, - stride_seq_idx_batch, - stride_seq_idx_seqlen, + stride_x_seqlen: tl.int64, + stride_x_head: tl.int64, + stride_x_hdim: tl.constexpr, + stride_b_seqlen: tl.int64, + stride_b_head: tl.int64, + stride_b_dstate: tl.constexpr, + stride_states_chunk: tl.int64, + stride_states_head: tl.int64, + stride_states_hdim: tl.int64, + stride_states_dstate: tl.constexpr, + stride_dt_head: tl.int64, + stride_dt_chunk: tl.int64, + stride_dt_csize: tl.constexpr, + stride_dA_cs_head: tl.int64, + stride_dA_cs_chunk: tl.int64, + stride_dA_cs_csize: tl.constexpr, + stride_seq_idx_seqlen: tl.constexpr, # Meta-parameters - HAS_SEQ_IDX: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, ): - pid_bc = tl.program_id(axis=1).to(tl.int64) - pid_c = pid_bc // batch - pid_b = pid_bc - pid_c * batch + pid_c = tl.program_id(axis=1).to(tl.int64) pid_h = tl.program_id(axis=2) num_pid_n = tl.cdiv(dstate, BLOCK_SIZE_N) pid_m = tl.program_id(axis=0) // num_pid_n pid_n = tl.program_id(axis=0) % num_pid_n - b_ptr += pid_b * stride_b_batch + pid_c * chunk_size * stride_b_seqlen + ( + b_ptr += pid_c * chunk_size * stride_b_seqlen + ( pid_h // nheads_ngroups_ratio) * stride_b_head - x_ptr += pid_b * stride_x_batch + pid_c * chunk_size * stride_x_seqlen + pid_h * stride_x_head - dt_ptr += pid_b * stride_dt_batch + pid_c * stride_dt_chunk + pid_h * stride_dt_head - dA_cumsum_ptr += pid_b * stride_dA_cs_batch + pid_c * stride_dA_cs_chunk + pid_h * stride_dA_cs_head - if HAS_SEQ_IDX: - seq_idx_ptr += pid_b * stride_seq_idx_batch + pid_c * chunk_size * stride_seq_idx_seqlen + x_ptr += pid_c * chunk_size * stride_x_seqlen + pid_h * stride_x_head + dt_ptr += pid_c * stride_dt_chunk + pid_h * stride_dt_head + dA_cumsum_ptr += pid_c * stride_dA_cs_chunk + pid_h * stride_dA_cs_head + + seq_idx_ptr += pid_c * chunk_size * stride_seq_idx_seqlen offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) @@ -259,13 +242,11 @@ def _chunk_state_fwd_kernel( dA_cs_last = tl.load(dA_cumsum_ptr + (chunk_size - 1) * stride_dA_cs_csize).to(tl.float32) dA_cumsum_ptrs = dA_cumsum_ptr + offs_k * stride_dA_cs_csize - if HAS_SEQ_IDX: - seq_idx_ptrs = seq_idx_ptr + offs_k * stride_seq_idx_seqlen + seq_idx_ptrs = seq_idx_ptr + offs_k * stride_seq_idx_seqlen chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) - if HAS_SEQ_IDX: - seq_idx_last = tl.load(seq_idx_ptr + - (chunk_size_limit - 1) * stride_seq_idx_seqlen) + seq_idx_last = tl.load(seq_idx_ptr + + (chunk_size_limit - 1) * stride_seq_idx_seqlen) acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) for k in range(0, chunk_size_limit, BLOCK_SIZE_K): @@ -280,29 +261,28 @@ def _chunk_state_fwd_kernel( dA_cs_k = tl.load(dA_cumsum_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0).to(tl.float32) - if HAS_SEQ_IDX: - seq_idx_k = tl.load(seq_idx_ptrs, - mask=offs_k < chunk_size_limit - k, - other=-1) + + seq_idx_k = tl.load(seq_idx_ptrs, + mask=offs_k < chunk_size_limit - k, + other=-1) dt_k = tl.load(dt_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0).to(tl.float32) - if not HAS_SEQ_IDX: - scale = tl.exp(dA_cs_last - dA_cs_k) * dt_k - else: - scale = tl.where(seq_idx_k == seq_idx_last, - tl.exp(dA_cs_last - dA_cs_k) * dt_k, 0.0) + + scale = tl.where(seq_idx_k == seq_idx_last, + tl.exp(dA_cs_last - dA_cs_k) * dt_k, 0.0) b *= scale[:, None] b = b.to(x_ptr.dtype.element_ty) acc += tl.dot(x, b) + x_ptrs += BLOCK_SIZE_K * stride_x_seqlen b_ptrs += BLOCK_SIZE_K * stride_b_seqlen dt_ptrs += BLOCK_SIZE_K * stride_dt_csize dA_cumsum_ptrs += BLOCK_SIZE_K * stride_dA_cs_csize - if HAS_SEQ_IDX: - seq_idx_ptrs += BLOCK_SIZE_K * stride_seq_idx_seqlen + seq_idx_ptrs += BLOCK_SIZE_K * stride_seq_idx_seqlen + states = acc.to(states_ptr.dtype.element_ty) - states_ptr += pid_b * stride_states_batch + pid_c * stride_states_chunk + pid_h * stride_states_head + states_ptr += pid_c * stride_states_chunk + pid_h * stride_states_head offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) states_ptrs = states_ptr + (offs_m[:, None] * stride_states_hdim + @@ -400,36 +380,35 @@ def _chunk_state_varlen_kernel( states_ptr, initstates_ptr, # Matrix dimensions - hdim, - dstate, - chunk_size, - seqlen, - nheads_ngroups_ratio, + hdim: tl.constexpr, + dstate: tl.constexpr, + chunk_size: tl.constexpr, + nheads_ngroups_ratio: tl.constexpr, # Strides - stride_x_seqlen, - stride_x_head, - stride_x_hdim, - stride_b_seqlen, - stride_b_head, - stride_b_dstate, - stride_dt_chunk, - stride_dt_head, - stride_dt_csize, - stride_dA_cs_chunk, - stride_dA_cs_head, - stride_dA_cs_csize, - stride_chunk_states_chunk, - stride_chunk_states_head, - stride_chunk_states_hdim, - stride_chunk_states_dstate, - stride_states_batch, - stride_states_head, - stride_states_hdim, - stride_states_dstate, - stride_init_states_batch, - stride_init_states_head, - stride_init_states_hdim, - stride_init_states_dstate, + stride_x_seqlen: tl.int64, + stride_x_head: tl.int64, + stride_x_hdim: tl.constexpr, + stride_b_seqlen: tl.int64, + stride_b_head: tl.int64, + stride_b_dstate: tl.constexpr, + stride_dt_head: tl.int64, + stride_dt_chunk: tl.int64, + stride_dt_csize: tl.constexpr, + stride_dA_cs_head: tl.int64, + stride_dA_cs_chunk: tl.int64, + stride_dA_cs_csize: tl.constexpr, + stride_chunk_states_chunk: tl.int64, + stride_chunk_states_head: tl.int64, + stride_chunk_states_hdim: tl.int64, + stride_chunk_states_dstate: tl.constexpr, + stride_states_batch: tl.int64, + stride_states_head: tl.int64, + stride_states_hdim: tl.int64, + stride_states_dstate: tl.constexpr, + stride_init_states_batch: tl.int64, + stride_init_states_head: tl.int64, + stride_init_states_hdim: tl.int64, + stride_init_states_dstate: tl.constexpr, # Meta-parameters BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, @@ -558,52 +537,47 @@ def _chunk_cumsum_fwd(dt, dt_bias=None, dt_softplus=False, dt_limit=(0.0, float("inf"))): - batch, seqlen, nheads = dt.shape + seqlen, nheads = dt.shape assert A.shape == (nheads, ) if dt_bias is not None: assert dt_bias.shape == (nheads, ) nchunks = math.ceil(seqlen / chunk_size) - dt_out = torch.empty(batch, - nheads, + dt_out = torch.empty(nheads, nchunks, chunk_size, device=dt.device, dtype=torch.float32) - dA_cumsum = torch.empty(batch, - nheads, + dA_cumsum = torch.empty(nheads, nchunks, chunk_size, device=dt.device, dtype=torch.float32) - grid_chunk_cs = lambda META: (batch, nchunks, + grid_chunk_cs = lambda META: (nchunks, triton.cdiv(nheads, META['BLOCK_SIZE_H'])) with torch.cuda.device(dt.device.index): _chunk_cumsum_fwd_kernel[grid_chunk_cs]( - dt, - A, - dt_bias, - dt_out, - dA_cumsum, - batch, - seqlen, - nheads, - chunk_size, - dt_limit[0], - dt_limit[1], - dt.stride(0), - dt.stride(1), - dt.stride(2), - A.stride(0), - dt_bias.stride(0) if dt_bias is not None else 0, - dt_out.stride(0), - dt_out.stride(2), - dt_out.stride(1), - dt_out.stride(3), - dA_cumsum.stride(0), - dA_cumsum.stride(2), - dA_cumsum.stride(1), - dA_cumsum.stride(3), - dt_softplus, + dt_ptr=dt, + A_ptr=A, + dt_bias_ptr=dt_bias, + dt_out_ptr=dt_out, + dA_cumsum_ptr=dA_cumsum, + seqlen=seqlen, + nheads=nheads, + chunk_size=chunk_size, + dt_min=dt_limit[0], + dt_max=dt_limit[1], + stride_dt_seqlen=dt.stride(0), + stride_dt_head=dt.stride(1), + stride_A_head=A.stride(0), + stride_dt_bias_head=dt_bias.stride(0) + if dt_bias is not None else 0, + stride_dt_out_head=dt_out.stride(0), + stride_dt_out_chunk=dt_out.stride(1), + stride_dt_out_csize=dt_out.stride(2), + stride_dA_cs_head=dA_cumsum.stride(0), + stride_dA_cs_chunk=dA_cumsum.stride(1), + stride_dA_cs_csize=dA_cumsum.stride(2), + DT_SOFTPLUS=dt_softplus, HAS_DT_BIAS=dt_bias is not None, BLOCK_SIZE_CHUNK=triton.next_power_of_2(chunk_size), ) @@ -617,63 +591,57 @@ def _chunk_state_fwd(B, seq_idx=None, states=None, states_in_fp32=True): - batch, seqlen, nheads, headdim = x.shape - _, _, nchunks, chunk_size = dt.shape - _, _, ngroups, dstate = B.shape + seqlen, nheads, headdim = x.shape + _, nchunks, chunk_size = dt.shape + _, ngroups, dstate = B.shape assert nheads % ngroups == 0 - assert B.shape == (batch, seqlen, ngroups, dstate) - assert dt.shape == (batch, nheads, nchunks, chunk_size) + assert B.shape == (seqlen, ngroups, dstate) + assert dt.shape == (nheads, nchunks, chunk_size) assert dA_cumsum.shape == dt.shape - if seq_idx is not None: - assert seq_idx.shape == (batch, seqlen) + + assert seq_idx is not None + assert seq_idx.shape == (seqlen, ) + if states is not None: - assert states.shape == (batch, nchunks, nheads, headdim, dstate) + assert states.shape == (nchunks, nheads, headdim, dstate) else: states_dtype = torch.float32 if states_in_fp32 else B.dtype - states = torch.empty((batch, nchunks, nheads, headdim, dstate), + states = torch.empty((nchunks, nheads, headdim, dstate), device=x.device, dtype=states_dtype) - grid = lambda META: ( - triton.cdiv(headdim, META['BLOCK_SIZE_M']) * triton.cdiv( - dstate, META['BLOCK_SIZE_N']), batch * nchunks, nheads) + + grid = lambda META: (triton.cdiv(headdim, META['BLOCK_SIZE_M']) * triton. + cdiv(dstate, META['BLOCK_SIZE_N']), nchunks, nheads) with torch.cuda.device(x.device.index): _chunk_state_fwd_kernel[grid]( - x, - B, - states, - dt, - dA_cumsum, - seq_idx, - headdim, - dstate, - chunk_size, - batch, - seqlen, - nheads // ngroups, - x.stride(0), - x.stride(1), - x.stride(2), - x.stride(3), - B.stride(0), - B.stride(1), - B.stride(2), - B.stride(-1), - states.stride(0), - states.stride(1), - states.stride(2), - states.stride(3), - states.stride(4), - dt.stride(0), - dt.stride(2), - dt.stride(1), - dt.stride(3), - dA_cumsum.stride(0), - dA_cumsum.stride(2), - dA_cumsum.stride(1), - dA_cumsum.stride(3), - *((seq_idx.stride(0), - seq_idx.stride(1)) if seq_idx is not None else (0, 0)), - HAS_SEQ_IDX=seq_idx is not None, + x_ptr=x, + b_ptr=B, + states_ptr=states, + dt_ptr=dt, + dA_cumsum_ptr=dA_cumsum, + seq_idx_ptr=seq_idx, + hdim=headdim, + dstate=dstate, + chunk_size=chunk_size, + seqlen=seqlen, + nheads_ngroups_ratio=nheads // ngroups, + stride_x_seqlen=x.stride(0), + stride_x_head=x.stride(1), + stride_x_hdim=x.stride(2), + stride_b_seqlen=B.stride(0), + stride_b_head=B.stride(1), + stride_b_dstate=B.stride(2), + stride_states_chunk=states.stride(0), + stride_states_head=states.stride(1), + stride_states_hdim=states.stride(2), + stride_states_dstate=states.stride(3), + stride_dt_head=dt.stride(0), + stride_dt_chunk=dt.stride(1), + stride_dt_csize=dt.stride(2), + stride_dA_cs_head=dA_cumsum.stride(0), + stride_dA_cs_chunk=dA_cumsum.stride(1), + stride_dA_cs_csize=dA_cumsum.stride(2), + stride_seq_idx_seqlen=seq_idx.stride(0), ) return states @@ -705,46 +673,52 @@ def chunk_state_varlen(B, dstate, dtype=chunk_states.dtype, device=chunk_states.device) + + initial_states_strides = ((initial_states.stride(0), + initial_states.stride(1), + initial_states.stride(2), + initial_states.stride(3)) + if initial_states is not None else (0, 0, 0, 0)) + grid = lambda META: (triton.cdiv(headdim, META['BLOCK_SIZE_M']) * triton. cdiv(dstate, META['BLOCK_SIZE_N']), batch, nheads) with torch.cuda.device(x.device.index): _chunk_state_varlen_kernel[grid]( - x, - B, - dt, - dA_cumsum, - chunk_states, - cu_seqlens, - states, - initial_states, - headdim, - dstate, - chunk_size, - total_seqlen, - nheads // ngroups, - x.stride(0), - x.stride(1), - x.stride(2), - B.stride(0), - B.stride(1), - B.stride(2), - dt.stride(1), - dt.stride(0), - dt.stride(2), - dA_cumsum.stride(1), - dA_cumsum.stride(0), - dA_cumsum.stride(2), - chunk_states.stride(0), - chunk_states.stride(1), - chunk_states.stride(2), - chunk_states.stride(3), - states.stride(0), - states.stride(1), - states.stride(2), - states.stride(3), - *((initial_states.stride(0), initial_states.stride(1), - initial_states.stride(2), - initial_states.stride(3)) if initial_states is not None else - (0, 0, 0, 0)), + x_ptr=x, + b_ptr=B, + dt_ptr=dt, + dA_cumsum_ptr=dA_cumsum, + chunk_states_ptr=chunk_states, + cu_seqlens_ptr=cu_seqlens, + states_ptr=states, + initstates_ptr=initial_states, + hdim=headdim, + dstate=dstate, + chunk_size=chunk_size, + nheads_ngroups_ratio=nheads // ngroups, + stride_x_seqlen=x.stride(0), + stride_x_head=x.stride(1), + stride_x_hdim=x.stride(2), + stride_b_seqlen=B.stride(0), + stride_b_head=B.stride(1), + stride_b_dstate=B.stride(2), + stride_dt_head=dt.stride(0), + stride_dt_chunk=dt.stride(1), + stride_dt_csize=dt.stride(2), + stride_dA_cs_head=dA_cumsum.stride(0), + stride_dA_cs_chunk=dA_cumsum.stride(1), + stride_dA_cs_csize=dA_cumsum.stride(2), + stride_chunk_states_chunk=chunk_states.stride(0), + stride_chunk_states_head=chunk_states.stride(1), + stride_chunk_states_hdim=chunk_states.stride(2), + stride_chunk_states_dstate=chunk_states.stride(3), + stride_states_batch=states.stride(0), + stride_states_head=states.stride(1), + stride_states_hdim=states.stride(2), + stride_states_dstate=states.stride(3), + stride_init_states_batch=initial_states_strides[0], + stride_init_states_head=initial_states_strides[1], + stride_init_states_hdim=initial_states_strides[2], + stride_init_states_dstate=initial_states_strides[3], HAS_INITSTATES=initial_states is not None) return states diff --git a/vllm/model_executor/layers/mamba/ops/ssd_combined.py b/vllm/model_executor/layers/mamba/ops/ssd_combined.py index fcc5c905bf77..37d6c2870812 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_combined.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_combined.py @@ -31,6 +31,7 @@ def _mamba_chunk_scan_combined_fwd(x, B, C, chunk_size, + out, D=None, z=None, dt_bias=None, @@ -41,14 +42,13 @@ def _mamba_chunk_scan_combined_fwd(x, cu_seqlens=None, dt_softplus=False, dt_limit=(0.0, float("inf")), - state_dtype=None, - out=None): + state_dtype=None): assert is_int_pow_2(chunk_size), "chunk_size must be integer power of 2" - batch, seqlen, nheads, headdim = x.shape - _, _, ngroups, dstate = B.shape + seqlen, nheads, headdim = x.shape + _, ngroups, dstate = B.shape assert nheads % ngroups == 0 - assert B.shape == (batch, seqlen, ngroups, dstate) - assert dt.shape == (batch, seqlen, nheads) + assert B.shape == (seqlen, ngroups, dstate) + assert dt.shape == (seqlen, nheads) assert A.shape == (nheads, ) assert C.shape == B.shape if z is not None: @@ -56,25 +56,24 @@ def _mamba_chunk_scan_combined_fwd(x, if D is not None: assert D.shape == (nheads, headdim) or D.shape == (nheads, ) if seq_idx is not None: - assert seq_idx.shape == (batch, seqlen) + assert seq_idx.shape == (seqlen, ) if B.stride(-1) != 1: B = B.contiguous() if C.stride(-1) != 1: C = C.contiguous() if x.stride(-1) != 1 and x.stride( - 1) != 1: # Either M or K dimension should be contiguous + 0) != 1: # Either M or K dimension should be contiguous x = x.contiguous() if z is not None and z.stride(-1) != 1 and z.stride( - 1) != 1: # Either M or K dimension should be contiguous + 0) != 1: # Either M or K dimension should be contiguous z = z.contiguous() if D is not None and D.stride(-1) != 1: D = D.contiguous() + assert cu_seqlens is not None, "Assuming varlen input - must supply cu_seqlens" + if initial_states is not None: - if cu_seqlens is None: - assert initial_states.shape == (batch, nheads, headdim, dstate) - else: - assert initial_states.shape == (len(cu_seqlens) - 1, nheads, - headdim, dstate) + assert initial_states.shape == (len(cu_seqlens) - 1, nheads, headdim, + dstate) # This function executes 5 sub-functions for computing mamba # - a good resource is the blog https://goombalab.github.io/blog/2024/mamba2-part3-algorithm/ @@ -114,18 +113,16 @@ def _mamba_chunk_scan_combined_fwd(x, # - this will ensure that states will be updated with the rightmost flushed seq_idx # of the previous chunk. This implies that the first chunk of states is either 0 # or equal to init_states of the first example. - states, final_states = _state_passing_fwd( + states = _state_passing_fwd( rearrange(states, "... p n -> ... (p n)"), - dA_cumsum, + dA_cumsum, # (nheads, nchunks, chunk_size) initial_states=rearrange(initial_states, "... p n -> ... (p n)") - if initial_states is not None else None, + if initial_states is not None else + None, # (batch, nheads, headdim*dstate) seq_idx=seq_idx, - chunk_size=chunk_size, out_dtype=state_dtype if state_dtype is not None else C.dtype, - is_cont_batched=cu_seqlens is not None, chunk_offsets=chunk_offsets) - states, final_states = (rearrange(t, "... (p n) -> ... p n", n=dstate) - for t in [states, final_states]) + states = rearrange(states, "... (p n) -> ... p n", n=dstate) # 4. Compute batched matrix multiply for C_j^T B_i terms CB = _bmm_chunk_fwd(C, @@ -144,87 +141,88 @@ def _mamba_chunk_scan_combined_fwd(x, # - in each (pseudo) chunk, we detect if the previous (pseudo) chunk had # a seq_idx change, in which case we take states information from # init_states. - out_x = _chunk_scan_fwd( + _chunk_scan_fwd( CB, x, dt, dA_cumsum, C, states, + out, # in-place update + seq_idx, D=D, z=z, - seq_idx=seq_idx, chunk_indices=chunk_indices, chunk_offsets=chunk_offsets, initial_states=initial_states, - out=out, ) - if cu_seqlens is None: - return out_x, dt, dA_cumsum, states, final_states - else: - assert batch == 1, "passing cu_seqlens to get the varlen states is only supported if batch dimension is 1" - varlen_states = chunk_state_varlen( - B.squeeze(0), - x.squeeze(0), - dt.squeeze(0), - dA_cumsum.squeeze(0), - cu_seqlens, - states.squeeze(0), - initial_states=initial_states, - ) - return out_x, dt, dA_cumsum, states, final_states, varlen_states - - -def mamba_chunk_scan_combined(x, - dt, - A, - B, - C, - chunk_size, - D=None, - z=None, - dt_bias=None, - initial_states=None, - seq_idx=None, - chunk_indices=None, - chunk_offsets=None, - cu_seqlens=None, - dt_softplus=False, - dt_limit=(0.0, float("inf")), - out=None, - return_final_states=False, - return_varlen_states=False, - state_dtype=None): + + varlen_states = chunk_state_varlen( + B, + x, + dt, + dA_cumsum, + cu_seqlens, + states, + initial_states=initial_states, + ) + + return varlen_states + + +def mamba_chunk_scan_combined_varlen( + x, + dt, + A, + B, + C, + chunk_size, + cu_seqlens, + seq_idx, + out, + D=None, + z=None, + dt_bias=None, + initial_states=None, + chunk_indices=None, + chunk_offsets=None, + dt_softplus=False, + dt_limit=(0.0, float("inf")), + state_dtype=None, +): """ Argument: - x: (batch, seqlen, nheads, headdim) - dt: (batch, seqlen, nheads) + x: (seqlen, nheads, headdim) + dt: (seqlen, nheads) A: (nheads) - B: (batch, seqlen, ngroups, dstate) - C: (batch, seqlen, ngroups, dstate) + B: (seqlen, ngroups, dstate) + C: (seqlen, ngroups, dstate) chunk_size: int + seq_idx: (seqlen) + cu_seqlens: (batch + 1) + out: (seqlen, nheads, headdim) preallocated output tensor D: (nheads, headdim) or (nheads,) - z: (batch, seqlen, nheads, headdim) + z: (seqlen, nheads, headdim) dt_bias: (nheads,) initial_states: (batch, nheads, headdim, dstate) - seq_idx: (batch, seqlen) - cu_seqlens: (num_sequences + 1) or None, only used if return_varlen_states is True dt_softplus: Whether to apply softplus to dt - out: Preallocated output tensor + out: (seqlen, nheads, headdim) preallocated output tensor state_dtype: The data type of the ssm state + Return: + varlen_states: (batch, nheads, headdim, dstate) """ - if not return_varlen_states: - cu_seqlens = None - else: - assert cu_seqlens is not None, "cu_seqlens must be provided if return_varlen_states is True" - out_x, dt_out, dA_cumsum, states, final_states, *rest = _mamba_chunk_scan_combined_fwd( + assert cu_seqlens is not None, "cu_seqlens must be provided assuming varlen input" + assert seq_idx is not None + + varlen_states = _mamba_chunk_scan_combined_fwd( x, dt, A, B, C, chunk_size, + out, D=D, z=z, dt_bias=dt_bias, @@ -235,14 +233,6 @@ def mamba_chunk_scan_combined(x, cu_seqlens=cu_seqlens, dt_softplus=dt_softplus, dt_limit=dt_limit, - out=out, state_dtype=state_dtype) - if not return_varlen_states: - if not return_final_states: - return - else: - return final_states - else: - varlen_states = rest[0] - return (varlen_states) if not return_final_states else (final_states, - varlen_states) + + return varlen_states diff --git a/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py b/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py index d61c3a8cdbe9..71a8a4b0a1c8 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py @@ -27,64 +27,46 @@ def _state_passing_fwd_kernel( # Pointers to matrices states_ptr, out_ptr, - final_states_ptr, dA_cs_ptr, initstates_ptr, seq_idx_ptr, chunk_offsets_ptr, chunk_meta_num, # Matrix dimensions - dim, + dim: tl.constexpr, nchunks, seqlen, - chunk_size, + chunk_size: tl.constexpr, # Strides - stride_states_batch, - stride_states_chunk, - stride_states_head, - stride_states_dim, - stride_out_batch, - stride_out_chunk, - stride_out_head, - stride_out_dim, - stride_final_states_batch, - stride_final_states_head, - stride_final_states_dim, - stride_dA_cs_batch, - stride_dA_cs_chunk, - stride_dA_cs_head, - stride_dA_cs_csize, - stride_initstates_batch, - stride_initstates_head, - stride_initstates_dim, - stride_seq_idx_batch, - stride_seq_idx_seqlen, + stride_states_chunk: tl.int64, + stride_states_head: tl.int64, + stride_states_dim: tl.constexpr, + stride_out_chunk: tl.int64, + stride_out_head: tl.int64, + stride_out_dim: tl.constexpr, + stride_dA_cs_head: tl.int64, + stride_dA_cs_chunk: tl.int64, + stride_dA_cs_csize: tl.constexpr, + stride_initstates_batch: tl.int64, + stride_initstates_head: tl.int64, + stride_initstates_dim: tl.constexpr, + stride_seq_idx_seqlen: tl.constexpr, # Meta-parameters HAS_INITSTATES: tl.constexpr, - HAS_SEQ_IDX: tl.constexpr, - IS_CONT_BATCHED: tl.constexpr, BLOCK_SIZE: tl.constexpr, ): - pid_b = tl.program_id(axis=1) - pid_h = tl.program_id(axis=2) + pid_h = tl.program_id(axis=1) pid_m = tl.program_id(axis=0) - states_ptr += pid_b * stride_states_batch + pid_h * stride_states_head - dA_cs_ptr += pid_b * stride_dA_cs_batch + pid_h * stride_dA_cs_head + ( - chunk_size - 1) * stride_dA_cs_csize - out_ptr += pid_b * stride_out_batch + pid_h * stride_out_head - final_states_ptr += pid_b * stride_final_states_batch + pid_h * stride_final_states_head + states_ptr += pid_h * stride_states_head + dA_cs_ptr += pid_h * stride_dA_cs_head + (chunk_size - + 1) * stride_dA_cs_csize + out_ptr += pid_h * stride_out_head if HAS_INITSTATES: initstates_ptr += pid_h * stride_initstates_head - if not IS_CONT_BATCHED: - initstates_ptr += pid_b * stride_initstates_batch - - if HAS_SEQ_IDX: - seq_idx_ptr += pid_b * stride_seq_idx_batch offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) states_ptrs = states_ptr + offs_m * stride_states_dim out_ptrs = out_ptr + offs_m * stride_out_dim - final_states_ptrs = final_states_ptr + offs_m * stride_final_states_dim # - states will be the past state of the sequence that continues on the current check if not HAS_INITSTATES: @@ -101,65 +83,63 @@ def _state_passing_fwd_kernel( out_ptrs += stride_out_chunk prev_seq_idx_chunk_end = 0 logical_chunk_idx = 0 - for c in range(nchunks): + for c in range(nchunks - 1): new_states = tl.load(states_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) dA_cs = tl.load(dA_cs_ptr).to(tl.float32) scale_mask = True - if HAS_SEQ_IDX: - # - the seq to pass forward is the one that is flushed to the right - # boundary. - # - that is given by seq_idx_chunk_end below: the sequence index at the end of the chunk. - seq_idx_chunk_end = tl.load(seq_idx_ptr + (min( - (c + 1) * chunk_size, seqlen) - 1) * stride_seq_idx_seqlen) - if HAS_INITSTATES: - if IS_CONT_BATCHED and prev_seq_idx_chunk_end != seq_idx_chunk_end: - # this means in the current chunk the rightmost flushed seq - # has changed. - # - so we do not propagate the state from previous chunk - # - but rather we load that sequence's init state - initstates_ptrs = initstates_ptr + seq_idx_chunk_end * stride_initstates_batch - - # - update state with seq_idx_new's init state - states = tl.load(initstates_ptrs, - mask=offs_m < dim, - other=0.0).to(tl.float32) - - # - we need to consider the cumsum only of the last sequence in the chunk - # - find its starting position (given by c_off of the logical chunk index) - # - and subtract the cumsum just before that position from the total cumsum - # - first, update the logical chunk index (add the number of sequences in the current physical chunk): - # sequence index at the start of the current chunk - seq_idx_chunk_start = tl.load(seq_idx_ptr + - min(c * chunk_size, seqlen) * - stride_seq_idx_seqlen) - logical_chunk_idx += seq_idx_chunk_end - seq_idx_chunk_start - # - load the chunk offset: - c_off = tl.load(chunk_offsets_ptr + logical_chunk_idx, - mask=logical_chunk_idx < chunk_meta_num, - other=0) - # - if offset is 0, then the sequence starts at the beginning of the chunk, and we don't need to subtract anything - if c_off > 0: - # - dA_cs_ptr currently points to the cumsum at the end of the chunk - subtract the chunk size and add the offset - dA_cs_boundary = tl.load( - dA_cs_ptr - (chunk_size - 1) * stride_dA_cs_csize + - (c_off - 1) * stride_dA_cs_csize, - mask=(c_off - 1) > -1 and c_off < chunk_size, - other=0.0) - dA_cs -= dA_cs_boundary - - # - increment logical chunk index for every physical chunk - logical_chunk_idx += 1 - else: - scale_mask = seq_idx_chunk_end == prev_seq_idx_chunk_end - prev_seq_idx_chunk_end = seq_idx_chunk_end + # - the seq to pass forward is the one that is flushed to the right + # boundary. + # - that is given by seq_idx_chunk_end below: the sequence index at the end of the chunk. + seq_idx_chunk_end = tl.load(seq_idx_ptr + + (min((c + 1) * chunk_size, seqlen) - 1) * + stride_seq_idx_seqlen) + + if HAS_INITSTATES: + if prev_seq_idx_chunk_end != seq_idx_chunk_end: + # this means in the current chunk the rightmost flushed seq + # has changed. + # - so we do not propagate the state from previous chunk + # - but rather we load that sequence's init state + initstates_ptrs = initstates_ptr + seq_idx_chunk_end * stride_initstates_batch + + # - update state with seq_idx_new's init state + states = tl.load(initstates_ptrs, mask=offs_m < dim, + other=0.0).to(tl.float32) + + # - we need to consider the cumsum only of the last sequence in the chunk + # - find its starting position (given by c_off of the logical chunk index) + # - and subtract the cumsum just before that position from the total cumsum + # - first, update the logical chunk index (add the number of sequences in the current physical chunk): + # sequence index at the start of the current chunk + seq_idx_chunk_start = tl.load(seq_idx_ptr + + min(c * chunk_size, seqlen) * + stride_seq_idx_seqlen) + logical_chunk_idx += seq_idx_chunk_end - seq_idx_chunk_start + # - load the chunk offset: + c_off = tl.load(chunk_offsets_ptr + logical_chunk_idx, + mask=logical_chunk_idx < chunk_meta_num, + other=0) + # - if offset is 0, then the sequence starts at the beginning of the chunk, and we don't need to subtract anything + if c_off > 0: + # - dA_cs_ptr currently points to the cumsum at the end of the chunk - subtract the chunk size and add the offset + dA_cs_boundary = tl.load( + dA_cs_ptr - (chunk_size - 1) * stride_dA_cs_csize + + (c_off - 1) * stride_dA_cs_csize, + mask=(c_off - 1) > -1 and c_off < chunk_size, + other=0.0) + dA_cs -= dA_cs_boundary + + # - increment logical chunk index for every physical chunk + logical_chunk_idx += 1 + else: + scale_mask = seq_idx_chunk_end == prev_seq_idx_chunk_end + prev_seq_idx_chunk_end = seq_idx_chunk_end scale = tl.where(scale_mask, tl.exp(dA_cs), 0.0) states = scale * states + new_states - if c < nchunks - 1: - tl.store(out_ptrs, states, mask=offs_m < dim) - else: - tl.store(final_states_ptrs, states, mask=offs_m < dim) + tl.store(out_ptrs, states, mask=offs_m < dim) + states_ptrs += stride_states_chunk dA_cs_ptr += stride_dA_cs_chunk out_ptrs += stride_out_chunk @@ -168,81 +148,53 @@ def _state_passing_fwd_kernel( def _state_passing_fwd( states, dA_cumsum, + seq_idx, + chunk_offsets, initial_states=None, - seq_idx=None, - chunk_size=None, out_dtype=None, - is_cont_batched=False, - chunk_offsets=None, ): - batch, nchunks, nheads, dim = states.shape - if chunk_size is None: - chunk_size = dA_cumsum.shape[-1] - else: - assert chunk_size == dA_cumsum.shape[-1] - assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size) - if initial_states is not None: - if is_cont_batched: - # - if cu_seqlens is provided, then the initial states - # are used for continuous batching. In which case we - # require seq_idx to be provided - assert seq_idx is not None, "seq_idx must be provided for continuous batching" - # - we also need chunk_offsets to be provided, to account - # for computation of dA_cumsum from the start of the - # sequence - assert chunk_offsets is not None, "chunk_offsets must be provided for continuous batching" - else: - # - this is the regular batching case, where initial - # states are used are for each example of the batch. - assert initial_states.shape == (batch, nheads, dim) - - if seq_idx is not None: - seqlen = seq_idx.shape[-1] - assert seq_idx.shape == (batch, seqlen) + nchunks, nheads, dim = states.shape + chunk_size = dA_cumsum.shape[-1] + assert dA_cumsum.shape == (nheads, nchunks, chunk_size) + seqlen = seq_idx.shape[-1] out_dtype = states.dtype if out_dtype is None else out_dtype - out = torch.empty((batch, nchunks, nheads, dim), + out = torch.empty((nchunks, nheads, dim), device=states.device, dtype=out_dtype) - final_states = torch.empty((batch, nheads, dim), - device=states.device, - dtype=torch.float32) - grid = lambda META: (triton.cdiv(dim, META['BLOCK_SIZE']), batch, nheads) + + initial_states_strides = ((initial_states.stride(0), + initial_states.stride(1), + initial_states.stride(2)) + if initial_states is not None else (0, 0, 0)) + + grid = lambda META: (triton.cdiv(dim, META['BLOCK_SIZE']), nheads) with torch.cuda.device(states.device.index): _state_passing_fwd_kernel[grid]( - states, - out, - final_states, - dA_cumsum, - initial_states, - seq_idx, - chunk_offsets, - len(chunk_offsets) if chunk_offsets is not None else 0, - dim, - nchunks, - seqlen if seq_idx is not None else 0, - chunk_size, - states.stride(0), - states.stride(1), - states.stride(2), - states.stride(3), - out.stride(0), - out.stride(1), - out.stride(2), - out.stride(3), - final_states.stride(0), - final_states.stride(1), - final_states.stride(2), - dA_cumsum.stride(0), - dA_cumsum.stride(2), - dA_cumsum.stride(1), - dA_cumsum.stride(3), - *((initial_states.stride(0), initial_states.stride(1), - initial_states.stride(2)) if initial_states is not None else - (0, 0, 0)), - *((seq_idx.stride(0), - seq_idx.stride(1)) if seq_idx is not None else (0, 0)), + states_ptr=states, + out_ptr=out, + dA_cs_ptr=dA_cumsum, + initstates_ptr=initial_states, + seq_idx_ptr=seq_idx, + chunk_offsets_ptr=chunk_offsets, + chunk_meta_num=len(chunk_offsets) + if chunk_offsets is not None else 0, + dim=dim, + nchunks=nchunks, + seqlen=seqlen if seq_idx is not None else 0, + chunk_size=chunk_size if seq_idx is not None else 0, + stride_states_chunk=states.stride(0), + stride_states_head=states.stride(1), + stride_states_dim=states.stride(2), + stride_out_chunk=out.stride(0), + stride_out_head=out.stride(1), + stride_out_dim=out.stride(2), + stride_dA_cs_head=dA_cumsum.stride(0), + stride_dA_cs_chunk=dA_cumsum.stride(1), + stride_dA_cs_csize=dA_cumsum.stride(2), + stride_initstates_batch=initial_states_strides[0], + stride_initstates_head=initial_states_strides[1], + stride_initstates_dim=initial_states_strides[2], + stride_seq_idx_seqlen=seq_idx.stride(0), HAS_INITSTATES=initial_states is not None, - HAS_SEQ_IDX=seq_idx is not None, - IS_CONT_BATCHED=is_cont_batched, ) - return out, final_states + return out diff --git a/vllm/model_executor/models/plamo2.py b/vllm/model_executor/models/plamo2.py index a7acf64f302b..03265b13de50 100644 --- a/vllm/model_executor/models/plamo2.py +++ b/vllm/model_executor/models/plamo2.py @@ -35,7 +35,7 @@ from vllm.model_executor.layers.mamba.ops.mamba_ssm import ( selective_state_update) from vllm.model_executor.layers.mamba.ops.ssd_combined import ( - mamba_chunk_scan_combined) + mamba_chunk_scan_combined_varlen) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( @@ -262,6 +262,7 @@ def forward_cuda( seq_idx_p = attn_metadata.seq_idx_p chunk_indices_p = attn_metadata.chunk_indices_p chunk_offsets_p = attn_metadata.chunk_offsets_p + query_start_loc_p = attn_metadata.query_start_loc_p # 1. Gated MLP's linear projection projected_states = self.in_proj(hidden_states) @@ -302,9 +303,6 @@ def forward_cuda( [num_decodes, num_prefills], dim=0, ) - query_start_loc_p = ( - attn_metadata.query_start_loc[-num_prefills - 1:] - - num_decodes if has_prefill else None) # Preallocate output tensor to avoid memcpy cost for merging prefill # and decode outputs @@ -356,17 +354,17 @@ def forward_cuda( has_initial_states_p[:, None, None, None], ssm_state[state_indices_tensor_p], 0) - varlen_state = mamba_chunk_scan_combined( - hidden_states_p.view(1, num_prefill_tokens, + varlen_state = mamba_chunk_scan_combined_varlen( + hidden_states_p.view(num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim), - dt.unsqueeze(0), + dt, self.A, - B.view(1, num_prefill_tokens, 1, -1), - C.view(1, num_prefill_tokens, 1, -1), + B.view(num_prefill_tokens, 1, -1), + C.view(num_prefill_tokens, 1, -1), chunk_size=chunk_size, D=self.D, - z=gate_p.view(1, num_prefill_tokens, + z=gate_p.view(num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim), dt_bias=self.dt_bias, seq_idx=seq_idx_p, @@ -374,11 +372,9 @@ def forward_cuda( chunk_offsets=chunk_offsets_p, cu_seqlens=query_start_loc_p, initial_states=initial_states, - return_varlen_states=True, - return_final_states=False, dt_softplus=True, dt_limit=(0.0, float("inf")), - out=preallocated_ssm_out_p.view(1, num_prefill_tokens, -1, + out=preallocated_ssm_out_p.view(num_prefill_tokens, -1, self.head_dim), state_dtype=ssm_state.dtype, ) diff --git a/vllm/v1/attention/backends/mamba2_attn.py b/vllm/v1/attention/backends/mamba2_attn.py index f45fc75334a2..6f16fda962ae 100644 --- a/vllm/v1/attention/backends/mamba2_attn.py +++ b/vllm/v1/attention/backends/mamba2_attn.py @@ -115,7 +115,7 @@ class Mamba2AttentionMetadata: num_prefill_tokens: int num_decodes: int num_decode_tokens: int - query_start_loc: torch.Tensor + query_start_loc_p: torch.Tensor seq_lens: torch.Tensor prep_initial_states: bool @@ -151,7 +151,7 @@ def build(self, common_attn_metadata: CommonAttentionMetadata, fast_build: bool = False) -> Mamba2AttentionMetadata: num_reqs = common_attn_metadata.num_reqs - query_start_loc = common_attn_metadata.query_start_loc + query_start_loc_p = None seq_lens = common_attn_metadata.seq_lens seq_idx_p = None @@ -179,7 +179,7 @@ def build(self, num_computed_tokens_cpu[num_reqs - num_prefills:num_reqs] > 0) prep_initial_states = torch.any(has_initial_states_cpu).item() has_initial_states_p = has_initial_states_cpu.to( - query_start_loc.device) + common_attn_metadata.query_start_loc.device) query_start_loc_p = common_attn_metadata.query_start_loc[ -num_prefills - 1:] - num_decode_tokens @@ -190,7 +190,6 @@ def build(self, device=query_start_loc_p.device), query_start_loc_p.diff(), output_size=num_prefill_tokens) - seq_idx_p.unsqueeze_(0) # We compute metadata for chunked prefill once at the top level # model forward and reuse them in mamba layers. If not needed, @@ -217,7 +216,7 @@ def build(self, num_prefill_tokens=num_prefill_tokens, num_decodes=num_decodes, num_decode_tokens=num_decode_tokens, - query_start_loc=query_start_loc, + query_start_loc_p=query_start_loc_p, seq_lens=seq_lens, prep_initial_states=prep_initial_states, chunk_size=self.chunk_size, From 9380098d46b7a356fcdae53810b8648d1449b5de Mon Sep 17 00:00:00 2001 From: Chauncey Date: Fri, 26 Sep 2025 20:46:17 +0800 Subject: [PATCH 014/166] [CI] Fix test_shared_storage_connector_hashes (#25748) Signed-off-by: chaunceyjiang --- .../kv_connector/unit/test_nixl_connector.py | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/tests/v1/kv_connector/unit/test_nixl_connector.py b/tests/v1/kv_connector/unit/test_nixl_connector.py index 24cc83c28614..6b4bd29f18a5 100644 --- a/tests/v1/kv_connector/unit/test_nixl_connector.py +++ b/tests/v1/kv_connector/unit/test_nixl_connector.py @@ -26,6 +26,8 @@ from vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector import ( KVConnectorRole, NixlAgentMetadata, NixlConnector, NixlConnectorMetadata, NixlConnectorWorker, NixlKVConnectorStats) +from vllm.distributed.kv_transfer.kv_transfer_state import ( + ensure_kv_transfer_shutdown, has_kv_transfer_group) from vllm.forward_context import ForwardContext from vllm.platforms.interface import Platform from vllm.sampling_params import SamplingParams @@ -35,6 +37,26 @@ from .utils import create_request, create_scheduler, create_vllm_config +@pytest.fixture(scope="module", autouse=True) +def clear_kv_transfer(): + """ + The test cases in this file use `VLLM_ENABLE_V1_MULTIPROCESSING=0`, + causing the global variable `_KV_CONNECTOR_AGENT` + to be assigned but never deleted. + + Since the current pytest process does not terminate and instead + continues running tests from other files, + this global variable remains in memory and interferes + with test cases in other modules. + + So we use this fixture to ensure that the global variable + `_KV_CONNECTOR_AGENT` is properly cleaned up after each test. + """ + yield + if has_kv_transfer_group(): + ensure_kv_transfer_shutdown() + + class FakeNixlWrapper: """Mock implementation of NixlWrapper for testing. From b13e5c7cd1db55120cadef1d782ee4c43b57dd74 Mon Sep 17 00:00:00 2001 From: "wang.yuqi" Date: Fri, 26 Sep 2025 20:47:34 +0800 Subject: [PATCH 015/166] [Bugfix] Properly abort pooling request. (#25734) Signed-off-by: wang.yuqi Co-authored-by: Cyrus Leung --- tests/v1/engine/test_output_processor.py | 33 ++++++++++++++++++++++++ vllm/v1/engine/output_processor.py | 9 ++++++- 2 files changed, 41 insertions(+), 1 deletion(-) diff --git a/tests/v1/engine/test_output_processor.py b/tests/v1/engine/test_output_processor.py index bdb40be99aa3..72c0a9a13e23 100644 --- a/tests/v1/engine/test_output_processor.py +++ b/tests/v1/engine/test_output_processor.py @@ -12,6 +12,7 @@ STOP_STRINGS, DummyOutputProcessorTestVectors, MockEngineCore) +from vllm import PoolingParams from vllm.logprobs import PromptLogprobs, SampleLogprobs from vllm.outputs import CompletionOutput, RequestOutput from vllm.sampling_params import RequestOutputKind, SamplingParams @@ -998,3 +999,35 @@ async def test_cumulative_output_collector_n(): third = [k for k in result.outputs if k.index == 2] assert len(third) == 1 assert third[0].text == "c" + + +@pytest.mark.parametrize("runner", ["generate", "pooling"]) +def test_abort_requests(runner: str, dummy_test_vectors): + output_processor = OutputProcessor(dummy_test_vectors.tokenizer, + log_stats=True) + requests = [ + EngineCoreRequest( + request_id=f"request-{idx}", + prompt_token_ids=prompt_tokens, + mm_features=None, + eos_token_id=None, + arrival_time=0, + lora_request=None, + cache_salt=None, + data_parallel_rank=None, + sampling_params=SamplingParams() if runner == "generate" else None, + pooling_params=PoolingParams( + task="embed") if runner == "pooling" else None, + ) for idx, prompt_tokens in enumerate(dummy_test_vectors.prompt_tokens) + ] + + for request in requests: + if runner == "generate": + output_kind = request.sampling_params.output_kind + else: + output_kind = request.pooling_params.output_kind + queue = RequestOutputCollector(output_kind=output_kind) + output_processor.add_request(request, None, queue=queue) + + for request in requests: + output_processor.abort_requests([request.request_id]) diff --git a/vllm/v1/engine/output_processor.py b/vllm/v1/engine/output_processor.py index c17dc3e204ec..38b2d6824b47 100644 --- a/vllm/v1/engine/output_processor.py +++ b/vllm/v1/engine/output_processor.py @@ -335,7 +335,14 @@ def abort_requests( # Produce final abort output. if req_state.queue is not None and ( request_output := req_state.make_request_output( - [], None, FinishReason.ABORT, None, None)): + new_token_ids=[], + # Set pooling_output is not None to + # correctly enter the abort pooling branch + pooling_output=torch.randn(0, device="cpu") + if req_state.detokenizer is None else None, + finish_reason=FinishReason.ABORT, + stop_reason=None, + kv_transfer_params=None)): req_state.queue.put(request_output) elif parent := self.parent_requests.get(request_id): # Abort children prior to removing the parent. From b1c49caf3272575dc0a7e9fea8172492c9597c27 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Fri, 26 Sep 2025 20:49:33 +0800 Subject: [PATCH 016/166] [CI/Build] Split up Distributed Tests (#25572) Signed-off-by: DarkLight1337 --- .buildkite/test-pipeline.yaml | 43 ++++++++++++------- .../test_sharded_state_loader.py | 3 +- 2 files changed, 28 insertions(+), 18 deletions(-) rename tests/{ => model_executor/model_loader}/test_sharded_state_loader.py (98%) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 7a1f38606062..2fc65c7fb658 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -870,25 +870,27 @@ steps: - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code -- label: Distributed Tests (2 GPUs) # 110min - timeout_in_minutes: 150 +- label: Distributed Tests (2 GPUs) # 68min + timeout_in_minutes: 90 mirror_hardwares: [amdexperimental] working_dir: "/vllm-workspace/tests" num_gpus: 2 source_file_dependencies: + - vllm/compilation/ - vllm/distributed/ - vllm/engine/ - vllm/executor/ - - vllm/model_executor/models/ - - tests/distributed/ - - vllm/compilation - vllm/worker/worker_base.py - - entrypoints/llm/test_collective_rpc.py + - vllm/v1/engine/ + - vllm/v1/worker/ + - tests/compile/test_basic_correctness.py + - tests/compile/test_wrapper.py + - tests/distributed/ + - tests/entrypoints/llm/test_collective_rpc.py - tests/v1/test_async_llm_dp.py - tests/v1/test_external_lb_dp.py - tests/v1/entrypoints/openai/test_multi_api_servers.py - - vllm/v1/engine/ - - vllm/v1/worker/ + - tests/v1/shutdown - tests/v1/worker/test_worker_memory_snapshot.py commands: - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py @@ -898,20 +900,29 @@ steps: - pytest -v -s ./compile/test_basic_correctness.py - pytest -v -s ./compile/test_wrapper.py - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed' + - pytest -v -s distributed/test_sequence_parallel.py + - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown + - pytest -v -s v1/worker/test_worker_memory_snapshot.py + +- label: Distributed Model Tests (2 GPUs) # 37min + timeout_in_minutes: 50 + mirror_hardwares: [amdexperimental] + working_dir: "/vllm-workspace/tests" + num_gpus: 2 + source_file_dependencies: + - vllm/model_executor/model_loader/sharded_state_loader.py + - vllm/model_executor/models/ + - tests/basic_correctness/ + - tests/model_executor/model_loader/test_sharded_state_loader.py + - tests/models/ + commands: - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)' + - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py # Avoid importing model tests that cause CUDA reinitialization error - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)' - pytest models/language -v -s -m 'distributed(num_gpus=2)' - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)' - # test sequence parallel - - pytest -v -s distributed/test_sequence_parallel.py - # this test fails consistently. - # TODO: investigate and fix - - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py - - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown - - pytest -v -s models/multimodal/generation/test_maverick.py - - pytest -v -s v1/worker/test_worker_memory_snapshot.py - label: Plugin Tests (2 GPUs) # 40min timeout_in_minutes: 60 diff --git a/tests/test_sharded_state_loader.py b/tests/model_executor/model_loader/test_sharded_state_loader.py similarity index 98% rename from tests/test_sharded_state_loader.py rename to tests/model_executor/model_loader/test_sharded_state_loader.py index fd5b5fad0999..785169f5d22e 100644 --- a/tests/test_sharded_state_loader.py +++ b/tests/model_executor/model_loader/test_sharded_state_loader.py @@ -91,8 +91,7 @@ def _run_generate(input_dir, queue: mp.Queue, **kwargs): @pytest.mark.parametrize("enable_lora", [False, True]) @pytest.mark.parametrize("tp_size", [1, 2]) def test_sharded_state_loader(enable_lora, tp_size, num_gpus_available, - llama_3p2_1b_files, - monkeypatch: pytest.MonkeyPatch): + llama_3p2_1b_files): if num_gpus_available < tp_size: pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}") From e3f4572df7ecc2011cf9b8efd1f56f4ba65a6413 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Fri, 26 Sep 2025 20:52:36 +0800 Subject: [PATCH 017/166] [CI/Build] Fix some V1 tests not being run (#25569) Signed-off-by: DarkLight1337 --- .buildkite/test-pipeline.yaml | 6 ++- tests/v1/test_kv_sharing.py | 97 ++--------------------------------- 2 files changed, 8 insertions(+), 95 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 2fc65c7fb658..0914c899aa5b 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -300,10 +300,12 @@ steps: - pytest -v -s v1/spec_decode - pytest -v -s v1/kv_connector/unit - pytest -v -s v1/metrics + - pytest -v -s v1/test_kv_sharing.py + - pytest -v -s v1/test_metrics_reader.py + - pytest -v -s v1/test_oracle.py + - pytest -v -s v1/test_request.py - pytest -v -s v1/test_serial_utils.py - pytest -v -s v1/test_utils.py - - pytest -v -s v1/test_oracle.py - - pytest -v -s v1/test_metrics_reader.py # Integration test for streaming correctness (requires special branch). - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine diff --git a/tests/v1/test_kv_sharing.py b/tests/v1/test_kv_sharing.py index 96848047145b..31a74101faf9 100644 --- a/tests/v1/test_kv_sharing.py +++ b/tests/v1/test_kv_sharing.py @@ -1,17 +1,10 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project -from unittest.mock import Mock - import torch -from vllm.v1.attention.backends.flash_attn import ( - FlashAttentionBackend, FlashAttentionMetadataBuilder) -from vllm.v1.attention.backends.flex_attention import ( - FlexAttentionBackend, FlexAttentionMetadataBuilder) from vllm.v1.kv_cache_interface import FullAttentionSpec, KVCacheGroupSpec -from vllm.v1.worker.utils import (AttentionGroup, - initialize_kv_cache_for_kv_sharing) +from vllm.v1.worker.utils import add_kv_sharing_layers_to_kv_cache_groups def new_kv_cache_spec(): @@ -37,56 +30,17 @@ def test_initialize_kv_cache_for_kv_sharing_different_attn_groups(): new_kv_cache_spec()), ] - attn_groups = [ - # KV cache group 0 has two attention groups - [ - AttentionGroup( - backend=FlashAttentionBackend, - metadata_builder=Mock(spec=FlashAttentionMetadataBuilder), - layer_names=["model.layers.0"], - ), - AttentionGroup( - backend=FlexAttentionBackend, - metadata_builder=Mock(spec=FlexAttentionMetadataBuilder), - layer_names=["model.layers.1"], - ), - ], - ] - - # Only layers 0 and 1 will have KV caches allocated - kv_caches = { - "model.layers.0": torch.zeros(1, 2, 3), - "model.layers.1": torch.ones(1, 2, 3), - } - - initialize_kv_cache_for_kv_sharing( + add_kv_sharing_layers_to_kv_cache_groups( shared_kv_cache_layers=shared_kv_cache_layers, kv_cache_groups=kv_cache_groups, - kv_caches=kv_caches, - attn_groups=attn_groups, ) - # Check that the KV caches were shared correctly - assert kv_caches["model.layers.2"].data_ptr( - ) == kv_caches["model.layers.0"].data_ptr() - assert kv_caches["model.layers.3"].data_ptr( - ) == kv_caches["model.layers.1"].data_ptr() - # Check that the layers were added to the correct KV cache group assert len(kv_cache_groups) == 1 assert kv_cache_groups[0].layer_names == [ "model.layers.0", "model.layers.1", "model.layers.2", "model.layers.3" ] - # Check that the layers were added to the attention groups - assert len(attn_groups) == 1 and len(attn_groups[0]) == 2 - assert attn_groups[0][0].layer_names == [ - "model.layers.0", "model.layers.2" - ] - assert attn_groups[0][1].layer_names == [ - "model.layers.1", "model.layers.3" - ] - def test_initialize_kv_cache_for_kv_sharing_same_attn_groups(): """ @@ -103,48 +57,17 @@ def test_initialize_kv_cache_for_kv_sharing_same_attn_groups(): new_kv_cache_spec()), ] - attn_groups = [ - # KV cache group 0 has a single attention group - # as all layers have the same flash attention backend - [ - AttentionGroup( - backend=FlashAttentionBackend, - metadata_builder=Mock(spec=FlashAttentionMetadataBuilder), - layer_names=["model.layers.0", "model.layers.1"], - ), - ], - ] - - kv_caches = { - "model.layers.0": torch.zeros(1, 2, 3), - "model.layers.1": torch.ones(1, 2, 3), - } - - initialize_kv_cache_for_kv_sharing( + add_kv_sharing_layers_to_kv_cache_groups( shared_kv_cache_layers=shared_kv_cache_layers, kv_cache_groups=kv_cache_groups, - kv_caches=kv_caches, - attn_groups=attn_groups, ) - # Check that the KV caches were shared correctly - assert kv_caches["model.layers.2"].data_ptr( - ) == kv_caches["model.layers.0"].data_ptr() - assert kv_caches["model.layers.3"].data_ptr( - ) == kv_caches["model.layers.1"].data_ptr() - # Check that the layers were added to the correct KV cache group assert len(kv_cache_groups) == 1 assert kv_cache_groups[0].layer_names == [ "model.layers.0", "model.layers.1", "model.layers.2", "model.layers.3" ] - # Check that the layers were added to the attention groups - assert len(attn_groups) == 1 and len(attn_groups[0]) == 1 - assert attn_groups[0][0].layer_names == [ - "model.layers.0", "model.layers.1", "model.layers.2", "model.layers.3" - ] - def test_initialize_kv_cache_for_kv_sharing_no_attn_groups(): """ @@ -162,23 +85,11 @@ def test_initialize_kv_cache_for_kv_sharing_no_attn_groups(): KVCacheGroupSpec(["model.layers.1"], new_kv_cache_spec()), ] - kv_caches = { - "model.layers.0": torch.zeros(1, 2, 3), - "model.layers.1": torch.ones(1, 2, 3), - } - - initialize_kv_cache_for_kv_sharing( + add_kv_sharing_layers_to_kv_cache_groups( shared_kv_cache_layers=shared_kv_cache_layers, kv_cache_groups=kv_cache_groups, - kv_caches=kv_caches, ) - # Check that the KV caches were shared correctly - assert kv_caches["model.layers.2"].data_ptr( - ) == kv_caches["model.layers.0"].data_ptr() - assert kv_caches["model.layers.3"].data_ptr( - ) == kv_caches["model.layers.1"].data_ptr() - # Check that the layers were added to the correct KV cache group assert len(kv_cache_groups) == 2 assert kv_cache_groups[0].layer_names == [ From 7f76f26d15a576fefd27ac2bea3e4dcf91f1d751 Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Fri, 26 Sep 2025 23:47:41 +0800 Subject: [PATCH 018/166] [Quantization] Add field to skip unquantized modules for GPTQ config (#25455) Signed-off-by: Isotr0py --- vllm/config/__init__.py | 1 + .../layers/quantization/base_config.py | 6 ++ .../layers/quantization/gptq.py | 52 +++++++++++++-- .../layers/quantization/gptq_marlin.py | 65 ++++++++++++++++--- .../layers/quantization/utils/gptq_utils.py | 52 ++++++++++++++- vllm/model_executor/models/keye.py | 12 +--- vllm/model_executor/models/minicpmo.py | 36 +--------- vllm/model_executor/models/ovis.py | 13 +--- vllm/model_executor/models/qwen2_5_vl.py | 13 +--- vllm/model_executor/models/qwen2_vl.py | 13 +--- vllm/model_executor/models/qwen3_moe.py | 28 ++------ vllm/model_executor/models/qwen3_next.py | 24 ++----- vllm/model_executor/models/qwen3_vl.py | 12 +--- vllm/model_executor/models/qwen3_vl_moe.py | 2 +- vllm/transformers_utils/config.py | 32 ++++++++- vllm/transformers_utils/utils.py | 11 +++- 16 files changed, 219 insertions(+), 153 deletions(-) diff --git a/vllm/config/__init__.py b/vllm/config/__init__.py index 2da9d8f4f3ea..59b65900b1e1 100644 --- a/vllm/config/__init__.py +++ b/vllm/config/__init__.py @@ -270,6 +270,7 @@ def _get_quantization_config( f"{model_config.dtype} is not supported for quantization " f"method {model_config.quantization}. Supported dtypes: " f"{supported_dtypes}") + quant_config.maybe_update_config(model_config.model) return quant_config return None diff --git a/vllm/model_executor/layers/quantization/base_config.py b/vllm/model_executor/layers/quantization/base_config.py index 6fd94afbe556..807a9866a18b 100644 --- a/vllm/model_executor/layers/quantization/base_config.py +++ b/vllm/model_executor/layers/quantization/base_config.py @@ -162,3 +162,9 @@ def apply_vllm_mapper( # noqa: B027 """ # TODO (@kylesayrs): add implementations for all subclasses pass + + def maybe_update_config(self, model_name: str): # noqa: B027 + """ + Interface to update values after config initialization. + """ + pass diff --git a/vllm/model_executor/layers/quantization/gptq.py b/vllm/model_executor/layers/quantization/gptq.py index 2272709f9309..0335b9c46b4d 100644 --- a/vllm/model_executor/layers/quantization/gptq.py +++ b/vllm/model_executor/layers/quantization/gptq.py @@ -7,6 +7,7 @@ from typing import Any, Optional, Union import torch +from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE from torch.nn.parameter import Parameter from vllm import _custom_ops as ops @@ -22,6 +23,8 @@ PackedColumnParameter, PackedvLLMParameter, RowvLLMParameter) +from vllm.transformers_utils.config import get_safetensors_params_metadata +from vllm.utils import is_list_of class GPTQConfig(QuantizationConfig): @@ -38,6 +41,7 @@ def __init__( lm_head_quantized: bool, dynamic: dict[str, dict[str, Union[int, bool]]], autoround_version: str = "", + modules_in_block_to_quantize: Optional[list[str]] = None, ) -> None: # GPTQModel use `dynamic` config property to allow per module # quantization config so each module can be individually optimized. @@ -75,15 +79,20 @@ def __init__( "Currently, only 2/3/4/8-bit weight quantization is " f"supported for GPTQ, but got {self.weight_bits} bits.") + self.modules_in_block_to_quantize = modules_in_block_to_quantize or [] + # used to identify GPTQ model quantized by autoround self.autoround_version = autoround_version def __repr__(self) -> str: - return (f"GPTQConfig(weight_bits={self.weight_bits}, " - f"group_size={self.group_size}, " - f"desc_act={self.desc_act}), " - f"lm_head_quantized={self.lm_head_quantized}), " - f"dynamic={self.dynamic}") + return ( + f"GPTQConfig(weight_bits={self.weight_bits}, " + f"group_size={self.group_size}, " + f"desc_act={self.desc_act}), " + f"lm_head_quantized={self.lm_head_quantized}, " + f"dynamic={self.dynamic}, " + f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})" + ) @classmethod def get_name(cls) -> QuantizationMethods: @@ -114,8 +123,10 @@ def from_config(cls, config: dict[str, Any]) -> "GPTQConfig": default=False) autoround_version = cls.get_from_keys_or(config, ["autoround_version"], default="") + modules_in_block_to_quantize = cls.get_from_keys_or( + config, ["modules_in_block_to_quantize"], default=None) return cls(weight_bits, group_size, desc_act, lm_head_quantized, - dynamic, autoround_version) + dynamic, autoround_version, modules_in_block_to_quantize) def get_quant_method( self, layer: torch.nn.Module, prefix: str @@ -136,6 +147,35 @@ def get_quant_method( return get_linear_quant_method(self, layer, prefix, GPTQLinearMethod) + def apply_vllm_mapper(self, hf_to_vllm_mapper): + if self.modules_in_block_to_quantize is not None: + self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list( + self.modules_in_block_to_quantize) + + def maybe_update_config(self, + model_name: str, + revision: Optional[str] = None): + if self.modules_in_block_to_quantize: + if is_list_of(self.modules_in_block_to_quantize, list): + # original modules_in_block_to_quantize: list[list[str]] + # flatten original modules_in_block_to_quantize + self.modules_in_block_to_quantize = [ + item for sublist in self.modules_in_block_to_quantize + for item in sublist + ] + return + + unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32] + metadata = get_safetensors_params_metadata(model_name, + revision=revision) + quant_layers: set[str] = { + param_name.rsplit(".", 1)[0] + for param_name, info in metadata.items() + if (dtype := info.get('dtype', None)) + and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes + } + self.modules_in_block_to_quantize = list(quant_layers) + class ExllamaState(Enum): diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 02188c3c224f..967e46c24378 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -5,6 +5,7 @@ from typing import Any, Callable, Optional, Union import torch +from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE import vllm.model_executor.layers.fused_moe # noqa from vllm import _custom_ops as ops @@ -35,6 +36,8 @@ RowvLLMParameter) from vllm.platforms import current_platform from vllm.scalar_type import scalar_types +from vllm.transformers_utils.config import get_safetensors_params_metadata +from vllm.utils import is_list_of logger = init_logger(__name__) @@ -71,10 +74,16 @@ class GPTQMarlinConfig(QuantizationConfig): (8, True): scalar_types.uint8b128, } - def __init__(self, weight_bits: int, group_size: int, desc_act: bool, - is_sym: bool, lm_head_quantized: bool, - dynamic: dict[str, dict[str, Union[int, bool]]], - full_config: dict[str, Any]) -> None: + def __init__( + self, + weight_bits: int, + group_size: int, + desc_act: bool, + is_sym: bool, + lm_head_quantized: bool, + dynamic: dict[str, dict[str, Union[int, bool]]], + full_config: dict[str, Any], + modules_in_block_to_quantize: Optional[list[str]] = None) -> None: super().__init__() if desc_act and group_size == -1: # In this case, act_order == True is the same as act_order == False @@ -121,15 +130,19 @@ def __init__(self, weight_bits: int, group_size: int, desc_act: bool, self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)] + self.modules_in_block_to_quantize = modules_in_block_to_quantize or [] # used to identify GPTQ model quantized by autoround self.autoround_version = full_config.get("autoround_version", "") def __repr__(self) -> str: - return (f"GPTQMarlinConfig(quant_type={self.quant_type}, " - f"group_size={self.group_size}, " - f"desc_act={self.desc_act}, " - f"lm_head_quantized={self.lm_head_quantized}), " - f"dynamic={self.dynamic}") + return ( + f"GPTQMarlinConfig(quant_type={self.quant_type}, " + f"group_size={self.group_size}, " + f"desc_act={self.desc_act}, " + f"lm_head_quantized={self.lm_head_quantized}, " + f"dynamic={self.dynamic}, " + f"modules_in_block_to_quantize={self.modules_in_block_to_quantize})" + ) @classmethod def get_name(cls) -> QuantizationMethods: @@ -158,8 +171,11 @@ def from_config(cls, config: dict[str, Any]) -> "GPTQMarlinConfig": is_sym = cls.get_from_keys(config, ["sym"]) lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"], default=False) + modules_in_block_to_quantize = cls.get_from_keys_or( + config, ["modules_in_block_to_quantize"], default=None) return cls(weight_bits, group_size, desc_act, is_sym, - lm_head_quantized, dynamic, config) + lm_head_quantized, dynamic, config, + modules_in_block_to_quantize) @classmethod def override_quantization_method( @@ -223,6 +239,35 @@ def is_gptq_marlin_compatible(cls, quant_config: dict[str, Any]): return check_marlin_supported(quant_type=cls.TYPE_MAP[(num_bits, sym)], group_size=group_size) + def apply_vllm_mapper(self, hf_to_vllm_mapper): + if self.modules_in_block_to_quantize is not None: + self.modules_in_block_to_quantize = hf_to_vllm_mapper.apply_list( + self.modules_in_block_to_quantize) + + def maybe_update_config(self, + model_name: str, + revision: Optional[str] = None): + if self.modules_in_block_to_quantize: + if is_list_of(self.modules_in_block_to_quantize, list): + # original modules_in_block_to_quantize: list[list[str]] + # flatten original modules_in_block_to_quantize + self.modules_in_block_to_quantize = [ + item for sublist in self.modules_in_block_to_quantize + for item in sublist + ] + return + + unquant_dtypes = [torch.float16, torch.bfloat16, torch.float32] + metadata = get_safetensors_params_metadata(model_name, + revision=revision) + quant_layers: set[str] = { + param_name.rsplit(".", 1)[0] + for param_name, info in metadata.items() + if (dtype := info.get('dtype', None)) + and _SAFETENSORS_TO_TORCH_DTYPE[dtype] not in unquant_dtypes + } + self.modules_in_block_to_quantize = list(quant_layers) + class GPTQMarlinLinearMethod(LinearMethodBase): """Linear method for GPTQ Marlin. diff --git a/vllm/model_executor/layers/quantization/utils/gptq_utils.py b/vllm/model_executor/layers/quantization/utils/gptq_utils.py index 4fbd0f5c4eff..41b833725b30 100644 --- a/vllm/model_executor/layers/quantization/utils/gptq_utils.py +++ b/vllm/model_executor/layers/quantization/utils/gptq_utils.py @@ -1,7 +1,9 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +from collections.abc import Mapping from copy import deepcopy from fractions import Fraction +from types import MappingProxyType from typing import Optional, Union import regex as re @@ -70,6 +72,49 @@ def get_dynamic_override( return default_value +def is_layer_gptq_quantized( + prefix: str, + quantized_layers: list[str], + fused_mapping: Mapping[str, list[str]] = MappingProxyType({}) +) -> bool: + # prefix: model.layers.0.self_attn.q_proj + # proj_name: q_proj + + # GPTQ's `modules_in_block_to_quantize`: + # Substr: ["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"] + # Full prefix ["model.layers.0.self_attn.q_proj"] + + proj_name = prefix.split(".")[-1] + + # Fused layers like gate_up_proj or qkv_proj will not be fused + # in the safetensors checkpoint. So, we convert the name + # from the fused version to unfused + check to make sure that + # each shard of the fused layer has the same scheme. + if proj_name in fused_mapping: + shard_prefixes = [ + prefix.replace(proj_name, shard_proj_name) + for shard_proj_name in fused_mapping[proj_name] + ] + + is_quantized = None + for shard_prefix in shard_prefixes: + is_shard_quantized = any(layer in shard_prefix + for layer in quantized_layers) + + if is_quantized is None: + is_quantized = is_shard_quantized + elif is_shard_quantized != is_quantized: + raise ValueError( + f"Detected some but not all shards of {prefix} " + "are quantized. All shards of fused layers " + "to have the same precision.") + else: + is_quantized = any(layer in prefix for layer in quantized_layers) + + assert is_quantized is not None + return is_quantized + + def get_linear_quant_method( config: QuantizationConfig, layer: torch.nn.Module, @@ -80,10 +125,15 @@ def get_linear_quant_method( parallel_lm_head_quantized = isinstance( layer, ParallelLMHead) and cloned_config.lm_head_quantized if isinstance(layer, LinearBase) or parallel_lm_head_quantized: + is_layer_quantized = is_layer_gptq_quantized( + prefix=prefix, + quantized_layers=cloned_config.modules_in_block_to_quantize, + fused_mapping=cloned_config.packed_modules_mapping) # False = skip module, None = no override, else = Positive match if get_dynamic_override( # noqa: E712 cloned_config, # noqa: E712 - layer_name=prefix) == False: # noqa: E712 + layer_name=prefix) == False or ( + not is_layer_quantized): # noqa: E712 if parallel_lm_head_quantized: return UnquantizedEmbeddingMethod() return UnquantizedLinearMethod() diff --git a/vllm/model_executor/models/keye.py b/vllm/model_executor/models/keye.py index 2e5e276cc1c7..3b6fdba22512 100644 --- a/vllm/model_executor/models/keye.py +++ b/vllm/model_executor/models/keye.py @@ -25,9 +25,6 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) from vllm.model_executor.models.module_mapping import MultiModelKeys @@ -1281,11 +1278,6 @@ def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]: raise ValueError("Only image or video modality is supported") - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)): - return None - return quant_config - def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: PretrainedConfig = vllm_config.model_config.hf_config @@ -1297,14 +1289,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.visual = KeyeSiglipVisionModel( config.vision_config, - quant_config=self._maybe_ignore_quant_config(quant_config), + quant_config=quant_config, prefix=maybe_prefix(prefix, "visual"), ) self.mlp_AR = self._build_projector( config, config.vision_config, - quant_config=self._maybe_ignore_quant_config(quant_config), + quant_config=quant_config, prefix=maybe_prefix(prefix, "mlp_AR"), ) diff --git a/vllm/model_executor/models/minicpmo.py b/vllm/model_executor/models/minicpmo.py index 225668d87fac..e5333fb652b1 100644 --- a/vllm/model_executor/models/minicpmo.py +++ b/vllm/model_executor/models/minicpmo.py @@ -28,7 +28,7 @@ import torch from torch import nn -from transformers import BatchFeature, PretrainedConfig +from transformers import BatchFeature from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.models.whisper.modeling_whisper import (ACT2FN, WhisperAttention, @@ -36,10 +36,6 @@ WhisperEncoder) from vllm.config import VllmConfig -from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, NestedTensors) @@ -548,36 +544,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.audio_token_id = None - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid vision encoder sections for some models. - # See: https://huggingface.co/openbmb/MiniCPM-o-2_6-int4 - if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)): - return None - return quant_config - - def init_vision_module( - self, - config: PretrainedConfig, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> nn.Module: - # MiniCPMO GPTQ model leave vpm unquantized. - quant_config = self._maybe_ignore_quant_config(quant_config) - return super().init_vision_module(config, quant_config, prefix) - - def init_resampler( - self, - embed_dim: int, - vision_dim: int, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> nn.Module: - # MiniCPMO GPTQ model leave resampler unquantized. - quant_config = self._maybe_ignore_quant_config(quant_config) - return super().init_resampler(embed_dim, vision_dim, quant_config, - prefix) - def init_audio_module(self, *, vllm_config: VllmConfig, prefix: str = ""): # Do not use parameters temporarily audio_config = self.config.audio_config diff --git a/vllm/model_executor/models/ovis.py b/vllm/model_executor/models/ovis.py index 052e143b27f6..bd525b6780e0 100644 --- a/vllm/model_executor/models/ovis.py +++ b/vllm/model_executor/models/ovis.py @@ -31,9 +31,6 @@ from vllm.model_executor.layers.linear import ReplicatedLinear from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.models.aimv2 import AIMv2Model from vllm.model_executor.models.siglip import SiglipVisionModel from vllm.model_executor.models.utils import (AutoWeightsLoader, flatten_bn, @@ -416,7 +413,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.visual_tokenizer = VisualTokenizer( config=config.visual_tokenizer_config, - quant_config=self._maybe_ignore_quant_config(quant_config), + quant_config=quant_config, prefix=f"{prefix}.visual_tokenizer", ) @@ -430,14 +427,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.make_empty_intermediate_tensors = ( self.get_language_model().make_empty_intermediate_tensors) - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid vision encoder sections for some models. - # See: https://huggingface.co/AIDC-AI/Ovis2-2B-GPTQ-Int4 - if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)): - return None - return quant_config - def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[OvisImagePatchInputs]: pixel_values = kwargs.pop("pixel_values", None) diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py index bd6c0b162cb4..6af6faa2b296 100644 --- a/vllm/model_executor/models/qwen2_5_vl.py +++ b/vllm/model_executor/models/qwen2_5_vl.py @@ -52,9 +52,6 @@ RowParallelLinear) # yapf: enable from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.module_mapping import MultiModelKeys from vllm.multimodal import MULTIMODAL_REGISTRY @@ -1015,8 +1012,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.visual = Qwen2_5_VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), - quant_config=self._maybe_ignore_quant_config( - self.quant_config), + quant_config=self.quant_config, prefix=maybe_prefix(prefix, "visual"), use_data_parallel=self.use_data_parallel, ) @@ -1032,13 +1028,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) - def _maybe_ignore_quant_config(self, config: Optional[QuantizationConfig]): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid vision encoder sections for some models. - if isinstance(config, (GPTQConfig, GPTQMarlinConfig)): - return None - return config - def _validate_and_reshape_mm_tensor(self, mm_input: object, name: str) -> torch.Tensor: if not isinstance(mm_input, (torch.Tensor, list)): diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index 14ea03444484..d4e195246bf1 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -50,9 +50,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.module_mapping import MultiModelKeys from vllm.multimodal import MULTIMODAL_REGISTRY @@ -1270,7 +1267,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.visual = Qwen2VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), - quant_config=self._maybe_ignore_quant_config(quant_config), + quant_config=quant_config, prefix=maybe_prefix(prefix, "visual"), use_data_parallel=self.use_data_parallel, ) @@ -1286,14 +1283,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid vision encoder sections for some models. - # See: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 - if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)): - return None - return quant_config - def _validate_and_reshape_mm_tensor(self, mm_input: object, name: str) -> torch.Tensor: if not isinstance(mm_input, (torch.Tensor, list)): diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py index 0661b3707ff4..cb2ff97a5df2 100644 --- a/vllm/model_executor/models/qwen3_moe.py +++ b/vllm/model_executor/models/qwen3_moe.py @@ -46,9 +46,6 @@ RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) @@ -149,24 +146,11 @@ def __init__( enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts) - self.gate = ReplicatedLinear( - config.hidden_size, - config.num_experts, - bias=False, - quant_config=self._maybe_ignore_quant_config(quant_config), - prefix=f"{prefix}.gate") - - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid gate quantization while AutoRound does. - # See: https://huggingface.co/Qwen/Qwen3-30B-A3B-GPTQ-Int4, - # and https://huggingface.co/jart25/Qwen3-Coder-30B-A3B-Instruct-Int4-gptq - if isinstance( - quant_config, - (GPTQConfig, - GPTQMarlinConfig)) and not quant_config.autoround_version: - return None - return quant_config + self.gate = ReplicatedLinear(config.hidden_size, + config.num_experts, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.gate") def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: assert hidden_states.dim( @@ -699,4 +683,4 @@ def load_weights(self, weights: Iterable[tuple[str, return loader.load_weights(weights) def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: - return self.model.get_expert_mapping() \ No newline at end of file + return self.model.get_expert_mapping() diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py index 356b5001a7dc..dc3153fcc826 100644 --- a/vllm/model_executor/models/qwen3_next.py +++ b/vllm/model_executor/models/qwen3_next.py @@ -41,9 +41,6 @@ from vllm.model_executor.layers.mamba.ops.causal_conv1d import ( causal_conv1d_fn, causal_conv1d_update) from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) @@ -119,12 +116,11 @@ def __init__( enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts) - self.gate = ReplicatedLinear( - config.hidden_size, - config.num_experts, - bias=False, - quant_config=self._maybe_ignore_quant_config(quant_config), - prefix=f"{prefix}.gate") + self.gate = ReplicatedLinear(config.hidden_size, + config.num_experts, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.gate") if config.shared_expert_intermediate_size > 0: self.shared_expert = Qwen3NextMLP( @@ -142,16 +138,6 @@ def __init__( 1, bias=False) - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid gate quantization while AutoRound does. - if isinstance( - quant_config, - (GPTQConfig, - GPTQMarlinConfig)) and not quant_config.autoround_version: - return None - return quant_config - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # NOTE: hidden_states can have either 1D or 2D shape. orig_shape = hidden_states.shape diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index ede477cde1a2..f3f11438eeee 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -50,9 +50,6 @@ RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.quantization.gptq import GPTQConfig -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQMarlinConfig) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.module_mapping import MultiModelKeys @@ -1058,7 +1055,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"): self.visual = Qwen3_VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), - quant_config=self._maybe_ignore_quant_config(quant_config), + quant_config=quant_config, prefix=maybe_prefix(prefix, "visual"), use_data_parallel=self.use_data_parallel, ) @@ -1116,13 +1113,6 @@ def _clear_deepstack_input_embeds(self, num_tokens: int) -> None: for idx in range(self.deepstack_num_level): self.deepstack_input_embeds[idx][:num_tokens].zero_() - def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): - # GPTQ configs do not have a list of ignored modules, however AutoGPTQ - # seems to avoid vision encoder sections for some models. - if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)): - return None - return quant_config - def _validate_and_reshape_mm_tensor(self, mm_input: object, name: str) -> torch.Tensor: if not isinstance(mm_input, (torch.Tensor, list)): diff --git a/vllm/model_executor/models/qwen3_vl_moe.py b/vllm/model_executor/models/qwen3_vl_moe.py index 7912cf3ea52b..52ea652b3765 100644 --- a/vllm/model_executor/models/qwen3_vl_moe.py +++ b/vllm/model_executor/models/qwen3_vl_moe.py @@ -322,7 +322,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.visual = Qwen3_VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), - quant_config=self._maybe_ignore_quant_config(quant_config), + quant_config=quant_config, prefix=maybe_prefix(prefix, "visual"), use_data_parallel=self.use_data_parallel, ) diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 9eed46678866..b0816cfb0702 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -4,6 +4,7 @@ import json import os import time +from dataclasses import asdict from functools import cache, partial from pathlib import Path from typing import Any, Callable, Literal, Optional, TypeVar, Union @@ -27,7 +28,8 @@ from vllm import envs from vllm.logger import init_logger from vllm.transformers_utils.config_parser_base import ConfigParserBase -from vllm.transformers_utils.utils import check_gguf_file +from vllm.transformers_utils.utils import (check_gguf_file, + parse_safetensors_file_metadata) if envs.VLLM_USE_MODELSCOPE: from modelscope import AutoConfig @@ -999,6 +1001,34 @@ def try_get_tokenizer_config( return None +def get_safetensors_params_metadata( + model: str, + *, + revision: Optional[str] = None, +) -> dict[str, Any]: + """ + Get the safetensors metadata for remote model repository. + """ + full_metadata = {} + if (model_path := Path(model)).exists(): + safetensors_to_check = model_path.glob("*.safetensors") + full_metadata = { + param_name: info + for file_path in safetensors_to_check if file_path.is_file() + for param_name, info in parse_safetensors_file_metadata( + file_path).items() + } + else: + repo_mt = try_get_safetensors_metadata(model, revision=revision) + if repo_mt and (files_mt := repo_mt.files_metadata): + full_metadata = { + param_name: asdict(info) + for file_mt in files_mt.values() + for param_name, info in file_mt.tensors.items() + } + return full_metadata + + def _download_mistral_config_file(model, revision) -> dict: config_file_name = "params.json" config_dict = get_hf_file_to_dict(config_file_name, model, revision) diff --git a/vllm/transformers_utils/utils.py b/vllm/transformers_utils/utils.py index 66c8fb797adc..2aaad8f949d0 100644 --- a/vllm/transformers_utils/utils.py +++ b/vllm/transformers_utils/utils.py @@ -2,10 +2,11 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import json +import struct from functools import cache from os import PathLike from pathlib import Path -from typing import Optional, Union +from typing import Any, Optional, Union from vllm.envs import VLLM_MODEL_REDIRECT_PATH from vllm.logger import init_logger @@ -97,3 +98,11 @@ def maybe_model_redirect(model: str) -> str: return redirect_model return model + + +def parse_safetensors_file_metadata( + path: Union[str, PathLike]) -> dict[str, Any]: + with open(path, "rb") as f: + length_of_metadata = struct.unpack(' Date: Fri, 26 Sep 2025 12:22:49 -0400 Subject: [PATCH 019/166] [BugFix] Fix using `dbo_decode_token_threshold` always (and ignoring `dbo_prefill_token_threshold`) (#25622) Signed-off-by: Lucas Wilkinson --- vllm/v1/worker/gpu_model_runner.py | 9 +++++++-- vllm/v1/worker/ubatch_splitting.py | 3 ++- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index cbf439aa697b..f199dbd991f4 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -1045,11 +1045,15 @@ def _prepare_inputs( num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens num_tokens_padded = num_tokens_unpadded + self.get_local_padding( num_tokens_unpadded) + uniform_decode = \ + (max_num_scheduled_tokens == self.uniform_decode_query_len) and \ + (total_num_scheduled_tokens == num_reqs * max_num_scheduled_tokens) ubatch_slices, num_tokens_after_padding = \ ubatch_split(num_scheduled_tokens, num_tokens_unpadded, num_tokens_padded, - self.vllm_config) + uniform_decode=uniform_decode, + vllm_config=self.vllm_config) self.seq_lens.np[:num_reqs] = ( self.input_batch.num_computed_tokens_cpu[:num_reqs] + @@ -2989,7 +2993,8 @@ def _dummy_run( num_scheduled_tokens, total_num_scheduled_tokens, total_num_scheduled_tokens, - self.vllm_config, + uniform_decode=uniform_decode, + vllm_config=self.vllm_config, ) # If we failed to microbatch, currently need to resynchronize diff --git a/vllm/v1/worker/ubatch_splitting.py b/vllm/v1/worker/ubatch_splitting.py index 30acb14ff58a..7767750aa604 100644 --- a/vllm/v1/worker/ubatch_splitting.py +++ b/vllm/v1/worker/ubatch_splitting.py @@ -139,6 +139,7 @@ def ubatch_split( num_scheduled_tokens_per_request: np.ndarray, num_tokens_unpadded: int, num_tokens_padded: int, + uniform_decode: bool, vllm_config: VllmConfig, ) -> tuple[Optional[UBatchSlices], Optional[torch.Tensor]]: """ @@ -164,7 +165,7 @@ def ubatch_split( should_attempt_ubatching = check_ubatch_thresholds( parallel_config, num_tokens_unpadded, - vllm_config, + uniform_decode=uniform_decode, ) # Don't microbatch unless every other DP worker is also microbatching From dcfeea4111243ca7a851f025f956a75b7bcfa2d0 Mon Sep 17 00:00:00 2001 From: Seiji Eicher <58963096+eicherseiji@users.noreply.github.com> Date: Fri, 26 Sep 2025 09:43:30 -0700 Subject: [PATCH 020/166] [ray][metrics] Replace ':' with '_' for OpenTelemetry compatibility in Ray (#25439) Signed-off-by: Seiji Eicher Signed-off-by: Seiji Eicher <58963096+eicherseiji@users.noreply.github.com> Co-authored-by: Rui Qiao <161574667+ruisearch42@users.noreply.github.com> --- tests/v1/metrics/test_ray_metrics.py | 39 +++++++++++++++++++++++++++- vllm/v1/metrics/ray_wrappers.py | 19 ++++++++++++++ 2 files changed, 57 insertions(+), 1 deletion(-) diff --git a/tests/v1/metrics/test_ray_metrics.py b/tests/v1/metrics/test_ray_metrics.py index 92f6c6f0e89c..0c9f83f049e4 100644 --- a/tests/v1/metrics/test_ray_metrics.py +++ b/tests/v1/metrics/test_ray_metrics.py @@ -8,7 +8,8 @@ from vllm.config import ModelDType from vllm.sampling_params import SamplingParams from vllm.v1.engine.async_llm import AsyncEngineArgs, AsyncLLM -from vllm.v1.metrics.ray_wrappers import RayPrometheusStatLogger +from vllm.v1.metrics.ray_wrappers import (RayPrometheusMetric, + RayPrometheusStatLogger) @pytest.fixture(scope="function", autouse=True) @@ -65,3 +66,39 @@ async def run(self): # Create the actor and call the async method actor = EngineTestActor.remote() # type: ignore[attr-defined] ray.get(actor.run.remote()) + + +def test_sanitized_opentelemetry_name(): + """Test the metric name sanitization logic for Ray.""" + + # Only a-z, A-Z, 0-9, _, test valid characters are preserved + valid_name = "valid_metric_123_abcDEF" + assert RayPrometheusMetric._get_sanitized_opentelemetry_name( + valid_name) == valid_name + + # Test dash, dot, are replaced + name_with_dash_dot = "metric-name.test" + expected = "metric_name_test" + assert RayPrometheusMetric._get_sanitized_opentelemetry_name( + name_with_dash_dot) == expected + + # Test colon is replaced with underscore + name_with_colon = "metric:name" + expected = "metric_name" + assert RayPrometheusMetric._get_sanitized_opentelemetry_name( + name_with_colon) == expected + + # Test multiple invalid characters are replaced + name_with_invalid = "metric:name@with#special%chars" + expected = "metric_name_with_special_chars" + assert RayPrometheusMetric._get_sanitized_opentelemetry_name( + name_with_invalid) == expected + + # Test mixed valid and invalid characters + complex_name = "vllm:engine_stats/time.latency_ms-99p" + expected = "vllm_engine_stats_time_latency_ms_99p" + assert RayPrometheusMetric._get_sanitized_opentelemetry_name( + complex_name) == expected + + # Test empty string + assert RayPrometheusMetric._get_sanitized_opentelemetry_name("") == "" diff --git a/vllm/v1/metrics/ray_wrappers.py b/vllm/v1/metrics/ray_wrappers.py index ae8f9447e9c8..609185753860 100644 --- a/vllm/v1/metrics/ray_wrappers.py +++ b/vllm/v1/metrics/ray_wrappers.py @@ -11,6 +11,7 @@ from ray.util.metrics import Metric except ImportError: ray_metrics = None +import regex as re class RayPrometheusMetric: @@ -42,6 +43,21 @@ def labels(self, *labels, **labelskwargs): return self + @staticmethod + def _get_sanitized_opentelemetry_name(name: str) -> str: + """ + For compatibility with Ray + OpenTelemetry, the metric name must be + sanitized. In particular, this replaces disallowed character (e.g., ':') + with '_' in the metric name. + Allowed characters: a-z, A-Z, 0-9, _ + + # ruff: noqa: E501 + Ref: https://github.com/open-telemetry/opentelemetry-cpp/blob/main/sdk/src/metrics/instrument_metadata_validator.cc#L22-L23 + Ref: https://github.com/ray-project/ray/blob/master/src/ray/stats/metric.cc#L107 + """ + + return re.sub(r"[^a-zA-Z0-9_]", "_", name) + class RayGaugeWrapper(RayPrometheusMetric): """Wraps around ray.util.metrics.Gauge to provide same API as @@ -58,6 +74,7 @@ def __init__(self, # implemented at the observability layer (Prometheus/Grafana). del multiprocess_mode labelnames_tuple = tuple(labelnames) if labelnames else None + name = self._get_sanitized_opentelemetry_name(name) self.metric = ray_metrics.Gauge(name=name, description=documentation, tag_keys=labelnames_tuple) @@ -79,6 +96,7 @@ def __init__(self, documentation: Optional[str] = "", labelnames: Optional[list[str]] = None): labelnames_tuple = tuple(labelnames) if labelnames else None + name = self._get_sanitized_opentelemetry_name(name) self.metric = ray_metrics.Counter(name=name, description=documentation, tag_keys=labelnames_tuple) @@ -99,6 +117,7 @@ def __init__(self, labelnames: Optional[list[str]] = None, buckets: Optional[list[float]] = None): labelnames_tuple = tuple(labelnames) if labelnames else None + name = self._get_sanitized_opentelemetry_name(name) boundaries = buckets if buckets else [] self.metric = ray_metrics.Histogram(name=name, description=documentation, From 471c031b4786d90d91bd21ef704cd711ba89fd2d Mon Sep 17 00:00:00 2001 From: Jiangyun Zhu Date: Sat, 27 Sep 2025 00:56:15 +0800 Subject: [PATCH 021/166] [Misc] fix unique_filepath (#25732) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: zjy0516 Co-authored-by: Luka Govedič --- tests/utils_/test_utils.py | 29 +++++++++++++++++++++-------- vllm/utils/__init__.py | 21 +++++++++++++++++++++ 2 files changed, 42 insertions(+), 8 deletions(-) diff --git a/tests/utils_/test_utils.py b/tests/utils_/test_utils.py index 608f517f6914..658ae7e7451a 100644 --- a/tests/utils_/test_utils.py +++ b/tests/utils_/test_utils.py @@ -23,15 +23,16 @@ from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config from vllm.transformers_utils.detokenizer_utils import ( convert_ids_list_to_tokens) -from vllm.utils import (CacheInfo, FlexibleArgumentParser, LRUCache, - MemorySnapshot, PlaceholderModule, StoreBoolean, - bind_kv_cache, common_broadcastable_dtype, - current_stream, deprecate_kwargs, get_open_port, - get_tcp_uri, is_lossless_cast, join_host_port, - make_zmq_path, make_zmq_socket, memory_profiling, - merge_async_iterators, sha256, split_host_port, - split_zmq_path, supports_kw, swap_dict_values) +# isort: off +from vllm.utils import ( + CacheInfo, FlexibleArgumentParser, LRUCache, MemorySnapshot, + PlaceholderModule, bind_kv_cache, common_broadcastable_dtype, + current_stream, deprecate_kwargs, get_open_port, get_tcp_uri, + is_lossless_cast, join_host_port, make_zmq_path, make_zmq_socket, + memory_profiling, merge_async_iterators, sha256, split_host_port, + split_zmq_path, supports_kw, swap_dict_values, unique_filepath) +# isort: on from ..utils import create_new_process_for_each_test, error_on_warning @@ -1032,3 +1033,15 @@ def test_load_config_file(tmp_path): # Assert that the processed arguments match the expected output assert processed_args == expected_args os.remove(str(config_file_path)) + + +def test_unique_filepath(): + temp_dir = tempfile.mkdtemp() + path_fn = lambda i: Path(temp_dir) / f"file_{i}.txt" + paths = set() + for i in range(10): + path = unique_filepath(path_fn) + path.write_text("test") + paths.add(path) + assert len(paths) == 10 + assert len(list(Path(temp_dir).glob("*.txt"))) == 10 diff --git a/vllm/utils/__init__.py b/vllm/utils/__init__.py index c502a69ea500..ba280d6dbe4a 100644 --- a/vllm/utils/__init__.py +++ b/vllm/utils/__init__.py @@ -45,6 +45,7 @@ from concurrent.futures.process import ProcessPoolExecutor from dataclasses import dataclass, field from functools import cache, lru_cache, partial, wraps +from pathlib import Path from types import MappingProxyType from typing import (TYPE_CHECKING, Any, Callable, Generic, Literal, NamedTuple, Optional, TextIO, TypeVar, Union, cast, overload) @@ -3536,3 +3537,23 @@ def set_env_var(key, value): del os.environ[key] else: os.environ[key] = old + + +def unique_filepath(fn: Callable[[int], Path]) -> Path: + """ + unique_filepath returns a unique path by trying + to include an integer in increasing order. + + fn should be a callable that returns a path that + includes the passed int at a fixed location. + + Note: This function has a TOCTOU race condition. + Caller should use atomic operations (e.g., open with 'x' mode) + when creating the file to ensure thread safety. + """ + i = 0 + while True: + p = fn(i) + if not p.exists(): + return p + i += 1 From e2f1a4a66327f3ad36db27bddb7c047c610909ae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=98=BF=E4=B8=B9=28adan=29?= <47373076+LDLINGLINGLING@users.noreply.github.com> Date: Sat, 27 Sep 2025 01:04:57 +0800 Subject: [PATCH 022/166] Eagle3 that supports the Minicpm3 model (#24243) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: liudan Co-authored-by: liudan Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Lucia Fang <116399278+luccafong@users.noreply.github.com> --- vllm/config/speculative.py | 2 +- vllm/model_executor/models/minicpm.py | 51 ++++++++++++++++++++++----- 2 files changed, 44 insertions(+), 9 deletions(-) diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index 2e4b3d3a6b20..5f462442148f 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -540,7 +540,7 @@ def _verify_args(self) -> Self: "speculative decoding is > 1, but got " f"{self.disable_by_batch_size=}") - eagle3_target_supported = ["llama", "qwen", "gpt_oss"] + eagle3_target_supported = ["llama", "qwen", "minicpm", "gpt_oss"] if self.method == "eagle3" and self.target_model_config and not any( supported_model in self.target_model_config.hf_text_config.model_type diff --git a/vllm/model_executor/models/minicpm.py b/vllm/model_executor/models/minicpm.py index 0986ea07406a..55fe3e2ae3ae 100644 --- a/vllm/model_executor/models/minicpm.py +++ b/vllm/model_executor/models/minicpm.py @@ -55,7 +55,7 @@ from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors -from .interfaces import SupportsLoRA, SupportsPP +from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP from .utils import (AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) @@ -381,6 +381,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.num_experts = getattr(self.config, "num_experts", 0) self._init_layers(prefix, config, cache_config, quant_config) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.aux_hidden_state_layers = tuple[int, ...]() + self.make_empty_intermediate_tensors = ( make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], self.config.hidden_size)) @@ -408,7 +411,8 @@ def forward( positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, - ) -> Union[torch.Tensor, IntermediateTensors]: + ) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor, + list[torch.Tensor]]]: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds @@ -419,18 +423,29 @@ def forward( hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] - for layer in islice(self.layers, self.start_layer, self.end_layer): + aux_hidden_states = [] + for idx, layer in enumerate( + islice(self.layers, self.start_layer, self.end_layer)): + if idx in self.aux_hidden_state_layers: + aux_hidden_states.append( + hidden_states + + residual if residual is not None else hidden_states) hidden_states, residual = layer( positions, hidden_states, residual, ) + if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": hidden_states, "residual": residual }) + hidden_states = self.norm(hidden_states) + + if len(aux_hidden_states) > 0: + return hidden_states, aux_hidden_states return hidden_states def load_weights(self, weights: Iterable[tuple[str, @@ -502,7 +517,7 @@ def load_weights(self, weights: Iterable[tuple[str, return loaded_params -class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP): +class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3): packed_modules_mapping = { "qkv_proj": [ "q_proj", @@ -568,16 +583,36 @@ def _init_model(self, *, vllm_config: VllmConfig, prefix: str = ""): def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) + def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None: + self.model.aux_hidden_state_layers = layers + + def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]: + num_layers = len(self.model.layers) + return (2, num_layers // 2, num_layers - 3) + def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, - ) -> Union[torch.Tensor, IntermediateTensors]: - hidden_states = self.model(input_ids, positions, intermediate_tensors, - inputs_embeds) / self.scale_width - return hidden_states + ) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor, + list[torch.Tensor]]]: + model_output = self.model(input_ids, positions, intermediate_tensors, + inputs_embeds) + + if isinstance(model_output, tuple) and len(model_output) == 2: + # Aux hidden states are present. + hidden_states, aux_hidden_states = model_output + hidden_states = hidden_states / self.scale_width + return hidden_states, aux_hidden_states + else: + # Only hidden states or IntermediateTensors + if isinstance(model_output, IntermediateTensors): + return model_output + else: + hidden_states = model_output / self.scale_width + return hidden_states def compute_logits( self, From b2819f024d2538d58c6ac9e4be4023ebab5e02ce Mon Sep 17 00:00:00 2001 From: Clouddude Date: Fri, 26 Sep 2025 13:26:33 -0400 Subject: [PATCH 023/166] [Doc]: improve CPU(x86) build-wheel-from-source section (#25617) Signed-off-by: Kosseila (CloudThrill) --- .../installation/cpu/x86.inc.md | 77 ++++++++++++++++++- 1 file changed, 75 insertions(+), 2 deletions(-) diff --git a/docs/getting_started/installation/cpu/x86.inc.md b/docs/getting_started/installation/cpu/x86.inc.md index 836da33f6531..00f3b726b1a0 100644 --- a/docs/getting_started/installation/cpu/x86.inc.md +++ b/docs/getting_started/installation/cpu/x86.inc.md @@ -20,7 +20,80 @@ vLLM supports basic model inferencing and serving on x86 CPU platform, with data # --8<-- [end:pre-built-wheels] # --8<-- [start:build-wheel-from-source] ---8<-- "docs/getting_started/installation/cpu/build.inc.md" +Install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run: + +```bash +sudo apt-get update -y +sudo apt-get install -y gcc-12 g++-12 libnuma-dev python3-dev +sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12 +``` + +Clone the vLLM project: + +```bash +git clone https://github.com/vllm-project/vllm.git vllm_source +cd vllm_source +``` + +Install the required dependencies: + +```bash +uv pip install -r requirements/cpu-build.txt --torch-backend cpu +uv pip install -r requirements/cpu.txt --torch-backend cpu +``` + +??? console "pip" + ```bash + pip install --upgrade pip + pip install -v -r requirements/cpu-build.txt --extra-index-url https://download.pytorch.org/whl/cpu + pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu + ``` + +Build and install vLLM: + +```bash +VLLM_TARGET_DEVICE=cpu uv pip install . --no-build-isolation +``` + +If you want to develop vLLM, install it in editable mode instead. + +```bash +VLLM_TARGET_DEVICE=cpu uv pip install -e . --no-build-isolation +``` + +Optionally, build a portable wheel which you can then install elsewhere: + +```bash +VLLM_TARGET_DEVICE=cpu uv build --wheel +``` + +```bash +uv pip install dist/*.whl +``` + +??? console "pip" + ```bash + VLLM_TARGET_DEVICE=cpu python -m build --wheel --no-isolation + ``` + + ```bash + pip install dist/*.whl + ``` + +!!! example "Troubleshooting" + - **NumPy ≥2.0 error**: Downgrade using `pip install "numpy<2.0"`. + - **CMake picks up CUDA**: Add `CMAKE_DISABLE_FIND_PACKAGE_CUDA=ON` to prevent CUDA detection during CPU builds, even if CUDA is installed. + - `AMD` requies at least 4th gen processors (Zen 4/Genoa) or higher to support [AVX512](https://www.phoronix.com/review/amd-zen4-avx512) to run vLLM on CPU. + - If you receive an error such as: `Could not find a version that satisfies the requirement torch==X.Y.Z+cpu+cpu`, consider updating [pyproject.toml](https://github.com/vllm-project/vllm/blob/main/pyproject.toml) to help pip resolve the dependency. + ```toml title="pyproject.toml" + [build-system] + requires = [ + "cmake>=3.26.1", + ... + "torch==X.Y.Z+cpu" # <------- + ] + ``` + - If you are building vLLM from source and not using the pre-built images, remember to set `LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD"` on x86 machines before running vLLM. # --8<-- [end:build-wheel-from-source] # --8<-- [start:pre-built-images] @@ -57,4 +130,4 @@ docker run --rm \ # --8<-- [end:build-image-from-source] # --8<-- [start:extra-information] -# --8<-- [end:extra-information] +# --8<-- [end:extra-information] \ No newline at end of file From aa5b385039522bfd4b86815e80efa48b4d6ca580 Mon Sep 17 00:00:00 2001 From: Frank Wang <41319051+frankwang28@users.noreply.github.com> Date: Fri, 26 Sep 2025 11:54:00 -0700 Subject: [PATCH 024/166] [Bugfix] Improve GLM4 MoE Reasoning Parser's is_reasoning_end Condition (#25355) Signed-off-by: frankwang28 Signed-off-by: Frank Wang <41319051+frankwang28@users.noreply.github.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Chauncey --- .../test_glm4_moe_reasoning_parser.py | 203 ++++++++++++++++++ vllm/reasoning/glm4_moe_reasoning_parser.py | 19 +- 2 files changed, 219 insertions(+), 3 deletions(-) create mode 100644 tests/reasoning/test_glm4_moe_reasoning_parser.py diff --git a/tests/reasoning/test_glm4_moe_reasoning_parser.py b/tests/reasoning/test_glm4_moe_reasoning_parser.py new file mode 100644 index 000000000000..4c5ec2c9b408 --- /dev/null +++ b/tests/reasoning/test_glm4_moe_reasoning_parser.py @@ -0,0 +1,203 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import pytest +from transformers import AutoTokenizer + +from tests.reasoning.utils import run_reasoning_extraction +from vllm.reasoning import ReasoningParser, ReasoningParserManager + +parser_name = "glm45" +start_token = "" +end_token = "" + +REASONING_MODEL_NAME = "zai-org/GLM-4.5" + + +@pytest.fixture(scope="module") +def glm45_tokenizer(): + return AutoTokenizer.from_pretrained(REASONING_MODEL_NAME) + + +WITH_THINK = { + "output": "This is a reasoning sectionThis is the rest", + "reasoning_content": "This is a reasoning section", + "content": "This is the rest", + "is_reasoning_end": True, +} + +WITH_THINK_STREAM = { + "output": "This is a reasoning sectionThis is the rest", + "reasoning_content": "This is a reasoning section", + "content": "This is the rest", + "is_reasoning_end": True, +} + +WITHOUT_THINK = { + "output": "This is the rest", + "reasoning_content": None, + "content": "This is the rest", + "is_reasoning_end": False, +} + +WITHOUT_THINK_STREAM = { + "output": "This is the rest", + "reasoning_content": None, + "content": "This is the rest", + "is_reasoning_end": False, +} + +COMPLETE_REASONING = { + "output": "This is a reasoning section", + "reasoning_content": "This is a reasoning section", + "content": None, + "is_reasoning_end": True, +} +MULTILINE_REASONING = { + "output": + "This is a reasoning\nsectionThis is the rest\nThat", + "reasoning_content": "This is a reasoning\nsection", + "content": "This is the rest\nThat", + "is_reasoning_end": True, +} +ONLY_OPEN_TAG = { + "output": "This is a reasoning section", + "reasoning_content": None, + "content": "This is a reasoning section", + "is_reasoning_end": False, +} + +ONLY_OPEN_TAG_STREAM = { + "output": "This is a reasoning section", + "reasoning_content": "This is a reasoning section", + "content": None, + "is_reasoning_end": False, +} + +TEST_CASES = [ + pytest.param( + False, + WITH_THINK, + id="with_think", + ), + pytest.param( + True, + WITH_THINK_STREAM, + id="with_think_stream", + ), + pytest.param( + False, + WITHOUT_THINK, + id="without_think", + ), + pytest.param( + True, + WITHOUT_THINK_STREAM, + id="without_think_stream", + ), + pytest.param( + False, + COMPLETE_REASONING, + id="complete_reasoning", + ), + pytest.param( + True, + COMPLETE_REASONING, + id="complete_reasoning_stream", + ), + pytest.param( + False, + MULTILINE_REASONING, + id="multiline_reasoning", + ), + pytest.param( + True, + MULTILINE_REASONING, + id="multiline_reasoning_stream", + ), + pytest.param( + False, + ONLY_OPEN_TAG, + id="only_open_tag", + ), + pytest.param( + True, + ONLY_OPEN_TAG_STREAM, + id="only_open_tag_stream", + ), +] + +STILL_REASONING_PROMPT = """[gMASK]<|system|> +You are a helpful assistant.<|user|> +What is the capital of France?<|assistant|> +The user is asking for the capital of""" + +DONE_REASONING_PROMPT = """[gMASK]<|system|> +You are a helpful assistant.<|user|> +What is the capital of France?<|assistant|> +The user is asking for the capital of France. +The capital of France is Paris.""" + +MULTI_TURN_STILL_REASONING_PROMPT = """[gMASK]<|system|> +You are a helpful assistant.<|user|> +What is the capital of France?<|assistant|> + +The capital of France is Paris.<|user|> +What about Chile?<|assistant|> +The user is asking for the capital of""" + +MULTI_TURN_DONE_REASONING_PROMPT = """[gMASK]<|system|> +You are a helpful assistant.<|user|> +What is the capital of France?<|assistant|> + +The capital of France is Paris.<|user|> +What about Chile?<|assistant|> +The user is asking for the capital of Chile. +The capital of Chile is Santiago.""" + +REASONING_END_TEST_CASES = [ + pytest.param(STILL_REASONING_PROMPT, False, id="still_reasoning"), + pytest.param(DONE_REASONING_PROMPT, True, id="done_reasoning"), + pytest.param(MULTI_TURN_STILL_REASONING_PROMPT, + False, + id="multi_turn_still_reasoning"), + pytest.param(MULTI_TURN_DONE_REASONING_PROMPT, + True, + id="multi_turn_done_reasoning") +] + + +@pytest.mark.parametrize("streaming, param_dict", TEST_CASES) +def test_reasoning( + streaming: bool, + param_dict: dict, + glm45_tokenizer, +): + output = glm45_tokenizer.tokenize(param_dict["output"]) + output_tokens: list[str] = [ + glm45_tokenizer.convert_tokens_to_string([token]) for token in output + ] + parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser( + parser_name)(glm45_tokenizer) + + reasoning, content = run_reasoning_extraction(parser, + output_tokens, + streaming=streaming) + + assert reasoning == param_dict["reasoning_content"] + assert content == param_dict["content"] + + output_ids = glm45_tokenizer.convert_tokens_to_ids(output) + is_reasoning_end = parser.is_reasoning_end(output_ids) + assert is_reasoning_end == param_dict["is_reasoning_end"] + + +@pytest.mark.parametrize("prompt, is_reasoning_end", REASONING_END_TEST_CASES) +def test_is_reasoning_end_full_prompt(prompt: str, is_reasoning_end: bool, + glm45_tokenizer): + parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser( + parser_name)(glm45_tokenizer) + tokens = glm45_tokenizer.tokenize(prompt) + token_ids = glm45_tokenizer.convert_tokens_to_ids(tokens) + check_is_reasoning_end = parser.is_reasoning_end(token_ids) + assert check_is_reasoning_end == is_reasoning_end diff --git a/vllm/reasoning/glm4_moe_reasoning_parser.py b/vllm/reasoning/glm4_moe_reasoning_parser.py index 11e828a7039f..8d7488afce68 100644 --- a/vllm/reasoning/glm4_moe_reasoning_parser.py +++ b/vllm/reasoning/glm4_moe_reasoning_parser.py @@ -30,6 +30,7 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs): super().__init__(tokenizer, *args, **kwargs) self.think_start_token = "" self.think_end_token = "" + self.assistant_token = "<|assistant|>" if not self.model_tokenizer: raise ValueError( @@ -38,14 +39,26 @@ def __init__(self, tokenizer: PreTrainedTokenizerBase, *args, **kwargs): self.think_start_token_id = self.vocab.get(self.think_start_token) self.think_end_token_id = self.vocab.get(self.think_end_token) + self.assistant_token_id = self.vocab.get(self.assistant_token) if (self.think_start_token_id is None - or self.think_end_token_id is None): + or self.think_end_token_id is None + or self.assistant_token_id is None): raise RuntimeError( "Glm4MoeModel reasoning parser could not locate " - "think start/end tokens in the tokenizer!") + "think start/end or assistant tokens in the tokenizer!") def is_reasoning_end(self, input_ids: list[int]) -> bool: - return self.think_end_token_id in input_ids + """ + GLM's chat template has tokens after every + <|assistant|> token. Thus, we need to check if is + after the most recent <|assistant|> token (if present). + """ + for token_id in input_ids[::-1]: + if token_id == self.think_end_token_id: + return True + elif token_id == self.assistant_token_id: + return False + return False def extract_content_ids(self, input_ids: list[int]) -> list[int]: """ From f74612d0a928ea6cad6e6c5b35eb90549215ab91 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Fri, 26 Sep 2025 15:00:46 -0400 Subject: [PATCH 025/166] [Docs] Add Toronto Meetup (#25773) Signed-off-by: mgoin --- README.md | 1 + docs/community/meetups.md | 1 + 2 files changed, 2 insertions(+) diff --git a/README.md b/README.md index 0c6e5aa6b31d..6772a9eae073 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,7 @@ Join us at the [PyTorch Conference, October 22-23](https://events.linuxfoundatio *Latest News* 🔥 +- [2025/09] We hosted [vLLM Toronto Meetup](https://luma.com/e80e0ymm) focused on tackling inference at scale and speculative decoding with speakers from NVIDIA and Red Hat! Please find the meetup slides [here](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing). - [2025/08] We hosted [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ) focusing on the ecosystem around vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA). - [2025/08] We hosted [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet). We shared V1 updates, disaggregated serving and MLLM speedups with speakers from Embedded LLM, AMD, WekaIO, and A*STAR. Please find the meetup slides [here](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing). - [2025/08] We hosted [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg) focusing on building, developing, and integrating with vLLM! Please find the meetup slides [here](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH). diff --git a/docs/community/meetups.md b/docs/community/meetups.md index a3004249b758..e821e2ac8114 100644 --- a/docs/community/meetups.md +++ b/docs/community/meetups.md @@ -2,6 +2,7 @@ We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below: +- [vLLM Toronto Meetup](https://luma.com/e80e0ymm), September 25th 2025. [[Slides]](https://docs.google.com/presentation/d/1IYJYmJcu9fLpID5N5RbW_vO0XLo0CGOR14IXOjB61V8/edit?usp=sharing) - [vLLM Shenzhen Meetup](https://mp.weixin.qq.com/s/k8ZBO1u2_2odgiKWH_GVTQ), August 30th 2025. [[Slides]](https://drive.google.com/drive/folders/1Ua2SVKVSu-wp5vou_6ElraDt2bnKhiEA) - [vLLM Singapore Meetup](https://www.sginnovate.com/event/vllm-sg-meet), August 27th 2025. [[Slides]](https://drive.google.com/drive/folders/1ncf3GyqLdqFaB6IeB834E5TZJPLAOiXZ?usp=sharing) - [vLLM Shanghai Meetup](https://mp.weixin.qq.com/s/pDmAXHcN7Iqc8sUKgJgGtg), August 23rd 2025. [[Slides]](https://drive.google.com/drive/folders/1OvLx39wnCGy_WKq8SiVKf7YcxxYI3WCH) From b05e292d21f0e02487dad59b0f761a842b896ffc Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Fri, 26 Sep 2025 15:23:00 -0400 Subject: [PATCH 026/166] [CI] Add E2E Blackwell Quantized MoE Test (#25723) Signed-off-by: mgoin --- .buildkite/test-pipeline.yaml | 19 ++- tests/quantization/test_blackwell_moe.py | 132 ++++++++++++++++++ tests/utils.py | 4 +- .../layers/fused_moe/flashinfer_trtllm_moe.py | 2 + 4 files changed, 155 insertions(+), 2 deletions(-) create mode 100644 tests/quantization/test_blackwell_moe.py diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 0914c899aa5b..c178fd372bcb 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -522,7 +522,7 @@ steps: # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now # we can only upgrade after this is resolved - pip install --pre torchao==0.13.0.dev20250814 --index-url https://download.pytorch.org/whl/nightly/cu128 - - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization + - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ - label: LM Eval Small Models # 53min timeout_in_minutes: 75 @@ -830,6 +830,23 @@ steps: - uv pip install --system 'gpt-oss[eval]==0.0.5' - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58 --server-args '--tensor-parallel-size 2' +- label: Blackwell Quantized MoE Test + timeout_in_minutes: 60 + working_dir: "/vllm-workspace/" + gpu: b200 + source_file_dependencies: + - tests/quantization/test_blackwell_moe.py + - vllm/model_executor/models/deepseek_v2.py + - vllm/model_executor/models/gpt_oss.py + - vllm/model_executor/models/llama4.py + - vllm/model_executor/layers/fused_moe + - vllm/model_executor/layers/quantization/compressed_tensors + - vllm/model_executor/layers/quantization/modelopt.py + - vllm/model_executor/layers/quantization/mxfp4.py + - vllm/v1/attention/backends/flashinfer.py + commands: + - pytest -s -v tests/quantization/test_blackwell_moe.py + ##### 1 GPU test ##### ##### multi gpus test ##### diff --git a/tests/quantization/test_blackwell_moe.py b/tests/quantization/test_blackwell_moe.py new file mode 100644 index 000000000000..c021126720af --- /dev/null +++ b/tests/quantization/test_blackwell_moe.py @@ -0,0 +1,132 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import json +import os + +import pytest + +from tests.utils import RemoteOpenAIServer +from vllm.platforms import current_platform + +if not current_platform.is_device_capability(100): + pytest.skip("This test only runs on Blackwell GPUs (SM100).", + allow_module_level=True) + +os.environ["FLASHINFER_NVCC_THREADS"] = "16" + +# dummy_hf_overrides = {"num_layers": 4, "num_hidden_layers": 4, +# "text_config": {"num_layers": 4, "num_hidden_layers": 4}} +dummy_hf_overrides = {"num_layers": 4, "num_hidden_layers": 4} + + +def can_initialize(model: str, extra_args: list[str]): + + # Server arguments + server_args = [ + "--max-model-len", + "2048", + "--max-num-batched-tokens", + "256", + "--load-format", + "dummy", + "--trust-remote-code", + "--limit-mm-per-prompt", + json.dumps({"image": 0}), + *extra_args, + ] + + # Launch server and make a simple request + with RemoteOpenAIServer( + model, + server_args, + max_wait_seconds=1000, # Due to FlashInfer compile + override_hf_configs=dummy_hf_overrides) as server: + client = server.get_client() + # Make a simple request to verify the server works + completion = client.completions.create( + model=model, + prompt=["Hello, World!"], + temperature=0, + max_tokens=2, + ) + print(completion) + assert completion.choices[0].text is not None + + +## Llama4 ## + + +@pytest.mark.skip(reason=( + "RuntimeError: run_moe() Expected a value of type " + "'Optional[List[Tensor]]' for argument '_9' but instead found type " + "'list'.")) +def test_llama4_fp8_tensor_moe_flashinfer_cutlass( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP8", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP8", []) + + +@pytest.mark.skip(reason="Works, but takes too long to run") +def test_llama4_fp8_tensor_moe_flashinfer_trtllm( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP8", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP8", []) + + +@pytest.mark.skip(reason="Works, but takes too long to run") +def test_llama4_nvfp4_moe_flashinfer_cutlass(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP4", []) + + +@pytest.mark.skip(reason="RuntimeError: No kernel found for the given options") +def test_llama4_nvfp4_moe_flashinfer_trtllm(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP4", []) + + +## DeepSeekV3 ## + + +def test_deepseek_fp8_block_moe_deep_gemm(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_DEEP_GEMM", "1") + can_initialize("deepseek-ai/DeepSeek-V3.1", []) + + +def test_deepseek_nvfp4_moe_flashinfer_cutlass( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") + can_initialize("nvidia/DeepSeek-R1-0528-FP4-v2", []) + + +@pytest.mark.skip(reason="RuntimeError: No kernel found for the given options") +def test_deepseek_nvfp4_moe_flashinfer_trtllm(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") + can_initialize("nvidia/DeepSeek-R1-0528-FP4-v2", []) + + +## GPT-OSS ## + + +def test_gptoss_mxfp4bf16_moe_flashinfer(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "1") + can_initialize("openai/gpt-oss-20b", []) + + +def test_gptoss_mxfp4mxfp8_moe_flashinfer_cutlass( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "1") + can_initialize("openai/gpt-oss-20b", []) + + +def test_gptoss_mxfp4mxfp8_moe_flashinfer_trtllm( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "1") + can_initialize("openai/gpt-oss-20b", []) diff --git a/tests/utils.py b/tests/utils.py index f630c57f46d8..ab6ccc7ad9f9 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -91,8 +91,10 @@ def _start_server(self, model: str, vllm_serve_args: list[str], env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn' if env_dict is not None: env.update(env_dict) + serve_cmd = ["vllm", "serve", model, *vllm_serve_args] + print(f"Launching RemoteOpenAIServer with: {' '.join(serve_cmd)}") self.proc: subprocess.Popen = subprocess.Popen( - ["vllm", "serve", model, *vllm_serve_args], + serve_cmd, env=env, stdout=sys.stdout, stderr=sys.stderr, diff --git a/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py b/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py index fe586a22e250..74bcffd8ca03 100644 --- a/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py +++ b/vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py @@ -40,6 +40,8 @@ def flashinfer_fused_moe_blockscale_fp8( assert global_num_experts % 4 == 0 assert top_k < (topk_group * global_num_experts / num_expert_group) assert block_shape == [128, 128] + # Routing kernel expects #experts <= #threads 256 + assert global_num_experts <= 256 a_q, a_sf = per_token_group_quant_fp8(x, block_shape[1]) # NOTE: scales of hidden states have to be transposed! From c3de86e505bdefa1ab21c25c2d1c813523836bb1 Mon Sep 17 00:00:00 2001 From: fhl2000 <63384265+fhl2000@users.noreply.github.com> Date: Sat, 27 Sep 2025 03:58:19 +0800 Subject: [PATCH 027/166] [V1] address post issues related to #20059 (part 1) (#23046) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: fhl2000 <63384265+fhl2000@users.noreply.github.com> Co-authored-by: Luka Govedič --- .../compile/piecewise/test_full_cudagraph.py | 86 +------------ tests/compile/test_config.py | 67 ++++++++++- tests/v1/attention/utils.py | 87 +++++++++++++- tests/v1/cudagraph/test_cudagraph_dispatch.py | 61 ++++------ tests/v1/cudagraph/test_cudagraph_mode.py | 84 ++----------- vllm/compilation/backends.py | 6 +- vllm/compilation/decorators.py | 4 +- ...cewise_backend.py => piecewise_backend.py} | 0 vllm/config/__init__.py | 37 ++++-- vllm/config/compilation.py | 113 ++++++++++++------ vllm/forward_context.py | 3 +- vllm/v1/cudagraph_dispatcher.py | 41 +++---- vllm/v1/worker/gpu_model_runner.py | 47 +++++--- 13 files changed, 346 insertions(+), 290 deletions(-) rename vllm/compilation/{cuda_piecewise_backend.py => piecewise_backend.py} (100%) diff --git a/tests/compile/piecewise/test_full_cudagraph.py b/tests/compile/piecewise/test_full_cudagraph.py index b02c1b565671..9906e49bb110 100644 --- a/tests/compile/piecewise/test_full_cudagraph.py +++ b/tests/compile/piecewise/test_full_cudagraph.py @@ -3,12 +3,11 @@ import contextlib import os import weakref -from dataclasses import dataclass -from typing import Optional import pytest from tests.utils import wait_for_gpu_memory_to_clear +from tests.v1.attention.utils import full_cg_backend_configs as backend_configs from vllm import LLM, SamplingParams from vllm.config import CompilationConfig from vllm.platforms import current_platform @@ -33,89 +32,6 @@ def temporary_environ(env_vars): os.environ[k] = v -@dataclass -class BackendConfig: - name: str - env_vars: dict - comp_config: dict - specific_gpu_arch: Optional[tuple] = None - - -# Define all backend configurations of full cudagraph to be tested -backend_configs = { - # FA3 on Hopper - "FA3": - BackendConfig(name="FA3", - env_vars={ - "VLLM_FLASH_ATTN_VERSION": "3", - "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", - }, - comp_config={ - "cudagraph_mode": "FULL", - }, - specific_gpu_arch=(9, 0)), - # FlashMLA on Hopper - "FlashMLA": - BackendConfig(name="FlashMLA", - env_vars={ - "VLLM_ATTENTION_BACKEND": "FLASHMLA", - }, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - }, - specific_gpu_arch=(9, 0)), - # FlashAttention MLA on Hopper - "FlashAttentionMLA": - BackendConfig(name="FlashAttentionMLA", - env_vars={ - "VLLM_ATTENTION_BACKEND": "FLASH_ATTN_MLA", - "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", - }, - comp_config={ - "cudagraph_mode": "FULL_DECODE_ONLY", - }, - specific_gpu_arch=(9, 0)), - # Cutlass MLA on Blackwell - "CutlassMLA": - BackendConfig( - name="CutlassMLA", - env_vars={ - "VLLM_USE_V1": "1", - "VLLM_ATTENTION_BACKEND": "CUTLASS_MLA", - "FORCE_NUM_KV_SPLITS": - "1", # TODO: remove this when hang issue is fixed - }, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - "cudagraph_capture_sizes": [16, 32, 64, 128, 256, 512], - }, - specific_gpu_arch=(10, 0)), - # FA2 - "FA2": - BackendConfig(name="FA2", - env_vars={ - "VLLM_FLASH_ATTN_VERSION": "2", - "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", - }, - comp_config={ - "cudagraph_mode": "FULL", - }), - # Triton Attention - "TritonAttn": - BackendConfig(name="TritonAttn", - env_vars={"VLLM_ATTENTION_BACKEND": "TRITON_ATTN"}, - comp_config={ - "cudagraph_mode": "FULL", - }), - # FlashInfer - "FlashInfer": - BackendConfig(name="FlashInfer", - env_vars={"VLLM_ATTENTION_BACKEND": "FLASHINFER"}, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - }), -} - test_params_full_cudagraph = [] # deepseek-ai/DeepSeek-V2-Lite with MLA diff --git a/tests/compile/test_config.py b/tests/compile/test_config.py index 7afd6251bbbd..17d3f0b37768 100644 --- a/tests/compile/test_config.py +++ b/tests/compile/test_config.py @@ -4,7 +4,7 @@ import vllm from vllm.compilation.counter import compilation_counter -from vllm.config import CompilationConfig, VllmConfig +from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig from vllm.utils import _is_torch_equal_or_newer @@ -106,7 +106,6 @@ def test_dynamo_as_is(vllm_runner, monkeypatch): def test_no_compilation(vllm_runner, monkeypatch): # Disable multiprocessing so that the counter is in the same process monkeypatch.setenv('VLLM_ENABLE_V1_MULTIPROCESSING', '0') - with ( compilation_counter.expect(num_graphs_seen=0, dynamo_as_is_count=0), @@ -131,3 +130,67 @@ def test_enforce_eager(vllm_runner, monkeypatch): enforce_eager=True, gpu_memory_utilization=0.4) as _): pass + + +def test_splitting_ops_dynamic(): + # Default config + config = VllmConfig() + assert config.compilation_config.cudagraph_mode == \ + CUDAGraphMode.FULL_AND_PIECEWISE + assert config.compilation_config.splitting_ops_contain_attention() + + # When use_inductor_graph_partition=True + if _is_torch_equal_or_newer('2.9.0.dev'): + # inductor graph partition is only available in PyTorch 2.9+. + # this is a fast config check so we are not using pytest.skip. + config = VllmConfig(compilation_config=CompilationConfig( + use_inductor_graph_partition=True, + splitting_ops=["silly_attention"])) + # should ignore splitting_ops + assert config.compilation_config.splitting_ops == [] + + # When attn_fusion pass enabled. + config = VllmConfig(compilation_config=CompilationConfig( + pass_config={ + "enable_attn_fusion": True, + "enable_noop": True + }, + custom_ops=["+quant_fp8"], + cudagraph_mode=CUDAGraphMode.PIECEWISE, + )) + assert config.compilation_config.splitting_ops == [] + # cudagraph mode also fall back to FULL + assert config.compilation_config.cudagraph_mode == \ + CUDAGraphMode.FULL + + # splitting_ops can not contain attention ops when attn_fusion + # pass enabled. + with pytest.raises(AssertionError): + config = VllmConfig(compilation_config=CompilationConfig( + pass_config={ + "enable_attn_fusion": True, + "enable_noop": True + }, + custom_ops=["+quant_fp8"], + cudagraph_mode=CUDAGraphMode.PIECEWISE, + # work around for accessing all attntion ops + splitting_ops=CompilationConfig()._attention_ops, + )) + + # When both use_inductor_graph_partition and attn_fusion pass enabled. + if _is_torch_equal_or_newer('2.9.0.dev'): + config = VllmConfig(compilation_config=CompilationConfig( + use_inductor_graph_partition=True, + pass_config={ + "enable_attn_fusion": True, + "enable_noop": True + }, + custom_ops=["+quant_fp8"], + cudagraph_mode=CUDAGraphMode.PIECEWISE, + )) + assert config.compilation_config.splitting_ops == [] + # enable_attn_fusion is directly support under + # use_inductor_graph_partition=True, and cudagraph_mode + # is unchanged. + assert config.compilation_config.cudagraph_mode == \ + CUDAGraphMode.PIECEWISE diff --git a/tests/v1/attention/utils.py b/tests/v1/attention/utils.py index 6f8c5ea50ef0..01b5de83a59a 100644 --- a/tests/v1/attention/utils.py +++ b/tests/v1/attention/utils.py @@ -3,7 +3,7 @@ """Utility functions for attention-related v1 tests.""" from dataclasses import dataclass -from typing import Union +from typing import Optional, Union import pytest import torch @@ -260,3 +260,88 @@ def create_dummy_kv_cache(block_size: int, dtype=dtype, device=device) return kv_cache + + +@dataclass +class BackendConfig: + name: str + env_vars: dict + comp_config: dict # compilation config + specific_gpu_arch: Optional[tuple] = None + + +# Define all backend configurations of full cudagraph to be tested +full_cg_backend_configs = { + # FA3 on Hopper + "FA3": + BackendConfig(name="FA3", + env_vars={ + "VLLM_ATTENTION_BACKEND": "FLASH_ATTN", + "VLLM_FLASH_ATTN_VERSION": "3", + "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", + }, + comp_config={ + "cudagraph_mode": "FULL", + }, + specific_gpu_arch=(9, 0)), + # FlashMLA on Hopper + "FlashMLA": + BackendConfig(name="FlashMLA", + env_vars={ + "VLLM_ATTENTION_BACKEND": "FLASHMLA", + }, + comp_config={ + "cudagraph_mode": "FULL_AND_PIECEWISE", + }, + specific_gpu_arch=(9, 0)), + # Cutlass MLA on Blackwell + "CutlassMLA": + BackendConfig( + name="CutlassMLA", + env_vars={ + "VLLM_USE_V1": "1", + "VLLM_ATTENTION_BACKEND": "CUTLASS_MLA", + "FORCE_NUM_KV_SPLITS": + "1", # TODO: remove this when hang issue is fixed + }, + comp_config={ + "cudagraph_mode": "FULL_AND_PIECEWISE", + }, + specific_gpu_arch=(10, 0)), + # FlashAttention MLA on Hopper + "FlashAttentionMLA": + BackendConfig(name="FlashAttentionMLA", + env_vars={ + "VLLM_ATTENTION_BACKEND": "FLASH_ATTN_MLA", + "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", + }, + comp_config={ + "cudagraph_mode": "FULL_DECODE_ONLY", + }, + specific_gpu_arch=(9, 0)), + # FA2 + "FA2": + BackendConfig(name="FA2", + env_vars={ + "VLLM_ATTENTION_BACKEND": "FLASH_ATTN", + "VLLM_FLASH_ATTN_VERSION": "2", + "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", + }, + comp_config={ + "cudagraph_mode": "FULL_AND_PIECEWISE", + }), + # Triton Attention + "TritonAttn": + BackendConfig(name="TritonAttn", + env_vars={"VLLM_ATTENTION_BACKEND": "TRITON_ATTN"}, + comp_config={ + "cudagraph_mode": "FULL_AND_PIECEWISE", + }), + # FlashInfer + "FlashInfer": + BackendConfig(name="FlashInfer", + env_vars={"VLLM_ATTENTION_BACKEND": "FLASHINFER"}, + comp_config={ + "cudagraph_mode": "FULL_AND_PIECEWISE", + }), +} diff --git a/tests/v1/cudagraph/test_cudagraph_dispatch.py b/tests/v1/cudagraph/test_cudagraph_dispatch.py index 64f2fa462802..b6b85e4440d0 100644 --- a/tests/v1/cudagraph/test_cudagraph_dispatch.py +++ b/tests/v1/cudagraph/test_cudagraph_dispatch.py @@ -45,39 +45,22 @@ def _create_vllm_config(compilation_config: CompilationConfig, class TestCudagraphDispatcher: @pytest.mark.parametrize( - "params", + "case_id,cudagraph_mode_str,compilation_level", [ # Test case 0: Full CG for mixed batches, no separate routine - { - "case_id": 0, - "cudagraph_mode": "FULL", - "compilation_level": CompilationLevel.NO_COMPILATION, - }, + (0, "FULL", CompilationLevel.NO_COMPILATION), # Test case 1: Full CG for uniform batches, piecewise for mixed - { - "case_id": 1, - "cudagraph_mode": "FULL_AND_PIECEWISE", - "compilation_level": CompilationLevel.PIECEWISE, - }, + (1, "FULL_AND_PIECEWISE", CompilationLevel.NO_COMPILATION), # Test case 2: Full CG for uniform batches, no CG for mixed - { - "case_id": 2, - "cudagraph_mode": "FULL_DECODE_ONLY", - "compilation_level": CompilationLevel.NO_COMPILATION, - }, + (2, "FULL_DECODE_ONLY", CompilationLevel.NO_COMPILATION), # Test case 3: Piecewise for all - { - "case_id": 3, - "cudagraph_mode": "PIECEWISE", - "compilation_level": CompilationLevel.PIECEWISE, - }, + (3, "PIECEWISE", CompilationLevel.PIECEWISE), ]) - def test_dispatcher(self, params): + def test_dispatcher(self, cudagraph_mode_str, compilation_level): # Setup dispatcher - comp_config = CompilationConfig( - cudagraph_mode=params["cudagraph_mode"], - level=params["compilation_level"], - cudagraph_capture_sizes=[1, 8]) + comp_config = CompilationConfig(cudagraph_mode=cudagraph_mode_str, + level=compilation_level, + cudagraph_capture_sizes=[1, 8]) config = _create_vllm_config(comp_config, max_num_seqs=8) dispatcher = CudagraphDispatcher(config) @@ -86,11 +69,11 @@ def test_dispatcher(self, params): uniform_decode_query_len=1) # Verify the key is initialized correctly - if params["cudagraph_mode"] in ["FULL_AND_PIECEWISE", "PIECEWISE"]: + if cudagraph_mode_str in ["FULL_AND_PIECEWISE", "PIECEWISE"]: assert len(dispatcher.cudagraph_keys[CUDAGraphMode.PIECEWISE]) == 2 else: assert len(dispatcher.cudagraph_keys[CUDAGraphMode.PIECEWISE]) == 0 - if params["cudagraph_mode"] not in ["NONE", "PIECEWISE"]: + if cudagraph_mode_str not in ["NONE", "PIECEWISE"]: assert len(dispatcher.cudagraph_keys[CUDAGraphMode.FULL]) == 2 else: assert len(dispatcher.cudagraph_keys[CUDAGraphMode.FULL]) == 0 @@ -99,10 +82,10 @@ def test_dispatcher(self, params): # 1. non-uniform batch, size in cudagraph size list desc_full_exact = BatchDescriptor(num_tokens=8, uniform_decode=False) rt_mode, key = dispatcher.dispatch(desc_full_exact) - if params["cudagraph_mode"] == "FULL": + if cudagraph_mode_str == "FULL": assert rt_mode == CUDAGraphMode.FULL assert key == desc_full_exact - elif params["cudagraph_mode"] in ["FULL_AND_PIECEWISE", "PIECEWISE"]: + elif cudagraph_mode_str in ["FULL_AND_PIECEWISE", "PIECEWISE"]: assert rt_mode == CUDAGraphMode.PIECEWISE assert key == desc_full_exact else: @@ -111,15 +94,13 @@ def test_dispatcher(self, params): # 2. uniform decode batch, size in cudagraph size list desc_uniform_exact = BatchDescriptor(num_tokens=8, uniform_decode=True) rt_mode, key = dispatcher.dispatch(desc_uniform_exact) - if params["cudagraph_mode"] == "FULL": + if cudagraph_mode_str == "FULL": assert rt_mode == CUDAGraphMode.FULL assert key == desc_uniform_exact.non_uniform - elif params["cudagraph_mode"] in [ - "FULL_DECODE_ONLY", "FULL_AND_PIECEWISE" - ]: + elif cudagraph_mode_str in ["FULL_DECODE_ONLY", "FULL_AND_PIECEWISE"]: assert rt_mode == CUDAGraphMode.FULL assert key == desc_uniform_exact - elif params["cudagraph_mode"] == "PIECEWISE": + elif cudagraph_mode_str == "PIECEWISE": assert rt_mode == CUDAGraphMode.PIECEWISE assert key == desc_uniform_exact.non_uniform else: @@ -131,6 +112,16 @@ def test_dispatcher(self, params): assert rt_mode == CUDAGraphMode.NONE assert key is None + # 4. Cascade attention should have a fall back mode + desc_full_exact = BatchDescriptor(num_tokens=8, uniform_decode=False) + rt_mode, key = dispatcher.dispatch(desc_full_exact, + use_cascade_attn=True) + if "PIECEWISE" in cudagraph_mode_str: # string contains check + assert rt_mode == CUDAGraphMode.PIECEWISE + assert key == desc_full_exact.non_uniform + else: + assert rt_mode == CUDAGraphMode.NONE + @pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda") class TestCUDAGraphWrapper: diff --git a/tests/v1/cudagraph/test_cudagraph_mode.py b/tests/v1/cudagraph/test_cudagraph_mode.py index 41a9493cbe58..c4116247bb7c 100644 --- a/tests/v1/cudagraph/test_cudagraph_mode.py +++ b/tests/v1/cudagraph/test_cudagraph_mode.py @@ -4,12 +4,11 @@ import os import weakref from contextlib import ExitStack -from dataclasses import dataclass -from typing import Optional import pytest from tests.utils import wait_for_gpu_memory_to_clear +from tests.v1.attention.utils import full_cg_backend_configs as backend_configs from vllm import LLM from vllm.config import CompilationConfig from vllm.platforms import current_platform @@ -34,74 +33,6 @@ def temporary_environ(env_vars): os.environ[k] = v -@dataclass -class BackendConfig: - name: str - env_vars: dict - comp_config: dict - specific_gpu_arch: Optional[tuple] = None - - -# Define all backend configurations of full cudagraph to be tested -backend_configs = { - # FA3 on Hopper - "FA3": - BackendConfig(name="FA3", - env_vars={ - "VLLM_FLASH_ATTN_VERSION": "3", - "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", - }, - comp_config={ - "cudagraph_mode": "FULL", - }, - specific_gpu_arch=(9, 0)), - # FlashMLA on Hopper - "FlashMLA": - BackendConfig(name="FlashMLA", - env_vars={ - "VLLM_ATTENTION_BACKEND": "FLASHMLA", - }, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - }, - specific_gpu_arch=(9, 0)), - # FlashAttention MLA on Hopper - "FlashAttentionMLA": - BackendConfig(name="FlashAttentionMLA", - env_vars={ - "VLLM_ATTENTION_BACKEND": "FLASH_ATTN_MLA", - "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", - }, - comp_config={ - "cudagraph_mode": "FULL_DECODE_ONLY", - }, - specific_gpu_arch=(9, 0)), - # FA2 - "FA2": - BackendConfig(name="FA2", - env_vars={ - "VLLM_FLASH_ATTN_VERSION": "2", - "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16", - }, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - }), - # Triton Attention - "TritonAttn": - BackendConfig(name="TritonAttn", - env_vars={"VLLM_ATTENTION_BACKEND": "TRITON_ATTN"}, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - }), - # FlashInfer - "FlashInfer": - BackendConfig(name="FlashInfer", - env_vars={"VLLM_ATTENTION_BACKEND": "FLASHINFER"}, - comp_config={ - "cudagraph_mode": "FULL_AND_PIECEWISE", - }), -} - # test attention backend and cudagraph_mode combo # (backend_name, cudagraph_mode, supported) combo_cases_1 = [ @@ -114,9 +45,10 @@ class BackendConfig: ] -@pytest.mark.parametrize("combo_case", combo_cases_1) -def test_backend_and_cudagraph_mode_combo(combo_case): - backend_name, cudagraph_mode, supported = combo_case +@pytest.mark.parametrize("backend_name, cudagraph_mode, supported", + combo_cases_1) +def test_backend_and_cudagraph_mode_combo(backend_name, cudagraph_mode, + supported): if backend_name == "FlashInfer": try: import flashinfer # noqa: F401 @@ -142,7 +74,7 @@ def test_backend_and_cudagraph_mode_combo(combo_case): compilation_config=CompilationConfig( level=3, cudagraph_mode=cudagraph_mode)) llm.generate(["Hello, my name is"] * 10) - + # when above code raises, `llm` may be undefined, so we need to catch that try: llm = weakref.proxy(llm) del llm @@ -173,7 +105,8 @@ def test_backend_and_cudagraph_mode_combo(combo_case): ] -@pytest.mark.parametrize("combo_case", combo_cases_2) +@pytest.mark.parametrize("backend_name,cudagraph_mode,compilation_level,"\ + "supported", combo_cases_2) def test_cudagraph_compilation_combo(combo_case): backend_name, cudagraph_mode, compilation_level, supported\ = combo_case @@ -192,6 +125,7 @@ def test_cudagraph_compilation_combo(combo_case): compilation_config=CompilationConfig( level=compilation_level, cudagraph_mode=cudagraph_mode)) llm.generate(["Hello, my name is"] * 10) + # when above code raises, `llm` may be undefined, so we need to catch that try: llm = weakref.proxy(llm) del llm diff --git a/vllm/compilation/backends.py b/vllm/compilation/backends.py index 17fc727b8fc7..335bbda5e4eb 100644 --- a/vllm/compilation/backends.py +++ b/vllm/compilation/backends.py @@ -340,15 +340,15 @@ def call_module(self, target: torch.fx.node.Target, num_graphs=len(self.compile_submod_names), runtime_shape=None) # Lazy import here to avoid circular import - from .cuda_piecewise_backend import PiecewiseBackend + from .piecewise_backend import PiecewiseBackend piecewise_backend = PiecewiseBackend( submod, self.vllm_config, index, len(self.compile_submod_names), sym_shape_indices, compiled_graph_for_dynamic_shape, self.vllm_backend) - if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE - and + if (self.compilation_config.cudagraph_mode.\ + has_piecewise_cudagraphs() and not self.compilation_config.use_inductor_graph_partition): # We're using Dynamo-based piecewise splitting, so we wrap # the whole subgraph with a static graph wrapper. diff --git a/vllm/compilation/decorators.py b/vllm/compilation/decorators.py index 6e9a36a2b0b9..fa38cfe49a91 100644 --- a/vllm/compilation/decorators.py +++ b/vllm/compilation/decorators.py @@ -336,7 +336,7 @@ def maybe_use_cudagraph_partition_wrapper(vllm_config: VllmConfig): from vllm.config import CUDAGraphMode compilation_config = vllm_config.compilation_config - if (compilation_config.cudagraph_mode != CUDAGraphMode.NONE + if (compilation_config.cudagraph_mode.has_piecewise_cudagraphs() and compilation_config.use_inductor_graph_partition): from torch._inductor.utils import CUDAGraphWrapperMetadata @@ -365,7 +365,7 @@ def customized_cudagraph_wrapper(f, yield - if (compilation_config.cudagraph_mode != CUDAGraphMode.NONE + if (compilation_config.cudagraph_mode.has_piecewise_cudagraphs() and compilation_config.use_inductor_graph_partition): torch._inductor.utils.set_customized_partition_wrappers(None) diff --git a/vllm/compilation/cuda_piecewise_backend.py b/vllm/compilation/piecewise_backend.py similarity index 100% rename from vllm/compilation/cuda_piecewise_backend.py rename to vllm/compilation/piecewise_backend.py diff --git a/vllm/config/__init__.py b/vllm/config/__init__.py index 59b65900b1e1..ecea90988ebc 100644 --- a/vllm/config/__init__.py +++ b/vllm/config/__init__.py @@ -459,15 +459,22 @@ def __post_init__(self): "to True to enable.") current_platform.check_and_update_config(self) - # final check of cudagraph mode after platform-specific update + # Do this after all the updates to compilation_config.level + if envs.VLLM_USE_V1 and \ + self.compilation_config.level == CompilationLevel.PIECEWISE: + self.compilation_config.set_splitting_ops_for_v1() + + # final check of cudagraph mode after all possible updates if envs.VLLM_USE_V1 and current_platform.is_cuda_alike(): - if self.compilation_config.cudagraph_mode == CUDAGraphMode.FULL \ + if self.compilation_config.cudagraph_mode.has_full_cudagraphs()\ and self.model_config is not None and \ - not self.model_config.disable_cascade_attn: - logger.info("CUDAGraphMode.FULL is not supported with " - "cascade attention currently. Disabling cascade" - "attention.") - self.model_config.disable_cascade_attn = True + not self.model_config.disable_cascade_attn and\ + not self.compilation_config.cudagraph_mode.\ + has_piecewise_cudagraphs(): + logger.warning_once( + "No piecewise cudagraph for executing cascade attention." + " Will fall back to eager execution if a batch runs " + "into cascade attentions") if self.compilation_config.cudagraph_mode\ .requires_piecewise_compilation(): @@ -477,6 +484,12 @@ def __post_init__(self): "when cudagraph_mode piecewise cudagraphs is used, "\ f"cudagraph_mode={self.compilation_config.cudagraph_mode}" + # final migrate the deprecated flags + self.compilation_config.use_cudagraph = self.compilation_config.\ + cudagraph_mode!= CUDAGraphMode.NONE + self.compilation_config.full_cuda_graph = self.compilation_config.\ + cudagraph_mode.has_full_cudagraphs() + if self.parallel_config.enable_dbo: a2a_backend = envs.VLLM_ALL2ALL_BACKEND assert a2a_backend in \ @@ -487,14 +500,14 @@ def __post_init__(self): "variable to deepep_low_latency or deepep_high_throughput and "\ "install the DeepEP kernels." + if not self.model_config.disable_cascade_attn: + self.model_config.disable_cascade_attn = True + logger.warning_once( + "Disabling cascade attention when DBO is enabled.") + if not self.instance_id: self.instance_id = random_uuid()[:5] - # Do this after all the updates to compilation_config.level - if envs.VLLM_USE_V1 and \ - self.compilation_config.level == CompilationLevel.PIECEWISE: - self.compilation_config.set_splitting_ops_for_v1() - if (envs.VLLM_USE_V1 and not self.scheduler_config.disable_hybrid_kv_cache_manager): # logger should only print warning message for hybrid models. As we diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py index 50fde9461a13..9735db98567d 100644 --- a/vllm/config/compilation.py +++ b/vllm/config/compilation.py @@ -61,9 +61,17 @@ def max_cudagraph_mode(self) -> 'CUDAGraphMode': def has_full_cudagraphs(self) -> bool: return self.max_cudagraph_mode() == CUDAGraphMode.FULL + def has_piecewise_cudagraphs(self) -> bool: + return self.requires_piecewise_compilation() + def separate_routine(self) -> bool: return isinstance(self.value, tuple) + def valid_runtime_modes(self) -> bool: + return self in [ + CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL + ] + @config @dataclass @@ -269,7 +277,8 @@ class CompilationConfig: Note that this is orthogonal to the cudagraph capture logic outside of compilation. Warning: This flag is deprecated and will be removed in the next major or - minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode instead. + minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode=PIECEWISE + instead. """ cudagraph_num_of_warmups: int = 0 """Number of warmup runs for cudagraph. @@ -294,7 +303,8 @@ class CompilationConfig: flag cannot be used together with splitting_ops. This may provide performance benefits for smaller models. Warning: This flag is deprecated and will be removed in the next major or - minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode instead. + minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode= + FULL_AND_PIECEWISE instead. """ use_inductor_graph_partition: bool = False @@ -464,7 +474,8 @@ def __post_init__(self) -> None: if not self.use_cudagraph: logger.warning("use_cudagraph is deprecated, use " "cudagraph_mode=NONE instead.") - if self.cudagraph_mode is not None: + if self.cudagraph_mode is not None and \ + self.cudagraph_mode != CUDAGraphMode.NONE: raise ValueError( "use_cudagraph and cudagraph_mode are mutually" " exclusive, prefer cudagraph_mode since " @@ -473,7 +484,8 @@ def __post_init__(self) -> None: if self.full_cuda_graph: logger.warning("full_cuda_graph is deprecated, use " "cudagraph_mode=FULL instead.") - if self.cudagraph_mode is not None: + if self.cudagraph_mode is not None and \ + not self.cudagraph_mode.has_full_cudagraphs(): raise ValueError("full_cuda_graph and cudagraph_mode are " "mutually exclusive, prefer cudagraph_mode " "since full_cuda_graph is deprecated.") @@ -570,48 +582,75 @@ def set_splitting_ops_for_v1(self): "set_splitting_ops_for_v1 should only be called when " "level is CompilationLevel.PIECEWISE") - use_inductor_graph_partition_msg = ( - "When use_inductor_graph_partition=True, splitting_ops " - "are ignored and set to an empty list. Instead, " - "\"tags=(torch._C.Tag.cudagraph_unsafe, ),\" is " - "used to annotate custom ops for graph partition.") + if self.use_inductor_graph_partition: + self.set_splitting_ops_for_inductor_graph_partition() + return + + if self.pass_config.enable_attn_fusion: + # here use_inductor_graph_partition is False + self.set_splitting_ops_for_attn_fusion() + return if self.splitting_ops is None: - if self.use_inductor_graph_partition: - # When using inductor graph partition, we set splitting_ops - # to be empty and rely on torch._C.Tag.cudagraph_unsafe to - # annotate custom ops as splitting ops. - logger.warning_once(use_inductor_graph_partition_msg) - self.splitting_ops = [] - else: - # NOTE: When using full cudagraph, instead of setting an empty - # list and capture the full cudagraph inside the flattened fx - # graph, we keep the piecewise fx graph structure but capture - # the full cudagraph outside the fx graph. This reduces some - # cpu overhead when the runtime batch_size is not cudagraph - # captured. see https://github.com/vllm-project/vllm/pull/20059 - # for details. make a copy to avoid mutating the class-level - # list via reference. - self.splitting_ops = list(self._attention_ops) + # NOTE: When using full cudagraph, instead of setting an empty + # list and capture the full cudagraph inside the flattened fx + # graph, we keep the piecewise fx graph structure but capture + # the full cudagraph outside the fx graph. This reduces some + # cpu overhead when the runtime batch_size is not cudagraph + # captured. see https://github.com/vllm-project/vllm/pull/20059 + # for details. Make a copy to avoid mutating the class-level + # list via reference. + self.splitting_ops = list(self._attention_ops) elif len(self.splitting_ops) == 0: logger.warning_once( - "Using piecewise compilation with empty " - "splitting_ops and use_inductor_graph_partition" - f"={self.use_inductor_graph_partition}.") - if (self.cudagraph_mode == CUDAGraphMode.PIECEWISE - and not self.use_inductor_graph_partition): + "Using piecewise compilation with empty splitting_ops") + if self.cudagraph_mode == CUDAGraphMode.PIECEWISE: + logger.warning_once( + "Piecewise compilation with empty splitting_ops do not" \ + "contains piecewise cudagraph. Setting cudagraph_" + "mode to NONE. Hint: If you are using attention backends " + "that support cudagraph, consider manually setting " + "cudagraph_mode to FULL or FULL_DECODE_ONLY to enable " + "full cudagraphs.") + self.cudagraph_mode = CUDAGraphMode.NONE + elif self.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE: logger.warning_once( - "When compilation level is piecewise with empty " - "splitting_ops, PIECEWISE cudagraph_mode will be " - "treated as FULL cudagraph_mode. Please ensure you are " - "using attention backends that support cudagraph or set " - "cudagraph_mode to NONE explicitly if encountering " - "any problems.") + "Piecewise compilation with empty splitting_ops do not " + "contains piecewise cudagraph. Setting cudagraph_mode " + "to FULL.") self.cudagraph_mode = CUDAGraphMode.FULL self.splitting_ops = [] - elif self.use_inductor_graph_partition: + + def set_splitting_ops_for_inductor_graph_partition(self): + assert self.use_inductor_graph_partition + use_inductor_graph_partition_msg = ( + "When use_inductor_graph_partition=True, splitting_ops " + "are ignored and set to an empty list. Instead, " + "\"tags=(torch._C.Tag.cudagraph_unsafe, ),\" is " + "used to annotate custom ops for graph partition.") + if self.splitting_ops is not None and \ + len(self.splitting_ops) > 0: logger.warning_once(use_inductor_graph_partition_msg) + self.splitting_ops = [] + + def set_splitting_ops_for_attn_fusion(self): + assert self.pass_config.enable_attn_fusion + if self.splitting_ops is None: self.splitting_ops = [] + if self.cudagraph_mode.has_piecewise_cudagraphs(): + logger.warning_once( + "enable_attn_fusion is incompatible with piecewise " + "cudagraph when use_inductor_graph_partition is off." + "In this case, splitting_ops will be set to empty " + "list, and cudagraph_mode will be set to FULL. " + "Please ensure you are using attention backends that " + "support cudagraph or set cudagraph_mode to NONE " + "explicitly if encountering any problems.") + self.cudagraph_mode = CUDAGraphMode.FULL + + assert not self.splitting_ops_contain_attention(), ( + "attention ops should not be in splitting_ops " + "when enable_attn_fusion is True") def splitting_ops_contain_attention(self) -> bool: return self.splitting_ops is not None and all( diff --git a/vllm/forward_context.py b/vllm/forward_context.py index 3b535423f7bc..2bf4e1804521 100644 --- a/vllm/forward_context.py +++ b/vllm/forward_context.py @@ -246,8 +246,7 @@ class ForwardContext: ubatch_slices: Optional[UBatchSlices] = None def __post_init__(self): - assert self.cudagraph_runtime_mode in [ - CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL], \ + assert self.cudagraph_runtime_mode.valid_runtime_modes(), \ f"Invalid cudagraph runtime mode: {self.cudagraph_runtime_mode}" diff --git a/vllm/v1/cudagraph_dispatcher.py b/vllm/v1/cudagraph_dispatcher.py index ea4fba8eeea6..2dbe2bfb8082 100644 --- a/vllm/v1/cudagraph_dispatcher.py +++ b/vllm/v1/cudagraph_dispatcher.py @@ -22,10 +22,10 @@ class CudagraphDispatcher: At runtime, the dispatch method generates the runtime cudagraph mode (FULL, PIECEWISE, or NONE for no cudagraph) and the valid key (batch descriptor) - based on the input key. After dispatching (communicate via forward context), - the cudagraph wrappers will trust the dispatch key to do either capturing - or replaying (if mode matched), or pass through to the underlying runnable - without cudagraph (if mode no match or mode is NONE). + based on the input key. After dispatching (communicated via forward + context), the cudagraph wrappers will trust the dispatch key to either + capture or replay (if the mode matches), or pass through to the underlying + runnable without cudagraph (if the mode does not match or mode is NONE). """ def __init__(self, vllm_config: VllmConfig): @@ -57,19 +57,15 @@ def __init__(self, vllm_config: VllmConfig): def add_cudagraph_key(self, runtime_mode: CUDAGraphMode, batch_descriptor: BatchDescriptor): assert runtime_mode in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL], \ - f"Invalid cudagraph runtime mode: {runtime_mode}" + f"Invalid cudagraph runtime mode for keys: {runtime_mode}" self.cudagraph_keys[runtime_mode].add(batch_descriptor) def initialize_cudagraph_keys(self, cudagraph_mode: CUDAGraphMode, uniform_decode_query_len: int): # This should be called only after attention backend is initialized. - # Note: we create all valid keys possible for cudagraph but do not - # guarantee all keys would be used. For example, we create keys for - # piecewise cudagraphs when it is piecewise compilation, which is always - # valid, but for attention backend support unified routine, we may not - # trigger capturing/replaying the piecewise cudagraphs depending on - # CompilationConfig.cudagraph_mode. In addition, if we allow lazy + # Note: we create all valid keys for cudagraph here but do not + # guarantee all keys would be used. For example, if we allow lazy # capturing in future PR, some keys may never be triggered. if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE: for bs in self.compilation_config.cudagraph_capture_sizes: @@ -94,10 +90,13 @@ def initialize_cudagraph_keys(self, cudagraph_mode: CUDAGraphMode, self.keys_initialized = True def dispatch( - self, batch_descriptor: BatchDescriptor + self, + batch_descriptor: BatchDescriptor, + use_cascade_attn: bool = False ) -> tuple[CUDAGraphMode, Optional[BatchDescriptor]]: """ - Given a batch descriptor, dispatch to a cudagraph mode. + Given conditions(e.g.,batch descriptor and if using cascade attention), + dispatch to a cudagraph runtime mode and the valid batch descriptor. A new batch descriptor is returned as we might dispatch a uniform batch to a graph that supports a more general batch (uniform to non-uniform). """ @@ -107,14 +106,16 @@ def dispatch( "initialized. No cudagraph will be used.") return CUDAGraphMode.NONE, None - # check if key exists for full cudagraph - if batch_descriptor in self.cudagraph_keys[CUDAGraphMode.FULL]: - return CUDAGraphMode.FULL, batch_descriptor - - # otherwise, check if non-uniform key exists non_uniform_key = batch_descriptor.non_uniform - if non_uniform_key in self.cudagraph_keys[CUDAGraphMode.FULL]: - return CUDAGraphMode.FULL, non_uniform_key + # if a batch use cascade attention, bypass checking full cudagraphs + if not use_cascade_attn: + # check if key exists for full cudagraph + if batch_descriptor in self.cudagraph_keys[CUDAGraphMode.FULL]: + return CUDAGraphMode.FULL, batch_descriptor + + # otherwise, check if non-uniform key exists + if non_uniform_key in self.cudagraph_keys[CUDAGraphMode.FULL]: + return CUDAGraphMode.FULL, non_uniform_key # also check if non-uniform key exists for more "general" # piecewise cudagraph diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index f199dbd991f4..2fac708905d0 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -923,11 +923,13 @@ def _prepare_inputs( ) -> tuple[PerLayerAttnMetadata, torch.Tensor, Optional[SpecDecodeMetadata], np.ndarray, Optional[CommonAttentionMetadata], int, Optional[UBatchSlices], - Optional[torch.Tensor]]: + Optional[torch.Tensor], bool]: """ :return: tuple[ attn_metadata: layer-to-attention_metadata mapping, - logits_indices, spec_decode_metadata + logits_indices, spec_decode_metadata, + num_scheduled_tokens, spec_decode_common_attn_metadata, + max_num_scheduled_tokens, use_cascade_attn ] """ total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens @@ -1135,6 +1137,7 @@ def _prepare_inputs( attn_metadata: PerLayerAttnMetadata = {} if ubatch_slices is not None: attn_metadata = [dict() for _ in range(len(ubatch_slices))] + use_cascade_attn = False # Used in the below loop. query_start_loc_cpu = self.query_start_loc.cpu[:num_reqs + 1] @@ -1251,9 +1254,15 @@ def _prepare_inputs( common_prefix_len=common_prefix_len, common_attn_metadata=common_attn_metadata, **extra_attn_metadata_args) + use_cascade_attn |= getattr(attn_metadata_i, "use_cascade", + False) for layer_name in attn_group.layer_names: attn_metadata[layer_name] = attn_metadata_i + # disable cascade attention when DBO + if ubatch_slices is not None: + use_cascade_attn = False + # Hot-Swap lora model if self.lora_config: self.set_active_loras(self.input_batch, num_scheduled_tokens) @@ -1261,7 +1270,7 @@ def _prepare_inputs( return (attn_metadata, logits_indices, spec_decode_metadata, num_scheduled_tokens, spec_decode_common_attn_metadata, max_num_scheduled_tokens, ubatch_slices, - num_tokens_after_padding) + num_tokens_after_padding, use_cascade_attn) def _compute_cascade_attn_prefix_len( self, @@ -2251,8 +2260,8 @@ def execute_model( # Prepare the decoder inputs. (attn_metadata, logits_indices, spec_decode_metadata, num_scheduled_tokens_np, spec_decode_common_attn_metadata, - max_query_len, ubatch_slices, num_tokens_after_padding - ) = self._prepare_inputs(scheduler_output) + max_query_len, ubatch_slices, num_tokens_after_padding, + use_cascade_attn) = self._prepare_inputs(scheduler_output) ( num_scheduled_tokens, @@ -2273,7 +2282,8 @@ def execute_model( batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens, uniform_decode=uniform_decode) cudagraph_runtime_mode, batch_descriptor = \ - self.cudagraph_dispatcher.dispatch(batch_descriptor) + self.cudagraph_dispatcher.dispatch(batch_descriptor, + use_cascade_attn) # This is currently to get around the assert in the DPMetadata # where it wants `num_tokens_across_dp` to align with `num_tokens` @@ -2701,16 +2711,15 @@ def reload_weights(self) -> None: "Cannot reload weights before model is loaded." model_loader = get_model_loader(self.load_config) logger.info("Reloading weights inplace...") - model = self.get_model() - model_loader.load_weights(model, model_config=self.model_config) + model_loader.load_weights(self.get_model(), + model_config=self.model_config) def save_tensorized_model( self, tensorizer_config: "TensorizerConfig", ) -> None: - model = self.get_model() TensorizerLoader.save_model( - model, + self.get_model(), tensorizer_config=tensorizer_config, model_config=self.model_config, ) @@ -2926,9 +2935,8 @@ def _dummy_run( (1 token) and prefill (multiple tokens) requests. remove_lora: If False, dummy LoRAs are not destroyed after the run """ - assert cudagraph_runtime_mode is None or cudagraph_runtime_mode in { - CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL - } + assert cudagraph_runtime_mode is None or \ + cudagraph_runtime_mode.valid_runtime_modes() # If cudagraph_mode.decode_mode() == FULL and # cudagraph_mode.separate_routine(). This means that we are using @@ -3113,7 +3121,8 @@ def _dummy_run( # filter out the valid batch descriptor _cg_mode, batch_descriptor = self.cudagraph_dispatcher.dispatch( BatchDescriptor(num_tokens=num_tokens, - uniform_decode=uniform_decode)) + uniform_decode=uniform_decode)) \ + if not is_profile else (CUDAGraphMode.NONE, None) if cudagraph_runtime_mode is not None: # we allow forcing NONE when the dispatcher disagrees to support # warm ups for cudagraph capture @@ -3453,8 +3462,8 @@ def _capture_cudagraphs(self, compilation_cases: list[int], cudagraph_runtime_mode: CUDAGraphMode, uniform_decode: bool): assert cudagraph_runtime_mode != CUDAGraphMode.NONE and \ - cudagraph_runtime_mode in [CUDAGraphMode.FULL, - CUDAGraphMode.PIECEWISE] + cudagraph_runtime_mode.valid_runtime_modes(), \ + f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}" # Only rank 0 should print progress bar during capture if is_global_first_rank(): @@ -3585,6 +3594,12 @@ def create_attn_groups( self.calculate_reorder_batch_threshold() def initialize_cudagraph_capture(self) -> None: + """ + Resolve the cudagraph_mode when there are multiple attention + backends with potential conflicting CUDA graph support. + Then initialize the cudagraph_dispatcher based on the resolved + cudagraph_mode. + """ min_cg_support = AttentionCGSupport.ALWAYS min_cg_builder_name = None From d093b42d27daf549db607ead5c8a4f342467c290 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Fri, 26 Sep 2025 17:11:40 -0400 Subject: [PATCH 028/166] [CI] Fix FlashInfer AOT in release docker image (#25730) Signed-off-by: mgoin --- .buildkite/release-pipeline.yaml | 2 +- docker/Dockerfile | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.buildkite/release-pipeline.yaml b/.buildkite/release-pipeline.yaml index 8c6ef7817aaf..7677d783fabc 100644 --- a/.buildkite/release-pipeline.yaml +++ b/.buildkite/release-pipeline.yaml @@ -76,7 +76,7 @@ steps: queue: arm64_cpu_queue_postmerge commands: - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7" - - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ." + - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg FLASHINFER_AOT_COMPILE=true --build-arg torch_cuda_arch_list='8.7 9.0 10.0+PTX 12.0' --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ." - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)" # Add job to create multi-arch manifest diff --git a/docker/Dockerfile b/docker/Dockerfile index 034f73736ca7..c0f55a7eeba0 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -404,6 +404,9 @@ RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH' FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0" fi echo "🏗️ Installing FlashInfer with AOT compilation for arches: ${FI_TORCH_CUDA_ARCH_LIST}" + export FLASHINFER_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" + # HACK: We need these to run flashinfer.aot before installing flashinfer, get from the package in the future + uv pip install --system cuda-python==$(echo $CUDA_VERSION | cut -d. -f1,2) pynvml==$(echo $CUDA_VERSION | cut -d. -f1) nvidia-nvshmem-cu$(echo $CUDA_VERSION | cut -d. -f1) # Build AOT kernels TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \ python3 -m flashinfer.aot From 55b306b828ed5e183207f30d5352709ec1c0f47e Mon Sep 17 00:00:00 2001 From: qizixi <22851944+zixi-qi@users.noreply.github.com> Date: Fri, 26 Sep 2025 15:27:05 -0700 Subject: [PATCH 029/166] [spec decode] Consolidate speculative decode method name for MTP (#25232) Signed-off-by: zixi-qi --- examples/offline_inference/spec_decode.py | 5 +- tests/v1/e2e/test_spec_decode.py | 65 ++++++++ tests/v1/spec_decode/test_mtp.py | 195 ++++++++++++++++++++++ vllm/config/speculative.py | 50 +++--- vllm/engine/arg_utils.py | 2 +- vllm/v1/spec_decode/eagle.py | 10 +- 6 files changed, 287 insertions(+), 40 deletions(-) create mode 100644 tests/v1/spec_decode/test_mtp.py diff --git a/examples/offline_inference/spec_decode.py b/examples/offline_inference/spec_decode.py index ce078bce0b75..af65b6d38e02 100644 --- a/examples/offline_inference/spec_decode.py +++ b/examples/offline_inference/spec_decode.py @@ -54,6 +54,7 @@ def parse_args(): "--method", type=str, default="eagle", + choices=["ngram", "eagle", "eagle3", "mtp"], ) parser.add_argument("--num-spec-tokens", type=int, default=2) parser.add_argument("--prompt-lookup-max", type=int, default=5) @@ -118,9 +119,9 @@ def main(args): "prompt_lookup_max": args.prompt_lookup_max, "prompt_lookup_min": args.prompt_lookup_min, } - elif args.method.endswith("mtp"): + elif args.method == "mtp": speculative_config = { - "method": args.method, + "method": "mtp", "num_speculative_tokens": args.num_spec_tokens, } else: diff --git a/tests/v1/e2e/test_spec_decode.py b/tests/v1/e2e/test_spec_decode.py index 66115f14c182..c4efd7548b81 100644 --- a/tests/v1/e2e/test_spec_decode.py +++ b/tests/v1/e2e/test_spec_decode.py @@ -15,6 +15,8 @@ from vllm.distributed import cleanup_dist_env_and_memory from vllm.platforms import current_platform +MTP_SIMILARITY_RATE = 0.8 + def get_test_prompts(mm_enabled: bool): prompt_types = ["repeat", "sentence"] @@ -222,3 +224,66 @@ def test_eagle_correctness( del spec_llm torch.cuda.empty_cache() cleanup_dist_env_and_memory() + + +@pytest.mark.parametrize(["model_setup", "mm_enabled"], [ + (("mtp", "XiaomiMiMo/MiMo-7B-Base", 1), False), + (("mtp", "ZixiQi/DeepSeek-V3-4layers-MTP-FP8", 1), False), +], + ids=["mimo", "deepseek"]) +def test_mtp_correctness( + monkeypatch: pytest.MonkeyPatch, + sampling_config: SamplingParams, + model_setup: tuple[str, str, int], + mm_enabled: bool, +): + # Generate test prompts inside the function instead of using fixture + test_prompts = get_test_prompts(mm_enabled) + ''' + Compare the outputs of a original LLM and a speculative LLM + should be the same when using MTP speculative decoding. + model_setup: (method, model_name, tp_size) + ''' + with monkeypatch.context() as m: + m.setenv("VLLM_USE_V1", "1") + m.setenv("VLLM_MLA_DISABLE", "1") + + method, model_name, tp_size = model_setup + + ref_llm = LLM(model=model_name, + max_model_len=2048, + tensor_parallel_size=tp_size, + trust_remote_code=True) + ref_outputs = ref_llm.chat(test_prompts, sampling_config) + del ref_llm + torch.cuda.empty_cache() + cleanup_dist_env_and_memory() + + spec_llm = LLM( + model=model_name, + trust_remote_code=True, + tensor_parallel_size=tp_size, + speculative_config={ + "method": method, + "num_speculative_tokens": 1, + "max_model_len": 2048, + }, + max_model_len=2048, + ) + spec_outputs = spec_llm.chat(test_prompts, sampling_config) + matches = 0 + misses = 0 + for ref_output, spec_output in zip(ref_outputs, spec_outputs): + if ref_output.outputs[0].text == spec_output.outputs[0].text: + matches += 1 + else: + misses += 1 + print(f"ref_output: {ref_output.outputs[0].text}") + print(f"spec_output: {spec_output.outputs[0].text}") + + # Heuristic: expect at least 80% of the prompts to match exactly + # Upon failure, inspect the outputs to check for inaccuracy. + assert matches > int(MTP_SIMILARITY_RATE * len(ref_outputs)) + del spec_llm + torch.cuda.empty_cache() + cleanup_dist_env_and_memory() diff --git a/tests/v1/spec_decode/test_mtp.py b/tests/v1/spec_decode/test_mtp.py new file mode 100644 index 000000000000..e4881859ece1 --- /dev/null +++ b/tests/v1/spec_decode/test_mtp.py @@ -0,0 +1,195 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +from unittest import mock + +import pytest +import torch + +from tests.v1.attention.utils import (BatchSpec, _Backend, + create_common_attn_metadata, + create_standard_kv_cache_spec, + get_attention_backend) +from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, + ParallelConfig, SchedulerConfig, SpeculativeConfig, + VllmConfig) +from vllm.config.load import LoadConfig +from vllm.model_executor.models.llama import LlamaForCausalLM +from vllm.platforms import current_platform +from vllm.v1.spec_decode.eagle import EagleProposer + +mimo_7b_dir = "XiaomiMiMo/MiMo-7B-Base" + + +def _create_mtp_proposer(num_speculative_tokens: int) -> EagleProposer: + """Create an MTP proposer with unified model configuration.""" + model_config = ModelConfig(model=mimo_7b_dir, + runner="generate", + max_model_len=100, + trust_remote_code=True) + + speculative_config = SpeculativeConfig( + target_model_config=model_config, + target_parallel_config=ParallelConfig(), + model=mimo_7b_dir, + method="mtp", + num_speculative_tokens=num_speculative_tokens, + ) + + vllm_config = VllmConfig( + model_config=model_config, + cache_config=CacheConfig(), + speculative_config=speculative_config, + device_config=DeviceConfig(device=current_platform.device_type), + parallel_config=ParallelConfig(), + load_config=LoadConfig(), + scheduler_config=SchedulerConfig()) + + return EagleProposer(vllm_config=vllm_config, + device=current_platform.device_type) + + +@mock.patch('vllm.v1.spec_decode.eagle.get_pp_group') +@mock.patch('vllm.v1.spec_decode.eagle.get_layers_from_vllm_config') +@mock.patch('vllm.v1.spec_decode.eagle.get_model') +def test_mtp_load_model_unified(mock_get_model, mock_get_layers, + mock_get_pp_group): + """Test MTP-specific model loading with unified model approach.""" + + # Setup mocks + mock_model = mock.MagicMock() + mock_model.model.embed_tokens.weight.shape = (131072, 4096) + mock_get_model.return_value = mock_model + + target_attn_layers = {"target_attn_1": mock.MagicMock()} + all_attn_layers = {**target_attn_layers, "draft_attn_1": mock.MagicMock()} + mock_get_layers.side_effect = [target_attn_layers, all_attn_layers] + + mock_pp_group = mock.MagicMock() + mock_pp_group.world_size = 1 + mock_get_pp_group.return_value = mock_pp_group + + # Create target model + class _TargetModelStub(LlamaForCausalLM): + model: mock.MagicMock + lm_head: mock.MagicMock + + target_model = mock.create_autospec(_TargetModelStub, instance=True) + target_model.model = mock.MagicMock() + target_model.model.embed_tokens.weight.shape = (131072, 4096) + target_model.lm_head = mock.MagicMock() + + # Create MTP proposer + proposer = _create_mtp_proposer(num_speculative_tokens=4) + proposer.load_model(target_model) + + # Verify MTP-specific behavior: + # Model is loaded + mock_get_model.assert_called_once() + # MTP shares lm_head with target model + assert proposer.model.lm_head == target_model.lm_head + # MTP shares embed_tokens with target model + assert proposer.model.model.embed_tokens == target_model.model.embed_tokens + + +@pytest.mark.parametrize("num_speculative_tokens", [1]) +def test_mtp_propose(num_speculative_tokens, monkeypatch): + """Test that MTP's forward method returns hidden states directly""" + + device = torch.device(current_platform.device_type) + batch_size = 2 + seq_lens = [5, 3] + total_tokens = sum(seq_lens) + vocab_size = 100 + + proposer = _create_mtp_proposer(num_speculative_tokens) + hidden_size = proposer.hidden_size + + # Mock the MTP model to verify it returns hidden states directly + model_mock = mock.MagicMock() + + # MTP returns hidden states directly + if num_speculative_tokens == 1: + model_mock.return_value = torch.zeros(total_tokens, + hidden_size, + device=device) + else: + # Multiple forward passes for multi-token speculation + forward_returns = [] + for i in range(num_speculative_tokens): + if i == 0: + h_states = torch.zeros(total_tokens, + hidden_size, + device=device) + else: + h_states = torch.zeros(batch_size, hidden_size, device=device) + forward_returns.append(h_states) + model_mock.side_effect = forward_returns + + # Mock compute_logits + def create_deterministic_logits(batch_size, vocab_size, token_offset): + logits = torch.full((batch_size, vocab_size), -100.0, device=device) + logits[:, token_offset] = 100.0 + return logits + + if num_speculative_tokens == 1: + model_mock.compute_logits.return_value = create_deterministic_logits( + batch_size, vocab_size, 42) + else: + logits_returns = [ + create_deterministic_logits(batch_size, vocab_size, 42 + i) + for i in range(num_speculative_tokens) + ] + model_mock.compute_logits.side_effect = logits_returns + + proposer.model = model_mock + proposer.attn_layer_names = ["layer.0"] + + # Prepare inputs + batch_spec = BatchSpec(seq_lens=seq_lens, query_lens=seq_lens) + common_attn_metadata = create_common_attn_metadata(batch_spec, + block_size=16, + device=device) + + target_token_ids = torch.randint(0, + vocab_size, (total_tokens, ), + device=device) + target_positions = torch.cat([ + torch.arange(seq_lens[0], device=device), + torch.arange(seq_lens[1], device=device) + ]) + target_hidden_states = torch.randn(total_tokens, + hidden_size, + device=device) + next_token_ids = torch.randint(0, + vocab_size, (batch_size, ), + dtype=torch.int32, + device=device) + sampling_metadata = mock.MagicMock() + + # Setup attention metadata + attn_metadata_builder_cls, _ = get_attention_backend(_Backend.FLASH_ATTN) + + attn_metadata_builder = attn_metadata_builder_cls( + kv_cache_spec=create_standard_kv_cache_spec(proposer.vllm_config), + layer_names=proposer.attn_layer_names, + vllm_config=proposer.vllm_config, + device=device, + ) + + proposer.runner = mock.MagicMock() + proposer.attn_metadata_builder = attn_metadata_builder + + # Run propose + result = proposer.propose(target_token_ids=target_token_ids, + target_positions=target_positions, + target_hidden_states=target_hidden_states, + next_token_ids=next_token_ids, + last_token_indices=None, + common_attn_metadata=common_attn_metadata, + sampling_metadata=sampling_metadata) + + # Verify the model was called correctly + assert model_mock.called + # Verify output shape + assert result.shape == (batch_size, num_speculative_tokens) diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index 5f462442148f..8b80ce13f96e 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -32,7 +32,9 @@ SpeculativeMethod = Literal["ngram", "eagle", "eagle3", "medusa", "mlp_speculator", "draft_model", "deepseek_mtp", "ernie_mtp", "qwen3_next_mtp", "mimo_mtp", - "longcat_flash_mtp"] + "longcat_flash_mtp", "mtp"] +MTP_MODEL_TYPES = ("deepseek_mtp", "mimo_mtp", "glm4_moe_mtp", "ernie_mtp", + "qwen3_next_mtp", "longcat_flash_mtp") @config @@ -207,11 +209,16 @@ def __post_init__(self): # can not be detected, it will be considered as the "draft_model" by # default. + if self.method in MTP_MODEL_TYPES: + logger.warning("method `%s` is deprecated and replaced with mtp.", + self.method) + self.method = "mtp" + if self.model is None and self.num_speculative_tokens is not None: - # TODO(Shangming): Refactor mtp configuration logic when supporting - if (self.target_model_config - and self.target_model_config.hf_text_config.model_type - in ("deepseek_v3", "mimo", "ernie4_5_moe", "qwen3_next")): + if self.method == "mtp": + assert ( + self.target_model_config + is not None), "target_model_config must be present for mtp" # use the draft model from the same model: self.model = self.target_model_config.model # Align the quantization of draft model for cases such as @@ -312,31 +319,13 @@ def __post_init__(self): "mlp_speculator"): self.method = "mlp_speculator" elif (self.draft_model_config.hf_config.model_type - in ("deepseek_mtp", "mimo_mtp", "glm4_moe_mtp")): - self.method = "deepseek_mtp" - if self.num_speculative_tokens > 1: - logger.warning( - "All Deepseek MTP models only have " \ - "one layer. Might need some code changes " \ - "to support multiple layers." - ) - elif (self.draft_model_config.hf_config.model_type == - "ernie_mtp"): - self.method = "ernie_mtp" + in MTP_MODEL_TYPES): + self.method = "mtp" if self.num_speculative_tokens > 1: logger.warning( - "All Ernie MTP models only have " \ - "one layer. Might need some code changes " \ - "to support multiple layers." - ) - elif (self.draft_model_config.hf_config.model_type == - "qwen3_next_mtp"): - self.method = "qwen3_next_mtp" - if self.num_speculative_tokens > 1: - logger.warning( - "All Qwen3Next MTP models only have " \ - "one layer. Might need some code changes " \ - "to support multiple layers." + "Enabling num_speculative_tokens > 1 will run" \ + "multiple times of forward on same MTP layer" \ + ",which may result in lower acceptance rate" \ ) elif (self.draft_model_config.hf_config.model_type in ("longcat_flash_mtp")): @@ -353,7 +342,7 @@ def __post_init__(self): "Speculative decoding with draft model is not " "supported yet. Please consider using other " "speculative decoding methods such as ngram, medusa, " - "eagle, or deepseek_mtp.") + "eagle, or mtp.") # Replace hf_config for EAGLE draft_model if self.method in ("eagle", "eagle3"): @@ -562,8 +551,7 @@ def num_lookahead_slots(self) -> int: return self.num_speculative_tokens def use_eagle(self) -> bool: - return self.method in ("eagle", "eagle3", "deepseek_mtp", "ernie_mtp", - "qwen3_next_mtp", "longcat_flash_mtp") + return self.method in ("eagle", "eagle3", "mtp") def __repr__(self) -> str: method = self.method diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 7b5ed67d0adb..8757f4b8b7ba 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1481,7 +1481,7 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool: raise NotImplementedError( "Draft model speculative decoding is not supported yet. " "Please consider using other speculative decoding methods " - "such as ngram, medusa, eagle, or deepseek_mtp.") + "such as ngram, medusa, eagle, or mtp.") V1_BACKENDS = [ "FLASH_ATTN", diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index 119f41d8580e..57da8346f497 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -222,8 +222,7 @@ def propose( hidden_states=self.hidden_states[:num_input_tokens], inputs_embeds=inputs_embeds, ) - if self.method in ("deepseek_mtp", "ernie_mtp", "qwen3_next_mtp", - "longcat_flash_mtp"): + if self.method == "mtp": last_hidden_states = ret_hidden_states hidden_states = last_hidden_states else: @@ -352,8 +351,7 @@ def propose( hidden_states=self.hidden_states[:input_batch_size], inputs_embeds=inputs_embeds, ) - if self.method in ("deepseek_mtp", "ernie_mtp", - "qwen3_next_mtp", "longcat_flash_mtp"): + if self.method == "mtp": last_hidden_states = ret_hidden_states hidden_states = ret_hidden_states else: @@ -888,10 +886,10 @@ def dummy_run( def _get_attention_metadata_builder( self) -> list[AttentionMetadataBuilder]: """Find and return the attention metadata builders for EAGLE layers. - + Returns: The metadata builders for EAGLE layers. - + Raises: AssertionError: If no metadata builders are found for EAGLE layers. """ From e31766e1fb63f645c9e8c2c848a62485d19841e3 Mon Sep 17 00:00:00 2001 From: Sage Moore Date: Fri, 26 Sep 2025 15:29:56 -0700 Subject: [PATCH 030/166] Reduce the Cuda Graph memory footprint when running with DBO (#25779) Signed-off-by: Sage Moore --- vllm/v1/worker/gpu_model_runner.py | 48 ++++++++++++---------------- vllm/v1/worker/gpu_ubatch_wrapper.py | 12 +++++++ 2 files changed, 32 insertions(+), 28 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 2fac708905d0..4fd4f9128c6e 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -3477,8 +3477,10 @@ def _capture_cudagraphs(self, compilation_cases: list[int], # We skip EPLB here since we don't want to record dummy metrics for num_tokens in compilation_cases: # We currently only capture ubatched graphs when its a FULL - # cudagraph and for uniform decode batches. - capture_ubatched_graph = self.parallel_config.enable_dbo \ + # cudagraph, a uniform decode batch, and the number of tokens + # is above the threshold. Otherwise we just capture a non-ubatched + # version of the graph + allow_microbatching = self.parallel_config.enable_dbo \ and cudagraph_runtime_mode == CUDAGraphMode.FULL \ and uniform_decode \ and check_ubatch_thresholds( @@ -3487,37 +3489,27 @@ def _capture_cudagraphs(self, compilation_cases: list[int], uniform_decode=uniform_decode, ) - # Currently we capture both microbatched and non-microbatched - # graphs when capture_ubatched_graph is True, this is because - # occasionally we will be forced out of microbatching due to other - # DP ranks not microbatching (usually caused by an empty second - # microbatch; once we resolve this, we can remove the - # non-microbatched graph capture). - allow_microbatching_options = [True, False] if \ - capture_ubatched_graph else [False] - for allow_microbatching in allow_microbatching_options: - for _ in range( - self.compilation_config.cudagraph_num_of_warmups): - # Use CUDAGraphRuntimeStyle.NONE (default) for warmup. - # But be careful, warm up with `NONE`is orthogonal to - # if we want to warm up attention or not. This is - # different from the case where `FULL` implies capture - # attention while `PIECEWISE` implies no attention. - force_attention = ( - cudagraph_runtime_mode == CUDAGraphMode.FULL) - self._dummy_run(num_tokens, - cudagraph_runtime_mode=CUDAGraphMode.NONE, - force_attention=force_attention, - uniform_decode=uniform_decode, - allow_microbatching=allow_microbatching, - skip_eplb=True, - remove_lora=False) + for _ in range(self.compilation_config.cudagraph_num_of_warmups): + # Use CUDAGraphRuntimeStyle.NONE (default) for warmup. + # But be careful, warm up with `NONE`is orthogonal to + # if we want to warm up attention or not. This is + # different from the case where `FULL` implies capture + # attention while `PIECEWISE` implies no attention. + force_attention = ( + cudagraph_runtime_mode == CUDAGraphMode.FULL) self._dummy_run(num_tokens, - cudagraph_runtime_mode=cudagraph_runtime_mode, + cudagraph_runtime_mode=CUDAGraphMode.NONE, + force_attention=force_attention, uniform_decode=uniform_decode, allow_microbatching=allow_microbatching, skip_eplb=True, remove_lora=False) + self._dummy_run(num_tokens, + cudagraph_runtime_mode=cudagraph_runtime_mode, + uniform_decode=uniform_decode, + allow_microbatching=allow_microbatching, + skip_eplb=True, + remove_lora=False) self.maybe_remove_all_loras(self.lora_config) def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None: diff --git a/vllm/v1/worker/gpu_ubatch_wrapper.py b/vllm/v1/worker/gpu_ubatch_wrapper.py index 5e4c1d32ab6c..39be8c74102e 100644 --- a/vllm/v1/worker/gpu_ubatch_wrapper.py +++ b/vllm/v1/worker/gpu_ubatch_wrapper.py @@ -330,6 +330,18 @@ def __call__(self, *args, **kwargs): # If there's no ubatching, just run the runnable object if ubatch_slices is None: + + # This is to account for the case where ubatching was aborted. + # When we capture full graphs we only capture one graph per shape, + # meaning that if we have a ubatched cudagraph for the current + # num_tokens, we don't have a non-ubatched one. Without this + # check, the cudagraph wrapper will try to capture a cudagraph + # for this shape during a normal run. + if cudagraph_runtime_mode is CUDAGraphMode.FULL: + assert batch_descriptor is not None + if batch_descriptor.num_tokens in self.cudagraphs: + cudagraph_runtime_mode = CUDAGraphMode.NONE + if cudagraph_runtime_mode in (CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE): return self.runnable(*args, **kwargs) From db06e13f2218b35408fdbbf4755fa65ac6cfcbdc Mon Sep 17 00:00:00 2001 From: Bram Wasti Date: Fri, 26 Sep 2025 19:59:09 -0400 Subject: [PATCH 031/166] Kernel-override Determinism [1/n] (#25603) Signed-off-by: Bram Wasti --- csrc/core/batch_invariant.hpp | 16 + csrc/layernorm_kernels.cu | 8 +- csrc/layernorm_quant_kernels.cu | 5 +- csrc/moe/topk_softmax_kernels.cu | 4 +- tests/v1/generation/test_batch_invariance.py | 290 +++++++++ vllm/model_executor/layers/batch_invariant.py | 561 ++++++++++++++++++ vllm/v1/attention/backends/flex_attention.py | 7 + vllm/v1/worker/gpu_model_runner.py | 3 + 8 files changed, 890 insertions(+), 4 deletions(-) create mode 100644 csrc/core/batch_invariant.hpp create mode 100644 tests/v1/generation/test_batch_invariance.py create mode 100644 vllm/model_executor/layers/batch_invariant.py diff --git a/csrc/core/batch_invariant.hpp b/csrc/core/batch_invariant.hpp new file mode 100644 index 000000000000..19e422e4b80c --- /dev/null +++ b/csrc/core/batch_invariant.hpp @@ -0,0 +1,16 @@ +#pragma once +#include +#include +#include + +namespace vllm { + +// vllm_kernel_override_batch_invariant(); returns true +// if env VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT=1 +inline bool vllm_kernel_override_batch_invariant() { + std::string env_key = "VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT"; + const char* val = std::getenv(env_key.c_str()); + return (val && std::atoi(val) != 0) ? 1 : 0; +} + +} // namespace vllm diff --git a/csrc/layernorm_kernels.cu b/csrc/layernorm_kernels.cu index 93c73d58390e..6c3685f6f7cd 100644 --- a/csrc/layernorm_kernels.cu +++ b/csrc/layernorm_kernels.cu @@ -1,6 +1,7 @@ #include "type_convert.cuh" #include "dispatch_utils.h" #include "cub_helpers.h" +#include "core/batch_invariant.hpp" #include #include @@ -413,7 +414,9 @@ void fused_add_rms_norm(torch::Tensor& input, // [..., hidden_size] wt_ptr % req_alignment_bytes == 0; bool offsets_are_multiple_of_vector_width = hidden_size % vector_width == 0 && input_stride % vector_width == 0; - if (ptrs_are_aligned && offsets_are_multiple_of_vector_width) { + bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant(); + if (ptrs_are_aligned && offsets_are_multiple_of_vector_width && + !batch_invariant_launch) { LAUNCH_FUSED_ADD_RMS_NORM(8); } else { LAUNCH_FUSED_ADD_RMS_NORM(0); @@ -459,7 +462,8 @@ void poly_norm(torch::Tensor& out, // [..., hidden_size] auto inp_ptr = reinterpret_cast(input.data_ptr()); auto out_ptr = reinterpret_cast(out.data_ptr()); bool ptrs_are_aligned = inp_ptr % 16 == 0 && out_ptr % 16 == 0; - if (ptrs_are_aligned && hidden_size % 8 == 0) { + bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant(); + if (ptrs_are_aligned && hidden_size % 8 == 0 && !batch_invariant_launch) { LAUNCH_FUSED_POLY_NORM(8); } else { LAUNCH_FUSED_POLY_NORM(0); diff --git a/csrc/layernorm_quant_kernels.cu b/csrc/layernorm_quant_kernels.cu index be134089bd6d..58c3d9c0981a 100644 --- a/csrc/layernorm_quant_kernels.cu +++ b/csrc/layernorm_quant_kernels.cu @@ -9,6 +9,7 @@ #include "quantization/fp8/common.cuh" #include "dispatch_utils.h" #include "cub_helpers.h" +#include "core/batch_invariant.hpp" #include #include @@ -240,7 +241,9 @@ void fused_add_rms_norm_static_fp8_quant( auto wt_ptr = reinterpret_cast(weight.data_ptr()); bool ptrs_are_aligned = inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0; - if (ptrs_are_aligned && hidden_size % 8 == 0 && input_stride % 8 == 0) { + bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant(); + if (ptrs_are_aligned && hidden_size % 8 == 0 && input_stride % 8 == 0 && + !batch_invariant_launch) { LAUNCH_FUSED_ADD_RMS_NORM(8); } else { LAUNCH_FUSED_ADD_RMS_NORM(0); diff --git a/csrc/moe/topk_softmax_kernels.cu b/csrc/moe/topk_softmax_kernels.cu index 53573ada86ba..eca021f1c186 100644 --- a/csrc/moe/topk_softmax_kernels.cu +++ b/csrc/moe/topk_softmax_kernels.cu @@ -21,6 +21,7 @@ #include #include "../cuda_compat.h" #include "../cub_helpers.h" +#include "../core/batch_invariant.hpp" #define MAX(a, b) ((a) > (b) ? (a) : (b)) #define MIN(a, b) ((a) < (b) ? (a) : (b)) @@ -405,7 +406,8 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f using Constants = detail::TopkConstants; static constexpr int VPT = Constants::VPT; static constexpr int ROWS_PER_WARP = Constants::ROWS_PER_WARP; - const int num_warps = (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP; + const bool batch_invariant_launch = vllm::vllm_kernel_override_batch_invariant(); + const int num_warps = batch_invariant_launch ? 32 : (num_rows + ROWS_PER_WARP - 1) / ROWS_PER_WARP; const int num_blocks = (num_warps + WARPS_PER_TB - 1) / WARPS_PER_TB; dim3 block_dim(WARP_SIZE_PARAM, WARPS_PER_TB); diff --git a/tests/v1/generation/test_batch_invariance.py b/tests/v1/generation/test_batch_invariance.py new file mode 100644 index 000000000000..b864f9a31836 --- /dev/null +++ b/tests/v1/generation/test_batch_invariance.py @@ -0,0 +1,290 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import contextlib +import os +import random +import string + +import pytest +import torch + +from vllm import LLM, SamplingParams + + +def _random_prompt(min_words: int = 1024, max_words: int = 1024 * 2) -> str: + # Lightweight random prompt generator to vary prompt lengths and content. + vocab = [ + "alpha", + "bravo", + "charlie", + "delta", + "echo", + "foxtrot", + "golf", + "hotel", + "india", + "juliet", + "kilo", + "lima", + "mike", + "november", + "oscar", + "papa", + "quebec", + "romeo", + "sierra", + "tango", + "uniform", + "victor", + "whiskey", + "xray", + "yankee", + "zulu", + ] + n = random.randint(min_words, max_words) + words = random.choices(vocab, k=n) + + # Add some noise and punctuation variability + if random.random() < 0.5: + words[0] = words[0].capitalize() + if random.random() < 0.2: + words.append("".join(random.choices(string.ascii_lowercase, k=5))) + punct = random.choice([".", "?", "!", "...", ""]) + return " ".join(words) + punct + + +@pytest.mark.timeout(1000) +def test_v1_generation_is_deterministic_across_batch_sizes_with_needle(): + """ + Ensures that the same request (the 'needle' prompt) yields identical output + whether run alone (bs=1) or mixed into a larger batch (e.g., bs=64), + using the high-level v1 LLM() API only (no manual batching). + + Strategy: + - Create two LLM engines with identical config except max_num_seqs: 1 vs N. + - Compute a baseline output for the needle prompt with the bs=1 engine. + - For many trials, generate a batch (size N) where the needle appears at a + random position among random filler prompts using the bs=N engine. + - Track how many trials match vs mismatch, and report totals at the end. + The test fails if any mismatches occur, but we still dump pass/fail + counts. + + Notes: + - Use seeded stochastic sampling with a fixed seed to test determinism. + - Outputs are intentionally longer and sampled at higher temperature/top_p + to produce a more random-sounding phrase, yet remain deterministic by + seed. + - Keep max_tokens and max_model_len bounded for speed and memory use. + """ + random.seed(12345) + + # Allow overrides from environment (useful for CI tuning) + # "facebook/opt-125m" is too small, doesn't reliably test determinism + model = os.getenv("VLLM_TEST_MODEL", "Qwen/Qwen3-1.7B") + num_trials = int(os.getenv("VLLM_NEEDLE_TRIALS", "5")) + batch_size = int(os.getenv("VLLM_NEEDLE_BATCH_SIZE", "64")) + assert batch_size >= 2, "Batch size should be >= 2 to mix needle." + + # Keep GPU memory usage low to avoid startup allocation failures. + gpu_mem_util = float(os.getenv("VLLM_GPU_MEMORY_UTILIZATION", "0.3")) + max_model_len = int(os.getenv("VLLM_MAX_MODEL_LEN", "4096")) + swap_space_gb = int(os.getenv("VLLM_SWAP_SPACE_GB", "4")) + + # Sampling parameters: longer outputs with a more random-sounding + # continuation,but still deterministic due to fixed seed. + temperature = float(os.getenv("VLLM_NEEDLE_TEMPERATURE", "0.0")) + top_p = float(os.getenv("VLLM_NEEDLE_TOP_P", "0.95")) + max_tokens = int(os.getenv("VLLM_NEEDLE_MAX_TOKENS", "128")) + + sampling = SamplingParams( + temperature=temperature, + top_p=top_p, + max_tokens=max_tokens, + seed=20240919, + ) + + needle_prompt = ("There once was a ") + + llm_bs1 = None + llm_bsN = None + try: + # Engine with bs=1 behavior + llm_bs1 = LLM_with_max_seqs( + model=model, + max_num_seqs=1, + gpu_memory_utilization=gpu_mem_util, + max_model_len=max_model_len, + swap_space=swap_space_gb, + ) + + # Baseline generation for the needle prompt alone. + baseline_out = llm_bs1.generate([needle_prompt], sampling) + assert len(baseline_out) == 1 + assert len(baseline_out[0].outputs) >= 1 + baseline_text = baseline_out[0].outputs[0].text + + # Engine with larger batch limit (e.g., 64) + llm_bsN = LLM_with_max_seqs( + model=model, + max_num_seqs=batch_size, + gpu_memory_utilization=gpu_mem_util, + max_model_len=max_model_len, + swap_space=swap_space_gb, + ) + + mismatches = 0 + + for trial in range(num_trials): + # Create a batch of size `batch_size` and insert the needle at + # a random index + prompts: list[str] = [] + needle_pos = random.randint(0, batch_size - 1) + for i in range(batch_size): + if i == needle_pos: + prompts.append(needle_prompt) + else: + prompts.append(_random_prompt()) + + # Generate with the larger-batch engine + outputs = llm_bsN.generate(prompts, sampling) + # Find the needle output by position + needle_output = outputs[needle_pos] + assert needle_output.prompt == needle_prompt + assert len(needle_output.outputs) >= 1 + text = needle_output.outputs[0].text + + if text != baseline_text: + mismatches += 1 + + passes = num_trials - mismatches + # Dump how many passed vs failed + print(f"[determinism] total={num_trials}, passed={passes}, " + f"failed={mismatches}, batch_size={batch_size}") + + if mismatches > 0: + pytest.fail( + f"Nondeterministic outputs detected: {mismatches} failed out " + f"of {num_trials} trials (batch_size={batch_size}).") + + finally: + # Ensure engines are shutdown to free GPU/VRAM across test sessions + if llm_bs1 is not None: + with contextlib.suppress(Exception): + llm_bs1.shutdown() + if llm_bsN is not None: + with contextlib.suppress(Exception): + llm_bsN.shutdown() + + +def _extract_step_logprobs(request_output): + if getattr(request_output, "outputs", None): + inner = request_output.outputs[0] + if hasattr(inner, "logprobs") and inner.logprobs is not None: + t = torch.tensor( + [ + inner.logprobs[i][tid].logprob + for i, tid in enumerate(inner.token_ids) + ], + dtype=torch.float32, + ) + return t + + return None + + +@pytest.mark.skipif( + not torch.cuda.is_available(), + reason="Requires CUDA to match production inference path.", +) +def test_logprobs_bitwise_batch_invariance_bs1_vs_bs2(): + + #model_name = os.getenv("VLLM_TEST_MODEL", "facebook/opt-125m") + model_name = os.getenv("VLLM_TEST_MODEL", "Qwen/Qwen3-1.7B") + tp_size = int(os.getenv("VLLM_TEST_TP_SIZE", "1")) + + # Force float32 to avoid precision-induced differences. + llm = LLM( + model=model_name, + tensor_parallel_size=tp_size, + enforce_eager=True, # helps reduce nondeterminism from some backends + ) + + prompts = [ + "The capital of France is", + "The capital of Germany is", + ] + + sp = SamplingParams( + temperature=0.0, + top_p=1.0, + max_tokens=8, + # Seed shouldn't matter at temperature=0, but keeping it stable anyway. + seed=1234, + logprobs=5, + ) + + # BS=1: run prompts individually and collect logprobs per step. + bs1_logprobs_per_prompt = [] + for p in prompts: + outs = llm.generate([p], sp, use_tqdm=False) + assert len(outs) == 1 + step_logprobs = _extract_step_logprobs(outs[0]) + if step_logprobs is None: + pytest.skip("Logits are not available on RequestOutput; " + "enable logprobs return to run this test.") + bs1_logprobs_per_prompt.append(step_logprobs) + + # BS=2: run prompts in a batch and collect logprobs per step for each + # prompt. + outs_batched = llm.generate(prompts, sp, use_tqdm=False) + assert len(outs_batched) == len(prompts) + bs2_logprobs_per_prompt = [] + for o in outs_batched: + step_logprobs = _extract_step_logprobs(o) + if step_logprobs is None: + pytest.skip("Logits are not available on RequestOutput; " + "enable logprobs return to run this test.") + bs2_logprobs_per_prompt.append(step_logprobs) + + # Compare step-by-step logprobs for each prompt between BS=1 and BS=2 runs. + for i, (logprobs_bs1, logprobs_bs2) in enumerate( + zip(bs1_logprobs_per_prompt, bs2_logprobs_per_prompt)): + assert len(logprobs_bs1) == len(logprobs_bs2), ( + f"Different number of generation steps for prompt index {i}: " + f"{len(logprobs_bs1)} (BS=1) vs {len(logprobs_bs2)} (BS=2)") + for t, (a, b) in enumerate(zip(logprobs_bs1, logprobs_bs2)): + assert a.shape == b.shape, ( + f"Logits shape mismatch at prompt {i}, step {t}: " + f"{a.shape} vs {b.shape}") + # Bitwise exact equality. + assert torch.equal( + a, b), (f"Bitwise logprobs mismatch at prompt {i}, step {t} " + f"(dtype={a.dtype}, shape={a.shape}).") + + +def LLM_with_max_seqs( + model: str, + max_num_seqs: int, + gpu_memory_utilization: float, + max_model_len: int, + swap_space: int, +) -> LLM: + """ + Helper to construct an LLM with a specific max_num_seqs (batch-size limit) + using the high-level v1 LLM API, while constraining memory usage. + """ + return LLM( + model=model, + max_num_seqs=max_num_seqs, + # Constrain GPU memory pool so test can run even on busy GPUs. + gpu_memory_utilization=gpu_memory_utilization, + # Keep KV cache footprint small while allowing longer outputs. + max_model_len=max_model_len, + # Allow some CPU offload if needed. + swap_space=swap_space, + # Keep things lean and CI-friendly. + dtype="float16", + # Single-GPU by default; override externally if desired. + tensor_parallel_size=int(os.getenv("VLLM_TP_SIZE", "1")), + trust_remote_code=os.getenv("VLLM_TRUST_REMOTE_CODE", "0") == "1", + ) diff --git a/vllm/model_executor/layers/batch_invariant.py b/vllm/model_executor/layers/batch_invariant.py new file mode 100644 index 000000000000..ae2c842af698 --- /dev/null +++ b/vllm/model_executor/layers/batch_invariant.py @@ -0,0 +1,561 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import contextlib +import os +from collections import namedtuple +from collections.abc import Callable +from typing import Any, Union + +import torch +import triton +import triton.language as tl + + +def _matmul_launch_metadata(grid: Callable[..., Any], kernel: Any, + args: dict[str, Any]) -> dict[str, Any]: + ret = {} + m, n, k = args["M"], args["N"], args["K"] + ret["name"] = f"{kernel.name} [M={m}, N={n}, K={k}]" + if "tiles_per_update" in args: + ret["name"] = (f"{kernel.name} [M={m}, N={n}, K={k}, " + f"tiles_per_update={args['tiles_per_update']:02}]") + if "c_ptr" in args: + bytes_per_elem = args["c_ptr"].element_size() + else: + bytes_per_elem = 1 if args["FP8_OUTPUT"] else 2 + ret[f"flops{bytes_per_elem * 8}"] = 2.0 * m * n * k + ret["bytes"] = bytes_per_elem * (m * k + n * k + m * n) + return ret + + +@triton.jit +def _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_SMS): + group_id = tile_id // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + (tile_id % group_size_m) + pid_n = (tile_id % num_pid_in_group) // group_size_m + return pid_m, pid_n + + +@triton.jit(launch_metadata=_matmul_launch_metadata) +def matmul_kernel_persistent( + a_ptr, + b_ptr, + c_ptr, # + bias_ptr, + M, + N, + K, # + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + BLOCK_SIZE_M: tl.constexpr, # + BLOCK_SIZE_N: tl.constexpr, # + BLOCK_SIZE_K: tl.constexpr, # + GROUP_SIZE_M: tl.constexpr, # + NUM_SMS: tl.constexpr, # + A_LARGE: tl.constexpr, + B_LARGE: tl.constexpr, + C_LARGE: tl.constexpr, + HAS_BIAS: tl.constexpr, +): + start_pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + k_tiles = tl.cdiv(K, BLOCK_SIZE_K) + num_tiles = num_pid_m * num_pid_n + + tile_id_c = start_pid - NUM_SMS + + offs_k_for_mask = tl.arange(0, BLOCK_SIZE_K) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + + for tile_id in tl.range(start_pid, num_tiles, NUM_SMS, flatten=True): + pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, + GROUP_SIZE_M, NUM_SMS) + start_m = pid_m * BLOCK_SIZE_M + start_n = pid_n * BLOCK_SIZE_N + offs_am = start_m + tl.arange(0, BLOCK_SIZE_M) + offs_bn = start_n + tl.arange(0, BLOCK_SIZE_N) + if A_LARGE: + offs_am = offs_am.to(tl.int64) + if B_LARGE: + offs_bn = offs_bn.to(tl.int64) + offs_am = tl.where(offs_am < M, offs_am, 0) + offs_bn = tl.where(offs_bn < N, offs_bn, 0) + offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), + BLOCK_SIZE_M) + offs_bn = tl.max_contiguous(tl.multiple_of(offs_bn, BLOCK_SIZE_N), + BLOCK_SIZE_N) + + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for ki in range(k_tiles): + if A_LARGE or B_LARGE: + offs_k = ki * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K).to( + tl.int64) + else: + offs_k = ki * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) + a_ptrs = a_ptr + (offs_am[:, None] * stride_am + + offs_k[None, :] * stride_ak) + b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + + offs_bn[None, :] * stride_bn) + + a = tl.load(a_ptrs, + mask=offs_k_for_mask[None, :] < K - ki * BLOCK_SIZE_K, + other=0.0) + b = tl.load(b_ptrs, + mask=offs_k_for_mask[:, None] < K - ki * BLOCK_SIZE_K, + other=0.0) + accumulator = tl.dot(a, b, accumulator) + + tile_id_c += NUM_SMS + pid_m, pid_n = _compute_pid(tile_id_c, num_pid_in_group, num_pid_m, + GROUP_SIZE_M, NUM_SMS) + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + if C_LARGE: + offs_cm = offs_cm.to(tl.int64) + offs_cn = offs_cn.to(tl.int64) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[ + None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + if HAS_BIAS: + bias_ptrs = bias_ptr + offs_cn + bias = tl.load(bias_ptrs, mask=offs_cn < N, + other=0.0).to(tl.float32) + accumulator += bias + if c_ptr.dtype.element_ty == tl.float8e4nv: + c = accumulator.to(tl.float8e4nv) + else: + c = accumulator.to(tl.float16) + tl.store(c_ptrs, c, mask=c_mask) + + +def matmul_persistent(a: torch.Tensor, + b: torch.Tensor, + bias: Union[torch.Tensor, None] = None): + # Check constraints. + assert a.shape[1] == b.shape[0], "Incompatible dimensions" + assert a.dtype == b.dtype, "Incompatible dtypes" + assert bias is None or bias.dim() == 1, ( + "Currently assuming bias is 1D, let Horace know if you run into this") + NUM_SMS = torch.cuda.get_device_properties("cuda").multi_processor_count + M, K = a.shape + K, N = b.shape + dtype = a.dtype + # Allocates output. + c = torch.empty((M, N), device=a.device, dtype=dtype) + + # 1D launch kernel where each block gets its own program. + def grid(META): + return (min( + NUM_SMS, + triton.cdiv(M, META["BLOCK_SIZE_M"]) * + triton.cdiv(N, META["BLOCK_SIZE_N"])), ) + + configs = { + torch.bfloat16: { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + "num_stages": 3, + "num_warps": 8, + }, + torch.float16: { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + "num_stages": 3, + "num_warps": 8, + }, + torch.float32: { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 32, + "GROUP_SIZE_M": 8, + "num_stages": 3, + "num_warps": 8, + }, + } + # print(a.device, b.device, c.device) + matmul_kernel_persistent[grid]( + a, + b, + c, # + bias, + M, + N, + K, # + a.stride(0), + a.stride(1), # + b.stride(0), + b.stride(1), # + c.stride(0), + c.stride(1), # + NUM_SMS=NUM_SMS, # + A_LARGE=a.numel() > 2**31, + B_LARGE=b.numel() > 2**31, + C_LARGE=c.numel() > 2**31, + HAS_BIAS=bias is not None, + **configs[dtype], + ) + return c + + +@triton.jit +def _log_softmax_kernel( + input_ptr, + output_ptr, + input_row_stride, + output_row_stride, + n_cols, + BLOCK_SIZE: tl.constexpr, +): + """ + Compute log_softmax along the last dimension of a 2D tensor. + Each block handles one row of the input tensor. + """ + # Get the row index for this block + row_idx = tl.program_id(0).to(tl.int64) + + # Compute base pointers for input and output rows + row_start_ptr = input_ptr + row_idx * input_row_stride + output_row_start_ptr = output_ptr + row_idx * output_row_stride + + # Step 1: Find maximum value in the row for numerical stability + max_val = -float("inf") + for col_offset in range(0, n_cols, BLOCK_SIZE): + col_idx = col_offset + tl.arange(0, BLOCK_SIZE) + mask = col_idx < n_cols + + # Load values + vals = tl.load(row_start_ptr + col_idx, mask=mask, other=-float("inf")) + + # Update maximum + max_val = tl.max(tl.maximum(vals, max_val)) + + # Step 2: Compute sum of exp(x - max_val) + sum_exp = 0.0 + for col_offset in range(0, n_cols, BLOCK_SIZE): + col_idx = col_offset + tl.arange(0, BLOCK_SIZE) + mask = col_idx < n_cols + + # Load values + vals = tl.load(row_start_ptr + col_idx, mask=mask, other=0.0) + + # Compute exp(x - max_val) and accumulate + exp_vals = tl.exp(vals - max_val) + sum_exp += tl.sum(tl.where(mask, exp_vals, 0.0)) + + # Compute log(sum_exp) + log_sum_exp = tl.log(sum_exp) + + # Step 3: Compute final log_softmax values: x - max_val - log_sum_exp + for col_offset in range(0, n_cols, BLOCK_SIZE): + col_idx = col_offset + tl.arange(0, BLOCK_SIZE) + mask = col_idx < n_cols + + # Load values + vals = tl.load(row_start_ptr + col_idx, mask=mask) + + # Compute log_softmax + output = vals - max_val - log_sum_exp + + # Store results + tl.store(output_row_start_ptr + col_idx, output, mask=mask) + + +def log_softmax(input: torch.Tensor, dim: int = -1) -> torch.Tensor: + """ + Compute log_softmax using Triton kernel. + + Args: + input: Input tensor + dim: Dimension along which to compute log_softmax + (only -1 or last dim supported) + >> Stashed changes + Returns: + Tensor with log_softmax applied along the specified dimension + """ + if dim != -1 and dim != input.ndim - 1: + raise ValueError("This implementation only supports log_softmax along " + "the last dimension") + + # Flatten all dimensions except the last one + original_shape = input.shape + input_2d = input.reshape(-1, input.shape[-1]) + input_2d = input_2d.contiguous() + + n_rows, n_cols = input_2d.shape + + # Allocate output tensor + output = torch.empty_like(input_2d) + + # Choose block size based on the number of columns + BLOCK_SIZE = 1024 + + # Launch kernel with one block per row + grid = (n_rows, ) + _log_softmax_kernel[grid]( + input_2d, + output, + input_2d.stride(0), + output.stride(0), + n_cols, + BLOCK_SIZE=BLOCK_SIZE, + ) + # Reshape output back to original shape + return output.reshape(original_shape) + + +@triton.jit +def mean_kernel( + input_ptr, + output_ptr, + input_stride0, + input_stride1, + input_stride2, + output_stride0, + output_stride1, + M, # size before reduction dim + N, # size of reduction dim + K, # size after reduction dim + BLOCK_SIZE: tl.constexpr, +): + """ + Kernel for computing mean along a single dimension. + Input is viewed as (M, N, K) where N is the dimension being reduced. + """ + # Program ID gives us which output element we're computing + pid = tl.program_id(0) + + # Compute output indices + m_idx = pid // K + k_idx = pid % K + + # Bounds check + if m_idx >= M or k_idx >= K: + return + + # Accumulate sum across reduction dimension + acc = 0.0 + for n_start in range(0, N, BLOCK_SIZE): + n_offsets = n_start + tl.arange(0, BLOCK_SIZE) + mask = n_offsets < N + + # Calculate input indices + input_idx = m_idx * input_stride0 + n_offsets * input_stride1 \ + + k_idx * input_stride2 + + # Load and accumulate + vals = tl.load(input_ptr + input_idx, mask=mask, other=0.0) + acc += tl.sum(vals) + + # Compute mean and store + mean_val = acc / N + output_idx = m_idx * output_stride0 + k_idx * output_stride1 + tl.store(output_ptr + output_idx, mean_val) + + +def mean_dim(input: torch.Tensor, + dim: int, + keepdim: bool = False, + dtype: Union[torch.dtype, None] = None) -> torch.Tensor: + """ + Triton implementation of torch.mean with single dimension reduction. + + Args: + input: Input tensor + dim: Single dimension along which to compute mean + keepdim: Whether to keep the reduced dimension + dtype: Output dtype. If None, uses input dtype + (or float32 for integer inputs) + + Returns: + Tensor with mean values along specified dimension + """ + # Validate inputs + assert input.is_cuda, "Input must be a CUDA tensor" + assert -input.ndim <= dim < input.ndim, ( + f"Invalid dimension {dim} for tensor with {input.ndim} dimensions") + + # Handle negative dim + if dim < 0: + dim = dim + input.ndim + + # Handle dtype + if dtype is None: + if input.dtype in [torch.int8, torch.int16, torch.int32, torch.int64]: + dtype = torch.float32 + else: + dtype = input.dtype + + # Convert input to appropriate dtype if needed + if input.dtype != dtype: + input = input.to(dtype) + + # Get input shape and strides + shape = list(input.shape) + + # Calculate dimensions for kernel + M = 1 + for i in range(dim): + M *= shape[i] + + N = shape[dim] + + K = 1 + for i in range(dim + 1, len(shape)): + K *= shape[i] + + # Reshape input to 3D view (M, N, K) + input_3d = input.reshape(M, N, K) + + # Create output shape + if keepdim: + output_shape = shape.copy() + output_shape[dim] = 1 + else: + output_shape = shape[:dim] + shape[dim + 1:] + + # Create output tensor + output = torch.empty(output_shape, dtype=dtype, device=input.device) + + # Reshape output for kernel + if keepdim: + output_2d = output.reshape(M, 1, K).squeeze(1) + else: + output_2d = output.reshape(M, K) + + # Launch kernel + grid = (M * K, ) + BLOCK_SIZE = 1024 + + mean_kernel[grid]( + input_3d, + output_2d, + input_3d.stride(0), + input_3d.stride(1), + input_3d.stride(2), + output_2d.stride(0), + output_2d.stride(1) if output_2d.ndim > 1 else 0, + M, + N, + K, + BLOCK_SIZE, + ) + + return output + + +def mm_batch_invariant(a, b): + return matmul_persistent(a, b) + + +def addmm_batch_invariant(bias, a, b): + return matmul_persistent(a, b, bias=bias) + + +def _log_softmax_batch_invariant(input, dim, _half_to_float): + assert not _half_to_float, "not implemented" + return log_softmax(input, dim=dim) + + +def mean_batch_invariant(input, + dim, + keepdim=False, + dtype: Union[torch.dtype, None] = None): + assert dtype is None or dtype == torch.float32, \ + f"unsupported dtype: {dtype}" + + result = input.to(torch.float32) + + # Sort dimensions to reduce from largest to smallest to handle shifting dims + # during iterative reduction. + sorted_dims = sorted([d % input.ndim for d in dim], reverse=True) + + # Iteratively apply a deterministic mean. + for d in sorted_dims: + result = mean_dim(result, dim=d, keepdim=True) + + if not keepdim: + # Squeeze the reduced dimensions. + for d in sorted_dims: + result = result.squeeze(d) + + return result + + +_batch_invariant_MODE = False +_batch_invariant_LIB = None + + +def is_batch_invariant_mode_enabled(): + return _batch_invariant_MODE + + +def enable_batch_invariant_mode(): + global _batch_invariant_MODE, _batch_invariant_LIB + if _batch_invariant_MODE: + return + + _batch_invariant_MODE = True + _batch_invariant_LIB = torch.library.Library("aten", "IMPL") + _batch_invariant_LIB.impl("aten::mm", mm_batch_invariant, "CUDA") + _batch_invariant_LIB.impl("aten::addmm", addmm_batch_invariant, "CUDA") + _batch_invariant_LIB.impl("aten::_log_softmax", + _log_softmax_batch_invariant, "CUDA") + _batch_invariant_LIB.impl("aten::mean.dim", mean_batch_invariant, "CUDA") + + +def disable_batch_invariant_mode(): + global _batch_invariant_MODE, _batch_invariant_LIB + if _batch_invariant_LIB is not None: + _batch_invariant_LIB._destroy() + _batch_invariant_MODE = False + _batch_invariant_LIB = None + + +@contextlib.contextmanager +def set_batch_invariant_mode(enabled: bool = True): + global _batch_invariant_MODE, _batch_invariant_LIB + old_data = (_batch_invariant_MODE, _batch_invariant_LIB) + if enabled: + enable_batch_invariant_mode() + else: + disable_batch_invariant_mode() + yield + if _batch_invariant_LIB is not None: + _batch_invariant_LIB._destroy() + _batch_invariant_MODE, _batch_invariant_LIB = old_data + + +AttentionBlockSize = namedtuple("AttentionBlockSize", ["block_m", "block_n"]) + + +def get_batch_invariant_attention_block_size() -> AttentionBlockSize: + return AttentionBlockSize(block_m=16, block_n=16) + + +def vllm_kernel_override_batch_invariant(): + env_key = "VLLM_KERNEL_OVERRIDE_BATCH_INVARIANT" + is_overridden = False + val = os.getenv(env_key, "0") + try: + is_overridden = int(val) != 0 + except ValueError: + is_overridden = False + return is_overridden + + +def init_batch_invariance(): + # this will hit all the csrc overrides as well + if vllm_kernel_override_batch_invariant(): + os.environ["VLLM_ATTENTION_BACKEND"] = "FLEX_ATTENTION" + enable_batch_invariant_mode() diff --git a/vllm/v1/attention/backends/flex_attention.py b/vllm/v1/attention/backends/flex_attention.py index c3358bfa74e9..807b8d987a2d 100644 --- a/vllm/v1/attention/backends/flex_attention.py +++ b/vllm/v1/attention/backends/flex_attention.py @@ -18,6 +18,8 @@ is_quantized_kv_cache) from vllm.config import VllmConfig from vllm.logger import init_logger +from vllm.model_executor.layers.batch_invariant import ( + vllm_kernel_override_batch_invariant) from vllm.utils import cdiv, is_torch_equal_or_newer from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder, CommonAttentionMetadata) @@ -839,6 +841,11 @@ def get_kernel_options(query, block_m, block_n, kernel_options: dict[str, Union[int, bool]] = { "FORCE_USE_FLEX_ATTENTION": True, } + if vllm_kernel_override_batch_invariant(): + kernel_options["BLOCK_M"] = 16 + kernel_options["BLOCK_N"] = 16 + kernel_options["IS_DIVISIBLE"] = False + return kernel_options if use_direct_build: kernel_options["BLOCK_M"] = block_m kernel_options["BLOCK_N"] = block_n diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 4fd4f9128c6e..f87a327d02a5 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -192,6 +192,9 @@ def __init__( from vllm.model_executor.models.utils import set_cpu_offload_max_bytes set_cpu_offload_max_bytes( int(self.cache_config.cpu_offload_gb * 1024**3)) + from vllm.model_executor.layers.batch_invariant import ( + init_batch_invariance) + init_batch_invariance() model_config = self.model_config cache_config = self.cache_config From af2c6ebdad61b7afe4a72dc0eb0bb84eb6677a1b Mon Sep 17 00:00:00 2001 From: Naman Lalit Date: Fri, 26 Sep 2025 17:07:36 -0700 Subject: [PATCH 032/166] [Bugfix] Optimize CpuGpuBuffer initialization (#25447) Signed-off-by: Naman Lalit --- vllm/v1/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/v1/utils.py b/vllm/v1/utils.py index ec4417290f61..ee0c1168f3cd 100644 --- a/vllm/v1/utils.py +++ b/vllm/v1/utils.py @@ -117,7 +117,7 @@ def __init__( dtype=dtype, device="cpu", pin_memory=pin_memory) - self.gpu = self.cpu.to(device) + self.gpu = torch.zeros_like(self.cpu, device=device) self.np: np.ndarray # To keep type hints simple (avoiding generics and subclasses), we # only conditionally create the numpy array attribute. This can cause From f05b91706812988f56057c5f797290a269aefc4d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jonas=20M=2E=20K=C3=BCbler?= <44084297+jmkuebler@users.noreply.github.com> Date: Sat, 27 Sep 2025 02:10:21 +0200 Subject: [PATCH 033/166] [Spec decode] automatically disable mm for text-only draft models (#25667) Signed-off-by: Jonas Kuebler --- tests/v1/e2e/test_spec_decode.py | 136 ++++++++++++++++--------------- vllm/v1/spec_decode/eagle.py | 14 ++++ 2 files changed, 83 insertions(+), 67 deletions(-) diff --git a/tests/v1/e2e/test_spec_decode.py b/tests/v1/e2e/test_spec_decode.py index c4efd7548b81..ea8d94722859 100644 --- a/tests/v1/e2e/test_spec_decode.py +++ b/tests/v1/e2e/test_spec_decode.py @@ -8,7 +8,7 @@ import pytest import torch -from tests.utils import get_attn_backend_list_based_on_platform +from tests.utils import get_attn_backend_list_based_on_platform, large_gpu_mark from vllm import LLM, SamplingParams from vllm.assets.base import VLLM_S3_BUCKET_URL from vllm.assets.image import VLM_IMAGES_DIR @@ -88,69 +88,66 @@ def test_ngram_correctness( Compare the outputs of an original LLM and a speculative LLM should be the same when using ngram speculative decoding. ''' - with monkeypatch.context() as m: - m.setenv("VLLM_USE_V1", "1") - test_prompts = get_test_prompts(mm_enabled=False) - - ref_llm = LLM(model=model_name, max_model_len=1024) - ref_outputs = ref_llm.chat(test_prompts, sampling_config) - del ref_llm - torch.cuda.empty_cache() - cleanup_dist_env_and_memory() - - spec_llm = LLM( - model=model_name, - speculative_config={ - "method": "ngram", - "prompt_lookup_max": 5, - "prompt_lookup_min": 3, - "num_speculative_tokens": 3, - }, - max_model_len=1024, - ) - spec_outputs = spec_llm.chat(test_prompts, sampling_config) - matches = 0 - misses = 0 - for ref_output, spec_output in zip(ref_outputs, spec_outputs): - if ref_output.outputs[0].text == spec_output.outputs[0].text: - matches += 1 - else: - misses += 1 - print(f"ref_output: {ref_output.outputs[0].text}") - print(f"spec_output: {spec_output.outputs[0].text}") - - # Heuristic: expect at least 66% of the prompts to match exactly - # Upon failure, inspect the outputs to check for inaccuracy. - assert matches >= int(0.66 * len(ref_outputs)) - del spec_llm - torch.cuda.empty_cache() - cleanup_dist_env_and_memory() - - -@pytest.mark.parametrize(["model_setup", "mm_enabled"], [ - (("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False), - (("eagle", "meta-llama/Llama-3.1-8B-Instruct", - "yuhuili/EAGLE-LLaMA3.1-Instruct-8B", 1), False), - (("eagle3", "meta-llama/Llama-3.1-8B-Instruct", - "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", 1), False), - pytest.param( - ("eagle", "meta-llama/Llama-4-Scout-17B-16E-Instruct", - "morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct", 4), - False, - marks=pytest.mark.skip(reason="Skipping due to CI OOM issues")), - pytest.param( - ("eagle", "meta-llama/Llama-4-Scout-17B-16E-Instruct", - "morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct", 4), - True, - marks=pytest.mark.skip(reason="Skipping due to CI OOM issues")), - (("eagle", "eagle618/deepseek-v3-random", - "eagle618/eagle-deepseek-v3-random", 1), False), -], - ids=[ - "qwen3_eagle3", "llama3_eagle", "llama3_eagle3", - "llama4_eagle", "llama4_eagle_mm", - "deepseek_eagle" - ]) + test_prompts = get_test_prompts(mm_enabled=False) + + ref_llm = LLM(model=model_name, max_model_len=1024) + ref_outputs = ref_llm.chat(test_prompts, sampling_config) + del ref_llm + torch.cuda.empty_cache() + cleanup_dist_env_and_memory() + + spec_llm = LLM( + model=model_name, + speculative_config={ + "method": "ngram", + "prompt_lookup_max": 5, + "prompt_lookup_min": 3, + "num_speculative_tokens": 3, + }, + max_model_len=1024, + ) + spec_outputs = spec_llm.chat(test_prompts, sampling_config) + matches = 0 + misses = 0 + for ref_output, spec_output in zip(ref_outputs, spec_outputs): + if ref_output.outputs[0].text == spec_output.outputs[0].text: + matches += 1 + else: + misses += 1 + print(f"ref_output: {ref_output.outputs[0].text}") + print(f"spec_output: {spec_output.outputs[0].text}") + + # Heuristic: expect at least 66% of the prompts to match exactly + # Upon failure, inspect the outputs to check for inaccuracy. + assert matches >= int(0.66 * len(ref_outputs)) + del spec_llm + torch.cuda.empty_cache() + cleanup_dist_env_and_memory() + + +@pytest.mark.parametrize( + ["model_setup", "mm_enabled"], + [ + (("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False), + (("eagle", "meta-llama/Llama-3.1-8B-Instruct", + "yuhuili/EAGLE-LLaMA3.1-Instruct-8B", 1), False), + (("eagle3", "meta-llama/Llama-3.1-8B-Instruct", + "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", 1), False), + pytest.param(("eagle", "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct", 4), + False, + marks=large_gpu_mark(min_gb=80)), # works on 4x H100 + pytest.param(("eagle", "meta-llama/Llama-4-Scout-17B-16E-Instruct", + "morgendave/EAGLE-Llama-4-Scout-17B-16E-Instruct", 4), + True, + marks=large_gpu_mark(min_gb=80)), # works on 4x H100 + (("eagle", "eagle618/deepseek-v3-random", + "eagle618/eagle-deepseek-v3-random", 1), False), + ], + ids=[ + "qwen3_eagle3", "llama3_eagle", "llama3_eagle3", "llama4_eagle", + "llama4_eagle_mm", "deepseek_eagle" + ]) @pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform()) def test_eagle_correctness( @@ -174,9 +171,14 @@ def test_eagle_correctness( model_setup: (method, model_name, eagle_model_name, tp_size) ''' with monkeypatch.context() as m: - m.setenv("VLLM_USE_V1", "1") - m.setenv("VLLM_MLA_DISABLE", "1") - m.setenv("VLLM_ATTENTION_BACKEND", attn_backend) + if "Llama-4-Scout" in model_setup[1] and attn_backend == "FLASH_ATTN": + # Scout requires default backend selection + # because vision encoder has head_dim 88 being incompatible + # with FLASH_ATTN and needs to fall back to Flex Attn + pass + else: + m.setenv("VLLM_MLA_DISABLE", "1") + m.setenv("VLLM_ATTENTION_BACKEND", attn_backend) if (attn_backend == "TRITON_ATTN" and not current_platform.is_rocm()): pytest.skip("TRITON_ATTN does not support " diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index 57da8346f497..394df48b4153 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -804,6 +804,20 @@ def load_model(self, target_model: nn.Module) -> None: self.attn_layer_names = list(draft_attn_layer_names) + if self.is_multimodal_model: + # Even if the target model is multimodal, we can also use + # text-only draft models + try: + dummy_input_ids = torch.tensor([[1]], + device=self.input_ids.device) + self.model.get_input_embeddings(dummy_input_ids, + multimodal_embeddings=None) + except (NotImplementedError, AttributeError, TypeError): + logger.warning( + "Draft model does not support multimodal inputs, " + "falling back to text-only mode") + self.is_multimodal_model = False + if supports_multimodal(target_model): # handle multimodality self.model.config.image_token_index = ( From cbf0ecac69bcb0768ac2ff7c599acd372cdcbee0 Mon Sep 17 00:00:00 2001 From: Zhuohan Li Date: Fri, 26 Sep 2025 17:16:40 -0700 Subject: [PATCH 034/166] [Core] Don't count preempted tokens in prefix cache hit rate (#25787) Signed-off-by: Zhuohan Li --- vllm/v1/core/kv_cache_manager.py | 24 ++++++++---- vllm/v1/core/sched/scheduler.py | 66 ++++++++++++++++---------------- vllm/v1/metrics/stats.py | 8 +++- vllm/v1/request.py | 3 ++ 4 files changed, 60 insertions(+), 41 deletions(-) diff --git a/vllm/v1/core/kv_cache_manager.py b/vllm/v1/core/kv_cache_manager.py index 401327f727a4..0af98e7ba2d8 100644 --- a/vllm/v1/core/kv_cache_manager.py +++ b/vllm/v1/core/kv_cache_manager.py @@ -27,8 +27,8 @@ class KVCacheBlocks: `blocks[i][j]` refers to the i-th kv_cache_group and the j-th block of tokens.We don't use block of tokens as the outer dimension because it assumes all - kv_cache_groups have the same number of blocks, which is true for now but - will be broken if we want to give different block_size to different + kv_cache_groups have the same number of blocks, which is true for now but + will be broken if we want to give different block_size to different kv_cache_groups in the future. """ @@ -184,9 +184,17 @@ def get_computed_blocks(self, if self.log_stats: assert self.prefix_cache_stats is not None - self.prefix_cache_stats.requests += 1 - self.prefix_cache_stats.queries += request.num_tokens - self.prefix_cache_stats.hits += num_new_computed_tokens + if request.num_preemptions > 0: + # Previously preempted request + self.prefix_cache_stats.preempted_requests += 1 + self.prefix_cache_stats.preempted_queries += request.num_tokens + self.prefix_cache_stats.preempted_hits += ( + num_new_computed_tokens) + else: + # New request + self.prefix_cache_stats.requests += 1 + self.prefix_cache_stats.queries += request.num_tokens + self.prefix_cache_stats.hits += num_new_computed_tokens return KVCacheBlocks(computed_blocks), num_new_computed_tokens @@ -209,10 +217,10 @@ def allocate_slots( already been computed locally (i.e. new_computed_blocks). num_new_computed_tokens: The number of new computed tokens just hitting the prefix caching, excluding external tokens. - new_computed_blocks: The cached blocks for the above new computed + new_computed_blocks: The cached blocks for the above new computed tokens. num_lookahead_tokens: The number of speculative tokens to allocate. - This is used by spec decode proposers with kv-cache such + This is used by spec decode proposers with kv-cache such as eagle. delay_cache_blocks: Whether to skip caching the blocks. This is used by P/D when allocating blocks used in a KV transfer @@ -365,7 +373,7 @@ def get_num_common_prefix_blocks( requests in the current step. Returns: - list[int]: The number of common prefix blocks for each kv cache + list[int]: The number of common prefix blocks for each kv cache group. """ assert request.status == RequestStatus.RUNNING diff --git a/vllm/v1/core/sched/scheduler.py b/vllm/v1/core/sched/scheduler.py index 7fc4776b0261..10d8f6bbda5c 100644 --- a/vllm/v1/core/sched/scheduler.py +++ b/vllm/v1/core/sched/scheduler.py @@ -251,46 +251,48 @@ def schedule(self) -> SchedulerOutput: req_index += 1 continue + # Schedule newly needed KV blocks for the request. while True: new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens, num_lookahead_tokens=self.num_lookahead_tokens) - if new_blocks is None: - # The request cannot be scheduled. - # Preempt the lowest-priority request. - if self.policy == SchedulingPolicy.PRIORITY: - preempted_req = max( - self.running, - key=lambda r: (r.priority, r.arrival_time), - ) - self.running.remove(preempted_req) - if preempted_req in scheduled_running_reqs: - scheduled_running_reqs.remove(preempted_req) - else: - preempted_req = self.running.pop() - - self.kv_cache_manager.free(preempted_req) - self.encoder_cache_manager.free(preempted_req) - preempted_req.status = RequestStatus.PREEMPTED - preempted_req.num_computed_tokens = 0 - if self.log_stats: - preempted_req.record_event( - EngineCoreEventType.PREEMPTED, scheduled_timestamp) - - self.waiting.prepend_request(preempted_req) - preempted_reqs.append(preempted_req) - if preempted_req == request: - # No more request to preempt. - can_schedule = False - break - else: + + if new_blocks is not None: # The request can be scheduled. - can_schedule = True break - if not can_schedule: + + # The request cannot be scheduled. + # Preempt the lowest-priority request. + if self.policy == SchedulingPolicy.PRIORITY: + preempted_req = max( + self.running, + key=lambda r: (r.priority, r.arrival_time), + ) + self.running.remove(preempted_req) + if preempted_req in scheduled_running_reqs: + scheduled_running_reqs.remove(preempted_req) + else: + preempted_req = self.running.pop() + + self.kv_cache_manager.free(preempted_req) + self.encoder_cache_manager.free(preempted_req) + preempted_req.status = RequestStatus.PREEMPTED + preempted_req.num_computed_tokens = 0 + preempted_req.num_preemptions += 1 + if self.log_stats: + preempted_req.record_event(EngineCoreEventType.PREEMPTED, + scheduled_timestamp) + + self.waiting.prepend_request(preempted_req) + preempted_reqs.append(preempted_req) + if preempted_req == request: + # No more request to preempt. Cannot schedule this request. + break + + if new_blocks is None: + # Cannot schedule this request. break - assert new_blocks is not None # Schedule the request. scheduled_running_reqs.append(request) diff --git a/vllm/v1/metrics/stats.py b/vllm/v1/metrics/stats.py index 296c39e8cdb5..a0d571318ba0 100644 --- a/vllm/v1/metrics/stats.py +++ b/vllm/v1/metrics/stats.py @@ -17,13 +17,19 @@ class PrefixCacheStats: """Stores prefix cache hit statistics.""" # Whether reset_prefix_cache was invoked. reset: bool = False - # The number of requests in this update. + # The number of new requests in this update. requests: int = 0 # The number of queries in these requests. Note that "queries" here # means the number of tokens that were queried from the cache. queries: int = 0 # The number of hits in these requests. hits: int = 0 + # The number of previously preempted requests in this update. + preempted_requests: int = 0 + # The `queries` number for preempted requests. + preempted_queries: int = 0 + # The `hits` number for preempted requests. + preempted_hits: int = 0 @dataclass diff --git a/vllm/v1/request.py b/vllm/v1/request.py index ff10fa00c1cf..dd0aea645d74 100644 --- a/vllm/v1/request.py +++ b/vllm/v1/request.py @@ -115,6 +115,9 @@ def __init__( # indicates that the output is corrupted self.num_nans_in_logits = 0 + # The number of requests being preempted by the scheduler + self.num_preemptions = 0 + self.block_hashes: list[BlockHash] = [] self.get_hash_new_full_blocks: Optional[Callable[ [], list[BlockHash]]] = None From b018331759142a29e97c43ba835fa1721dcfb062 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Fri, 26 Sep 2025 21:23:52 -0400 Subject: [PATCH 035/166] Add option to restrict media domains (#25783) Signed-off-by: Chenheli Hua Signed-off-by: Russell Bryant Co-authored-by: Chenheli Hua --- docs/features/multimodal_inputs.md | 4 +++ docs/usage/security.md | 6 ++++ .../entrypoints/openai/test_lora_resolvers.py | 1 + tests/entrypoints/openai/test_serving_chat.py | 1 + tests/multimodal/test_utils.py | 33 ++++++++++++++++++- vllm/config/model.py | 3 ++ vllm/config/speculative.py | 2 ++ vllm/engine/arg_utils.py | 5 +++ vllm/entrypoints/chat_utils.py | 6 ++++ vllm/entrypoints/llm.py | 4 +++ vllm/multimodal/utils.py | 16 +++++++++ 11 files changed, 80 insertions(+), 1 deletion(-) diff --git a/docs/features/multimodal_inputs.md b/docs/features/multimodal_inputs.md index 7fb033723500..bcc48e756046 100644 --- a/docs/features/multimodal_inputs.md +++ b/docs/features/multimodal_inputs.md @@ -6,6 +6,10 @@ This page teaches you how to pass multi-modal inputs to [multi-modal models][sup We are actively iterating on multi-modal support. See [this RFC](gh-issue:4194) for upcoming changes, and [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose) if you have any feedback or feature requests. +!!! tip + When serving multi-modal models, consider setting `--allowed-media-domains` to restrict domain that vLLM can access to prevent it from accessing arbitrary endpoints that can potentially be vulnerable to Server-Side Request Forgery (SSRF) attacks. You can provide a list of domains for this arg. For example: `--allowed-media-domains upload.wikimedia.org github.com www.bogotobogo.com` + This restriction is especially important if you run vLLM in a containerized environment where the vLLM pods may have unrestricted access to internal networks. + ## Offline Inference To input multi-modal data, follow this schema in [vllm.inputs.PromptType][]: diff --git a/docs/usage/security.md b/docs/usage/security.md index d54e2bb37ec0..5d85e889c80c 100644 --- a/docs/usage/security.md +++ b/docs/usage/security.md @@ -60,6 +60,12 @@ Key points from the PyTorch security guide: - Implement proper authentication and authorization for management interfaces - Follow the principle of least privilege for all system components +### 4. **Restrict Domains Access for Media URLs:** + +Restrict domains that vLLM can access for media URLs by setting +`--allowed-media-domains` to prevent Server-Side Request Forgery (SSRF) attacks. +(e.g. `--allowed-media-domains upload.wikimedia.org github.com www.bogotobogo.com`) + ## Security and Firewalls: Protecting Exposed vLLM Systems While vLLM is designed to allow unsafe network services to be isolated to diff --git a/tests/entrypoints/openai/test_lora_resolvers.py b/tests/entrypoints/openai/test_lora_resolvers.py index 9d5ee84a1956..0561158dcf65 100644 --- a/tests/entrypoints/openai/test_lora_resolvers.py +++ b/tests/entrypoints/openai/test_lora_resolvers.py @@ -45,6 +45,7 @@ class MockModelConfig: logits_processor_pattern: Optional[str] = None diff_sampling_param: Optional[dict] = None allowed_local_media_path: str = "" + allowed_media_domains: Optional[list[str]] = None encoder_config = None generation_config: str = "auto" skip_tokenizer_init: bool = False diff --git a/tests/entrypoints/openai/test_serving_chat.py b/tests/entrypoints/openai/test_serving_chat.py index bfed760822cd..07f39fe2b9bd 100644 --- a/tests/entrypoints/openai/test_serving_chat.py +++ b/tests/entrypoints/openai/test_serving_chat.py @@ -240,6 +240,7 @@ class MockModelConfig: logits_processor_pattern = None diff_sampling_param: Optional[dict] = None allowed_local_media_path: str = "" + allowed_media_domains: Optional[list[str]] = None encoder_config = None generation_config: str = "auto" media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict) diff --git a/tests/multimodal/test_utils.py b/tests/multimodal/test_utils.py index f6a93bae2afc..d1a7882a4c37 100644 --- a/tests/multimodal/test_utils.py +++ b/tests/multimodal/test_utils.py @@ -66,7 +66,12 @@ async def test_fetch_image_http(image_url: str): @pytest.mark.parametrize("suffix", get_supported_suffixes()) async def test_fetch_image_base64(url_images: dict[str, Image.Image], raw_image_url: str, suffix: str): - connector = MediaConnector() + connector = MediaConnector( + # Domain restriction should not apply to data URLs. + allowed_media_domains=[ + "www.bogotobogo.com", + "github.com", + ]) url_image = url_images[raw_image_url] try: @@ -387,3 +392,29 @@ def test_argsort_mm_positions(case): modality_idxs = argsort_mm_positions(mm_positions) assert modality_idxs == expected_modality_idxs + + +@pytest.mark.asyncio +@pytest.mark.parametrize("video_url", TEST_VIDEO_URLS) +@pytest.mark.parametrize("num_frames", [-1, 32, 1800]) +async def test_allowed_media_domains(video_url: str, num_frames: int): + connector = MediaConnector( + media_io_kwargs={"video": { + "num_frames": num_frames, + }}, + allowed_media_domains=[ + "www.bogotobogo.com", + "github.com", + ]) + + video_sync, metadata_sync = connector.fetch_video(video_url) + video_async, metadata_async = await connector.fetch_video_async(video_url) + assert np.array_equal(video_sync, video_async) + assert metadata_sync == metadata_async + + disallowed_url = "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png" + with pytest.raises(ValueError): + _, _ = connector.fetch_video(disallowed_url) + + with pytest.raises(ValueError): + _, _ = await connector.fetch_video_async(disallowed_url) diff --git a/vllm/config/model.py b/vllm/config/model.py index da01d6d4480c..b2b68abd2c1d 100644 --- a/vllm/config/model.py +++ b/vllm/config/model.py @@ -137,6 +137,9 @@ class ModelConfig: """Allowing API requests to read local images or videos from directories specified by the server file system. This is a security risk. Should only be enabled in trusted environments.""" + allowed_media_domains: Optional[list[str]] = None + """If set, only media URLs that belong to this domain can be used for + multi-modal inputs. """ revision: Optional[str] = None """The specific model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version.""" diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index 8b80ce13f96e..cb4f0ae2cee0 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -288,6 +288,8 @@ def __post_init__(self): trust_remote_code, allowed_local_media_path=self.target_model_config. allowed_local_media_path, + allowed_media_domains=self.target_model_config. + allowed_media_domains, dtype=self.target_model_config.dtype, seed=self.target_model_config.seed, revision=self.revision, diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 8757f4b8b7ba..6bb794177db8 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -297,6 +297,8 @@ class EngineArgs: tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode trust_remote_code: bool = ModelConfig.trust_remote_code allowed_local_media_path: str = ModelConfig.allowed_local_media_path + allowed_media_domains: Optional[ + list[str]] = ModelConfig.allowed_media_domains download_dir: Optional[str] = LoadConfig.download_dir safetensors_load_strategy: str = LoadConfig.safetensors_load_strategy load_format: Union[str, LoadFormats] = LoadConfig.load_format @@ -531,6 +533,8 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: **model_kwargs["hf_config_path"]) model_group.add_argument("--allowed-local-media-path", **model_kwargs["allowed_local_media_path"]) + model_group.add_argument("--allowed-media-domains", + **model_kwargs["allowed_media_domains"]) model_group.add_argument("--revision", **model_kwargs["revision"]) model_group.add_argument("--code-revision", **model_kwargs["code_revision"]) @@ -997,6 +1001,7 @@ def create_model_config(self) -> ModelConfig: tokenizer_mode=self.tokenizer_mode, trust_remote_code=self.trust_remote_code, allowed_local_media_path=self.allowed_local_media_path, + allowed_media_domains=self.allowed_media_domains, dtype=self.dtype, seed=self.seed, revision=self.revision, diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index df49119d8642..4e1ecb9ed4c5 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -632,6 +632,10 @@ def model_cls(self) -> type[SupportsMultiModal]: def allowed_local_media_path(self): return self._model_config.allowed_local_media_path + @property + def allowed_media_domains(self): + return self._model_config.allowed_media_domains + @property def mm_registry(self): return MULTIMODAL_REGISTRY @@ -832,6 +836,7 @@ def __init__(self, tracker: MultiModalItemTracker) -> None: self._connector = MediaConnector( media_io_kwargs=media_io_kwargs, allowed_local_media_path=tracker.allowed_local_media_path, + allowed_media_domains=tracker.allowed_media_domains, ) def parse_image( @@ -916,6 +921,7 @@ def __init__(self, tracker: AsyncMultiModalItemTracker) -> None: self._connector = MediaConnector( media_io_kwargs=media_io_kwargs, allowed_local_media_path=tracker.allowed_local_media_path, + allowed_media_domains=tracker.allowed_media_domains, ) def parse_image( diff --git a/vllm/entrypoints/llm.py b/vllm/entrypoints/llm.py index dfe535b95917..862f383e4ecb 100644 --- a/vllm/entrypoints/llm.py +++ b/vllm/entrypoints/llm.py @@ -86,6 +86,8 @@ class LLM: or videos from directories specified by the server file system. This is a security risk. Should only be enabled in trusted environments. + allowed_media_domains: If set, only media URLs that belong to this + domain can be used for multi-modal inputs. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, @@ -169,6 +171,7 @@ def __init__( skip_tokenizer_init: bool = False, trust_remote_code: bool = False, allowed_local_media_path: str = "", + allowed_media_domains: Optional[list[str]] = None, tensor_parallel_size: int = 1, dtype: ModelDType = "auto", quantization: Optional[QuantizationMethods] = None, @@ -264,6 +267,7 @@ def __init__( skip_tokenizer_init=skip_tokenizer_init, trust_remote_code=trust_remote_code, allowed_local_media_path=allowed_local_media_path, + allowed_media_domains=allowed_media_domains, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, diff --git a/vllm/multimodal/utils.py b/vllm/multimodal/utils.py index 9b158267040a..1f1eea6bfee7 100644 --- a/vllm/multimodal/utils.py +++ b/vllm/multimodal/utils.py @@ -50,6 +50,7 @@ def __init__( connection: HTTPConnection = global_http_connection, *, allowed_local_media_path: str = "", + allowed_media_domains: Optional[list[str]] = None, ) -> None: """ Args: @@ -82,6 +83,9 @@ def __init__( allowed_local_media_path_ = None self.allowed_local_media_path = allowed_local_media_path_ + if allowed_media_domains is None: + allowed_media_domains = [] + self.allowed_media_domains = allowed_media_domains def _load_data_url( self, @@ -115,6 +119,14 @@ def _load_file_url( return media_io.load_file(filepath) + def _assert_url_in_allowed_media_domains(self, url_spec) -> None: + if self.allowed_media_domains and url_spec.hostname not in \ + self.allowed_media_domains: + raise ValueError( + f"The URL must be from one of the allowed domains: " + f"{self.allowed_media_domains}. Input URL domain: " + f"{url_spec.hostname}") + def load_from_url( self, url: str, @@ -125,6 +137,8 @@ def load_from_url( url_spec = urlparse(url) if url_spec.scheme.startswith("http"): + self._assert_url_in_allowed_media_domains(url_spec) + connection = self.connection data = connection.get_bytes(url, timeout=fetch_timeout) @@ -150,6 +164,8 @@ async def load_from_url_async( loop = asyncio.get_running_loop() if url_spec.scheme.startswith("http"): + self._assert_url_in_allowed_media_domains(url_spec) + connection = self.connection data = await connection.async_get_bytes(url, timeout=fetch_timeout) future = loop.run_in_executor(global_thread_pool, From 3052fc5e11291d869362d90e9fabb391770356d1 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Fri, 26 Sep 2025 21:54:09 -0400 Subject: [PATCH 036/166] Add flashinfer-build.sh and register precompiled cu128 wheel in Dockerfile (#25782) Signed-off-by: mgoin --- docker/Dockerfile | 30 ++++++++++++------- tools/flashinfer-build.sh | 63 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 83 insertions(+), 10 deletions(-) create mode 100644 tools/flashinfer-build.sh diff --git a/docker/Dockerfile b/docker/Dockerfile index c0f55a7eeba0..fad62be798a1 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -391,18 +391,28 @@ RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH' git clone --depth 1 --recursive --shallow-submodules \ --branch ${FLASHINFER_GIT_REF} \ ${FLASHINFER_GIT_REPO} flashinfer + # Exclude CUDA arches for older versions (11.x and 12.0-12.7) + # TODO: Update this to allow setting TORCH_CUDA_ARCH_LIST as a build arg. + if [[ "${CUDA_VERSION}" == 11.* ]]; then + FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9" + elif [[ "${CUDA_VERSION}" == 12.[0-7]* ]]; then + FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a" + else + # CUDA 12.8+ supports 10.0a and 12.0 + FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0" + fi pushd flashinfer - if [ "${FLASHINFER_AOT_COMPILE}" = "true" ]; then - # Exclude CUDA arches for older versions (11.x and 12.0-12.7) - # TODO: Update this to allow setting TORCH_CUDA_ARCH_LIST as a build arg. - if [[ "${CUDA_VERSION}" == 11.* ]]; then - FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9" - elif [[ "${CUDA_VERSION}" == 12.[0-7]* ]]; then - FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a" - else - # CUDA 12.8+ supports 10.0a and 12.0 - FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0" + if [[ "${CUDA_VERSION}" == 12.8.* ]] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then + # NOTE: To make new precompiled wheels, see tools/flashinfer-build.sh + echo "🏗️ Installing FlashInfer from pre-compiled wheel" + uv pip install --system https://wheels.vllm.ai/flashinfer-python/flashinfer_python-0.3.1-cp39-abi3-manylinux1_x86_64.whl \ + --extra-index-url ${PYTORCH_CUDA_INDEX_BASE_URL}/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') + if [ "${FLASHINFER_AOT_COMPILE}" = "true" ]; then + # Download pre-compiled cubins + TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" \ + python3 -m flashinfer --download-cubin || echo "WARNING: Failed to download flashinfer cubins." fi + elif [ "${FLASHINFER_AOT_COMPILE}" = "true" ]; then echo "🏗️ Installing FlashInfer with AOT compilation for arches: ${FI_TORCH_CUDA_ARCH_LIST}" export FLASHINFER_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" # HACK: We need these to run flashinfer.aot before installing flashinfer, get from the package in the future diff --git a/tools/flashinfer-build.sh b/tools/flashinfer-build.sh new file mode 100644 index 000000000000..6c14d87348c3 --- /dev/null +++ b/tools/flashinfer-build.sh @@ -0,0 +1,63 @@ +#!/usr/bin/env bash +# This script is used to build FlashInfer wheels with AOT kernels + +set -ex + +# FlashInfer configuration +FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git" +FLASHINFER_GIT_REF="${FLASHINFER_GIT_REF}" +CUDA_VERSION="${CUDA_VERSION}" +BUILD_WHEEL="${BUILD_WHEEL:-true}" + +if [[ -z "${FLASHINFER_GIT_REF}" ]]; then + echo "❌ FLASHINFER_GIT_REF must be specified" >&2 + exit 1 +fi + +if [[ -z "${CUDA_VERSION}" ]]; then + echo "❌ CUDA_VERSION must be specified" >&2 + exit 1 +fi + +echo "🏗️ Building FlashInfer ${FLASHINFER_GIT_REF} for CUDA ${CUDA_VERSION}" + +# Clone FlashInfer +git clone --depth 1 --recursive --shallow-submodules \ + --branch ${FLASHINFER_GIT_REF} \ + ${FLASHINFER_GIT_REPO} flashinfer + +# Set CUDA arch list based on CUDA version +# Exclude CUDA arches for older versions (11.x and 12.0-12.7) +if [[ "${CUDA_VERSION}" == 11.* ]]; then + FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9" +elif [[ "${CUDA_VERSION}" == 12.[0-7]* ]]; then + FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a" +else + # CUDA 12.8+ supports 10.0a and 12.0 + FI_TORCH_CUDA_ARCH_LIST="7.5 8.0 8.9 9.0a 10.0a 12.0" +fi + +echo "🏗️ Building FlashInfer AOT for arches: ${FI_TORCH_CUDA_ARCH_LIST}" + +pushd flashinfer + # Make sure the wheel is built for the correct CUDA version + export UV_TORCH_BACKEND=cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.') + + # Build AOT kernels + export TORCH_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" + export FLASHINFER_CUDA_ARCH_LIST="${FI_TORCH_CUDA_ARCH_LIST}" + python3 -m flashinfer.aot + + if [[ "${BUILD_WHEEL}" == "true" ]]; then + # Build wheel for distribution + uv build --no-build-isolation --wheel --out-dir ../flashinfer-dist . + echo "✅ FlashInfer wheel built successfully in flashinfer-dist/" + else + # Install directly (for Dockerfile) + uv pip install --system --no-build-isolation --force-reinstall . + echo "✅ FlashInfer installed successfully" + fi +popd + +# Cleanup +rm -rf flashinfer \ No newline at end of file From 2d6c910e6d6e43a4f30b98efa70388aff73a9140 Mon Sep 17 00:00:00 2001 From: WeiQing Chen <40507679+david6666666@users.noreply.github.com> Date: Sat, 27 Sep 2025 11:35:47 +0800 Subject: [PATCH 037/166] [Multimodal][Speculative Decoding]Eagle Eagle3 mm support, enablement on qwen2.5vl (#22872) Signed-off-by: Junhong Signed-off-by: Junhong Liu <98734602+LJH-LBJ@users.noreply.github.com> Co-authored-by: Junhong Co-authored-by: LJH-LBJ <98734602+LJH-LBJ@users.noreply.github.com> --- tests/models/registry.py | 3 + tests/v1/e2e/test_spec_decode.py | 9 +- vllm/benchmarks/datasets.py | 80 +++++++++++++++ vllm/model_executor/models/llama_eagle3.py | 27 ++++-- vllm/model_executor/models/qwen2_5_vl.py | 11 ++- vllm/model_executor/models/registry.py | 1 + vllm/v1/spec_decode/eagle.py | 108 +++++++++++++++------ vllm/v1/worker/gpu_model_runner.py | 16 ++- 8 files changed, 210 insertions(+), 45 deletions(-) diff --git a/tests/models/registry.py b/tests/models/registry.py index 6a6e2538559f..e321acc873c6 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -651,6 +651,9 @@ def check_available_online( "MiMoMTPModel": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL", trust_remote_code=True, speculative_model="XiaomiMiMo/MiMo-7B-RL"), + "Eagle3Qwen2_5vlForCausalLM": _HfExamplesInfo( + "Qwen/Qwen2.5-VL-7B-Instruct", + speculative_model="Rayzl/qwen2.5-vl-7b-eagle3-sgl"), "Qwen3NextMTP": _HfExamplesInfo("Qwen/Qwen3-Next-80B-A3B-Instruct", min_transformers_version="4.56.3"), } diff --git a/tests/v1/e2e/test_spec_decode.py b/tests/v1/e2e/test_spec_decode.py index ea8d94722859..8f048775352e 100644 --- a/tests/v1/e2e/test_spec_decode.py +++ b/tests/v1/e2e/test_spec_decode.py @@ -129,6 +129,11 @@ def test_ngram_correctness( ["model_setup", "mm_enabled"], [ (("eagle3", "Qwen/Qwen3-8B", "AngelSlim/Qwen3-8B_eagle3", 1), False), + pytest.param(("eagle3", "Qwen/Qwen2.5-VL-7B-Instruct", + "Rayzl/qwen2.5-vl-7b-eagle3-sgl", 1), + False, + marks=pytest.mark.skip(reason="Skipping due to its " \ + "head_dim not being a a multiple of 32")), (("eagle", "meta-llama/Llama-3.1-8B-Instruct", "yuhuili/EAGLE-LLaMA3.1-Instruct-8B", 1), False), (("eagle3", "meta-llama/Llama-3.1-8B-Instruct", @@ -145,8 +150,8 @@ def test_ngram_correctness( "eagle618/eagle-deepseek-v3-random", 1), False), ], ids=[ - "qwen3_eagle3", "llama3_eagle", "llama3_eagle3", "llama4_eagle", - "llama4_eagle_mm", "deepseek_eagle" + "qwen3_eagle3", "qwen2_5_vl_eagle3", "llama3_eagle", "llama3_eagle3", + "llama4_eagle", "llama4_eagle_mm", "deepseek_eagle" ]) @pytest.mark.parametrize("attn_backend", get_attn_backend_list_based_on_platform()) diff --git a/vllm/benchmarks/datasets.py b/vllm/benchmarks/datasets.py index 68a937d5750e..f0c0d829a393 100644 --- a/vllm/benchmarks/datasets.py +++ b/vllm/benchmarks/datasets.py @@ -1450,6 +1450,13 @@ def get_samples(args, tokenizer) -> list[SampleRequest]: ): dataset_class = MLPerfDataset args.hf_split = "train" + elif ( + args.dataset_path in MMStarDataset.SUPPORTED_DATASET_PATHS + or args.hf_name in MMStarDataset.SUPPORTED_DATASET_PATHS + ): + dataset_class = MMStarDataset + args.hf_split = "val" + args.hf_subset = None else: supported_datasets = set([ dataset_name for cls in HuggingFaceDataset.__subclasses__() @@ -2721,3 +2728,76 @@ def _generate_exact_length_tokens(target_length: int) -> list[int]: random.shuffle(requests) return requests + + +# ----------------------------------------------------------------------------- +# MMStar Dataset Implementation +# ----------------------------------------------------------------------------- + + +class MMStarDataset(HuggingFaceDataset): + """ + Lin-Chen/MMStar: https://huggingface.co/datasets/Lin-Chen/MMStar + refer to: https://github.com/sgl-project/SpecForge/pull/106 + """ + DEFAULT_OUTPUT_LEN = 128 + SUPPORTED_DATASET_PATHS = {"Lin-Chen/MMStar"} + IS_MULTIMODAL = True + + def sample( + self, + tokenizer: PreTrainedTokenizerBase, + num_requests: int, + output_len: Optional[int] = None, + enable_multimodal_chat: bool = False, + request_id_prefix: str = "", + no_oversample: bool = False, + **kwargs, + ) -> list[SampleRequest]: + # If --hf-output-len is not set, use the default output length. + output_len = (output_len + if output_len is not None else self.DEFAULT_OUTPUT_LEN) + sampled_requests: list[SampleRequest] = [] + + for ind, item in enumerate(self.data): + if len(sampled_requests) >= num_requests: + break + # Split the question text from options + # (keep only the part before "Options:"). + full_q: str = item.get("question", "") + question_text = full_q.split("Options:", 1)[0].strip() + + # Multimodal image content. + mm_content = process_image(item["image"]) + + # Compute prompt token length (note: this is plain text length + # if enable_multimodal_chat is False). + prompt_len = len(tokenizer(question_text).input_ids) + + if enable_multimodal_chat: + # If multimodal content should be embedded in the chat message, + # convert to [{"role":"user","content":[...]}] + prompt = self.apply_multimodal_chat_transformation( + question_text, mm_content + ) + mm_for_request = None # Already embedded in chat content. + else: + # Default: prompt is plain text, + # image is in mm_content for the bench to assemble. + prompt = question_text + mm_for_request = mm_content + + sampled_requests.append( + SampleRequest( + prompt=prompt, + prompt_len=prompt_len, + expected_output_len=output_len, + multi_modal_data=mm_for_request, + request_id=request_id_prefix + str(ind), + ) + ) + + self.maybe_oversample_requests( + sampled_requests, num_requests, request_id_prefix, no_oversample + ) + return sampled_requests diff --git a/vllm/model_executor/models/llama_eagle3.py b/vllm/model_executor/models/llama_eagle3.py index b99a1547918e..55b6ae6ee0e9 100644 --- a/vllm/model_executor/models/llama_eagle3.py +++ b/vllm/model_executor/models/llama_eagle3.py @@ -8,7 +8,6 @@ import torch.nn as nn from transformers import LlamaConfig -from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config from vllm.logger import init_logger from vllm.model_executor.layers.layernorm import RMSNorm @@ -19,6 +18,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.models.interfaces import MultiModalEmbeddings from vllm.model_executor.models.llama import (LlamaDecoderLayer, LlamaForCausalLM) @@ -102,7 +102,6 @@ def forward( return hidden_states, residual -@support_torch_compile class LlamaModel(nn.Module): def __init__( @@ -145,13 +144,21 @@ def __init__( eps=self.config.rms_norm_eps, ) + def get_input_embeddings( + self, + input_ids: torch.Tensor, + ) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, hidden_states: torch.Tensor, + input_embeds: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, torch.Tensor]: - input_embeds = self.embed_tokens(input_ids) + if input_embeds is None: + input_embeds = self.get_input_embeddings(input_ids) assert hidden_states.shape[-1] == input_embeds.shape[-1] residual = None @@ -239,11 +246,7 @@ def forward( hidden_states: torch.Tensor, inputs_embeds: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, torch.Tensor]: - if inputs_embeds is not None: - raise NotImplementedError( - f"{type(self).__name__} does not support multimodal inputs yet." - ) - return self.model(input_ids, positions, hidden_states) + return self.model(input_ids, positions, hidden_states, inputs_embeds) def compute_logits( self, @@ -299,3 +302,11 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): skip_substrs=skip_substrs, ) loader.load_weights(model_weights.items()) + + def get_input_embeddings( + self, + input_ids: torch.Tensor, + multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + ) -> torch.Tensor: + inputs_embeds = self.model.get_input_embeddings(input_ids) + return inputs_embeds diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py index 6af6faa2b296..3199f53a0539 100644 --- a/vllm/model_executor/models/qwen2_5_vl.py +++ b/vllm/model_executor/models/qwen2_5_vl.py @@ -68,7 +68,7 @@ from vllm.utils import is_pin_memory_available from vllm.utils.tensor_schema import TensorSchema, TensorShape -from .interfaces import (MultiModalEmbeddings, SupportsLoRA, +from .interfaces import (MultiModalEmbeddings, SupportsEagle3, SupportsLoRA, SupportsMultiModal, SupportsMultiModalPruning, SupportsPP, SupportsQuant) from .qwen2_vl import Qwen2VLDummyInputsBuilder as Qwen2_5_VLDummyInputsBuilder @@ -965,7 +965,7 @@ def get_replacement_qwen2vl(item_idx: int, modality: str): dummy_inputs=Qwen2_5_VLDummyInputsBuilder) class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, - SupportsQuant, + SupportsQuant, SupportsEagle3, SupportsMultiModalPruning): packed_modules_mapping = { @@ -1028,6 +1028,13 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) + def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None: + self.language_model.model.aux_hidden_state_layers = layers + + def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]: + num_layers = len(self.language_model.model.layers) + return (2, num_layers // 2, num_layers - 3) + def _validate_and_reshape_mm_tensor(self, mm_input: object, name: str) -> torch.Tensor: if not isinstance(mm_input, (torch.Tensor, list)): diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 10e9aa4db078..0471164ab8a6 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -286,6 +286,7 @@ "EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"), "Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"), "LlamaForCausalLMEagle3": ("llama_eagle3", "Eagle3LlamaForCausalLM"), + "Eagle3Qwen2_5vlForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"), "EagleDeepSeekMTPModel": ("deepseek_eagle", "EagleDeepseekV3ForCausalLM"), "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"), "ErnieMTPModel": ("ernie_mtp", "ErnieMTP"), diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index 394df48b4153..51e54e0dc337 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -80,9 +80,17 @@ def __init__( self.input_ids = torch.zeros(self.max_num_tokens, dtype=torch.int32, device=device) - self.positions = torch.zeros(self.max_num_tokens, - dtype=torch.int64, - device=device) + self.uses_mrope = self.vllm_config.model_config.uses_mrope + if self.uses_mrope: + # M-RoPE need (3, max_num_tokens) + self.mrope_positions = torch.zeros((3, self.max_num_tokens), + dtype=torch.int64, + device=device) + else: + # RoPE need (max_num_tokens,) + self.positions = torch.zeros(self.max_num_tokens, + dtype=torch.int64, + device=device) self.hidden_states = torch.zeros( (self.max_num_tokens, self.hidden_size), dtype=self.dtype, @@ -143,11 +151,22 @@ def __init__( dtype=torch.int32, ).repeat(max_batch_size, 1) + def _get_positions(self, num_tokens: int): + if self.uses_mrope: + return self.mrope_positions[:, :num_tokens] + return self.positions[:num_tokens] + + def _set_positions(self, num_tokens: int, positions: torch.Tensor): + if self.uses_mrope: + self.mrope_positions[:, :num_tokens] = positions + else: + self.positions[:num_tokens] = positions + def propose( self, # [num_tokens] target_token_ids: torch.Tensor, - # [num_tokens] + # [num_tokens] or [3, num_tokens] when M-RoPE is enabled target_positions: torch.Tensor, # [num_tokens, hidden_size] target_hidden_states: torch.Tensor, @@ -198,7 +217,7 @@ def propose( else: num_input_tokens = num_tokens # copy inputs to buffer for cudagraph - self.positions[:num_tokens] = target_positions + self._set_positions(num_tokens, target_positions) self.hidden_states[:num_tokens] = target_hidden_states if self.is_multimodal_model: input_ids = self.input_ids[:num_tokens] @@ -218,7 +237,7 @@ def propose( num_tokens=num_input_tokens): ret_hidden_states = self.model( input_ids=input_ids, - positions=self.positions[:num_input_tokens], + positions=self._get_positions(num_input_tokens), hidden_states=self.hidden_states[:num_input_tokens], inputs_embeds=inputs_embeds, ) @@ -235,7 +254,10 @@ def propose( draft_token_ids = logits.argmax(dim=-1) return draft_token_ids.view(-1, 1) - positions = target_positions[last_token_indices] + if self.uses_mrope: + positions = target_positions[:, last_token_indices] + else: + positions = target_positions[last_token_indices] if self.method in ("deepseek_mtp", "ernie_mtp", "longcat_flash_mtp"): hidden_states = self.hidden_states[last_token_indices] else: @@ -282,25 +304,34 @@ def propose( # cast to int32 is crucial when eagle model is compiled. # tensor.argmax() returns int64 by default. input_ids = draft_token_ids_list[-1].int() - positions += 1 - - # NOTE(woosuk): We should handle the case where the draft model - # generates tokens beyond the max model length. Since it is complex - # to remove such requests from the batch, we keep them in the batch - # but adjust the position ids and slot mappings to avoid the - # out-of-range access during the model execution. The draft tokens - # generated with this adjustment should be ignored. - exceeds_max_model_len = positions >= self.max_model_len - # Mask out the position ids that exceed the max model length. - # Otherwise, we may get out-of-range error in RoPE. - clamped_positions = torch.where(exceeds_max_model_len, 0, - positions) + if self.uses_mrope: + positions += 1 + # NOTE(woosuk): We should handle the case where the draft model + # generates tokens beyond the max model length. + # Since it is complex to remove such requests from the batch, + # we keep them in the batch but adjust the position ids + # and slot mappings to avoid the + # out-of-range access during the model execution. + # The draft tokens generated with this adjustment + # should be ignored. + exceeds_max_model_len = positions[0] >= self.max_model_len + # Mask out the position ids that exceed the max model length. + # Otherwise, we may get out-of-range error in RoPE. + clamped_positions = torch.where\ + (exceeds_max_model_len.unsqueeze(0), \ + torch.zeros_like(positions), positions) + else: + positions += 1 + exceeds_max_model_len = positions >= self.max_model_len + clamped_positions = torch.where(exceeds_max_model_len, 0, + positions) # Increment the sequence lengths. common_attn_metadata.seq_lens += 1 common_attn_metadata.seq_lens_cpu += 1 # For the requests that exceed the max model length, we set the # sequence length to 1 to minimize their overheads in attention. + common_attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1) @@ -308,13 +339,22 @@ def propose( common_attn_metadata.seq_lens_cpu - 1 # Compute the slot mapping. - block_numbers = clamped_positions // self.block_size + if self.uses_mrope: + # all dimensions of positions are the same + block_numbers = clamped_positions[0] // self.block_size + else: + block_numbers = clamped_positions // self.block_size block_ids = common_attn_metadata.block_table_tensor.gather( dim=1, index=block_numbers.view(-1, 1)) block_ids = block_ids.view(-1) - common_attn_metadata.slot_mapping = ( - block_ids * self.block_size + - clamped_positions % self.block_size) + if self.uses_mrope: + common_attn_metadata.slot_mapping = ( + block_ids * self.block_size + + clamped_positions[0] % self.block_size) + else: + common_attn_metadata.slot_mapping = ( + block_ids * self.block_size + + clamped_positions % self.block_size) # Mask out the slot mappings that exceed the max model length. # Otherwise, the KV cache will be inadvertently updated with the # padding tokens. @@ -330,7 +370,7 @@ def propose( # copy inputs to buffer for cudagraph self.input_ids[:batch_size] = input_ids - self.positions[:batch_size] = clamped_positions + self._set_positions(batch_size, clamped_positions) self.hidden_states[:batch_size] = hidden_states if self.is_multimodal_model: inputs_embeds = self.model.get_input_embeddings(input_ids) @@ -347,7 +387,7 @@ def propose( num_tokens=input_batch_size): ret_hidden_states = self.model( input_ids=input_ids, - positions=self.positions[:input_batch_size], + positions=self._get_positions(input_batch_size), hidden_states=self.hidden_states[:input_batch_size], inputs_embeds=inputs_embeds, ) @@ -787,6 +827,11 @@ def prepare_inputs( return spec_common_attn_metadata, token_indices + def get_model_name(self, model: nn.Module) -> str: + if hasattr(model, 'module'): # multi-GPU + model = model.module + return model.__class__.__name__ + def load_model(self, target_model: nn.Module) -> None: draft_model_config = \ self.vllm_config.speculative_config.draft_model_config @@ -820,8 +865,13 @@ def load_model(self, target_model: nn.Module) -> None: if supports_multimodal(target_model): # handle multimodality - self.model.config.image_token_index = ( - target_model.config.image_token_index) + if (self.get_model_name(target_model) == + "Qwen2_5_VLForConditionalGeneration"): + self.model.config.image_token_index = ( + target_model.config.image_token_id) + else: + self.model.config.image_token_index = ( + target_model.config.image_token_index) target_language_model = target_model.get_language_model() else: target_language_model = target_model @@ -892,7 +942,7 @@ def dummy_run( self.model( input_ids=input_ids, - positions=self.positions[:num_tokens], + positions=self._get_positions(num_tokens), hidden_states=self.hidden_states[:num_tokens], inputs_embeds=inputs_embeds, ) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index f87a327d02a5..22a177dd7cc7 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -442,6 +442,16 @@ def __init__( device="cpu", pin_memory=self.pin_memory) + def _get_positions(self, num_tokens: Any): + if isinstance(num_tokens, int): + if self.uses_mrope: + return self.mrope_positions.gpu[:, :num_tokens] + return self.positions.gpu[:num_tokens] + else: + if self.uses_mrope: + return self.mrope_positions.gpu[:, num_tokens] + return self.positions.gpu[num_tokens] + def _make_buffer(self, *size: Union[int, torch.SymInt], dtype: torch.dtype, @@ -2544,8 +2554,7 @@ def propose_draft_token_ids( token_indices_to_sample = None # input_ids can be None for multimodal models. target_token_ids = self.input_ids.gpu[:num_scheduled_tokens] - # TODO(woosuk): Support M-RoPE. - target_positions = self.positions.gpu[:num_scheduled_tokens] + target_positions = self._get_positions(num_scheduled_tokens) if self.use_aux_hidden_state_outputs: assert aux_hidden_states is not None target_hidden_states = torch.cat( @@ -2570,8 +2579,7 @@ def propose_draft_token_ids( valid_sampled_tokens_count) target_token_ids = self.input_ids.gpu[token_indices] - # TODO(woosuk): Support M-RoPE. - target_positions = self.positions.gpu[token_indices] + target_positions = self._get_positions(token_indices) if self.use_aux_hidden_state_outputs: assert aux_hidden_states is not None target_hidden_states = torch.cat( From be7a248fc9210161f017496c6a3ca9a7dacde15e Mon Sep 17 00:00:00 2001 From: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Date: Fri, 26 Sep 2025 23:44:52 -0400 Subject: [PATCH 038/166] [Bugfix] Allow Only SDPA Backend for ViT on B200 for Qwen3-VL (#25788) --- vllm/model_executor/models/qwen2_5_vl.py | 73 ++++++++++++------------ vllm/model_executor/models/qwen3_vl.py | 53 ++++++++++++----- 2 files changed, 75 insertions(+), 51 deletions(-) diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py index 3199f53a0539..adb21373056c 100644 --- a/vllm/model_executor/models/qwen2_5_vl.py +++ b/vllm/model_executor/models/qwen2_5_vl.py @@ -274,6 +274,8 @@ def __init__( quant_config: Optional[QuantizationConfig] = None, prefix: str = "", use_data_parallel: bool = False, + attn_backend: _Backend = _Backend.TORCH_SDPA, + use_upstream_fa: bool = False, ) -> None: super().__init__() # Per attention head and per partition values. @@ -300,25 +302,8 @@ def __init__( quant_config=quant_config, prefix=f"{prefix}.proj", disable_tp=use_data_parallel) - - # Detect attention implementation. - self.attn_backend = get_vit_attn_backend( - head_size=self.hidden_size_per_attention_head, - dtype=torch.get_default_dtype()) - self.use_upstream_fa = False - if self.attn_backend != _Backend.FLASH_ATTN and \ - check_upstream_fa_availability( - torch.get_default_dtype()): - self.attn_backend = _Backend.FLASH_ATTN - self.use_upstream_fa = True - - if self.attn_backend not in { - _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS, - _Backend.ROCM_AITER_FA - }: - raise RuntimeError( - f"Qwen2.5-VL does not support {self.attn_backend} backend now." - ) + self.attn_backend = attn_backend + self.use_upstream_fa = use_upstream_fa self.is_flash_attn_backend = self.attn_backend in { _Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA } @@ -443,6 +428,8 @@ def __init__( quant_config: Optional[QuantizationConfig] = None, prefix: str = "", use_data_parallel: bool = False, + attn_backend: _Backend = _Backend.TORCH_SDPA, + use_upstream_fa: bool = False, ) -> None: super().__init__() if norm_layer is None: @@ -455,7 +442,9 @@ def __init__( projection_size=dim, quant_config=quant_config, prefix=f"{prefix}.attn", - use_data_parallel=use_data_parallel) + use_data_parallel=use_data_parallel, + attn_backend=attn_backend, + use_upstream_fa=use_upstream_fa) self.mlp = Qwen2_5_VisionMLP(dim, mlp_hidden_dim, act_fn=act_fn, @@ -627,17 +616,35 @@ def __init__( head_dim = self.hidden_size // self.num_heads self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) + use_upstream_fa = False + self.attn_backend = get_vit_attn_backend( + head_size=head_dim, dtype=torch.get_default_dtype()) + if self.attn_backend != _Backend.FLASH_ATTN and \ + check_upstream_fa_availability( + torch.get_default_dtype()): + self.attn_backend = _Backend.FLASH_ATTN + use_upstream_fa = True + + if self.attn_backend not in { + _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS, + _Backend.ROCM_AITER_FA + }: + raise RuntimeError( + f"Qwen2.5-VL does not support {self.attn_backend} backend now." + ) + self.blocks = nn.ModuleList([ - Qwen2_5_VisionBlock(dim=self.hidden_size, - num_heads=self.num_heads, - mlp_hidden_dim=vision_config.intermediate_size, - act_fn=get_act_and_mul_fn( - vision_config.hidden_act), - norm_layer=norm_layer, - quant_config=quant_config, - prefix=f"{prefix}.blocks.{layer_idx}", - use_data_parallel=use_data_parallel) - for layer_idx in range(depth) + Qwen2_5_VisionBlock( + dim=self.hidden_size, + num_heads=self.num_heads, + mlp_hidden_dim=vision_config.intermediate_size, + act_fn=get_act_and_mul_fn(vision_config.hidden_act), + norm_layer=norm_layer, + quant_config=quant_config, + prefix=f"{prefix}.blocks.{layer_idx}", + use_data_parallel=use_data_parallel, + attn_backend=self.attn_backend, + use_upstream_fa=use_upstream_fa) for layer_idx in range(depth) ]) self.merger = Qwen2_5_VisionPatchMerger( d_model=vision_config.out_hidden_size, @@ -648,12 +655,6 @@ def __init__( prefix=f"{prefix}.merger", use_data_parallel=use_data_parallel, ) - self.attn_backend = get_vit_attn_backend( - head_size=head_dim, dtype=torch.get_default_dtype()) - if self.attn_backend != _Backend.FLASH_ATTN and \ - check_upstream_fa_availability( - torch.get_default_dtype()): - self.attn_backend = _Backend.FLASH_ATTN @property def dtype(self) -> torch.dtype: diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index f3f11438eeee..f1aeb99a4d37 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -63,7 +63,7 @@ PromptReplacement, PromptUpdate, PromptUpdateDetails) from vllm.multimodal.profiling import BaseDummyInputsBuilder -from vllm.platforms import _Backend +from vllm.platforms import _Backend, current_platform from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import uses_mrope from vllm.utils import is_list_of @@ -158,6 +158,8 @@ def __init__( quant_config: Optional[QuantizationConfig] = None, prefix: str = "", use_data_parallel: bool = False, + attn_backend: _Backend = _Backend.TORCH_SDPA, + use_upstream_fa: bool = False, ) -> None: super().__init__() if norm_layer is None: @@ -170,7 +172,9 @@ def __init__( projection_size=dim, quant_config=quant_config, prefix=f"{prefix}.attn", - use_data_parallel=use_data_parallel) + use_data_parallel=use_data_parallel, + attn_backend=attn_backend, + use_upstream_fa=use_upstream_fa) self.mlp = Qwen3_VisionMLP(dim, mlp_hidden_dim, act_fn=act_fn, @@ -287,19 +291,6 @@ def __init__( head_dim = self.hidden_size // self.num_heads self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) - self.blocks = nn.ModuleList([ - Qwen3_VisionBlock( - dim=self.hidden_size, - num_heads=self.num_heads, - mlp_hidden_dim=vision_config.intermediate_size, - act_fn=_ACTIVATION_REGISTRY[vision_config.hidden_act], - norm_layer=norm_layer, - quant_config=quant_config, - prefix=f"{prefix}.blocks.{layer_idx}", - use_data_parallel=use_data_parallel) - for layer_idx in range(vision_config.depth) - ]) - self.merger = Qwen3_VisionPatchMerger( d_model=vision_config.out_hidden_size, context_dim=self.hidden_size, @@ -325,10 +316,42 @@ def __init__( self.attn_backend = get_vit_attn_backend( head_size=head_dim, dtype=torch.get_default_dtype()) + use_upstream_fa = False if self.attn_backend != _Backend.FLASH_ATTN and \ check_upstream_fa_availability( torch.get_default_dtype()): self.attn_backend = _Backend.FLASH_ATTN + use_upstream_fa = True + + if self.attn_backend not in { + _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS, + _Backend.ROCM_AITER_FA + }: + raise RuntimeError( + f"Qwen3-VL does not support {self.attn_backend} backend now.") + if current_platform.is_device_capability( + 100) and self.attn_backend != _Backend.TORCH_SDPA: + # TODO(Roger/Wentao): remove this after FA + # or XFORMERS's issue fixed on Blackwell + logger.info_once("Qwen3-VL vision attention does not support " + f"{self.attn_backend} backend on Blackwell now. " + "Vision attention backend is set to TORCH_SDPA.") + self.attn_backend = _Backend.TORCH_SDPA + + self.blocks = nn.ModuleList([ + Qwen3_VisionBlock( + dim=self.hidden_size, + num_heads=self.num_heads, + mlp_hidden_dim=vision_config.intermediate_size, + act_fn=_ACTIVATION_REGISTRY[vision_config.hidden_act], + norm_layer=norm_layer, + quant_config=quant_config, + prefix=f"{prefix}.blocks.{layer_idx}", + use_data_parallel=use_data_parallel, + attn_backend=self.attn_backend, + use_upstream_fa=use_upstream_fa) + for layer_idx in range(vision_config.depth) + ]) @property def dtype(self) -> torch.dtype: From 0ebc20706ce374445cac4fe214c1a2ec990426b2 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Sat, 27 Sep 2025 12:45:20 +0800 Subject: [PATCH 039/166] [CI/Build] Consolidate model loader tests and requirements (#25765) Signed-off-by: DarkLight1337 --- .buildkite/test-pipeline.yaml | 19 ++----- .github/mergify.yml | 2 +- docker/Dockerfile | 2 +- requirements/nightly_torch_test.txt | 3 +- requirements/rocm.txt | 5 +- requirements/test.in | 3 +- requirements/test.txt | 10 ++-- setup.py | 5 +- tests/model_executor/conftest.py | 52 ------------------- .../fastsafetensors_loader/__init__.py | 0 .../test_fastsafetensors_loader.py | 0 .../test_weight_utils.py | 0 .../runai_model_streamer}/__init__.py | 0 .../test_runai_model_streamer_loader.py | 0 .../runai_model_streamer}/test_runai_utils.py | 0 .../test_weight_utils.py | 0 .../tensorizer_loader/__init__.py | 0 .../tensorizer_loader/conftest.py | 0 .../tensorizer_loader/test_tensorizer.py | 2 +- .../model_loader/weight_utils.py | 35 +++++++++++-- 20 files changed, 48 insertions(+), 90 deletions(-) delete mode 100644 tests/model_executor/conftest.py rename tests/{ => model_executor/model_loader}/fastsafetensors_loader/__init__.py (100%) rename tests/{ => model_executor/model_loader}/fastsafetensors_loader/test_fastsafetensors_loader.py (100%) rename tests/{ => model_executor/model_loader}/fastsafetensors_loader/test_weight_utils.py (100%) rename tests/{runai_model_streamer_test => model_executor/model_loader/runai_model_streamer}/__init__.py (100%) rename tests/{runai_model_streamer_test => model_executor/model_loader/runai_model_streamer}/test_runai_model_streamer_loader.py (100%) rename tests/{runai_model_streamer_test => model_executor/model_loader/runai_model_streamer}/test_runai_utils.py (100%) rename tests/{runai_model_streamer_test => model_executor/model_loader/runai_model_streamer}/test_weight_utils.py (100%) rename tests/{ => model_executor/model_loader}/tensorizer_loader/__init__.py (100%) rename tests/{ => model_executor/model_loader}/tensorizer_loader/conftest.py (100%) rename tests/{ => model_executor/model_loader}/tensorizer_loader/test_tensorizer.py (99%) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index c178fd372bcb..82a3b2fc199e 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -465,29 +465,18 @@ steps: commands: - pytest -v -s kernels/mamba -- label: Tensorizer Test # 14min - timeout_in_minutes: 25 - mirror_hardwares: [amdexperimental] - source_file_dependencies: - - vllm/model_executor/model_loader - - tests/tensorizer_loader - - tests/entrypoints/openai/test_tensorizer_entrypoint.py - commands: - - apt-get update && apt-get install -y curl libsodium23 - - export VLLM_WORKER_MULTIPROC_METHOD=spawn - - pytest -v -s tensorizer_loader - - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py - -- label: Model Executor Test # 7min - timeout_in_minutes: 20 +- label: Model Executor Test # ??? + timeout_in_minutes: 60 mirror_hardwares: [amdexperimental] source_file_dependencies: - vllm/model_executor - tests/model_executor + - tests/entrypoints/openai/test_tensorizer_entrypoint.py commands: - apt-get update && apt-get install -y curl libsodium23 - export VLLM_WORKER_MULTIPROC_METHOD=spawn - pytest -v -s model_executor + - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py - label: Benchmarks # 11min timeout_in_minutes: 20 diff --git a/.github/mergify.yml b/.github/mergify.yml index 75ee3e3c55b4..923f708ea10c 100644 --- a/.github/mergify.yml +++ b/.github/mergify.yml @@ -274,7 +274,7 @@ pull_request_rules: - files~=^vllm/model_executor/model_loader/tensorizer.py - files~=^vllm/model_executor/model_loader/tensorizer_loader.py - files~=^tests/entrypoints/openai/test_tensorizer_entrypoint.py - - files~=^tests/tensorizer_loader/ + - files~=^tests/model_executor/model_loader/tensorizer_loader/ actions: assign: users: diff --git a/docker/Dockerfile b/docker/Dockerfile index fad62be798a1..c2b855be4403 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -546,7 +546,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ else \ BITSANDBYTES_VERSION="0.46.1"; \ fi; \ - uv pip install --system accelerate hf_transfer modelscope "bitsandbytes>=${BITSANDBYTES_VERSION}" 'timm>=1.0.17' boto3 runai-model-streamer runai-model-streamer[s3] + uv pip install --system accelerate hf_transfer modelscope "bitsandbytes>=${BITSANDBYTES_VERSION}" 'timm>=1.0.17' 'runai-model-streamer[s3]>=0.14.0' ENV VLLM_USAGE_SOURCE production-docker-image diff --git a/requirements/nightly_torch_test.txt b/requirements/nightly_torch_test.txt index a529bf4504e4..790a18f28b7f 100644 --- a/requirements/nightly_torch_test.txt +++ b/requirements/nightly_torch_test.txt @@ -43,7 +43,6 @@ tritonclient==2.51.0 numba == 0.60.0; python_version == '3.9' # v0.61 doesn't support Python 3.9. Required for N-gram speculative decoding numba == 0.61.2; python_version > '3.9' numpy -runai-model-streamer==0.11.0 -runai-model-streamer-s3==0.11.0 +runai-model-streamer[s3]==0.14.0 fastsafetensors>=0.1.10 pydantic>=2.10 # 2.9 leads to error on python 3.10 diff --git a/requirements/rocm.txt b/requirements/rocm.txt index c129dd345c81..c4aabe2a7314 100644 --- a/requirements/rocm.txt +++ b/requirements/rocm.txt @@ -5,8 +5,6 @@ numba == 0.60.0; python_version == '3.9' # v0.61 doesn't support Python 3.9. Req numba == 0.61.2; python_version > '3.9' # Dependencies for AMD GPUs -boto3 -botocore datasets ray[cgraph]>=2.48.0 # Ray Compiled Graph, required for pipeline parallelism in V1. peft @@ -15,7 +13,6 @@ tensorizer==2.10.1 packaging>=24.2 setuptools>=77.0.3,<80.0.0 setuptools-scm>=8 -runai-model-streamer==0.11.0 -runai-model-streamer-s3==0.11.0 +runai-model-streamer[s3]==0.14.0 conch-triton-kernels==1.2.1 timm>=1.0.17 \ No newline at end of file diff --git a/requirements/test.in b/requirements/test.in index 451bd7387910..c9496c61a7e4 100644 --- a/requirements/test.in +++ b/requirements/test.in @@ -51,8 +51,7 @@ tritonclient==2.51.0 numba == 0.60.0; python_version == '3.9' # v0.61 doesn't support Python 3.9. Required for N-gram speculative decoding numba == 0.61.2; python_version > '3.9' numpy -runai-model-streamer==0.11.0 -runai-model-streamer-s3==0.11.0 +runai-model-streamer[s3]==0.14.0 fastsafetensors>=0.1.10 pydantic>=2.10 # 2.9 leads to error on python 3.10 decord==0.6.0 diff --git a/requirements/test.txt b/requirements/test.txt index 3519aa524f41..912e04b2606c 100644 --- a/requirements/test.txt +++ b/requirements/test.txt @@ -72,7 +72,9 @@ blobfile==3.0.0 bm25s==0.2.13 # via mteb boto3==1.35.57 - # via tensorizer + # via + # runai-model-streamer-s3 + # tensorizer botocore==1.35.57 # via # boto3 @@ -925,10 +927,10 @@ rsa==4.9.1 # via google-auth rtree==1.4.0 # via torchgeo -runai-model-streamer==0.11.0 - # via -r requirements/test.in -runai-model-streamer-s3==0.11.0 +runai-model-streamer==0.14.0 # via -r requirements/test.in +runai-model-streamer-s3==0.14.0 + # via runai-model-streamer s3transfer==0.10.3 # via boto3 sacrebleu==2.4.3 diff --git a/setup.py b/setup.py index e4c40d22b928..a8fec8a028d0 100644 --- a/setup.py +++ b/setup.py @@ -654,10 +654,7 @@ def _read_requirements(filename: str) -> list[str]: "bench": ["pandas", "datasets"], "tensorizer": ["tensorizer==2.10.1"], "fastsafetensors": ["fastsafetensors >= 0.1.10"], - "runai": [ - "runai-model-streamer >= 0.14.0", "runai-model-streamer-gcs", - "google-cloud-storage", "runai-model-streamer-s3", "boto3" - ], + "runai": ["runai-model-streamer[s3,gcs] >= 0.14.0"], "audio": ["librosa", "soundfile", "mistral_common[audio]"], # Required for audio processing "video": [], # Kept for backwards compatibility diff --git a/tests/model_executor/conftest.py b/tests/model_executor/conftest.py deleted file mode 100644 index c6d89d849e9f..000000000000 --- a/tests/model_executor/conftest.py +++ /dev/null @@ -1,52 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the vLLM project - -import pytest - - -@pytest.fixture -def sample_regex(): - return (r"((25[0-5]|(2[0-4]|1\d|[1-9]|)\d)\.){3}" - r"(25[0-5]|(2[0-4]|1\d|[1-9]|)\d)") - - -@pytest.fixture -def sample_json_schema(): - return { - "type": "object", - "properties": { - "name": { - "type": "string" - }, - "age": { - "type": "integer" - }, - "skills": { - "type": "array", - "items": { - "type": "string", - "maxLength": 10 - }, - "minItems": 3 - }, - "work_history": { - "type": "array", - "items": { - "type": "object", - "properties": { - "company": { - "type": "string" - }, - "duration": { - "type": "number" - }, - "position": { - "type": "string" - } - }, - "required": ["company", "position"] - } - } - }, - "required": ["name", "age", "skills", "work_history"] - } diff --git a/tests/fastsafetensors_loader/__init__.py b/tests/model_executor/model_loader/fastsafetensors_loader/__init__.py similarity index 100% rename from tests/fastsafetensors_loader/__init__.py rename to tests/model_executor/model_loader/fastsafetensors_loader/__init__.py diff --git a/tests/fastsafetensors_loader/test_fastsafetensors_loader.py b/tests/model_executor/model_loader/fastsafetensors_loader/test_fastsafetensors_loader.py similarity index 100% rename from tests/fastsafetensors_loader/test_fastsafetensors_loader.py rename to tests/model_executor/model_loader/fastsafetensors_loader/test_fastsafetensors_loader.py diff --git a/tests/fastsafetensors_loader/test_weight_utils.py b/tests/model_executor/model_loader/fastsafetensors_loader/test_weight_utils.py similarity index 100% rename from tests/fastsafetensors_loader/test_weight_utils.py rename to tests/model_executor/model_loader/fastsafetensors_loader/test_weight_utils.py diff --git a/tests/runai_model_streamer_test/__init__.py b/tests/model_executor/model_loader/runai_model_streamer/__init__.py similarity index 100% rename from tests/runai_model_streamer_test/__init__.py rename to tests/model_executor/model_loader/runai_model_streamer/__init__.py diff --git a/tests/runai_model_streamer_test/test_runai_model_streamer_loader.py b/tests/model_executor/model_loader/runai_model_streamer/test_runai_model_streamer_loader.py similarity index 100% rename from tests/runai_model_streamer_test/test_runai_model_streamer_loader.py rename to tests/model_executor/model_loader/runai_model_streamer/test_runai_model_streamer_loader.py diff --git a/tests/runai_model_streamer_test/test_runai_utils.py b/tests/model_executor/model_loader/runai_model_streamer/test_runai_utils.py similarity index 100% rename from tests/runai_model_streamer_test/test_runai_utils.py rename to tests/model_executor/model_loader/runai_model_streamer/test_runai_utils.py diff --git a/tests/runai_model_streamer_test/test_weight_utils.py b/tests/model_executor/model_loader/runai_model_streamer/test_weight_utils.py similarity index 100% rename from tests/runai_model_streamer_test/test_weight_utils.py rename to tests/model_executor/model_loader/runai_model_streamer/test_weight_utils.py diff --git a/tests/tensorizer_loader/__init__.py b/tests/model_executor/model_loader/tensorizer_loader/__init__.py similarity index 100% rename from tests/tensorizer_loader/__init__.py rename to tests/model_executor/model_loader/tensorizer_loader/__init__.py diff --git a/tests/tensorizer_loader/conftest.py b/tests/model_executor/model_loader/tensorizer_loader/conftest.py similarity index 100% rename from tests/tensorizer_loader/conftest.py rename to tests/model_executor/model_loader/tensorizer_loader/conftest.py diff --git a/tests/tensorizer_loader/test_tensorizer.py b/tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py similarity index 99% rename from tests/tensorizer_loader/test_tensorizer.py rename to tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py index e00d7c2f80c6..f50f04696738 100644 --- a/tests/tensorizer_loader/test_tensorizer.py +++ b/tests/model_executor/model_loader/tensorizer_loader/test_tensorizer.py @@ -14,6 +14,7 @@ import torch import vllm.model_executor.model_loader.tensorizer +from tests.utils import VLLM_PATH, RemoteOpenAIServer from vllm import LLM, SamplingParams from vllm.engine.arg_utils import EngineArgs # yapf: disable @@ -27,7 +28,6 @@ # yapf: enable from vllm.utils import PlaceholderModule -from ..utils import VLLM_PATH, RemoteOpenAIServer from .conftest import DummyExecutor, assert_from_collective_rpc try: diff --git a/vllm/model_executor/model_loader/weight_utils.py b/vllm/model_executor/model_loader/weight_utils.py index cad32fee1d0f..f52d9dd2f534 100644 --- a/vllm/model_executor/model_loader/weight_utils.py +++ b/vllm/model_executor/model_loader/weight_utils.py @@ -639,6 +639,19 @@ def runai_safetensors_weights_iterator( yield from tensor_iter +def _init_loader( + pg: torch.distributed.ProcessGroup, + device: torch.device, + f_list: list[str], + *, + nogds: bool = False, +): + loader = SafeTensorsFileLoader(pg, device, nogds=nogds) + rank_file_map = {i: [f] for i, f in enumerate(f_list)} + loader.add_filenames(rank_file_map) + return loader + + def fastsafetensors_weights_iterator( hf_weights_files: list[str], use_tqdm_on_load: bool, @@ -656,17 +669,31 @@ def fastsafetensors_weights_iterator( for i in range(0, len(hf_weights_files), pg.size()) ] + nogds = False + for f_list in tqdm( weight_files_sub_lists, desc="Loading safetensors using Fastsafetensor loader", disable=not enable_tqdm(use_tqdm_on_load), bar_format=_BAR_FORMAT, ): - loader = SafeTensorsFileLoader(pg, device) - rank_file_map = {i: [f] for i, f in enumerate(f_list)} - loader.add_filenames(rank_file_map) + loader = _init_loader(pg, device, f_list, nogds=nogds) try: - fb = loader.copy_files_to_device() + try: + fb = loader.copy_files_to_device() + except RuntimeError as e: + if "gds" not in str(e): + raise + + loader.close() + nogds = True + logger.warning_once( + "GDS not enabled, setting `nogds=True`.\n" + "For more information, see: https://github.com/foundation-model-stack/fastsafetensors?tab=readme-ov-file#basic-api-usages" + ) + loader = _init_loader(pg, device, f_list, nogds=nogds) + fb = loader.copy_files_to_device() + try: keys = list(fb.key_to_rank_lidx.keys()) for k in keys: From 7f947481d1a87025042d8b9d3c2b8b53dee68fdb Mon Sep 17 00:00:00 2001 From: 22quinn <33176974+22quinn@users.noreply.github.com> Date: Fri, 26 Sep 2025 21:57:27 -0700 Subject: [PATCH 040/166] [CI/Build] Add timing to Model Executor Test (#25799) Signed-off-by: 22quinn <33176974+22quinn@users.noreply.github.com> --- .buildkite/test-pipeline.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 82a3b2fc199e..c6c4e2a2309f 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -465,8 +465,8 @@ steps: commands: - pytest -v -s kernels/mamba -- label: Model Executor Test # ??? - timeout_in_minutes: 60 +- label: Model Executor Test # 23min + timeout_in_minutes: 35 mirror_hardwares: [amdexperimental] source_file_dependencies: - vllm/model_executor From c11b2d848314e679f151e00078ef858011972768 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Sat, 27 Sep 2025 13:51:15 +0800 Subject: [PATCH 041/166] [CI/Build] Reorganize root-level V1 tests (#25767) Signed-off-by: DarkLight1337 --- .../scripts/hardware_ci/run-xpu-test.sh | 3 +- .buildkite/test-pipeline.yaml | 23 +++---- tests/v1/{ => core}/test_kv_sharing.py | 0 tests/v1/distributed/__init__.py | 0 .../v1/{ => distributed}/test_async_llm_dp.py | 0 .../{ => distributed}/test_external_lb_dp.py | 0 .../v1/{ => distributed}/test_hybrid_lb_dp.py | 2 +- .../{ => distributed}/test_internal_lb_dp.py | 2 +- .../openai/test_multi_api_servers.py | 2 +- tests/v1/{ => metrics}/test_metrics_reader.py | 0 tests/v1/{test_utils.py => utils.py} | 61 ------------------ tests/v1/worker/test_utils.py | 63 +++++++++++++++++++ 12 files changed, 75 insertions(+), 81 deletions(-) rename tests/v1/{ => core}/test_kv_sharing.py (100%) create mode 100644 tests/v1/distributed/__init__.py rename tests/v1/{ => distributed}/test_async_llm_dp.py (100%) rename tests/v1/{ => distributed}/test_external_lb_dp.py (100%) rename tests/v1/{ => distributed}/test_hybrid_lb_dp.py (99%) rename tests/v1/{ => distributed}/test_internal_lb_dp.py (99%) rename tests/v1/{ => metrics}/test_metrics_reader.py (100%) rename tests/v1/{test_utils.py => utils.py} (67%) create mode 100644 tests/v1/worker/test_utils.py diff --git a/.buildkite/scripts/hardware_ci/run-xpu-test.sh b/.buildkite/scripts/hardware_ci/run-xpu-test.sh index 1fc3dbd8c21f..6b9c0121c4aa 100644 --- a/.buildkite/scripts/hardware_ci/run-xpu-test.sh +++ b/.buildkite/scripts/hardware_ci/run-xpu-test.sh @@ -44,7 +44,6 @@ docker run \ pytest -v -s v1/structured_output pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py --ignore=v1/spec_decode/test_tree_attention.py pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py + pytest -v -s v1/test_metrics pytest -v -s v1/test_serial_utils.py - pytest -v -s v1/test_utils.py - pytest -v -s v1/test_metrics_reader.py ' diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index c6c4e2a2309f..e603c1582e1f 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -159,10 +159,7 @@ steps: - examples/offline_inference/rlhf.py - examples/offline_inference/rlhf_colocate.py - tests/examples/offline_inference/data_parallel.py - - tests/v1/test_async_llm_dp.py - - tests/v1/test_external_lb_dp.py - - tests/v1/test_internal_lb_dp.py - - tests/v1/test_hybrid_lb_dp.py + - tests/v1/distributed - tests/v1/engine/test_engine_core_client.py - tests/distributed/test_symm_mem_allreduce.py commands: @@ -180,10 +177,10 @@ steps: - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py # test with internal dp - python3 ../examples/offline_inference/data_parallel.py --enforce-eager - - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py - - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py - - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_internal_lb_dp.py - - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_hybrid_lb_dp.py + - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py + - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py + - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py + - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp - pytest -v -s distributed/test_utils.py - pytest -v -s compile/test_basic_correctness.py @@ -300,12 +297,9 @@ steps: - pytest -v -s v1/spec_decode - pytest -v -s v1/kv_connector/unit - pytest -v -s v1/metrics - - pytest -v -s v1/test_kv_sharing.py - - pytest -v -s v1/test_metrics_reader.py - pytest -v -s v1/test_oracle.py - pytest -v -s v1/test_request.py - pytest -v -s v1/test_serial_utils.py - - pytest -v -s v1/test_utils.py # Integration test for streaming correctness (requires special branch). - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine @@ -895,14 +889,13 @@ steps: - tests/compile/test_wrapper.py - tests/distributed/ - tests/entrypoints/llm/test_collective_rpc.py - - tests/v1/test_async_llm_dp.py - - tests/v1/test_external_lb_dp.py + - tests/v1/distributed - tests/v1/entrypoints/openai/test_multi_api_servers.py - tests/v1/shutdown - tests/v1/worker/test_worker_memory_snapshot.py commands: - - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py - - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py + - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py + - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py - pytest -v -s entrypoints/llm/test_collective_rpc.py - pytest -v -s ./compile/test_basic_correctness.py diff --git a/tests/v1/test_kv_sharing.py b/tests/v1/core/test_kv_sharing.py similarity index 100% rename from tests/v1/test_kv_sharing.py rename to tests/v1/core/test_kv_sharing.py diff --git a/tests/v1/distributed/__init__.py b/tests/v1/distributed/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/v1/test_async_llm_dp.py b/tests/v1/distributed/test_async_llm_dp.py similarity index 100% rename from tests/v1/test_async_llm_dp.py rename to tests/v1/distributed/test_async_llm_dp.py diff --git a/tests/v1/test_external_lb_dp.py b/tests/v1/distributed/test_external_lb_dp.py similarity index 100% rename from tests/v1/test_external_lb_dp.py rename to tests/v1/distributed/test_external_lb_dp.py diff --git a/tests/v1/test_hybrid_lb_dp.py b/tests/v1/distributed/test_hybrid_lb_dp.py similarity index 99% rename from tests/v1/test_hybrid_lb_dp.py rename to tests/v1/distributed/test_hybrid_lb_dp.py index 552436f818d7..21d8009a6dbb 100644 --- a/tests/v1/test_hybrid_lb_dp.py +++ b/tests/v1/distributed/test_hybrid_lb_dp.py @@ -12,7 +12,7 @@ import requests from tests.utils import RemoteOpenAIServer -from tests.v1.test_utils import check_request_balancing +from tests.v1.utils import check_request_balancing from vllm.platforms import current_platform MODEL_NAME = "ibm-research/PowerMoE-3b" diff --git a/tests/v1/test_internal_lb_dp.py b/tests/v1/distributed/test_internal_lb_dp.py similarity index 99% rename from tests/v1/test_internal_lb_dp.py rename to tests/v1/distributed/test_internal_lb_dp.py index e965645711ee..3f9defd13dea 100644 --- a/tests/v1/test_internal_lb_dp.py +++ b/tests/v1/distributed/test_internal_lb_dp.py @@ -13,7 +13,7 @@ import requests from tests.utils import RemoteOpenAIServer -from tests.v1.test_utils import check_request_balancing +from tests.v1.utils import check_request_balancing from vllm.platforms import current_platform MODEL_NAME = "ibm-research/PowerMoE-3b" diff --git a/tests/v1/entrypoints/openai/test_multi_api_servers.py b/tests/v1/entrypoints/openai/test_multi_api_servers.py index f7c31b0c4377..35f75191d9c8 100644 --- a/tests/v1/entrypoints/openai/test_multi_api_servers.py +++ b/tests/v1/entrypoints/openai/test_multi_api_servers.py @@ -8,7 +8,7 @@ import pytest_asyncio from tests.utils import RemoteOpenAIServer -from tests.v1.test_utils import check_request_balancing +from tests.v1.utils import check_request_balancing MODEL_NAME = "ibm-research/PowerMoE-3b" diff --git a/tests/v1/test_metrics_reader.py b/tests/v1/metrics/test_metrics_reader.py similarity index 100% rename from tests/v1/test_metrics_reader.py rename to tests/v1/metrics/test_metrics_reader.py diff --git a/tests/v1/test_utils.py b/tests/v1/utils.py similarity index 67% rename from tests/v1/test_utils.py rename to tests/v1/utils.py index 00d98a873a31..b3f560c11e8f 100644 --- a/tests/v1/test_utils.py +++ b/tests/v1/utils.py @@ -1,71 +1,10 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project - import pytest import regex as re import requests -import torch from tests.utils import RemoteOpenAIServer -from vllm.v1.worker.utils import bind_kv_cache - - -def test_bind_kv_cache(): - from vllm.attention import Attention - - ctx = { - 'layers.0.self_attn': Attention(32, 128, 0.1), - 'layers.1.self_attn': Attention(32, 128, 0.1), - 'layers.2.self_attn': Attention(32, 128, 0.1), - 'layers.3.self_attn': Attention(32, 128, 0.1), - } - kv_cache = { - 'layers.0.self_attn': torch.zeros((1, )), - 'layers.1.self_attn': torch.zeros((1, )), - 'layers.2.self_attn': torch.zeros((1, )), - 'layers.3.self_attn': torch.zeros((1, )), - } - runner_kv_caches: list[torch.Tensor] = [] - bind_kv_cache(kv_cache, ctx, runner_kv_caches) - assert ctx['layers.0.self_attn'].kv_cache[0] is kv_cache[ - 'layers.0.self_attn'] - assert ctx['layers.1.self_attn'].kv_cache[0] is kv_cache[ - 'layers.1.self_attn'] - assert ctx['layers.2.self_attn'].kv_cache[0] is kv_cache[ - 'layers.2.self_attn'] - assert ctx['layers.3.self_attn'].kv_cache[0] is kv_cache[ - 'layers.3.self_attn'] - - assert runner_kv_caches[0] is kv_cache['layers.0.self_attn'] - assert runner_kv_caches[1] is kv_cache['layers.1.self_attn'] - assert runner_kv_caches[2] is kv_cache['layers.2.self_attn'] - assert runner_kv_caches[3] is kv_cache['layers.3.self_attn'] - - -def test_bind_kv_cache_non_attention(): - from vllm.attention import Attention - - # example from Jamba PP=2 - ctx = { - 'model.layers.20.attn': Attention(32, 128, 0.1), - 'model.layers.28.attn': Attention(32, 128, 0.1), - } - kv_cache = { - 'model.layers.20.attn': torch.zeros((1, )), - 'model.layers.28.attn': torch.zeros((1, )), - } - - runner_kv_caches: list[torch.Tensor] = [] - bind_kv_cache(kv_cache, ctx, runner_kv_caches) - - assert ctx['model.layers.20.attn'].kv_cache[0] is kv_cache[ - 'model.layers.20.attn'] - assert ctx['model.layers.28.attn'].kv_cache[0] is kv_cache[ - 'model.layers.28.attn'] - - assert runner_kv_caches[0] is kv_cache['model.layers.20.attn'] - assert runner_kv_caches[1] is kv_cache['model.layers.28.attn'] - # Prometheus metrics utilities for testing diff --git a/tests/v1/worker/test_utils.py b/tests/v1/worker/test_utils.py new file mode 100644 index 000000000000..fd0e630ce178 --- /dev/null +++ b/tests/v1/worker/test_utils.py @@ -0,0 +1,63 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import torch + +from vllm.v1.worker.utils import bind_kv_cache + + +def test_bind_kv_cache(): + from vllm.attention import Attention + + ctx = { + 'layers.0.self_attn': Attention(32, 128, 0.1), + 'layers.1.self_attn': Attention(32, 128, 0.1), + 'layers.2.self_attn': Attention(32, 128, 0.1), + 'layers.3.self_attn': Attention(32, 128, 0.1), + } + kv_cache = { + 'layers.0.self_attn': torch.zeros((1, )), + 'layers.1.self_attn': torch.zeros((1, )), + 'layers.2.self_attn': torch.zeros((1, )), + 'layers.3.self_attn': torch.zeros((1, )), + } + runner_kv_caches: list[torch.Tensor] = [] + bind_kv_cache(kv_cache, ctx, runner_kv_caches) + assert ctx['layers.0.self_attn'].kv_cache[0] is kv_cache[ + 'layers.0.self_attn'] + assert ctx['layers.1.self_attn'].kv_cache[0] is kv_cache[ + 'layers.1.self_attn'] + assert ctx['layers.2.self_attn'].kv_cache[0] is kv_cache[ + 'layers.2.self_attn'] + assert ctx['layers.3.self_attn'].kv_cache[0] is kv_cache[ + 'layers.3.self_attn'] + + assert runner_kv_caches[0] is kv_cache['layers.0.self_attn'] + assert runner_kv_caches[1] is kv_cache['layers.1.self_attn'] + assert runner_kv_caches[2] is kv_cache['layers.2.self_attn'] + assert runner_kv_caches[3] is kv_cache['layers.3.self_attn'] + + +def test_bind_kv_cache_non_attention(): + from vllm.attention import Attention + + # example from Jamba PP=2 + ctx = { + 'model.layers.20.attn': Attention(32, 128, 0.1), + 'model.layers.28.attn': Attention(32, 128, 0.1), + } + kv_cache = { + 'model.layers.20.attn': torch.zeros((1, )), + 'model.layers.28.attn': torch.zeros((1, )), + } + + runner_kv_caches: list[torch.Tensor] = [] + bind_kv_cache(kv_cache, ctx, runner_kv_caches) + + assert ctx['model.layers.20.attn'].kv_cache[0] is kv_cache[ + 'model.layers.20.attn'] + assert ctx['model.layers.28.attn'].kv_cache[0] is kv_cache[ + 'model.layers.28.attn'] + + assert runner_kv_caches[0] is kv_cache['model.layers.20.attn'] + assert runner_kv_caches[1] is kv_cache['model.layers.28.attn'] From fbb7895a4fefd986a8a3ae22ceb4780f40a33a9f Mon Sep 17 00:00:00 2001 From: 22quinn <33176974+22quinn@users.noreply.github.com> Date: Sat, 27 Sep 2025 00:47:29 -0700 Subject: [PATCH 042/166] [Misc] Fix codeowners override for v1 sample and attention (#25037) Signed-off-by: 22quinn <33176974+22quinn@users.noreply.github.com> --- .github/CODEOWNERS | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 9d749fe8d323..0b9c054b968a 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -12,8 +12,6 @@ /vllm/model_executor/layers/mamba @tdoublep /vllm/model_executor/model_loader @22quinn /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche -/vllm/v1/attention @LucasWilkinson -/vllm/v1/sample @22quinn @houseroad /vllm/vllm_flash_attn @LucasWilkinson /vllm/lora @jeejeelee /vllm/reasoning @aarnphm @chaunceyjiang @@ -28,11 +26,13 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson # vLLM V1 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat -/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett -/vllm/v1/spec_decode @benchislett @luccafong +/vllm/v1/attention @LucasWilkinson /vllm/v1/attention/backends/flashinfer.py @mgoin /vllm/v1/attention/backends/triton_attn.py @tdoublep /vllm/v1/core @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat @heheda12345 @ApostaC +/vllm/v1/sample @22quinn @houseroad @njhill +/vllm/v1/spec_decode @benchislett @luccafong +/vllm/v1/structured_output @mgoin @russellb @aarnphm @benchislett /vllm/v1/kv_cache_interface.py @heheda12345 /vllm/v1/offloading @ApostaC @@ -54,7 +54,7 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson /tests/weight_loading @mgoin @youkaichao @yewentao256 /tests/lora @jeejeelee /tests/models/language/generation/test_hybrid.py @tdoublep -/tests/v1/kv_connector/nixl_integration @NickLucche +/tests/v1/kv_connector/nixl_integration @NickLucche /tests/v1/kv_connector @ApostaC /tests/v1/offloading @ApostaC From 1687706f6c8881b3ab92745f369973e1a60cbdef Mon Sep 17 00:00:00 2001 From: Roger Wang Date: Sat, 27 Sep 2025 00:57:07 -0700 Subject: [PATCH 043/166] [Misc] Update openai client example file for multimodal (#25795) Signed-off-by: Roger Wang Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- ...i_chat_completion_client_for_multimodal.py | 63 +++++++++++-------- 1 file changed, 36 insertions(+), 27 deletions(-) diff --git a/examples/online_serving/openai_chat_completion_client_for_multimodal.py b/examples/online_serving/openai_chat_completion_client_for_multimodal.py index 37216a5cfe57..5d515fbfb671 100644 --- a/examples/online_serving/openai_chat_completion_client_for_multimodal.py +++ b/examples/online_serving/openai_chat_completion_client_for_multimodal.py @@ -38,11 +38,13 @@ base_url=openai_api_base, ) +headers = {"User-Agent": "vLLM Example Client"} + def encode_base64_content_from_url(content_url: str) -> str: """Encode a content retrieved from a remote url to base64 format.""" - with requests.get(content_url) as response: + with requests.get(content_url, headers=headers) as response: response.raise_for_status() result = base64.b64encode(response.content).decode("utf-8") @@ -50,19 +52,19 @@ def encode_base64_content_from_url(content_url: str) -> str: # Text-only inference -def run_text_only(model: str) -> None: +def run_text_only(model: str, max_completion_tokens: int) -> None: chat_completion = client.chat.completions.create( messages=[{"role": "user", "content": "What's the capital of France?"}], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion.choices[0].message.content - print("Chat completion output:", result) + print("Chat completion output:\n", result) # Single-image input inference -def run_single_image(model: str) -> None: +def run_single_image(model: str, max_completion_tokens: int) -> None: ## Use image url in the payload image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" chat_completion_from_url = client.chat.completions.create( @@ -79,11 +81,11 @@ def run_single_image(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_url.choices[0].message.content - print("Chat completion output from image url:", result) + print("Chat completion output from image url:\n", result) ## Use base64 encoded image in the payload image_base64 = encode_base64_content_from_url(image_url) @@ -101,7 +103,7 @@ def run_single_image(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_base64.choices[0].message.content @@ -109,7 +111,7 @@ def run_single_image(model: str) -> None: # Multi-image input inference -def run_multi_image(model: str) -> None: +def run_multi_image(model: str, max_completion_tokens: int) -> None: image_url_duck = "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg" image_url_lion = "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg" chat_completion_from_url = client.chat.completions.create( @@ -130,15 +132,15 @@ def run_multi_image(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_url.choices[0].message.content - print("Chat completion output:", result) + print("Chat completion output:\n", result) # Video input inference -def run_video(model: str) -> None: +def run_video(model: str, max_completion_tokens: int) -> None: video_url = "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ForBiggerFun.mp4" video_base64 = encode_base64_content_from_url(video_url) @@ -157,11 +159,11 @@ def run_video(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_url.choices[0].message.content - print("Chat completion output from image url:", result) + print("Chat completion output from video url:\n", result) ## Use base64 encoded video in the payload chat_completion_from_base64 = client.chat.completions.create( @@ -178,15 +180,15 @@ def run_video(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_base64.choices[0].message.content - print("Chat completion output from base64 encoded image:", result) + print("Chat completion output from base64 encoded video:\n", result) # Audio input inference -def run_audio(model: str) -> None: +def run_audio(model: str, max_completion_tokens: int) -> None: from vllm.assets.audio import AudioAsset audio_url = AudioAsset("winning_call").url @@ -211,11 +213,11 @@ def run_audio(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_base64.choices[0].message.content - print("Chat completion output from input audio:", result) + print("Chat completion output from input audio:\n", result) # HTTP URL chat_completion_from_url = client.chat.completions.create( @@ -235,11 +237,11 @@ def run_audio(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_url.choices[0].message.content - print("Chat completion output from audio url:", result) + print("Chat completion output from audio url:\n", result) # base64 URL chat_completion_from_base64 = client.chat.completions.create( @@ -259,14 +261,14 @@ def run_audio(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_base64.choices[0].message.content - print("Chat completion output from base64 encoded audio:", result) + print("Chat completion output from base64 encoded audio:\n", result) -def run_multi_audio(model: str) -> None: +def run_multi_audio(model: str, max_completion_tokens: int) -> None: from vllm.assets.audio import AudioAsset # Two different audios to showcase batched inference. @@ -300,11 +302,11 @@ def run_multi_audio(model: str) -> None: } ], model=model, - max_completion_tokens=64, + max_completion_tokens=max_completion_tokens, ) result = chat_completion_from_base64.choices[0].message.content - print("Chat completion output from input audio:", result) + print("Chat completion output from input audio:\n", result) example_function_map = { @@ -330,13 +332,20 @@ def parse_args(): choices=list(example_function_map.keys()), help="Conversation type with multimodal data.", ) + parser.add_argument( + "--max-completion-tokens", + "-n", + type=int, + default=128, + help="Maximum number of tokens to generate for each completion.", + ) return parser.parse_args() def main(args) -> None: chat_type = args.chat_type model = get_first_model(client) - example_function_map[chat_type](model) + example_function_map[chat_type](model, args.max_completion_tokens) if __name__ == "__main__": From 1a97bb5f8ad8cf38805cbae7a1e3e4154833462d Mon Sep 17 00:00:00 2001 From: Xiaohan Zou Date: Sat, 27 Sep 2025 03:59:22 -0400 Subject: [PATCH 044/166] [Bugfix] Add missing `image_size` for phi4_multimodal (#25796) --- vllm/model_executor/models/phi4_multimodal.py | 1 + 1 file changed, 1 insertion(+) diff --git a/vllm/model_executor/models/phi4_multimodal.py b/vllm/model_executor/models/phi4_multimodal.py index d2a3a8cc0496..bdc831354c11 100644 --- a/vllm/model_executor/models/phi4_multimodal.py +++ b/vllm/model_executor/models/phi4_multimodal.py @@ -786,6 +786,7 @@ def _find_target_aspect_ratio( target_ratios, orig_width, orig_height, + image_size, ) # calculate the target width and height From 1031f21f5c87a55623f993f9edff627df90ee44e Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Sat, 27 Sep 2025 16:15:12 +0800 Subject: [PATCH 045/166] [Bugfix] Merge MM embeddings by index instead of token IDs (#16229) Signed-off-by: DarkLight1337 Signed-off-by: NickLucche Signed-off-by: Roger Wang Co-authored-by: NickLucche Co-authored-by: Roger Wang --- docs/contributing/model/multimodal.md | 33 +------ vllm/config/model.py | 7 +- vllm/model_executor/models/aria.py | 25 ++--- vllm/model_executor/models/aya_vision.py | 27 ++---- vllm/model_executor/models/bert.py | 12 +++ vllm/model_executor/models/bert_with_rope.py | 6 ++ vllm/model_executor/models/blip2.py | 22 ++--- vllm/model_executor/models/chameleon.py | 24 ++--- vllm/model_executor/models/chatglm.py | 3 + vllm/model_executor/models/cohere2_vision.py | 27 ++---- vllm/model_executor/models/deepseek_eagle.py | 6 ++ vllm/model_executor/models/deepseek_mtp.py | 6 ++ vllm/model_executor/models/deepseek_vl2.py | 25 ++--- vllm/model_executor/models/dots_ocr.py | 44 +++------ vllm/model_executor/models/ernie45_vl.py | 31 +++--- vllm/model_executor/models/ernie_mtp.py | 6 ++ vllm/model_executor/models/fuyu.py | 26 ++--- vllm/model_executor/models/gemma3_mm.py | 26 ++--- vllm/model_executor/models/gemma3n_mm.py | 23 +++-- vllm/model_executor/models/glm4_1v.py | 17 ---- vllm/model_executor/models/glm4_moe_mtp.py | 6 ++ vllm/model_executor/models/glm4v.py | 35 ++----- vllm/model_executor/models/granite_speech.py | 34 ++++--- vllm/model_executor/models/hunyuan_v1.py | 3 + .../models/hyperclovax_vision.py | 35 ++----- vllm/model_executor/models/idefics3.py | 31 ++---- vllm/model_executor/models/interfaces.py | 83 ++++++++++++++-- vllm/model_executor/models/interfaces_base.py | 24 ++++- vllm/model_executor/models/interns1.py | 47 ++++++---- vllm/model_executor/models/internvl.py | 46 +++++---- vllm/model_executor/models/keye.py | 18 ---- vllm/model_executor/models/kimi_vl.py | 29 +----- vllm/model_executor/models/lfm2.py | 3 + vllm/model_executor/models/llama4_eagle.py | 31 ++---- vllm/model_executor/models/llama_eagle.py | 6 ++ vllm/model_executor/models/llama_eagle3.py | 17 +--- vllm/model_executor/models/llava.py | 26 ++--- vllm/model_executor/models/llava_next.py | 31 +++--- .../model_executor/models/llava_next_video.py | 23 ++--- vllm/model_executor/models/llava_onevision.py | 13 --- vllm/model_executor/models/midashenglm.py | 25 ++--- vllm/model_executor/models/mimo_mtp.py | 6 ++ vllm/model_executor/models/minicpmv.py | 27 ++---- vllm/model_executor/models/minimax_text_01.py | 10 +- vllm/model_executor/models/minimax_vl_01.py | 25 ++--- vllm/model_executor/models/mistral3.py | 26 ++--- vllm/model_executor/models/mllama4.py | 30 ++---- vllm/model_executor/models/modernbert.py | 9 ++ vllm/model_executor/models/molmo.py | 32 ++----- .../model_executor/models/nano_nemotron_vl.py | 43 +++------ vllm/model_executor/models/nemotron_vl.py | 37 +++++--- vllm/model_executor/models/olmo2.py | 6 ++ vllm/model_executor/models/ovis.py | 21 +---- vllm/model_executor/models/ovis2_5.py | 18 +--- vllm/model_executor/models/paligemma.py | 23 ++--- vllm/model_executor/models/phi3v.py | 44 ++++++--- vllm/model_executor/models/phi4_multimodal.py | 18 +--- vllm/model_executor/models/phi4mm.py | 14 --- vllm/model_executor/models/pixtral.py | 26 ++--- .../models/qwen2_5_omni_thinker.py | 28 +++--- vllm/model_executor/models/qwen2_5_vl.py | 13 --- vllm/model_executor/models/qwen2_audio.py | 23 ++--- vllm/model_executor/models/qwen2_vl.py | 13 --- vllm/model_executor/models/qwen3_vl.py | 89 ++++++++++++------ vllm/model_executor/models/qwen_vl.py | 25 ++--- vllm/model_executor/models/roberta.py | 3 + vllm/model_executor/models/skyworkr1v.py | 36 ++++--- vllm/model_executor/models/solar.py | 3 + vllm/model_executor/models/step3_text.py | 3 + vllm/model_executor/models/step3_vl.py | 52 +++++----- vllm/model_executor/models/tarsier.py | 25 ++--- vllm/model_executor/models/terratorch.py | 3 + vllm/model_executor/models/transformers.py | 64 ++++++++++--- vllm/model_executor/models/ultravox.py | 36 +++---- vllm/model_executor/models/utils.py | 94 ++++++++----------- vllm/model_executor/models/voxtral.py | 29 ++---- vllm/model_executor/models/whisper.py | 10 +- vllm/v1/spec_decode/eagle.py | 46 +++++---- vllm/v1/worker/gpu_model_runner.py | 52 +++++++--- vllm/v1/worker/tpu_model_runner.py | 79 +++++++++++++--- 80 files changed, 965 insertions(+), 1138 deletions(-) diff --git a/docs/contributing/model/multimodal.md b/docs/contributing/model/multimodal.md index 87d34d207cde..1d72fe97b966 100644 --- a/docs/contributing/model/multimodal.md +++ b/docs/contributing/model/multimodal.md @@ -66,35 +66,12 @@ Further update the model as follows: !!! important The returned `multimodal_embeddings` must be either a **3D [torch.Tensor][]** of shape `(num_items, feature_size, hidden_size)`, or a **list / tuple of 2D [torch.Tensor][]'s** of shape `(feature_size, hidden_size)`, so that `multimodal_embeddings[i]` retrieves the embeddings generated from the `i`-th multimodal data item (e.g, image) of the request. -- Implement [get_input_embeddings][vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings] to merge `multimodal_embeddings` with text embeddings from the `input_ids`. If input processing for the model is implemented correctly (see sections below), then you can leverage the utility function we provide to easily merge the embeddings. - - ??? code - - ```python - from .utils import merge_multimodal_embeddings - - class YourModelForImage2Seq(nn.Module): - ... - - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - - # `get_input_embeddings` should already be implemented for the language - # model as one of the requirements of basic vLLM model implementation. - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - - if multimodal_embeddings is not None: - inputs_embeds = merge_multimodal_embeddings( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - multimodal_embeddings=multimodal_embeddings, - placeholder_token_id=self.config.image_token_index) +!!! note + By default, vLLM merges the multimodal embeddings into text embeddings depending on the information of their locations defined in + [PlaceholderRange][vllm.multimodal.inputs.PlaceholderRange] from input processing. + This logic can be found at [get_input_embeddings][vllm.model_executor.models.interfaces.SupportsMultiModal.get_input_embeddings]. - return inputs_embeds - ``` + You may override this method if additional logic is required for your model when merging embeddings. - Implement [get_language_model][vllm.model_executor.models.interfaces.SupportsMultiModal.get_language_model] getter to provide stable access to the underlying language model. diff --git a/vllm/config/model.py b/vllm/config/model.py index b2b68abd2c1d..3fb448ebbf36 100644 --- a/vllm/config/model.py +++ b/vllm/config/model.py @@ -509,9 +509,14 @@ def _task_to_convert(task: TaskOption) -> ConvertType: else: # task == "auto" pass else: + debug_info = { + "architectures": architectures, + "is_generative_model": is_generative_model, + "is_pooling_model": is_pooling_model, + } raise AssertionError("The model should be a generative or " "pooling model when task is set to " - f"{self.task!r}.") + f"{self.task!r}. Found: {debug_info}") self.runner = runner self.convert = convert diff --git a/vllm/model_executor/models/aria.py b/vllm/model_executor/models/aria.py index 35c1adbdd00b..6cef5e134a4b 100644 --- a/vllm/model_executor/models/aria.py +++ b/vllm/model_executor/models/aria.py @@ -38,8 +38,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsQuant from .llama import LlamaDecoderLayer, LlamaMLP, LlamaModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - is_pp_missing_parameter, maybe_prefix, - merge_multimodal_embeddings) + is_pp_missing_parameter, maybe_prefix) class AriaImagePixelInputs(TensorSchema): @@ -605,19 +604,6 @@ def get_multimodal_embeddings(self, multimodal_embeddings = self._process_image_input(image_input) return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.config.image_token_index) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -628,10 +614,11 @@ def forward( ) -> Union[torch.Tensor, IntermediateTensors]: if inputs_embeds is None: multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - # always pass the input via `inputs_embeds` - # to make sure the computation graph is consistent - inputs_embeds = self.get_input_embeddings(input_ids, - multimodal_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + multimodal_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model( diff --git a/vllm/model_executor/models/aya_vision.py b/vllm/model_executor/models/aya_vision.py index 6fd8c2fb5c56..eab996e9ba22 100644 --- a/vllm/model_executor/models/aya_vision.py +++ b/vllm/model_executor/models/aya_vision.py @@ -33,8 +33,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) class AyaVisionImagePixelInputs(TensorSchema): @@ -417,23 +416,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input, **kwargs) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - multimodal_embeddings=multimodal_embeddings, - placeholder_token_id=self.config.image_token_index, - ) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -449,8 +431,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model( diff --git a/vllm/model_executor/models/bert.py b/vllm/model_executor/models/bert.py index ee32587f6b1b..c984845204c4 100644 --- a/vllm/model_executor/models/bert.py +++ b/vllm/model_executor/models/bert.py @@ -348,6 +348,9 @@ def __init__( self.encoder = BertEncoder(vllm_config=vllm_config, prefix=f"{prefix}.encoder") + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -457,6 +460,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): prefix=maybe_prefix(prefix, "model")) self.pooler = self._build_pooler(pooler_config) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -588,6 +594,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): ), }) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.bert.get_input_embeddings(input_ids) + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): loader = AutoWeightsLoader(self) loaded_params = loader.load_weights(weights) @@ -637,6 +646,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): Pooler.for_encode(pooler_config), }) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.bert.get_input_embeddings(input_ids) + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): loader = AutoWeightsLoader(self) loaded_params = loader.load_weights(weights) diff --git a/vllm/model_executor/models/bert_with_rope.py b/vllm/model_executor/models/bert_with_rope.py index bfc1408ddf88..4e1eba32d259 100644 --- a/vllm/model_executor/models/bert_with_rope.py +++ b/vllm/model_executor/models/bert_with_rope.py @@ -426,6 +426,9 @@ def __init__(self, prefix=f"{prefix}.encoder") self.pooler = BertPooler(self.config) if add_pooling_layer else None + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -673,6 +676,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): loaded_params = loader.load_weights(weights) return loaded_params + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.new.get_input_embeddings(input_ids) + def forward( self, input_ids: Optional[torch.Tensor], diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py index b7455fba62c0..4d1850d07b28 100644 --- a/vllm/model_executor/models/blip2.py +++ b/vllm/model_executor/models/blip2.py @@ -27,7 +27,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP, SupportsQuant) from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) # We use this internally as placeholders since there is no image token # defined on the HuggingFace repo @@ -631,19 +631,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self._process_image_input(image_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - _IMAGE_TOKEN_ID) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -689,8 +676,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == _IMAGE_TOKEN_ID, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/chameleon.py b/vllm/model_executor/models/chameleon.py index 79d648d749c6..f9740adb151b 100644 --- a/vllm/model_executor/models/chameleon.py +++ b/vllm/model_executor/models/chameleon.py @@ -44,7 +44,7 @@ SupportsQuant) from .utils import (flatten_bn, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) logger = init_logger(__name__) @@ -1002,20 +1002,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self.model.get_input_embeddings(image_tokens) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - - inputs_embeds = self.model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.model.vocabulary_mapping.image_token_id) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1032,8 +1018,12 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + image_token_id = self.model.vocabulary_mapping.image_token_id + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == image_token_id, + ) input_ids = None hidden_states = self.model(input_ids, diff --git a/vllm/model_executor/models/chatglm.py b/vllm/model_executor/models/chatglm.py index 879508400222..c182201fe256 100644 --- a/vllm/model_executor/models/chatglm.py +++ b/vllm/model_executor/models/chatglm.py @@ -433,6 +433,9 @@ def __init__( self.make_empty_intermediate_tensors = ( self.transformer.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.transformer.get_input_embeddings(input_ids) + def compute_logits( self, hidden_states: torch.Tensor, diff --git a/vllm/model_executor/models/cohere2_vision.py b/vllm/model_executor/models/cohere2_vision.py index 6d67eb68d51a..99edcba4d874 100644 --- a/vllm/model_executor/models/cohere2_vision.py +++ b/vllm/model_executor/models/cohere2_vision.py @@ -37,8 +37,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) class Cohere2VisionImagePixelInputs(TensorSchema): @@ -430,23 +429,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input, **kwargs) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - multimodal_embeddings=multimodal_embeddings, - placeholder_token_id=self.config.image_token_id, - ) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -462,8 +444,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_id, + ) input_ids = None hidden_states = self.language_model.model( diff --git a/vllm/model_executor/models/deepseek_eagle.py b/vllm/model_executor/models/deepseek_eagle.py index ed7e7614800f..c42a66d86912 100644 --- a/vllm/model_executor/models/deepseek_eagle.py +++ b/vllm/model_executor/models/deepseek_eagle.py @@ -66,6 +66,9 @@ def __init__( self.norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -205,6 +208,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.logits_processor = LogitsProcessor(self.config.vocab_size, scale=logit_scale) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/deepseek_mtp.py b/vllm/model_executor/models/deepseek_mtp.py index 92f311ab465b..a4623ff13cec 100644 --- a/vllm/model_executor/models/deepseek_mtp.py +++ b/vllm/model_executor/models/deepseek_mtp.py @@ -101,6 +101,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): ) self.logits_processor = LogitsProcessor(config.vocab_size) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -142,6 +145,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): prefix=maybe_prefix( prefix, "model")) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/deepseek_vl2.py b/vllm/model_executor/models/deepseek_vl2.py index c8ed759d2e97..b98008c83bdc 100644 --- a/vllm/model_executor/models/deepseek_vl2.py +++ b/vllm/model_executor/models/deepseek_vl2.py @@ -41,8 +41,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) # The image token id may be various _IMAGE_TOKEN = "" @@ -346,7 +345,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): model_config = vllm_config.model_config tokenizer = cached_tokenizer_from_config(model_config) - self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN] + self.image_token_id: int = tokenizer.vocab[_IMAGE_TOKEN] self.vision = self._init_vision_module(self.vision_config, quant_config, @@ -605,19 +604,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self._process_image_input(image_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.image_token_id) - return inputs_embeds - def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, @@ -632,8 +618,11 @@ def forward(self, # condition is for v0 compatibility elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.image_token_id, + ) input_ids = None hidden_states = self.language_model(input_ids, diff --git a/vllm/model_executor/models/dots_ocr.py b/vllm/model_executor/models/dots_ocr.py index 2db350c892ae..4845f19bcbc4 100644 --- a/vllm/model_executor/models/dots_ocr.py +++ b/vllm/model_executor/models/dots_ocr.py @@ -34,8 +34,7 @@ Qwen2VLProcessingInfo) from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, - maybe_prefix, - merge_multimodal_embeddings) + maybe_prefix) from vllm.model_executor.models.vision import get_vit_attn_backend from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import MultiModalDataDict @@ -796,33 +795,17 @@ def _process_image_input( def get_language_model(self) -> torch.nn.Module: return self.language_model - def get_multimodal_embeddings( - self, **kwargs: object) -> Optional[MultiModalEmbeddings]: + def get_multimodal_embeddings(self, + **kwargs: object) -> MultiModalEmbeddings: image_input = self._parse_and_validate_image_input(**kwargs) if image_input is None: return [] vision_embeddings = self._process_image_input(image_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_id, - ) - - return inputs_embeds - def forward( self, - input_ids: Optional[torch.Tensor], + input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, @@ -830,17 +813,14 @@ def forward( ) -> Union[torch.Tensor, IntermediateTensors]: if intermediate_tensors is not None: inputs_embeds = None - elif inputs_embeds is None and kwargs.get("pixel_values") is not None: - image_input = self._parse_and_validate_image_input(**kwargs) - if image_input is None: - inputs_embeds = None - else: - assert input_ids is not None - inputs_embeds = self.get_multimodal_embeddings( - input_ids, - image_input=image_input, - ) - input_ids = None + elif inputs_embeds is None: + vision_embeddings = self.get_multimodal_embeddings(**kwargs) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_id, + ) + input_ids = None hidden_states = self.language_model( input_ids=input_ids, diff --git a/vllm/model_executor/models/ernie45_vl.py b/vllm/model_executor/models/ernie45_vl.py index 74b358034ef3..a73ec4f88ffe 100644 --- a/vllm/model_executor/models/ernie45_vl.py +++ b/vllm/model_executor/models/ernie45_vl.py @@ -60,8 +60,7 @@ from .ernie45_vl_moe import Ernie4_5_VLMoeForCausalLM from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) -from .utils import (AutoWeightsLoader, WeightsMapper, maybe_prefix, - merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix from .vision import get_vit_attn_backend logger = init_logger(__name__) @@ -1467,18 +1466,24 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - - if multimodal_embeddings is None: - return inputs_embeds - - self._set_visual_token_mask(input_ids) - inputs_embeds = merge_multimodal_embeddings(input_ids, inputs_embeds, - multimodal_embeddings, - [self.config.im_patch_id]) - return inputs_embeds + if multimodal_embeddings is not None and len( + multimodal_embeddings) > 0: + self._set_visual_token_mask(input_ids) + + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward( self, diff --git a/vllm/model_executor/models/ernie_mtp.py b/vllm/model_executor/models/ernie_mtp.py index 288fbe736c32..3b24bf2f1ef8 100644 --- a/vllm/model_executor/models/ernie_mtp.py +++ b/vllm/model_executor/models/ernie_mtp.py @@ -116,6 +116,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): ) self.logits_processor = LogitsProcessor(config.vocab_size) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -160,6 +163,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py index 53e9e6fe6e46..b99fe33a1dcc 100644 --- a/vllm/model_executor/models/fuyu.py +++ b/vllm/model_executor/models/fuyu.py @@ -42,8 +42,7 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, maybe_prefix, - merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, WeightsMapper, flatten_bn, maybe_prefix # Cannot find the following 2 numbers from hf config. _IMAGE_TOKEN_ID = 71011 @@ -342,22 +341,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - _IMAGE_TOKEN_ID, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -373,8 +356,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == _IMAGE_TOKEN_ID, + ) input_ids = None hidden_states = self.language_model( diff --git a/vllm/model_executor/models/gemma3_mm.py b/vllm/model_executor/models/gemma3_mm.py index 0630ee07c347..be75e36fe23b 100644 --- a/vllm/model_executor/models/gemma3_mm.py +++ b/vllm/model_executor/models/gemma3_mm.py @@ -37,8 +37,7 @@ SupportsMultiModal, SupportsPP) from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) logger = init_logger(__name__) @@ -588,22 +587,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - return inputs_embeds - def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, @@ -618,8 +601,11 @@ def forward(self, elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) if (vision_embeddings is not None) and len(vision_embeddings) != 0: kwargs = self.prepare_attn_masks( input_ids, diff --git a/vllm/model_executor/models/gemma3n_mm.py b/vllm/model_executor/models/gemma3n_mm.py index 2acdba54a257..b23437a08e5a 100644 --- a/vllm/model_executor/models/gemma3n_mm.py +++ b/vllm/model_executor/models/gemma3n_mm.py @@ -632,8 +632,10 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) # NOTE (NickLucche) Each pass needs tokens to compute PLE so we cache # them here, as the model forward has only access to the input_embeds. if input_ids is not None: @@ -645,15 +647,16 @@ def get_input_embeddings( self.per_layer_embeddings[:per_layer_inputs.shape[0]].copy_( per_layer_inputs) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - # NOTE: this order of processing mm items is important - [self.config.image_token_id, self.config.audio_token_id]) - return inputs_embeds + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward(self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/glm4_1v.py b/vllm/model_executor/models/glm4_1v.py index b088e0c0dd24..dbb5431ae491 100644 --- a/vllm/model_executor/models/glm4_1v.py +++ b/vllm/model_executor/models/glm4_1v.py @@ -1552,23 +1552,6 @@ def get_multimodal_embeddings( multimodal_embeddings += video_embeddings return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if (multimodal_embeddings is not None - and len(multimodal_embeddings) != 0 - and all(embed.numel() > 0 for embed in multimodal_embeddings)): - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - [self.config.image_token_id, self.config.video_token_id], - ) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/glm4_moe_mtp.py b/vllm/model_executor/models/glm4_moe_mtp.py index c572978e6220..826d541e571b 100644 --- a/vllm/model_executor/models/glm4_moe_mtp.py +++ b/vllm/model_executor/models/glm4_moe_mtp.py @@ -132,6 +132,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): ) self.logits_processor = LogitsProcessor(config.vocab_size) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -173,6 +176,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): prefix=maybe_prefix( prefix, "model")) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/glm4v.py b/vllm/model_executor/models/glm4v.py index bf33575859ae..ace9c05daf15 100644 --- a/vllm/model_executor/models/glm4v.py +++ b/vllm/model_executor/models/glm4v.py @@ -43,7 +43,7 @@ from .chatglm import ChatGLMBaseModel, ChatGLMModel from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) -from .utils import flatten_bn, merge_multimodal_embeddings +from .utils import flatten_bn, isin_list class GLMVImagePixelInputs(TensorSchema): @@ -607,28 +607,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self._process_image_input(image_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.transformer.get_input_embeddings(input_ids) - - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - multimodal_embeddings=multimodal_embeddings, - placeholder_token_id=[ - self.config.boi_token_id, - self.config.pad_token_id, - self.config.eoi_token_id, - ], - ) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -644,8 +622,15 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=isin_list(input_ids, [ + self.config.boi_token_id, + self.config.pad_token_id, + self.config.eoi_token_id, + ]), + ) input_ids = None hidden_states = self.transformer(input_ids, positions, diff --git a/vllm/model_executor/models/granite_speech.py b/vllm/model_executor/models/granite_speech.py index a5849184339b..8a02da58ea0b 100644 --- a/vllm/model_executor/models/granite_speech.py +++ b/vllm/model_executor/models/granite_speech.py @@ -52,8 +52,7 @@ from .blip2 import Blip2QFormerModel from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) -from .utils import (AutoWeightsLoader, embed_multimodal, - init_vllm_registered_model, maybe_prefix) +from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix ### Audio Input @@ -720,6 +719,9 @@ def _process_audio_input( # Split variable length features into a tuple return torch.split(masked_embeds, audio_input["audio_embed_sizes"]) + def get_language_model(self) -> torch.nn.Module: + return self.language_model + def get_multimodal_embeddings( self, **kwargs: object, @@ -728,7 +730,7 @@ def get_multimodal_embeddings( audio_input = self._parse_and_validate_audio_input(**kwargs) if audio_input is None: return [] - return None + audio_features = self._process_audio_input(audio_input) return audio_features @@ -736,19 +738,21 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + # Multi-modal token ID may exceed vocab size + handle_oov_mm_token: bool = True, ) -> torch.Tensor: - """Compute the merged LLM / audio embeddings.""" - if multimodal_embeddings is None \ - or len(multimodal_embeddings) == 0: - return self.language_model.get_input_embeddings(input_ids) + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) - inputs_embeds = embed_multimodal( + return super().get_input_embeddings( input_ids, - self.config.audio_token_index, - self.language_model.model.get_input_embeddings, - multimodal_embeddings, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, ) - return inputs_embeds def forward( self, @@ -765,7 +769,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: audio_embeds = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, audio_embeds) + inputs_embeds = self.get_input_embeddings( + input_ids, + audio_embeds, + is_multimodal=input_ids == self.config.audio_token_index, + ) input_ids = None model_output = self.language_model(input_ids, positions, diff --git a/vllm/model_executor/models/hunyuan_v1.py b/vllm/model_executor/models/hunyuan_v1.py index 8a23a6b45bc7..d28c97116790 100644 --- a/vllm/model_executor/models/hunyuan_v1.py +++ b/vllm/model_executor/models/hunyuan_v1.py @@ -989,6 +989,9 @@ def update_physical_experts_metadata( moe.n_redundant_experts = self.num_redundant_experts moe.experts.update_expert_map() + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/hyperclovax_vision.py b/vllm/model_executor/models/hyperclovax_vision.py index 4d39ff9ae79e..f851688bf7ba 100644 --- a/vllm/model_executor/models/hyperclovax_vision.py +++ b/vllm/model_executor/models/hyperclovax_vision.py @@ -45,8 +45,8 @@ from .clip import CLIPVisionModel from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel -from .utils import (AutoWeightsLoader, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) +from .utils import (AutoWeightsLoader, init_vllm_registered_model, isin_list, + maybe_prefix) from .vision import get_vision_encoder_info EOT = "<|endofturn|>" @@ -691,7 +691,7 @@ def get_language_model(self) -> torch.nn.Module: def get_multimodal_embeddings( self, **kwargs: Unpack[HCXVisionMultimodalInputs], - ) -> Optional[MultiModalEmbeddings]: + ) -> MultiModalEmbeddings: multimodal_embeddings = list() if kwargs.get("pixel_values_images") is not None: @@ -736,26 +736,6 @@ def get_multimodal_embeddings( multimodal_embeddings.append(_multimodal_embeddings_videos) return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - placeholder_token_id=[ - self.config.image_token_id, - self.config.video_token_id, - ], - ) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -771,8 +751,13 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - multimodal_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + multimodal_embeddings, + is_multimodal=isin_list( + input_ids, + [self.config.image_token_id, self.config.video_token_id]), + ) input_ids = None hidden_states = self.language_model.model(input_ids, positions, diff --git a/vllm/model_executor/models/idefics3.py b/vllm/model_executor/models/idefics3.py index 79e130119ae8..3334ee224253 100644 --- a/vllm/model_executor/models/idefics3.py +++ b/vllm/model_executor/models/idefics3.py @@ -52,8 +52,7 @@ # yapf: enable from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal from .llama import LlamaModel -from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix, - merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, flatten_bn, maybe_prefix class Idefics3ImagePixelInputs(TensorSchema): @@ -539,10 +538,7 @@ def image_pixels_to_features( return image_hidden_states - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.text_model.get_input_embeddings(input_ids) def forward( @@ -695,22 +691,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -726,8 +706,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_id, + ) input_ids = None hidden_states = self.model.text_model(input_ids, diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py index f13e590cd243..d40df9b43dd4 100644 --- a/vllm/model_executor/models/interfaces.py +++ b/vllm/model_executor/models/interfaces.py @@ -2,8 +2,8 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from collections.abc import Iterable, Mapping, MutableSequence -from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol, - Union, overload, runtime_checkable) +from typing import (TYPE_CHECKING, Callable, ClassVar, Literal, Optional, + Protocol, Union, overload, runtime_checkable) import numpy as np import torch @@ -20,7 +20,7 @@ QuantizationConfig) from vllm.utils import supports_kw -from .interfaces_base import is_pooling_model +from .interfaces_base import VllmModel, is_pooling_model if TYPE_CHECKING: from vllm.config import VllmConfig @@ -90,7 +90,7 @@ def get_multimodal_embeddings(self, """ ... - def get_language_model(self) -> torch.nn.Module: + def get_language_model(self) -> VllmModel: """ Returns the underlying language model used for text generation. @@ -102,17 +102,84 @@ def get_language_model(self) -> torch.nn.Module: """ ... + @overload + def get_input_embeddings(self, input_ids: Tensor) -> Tensor: + ... + + @overload + def get_input_embeddings( + self, + input_ids: Tensor, + multimodal_embeddings: MultiModalEmbeddings, + *, + is_multimodal: torch.Tensor, + handle_oov_mm_token: bool = False, + ) -> Tensor: + ... + + def _get_text_embeddings( + self, + input_ids: Tensor, + get_input_embeddings: Callable[[Tensor], Tensor], + *, + is_multimodal: Optional[Tensor], + handle_oov_mm_token: bool, + ) -> Tensor: + if handle_oov_mm_token and is_multimodal is not None: + is_text = ~is_multimodal + text_embeds = get_input_embeddings(input_ids[is_text]) + + return torch.empty( + (input_ids.shape[0], text_embeds.shape[1]), + dtype=text_embeds.dtype, + device=text_embeds.device, + ).masked_scatter_(is_text.unsqueeze_(-1), text_embeds) + + return get_input_embeddings(input_ids) + def get_input_embeddings( self, input_ids: Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[Tensor] = None, + handle_oov_mm_token: bool = False, ) -> Tensor: """ - Returns the input embeddings merged from the text embeddings from - input_ids and the multimodal embeddings generated from multimodal - kwargs. + Apply token embeddings to `input_ids`. + + If `multimodal_embeddings` is passed, scatter them into + `input_ids` according to the mask `is_multimodal`. + + In case the multi-modal token IDs exceed the vocabulary size of + the language model, you can set `handle_oov_mm_token=False` + to avoid calling the language model's `get_input_embeddings` method + on those tokens. Note however that doing so increases memory usage + as an additional buffer is needed to hold the input embeddings. """ - ... + from .utils import _merge_multimodal_embeddings + + inputs_embeds = self._get_text_embeddings( + input_ids, + self.get_language_model().get_input_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) + + if multimodal_embeddings is None or len(multimodal_embeddings) == 0: + return inputs_embeds + + if is_multimodal is None: + raise ValueError( + "`get_input_embeddings` now requires `is_multimodal` arg, " + "please update your model runner according to " + "https://github.com/vllm-project/vllm/pull/16229.") + + return _merge_multimodal_embeddings( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + ) @runtime_checkable diff --git a/vllm/model_executor/models/interfaces_base.py b/vllm/model_executor/models/interfaces_base.py index 8fdf70e35a2b..84146db0943c 100644 --- a/vllm/model_executor/models/interfaces_base.py +++ b/vllm/model_executor/models/interfaces_base.py @@ -41,6 +41,13 @@ def __init__( ) -> None: ... + def get_input_embeddings( + self, + input_ids: torch.Tensor, + ) -> torch.Tensor: + """Apply token embeddings to `input_ids`.""" + ... + def forward( self, input_ids: torch.Tensor, @@ -54,6 +61,19 @@ def _check_vllm_model_init(model: Union[type[object], object]) -> bool: return supports_kw(model_init, "vllm_config") +def _check_vllm_model_get_input_embeddings( + model: Union[type[object], object]) -> bool: + model_get_input_embeddings = getattr(model, "get_input_embeddings", None) + if not callable(model_get_input_embeddings): + logger.warning( + "The model (%s) is missing the `get_input_embeddings` method.", + model, + ) + return False + + return True + + def _check_vllm_model_forward(model: Union[type[object], object]) -> bool: model_forward = getattr(model, "forward", None) if not callable(model_forward): @@ -88,7 +108,9 @@ def is_vllm_model(model: object) -> TypeIs[VllmModel]: def is_vllm_model( model: Union[type[object], object], ) -> Union[TypeIs[type[VllmModel]], TypeIs[VllmModel]]: - return _check_vllm_model_init(model) and _check_vllm_model_forward(model) + return (_check_vllm_model_init(model) + and _check_vllm_model_get_input_embeddings(model) + and _check_vllm_model_forward(model)) @runtime_checkable diff --git a/vllm/model_executor/models/interns1.py b/vllm/model_executor/models/interns1.py index 197d629b906f..545dad1a96f5 100644 --- a/vllm/model_executor/models/interns1.py +++ b/vllm/model_executor/models/interns1.py @@ -40,8 +40,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, isin_list, maybe_prefix) class InternS1MultiModalProjector(nn.Module): @@ -767,24 +766,24 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - context_token_ids = [ - token_id for token_id in (self.img_context_token_id, - self.video_context_token_id) - if token_id is not None - ] - assert len(context_token_ids) >= 1 + if multimodal_embeddings is not None and len( + multimodal_embeddings) > 0: self._set_visual_token_mask(input_ids) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - context_token_ids, - ) - return inputs_embeds + + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward( self, @@ -802,9 +801,17 @@ def forward( # NOTE: In v1, inputs_embeds is always generated at model runner, this # condition is for v0 compatibility. elif inputs_embeds is None: + context_token_ids = [ + token_id for token_id in (self.img_context_token_id, + self.video_context_token_id) + if token_id is not None + ] vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=isin_list(input_ids, context_token_ids), + ) input_ids = None forward_kwargs = { diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index f4004e518e3b..78aac8541434 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -43,7 +43,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) + isin_list, maybe_prefix) IMG_START = '' IMG_END = '' @@ -1339,24 +1339,24 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - context_token_ids = [ - token_id for token_id in (self.img_context_token_id, - self.video_context_token_id) - if token_id is not None - ] - assert len(context_token_ids) >= 1 + if multimodal_embeddings is not None and len( + multimodal_embeddings) > 0: self._set_visual_token_mask(input_ids) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - context_token_ids, - ) - return inputs_embeds + + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward( self, @@ -1374,9 +1374,17 @@ def forward( # NOTE: In v1, inputs_embeds is always generated at model runner, this # condition is for v0 compatibility. elif inputs_embeds is None: + context_token_ids = [ + token_id for token_id in (self.img_context_token_id, + self.video_context_token_id) + if token_id is not None + ] vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=isin_list(input_ids, context_token_ids), + ) input_ids = None forward_kwargs = { diff --git a/vllm/model_executor/models/keye.py b/vllm/model_executor/models/keye.py index 3b6fdba22512..62a71b7b1fa8 100644 --- a/vllm/model_executor/models/keye.py +++ b/vllm/model_executor/models/keye.py @@ -1450,24 +1450,6 @@ def get_multimodal_embeddings( multimodal_embeddings += video_embeddings return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - [ - self.config.image_token_id, - self.config.video_token_id, - ], - ) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/kimi_vl.py b/vllm/model_executor/models/kimi_vl.py index 503627865c4a..db032736f914 100644 --- a/vllm/model_executor/models/kimi_vl.py +++ b/vllm/model_executor/models/kimi_vl.py @@ -66,7 +66,6 @@ from vllm.model_executor.models.interfaces import (SupportsMultiModal, SupportsPP) from vllm.model_executor.models.moonvit import MoonVitPretrainedModel -from vllm.model_executor.models.utils import merge_multimodal_embeddings from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, MultiModalKwargsItems, NestedTensors) @@ -424,26 +423,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self._process_image_input(image_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[NestedTensors] = None, - ) -> torch.Tensor: - - # `get_input_embeddings` should already be implemented for the language - # model as one of the requirements of basic vLLM model implementation. - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - - if multimodal_embeddings is not None and len( - multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - multimodal_embeddings=multimodal_embeddings, - placeholder_token_id=self.config.media_placeholder_token_id) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -462,14 +441,12 @@ def forward( if image_input is None: inputs_embeds = None else: - inputs_embeds = self.get_input_embeddings(input_ids) image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( + inputs_embeds = self.get_input_embeddings( input_ids, - inputs_embeds, image_embeds, - placeholder_token_id=self.config. - media_placeholder_token_id, + is_multimodal=input_ids == + self.config.media_placeholder_token_id, ) input_ids = None diff --git a/vllm/model_executor/models/lfm2.py b/vllm/model_executor/models/lfm2.py index 53c36e4e52d8..f9def222a1ec 100644 --- a/vllm/model_executor/models/lfm2.py +++ b/vllm/model_executor/models/lfm2.py @@ -522,6 +522,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None: self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/llama4_eagle.py b/vllm/model_executor/models/llama4_eagle.py index a203af53205c..235275c0940a 100644 --- a/vllm/model_executor/models/llama4_eagle.py +++ b/vllm/model_executor/models/llama4_eagle.py @@ -37,9 +37,9 @@ from vllm.model_executor.models.llama4 import (Llama4DecoderLayer, Llama4ForCausalLM) from vllm.model_executor.models.utils import extract_layer_index -from vllm.multimodal.inputs import NestedTensors -from .utils import AutoWeightsLoader, maybe_prefix, merge_multimodal_embeddings +from .interfaces import SupportsMultiModal +from .utils import AutoWeightsLoader, maybe_prefix logger = init_logger(__name__) @@ -79,10 +79,7 @@ def __init__( self.norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( @@ -194,6 +191,11 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.logits_processor = LogitsProcessor(self.config.vocab_size, scale=logit_scale) + def get_language_model(self) -> torch.nn.Module: + return self.model + + get_input_embeddings = SupportsMultiModal.get_input_embeddings # type: ignore + def forward( self, input_ids: torch.Tensor, @@ -220,20 +222,3 @@ def transform(inputs): skip_prefixes=(["lm_head."]), ) loader.load_weights(map(transform, weights)) - - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[NestedTensors] = None, - ) -> torch.Tensor: - inputs_embeds = self.model.get_input_embeddings(input_ids) - - if multimodal_embeddings is not None: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - - return inputs_embeds diff --git a/vllm/model_executor/models/llama_eagle.py b/vllm/model_executor/models/llama_eagle.py index 2ff2d54a83aa..d6e6fd3fcfe9 100644 --- a/vllm/model_executor/models/llama_eagle.py +++ b/vllm/model_executor/models/llama_eagle.py @@ -73,6 +73,9 @@ def __init__( self.config.hidden_size, bias=False) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -149,6 +152,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.logits_processor = LogitsProcessor(self.config.vocab_size, scale=logit_scale) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/llama_eagle3.py b/vllm/model_executor/models/llama_eagle3.py index 55b6ae6ee0e9..34b8ea0ca536 100644 --- a/vllm/model_executor/models/llama_eagle3.py +++ b/vllm/model_executor/models/llama_eagle3.py @@ -18,7 +18,6 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models.interfaces import MultiModalEmbeddings from vllm.model_executor.models.llama import (LlamaDecoderLayer, LlamaForCausalLM) @@ -144,10 +143,7 @@ def __init__( eps=self.config.rms_norm_eps, ) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( @@ -239,6 +235,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): requires_grad=False, ) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -302,11 +301,3 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): skip_substrs=skip_substrs, ) loader.load_weights(model_weights.items()) - - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.model.get_input_embeddings(input_ids) - return inputs_embeds diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 4d8ed95b6cc8..6f3cfd88aee2 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -41,8 +41,7 @@ from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vision_encoder_info @@ -676,22 +675,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -744,8 +727,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index c9133fde1455..e132389c4f06 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -25,8 +25,8 @@ LlavaDummyInputsBuilder, LlavaLikeConfig, LlavaMultiModalProjector, init_vision_tower_for_llava) from .siglip import SiglipVisionModel -from .utils import (AutoWeightsLoader, WeightsMapper, embed_multimodal, - flatten_bn, init_vllm_registered_model, maybe_prefix) +from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, + init_vllm_registered_model, maybe_prefix) class LlavaNextImagePixelInputs(TensorSchema): @@ -474,19 +474,21 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + # Multi-modal token ID may exceed vocab size + handle_oov_mm_token: bool = True, ) -> torch.Tensor: + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) - if multimodal_embeddings is None \ - or len(multimodal_embeddings) == 0: - return self.language_model.get_input_embeddings(input_ids) - - inputs_embeds = embed_multimodal( + return super().get_input_embeddings( input_ids, - self.config.image_token_index, - self.language_model.model.get_input_embeddings, - multimodal_embeddings, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, ) - return inputs_embeds def forward( self, @@ -549,8 +551,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/llava_next_video.py b/vllm/model_executor/models/llava_next_video.py index 610fb188d57d..2642d8c77cf3 100644 --- a/vllm/model_executor/models/llava_next_video.py +++ b/vllm/model_executor/models/llava_next_video.py @@ -30,8 +30,7 @@ from .llava import init_vision_tower_for_llava from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vision_encoder_info @@ -415,19 +414,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self._process_video_pixels(video_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.config.video_token_index) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -449,8 +435,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.video_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/llava_onevision.py b/vllm/model_executor/models/llava_onevision.py index cee9ddaf94cc..906858f4e2f4 100644 --- a/vllm/model_executor/models/llava_onevision.py +++ b/vllm/model_executor/models/llava_onevision.py @@ -850,19 +850,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - [self.config.image_token_index, self.config.video_token_index]) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/midashenglm.py b/vllm/model_executor/models/midashenglm.py index 82648ba668ca..0bf04e0e7e2f 100644 --- a/vllm/model_executor/models/midashenglm.py +++ b/vllm/model_executor/models/midashenglm.py @@ -54,8 +54,7 @@ from vllm.transformers_utils.configs.midashenglm import DashengConfig from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP -from .utils import (AutoWeightsLoader, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix _Tuple2 = Union[int, tuple[int, int], Sequence[int]] @@ -744,21 +743,6 @@ def get_multimodal_embeddings(self, return [] return self._process_audio_input(audio_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.decoder.get_input_embeddings(input_ids) - if multimodal_embeddings and len(multimodal_embeddings) > 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.audio_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -771,8 +755,11 @@ def forward( inputs_embeds = None elif inputs_embeds is None: multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - multimodal_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + multimodal_embeddings, + is_multimodal=input_ids == self.config.audio_token_id, + ) input_ids = None return self.decoder.model(input_ids, diff --git a/vllm/model_executor/models/mimo_mtp.py b/vllm/model_executor/models/mimo_mtp.py index b4abe458e477..9c1e36094c4a 100644 --- a/vllm/model_executor/models/mimo_mtp.py +++ b/vllm/model_executor/models/mimo_mtp.py @@ -117,6 +117,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.logits_processor = LogitsProcessor(config.vocab_size) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -158,6 +161,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.config.hidden_size, prefix=maybe_prefix(prefix, "lm_head")) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py index a17c4f004d75..bffc9a0c125e 100644 --- a/vllm/model_executor/models/minicpmv.py +++ b/vllm/model_executor/models/minicpmv.py @@ -71,8 +71,7 @@ from .idefics2_vision_model import Idefics2VisionTransformer from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) -from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix, - merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, flatten_bn, isin_list, maybe_prefix # For profile run _MAX_FRAMES_PER_VIDEO = 16 @@ -1144,23 +1143,6 @@ def get_multimodal_embeddings(self, return self._process_multimodal_inputs(modalities) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.llm.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - assert len(self.mm_token_ids) > 0 - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - list(self.mm_token_ids), - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1178,8 +1160,11 @@ def forward( elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=isin_list(input_ids, list(self.mm_token_ids)), + ) input_ids = None hidden_states = self.llm.model( diff --git a/vllm/model_executor/models/minimax_text_01.py b/vllm/model_executor/models/minimax_text_01.py index cc9a959f6331..a92890c9f7b5 100644 --- a/vllm/model_executor/models/minimax_text_01.py +++ b/vllm/model_executor/models/minimax_text_01.py @@ -592,10 +592,7 @@ def _clear_prefill_cache(self, attn_metadata, dtype=torch.long) minimax_cache_tensors[:, slots_tensor, ...] = 0 - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward(self, @@ -687,10 +684,7 @@ def get_seqlen_agnostic_capture_inputs(self, batch_size: int): return self.model.minimax_cache.get_seqlen_agnostic_capture_inputs( batch_size) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) def forward(self, diff --git a/vllm/model_executor/models/minimax_vl_01.py b/vllm/model_executor/models/minimax_vl_01.py index d81ac8c704e7..d41b9d3f14fe 100644 --- a/vllm/model_executor/models/minimax_vl_01.py +++ b/vllm/model_executor/models/minimax_vl_01.py @@ -28,7 +28,7 @@ from .pixtral import PixtralHFVisionModel from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) class MiniMaxVL01ImagePixelInputs(TensorSchema): @@ -218,22 +218,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None: self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - return inputs_embeds - def get_language_model(self) -> torch.nn.Module: return self.language_model @@ -403,8 +387,11 @@ def forward( inputs_embeds = None elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/mistral3.py b/vllm/model_executor/models/mistral3.py index ba6da4403ae1..31571ce962d1 100644 --- a/vllm/model_executor/models/mistral3.py +++ b/vllm/model_executor/models/mistral3.py @@ -38,8 +38,7 @@ SupportsMultiModal, SupportsPP) from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vision_encoder_info @@ -524,22 +523,6 @@ def get_multimodal_embeddings(self, return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -592,8 +575,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/mllama4.py b/vllm/model_executor/models/mllama4.py index 79e315f79489..3af5267928cd 100644 --- a/vllm/model_executor/models/mllama4.py +++ b/vllm/model_executor/models/mllama4.py @@ -42,7 +42,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, - MultiModalKwargsItems, NestedTensors) + MultiModalKwargsItems) from vllm.multimodal.parse import (ImageProcessorItems, ImageSize, MultiModalDataItems) from vllm.multimodal.processing import (BaseMultiModalProcessor, @@ -56,8 +56,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .llama4 import Llama4ForCausalLM -from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix, - merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, flatten_bn, maybe_prefix from .vision import run_dp_sharded_vision_model @@ -813,24 +812,6 @@ def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings: return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[NestedTensors] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - - if multimodal_embeddings is not None and len( - multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -846,8 +827,11 @@ def forward( # this condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None return self.language_model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/modernbert.py b/vllm/model_executor/models/modernbert.py index 1d5da3139de9..e4a51b369737 100644 --- a/vllm/model_executor/models/modernbert.py +++ b/vllm/model_executor/models/modernbert.py @@ -43,6 +43,9 @@ def __init__(self, config: ModernBertConfig): eps=config.layer_norm_eps, bias=config.norm_bias) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.tok_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -220,6 +223,9 @@ def __init__( eps=config.norm_eps, bias=config.norm_bias) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embeddings.get_input_embeddings(input_ids) + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: weights = self.hf_to_vllm_mapper.apply(weights) @@ -333,6 +339,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): ), }) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): self_weights = [] diff --git a/vllm/model_executor/models/molmo.py b/vllm/model_executor/models/molmo.py index 201bf83cac58..054caee9e8a4 100644 --- a/vllm/model_executor/models/molmo.py +++ b/vllm/model_executor/models/molmo.py @@ -58,7 +58,7 @@ from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) # TODO: hard-coded for now. Consider making it configurable. VIT_LAYERS = [-2, -9] @@ -819,10 +819,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size)) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( @@ -1481,24 +1478,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - assert self.img_patch_id is not None - - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.img_patch_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.LongTensor, @@ -1515,8 +1494,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.img_patch_id, + ) input_ids = None hidden_states = self.model(input_ids, diff --git a/vllm/model_executor/models/nano_nemotron_vl.py b/vllm/model_executor/models/nano_nemotron_vl.py index 2b68d40cf2c6..505806a15c89 100644 --- a/vllm/model_executor/models/nano_nemotron_vl.py +++ b/vllm/model_executor/models/nano_nemotron_vl.py @@ -35,8 +35,7 @@ from vllm.model_executor.models.radio import RadioModel from vllm.model_executor.models.utils import (flatten_bn, init_vllm_registered_model, - maybe_prefix, - merge_multimodal_embeddings) + isin_list, maybe_prefix) from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, MultiModalKwargs, MultiModalKwargsItems, @@ -1096,8 +1095,8 @@ def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict: return modalities - def get_multimodal_embeddings( - self, **kwargs: object) -> Optional[MultiModalEmbeddings]: + def get_multimodal_embeddings(self, + **kwargs: object) -> MultiModalEmbeddings: # Validate the multimodal input keyword arguments modalities = self._parse_and_validate_multimodal_inputs(**kwargs) if modalities is None: @@ -1121,30 +1120,6 @@ def get_multimodal_embeddings( return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - - if (multimodal_embeddings is not None - and len(multimodal_embeddings) != 0): - context_token_ids = [ - token_id for token_id in (self.img_context_token_id, - self.video_context_token_id) - if token_id is not None - ] - assert len(context_token_ids) >= 1 - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - context_token_ids, - ) - - return inputs_embeds - def get_language_model(self) -> torch.nn.Module: return self.language_model @@ -1163,9 +1138,17 @@ def forward( # NOTE: In v1, inputs_embeds is always generated at model runner, this # condition is for v0 compatibility. elif inputs_embeds is None: + context_token_ids = [ + token_id for token_id in (self.img_context_token_id, + self.video_context_token_id) + if token_id is not None + ] vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=isin_list(input_ids, context_token_ids), + ) input_ids = None hidden_states = self.language_model( diff --git a/vllm/model_executor/models/nemotron_vl.py b/vllm/model_executor/models/nemotron_vl.py index 3abbff8c717d..2627a262e958 100644 --- a/vllm/model_executor/models/nemotron_vl.py +++ b/vllm/model_executor/models/nemotron_vl.py @@ -38,7 +38,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) IMG_START = '' IMG_END = '' @@ -576,20 +576,24 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - context_token_ids = [self.img_context_token_id] - assert len(context_token_ids) >= 1 + if multimodal_embeddings is not None and len( + multimodal_embeddings) > 0: self._set_visual_token_mask(input_ids) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - context_token_ids, - ) - return inputs_embeds + + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward( self, @@ -608,8 +612,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.img_context_token_id, + ) input_ids = None forward_kwargs = { diff --git a/vllm/model_executor/models/olmo2.py b/vllm/model_executor/models/olmo2.py index 2e0b1fb2a13f..e7e30ee8df0f 100644 --- a/vllm/model_executor/models/olmo2.py +++ b/vllm/model_executor/models/olmo2.py @@ -295,6 +295,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): make_empty_intermediate_tensors_factory(["hidden_states"], self.config.hidden_size)) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + def forward( self, input_ids: torch.Tensor, @@ -408,6 +411,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/ovis.py b/vllm/model_executor/models/ovis.py index bd525b6780e0..8503d3f71d1c 100644 --- a/vllm/model_executor/models/ovis.py +++ b/vllm/model_executor/models/ovis.py @@ -48,7 +48,6 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP -from .utils import merge_multimodal_embeddings # Cannot find the following number from hf config. IMAGE_TOKEN = "" @@ -501,19 +500,6 @@ def get_multimodal_embeddings(self, return image_features - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.llm.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.image_pad_token_id) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -529,8 +515,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.image_pad_token_id, + ) input_ids = None # up until here we have an inputs_embeds 100% numerical identity diff --git a/vllm/model_executor/models/ovis2_5.py b/vllm/model_executor/models/ovis2_5.py index f18e38ce154d..2ecc7bff07e0 100644 --- a/vllm/model_executor/models/ovis2_5.py +++ b/vllm/model_executor/models/ovis2_5.py @@ -585,17 +585,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.llm.get_input_embeddings(input_ids) - if multimodal_embeddings is not None: - tmp = torch.concat(multimodal_embeddings, dim=0) - inputs_embeds[input_ids == self.image_pad_token_id] = tmp - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -612,8 +601,11 @@ def forward( elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.image_pad_token_id, + ) input_ids = None # up until here we have a inputs_embeds 100% numerical identity diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py index aef510230461..f07f444819f4 100644 --- a/vllm/model_executor/models/paligemma.py +++ b/vllm/model_executor/models/paligemma.py @@ -26,8 +26,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vision_encoder_info logger = init_logger(__name__) @@ -362,19 +361,6 @@ def get_multimodal_embeddings(self, vision_embeddings = vision_embeddings * (self.config.hidden_size**-0.5) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.config.image_token_index) - return inputs_embeds - def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, @@ -388,8 +374,11 @@ def forward(self, # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index a2b201fe4228..ea34c8d92f13 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -51,9 +51,9 @@ from .clip import CLIPVisionModel from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP, SupportsQuant) -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) +from .utils import (AutoWeightsLoader, WeightsMapper, + _merge_multimodal_embeddings, flatten_bn, + init_vllm_registered_model, maybe_prefix) logger = init_logger(__name__) @@ -643,14 +643,31 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.embed_tokens(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.image_token_id) - return inputs_embeds + inputs_embeds = self._get_text_embeddings( + input_ids, + self.embed_tokens, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) + + if multimodal_embeddings is None or len(multimodal_embeddings) == 0: + return inputs_embeds + + if is_multimodal is None: + raise ValueError( + "`get_input_embeddings` now requires `is_multimodal` arg, " + "please update your model runner according to " + "https://github.com/vllm-project/vllm/pull/16229.") + + return _merge_multimodal_embeddings( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + ) def forward(self, input_ids: torch.Tensor, @@ -666,8 +683,11 @@ def forward(self, # condition is for v0 compatibility elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=self.image_token_id, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/phi4_multimodal.py b/vllm/model_executor/models/phi4_multimodal.py index bdc831354c11..e8b79717d75d 100644 --- a/vllm/model_executor/models/phi4_multimodal.py +++ b/vllm/model_executor/models/phi4_multimodal.py @@ -1342,12 +1342,12 @@ def _process_image_input( image_attention_mask) return image_embeds - def get_multimodal_embeddings( - self, **kwargs: object) -> Optional[MultiModalEmbeddings]: + def get_multimodal_embeddings(self, + **kwargs: object) -> MultiModalEmbeddings: modalities = self._parse_and_validate_multimodal_inputs(**kwargs) if not modalities: - return None + return [] # The result multimodal_embeddings is tuple of tensors, with each # tensor corresponding to a multimodal data item (image or video). @@ -1371,18 +1371,6 @@ def get_multimodal_embeddings( return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - [_IMAGE_PLACEHOLDER_TOKEN_ID, _AUDIO_PLACEHOLDER_TOKEN_ID]) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/phi4mm.py b/vllm/model_executor/models/phi4mm.py index 47b5ad55ab2d..15b09c7ae2bc 100644 --- a/vllm/model_executor/models/phi4mm.py +++ b/vllm/model_executor/models/phi4mm.py @@ -1151,7 +1151,6 @@ def get_multimodal_embeddings(self, modalities = self._parse_and_validate_multimodal_inputs(**kwargs) if not modalities: return [] - return None # The result multimodal_embeddings is tuple of tensors, with each # tensor corresponding to a multimodal data item (image or video). @@ -1175,19 +1174,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.model.embed_tokens(input_ids) - if multimodal_embeddings is not None and len( - multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - [_IMAGE_PLACEHOLDER_TOKEN_ID, _AUDIO_PLACEHOLDER_TOKEN_ID]) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index 7b197844c8b6..2c04b6f0f4f9 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -50,8 +50,7 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP -from .utils import (flatten_bn, init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) +from .utils import flatten_bn, init_vllm_registered_model, maybe_prefix from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs try: @@ -433,22 +432,6 @@ def get_multimodal_embeddings(self, return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.vision_args.image_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -465,8 +448,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.vision_args.image_token_id, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/qwen2_5_omni_thinker.py b/vllm/model_executor/models/qwen2_5_omni_thinker.py index 5f27230c913b..bfa398ee43b5 100644 --- a/vllm/model_executor/models/qwen2_5_omni_thinker.py +++ b/vllm/model_executor/models/qwen2_5_omni_thinker.py @@ -865,24 +865,26 @@ def get_multimodal_embeddings(self, multimodal_embeddings += audio_embeddings return multimodal_embeddings + # TODO (ywang96): support overlapping modality embeddings so that + # `use_audio_in_video` will work on V1. def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - - # TODO (ywang96): support overlapping modality embeddings so that - # `use_audio_in_video` will work on V1. - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, [ - self.config.image_token_index, - self.config.video_token_index, - self.config.audio_token_index - ]) - return inputs_embeds + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def get_multimodal_embeddings_v0( self, **kwargs: object) -> Optional[NestedTensors]: diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py index adb21373056c..5b092b42205f 100644 --- a/vllm/model_executor/models/qwen2_5_vl.py +++ b/vllm/model_executor/models/qwen2_5_vl.py @@ -1365,19 +1365,6 @@ def get_multimodal_embeddings(self, multimodal_embeddings += video_embeddings return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - [self.config.image_token_id, self.config.video_token_id]) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/qwen2_audio.py b/vllm/model_executor/models/qwen2_audio.py index 762ab42e5929..9dfa29eef5ce 100644 --- a/vllm/model_executor/models/qwen2_audio.py +++ b/vllm/model_executor/models/qwen2_audio.py @@ -49,8 +49,7 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP -from .utils import (AutoWeightsLoader, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix # # === Audio Inputs === # @@ -438,19 +437,6 @@ def get_multimodal_embeddings(self, masked_audio_features = self._process_audio_input(audio_input) return masked_audio_features - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.config.audio_token_index) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -467,8 +453,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - multimodal_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + multimodal_embeddings, + is_multimodal=input_ids == self.config.audio_token_index, + ) input_ids = None hidden_states = self.language_model.model(input_ids, diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index d4e195246bf1..8192c3ce05dd 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -1459,19 +1459,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - [self.config.image_token_id, self.config.video_token_id]) - return inputs_embeds - def get_input_embeddings_v0( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index f1aeb99a4d37..5d0b66f91ace 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -79,7 +79,8 @@ from .qwen2_vl import Qwen2VLProcessingInfo from .qwen3 import Qwen3ForCausalLM, Qwen3Model from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper, - maybe_prefix, merge_multimodal_embeddings) + _merge_multimodal_embeddings, maybe_prefix, + merge_multimodal_embeddings) from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model logger = init_logger(__name__) @@ -1324,17 +1325,22 @@ def get_multimodal_embeddings( return multimodal_embeddings def _compute_deepstack_embeds( - self, input_ids: torch.Tensor, inputs_embeds: torch.Tensor, - multimodal_embeddings: MultiModalEmbeddings) -> torch.Tensor: - visual_lens = [ - x.shape[0] if isinstance(x, torch.Tensor) else len(x) - for x in multimodal_embeddings - ] + self, + inputs_embeds: torch.Tensor, + multimodal_embeddings: MultiModalEmbeddings, + is_multimodal: torch.Tensor, + ) -> tuple[torch.Tensor, MultiModalEmbeddings]: + visual_lens = [len(x) for x in multimodal_embeddings] multimodal_embeddings_cat = torch.cat(multimodal_embeddings, dim=0) - multimodal_embeddings_main, multimodal_embeddings_multiscale = torch.split( # noqa:E501 - multimodal_embeddings_cat, [self.visual_dim, self.multiscale_dim], - dim=-1) + ( + multimodal_embeddings_main, + multimodal_embeddings_multiscale, + ) = torch.split( + multimodal_embeddings_cat, + [self.visual_dim, self.multiscale_dim], + dim=-1, + ) multimodal_embeddings = torch.split(multimodal_embeddings_main, visual_lens, @@ -1346,39 +1352,62 @@ def _compute_deepstack_embeds( inputs_embeds.size(0), self.deepstack_num_level * inputs_embeds.size(1)) - deepstack_input_embeds = merge_multimodal_embeddings( - input_ids, - deepstack_input_embeds, - multimodal_embeddings_multiscale, - placeholder_token_id=[ - self.config.image_token_id, self.config.video_token_id - ], + deepstack_input_embeds = _merge_multimodal_embeddings( + inputs_embeds=deepstack_input_embeds, + multimodal_embeddings=multimodal_embeddings_multiscale, + is_multimodal=is_multimodal, ) deepstack_input_embeds = deepstack_input_embeds.view( inputs_embeds.shape[0], self.deepstack_num_level, self.visual_dim) deepstack_input_embeds = deepstack_input_embeds.permute(1, 0, 2) + return deepstack_input_embeds, multimodal_embeddings def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - deepstack_input_embeds = None - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None: - if self.use_deepstack: - deepstack_input_embeds, multimodal_embeddings = self._compute_deepstack_embeds( # noqa:E501 - input_ids, inputs_embeds, multimodal_embeddings) - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - [self.config.image_token_id, self.config.video_token_id]) + inputs_embeds = self._get_text_embeddings( + input_ids, + self.language_model.get_input_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) + + if multimodal_embeddings is None or len(multimodal_embeddings) == 0: + return inputs_embeds + + if is_multimodal is None: + raise ValueError( + "`get_input_embeddings` now requires `is_multimodal` arg, " + "please update your model runner according to " + "https://github.com/vllm-project/vllm/pull/16229.") if self.use_deepstack: - if deepstack_input_embeds is None: - deepstack_input_embeds = torch.zeros_like( - inputs_embeds).unsqueeze(0).repeat( - self.deepstack_num_level, 1, 1).contiguous() + ( + deepstack_input_embeds, + multimodal_embeddings, + ) = self._compute_deepstack_embeds( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + ) + else: + deepstack_input_embeds = None + + inputs_embeds = _merge_multimodal_embeddings( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + ) + + if deepstack_input_embeds is not None: + deepstack_input_embeds = torch.zeros_like(inputs_embeds).unsqueeze( + 0).repeat(self.deepstack_num_level, 1, 1).contiguous() self._set_deepstack_input_embeds(deepstack_input_embeds) return inputs_embeds diff --git a/vllm/model_executor/models/qwen_vl.py b/vllm/model_executor/models/qwen_vl.py index 90200f319464..dc11b60604a9 100644 --- a/vllm/model_executor/models/qwen_vl.py +++ b/vllm/model_executor/models/qwen_vl.py @@ -45,7 +45,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .qwen import QWenBaseModel, QWenModel -from .utils import flatten_bn, merge_multimodal_embeddings +from .utils import flatten_bn class QwenImagePixelInputs(TensorSchema): @@ -756,21 +756,6 @@ def get_multimodal_embeddings(self, vision_embeddings = self._process_image_input(image_input) return vision_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.transformer.get_input_embeddings(input_ids) - - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.transformer.visual.image_pad_id) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -786,8 +771,12 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == + self.transformer.visual.image_pad_id, + ) input_ids = None hidden_states = self.transformer(input_ids, positions, diff --git a/vllm/model_executor/models/roberta.py b/vllm/model_executor/models/roberta.py index ba405be41687..53e698c4fa80 100644 --- a/vllm/model_executor/models/roberta.py +++ b/vllm/model_executor/models/roberta.py @@ -218,6 +218,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): loader = AutoWeightsLoader(self) return loader.load_weights(weights, mapper=self.jina_to_vllm_mapper) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.roberta.get_input_embeddings(input_ids) + def forward( self, input_ids: Optional[torch.Tensor], diff --git a/vllm/model_executor/models/skyworkr1v.py b/vllm/model_executor/models/skyworkr1v.py index 893ce4497c31..f9a107c06085 100644 --- a/vllm/model_executor/models/skyworkr1v.py +++ b/vllm/model_executor/models/skyworkr1v.py @@ -38,7 +38,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) IMG_START = '' IMG_END = '' @@ -842,19 +842,24 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - assert self.img_context_token_id is not None + if multimodal_embeddings is not None and len( + multimodal_embeddings) > 0: self._set_visual_token_mask(input_ids) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.img_context_token_id, - ) - return inputs_embeds + + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward( self, @@ -873,8 +878,11 @@ def forward( # condition is for v0 compatibility. elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.img_context_token_id, + ) input_ids = None forward_kwargs = { diff --git a/vllm/model_executor/models/solar.py b/vllm/model_executor/models/solar.py index c774171b9dcd..c5b82b0ca4a0 100644 --- a/vllm/model_executor/models/solar.py +++ b/vllm/model_executor/models/solar.py @@ -483,6 +483,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py index 0cce0c78f8dc..0fe723d59483 100644 --- a/vllm/model_executor/models/step3_text.py +++ b/vllm/model_executor/models/step3_text.py @@ -395,6 +395,9 @@ def __init__( self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, diff --git a/vllm/model_executor/models/step3_vl.py b/vllm/model_executor/models/step3_vl.py index 5f6ad5885043..ad295ef44732 100644 --- a/vllm/model_executor/models/step3_vl.py +++ b/vllm/model_executor/models/step3_vl.py @@ -25,7 +25,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, - MultiModalKwargsItems, NestedTensors) + MultiModalKwargsItems) from vllm.multimodal.parse import ImageSize, MultiModalDataItems from vllm.multimodal.processing import (BaseMultiModalProcessor, BaseProcessingInfo, PromptReplacement, @@ -37,8 +37,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import run_dp_sharded_vision_model @@ -996,10 +995,13 @@ def _process_image_input( 1 else cur_feature[0]) return merged_image_features - def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]: + def get_language_model(self) -> torch.nn.Module: + return self.language_model + + def get_multimodal_embeddings(self, **kwargs) -> MultiModalEmbeddings: image_input = self._parse_and_validate_image_input(**kwargs) if image_input is None: - return None + return [] vision_embeddings = self._process_image_input(image_input) return vision_embeddings @@ -1007,24 +1009,21 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + # Multi-modal token ID may exceed vocab size + handle_oov_mm_token: bool = True, ) -> torch.Tensor: - if multimodal_embeddings is None: - inputs_embeds = self.language_model.model.get_input_embeddings( - input_ids) - else: - is_text = input_ids != self.config.image_token_id - text_ids = input_ids[is_text] - text_embeds = self.language_model.model.get_input_embeddings( - text_ids) - inputs_embeds = torch.empty(input_ids.shape[0], - text_embeds.shape[-1], - dtype=text_embeds.dtype, - device=text_embeds.device) - inputs_embeds[is_text] = text_embeds - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.config.image_token_id) - return inputs_embeds + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward( self, @@ -1038,10 +1037,11 @@ def forward( inputs_embeds = None elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - # always pass the input via `inputs_embeds` - # to make sure the computation graph is consistent - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_id, + ) input_ids = None hidden_states = self.language_model(input_ids, diff --git a/vllm/model_executor/models/tarsier.py b/vllm/model_executor/models/tarsier.py index 3660efdc079a..1145bea41480 100644 --- a/vllm/model_executor/models/tarsier.py +++ b/vllm/model_executor/models/tarsier.py @@ -40,7 +40,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) from .vision import VisionEncoderInfo, get_vision_encoder_info @@ -589,22 +589,6 @@ def get_multimodal_embeddings(self, return [] return self._process_image_input(image_input) - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None \ - and len(multimodal_embeddings) != 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - multimodal_embeddings, - self.config.image_token_index, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -617,8 +601,11 @@ def forward( inputs_embeds = None elif inputs_embeds is None: vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - vision_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + vision_embeddings, + is_multimodal=input_ids == self.config.image_token_index, + ) input_ids = None hidden_states = self.language_model.model( input_ids=input_ids, diff --git a/vllm/model_executor/models/terratorch.py b/vllm/model_executor/models/terratorch.py index b9dfa8e9b6f5..938b02e3e04b 100644 --- a/vllm/model_executor/models/terratorch.py +++ b/vllm/model_executor/models/terratorch.py @@ -233,6 +233,9 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: # We do not really use any input tokens and therefore no embeddings # to be calculated. However, due to the mandatory token ids in diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py index 19dd242f16eb..3d7b06633f34 100644 --- a/vllm/model_executor/models/transformers.py +++ b/vllm/model_executor/models/transformers.py @@ -52,8 +52,8 @@ from vllm.sequence import IntermediateTensors from vllm.utils import is_list_of -from .interfaces import (SupportsLoRA, SupportsMultiModal, SupportsPP, - SupportsQuant) +from .interfaces import (MultiModalEmbeddings, SupportsLoRA, + SupportsMultiModal, SupportsPP, SupportsQuant) from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper, flatten_bn, make_empty_intermediate_tensors_factory, maybe_prefix) @@ -797,6 +797,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): else: self.lm_head = PPMissingLayer() + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings()(input_ids) + def compute_logits( self, hidden_states: torch.Tensor, @@ -873,13 +876,19 @@ def forward( multimodal_embeds = self.get_multimodal_embeddings(**kwargs) if multimodal_embeds is not None: inputs_embeds = self.get_input_embeddings( - input_ids, multimodal_embeds) + input_ids, + multimodal_embeds, + is_multimodal=input_ids == self.config.image_token_id, + ) input_ids = None model_output = super().forward(input_ids, positions, intermediate_tensors, inputs_embeds) return model_output + def get_language_model(self) -> torch.nn.Module: + return self.model + def get_multimodal_embeddings(self, **kwargs): pixel_values = kwargs.pop("pixel_values", None) pixel_values = pixel_values if pixel_values is not None else kwargs.pop( @@ -934,15 +943,42 @@ def get_multimodal_embeddings(self, **kwargs): def get_input_embeddings( self, input_ids: torch.Tensor, - multimodal_embeddings=None, + multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: - inputs_embeds = self.model.get_input_embeddings()(input_ids) - if (multimodal_embeddings is not None - and len(multimodal_embeddings) != 0): - mask = (input_ids == self.config.image_token_id) - mask = mask.unsqueeze(-1).expand_as(inputs_embeds) - multimodal_embeddings = torch.cat(multimodal_embeddings) - - inputs_embeds = inputs_embeds.masked_scatter( - mask, multimodal_embeddings) - return inputs_embeds + """ + Apply token embeddings to `input_ids`. + + If `multimodal_embeddings` is passed, scatter them into + `input_ids` according to the mask `is_multimodal`. + + In case the multi-modal token IDs exceed the vocabulary size of + the language model, you can set `handle_oov_mm_token=False` + to avoid calling the language model's `get_input_embeddings` method + on those tokens. + """ + from .utils import _merge_multimodal_embeddings + + inputs_embeds = self._get_text_embeddings( + input_ids, + self.model.get_input_embeddings(), + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) + + if multimodal_embeddings is None or len(multimodal_embeddings) == 0: + return inputs_embeds + + if is_multimodal is None: + raise ValueError( + "`get_input_embeddings` now requires `is_multimodal` arg, " + "please update your model runner according to " + "https://github.com/vllm-project/vllm/pull/16229.") + + return _merge_multimodal_embeddings( + inputs_embeds=inputs_embeds, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + ) diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py index 12ae9487ad9d..77e886c22e63 100644 --- a/vllm/model_executor/models/ultravox.py +++ b/vllm/model_executor/models/ultravox.py @@ -33,8 +33,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) _AUDIO_PLACEHOLDER_OVERRIDE = "<|audio|>" _MAX_ENCODER_BATCH_SIZE = 16 @@ -555,19 +554,21 @@ def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + # Multi-modal token ID may exceed vocab size + handle_oov_mm_token: bool = True, ) -> torch.Tensor: - # The audio token index is not included in the embedding table - # We need to remove it before embedding lookup - safe_input_ids = input_ids.clone() - safe_input_ids[safe_input_ids == self.config.audio_token_index] = 0 - inputs_embeds = self.language_model.get_input_embeddings( - safe_input_ids) - if multimodal_embeddings is not None and len( - multimodal_embeddings) > 0: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, - self.config.audio_token_index) - return inputs_embeds + # This is to satisfy the type checker for each overload + if multimodal_embeddings is None or is_multimodal is None: + return super().get_input_embeddings(input_ids) + + return super().get_input_embeddings( + input_ids, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, + handle_oov_mm_token=handle_oov_mm_token, + ) def forward(self, input_ids: torch.Tensor, @@ -601,8 +602,11 @@ def forward(self, elif inputs_embeds is None: multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - multimodal_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + multimodal_embeddings, + is_multimodal=input_ids == self.config.audio_token_index, + ) input_ids = None language_model = self.language_model diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index 51cd41c864f0..7b3f20c6b28a 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -4,7 +4,7 @@ import itertools from collections.abc import Iterable, Mapping from dataclasses import dataclass, field -from typing import Any, Callable, Literal, Optional, Protocol, Union, overload +from typing import Any, Literal, Optional, Protocol, Union, overload import torch import torch.nn as nn @@ -391,8 +391,8 @@ def _embedding_count_expression(embeddings: NestedTensors) -> str: def _merge_multimodal_embeddings( inputs_embeds: torch.Tensor, - is_multimodal: torch.Tensor, multimodal_embeddings: NestedTensors, + is_multimodal: torch.Tensor, ) -> torch.Tensor: """ Merge ``multimodal_embeddings`` into ``inputs_embeds`` by overwriting the @@ -402,61 +402,35 @@ def _merge_multimodal_embeddings( Note: This updates ``inputs_embeds`` in place. """ - flattened = _flatten_embeddings(multimodal_embeddings) + if len(multimodal_embeddings) == 0: + return inputs_embeds + + mm_embeds_flat = _flatten_embeddings(multimodal_embeddings) + input_dtype = inputs_embeds.dtype + try: - # This is equivalent to: inputs_embeds[is_multimodal] = flattened. + # For debugging + # inputs_embeds[is_multimodal] = mm_embeds_flat.to(dtype=input_dtype) + + # NOTE: This can avoid D2H sync (#22105), but fails to + # raise an error if is_multimodal.sum() < len(mm_embeds_flat) inputs_embeds.masked_scatter_(is_multimodal.unsqueeze(-1), - flattened.to(dtype=inputs_embeds.dtype)) + mm_embeds_flat.to(dtype=input_dtype)) except RuntimeError as e: + num_actual_tokens = len(mm_embeds_flat) num_expected_tokens = is_multimodal.sum().item() - assert isinstance(num_expected_tokens, int) - if flattened.shape[0] != num_expected_tokens: + if num_actual_tokens != num_expected_tokens: expr = _embedding_count_expression(multimodal_embeddings) + raise ValueError( - f"Attempted to assign {expr} = {flattened.shape[0]} " + f"Attempted to assign {expr} = {num_actual_tokens} " f"multimodal tokens to {num_expected_tokens} placeholders" ) from e - else: - raise ValueError("Error during masked scatter operation") from e - - return inputs_embeds - - -def embed_multimodal( - input_ids: torch.Tensor, - multimodal_token_id: int, - get_text_embeds: Callable[[torch.Tensor], torch.Tensor], - multimodal_embeds: NestedTensors, -) -> torch.Tensor: - """ - Embed token IDs and multimodal inputs and combine their embeddings. - - ``multimodal_token_id`` is used to determine whether a token ID should - be embedded using ``get_text_embeds`` or ``get_multimodal_embeds``. - Compared to ``merge_multimodal_embeddings`, this avoids running - ``get_text_embeds`` on ``input_ids[input_ids == multimodal_token_id]`` - which causes issues when the placeholder token ID exceeds the - vocabulary size of the language model. - """ - is_multimodal = input_ids == multimodal_token_id - is_text = ~is_multimodal - - text_embeds = get_text_embeds(input_ids[is_text]) - merged_embeds = torch.empty( - (input_ids.shape[0], text_embeds.shape[1]), - dtype=text_embeds.dtype, - device=text_embeds.device, - ) + raise ValueError("Error during masked scatter operation") from e - merged_embeds[is_text] = text_embeds - - return _merge_multimodal_embeddings( - merged_embeds, - is_multimodal, - multimodal_embeds, - ) + return inputs_embeds def merge_multimodal_embeddings( @@ -491,23 +465,29 @@ def merge_multimodal_embeddings( This updates ``inputs_embeds`` in place. """ if isinstance(placeholder_token_id, list): - placeholder_token_id = torch.tensor( - placeholder_token_id, - pin_memory=is_pin_memory_available()).to(device=input_ids.device, - non_blocking=True) - return _merge_multimodal_embeddings( - inputs_embeds, - torch.isin(input_ids, placeholder_token_id), - multimodal_embeddings, - ) + is_multimodal = isin_list(input_ids, placeholder_token_id) + else: + is_multimodal = (input_ids == placeholder_token_id) return _merge_multimodal_embeddings( inputs_embeds, - (input_ids == placeholder_token_id), - multimodal_embeddings, + multimodal_embeddings=multimodal_embeddings, + is_multimodal=is_multimodal, ) +def isin_list( + elements: torch.Tensor, + test_elements_list: list[int], +) -> torch.Tensor: + test_elements = torch.tensor( + test_elements_list, + pin_memory=is_pin_memory_available(), + ).to(device=elements.device, non_blocking=True) + + return torch.isin(elements, test_elements) + + class LayerFn(Protocol): def __call__(self, prefix: str) -> torch.nn.Module: diff --git a/vllm/model_executor/models/voxtral.py b/vllm/model_executor/models/voxtral.py index b33e8d09c4be..f93e7ccfd06f 100644 --- a/vllm/model_executor/models/voxtral.py +++ b/vllm/model_executor/models/voxtral.py @@ -45,10 +45,8 @@ from vllm.transformers_utils.tokenizer import (MistralTokenizer, cached_tokenizer_from_config) -from .interfaces import (MultiModalEmbeddings, SupportsLoRA, - SupportsMultiModal, SupportsTranscription) -from .utils import (flatten_bn, init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) +from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsTranscription +from .utils import flatten_bn, init_vllm_registered_model, maybe_prefix logger = init_logger(__name__) @@ -376,9 +374,14 @@ def forward( # NOTE: In v1, inputs_embeds is always generated at model runner, this # condition is for v0 compatibility. elif inputs_embeds is None: + audio_encoder = self.tokenizer.instruct.audio_encoder + audio_tok_id = audio_encoder.audio_token audio_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings(input_ids, - audio_embeddings) + inputs_embeds = self.get_input_embeddings( + input_ids, + audio_embeddings, + is_multimodal=input_ids == audio_tok_id, + ) input_ids = None hidden_states = self.language_model.model(input_ids, @@ -421,20 +424,6 @@ def get_multimodal_embeddings( return audio_embeddings - def get_input_embeddings( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[MultiModalEmbeddings] = None, - ) -> torch.Tensor: - audio_encoder = self.tokenizer.instruct.audio_encoder - audio_tok_id = audio_encoder.audio_token - - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is not None: - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, multimodal_embeddings, audio_tok_id) - return inputs_embeds - def _parse_and_validate_audio_arrays( self, **kwargs: object) -> Union[list[torch.Tensor], None]: audio_arrays = kwargs.pop("audio_arrays", None) diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py index de3e4f0592a6..7beeeddf988f 100644 --- a/vllm/model_executor/models/whisper.py +++ b/vllm/model_executor/models/whisper.py @@ -579,10 +579,7 @@ def forward( hidden_states = self.layer_norm(hidden_states) return hidden_states - def get_input_embeddings( - self, - input_ids: torch.Tensor, - ) -> torch.Tensor: + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) @@ -916,7 +913,10 @@ def get_multimodal_embeddings(self, def get_input_embeddings( self, input_ids: torch.Tensor, - multimodal_embeddings: Optional[NestedTensors] = None, + multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + *, + is_multimodal: Optional[torch.Tensor] = None, + handle_oov_mm_token: bool = False, ) -> torch.Tensor: # This method just returns the decoder sequence embeddings since # Whisper does not have encoder text tokens. diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index 51e54e0dc337..1b5bafb9ca1b 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -18,6 +18,7 @@ from vllm.model_executor.model_loader import get_model from vllm.model_executor.models import supports_multimodal from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM +from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.platforms import current_platform from vllm.utils import is_pin_memory_available from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata @@ -64,8 +65,10 @@ def __init__( # hidden size (e.g., Llama 3.3 70B). self.hidden_size = self.draft_model_config.get_hidden_size() - self.is_multimodal_model = vllm_config.model_config \ - .is_multimodal_model + # Multi-modal data support + self.mm_registry = MULTIMODAL_REGISTRY + self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs( + vllm_config.model_config) self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None @@ -175,7 +178,8 @@ def propose( last_token_indices: Optional[torch.Tensor], common_attn_metadata: CommonAttentionMetadata, sampling_metadata: SamplingMetadata, - mm_embeds: Optional[list[torch.Tensor]] = None, + mm_embed_inputs: Optional[tuple[list[torch.Tensor], + torch.Tensor]] = None, ) -> torch.Tensor: num_tokens = target_token_ids.shape[0] batch_size = next_token_ids.shape[0] @@ -219,18 +223,21 @@ def propose( # copy inputs to buffer for cudagraph self._set_positions(num_tokens, target_positions) self.hidden_states[:num_tokens] = target_hidden_states - if self.is_multimodal_model: - input_ids = self.input_ids[:num_tokens] - inputs_embeds = self.model.get_input_embeddings( - input_ids, - multimodal_embeddings=mm_embeds or None, + + if self.supports_mm_inputs: + mm_embeds, is_mm_embed = mm_embed_inputs or (None, None) + + self.inputs_embeds[:num_tokens] = self.model.get_input_embeddings( + self.input_ids[:num_tokens], + multimodal_embeddings=mm_embeds, + is_multimodal=is_mm_embed, ) - self.inputs_embeds[:num_tokens] = inputs_embeds - inputs_embeds = self.inputs_embeds[:num_input_tokens] + input_ids = None + inputs_embeds = self.inputs_embeds[:num_input_tokens] else: - inputs_embeds = None input_ids = self.input_ids[:num_input_tokens] + inputs_embeds = None with set_forward_context(per_layer_attn_metadata, self.vllm_config, @@ -372,14 +379,15 @@ def propose( self.input_ids[:batch_size] = input_ids self._set_positions(batch_size, clamped_positions) self.hidden_states[:batch_size] = hidden_states - if self.is_multimodal_model: - inputs_embeds = self.model.get_input_embeddings(input_ids) - self.inputs_embeds[:batch_size] = inputs_embeds - inputs_embeds = self.inputs_embeds[:input_batch_size] + if self.supports_mm_inputs: + self.inputs_embeds[:batch_size] = \ + self.model.get_input_embeddings(input_ids) + input_ids = None + inputs_embeds = self.inputs_embeds[:input_batch_size] else: - inputs_embeds = None input_ids = self.input_ids[:input_batch_size] + inputs_embeds = None # Run the model. with set_forward_context(per_layer_attn_metadata, @@ -849,7 +857,7 @@ def load_model(self, target_model: nn.Module) -> None: self.attn_layer_names = list(draft_attn_layer_names) - if self.is_multimodal_model: + if self.supports_mm_inputs: # Even if the target model is multimodal, we can also use # text-only draft models try: @@ -861,7 +869,7 @@ def load_model(self, target_model: nn.Module) -> None: logger.warning( "Draft model does not support multimodal inputs, " "falling back to text-only mode") - self.is_multimodal_model = False + self.supports_mm_inputs = False if supports_multimodal(target_model): # handle multimodality @@ -933,7 +941,7 @@ def dummy_run( ) -> None: with set_forward_context(None, self.vllm_config, num_tokens=num_tokens): - if self.is_multimodal_model: + if self.supports_mm_inputs: input_ids = None inputs_embeds = self.inputs_embeds[:num_tokens] else: diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 22a177dd7cc7..1bae0d4ce4d1 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -368,6 +368,11 @@ def __init__( self.num_accepted_tokens = self._make_buffer(self.max_num_reqs, dtype=torch.int64) + # Only relevant for multimodal models + if self.supports_mm_inputs: + self.is_mm_embed = self._make_buffer(self.max_num_tokens, + dtype=torch.bool) + # Only relevant for models using M-RoPE (e.g, Qwen2-VL) if self.uses_mrope: # NOTE: `mrope_positions` is implemented with one additional dummy @@ -1627,9 +1632,16 @@ def _gather_mm_embeddings( self, scheduler_output: "SchedulerOutput", shift_computed_tokens: int = 0, - ) -> list[torch.Tensor]: + ) -> tuple[list[torch.Tensor], torch.Tensor]: + total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens + + mm_embeds = list[torch.Tensor]() + is_mm_embed = self.is_mm_embed.cpu + is_mm_embed[:total_num_scheduled_tokens] = False + + req_start_idx = 0 should_sync_mrope_positions = False - mm_embeds: list[torch.Tensor] = [] + for req_id in self.input_batch.req_ids: mm_embeds_req: list[torch.Tensor] = [] @@ -1638,6 +1650,7 @@ def _gather_mm_embeddings( req_state = self.requests[req_id] num_computed_tokens = \ req_state.num_computed_tokens + shift_computed_tokens + for mm_feature in req_state.mm_features: pos_info = mm_feature.mm_position start_pos = pos_info.offset @@ -1670,6 +1683,10 @@ def _gather_mm_embeddings( if (is_embed := pos_info.is_embed) is not None: is_embed = is_embed[start_idx:end_idx] + req_start_pos = req_start_idx + start_pos - num_computed_tokens + is_mm_embed[req_start_pos+start_idx:req_start_pos + end_idx] \ + = True if is_embed is None else is_embed + mm_embeds_item = gather_mm_placeholders( encoder_output[start_idx:end_idx], is_embed=is_embed, @@ -1677,6 +1694,7 @@ def _gather_mm_embeddings( mm_embeds_req.append(mm_embeds_item) if self.is_multimodal_pruning_enabled and self.uses_mrope: + assert req_state.mrope_positions is not None should_sync_mrope_positions = True mm_embeds_req, new_mrope_positions, new_delta = ( self.model.recompute_mrope_positions( @@ -1685,18 +1703,19 @@ def _gather_mm_embeddings( mrope_positions=req_state.mrope_positions, num_computed_tokens=req_state.num_computed_tokens, )) - assert req_state.mrope_positions is not None req_state.mrope_positions.copy_(new_mrope_positions) req_state.mrope_position_delta = new_delta mm_embeds.extend(mm_embeds_req) + req_start_idx += num_scheduled_tokens + + is_mm_embed = self.is_mm_embed.copy_to_gpu(total_num_scheduled_tokens) if should_sync_mrope_positions: self._calc_mrope_positions(scheduler_output) - self.mrope_positions.copy_to_gpu( - scheduler_output.total_num_scheduled_tokens) + self.mrope_positions.copy_to_gpu(total_num_scheduled_tokens) - return mm_embeds + return mm_embeds, is_mm_embed def _extract_encoder_inputs( self, @@ -1990,14 +2009,16 @@ def _preprocess( and not self.model_config.is_encoder_decoder): # Run the multimodal encoder if any. self._execute_mm_encoder(scheduler_output) - mm_embeds = self._gather_mm_embeddings(scheduler_output) + mm_embeds, is_mm_embed = self._gather_mm_embeddings( + scheduler_output) # NOTE(woosuk): To unify token ids and soft tokens (vision # embeddings), we always use embeddings (rather than token ids) # as input to the multimodal model, even when the input is text. inputs_embeds_scheduled = self.model.get_input_embeddings( - input_ids=self.input_ids.gpu[:num_scheduled_tokens], - multimodal_embeddings=mm_embeds or None, + self.input_ids.gpu[:num_scheduled_tokens], + multimodal_embeddings=mm_embeds, + is_multimodal=is_mm_embed, ) # TODO(woosuk): Avoid the copy. Optimize. @@ -2586,10 +2607,14 @@ def propose_draft_token_ids( [h[token_indices] for h in aux_hidden_states], dim=-1) else: target_hidden_states = hidden_states[token_indices] - mm_embeds = None + if self.supports_mm_inputs: - mm_embeds = self._gather_mm_embeddings(scheduler_output, - shift_computed_tokens=1) + mm_embed_inputs = self._gather_mm_embeddings( + scheduler_output, + shift_computed_tokens=1, + ) + else: + mm_embed_inputs = None draft_token_ids = self.drafter.propose( target_token_ids=target_token_ids, @@ -2599,8 +2624,9 @@ def propose_draft_token_ids( last_token_indices=token_indices_to_sample, sampling_metadata=sampling_metadata, common_attn_metadata=common_attn_metadata, - mm_embeds=mm_embeds, + mm_embed_inputs=mm_embed_inputs, ) + return draft_token_ids def update_config(self, overrides: dict[str, Any]) -> None: diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py index a330f50875a8..2405f978ca73 100644 --- a/vllm/v1/worker/tpu_model_runner.py +++ b/vllm/v1/worker/tpu_model_runner.py @@ -263,6 +263,13 @@ def __init__( pin_memory=self.pin_memory) self.seq_lens_np = self.seq_lens_cpu.numpy() + # Only relevant for multimodal models + if self.supports_mm_inputs: + self.is_mm_embed_cpu = torch.zeros(self.max_num_tokens, + dtype=torch.bool, + device="cpu", + pin_memory=self.pin_memory) + # Range tensor with values [0 .. self.max_num_tokens - 1]. # Used to initialize positions / context_lens / seq_lens # Keep in int64 to avoid overflow with long context @@ -879,13 +886,22 @@ def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"): def _gather_mm_embeddings( self, scheduler_output: "SchedulerOutput", - ) -> list[torch.Tensor]: - mm_embeds: list[torch.Tensor] = [] + ) -> tuple[list[torch.Tensor], torch.Tensor]: + total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens + padded_total_num_scheduled_tokens = _get_padded_token_len( + self.num_tokens_paddings, total_num_scheduled_tokens) + + is_mm_embed = self.is_mm_embed_cpu + is_mm_embed[:padded_total_num_scheduled_tokens] = False + mm_embeds = list[torch.Tensor]() + req_start_idx = 0 + for req_id in self.input_batch.req_ids: num_scheduled_tokens = scheduler_output.num_scheduled_tokens[ req_id] req_state = self.requests[req_id] num_computed_tokens = req_state.num_computed_tokens + # TODO unroll loop and assume/enforce --disable_chunked_mm_input # NOTE (NickLucche) here we diverge from logic in other runners, as # we assume to only have whole mm items to process. Hence we avoid @@ -906,26 +922,53 @@ def _gather_mm_embeddings( # The encoder output is already processed and stored # in the decoder's KV cache. continue + + start_idx = max(num_computed_tokens - start_pos, 0) + end_idx = min( + num_computed_tokens - start_pos + num_scheduled_tokens, + num_encoder_tokens, + ) + assert start_idx < end_idx + mm_hash = mm_feature.identifier encoder_output = self.encoder_cache.get(mm_hash, None) assert encoder_output is not None,\ f"Encoder cache miss for {mm_hash}." + assert pos_info.is_embed is None, "Expected all positions to"\ " be contiguous and embeddings." - encoder_output = self.encoder_cache[mm_hash] + + req_start_pos = req_start_idx + start_pos - num_computed_tokens + is_mm_embed[req_start_pos+start_idx:req_start_pos + end_idx] \ + = True + + # Only whole mm items are processed mm_embeds.append(encoder_output) - return mm_embeds - def _get_model_inputs(self, input_ids: torch.Tensor, - mm_embeds: list[torch.Tensor]): + req_start_idx += num_scheduled_tokens + + is_mm_embed = is_mm_embed[:padded_total_num_scheduled_tokens] \ + .to(self.device) + + return mm_embeds, is_mm_embed + + def _get_model_inputs( + self, + input_ids: torch.Tensor, + mm_embed_inputs: Optional[tuple[list[torch.Tensor], torch.Tensor]], + ): if self.supports_mm_inputs: + mm_embeds, is_mm_embed = mm_embed_inputs or (None, None) + # NOTE(woosuk): To unify token ids and soft tokens (vision # embeddings), we always use embeddings (rather than token ids) # as input to the multimodal model, even when the input is text. inputs_embeds = self.model.get_input_embeddings( - input_ids=input_ids, + input_ids, multimodal_embeddings=mm_embeds, + is_multimodal=is_mm_embed, ) + return None, inputs_embeds else: # For text-only models, we use token ids as input. @@ -953,9 +996,10 @@ def execute_model( if self.supports_mm_inputs: # Run the multimodal encoder if any. self._execute_mm_encoder(scheduler_output) - mm_embeds = self._gather_mm_embeddings(scheduler_output) + mm_embed_inputs = self._gather_mm_embeddings(scheduler_output) else: - mm_embeds = [] + mm_embed_inputs = None + torch_xla.sync(wait=False) # Prepare inputs, the requests might be split into multiple # executions, combine the result of each execution. @@ -972,7 +1016,7 @@ def execute_model( attn_metadata, logits_indices, padded_num_reqs, num_reqs,\ end_index = self._prepare_inputs(scheduler_output, start_index) input_ids, inputs_embeds = self._get_model_inputs( - self.input_ids, mm_embeds) + self.input_ids, mm_embed_inputs) torch_xla.sync(wait=False) # Run the decoder with set_forward_context( @@ -1325,9 +1369,15 @@ def _precompile_mm_encoder(self) -> None: hf_config.image_token_index placeholders_ids = placeholders_ids.to(self.device) + + mm_mask = torch.tensor([False] * num_tokens) + mm_mask[:items_size] = True + mm_mask = mm_mask.to(self.device) # Assign outputs or the graph will be cut short. - a, b = self._get_model_inputs(placeholders_ids, - [mm_embeds]) + a, b = self._get_model_inputs( + placeholders_ids, + mm_embed_inputs=([mm_embeds], mm_mask), + ) assert a is None torch_xla.sync(wait=False) @@ -1338,7 +1388,10 @@ def _precompile_mm_encoder(self) -> None: dtype=torch.int32, device="cpu") placeholders_ids = placeholders_ids.to(self.device) - a, b = self._get_model_inputs(placeholders_ids, []) + a, b = self._get_model_inputs( + placeholders_ids, + mm_embed_inputs=None, + ) assert a is None torch_xla.sync(wait=False) From cdebe9e54ed43ff24ef6e351547d7d0dc468dd83 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Sat, 27 Sep 2025 06:09:26 -0400 Subject: [PATCH 046/166] Validate API tokens in constant time (#25781) Signed-off-by: rentianyue-jk Signed-off-by: Russell Bryant Co-authored-by: rentianyue-jk --- vllm/entrypoints/openai/api_server.py | 28 +++++++++++++++++++++++---- 1 file changed, 24 insertions(+), 4 deletions(-) diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index 97cbda63bf42..d054e2826744 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -3,12 +3,14 @@ import asyncio import gc +import hashlib import importlib import inspect import json import multiprocessing import multiprocessing.forkserver as forkserver import os +import secrets import signal import socket import tempfile @@ -1252,7 +1254,7 @@ def load_log_config(log_config_file: Optional[str]) -> Optional[dict]: class AuthenticationMiddleware: """ Pure ASGI middleware that authenticates each request by checking - if the Authorization header exists and equals "Bearer {api_key}". + if the Authorization Bearer token exists and equals anyof "{api_key}". Notes ----- @@ -1263,7 +1265,26 @@ class AuthenticationMiddleware: def __init__(self, app: ASGIApp, tokens: list[str]) -> None: self.app = app - self.api_tokens = {f"Bearer {token}" for token in tokens} + self.api_tokens = [ + hashlib.sha256(t.encode("utf-8")).digest() for t in tokens + ] + + def verify_token(self, headers: Headers) -> bool: + authorization_header_value = headers.get("Authorization") + if not authorization_header_value: + return False + + scheme, _, param = authorization_header_value.partition(" ") + if scheme.lower() != "bearer": + return False + + param_hash = hashlib.sha256(param.encode("utf-8")).digest() + + token_match = False + for token_hash in self.api_tokens: + token_match |= secrets.compare_digest(param_hash, token_hash) + + return token_match def __call__(self, scope: Scope, receive: Receive, send: Send) -> Awaitable[None]: @@ -1276,8 +1297,7 @@ def __call__(self, scope: Scope, receive: Receive, url_path = URL(scope=scope).path.removeprefix(root_path) headers = Headers(scope=scope) # Type narrow to satisfy mypy. - if url_path.startswith("/v1") and headers.get( - "Authorization") not in self.api_tokens: + if url_path.startswith("/v1") and not self.verify_token(headers): response = JSONResponse(content={"error": "Unauthorized"}, status_code=401) return response(scope, receive, send) From 04df893a720df0df959eb883c457196338b290d4 Mon Sep 17 00:00:00 2001 From: Russell Bryant Date: Sat, 27 Sep 2025 06:46:49 -0400 Subject: [PATCH 047/166] Add filtering for chat template kwargs (#25794) Signed-off-by: Isotr0py Co-authored-by: Isotr0py --- tests/entrypoints/test_chat_utils.py | 85 +++++++++++++++++++++++++ vllm/entrypoints/chat_utils.py | 54 +++++++++++++++- vllm/entrypoints/openai/api_server.py | 1 + vllm/entrypoints/openai/cli_args.py | 10 ++- vllm/entrypoints/openai/serving_chat.py | 14 +++- 5 files changed, 158 insertions(+), 6 deletions(-) diff --git a/tests/entrypoints/test_chat_utils.py b/tests/entrypoints/test_chat_utils.py index 78370d199b56..a268f573ef90 100644 --- a/tests/entrypoints/test_chat_utils.py +++ b/tests/entrypoints/test_chat_utils.py @@ -19,6 +19,7 @@ parse_chat_messages, parse_chat_messages_futures, resolve_chat_template_content_format, + resolve_chat_template_kwargs, resolve_hf_chat_template) from vllm.multimodal import MultiModalDataDict, MultiModalUUIDDict from vllm.multimodal.utils import (encode_audio_base64, encode_image_base64, @@ -37,6 +38,7 @@ QWEN2VL_MODEL_ID = "Qwen/Qwen2-VL-2B-Instruct" QWEN25VL_MODEL_ID = "Qwen/Qwen2.5-VL-3B-Instruct" QWEN25OMNI_MODEL_ID = "Qwen/Qwen2.5-Omni-7B" +QWEN3_MODEL_ID = "Qwen/Qwen3-8B" LLAMA_GUARD_MODEL_ID = "meta-llama/Llama-Guard-3-1B" HERMES_MODEL_ID = "NousResearch/Hermes-3-Llama-3.1-8B" MISTRAL_MODEL_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" @@ -2255,6 +2257,89 @@ def test_resolve_hf_chat_template(sample_json_schema, model, use_tools): assert isinstance(chat_template, str) +@pytest.mark.parametrize( + "model, expected_kwargs", + [ + ( + QWEN2VL_MODEL_ID, + { + "add_vision_id", "add_generation_prompt", + "continue_final_message", "tools" + }, + ), + ( + QWEN3_MODEL_ID, + { + "enable_thinking", "add_generation_prompt", + "continue_final_message", "tools" + }, + ), + ], +) +def test_resolve_hf_chat_template_kwargs(sample_json_schema, model, + expected_kwargs): + """checks that chat_template is a dict type for HF models.""" + model_info = HF_EXAMPLE_MODELS.find_hf_info(model) + model_info.check_available_online(on_fail="skip") + + tools = ([{ + "type": "function", + "function": { + "name": "dummy_function_name", + "description": "This is a dummy function", + "parameters": sample_json_schema, + }, + }]) + + chat_template_kwargs = { + # both unused + "unsed_kwargs_1": 123, + "unsed_kwargs_2": "abc", + # should not appear + "chat_template": "{% Hello world! %}", + # used by tokenizer + "continue_final_message": True, + "tools": tools, + # both used by Qwen2-VL and Qwen3 + "add_generation_prompt": True, + # only used by Qwen2-VL + "add_vision_id": True, + # only used by Qwen3 + "enable_thinking": True, + } + + model_config = ModelConfig( + model, + tokenizer=model_info.tokenizer or model, + tokenizer_mode=model_info.tokenizer_mode, + revision=model_info.revision, + trust_remote_code=model_info.trust_remote_code, + hf_overrides=model_info.hf_overrides, + skip_tokenizer_init=model_info.skip_tokenizer_init, + enforce_eager=model_info.enforce_eager, + dtype=model_info.dtype) + + # Build the tokenizer + tokenizer = get_tokenizer( + model, + trust_remote_code=model_config.trust_remote_code, + ) + + # Test detecting the tokenizer's chat_template + chat_template = resolve_hf_chat_template( + tokenizer, + chat_template=None, + tools=tools, + model_config=model_config, + ) + resolved_chat_template_kwargs = resolve_chat_template_kwargs( + tokenizer, + chat_template=chat_template, + chat_template_kwargs=chat_template_kwargs, + ) + assert set(resolved_chat_template_kwargs.keys()) == expected_kwargs + + # NOTE: Qwen2-Audio default chat template is specially defined inside # processor class instead of using `tokenizer_config.json` # yapf: disable diff --git a/vllm/entrypoints/chat_utils.py b/vllm/entrypoints/chat_utils.py index 4e1ecb9ed4c5..6b0ed23277d3 100644 --- a/vllm/entrypoints/chat_utils.py +++ b/vllm/entrypoints/chat_utils.py @@ -11,7 +11,12 @@ from typing import (Any, Callable, Generic, Literal, Optional, TypeVar, Union, cast) +import jinja2 +import jinja2.ext +import jinja2.meta import jinja2.nodes +import jinja2.parser +import jinja2.sandbox import transformers.utils.chat_template_utils as hf_chat_utils # yapf conflicts with isort for this block # yapf: disable @@ -50,7 +55,7 @@ # yapf: enable from vllm.transformers_utils.processor import cached_get_processor from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer -from vllm.utils import random_uuid +from vllm.utils import random_uuid, supports_kw logger = init_logger(__name__) @@ -1554,6 +1559,46 @@ def parse_chat_messages_futures( return conversation, mm_tracker.all_mm_data(), mm_tracker.all_mm_uuids() +# adapted from https://github.com/huggingface/transformers/blob/v4.56.2/src/transformers/utils/chat_template_utils.py#L398-L412 +# only preserve the parse function used to resolve chat template kwargs +class AssistantTracker(jinja2.ext.Extension): + tags = {"generation"} + + def parse(self, parser: jinja2.parser.Parser) -> jinja2.nodes.CallBlock: + lineno = next(parser.stream).lineno + body = parser.parse_statements(["name:endgeneration"], drop_needle=True) + call = self.call_method("_generation_support") + call_block = jinja2.nodes.CallBlock(call, [], [], body) + return call_block.set_lineno(lineno) + + +def resolve_chat_template_kwargs( + tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], + chat_template: str, + chat_template_kwargs: dict[str, Any], +) -> dict[str, Any]: + fn_kw = { + k for k in chat_template_kwargs + if supports_kw(tokenizer.apply_chat_template, k, allow_var_kwargs=False) + } + + env = jinja2.sandbox.ImmutableSandboxedEnvironment( + trim_blocks=True, + lstrip_blocks=True, + extensions=[AssistantTracker, jinja2.ext.loopcontrols], + ) + parsed_content = env.parse(chat_template) + template_vars = jinja2.meta.find_undeclared_variables(parsed_content) + + # We exclude chat_template from kwargs here, because + # chat template has been already resolved at this stage + unexpected_vars = {"chat_template"} + accept_vars = (fn_kw | template_vars) - unexpected_vars + return { + k: v for k, v in chat_template_kwargs.items() if k in accept_vars + } + + def apply_hf_chat_template( tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast], conversation: list[ConversationMessage], @@ -1579,12 +1624,17 @@ def apply_hf_chat_template( ) try: + resolved_kwargs = resolve_chat_template_kwargs( + tokenizer=tokenizer, + chat_template=hf_chat_template, + chat_template_kwargs=kwargs, + ) return tokenizer.apply_chat_template( conversation=conversation, # type: ignore[arg-type] tools=tools, # type: ignore[arg-type] chat_template=hf_chat_template, tokenize=tokenize, - **kwargs, + **resolved_kwargs, ) # External library exceptions can sometimes occur despite the framework's diff --git a/vllm/entrypoints/openai/api_server.py b/vllm/entrypoints/openai/api_server.py index d054e2826744..15844d3162fe 100644 --- a/vllm/entrypoints/openai/api_server.py +++ b/vllm/entrypoints/openai/api_server.py @@ -1716,6 +1716,7 @@ async def init_app_state( request_logger=request_logger, chat_template=resolved_chat_template, chat_template_content_format=args.chat_template_content_format, + trust_request_chat_template=args.trust_request_chat_template, return_tokens_as_token_ids=args.return_tokens_as_token_ids, enable_auto_tools=args.enable_auto_tool_choice, exclude_tools_when_tool_choice_none=args. diff --git a/vllm/entrypoints/openai/cli_args.py b/vllm/entrypoints/openai/cli_args.py index 1c2a6f58197d..a306c2bb7cb5 100644 --- a/vllm/entrypoints/openai/cli_args.py +++ b/vllm/entrypoints/openai/cli_args.py @@ -103,9 +103,13 @@ class FrontendArgs: chat_template_content_format: ChatTemplateContentFormatOption = "auto" """The format to render message content within a chat template. -* "string" will render the content as a string. Example: `"Hello World"` -* "openai" will render the content as a list of dictionaries, similar to OpenAI -schema. Example: `[{"type": "text", "text": "Hello world!"}]`""" + * "string" will render the content as a string. Example: `"Hello World"` + * "openai" will render the content as a list of dictionaries, similar to + OpenAI schema. Example: `[{"type": "text", "text": "Hello world!"}]`""" + trust_request_chat_template: bool = False + """Whether to trust the chat template provided in the request. If False, + the server will always use the chat template specified by `--chat-template` + or the ones from tokenizer.""" response_role: str = "assistant" """The role name to return if `request.add_generation_prompt=true`.""" ssl_keyfile: Optional[str] = None diff --git a/vllm/entrypoints/openai/serving_chat.py b/vllm/entrypoints/openai/serving_chat.py index 0780448ad733..ab4bf75102f4 100644 --- a/vllm/entrypoints/openai/serving_chat.py +++ b/vllm/entrypoints/openai/serving_chat.py @@ -68,6 +68,7 @@ def __init__( request_logger: Optional[RequestLogger], chat_template: Optional[str], chat_template_content_format: ChatTemplateContentFormatOption, + trust_request_chat_template: bool = False, return_tokens_as_token_ids: bool = False, reasoning_parser: str = "", enable_auto_tools: bool = False, @@ -89,6 +90,7 @@ def __init__( self.response_role = response_role self.chat_template = chat_template self.chat_template_content_format: Final = chat_template_content_format + self.trust_request_chat_template = trust_request_chat_template self.enable_log_outputs = enable_log_outputs # set up tool use @@ -220,6 +222,16 @@ async def create_chat_completion( if not self.use_harmony: # Common case. + request_chat_template = request.chat_template + chat_template_kwargs = request.chat_template_kwargs + if not self.trust_request_chat_template and ( + request_chat_template is not None or + (chat_template_kwargs and + chat_template_kwargs.get("chat_template") is not None)): + return self.create_error_response( + "Chat template is passed with request, but " + "--trust-request-chat-template is not set. " + "Refused request with untrusted chat template.") ( conversation, request_prompts, @@ -228,7 +240,7 @@ async def create_chat_completion( request, tokenizer, request.messages, - chat_template=request.chat_template or self.chat_template, + chat_template=request_chat_template or self.chat_template, chat_template_content_format=self. chat_template_content_format, add_generation_prompt=request.add_generation_prompt, From d85bd4678adb56b0537ddd78cdda4ed4b756eec4 Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Sat, 27 Sep 2025 13:18:20 +0100 Subject: [PATCH 048/166] Fix GPTQ model loading in Transformers backend (#25770) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Isotr0py --- tests/models/test_transformers.py | 10 +++++++--- vllm/model_executor/models/transformers.py | 22 +++++++++++++++++----- vllm/model_executor/models/utils.py | 7 +++++-- 3 files changed, 29 insertions(+), 10 deletions(-) diff --git a/tests/models/test_transformers.py b/tests/models/test_transformers.py index 1817d4aeee9f..e4b5e7c24453 100644 --- a/tests/models/test_transformers.py +++ b/tests/models/test_transformers.py @@ -100,10 +100,9 @@ def test_distributed( kwargs_test=kwargs) -@pytest.mark.skipif( - current_platform.is_rocm(), - reason="bitsandbytes quantization is currently not supported in rocm.") @pytest.mark.parametrize("model, quantization_kwargs", [ + ("TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ", {}), + ("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ", {}), ( "meta-llama/Llama-3.2-1B-Instruct", { @@ -121,6 +120,11 @@ def test_quantization( max_tokens: int, num_logprobs: int, ) -> None: + if (current_platform.is_rocm() + and quantization_kwargs.get("quantization", "") == "bitsandbytes"): + pytest.skip( + "bitsandbytes quantization is currently not supported in rocm.") + with vllm_runner( model, model_impl="auto", enforce_eager=True, **quantization_kwargs) as vllm_model: # type: ignore[arg-type] diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py index 3d7b06633f34..7cfb639f675d 100644 --- a/vllm/model_executor/models/transformers.py +++ b/vllm/model_executor/models/transformers.py @@ -447,7 +447,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.device_config: DeviceConfig = vllm_config.device_config self.model_config: ModelConfig = vllm_config.model_config self.parallel_config: ParallelConfig = vllm_config.parallel_config - self.quant_config: QuantizationConfig = vllm_config.quant_config + self.quant_config: Optional[ + QuantizationConfig] = vllm_config.quant_config self.pp_group = get_pp_group() self.pp_size = self.pp_group.world_size @@ -456,7 +457,18 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): # Weights to skip in `self.load_weights` self.skip_prefixes: list[str] = [] + """Skip loading weights whose qualname starts with these prefixes.""" self.skip_substrs: list[str] = [] + """Skip loading weights whose qualname contains these substrings.""" + self.ignore_unexpected_prefixes: list[str] = [] + """Ignore unexpected weights whose qualname starts with these prefixes. + """ + self.ignore_unexpected_suffixes: list[str] = [] + """Ignore unexpected weights whose qualname ends with these suffixes.""" + + # Skip loading extra bias for GPTQ models. + if self.quant_config and "gptq" in self.quant_config.get_name(): + self.ignore_unexpected_suffixes.append(".bias") # Set correct attn and init on "meta" to delay allocating GPU tensors # TODO: @raushan, use the public `model.set_attn_implementation()` @@ -563,9 +575,7 @@ def tensor_parallel(self): raise ValueError( f"{type(self.model)} does not support tensor parallel. {tip}") - def _tensor_parallel(module: nn.Module, - prefix: str = "", - tp_plan=None): + def _tensor_parallel(module: nn.Module, prefix: str, tp_plan=None): tp_plan = tp_plan or {} # If the current module is a PreTrainedModel, set the tp_plan for @@ -597,7 +607,7 @@ def _tensor_parallel(module: nn.Module, prefix=qual_name, tp_plan=tp_plan) - _tensor_parallel(self.model) + _tensor_parallel(self.model, prefix="model") def create_attention_instances( self, @@ -696,6 +706,8 @@ def load_weights(self, weights: Iterable[tuple[str, self, skip_prefixes=self.skip_prefixes, skip_substrs=self.skip_substrs, + ignore_unexpected_prefixes=self.ignore_unexpected_prefixes, + ignore_unexpected_suffixes=self.ignore_unexpected_suffixes, ) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index 7b3f20c6b28a..bb6a0bd02202 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -109,6 +109,7 @@ def __init__( skip_prefixes: Optional[list[str]] = None, skip_substrs: Optional[list[str]] = None, ignore_unexpected_prefixes: Optional[list[str]] = None, + ignore_unexpected_suffixes: Optional[list[str]] = None, ) -> None: super().__init__() @@ -116,6 +117,7 @@ def __init__( self.skip_prefixes = skip_prefixes or [] self.skip_substrs = skip_substrs or [] self.ignore_unexpected_prefixes = ignore_unexpected_prefixes or [] + self.ignore_unexpected_suffixes = ignore_unexpected_suffixes or [] # update default skip_substrs self.skip_substrs += self.ROTARY_EMBEDS_UNUSED_WEIGHTS @@ -149,8 +151,9 @@ def _can_skip(self, qualname: str) -> bool: or any(substr in qualname for substr in self.skip_substrs)) def _can_ignore_unexpected(self, qualname: str) -> bool: - return any( - qualname.startswith(p) for p in self.ignore_unexpected_prefixes) + iup = (qualname.startswith(p) for p in self.ignore_unexpected_prefixes) + ius = (qualname.endswith(s) for s in self.ignore_unexpected_suffixes) + return any(iup) or any(ius) def _load_param( self, From 2fcf6dc69f67ea07a44a05c773044fb3cc341ff5 Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Sat, 27 Sep 2025 10:13:11 -0400 Subject: [PATCH 049/166] [Bugfix] Fix triton import precommit failure (#25803) Signed-off-by: Tyler Michael Smith --- vllm/model_executor/layers/batch_invariant.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vllm/model_executor/layers/batch_invariant.py b/vllm/model_executor/layers/batch_invariant.py index ae2c842af698..c025d509d862 100644 --- a/vllm/model_executor/layers/batch_invariant.py +++ b/vllm/model_executor/layers/batch_invariant.py @@ -7,8 +7,8 @@ from typing import Any, Union import torch -import triton -import triton.language as tl + +from vllm.triton_utils import tl, triton def _matmul_launch_metadata(grid: Callable[..., Any], kernel: Any, From b106db1a3b12d49e358666827d8c832a1f84aafb Mon Sep 17 00:00:00 2001 From: Tyler Michael Smith Date: Sat, 27 Sep 2025 10:22:28 -0400 Subject: [PATCH 050/166] [Bugfix][WideEP] Apply TP Attn + EP MoE fix to other models (#24982) Signed-off-by: Tyler Michael Smith --- vllm/config/parallel.py | 18 +++ .../device_communicators/all2all.py | 122 ++++++++++++------ .../base_device_communicator.py | 28 ++-- .../device_communicators/cuda_communicator.py | 22 ++-- .../device_communicators/xpu_communicator.py | 16 ++- vllm/distributed/parallel_state.py | 17 ++- vllm/forward_context.py | 97 +++++++++----- vllm/model_executor/layers/fused_moe/layer.py | 119 +++++++++-------- vllm/model_executor/models/aria.py | 16 +-- vllm/model_executor/models/deepseek_v2.py | 58 +-------- vllm/model_executor/models/ernie_mtp.py | 16 +-- vllm/model_executor/models/glm4.py | 16 ++- vllm/model_executor/models/gpt_oss.py | 42 ++++-- vllm/model_executor/models/granitemoe.py | 38 ++++-- vllm/model_executor/models/llama.py | 25 ++-- vllm/model_executor/models/llama4.py | 44 ++++--- vllm/model_executor/models/llama4_eagle.py | 4 +- vllm/model_executor/models/llama_eagle.py | 8 +- vllm/model_executor/models/llama_eagle3.py | 27 ++-- vllm/model_executor/models/qwen3_moe.py | 60 +++++---- vllm/model_executor/models/qwen3_next.py | 70 +++++----- vllm/model_executor/models/qwen3_next_mtp.py | 6 +- vllm/model_executor/models/utils.py | 48 ++++++- 23 files changed, 541 insertions(+), 376 deletions(-) diff --git a/vllm/config/parallel.py b/vllm/config/parallel.py index f80eb1adc7fd..8b980458ddaf 100644 --- a/vllm/config/parallel.py +++ b/vllm/config/parallel.py @@ -279,6 +279,24 @@ def stateless_init_dp_group(self) -> ProcessGroup: assert last_exc is not None raise last_exc + # The all_reduce at the end of attention (during o_proj) means that + # inputs are replicated across each rank of the tensor parallel group. + # If using expert-parallelism with DeepEP All2All ops, replicated + # tokens results in useless duplicate computation and communication. + # + # In this case, ensure the input to the experts is sequence parallel + # to avoid the excess work. + # + # Not needed for pplx-kernels as it can handle duplicate input tokens. + @property + def use_sequence_parallel_moe(self) -> bool: + return (envs.VLLM_ALL2ALL_BACKEND + in ("allgather_reducescatter", "naive", + "deepep_high_throughput", "deepep_low_latency") + and self.enable_expert_parallel + and self.tensor_parallel_size > 1 + and self.data_parallel_size > 1) + @staticmethod def has_unfinished_dp(dp_group: ProcessGroup, has_unfinished: bool) -> bool: diff --git a/vllm/distributed/device_communicators/all2all.py b/vllm/distributed/device_communicators/all2all.py index 661ed939608a..bb3fd657facd 100644 --- a/vllm/distributed/device_communicators/all2all.py +++ b/vllm/distributed/device_communicators/all2all.py @@ -6,7 +6,7 @@ import torch.distributed as dist import vllm.envs as envs -from vllm.distributed import get_dp_group +from vllm.distributed import get_dp_group, get_ep_group from vllm.forward_context import get_forward_context from vllm.logger import init_logger from vllm.utils import has_deep_ep, has_pplx @@ -34,41 +34,60 @@ def __init__(self, cpu_group): super().__init__(cpu_group) def naive_multicast(self, x: torch.Tensor, - cu_tokens_across_dp_cpu: torch.Tensor): + cu_tokens_across_sp_cpu: torch.Tensor, + is_sequence_parallel: bool) -> torch.Tensor: assert (len(x.shape) == 2) - buffer = torch.empty((cu_tokens_across_dp_cpu[-1], x.size(1)), + buffer = torch.empty((cu_tokens_across_sp_cpu[-1], x.size(1)), device=x.device, dtype=x.dtype) - start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[ - self.dp_rank - 1] - end = cu_tokens_across_dp_cpu[self.dp_rank] + rank = self.rank if is_sequence_parallel else self.dp_rank + world_size = (self.world_size + if is_sequence_parallel else self.dp_world_size) + + start = 0 if rank == 0 else cu_tokens_across_sp_cpu[rank - 1] + end = cu_tokens_across_sp_cpu[rank] buffer[start:end, :].copy_(x) - for idx in range(self.dp_world_size): - start = 0 if idx == 0 else cu_tokens_across_dp_cpu[idx - 1] - end = cu_tokens_across_dp_cpu[idx] - self.dp_group.broadcast(buffer[start:end, :], idx) + for idx in range(world_size): + start = 0 if idx == 0 else cu_tokens_across_sp_cpu[idx - 1] + end = cu_tokens_across_sp_cpu[idx] + get_ep_group().broadcast(buffer[start:end, :], idx) return buffer - def dispatch(self, hidden_states: torch.Tensor, - router_logits: torch.Tensor): - sizes = get_forward_context( - ).dp_metadata.get_chunk_sizes_across_dp_rank() - hidden_states, router_logits = get_dp_group().all_gatherv( - [hidden_states, router_logits], - dim=0, - sizes=sizes, - ) - + def dispatch( + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: + sp_size = self.tp_group.world_size if is_sequence_parallel else 1 + dp_metadata = get_forward_context().dp_metadata + cu_tokens_across_sp_cpu = dp_metadata.cu_tokens_across_sp(sp_size) + + hidden_states = self.naive_multicast(hidden_states, + cu_tokens_across_sp_cpu, + is_sequence_parallel) + router_logits = self.naive_multicast(router_logits, + cu_tokens_across_sp_cpu, + is_sequence_parallel) return hidden_states, router_logits - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: - sizes = get_forward_context( - ).dp_metadata.get_chunk_sizes_across_dp_rank() - hidden_states = get_dp_group().reduce_scatterv(hidden_states, - dim=0, - sizes=sizes) + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: + + ep_rank = self.rank if is_sequence_parallel else self.dp_rank + + dp_metadata = get_forward_context().dp_metadata + sp_size = self.tp_group.world_size if is_sequence_parallel else 1 + cu_tokens_across_sp_cpu = dp_metadata.cu_tokens_across_sp(sp_size) + + start = 0 if ep_rank == 0 else cu_tokens_across_sp_cpu[ep_rank - 1] + end = cu_tokens_across_sp_cpu[ep_rank] + + all_hidden_states = get_ep_group().all_reduce(hidden_states) + hidden_states = all_hidden_states[start:end, :] return hidden_states def destroy(self): @@ -84,29 +103,40 @@ class AgRsAll2AllManager(All2AllManagerBase): def __init__(self, cpu_group): super().__init__(cpu_group) - def dispatch(self, hidden_states: torch.Tensor, - router_logits: torch.Tensor): + def dispatch( + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: """ Gather hidden_states and router_logits from all dp ranks. """ sizes = get_forward_context( ).dp_metadata.get_chunk_sizes_across_dp_rank() - hidden_states, router_logits = get_dp_group().all_gatherv( + + dist_group = get_ep_group() if is_sequence_parallel else get_dp_group() + assert sizes[dist_group.rank_in_group] == hidden_states.shape[0] + hidden_states, router_logits = dist_group.all_gatherv( [hidden_states, router_logits], dim=0, sizes=sizes, ) return hidden_states, router_logits - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: """ Reduce-scatter hidden_states across all dp ranks. """ sizes = get_forward_context( ).dp_metadata.get_chunk_sizes_across_dp_rank() - hidden_states = get_dp_group().reduce_scatterv(hidden_states, - dim=0, - sizes=sizes) + + dist_group = get_ep_group() if is_sequence_parallel else get_dp_group() + hidden_states = dist_group.reduce_scatterv(hidden_states, + dim=0, + sizes=sizes) return hidden_states def destroy(self): @@ -148,11 +178,17 @@ def get_handle(self, kwargs): kwargs, pplx.AllToAll.internode if self.internode else pplx.AllToAll.intranode) - def dispatch(self, hidden_states: torch.Tensor, - router_logits: torch.Tensor): + def dispatch( + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: raise NotImplementedError - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: raise NotImplementedError def destroy(self): @@ -184,11 +220,17 @@ def __init__(self, cpu_group): def get_handle(self, kwargs): raise NotImplementedError - def dispatch(self, hidden_states: torch.Tensor, - router_logits: torch.Tensor): + def dispatch( + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: raise NotImplementedError - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: raise NotImplementedError def destroy(self): @@ -395,4 +437,4 @@ def cleanup(self): self.workspace_tensor = None self.prepare_workspace_tensor = None self.mapping = None - self.initialized = False \ No newline at end of file + self.initialized = False diff --git a/vllm/distributed/device_communicators/base_device_communicator.py b/vllm/distributed/device_communicators/base_device_communicator.py index 586441c91783..a42081fb0c15 100644 --- a/vllm/distributed/device_communicators/base_device_communicator.py +++ b/vllm/distributed/device_communicators/base_device_communicator.py @@ -28,6 +28,8 @@ def get_or_create(self, kwargs, func): class All2AllManagerBase: + rank: int + world_size: int def __init__(self, cpu_group): self.cpu_group = cpu_group @@ -40,6 +42,7 @@ def __init__(self, cpu_group): # all2all lives in ep group, which is merged from dp and tp group self.dp_group = get_dp_group() self.tp_group = get_tp_group() + # no self.ep_group since self.ep_group is still in construction # when we create this object self.dp_rank = self.dp_group.rank_in_group @@ -60,17 +63,21 @@ def get_handle(self, kwargs): # and reuse it for the same config. raise NotImplementedError + def dispatch(self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False): + raise NotImplementedError + def set_num_sms(self, num_sms: int): pass def max_sms_used(self) -> Optional[int]: return None # None means it could use the whole GPU - def dispatch(self, hidden_states: torch.Tensor, - router_logits: torch.Tensor): - raise NotImplementedError - - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False): raise NotImplementedError def destroy(self): @@ -267,15 +274,20 @@ def prepare_communication_buffer_for_model(self, module.quant_method.init_prepare_finalize(module) def dispatch( - self, hidden_states: torch.Tensor, - router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: """ Dispatch the hidden states and router logits to the appropriate device. This is a no-op in the base class. """ return hidden_states, router_logits - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: """ Combine the hidden states and router logits from the appropriate device. This is a no-op in the base class. diff --git a/vllm/distributed/device_communicators/cuda_communicator.py b/vllm/distributed/device_communicators/cuda_communicator.py index bab372b722db..30d1bf10138b 100644 --- a/vllm/distributed/device_communicators/cuda_communicator.py +++ b/vllm/distributed/device_communicators/cuda_communicator.py @@ -39,10 +39,6 @@ def __init__(self, use_custom_allreduce = _ENABLE_CUSTOM_ALL_REDUCE use_torch_symm_mem = envs.VLLM_ALLREDUCE_USE_SYMM_MEM - # ep does not use pynccl - use_pynccl = "ep" not in unique_name - - self.use_pynccl = use_pynccl self.use_custom_allreduce = use_custom_allreduce self.use_torch_symm_mem = use_torch_symm_mem @@ -57,7 +53,7 @@ def __init__(self, SymmMemCommunicator) self.pynccl_comm: Optional[PyNcclCommunicator] = None - if use_pynccl and self.world_size > 1: + if self.world_size > 1: self.pynccl_comm = PyNcclCommunicator( group=self.cpu_group, device=self.device, @@ -308,14 +304,20 @@ def _all_gather_single(input_: torch.Tensor, return output_list def dispatch( - self, hidden_states: torch.Tensor, - router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: assert self.all2all_manager is not None hidden_states, router_logits = self.all2all_manager.dispatch( - hidden_states, router_logits) + hidden_states, router_logits, is_sequence_parallel) return hidden_states, router_logits - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: assert self.all2all_manager is not None - hidden_states = self.all2all_manager.combine(hidden_states) + hidden_states = self.all2all_manager.combine(hidden_states, + is_sequence_parallel) return hidden_states diff --git a/vllm/distributed/device_communicators/xpu_communicator.py b/vllm/distributed/device_communicators/xpu_communicator.py index b236bae261e0..27bd176554af 100644 --- a/vllm/distributed/device_communicators/xpu_communicator.py +++ b/vllm/distributed/device_communicators/xpu_communicator.py @@ -75,14 +75,20 @@ def broadcast(self, input_: torch.Tensor, src: int = 0) -> None: dist.broadcast(input_, src=src, group=self.device_group) def dispatch( - self, hidden_states: torch.Tensor, - router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: assert self.all2all_manager is not None hidden_states, router_logits = self.all2all_manager.dispatch( - hidden_states, router_logits) + hidden_states, router_logits, is_sequence_parallel) return hidden_states, router_logits - def combine(self, hidden_states: torch.Tensor) -> torch.Tensor: + def combine(self, + hidden_states: torch.Tensor, + is_sequence_parallel: bool = False) -> torch.Tensor: assert self.all2all_manager is not None - hidden_states = self.all2all_manager.combine(hidden_states) + hidden_states = self.all2all_manager.combine(hidden_states, + is_sequence_parallel) return hidden_states diff --git a/vllm/distributed/parallel_state.py b/vllm/distributed/parallel_state.py index 69f98eb54f36..638170963e2b 100644 --- a/vllm/distributed/parallel_state.py +++ b/vllm/distributed/parallel_state.py @@ -871,17 +871,24 @@ def prepare_communication_buffer_for_model(self, model: torch.nn.Module): model) def dispatch( - self, hidden_states: torch.Tensor, - router_logits: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + self, + hidden_states: torch.Tensor, + router_logits: torch.Tensor, + is_sequence_parallel: bool = False + ) -> tuple[torch.Tensor, torch.Tensor]: if self.device_communicator is not None: return self.device_communicator.dispatch(hidden_states, - router_logits) + router_logits, + is_sequence_parallel) else: return hidden_states, router_logits - def combine(self, hidden_states) -> torch.Tensor: + def combine(self, + hidden_states, + is_sequence_parallel: bool = False) -> torch.Tensor: if self.device_communicator is not None: - return self.device_communicator.combine(hidden_states) + return self.device_communicator.combine(hidden_states, + is_sequence_parallel) else: return hidden_states diff --git a/vllm/forward_context.py b/vllm/forward_context.py index 2bf4e1804521..09defade00dc 100644 --- a/vllm/forward_context.py +++ b/vllm/forward_context.py @@ -49,16 +49,29 @@ def non_uniform(self) -> "BatchDescriptor": return BatchDescriptor(self.num_tokens, uniform_decode=False) -def _compute_chunked_local_num_tokens(num_tokens_across_dp_cpu: list[int], +def _compute_sp_num_tokens(num_tokens_across_dp_cpu: torch.Tensor, + sequence_parallel_size: int) -> list[int]: + sp_tokens = ((num_tokens_across_dp_cpu + sequence_parallel_size - 1) // + sequence_parallel_size) + + sp_tokens = sp_tokens.repeat_interleave(sequence_parallel_size) + return sp_tokens.tolist() + + +def _compute_chunked_local_num_tokens(num_tokens_across_dp_cpu: torch.Tensor, + sequence_parallel_size: int, max_num_tokens: int, chunk_idx: int) -> list[int]: - dp_size = len(num_tokens_across_dp_cpu) - local_size = [-1] * dp_size - for i in range(dp_size): - dp_tokens = num_tokens_across_dp_cpu[i] + sp_tokens = _compute_sp_num_tokens(num_tokens_across_dp_cpu, + sequence_parallel_size) + sp_size = len(sp_tokens) + + local_size = [-1] * sp_size + for i in range(sp_size): + # Take into account sharding if MoE activation is sequence parallel. local_size[i] = min(max_num_tokens, - dp_tokens - (max_num_tokens * chunk_idx)) + sp_tokens[i] - (max_num_tokens * chunk_idx)) if local_size[i] <= 0: local_size[i] = 1 # ensure lockstep even if done return local_size @@ -67,7 +80,9 @@ def _compute_chunked_local_num_tokens(num_tokens_across_dp_cpu: list[int], @dataclass class DPMetadata: max_tokens_across_dp_cpu: torch.Tensor - cu_tokens_across_dp_cpu: torch.Tensor + num_tokens_across_dp_cpu: torch.Tensor + + # NOTE: local_sizes should only be set by the chunked_sizes context manager local_sizes: Optional[list[int]] = None @staticmethod @@ -98,6 +113,17 @@ def num_tokens_across_dp(num_tokens: int, dp_size: int, dist.all_reduce(num_tokens_tensor, group=group) return num_tokens_tensor.cpu() + # Get the cumulative tokens across sequence parallel ranks. + # In this case the input to the MoEs will be distributed w.r.t both + # DP and TP rank. + # When sp_size==1, this is just the cummulative num tokens across DP. + def cu_tokens_across_sp(self, sp_size: int) -> torch.Tensor: + num_tokens_across_sp_cpu = ( + (self.num_tokens_across_dp_cpu - 1 + sp_size) // sp_size) + num_tokens_across_sp_cpu = ( + num_tokens_across_sp_cpu.repeat_interleave(sp_size)) + return torch.cumsum(num_tokens_across_sp_cpu, dim=0) + @staticmethod def should_ubatch_across_dp( should_ubatch: bool, orig_num_tokens_per_ubatch: int, @@ -147,10 +173,10 @@ def should_ubatch_across_dp( @staticmethod def make( - parallel_config: ParallelConfig, - attn_metadata: Any, - num_tokens: int, - num_tokens_across_dp: Optional[torch.Tensor] = None + parallel_config: ParallelConfig, + attn_metadata: Any, + num_tokens: int, + num_tokens_across_dp_cpu: Optional[torch.Tensor] = None ) -> "DPMetadata": assert parallel_config.data_parallel_size > 1 @@ -167,18 +193,18 @@ def make( # If num_tokens_across_dp is None, it will be computed by all_reduce # Otherwise, num_tokens_across_dp[dp_rank] should be equal to batchsize - assert (num_tokens_across_dp is None or num_tokens_across_dp[dp_rank] - == batchsize), f"{num_tokens_across_dp[dp_rank]} {batchsize}" - if num_tokens_across_dp is None: - num_tokens_across_dp = DPMetadata.num_tokens_across_dp( + assert (num_tokens_across_dp_cpu is None + or num_tokens_across_dp_cpu[dp_rank] == batchsize + ), f"{num_tokens_across_dp_cpu[dp_rank]} {batchsize}" + if num_tokens_across_dp_cpu is None: + num_tokens_across_dp_cpu = DPMetadata.num_tokens_across_dp( batchsize, dp_size, dp_rank) - max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp) - cu_tokens_across_dp_cpu = torch.cumsum(num_tokens_across_dp, dim=0) - return DPMetadata(max_tokens_across_dp_cpu, cu_tokens_across_dp_cpu, - num_tokens_across_dp) + max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp_cpu) + return DPMetadata(max_tokens_across_dp_cpu, num_tokens_across_dp_cpu) @contextmanager - def chunked_sizes(self, max_chunk_size_per_rank: int, chunk_idx: int): + def chunked_sizes(self, sequence_parallel_size: int, + max_chunk_size_per_rank: int, chunk_idx: int): """ Context manager to compute and temporarily set the per-rank local token sizes for a specific chunk during chunked forward execution. @@ -192,31 +218,40 @@ def chunked_sizes(self, max_chunk_size_per_rank: int, chunk_idx: int): `chunk_idx`, this context manager sets `self.local_sizes` to the number of tokens to process in that chunk on each rank. - It uses cumulative sizes (`cu_tokens_across_dp_cpu`) to derive the - number of tokens per rank, and calls `_compute_chunked_local_num_tokens` - to determine the chunk-wise split. - `self.local_sizes` is only valid inside the context. Args: + sequence_parallel_size: When Attn is TP and MoE layers are EP, + we use SP between the layers to avoid + redundant ops. We need this value to + compute the chunked sizes. max_chunk_size_per_rank: The max number of tokens each rank is allowed to process in this chunk. chunk_idx: The index of the chunk to compute sizes for. """ - cu_sizes = self.cu_tokens_across_dp_cpu - num_tokens_across_dp_cpu = [ - (cu_sizes[i] - - cu_sizes[i - 1]).item() if i > 0 else cu_sizes[0].item() - for i in range(len(cu_sizes)) - ] self.local_sizes = _compute_chunked_local_num_tokens( - num_tokens_across_dp_cpu, max_chunk_size_per_rank, chunk_idx) + self.num_tokens_across_dp_cpu, sequence_parallel_size, + max_chunk_size_per_rank, chunk_idx) + try: + yield self.local_sizes + finally: + self.local_sizes = None + + @contextmanager + def sp_local_sizes(self, sequence_parallel_size: int): + """ + Context mamager for setting self.local_sizes. Same as self.chunked_sizes + but without any chunking. + """ + self.local_sizes = _compute_sp_num_tokens( + self.num_tokens_across_dp_cpu, sequence_parallel_size) try: yield self.local_sizes finally: self.local_sizes = None def get_chunk_sizes_across_dp_rank(self) -> Optional[list[int]]: + assert self.local_sizes is not None return self.local_sizes diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py index eccae8b2a7af..8de1d14d46b3 100644 --- a/vllm/model_executor/layers/fused_moe/layer.py +++ b/vllm/model_executor/layers/fused_moe/layer.py @@ -3,6 +3,7 @@ from abc import abstractmethod from collections.abc import Iterable +from contextlib import nullcontext from enum import Enum from typing import Callable, Literal, Optional, Union, get_args, overload @@ -983,8 +984,7 @@ def __init__( if dp_size is not None else get_dp_group().world_size) self.is_sequence_parallel = is_sequence_parallel - if self.is_sequence_parallel: - self.sp_size = tp_size_ + self.sp_size = tp_size_ if is_sequence_parallel else 1 self.moe_parallel_config: FusedMoEParallelConfig = ( FusedMoEParallelConfig.make( @@ -1966,7 +1966,8 @@ def process_chunk(chunk_start, chunk_end, skip_result_store=False): # clamp start and end chunk_start = min(chunk_start, num_tokens - 1) chunk_end = min(chunk_end, num_tokens) - with ctx.dp_metadata.chunked_sizes(moe_dp_chunk_size_per_rank, + with ctx.dp_metadata.chunked_sizes(self.sp_size, + moe_dp_chunk_size_per_rank, chunk_idx): process_chunk(chunk_start, chunk_end, @@ -2011,65 +2012,73 @@ def forward_impl( else: shared_output = None - if do_naive_dispatch_combine: - hidden_states, router_logits = get_ep_group().dispatch( - hidden_states, router_logits) + ctx = get_forward_context() + sp_ctx = ctx.dp_metadata.sp_local_sizes( + self.sp_size) if ctx.dp_metadata else nullcontext() - # Matrix multiply. - final_hidden_states = self.quant_method.apply( - layer=self, - x=hidden_states, - router_logits=router_logits, - top_k=self.top_k, - renormalize=self.renormalize, - use_grouped_topk=self.use_grouped_topk, - global_num_experts=self.global_num_experts, - expert_map=self.expert_map, - topk_group=self.topk_group, - num_expert_group=self.num_expert_group, - custom_routing_function=self.custom_routing_function, - scoring_func=self.scoring_func, - routed_scaling_factor=self.routed_scaling_factor, - e_score_correction_bias=self.e_score_correction_bias, - activation=self.activation, - apply_router_weight_on_input=self.apply_router_weight_on_input, - enable_eplb=self.enable_eplb, - expert_load_view=self.expert_load_view, - logical_to_physical_map=self.logical_to_physical_map, - logical_replica_count=self.logical_replica_count, - ) + with sp_ctx: + if do_naive_dispatch_combine: + hidden_states, router_logits = get_ep_group().dispatch( + hidden_states, router_logits, self.is_sequence_parallel) - if shared_output is not None: - assert not isinstance(final_hidden_states, tuple) - assert self.shared_experts is not None - final_hidden_states = ( - shared_output, - final_hidden_states, + # Matrix multiply. + final_hidden_states = self.quant_method.apply( + layer=self, + x=hidden_states, + router_logits=router_logits, + top_k=self.top_k, + renormalize=self.renormalize, + use_grouped_topk=self.use_grouped_topk, + global_num_experts=self.global_num_experts, + expert_map=self.expert_map, + topk_group=self.topk_group, + num_expert_group=self.num_expert_group, + custom_routing_function=self.custom_routing_function, + scoring_func=self.scoring_func, + routed_scaling_factor=self.routed_scaling_factor, + e_score_correction_bias=self.e_score_correction_bias, + activation=self.activation, + apply_router_weight_on_input=self.apply_router_weight_on_input, + enable_eplb=self.enable_eplb, + expert_load_view=self.expert_load_view, + logical_to_physical_map=self.logical_to_physical_map, + logical_replica_count=self.logical_replica_count, ) - elif self.zero_expert_num is not None and self.zero_expert_num > 0: - assert isinstance(final_hidden_states, tuple) - final_hidden_states, zero_expert_result = final_hidden_states - def reduce_output(states: torch.Tensor, - do_combine: bool = True) -> torch.Tensor: - if do_naive_dispatch_combine and do_combine: - states = get_ep_group().combine(states) + if shared_output is not None: + assert not isinstance(final_hidden_states, tuple) + assert self.shared_experts is not None + final_hidden_states = ( + shared_output, + final_hidden_states, + ) + elif self.zero_expert_num is not None and self.zero_expert_num > 0: + assert isinstance(final_hidden_states, tuple) + final_hidden_states, zero_expert_result = final_hidden_states + + def reduce_output(states: torch.Tensor, + do_combine: bool = True) -> torch.Tensor: + if do_naive_dispatch_combine and do_combine: + states = get_ep_group().combine(states, + self.is_sequence_parallel) - if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1): - states = self.maybe_all_reduce_tensor_model_parallel(states) + if (not self.is_sequence_parallel and self.reduce_results + and (self.tp_size > 1 or self.ep_size > 1)): + states = self.maybe_all_reduce_tensor_model_parallel( + states) - return states + return states - if self.shared_experts is not None: - return ( - reduce_output(final_hidden_states[0], do_combine=False), - reduce_output(final_hidden_states[1]), - ) - elif self.zero_expert_num is not None and self.zero_expert_num > 0: - assert isinstance(final_hidden_states, torch.Tensor) - return reduce_output(final_hidden_states) + zero_expert_result - else: - return reduce_output(final_hidden_states) + if self.shared_experts is not None: + return ( + reduce_output(final_hidden_states[0], do_combine=False), + reduce_output(final_hidden_states[1]), + ) + elif self.zero_expert_num is not None and self.zero_expert_num > 0: + assert isinstance(final_hidden_states, torch.Tensor) + return reduce_output(final_hidden_states) + zero_expert_result + else: + return reduce_output(final_hidden_states) @classmethod def make_expert_params_mapping( diff --git a/vllm/model_executor/models/aria.py b/vllm/model_executor/models/aria.py index 6cef5e134a4b..e0d7af0b1c3e 100644 --- a/vllm/model_executor/models/aria.py +++ b/vllm/model_executor/models/aria.py @@ -9,7 +9,7 @@ from transformers.models.aria.modeling_aria import AriaCrossAttention from transformers.models.aria.processing_aria import AriaProcessor -from vllm.config import CacheConfig, QuantizationConfig, VllmConfig +from vllm.config import QuantizationConfig, VllmConfig from vllm.distributed import get_tensor_model_parallel_rank from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.fused_moe import FusedMoE @@ -297,14 +297,12 @@ class AriaTextDecoderLayer(LlamaDecoderLayer): Experts (MoE) Layer. """ - def __init__( - self, - config: AriaTextConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> None: - super().__init__(config, cache_config, quant_config, prefix) + def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None: + super().__init__(vllm_config, prefix) + + config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + self.mlp = AriaTextMoELayer(config, quant_config=quant_config, prefix=f"{prefix}.mlp") diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py index aab522390a7a..2e0bcbe5d2e5 100644 --- a/vllm/model_executor/models/deepseek_v2.py +++ b/vllm/model_executor/models/deepseek_v2.py @@ -32,7 +32,6 @@ from torch import nn from transformers import DeepseekV2Config, DeepseekV3Config -import vllm.envs as envs from vllm.attention import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, ParallelConfig, VllmConfig @@ -56,8 +55,8 @@ ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.sequence import IntermediateTensors -from vllm.utils import cdiv, direct_register_custom_op from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP from .utils import (PPMissingLayer, is_pp_missing_parameter, @@ -108,43 +107,6 @@ def forward(self, x): return x -# Chunk x along the num_tokens axis for sequence parallelism -# NOTE: This is wrapped in a torch custom op to work around the following issue: -# The output tensor can have a sequence length 0 at small input sequence lengths -# even though we explicitly pad to avoid this. -def sequence_parallel_chunk(x: torch.Tensor) -> torch.Tensor: - tp_size = get_tensor_model_parallel_world_size() - tp_rank = get_tensor_model_parallel_rank() - - # all_gather needs the sequence length to be divisible by tp_size - seq_len = x.size(0) - remainder = seq_len % tp_size - if remainder != 0: - pad_len = tp_size - remainder - x = nn.functional.pad(x, (0, 0, 0, pad_len)) - - chunk = x.shape[0] // tp_size - start = tp_rank * chunk - return torch.narrow(x, 0, start, chunk) - - -def sequence_parallel_chunk_fake(x: torch.Tensor) -> torch.Tensor: - tp_size = get_tensor_model_parallel_world_size() - seq_len = cdiv(x.size(0), tp_size) - shape = list(x.shape) - shape[0] = seq_len - out = torch.empty(shape, dtype=x.dtype, device=x.device) - return out - - -direct_register_custom_op( - op_name="sequence_parallel_chunk", - op_func=sequence_parallel_chunk, - fake_impl=sequence_parallel_chunk_fake, - tags=(torch.Tag.needs_fixed_stride_order, ), -) - - class DeepseekV2MoE(nn.Module): def __init__( @@ -166,20 +128,7 @@ def __init__( self.n_routed_experts: int = config.n_routed_experts self.n_shared_experts: int = config.n_shared_experts - # The all_reduce at the end of attention (during o_proj) means that - # inputs are replicated across each rank of the tensor parallel group. - # If using expert-parallelism with DeepEP All2All ops, replicated - # tokens results in useless duplicate computation and communication. - # - # In this case, ensure the input to the experts is sequence parallel - # to avoid the excess work. - # - # Not needed for pplx-kernels as it can handle duplicate input tokens. - self.is_sequence_parallel = (envs.VLLM_ALL2ALL_BACKEND - in ("deepep_high_throughput", - "deepep_low_latency") - and parallel_config.enable_expert_parallel - and self.tp_size > 1) + self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe if config.hidden_act != "silu": raise ValueError(f"Unsupported activation: {config.hidden_act}. " @@ -278,8 +227,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # TODO: We can replace the all_reduce at the end of attn with a # reduce_scatter instead of chunking here. if self.is_sequence_parallel: - hidden_states = torch.ops.vllm.sequence_parallel_chunk( - hidden_states) + hidden_states = sequence_parallel_chunk(hidden_states) # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) diff --git a/vllm/model_executor/models/ernie_mtp.py b/vllm/model_executor/models/ernie_mtp.py index 3b24bf2f1ef8..2e6ef2d476a6 100644 --- a/vllm/model_executor/models/ernie_mtp.py +++ b/vllm/model_executor/models/ernie_mtp.py @@ -29,10 +29,9 @@ import torch.nn as nn from transformers import PretrainedConfig -from vllm.config import CacheConfig, ModelConfig, VllmConfig +from vllm.config import VllmConfig from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader @@ -47,13 +46,11 @@ class ErnieMultiTokenPredictorLayer(nn.Module): def __init__( self, - config: PretrainedConfig, + vllm_config: VllmConfig, prefix: str, - model_config: ModelConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() + config = vllm_config.model_config.hf_config self.mtp_emb_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -62,8 +59,7 @@ def __init__( self.mtp_linear_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False) - self.mtp_block = LlamaDecoderLayer(config, cache_config, quant_config, - prefix) + self.mtp_block = LlamaDecoderLayer(vllm_config, prefix) def forward( self, @@ -102,10 +98,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.layers = torch.nn.ModuleDict({ str(idx): ErnieMultiTokenPredictorLayer( - config, + vllm_config, f"{prefix}.layers.{idx}", - model_config=vllm_config.model_config, - cache_config=vllm_config.cache_config, ) for idx in range(self.mtp_start_layer_idx, self.mtp_start_layer_idx + self.num_mtp_layers) diff --git a/vllm/model_executor/models/glm4.py b/vllm/model_executor/models/glm4.py index b9d5e24e9f6f..f49f21a40f82 100644 --- a/vllm/model_executor/models/glm4.py +++ b/vllm/model_executor/models/glm4.py @@ -136,14 +136,16 @@ def forward( class Glm4DecoderLayer(nn.Module): - def __init__( - self, - config: Glm4Config, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> None: + def __init__(self, + vllm_config: VllmConfig, + prefix: str = "", + config: Optional[Glm4Config] = None) -> None: super().__init__() + + config = config or vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 1000000) rope_scaling = getattr(config, "rope_scaling", None) diff --git a/vllm/model_executor/models/gpt_oss.py b/vllm/model_executor/models/gpt_oss.py index 7c755a00e1c9..47ba5084d608 100644 --- a/vllm/model_executor/models/gpt_oss.py +++ b/vllm/model_executor/models/gpt_oss.py @@ -13,7 +13,8 @@ from vllm.config import CacheConfig, VllmConfig from vllm.distributed import (get_ep_group, get_pp_group, get_tensor_model_parallel_rank, - get_tensor_model_parallel_world_size) + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_gather) from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (QKVParallelLinear, @@ -24,6 +25,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.sequence import IntermediateTensors from vllm.utils import cdiv @@ -132,12 +134,18 @@ class MLPBlock(torch.nn.Module): def __init__( self, - config: GptOssConfig, + vllm_config: VllmConfig, layer_idx: int, - quant_config: QuantizationConfig, prefix: str = "", ): super().__init__() + + config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + parallel_config = vllm_config.parallel_config + + self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe + self.layer_idx = layer_idx self.num_experts = config.num_local_experts self.experts_per_token = config.num_experts_per_tok @@ -155,11 +163,20 @@ def __init__( prefix=f"{prefix}.experts", apply_router_weight_on_input=False, has_bias=True, - activation="swigluoai") + activation="swigluoai", + is_sequence_parallel=self.is_sequence_parallel) def forward(self, x: torch.Tensor) -> torch.Tensor: + num_tokens = x.shape[0] + if self.is_sequence_parallel: + x = sequence_parallel_chunk(x) + g = self.router(x) x = self.experts(hidden_states=x, router_logits=g) + + if self.is_sequence_parallel: + x = tensor_model_parallel_all_gather(x.contiguous(), 0) + x = x[:num_tokens] return x @@ -167,19 +184,20 @@ class TransformerBlock(torch.nn.Module): def __init__( self, - config: GptOssConfig, - cache_config: CacheConfig, - quant_config: QuantizationConfig, + vllm_config: VllmConfig, prefix: str = "", ): super().__init__() + + config = vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + self.layer_idx = extract_layer_index(prefix) self.attn = OAIAttention(config, prefix=f"{prefix}.attn", cache_config=cache_config) - self.mlp = MLPBlock(config, + self.mlp = MLPBlock(vllm_config, self.layer_idx, - quant_config=quant_config, prefix=f"{prefix}.mlp") self.input_layernorm = RMSNorm(config.hidden_size, eps=1e-5) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=1e-5) @@ -216,8 +234,6 @@ def __init__( ): super().__init__() self.config = vllm_config.model_config.hf_config - self.cache_config = vllm_config.cache_config - self.quant_config = vllm_config.quant_config self.parallel_config = vllm_config.parallel_config self.config.hidden_size = self.config.hidden_size self.embedding = VocabParallelEmbedding( @@ -227,9 +243,7 @@ def __init__( self.start_layer, self.end_layer, self.layers = make_layers( self.config.num_hidden_layers, lambda prefix: TransformerBlock( - self.config, - cache_config=self.cache_config, - quant_config=self.quant_config, + vllm_config, prefix=prefix, ), prefix=f"{prefix}.layers", diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py index 47ac22c4aeaa..76a5745a4f51 100644 --- a/vllm/model_executor/models/granitemoe.py +++ b/vllm/model_executor/models/granitemoe.py @@ -29,12 +29,13 @@ import torch from torch import nn -from transformers.models.granitemoe import GraniteMoeConfig from vllm.attention import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig -from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size +from vllm.distributed import (get_pp_group, + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_gather) from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (QKVParallelLinear, @@ -48,6 +49,7 @@ DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.sequence import IntermediateTensors from .interfaces import SupportsLoRA, SupportsPP @@ -71,9 +73,11 @@ def __init__(self, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, tp_size: Optional[int] = None, + is_sequence_parallel=False, prefix: str = ""): super().__init__() self.hidden_size = hidden_size + self.is_sequence_parallel = is_sequence_parallel # Gate always runs at half / full precision for now. self.gate = ReplicatedLinear(hidden_size, @@ -92,15 +96,27 @@ def __init__(self, renormalize=True, quant_config=quant_config, tp_size=tp_size, - prefix=f"{prefix}.experts") + prefix=f"{prefix}.experts", + is_sequence_parallel=self.is_sequence_parallel) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # NOTE: hidden_states can have either 1D or 2D shape. orig_shape = hidden_states.shape hidden_states = hidden_states.view(-1, self.hidden_size) + + if self.is_sequence_parallel: + hidden_states = sequence_parallel_chunk(hidden_states) + # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) final_hidden_states = self.experts(hidden_states, router_logits) + + if self.is_sequence_parallel: + final_hidden_states = tensor_model_parallel_all_gather( + final_hidden_states, 0) + num_tokens = orig_shape[0] + final_hidden_states = final_hidden_states[:num_tokens] + return final_hidden_states.view(orig_shape) @@ -191,12 +207,16 @@ class GraniteMoeDecoderLayer(nn.Module): def __init__( self, - config: GraniteMoeConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, + vllm_config: VllmConfig, prefix: str = "", ) -> None: super().__init__() + + config = vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + parallel_config = vllm_config.parallel_config + self.hidden_size = config.hidden_size # Requires transformers > 4.32.0 rope_theta = getattr(config, "rope_theta", 10000) @@ -218,6 +238,7 @@ def __init__( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, quant_config=quant_config, + is_sequence_parallel=parallel_config.use_sequence_parallel_moe, prefix=f"{prefix}.block_sparse_moe") self.input_layernorm = RMSNorm(config.hidden_size, @@ -255,7 +276,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config - cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config lora_config = vllm_config.lora_config @@ -275,9 +295,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, - lambda prefix: GraniteMoeDecoderLayer( - config, cache_config, quant_config=quant_config, prefix=prefix - ), + lambda prefix: GraniteMoeDecoderLayer(vllm_config, prefix=prefix), prefix=f"{prefix}.layers") self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py index 1b03cbef501b..c7dd134ea47e 100644 --- a/vllm/model_executor/models/llama.py +++ b/vllm/model_executor/models/llama.py @@ -68,6 +68,7 @@ def __init__( bias: bool = False, prefix: str = "", reduce_results: bool = True, + disable_tp: bool = False, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( @@ -75,6 +76,7 @@ def __init__( output_sizes=[intermediate_size] * 2, bias=bias, quant_config=quant_config, + disable_tp=disable_tp, prefix=f"{prefix}.gate_up_proj", ) self.down_proj = RowParallelLinear( @@ -83,6 +85,7 @@ def __init__( bias=bias, quant_config=quant_config, reduce_results=reduce_results, + disable_tp=disable_tp, prefix=f"{prefix}.down_proj", ) if hidden_act != "silu": @@ -237,14 +240,16 @@ def _init_rotary_emb(self, config: LlamaConfig, class LlamaDecoderLayer(nn.Module): - def __init__( - self, - config: LlamaConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> None: + def __init__(self, + vllm_config: VllmConfig, + prefix: str = "", + config: Optional[LlamaConfig] = None) -> None: super().__init__() + + config = config or vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) @@ -335,7 +340,6 @@ def __init__(self, super().__init__() config = vllm_config.model_config.hf_config - cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config lora_config = vllm_config.lora_config @@ -357,10 +361,7 @@ def __init__(self, self.embed_tokens = PPMissingLayer() self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, - lambda prefix: layer_type(config=config, - cache_config=cache_config, - quant_config=quant_config, - prefix=prefix), + lambda prefix: layer_type(vllm_config=vllm_config, prefix=prefix), prefix=f"{prefix}.layers", ) if get_pp_group().is_last_rank: diff --git a/vllm/model_executor/models/llama4.py b/vllm/model_executor/models/llama4.py index ddd7e6a5936e..32d4f69c6bf1 100644 --- a/vllm/model_executor/models/llama4.py +++ b/vllm/model_executor/models/llama4.py @@ -28,7 +28,8 @@ from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig -from vllm.distributed import get_tensor_model_parallel_world_size +from vllm.distributed import (get_tensor_model_parallel_world_size, + tensor_model_parallel_all_gather) from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (QKVParallelLinear, @@ -39,6 +40,7 @@ from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.models.utils import sequence_parallel_chunk from .llama import LlamaForCausalLM, LlamaMLP, LlamaModel from .utils import (AutoWeightsLoader, extract_layer_index, fast_topk, @@ -59,13 +61,16 @@ def custom_routing_function( router_scores = torch.sigmoid(router_scores.float()) return (router_scores, router_indices.to(torch.int32)) - def __init__(self, - config: Llama4TextConfig, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = ""): + def __init__(self, vllm_config: VllmConfig, prefix: str = ""): super().__init__() + + config = vllm_config.model_config.hf_config + parallel_config = vllm_config.parallel_config + quant_config = vllm_config.quant_config + self.tp_size = get_tensor_model_parallel_world_size() self.top_k = config.num_experts_per_tok + self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe intermediate_size_moe = config.intermediate_size self.router = ReplicatedLinear(config.hidden_size, @@ -82,6 +87,7 @@ def __init__(self, bias=False, prefix=f"{prefix}.shared_expert", reduce_results=False, + disable_tp=self.is_sequence_parallel, ) self.experts = SharedFusedMoE( @@ -96,9 +102,14 @@ def __init__(self, renormalize=False, quant_config=quant_config, prefix=f"{prefix}.experts", + is_sequence_parallel=self.is_sequence_parallel, ) def forward(self, hidden_states): + num_tokens = hidden_states.shape[0] + if self.is_sequence_parallel: + hidden_states = sequence_parallel_chunk(hidden_states) + router_logits, _ = self.router(hidden_states) shared_out, routed_out = self.experts( @@ -107,7 +118,10 @@ def forward(self, hidden_states): ) experts_out = routed_out + shared_out - if self.tp_size > 1: + if self.is_sequence_parallel: + experts_out = tensor_model_parallel_all_gather(experts_out, 0) + experts_out = experts_out[:num_tokens] + elif self.tp_size > 1: experts_out = self.experts.maybe_all_reduce_tensor_model_parallel( experts_out) @@ -257,15 +271,16 @@ def forward( class Llama4DecoderLayer(nn.Module): - def __init__( - self, - config: Llama4TextConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> None: + def __init__(self, + vllm_config: VllmConfig, + prefix: str = "", + config: Optional[Llama4TextConfig] = None) -> None: super().__init__() + config = config or vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + self.layer_idx = extract_layer_index(prefix) self.global_layer = config.no_rope_layers[self.layer_idx] == 0 self.hidden_size = config.hidden_size @@ -291,8 +306,7 @@ def __init__( self.layer_idx + 1) % config.interleave_moe_layer_step == 0 if is_moe_layer: self.feed_forward = Llama4MoE( - config=config, - quant_config=quant_config, + vllm_config=vllm_config, prefix=f"{prefix}.feed_forward", ) else: diff --git a/vllm/model_executor/models/llama4_eagle.py b/vllm/model_executor/models/llama4_eagle.py index 235275c0940a..0768edd08315 100644 --- a/vllm/model_executor/models/llama4_eagle.py +++ b/vllm/model_executor/models/llama4_eagle.py @@ -68,9 +68,9 @@ def __init__( self.layers = nn.ModuleList([ Llama4DecoderLayer( - self.config, - quant_config=quant_config, + vllm_config=vllm_config, prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"), + config=self.config, ) for i in range(self.config.num_hidden_layers) ]) self.fc = torch.nn.Linear(self.config.hidden_size * 2, diff --git a/vllm/model_executor/models/llama_eagle.py b/vllm/model_executor/models/llama_eagle.py index d6e6fd3fcfe9..d7d6b1745fc8 100644 --- a/vllm/model_executor/models/llama_eagle.py +++ b/vllm/model_executor/models/llama_eagle.py @@ -28,11 +28,12 @@ class LlamaDecoderLayer(LlamaDecoderLayer): def __init__( self, - config: LlamaConfig, + vllm_config: VllmConfig, disable_input_layernorm: bool, prefix: str = "", + config: Optional[LlamaConfig] = None, ) -> None: - super().__init__(config, prefix=prefix) + super().__init__(vllm_config, prefix=prefix, config=config) # Skip the input_layernorm # https://github.com/SafeAILab/EAGLE/blob/35c78f6cdc19a73e05cf5c330b4c358dad970c6a/eagle/model/cnets.py#L427 @@ -64,9 +65,10 @@ def __init__( self.layers = nn.ModuleList([ LlamaDecoderLayer( - self.config, + vllm_config, i == 0, prefix=maybe_prefix(prefix, f"layers.{i + start_layer_id}"), + config=self.config, ) for i in range(self.config.num_hidden_layers) ]) self.fc = torch.nn.Linear(self.config.hidden_size * 2, diff --git a/vllm/model_executor/models/llama_eagle3.py b/vllm/model_executor/models/llama_eagle3.py index 34b8ea0ca536..7192a76c8749 100644 --- a/vllm/model_executor/models/llama_eagle3.py +++ b/vllm/model_executor/models/llama_eagle3.py @@ -8,13 +8,11 @@ import torch.nn as nn from transformers import LlamaConfig -from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config +from vllm.config import VllmConfig, get_current_vllm_config from vllm.logger import init_logger from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import QKVParallelLinear from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader @@ -28,17 +26,14 @@ class LlamaDecoderLayer(LlamaDecoderLayer): - def __init__( - self, - config: LlamaConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> None: - super().__init__(config, - cache_config=cache_config, - quant_config=quant_config, - prefix=prefix) + def __init__(self, + vllm_config: VllmConfig, + prefix: str = "", + config: Optional[LlamaConfig] = None) -> None: + super().__init__(vllm_config, prefix=prefix, config=config) + + config = config or vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config # override qkv self.self_attn.qkv_proj = QKVParallelLinear( @@ -125,9 +120,9 @@ def __init__( self.layers = nn.ModuleList([ LlamaDecoderLayer( - config=self.config, - cache_config=current_vllm_config.cache_config, + current_vllm_config, prefix=maybe_prefix(prefix, f"layers.{start_layer_id}"), + config=self.config, ) ]) if hasattr(self.config, "target_hidden_size"): diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py index cb2ff97a5df2..45b9c656a4bb 100644 --- a/vllm/model_executor/models/qwen3_moe.py +++ b/vllm/model_executor/models/qwen3_moe.py @@ -29,13 +29,13 @@ import torch from torch import nn -from transformers import Qwen3MoeConfig from vllm.attention import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config from vllm.distributed import (get_ep_group, get_pp_group, - get_tensor_model_parallel_world_size) + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_gather) from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.fused_moe import FusedMoE @@ -51,6 +51,7 @@ ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.sequence import IntermediateTensors from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP @@ -101,12 +102,15 @@ class Qwen3MoeSparseMoeBlock(nn.Module): def __init__( self, - config: Qwen3MoeConfig, - quant_config: Optional[QuantizationConfig] = None, + vllm_config: VllmConfig, prefix: str = "", - enable_eplb: bool = False, ): super().__init__() + + config = vllm_config.model_config.hf_config + parallel_config = vllm_config.parallel_config + quant_config = vllm_config.quant_config + self.tp_size = get_tensor_model_parallel_world_size() self.ep_group = get_ep_group().device_group @@ -114,6 +118,8 @@ def __init__( self.ep_size = self.ep_group.size() self.n_routed_experts = config.num_experts + self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe + if self.tp_size > config.num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " @@ -122,7 +128,7 @@ def __init__( # Load balancing settings. vllm_config = get_current_vllm_config() eplb_config = vllm_config.parallel_config.eplb_config - self.enable_eplb = enable_eplb + self.enable_eplb = parallel_config.enable_eplb self.n_logical_experts = self.n_routed_experts self.n_redundant_experts = eplb_config.num_redundant_experts @@ -144,7 +150,8 @@ def __init__( quant_config=quant_config, prefix=f"{prefix}.experts", enable_eplb=self.enable_eplb, - num_redundant_experts=self.n_redundant_experts) + num_redundant_experts=self.n_redundant_experts, + is_sequence_parallel=self.is_sequence_parallel) self.gate = ReplicatedLinear(config.hidden_size, config.num_experts, @@ -156,14 +163,22 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: assert hidden_states.dim( ) <= 2, "Qwen3MoeSparseMoeBlock only supports 1D or 2D inputs" is_input_1d = hidden_states.dim() == 1 - hidden_dim = hidden_states.shape[-1] + num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) + if self.is_sequence_parallel: + hidden_states = sequence_parallel_chunk(hidden_states) + # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) final_hidden_states = self.experts(hidden_states=hidden_states, router_logits=router_logits) + if self.is_sequence_parallel: + final_hidden_states = tensor_model_parallel_all_gather( + final_hidden_states, 0) + final_hidden_states = final_hidden_states[:num_tokens] + # return to 1d if input is 1d return final_hidden_states.squeeze(0) if is_input_1d else \ final_hidden_states @@ -275,15 +290,13 @@ def forward( class Qwen3MoeDecoderLayer(nn.Module): - def __init__( - self, - config: Qwen3MoeConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - enable_eplb: bool = False, - ) -> None: + def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None: super().__init__() + + config = vllm_config.model_config.hf_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) @@ -315,10 +328,8 @@ def __init__( if (layer_idx not in mlp_only_layers) and ( config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0): - self.mlp = Qwen3MoeSparseMoeBlock(config=config, - quant_config=quant_config, - prefix=f"{prefix}.mlp", - enable_eplb=enable_eplb) + self.mlp = Qwen3MoeSparseMoeBlock(vllm_config=vllm_config, + prefix=f"{prefix}.mlp") else: self.mlp = Qwen3MoeMLP(hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, @@ -362,10 +373,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config.get_text_config() - cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config parallel_config = vllm_config.parallel_config - enable_eplb = parallel_config.enable_eplb eplb_config = parallel_config.eplb_config self.num_redundant_experts = eplb_config.num_redundant_experts @@ -379,11 +388,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): prefix=f"{prefix}.embed_tokens") self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, - lambda prefix: Qwen3MoeDecoderLayer(config=config, - cache_config=cache_config, - quant_config=quant_config, - prefix=prefix, - enable_eplb=enable_eplb), + lambda prefix: Qwen3MoeDecoderLayer(vllm_config=vllm_config, + prefix=prefix), prefix=f"{prefix}.layers", ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) diff --git a/vllm/model_executor/models/qwen3_next.py b/vllm/model_executor/models/qwen3_next.py index dc3153fcc826..14d19874a51e 100644 --- a/vllm/model_executor/models/qwen3_next.py +++ b/vllm/model_executor/models/qwen3_next.py @@ -17,7 +17,8 @@ VllmConfig, get_current_vllm_config) from vllm.distributed import (divide, get_ep_group, get_pp_group, get_tensor_model_parallel_rank, - get_tensor_model_parallel_world_size) + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_gather) from vllm.forward_context import ForwardContext, get_forward_context from vllm.logger import init_logger from vllm.model_executor.layers.fla.ops import ( @@ -47,6 +48,7 @@ from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, sharded_weight_loader) from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP +from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors @@ -69,14 +71,13 @@ class Qwen3NextSparseMoeBlock(nn.Module): - def __init__( - self, - config: Qwen3NextConfig, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - enable_eplb: bool = False, - ): + def __init__(self, vllm_config: VllmConfig, prefix: str = ""): super().__init__() + + config = vllm_config.model_config.hf_config + parallel_config = vllm_config.parallel_config + quant_config = vllm_config.quant_config + self.tp_size = get_tensor_model_parallel_world_size() self.ep_group = get_ep_group().device_group @@ -84,6 +85,8 @@ def __init__( self.ep_size = self.ep_group.size() self.n_routed_experts = config.num_experts + self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe + if self.tp_size > config.num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " @@ -92,7 +95,7 @@ def __init__( # Load balancing settings. vllm_config = get_current_vllm_config() eplb_config = vllm_config.parallel_config.eplb_config - self.enable_eplb = enable_eplb + self.enable_eplb = parallel_config.enable_eplb self.n_logical_experts = self.n_routed_experts self.n_redundant_experts = eplb_config.num_redundant_experts @@ -114,7 +117,8 @@ def __init__( quant_config=quant_config, prefix=f"{prefix}.experts", enable_eplb=self.enable_eplb, - num_redundant_experts=self.n_redundant_experts) + num_redundant_experts=self.n_redundant_experts, + is_sequence_parallel=self.is_sequence_parallel) self.gate = ReplicatedLinear(config.hidden_size, config.num_experts, @@ -141,9 +145,12 @@ def __init__( def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # NOTE: hidden_states can have either 1D or 2D shape. orig_shape = hidden_states.shape - hidden_dim = hidden_states.shape[-1] + num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) + if self.is_sequence_parallel: + hidden_states = sequence_parallel_chunk(hidden_states) + shared_output = None if self.shared_expert is not None: shared_output = self.shared_expert(hidden_states) @@ -158,7 +165,12 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if shared_output is not None: final_hidden_states = final_hidden_states + shared_output - if self.tp_size > 1: + + if self.is_sequence_parallel: + final_hidden_states = tensor_model_parallel_all_gather( + final_hidden_states, 0) + final_hidden_states = final_hidden_states[:num_tokens] + elif self.tp_size > 1: final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel( # noqa E501 final_hidden_states) @@ -719,17 +731,17 @@ class Qwen3NextDecoderLayer(nn.Module): def __init__( self, - config: Qwen3NextConfig, + vllm_config: VllmConfig, layer_type: str, - model_config: Optional[ModelConfig] = None, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - speculative_config: Optional[SpeculativeConfig] = None, prefix: str = "", - enable_eplb: bool = False, ) -> None: super().__init__() - self.config = config + + config = vllm_config.model_config.hf_config + model_config = vllm_config.model_config + cache_config = vllm_config.cache_config + quant_config = vllm_config.quant_config + speculative_config = vllm_config.speculative_config self.layer_type = layer_type self.layer_idx = extract_layer_index(prefix) @@ -759,10 +771,8 @@ def __init__( config.num_experts > 0 and (self.layer_idx + 1) % config.decoder_sparse_step == 0): self.mlp = Qwen3NextSparseMoeBlock( - config=config, - quant_config=quant_config, + vllm_config=vllm_config, prefix=f"{prefix}.mlp", - enable_eplb=enable_eplb, ) else: self.mlp = Qwen3NextMLP( @@ -783,14 +793,14 @@ def __init__( torch.zeros( 1, 1, - self.config.hidden_size, + config.hidden_size, dtype=config.torch_dtype, ), ) self.ffn_layer_scale = torch.nn.Parameter( torch.zeros( 1, 1, - self.config.hidden_size, + config.hidden_size, dtype=config.torch_dtype, ), ) @@ -858,13 +868,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: Qwen3NextConfig = vllm_config.model_config.hf_config - model_config = vllm_config.model_config - cache_config = vllm_config.cache_config - quant_config = vllm_config.quant_config parallel_config = vllm_config.parallel_config lora_config = vllm_config.lora_config - speculative_config = vllm_config.speculative_config - enable_eplb = parallel_config.enable_eplb eplb_config = parallel_config.eplb_config self.num_redundant_experts = eplb_config.num_redundant_experts @@ -881,14 +886,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): def get_layer(prefix: str): return Qwen3NextDecoderLayer( - config, + vllm_config, layer_type=config.layer_types[extract_layer_index(prefix)], - model_config=model_config, - cache_config=cache_config, - quant_config=quant_config, - speculative_config=speculative_config, prefix=prefix, - enable_eplb=enable_eplb, ) self.start_layer, self.end_layer, self.layers = make_layers( diff --git a/vllm/model_executor/models/qwen3_next_mtp.py b/vllm/model_executor/models/qwen3_next_mtp.py index c054339842e6..e950699a0c49 100644 --- a/vllm/model_executor/models/qwen3_next_mtp.py +++ b/vllm/model_executor/models/qwen3_next_mtp.py @@ -38,7 +38,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() model_config = vllm_config.model_config - cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config lora_config = vllm_config.lora_config config: Qwen3NextConfig = model_config.hf_config @@ -68,11 +67,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.layers = torch.nn.ModuleList( Qwen3NextDecoderLayer( - config, + vllm_config, layer_type="full_attention", - model_config=model_config, - cache_config=cache_config, - quant_config=quant_config, prefix=f'{prefix}.layers.{idx}', ) for idx in range(self.num_mtp_layers)) diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index bb6a0bd02202..4bf151fbf62d 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -13,11 +13,14 @@ import vllm.envs as envs from vllm.config import VllmConfig +from vllm.distributed import (get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size) from vllm.logger import init_logger from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.multimodal import NestedTensors from vllm.sequence import IntermediateTensors -from vllm.utils import (get_cuda_view_from_cpu_tensor, is_pin_memory_available, +from vllm.utils import (cdiv, direct_register_custom_op, + get_cuda_view_from_cpu_tensor, is_pin_memory_available, is_uva_available) logger = init_logger(__name__) @@ -743,3 +746,46 @@ def get_model_hidden_size(hf_config: PretrainedConfig) -> int: return hf_config.hidden_size text_config = hf_config.get_text_config() return text_config.hidden_size + + +# Chunk x along the num_tokens axis for sequence parallelism +# NOTE: This is wrapped in a torch custom op to work around the following issue: +# The output tensor can have a sequence length 0 at small input sequence lengths +# even though we explicitly pad to avoid this. +def sequence_parallel_chunk(x: torch.Tensor) -> torch.Tensor: + return torch.ops.vllm.sequence_parallel_chunk_impl(x) + + +def sequence_parallel_chunk_impl(x: torch.Tensor) -> torch.Tensor: + tp_size = get_tensor_model_parallel_world_size() + tp_rank = get_tensor_model_parallel_rank() + + # all_gather needs the sequence length to be divisible by tp_size + seq_len = x.size(0) + remainder = seq_len % tp_size + if remainder != 0: + pad_len = tp_size - remainder + y = nn.functional.pad(x, (0, 0, 0, pad_len)) + else: + y = x + + chunk = y.shape[0] // tp_size + start = tp_rank * chunk + return torch.narrow(y, 0, start, chunk) + + +def sequence_parallel_chunk_impl_fake(x: torch.Tensor) -> torch.Tensor: + tp_size = get_tensor_model_parallel_world_size() + seq_len = cdiv(x.size(0), tp_size) + shape = list(x.shape) + shape[0] = seq_len + out = torch.empty(shape, dtype=x.dtype, device=x.device) + return out + + +direct_register_custom_op( + op_name="sequence_parallel_chunk_impl", + op_func=sequence_parallel_chunk_impl, + fake_impl=sequence_parallel_chunk_impl_fake, + tags=(torch.Tag.needs_fixed_stride_order, ), +) From bc9b9704f56ef286487ac39c255982b806c3487d Mon Sep 17 00:00:00 2001 From: yyzxw <34639446+yyzxw@users.noreply.github.com> Date: Sat, 27 Sep 2025 23:00:35 +0800 Subject: [PATCH 051/166] [docs] transcriptions API audio upload (#25446) Signed-off-by: zxw <1020938856@qq.com> --- docs/serving/openai_compatible_server.md | 81 +++++++++++++++++++++++- 1 file changed, 80 insertions(+), 1 deletion(-) diff --git a/docs/serving/openai_compatible_server.md b/docs/serving/openai_compatible_server.md index bac3f6c1fe90..1ffe9c9ade20 100644 --- a/docs/serving/openai_compatible_server.md +++ b/docs/serving/openai_compatible_server.md @@ -351,13 +351,92 @@ you can use the [official OpenAI Python client](https://github.com/openai/openai To use the Transcriptions API, please install with extra audio dependencies using `pip install vllm[audio]`. Code example: - #### API Enforced Limits Set the maximum audio file size (in MB) that VLLM will accept, via the `VLLM_MAX_AUDIO_CLIP_FILESIZE_MB` environment variable. Default is 25 MB. +#### Uploading Audio Files + +The Transcriptions API supports uploading audio files in various formats including FLAC, MP3, MP4, MPEG, MPGA, M4A, OGG, WAV, and WEBM. + +**Using OpenAI Python Client:** + +??? code + + ```python + from openai import OpenAI + + client = OpenAI( + base_url="http://localhost:8000/v1", + api_key="token-abc123", + ) + + # Upload audio file from disk + with open("audio.mp3", "rb") as audio_file: + transcription = client.audio.transcriptions.create( + model="openai/whisper-large-v3-turbo", + file=audio_file, + language="en", + response_format="verbose_json" + ) + + print(transcription.text) + ``` + +**Using curl with multipart/form-data:** + +??? code + + ```bash + curl -X POST "http://localhost:8000/v1/audio/transcriptions" \ + -H "Authorization: Bearer token-abc123" \ + -F "file=@audio.mp3" \ + -F "model=openai/whisper-large-v3-turbo" \ + -F "language=en" \ + -F "response_format=verbose_json" + ``` + +**Supported Parameters:** + +- `file`: The audio file to transcribe (required) +- `model`: The model to use for transcription (required) +- `language`: The language code (e.g., "en", "zh") (optional) +- `prompt`: Optional text to guide the transcription style (optional) +- `response_format`: Format of the response ("json", "text") (optional) +- `temperature`: Sampling temperature between 0 and 1 (optional) + +For the complete list of supported parameters including sampling parameters and vLLM extensions, see the [protocol definitions](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/protocol.py#L2182). + +**Response Format:** + +For `verbose_json` response format: + +??? code + + ```json + { + "text": "Hello, this is a transcription of the audio file.", + "language": "en", + "duration": 5.42, + "segments": [ + { + "id": 0, + "seek": 0, + "start": 0.0, + "end": 2.5, + "text": "Hello, this is a transcription", + "tokens": [50364, 938, 428, 307, 275, 28347], + "temperature": 0.0, + "avg_logprob": -0.245, + "compression_ratio": 1.235, + "no_speech_prob": 0.012 + } + ] + } + ``` + #### Extra Parameters The following [sampling parameters][sampling-params] are supported. From c4b673cf9ff4b6914760bc9be82bbcd234343459 Mon Sep 17 00:00:00 2001 From: Peter Pan Date: Sat, 27 Sep 2025 23:02:40 +0800 Subject: [PATCH 052/166] [env] default nixl side port conflicts with kv-event zmq port (#25056) Signed-off-by: Peter Pan --- vllm/envs.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vllm/envs.py b/vllm/envs.py index 832d031f998e..94b0dece9655 100755 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -155,7 +155,7 @@ VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256 VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost" - VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5557 + VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600 VLLM_ALL2ALL_BACKEND: Literal["naive", "pplx", "deepep_high_throughput", "deepep_low_latency", @@ -1220,7 +1220,7 @@ def get_vllm_port() -> Optional[int]: # Port used for NIXL handshake between remote agents. "VLLM_NIXL_SIDE_CHANNEL_PORT": - lambda: int(os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5557")), + lambda: int(os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5600")), # all2all backend for vllm's expert parallel communication # Available options: From 66b2a3132b87762491b03e3487ba6e521d42df0c Mon Sep 17 00:00:00 2001 From: "Patrick C. Toulme" <135739773+patrick-toulme@users.noreply.github.com> Date: Sat, 27 Sep 2025 11:40:59 -0400 Subject: [PATCH 053/166] [Core] Refactor self.model() to call a helper for subclassing. (#25084) Signed-off-by: Patrick Toulme Signed-off-by: Patrick Toulme --- vllm/v1/worker/gpu_model_runner.py | 34 +++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 1bae0d4ce4d1..2354e8222e7a 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -2268,6 +2268,38 @@ def synchronize_input_prep(self): finally: self.prepare_inputs_event.record() + def _model_forward( + self, + input_ids: Optional[torch.Tensor] = None, + positions: Optional[torch.Tensor] = None, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + **model_kwargs: dict[str, Any], + ) -> Any: + """Helper method to call the model forward pass. + + This method can be overridden by subclasses for model execution. + Motivation: We can inspect only this method versus + the whole execute_model, which has additional logic. + + Args: + input_ids: Input token IDs + positions: Token positions + intermediate_tensors: Tensors from previous pipeline stages + inputs_embeds: Input embeddings (alternative to input_ids) + **model_kwargs: Additional model arguments + + Returns: + Model output tensor + """ + return self.model( + input_ids=input_ids, + positions=positions, + intermediate_tensors=intermediate_tensors, + inputs_embeds=inputs_embeds, + **model_kwargs, + ) + @torch.inference_mode() def execute_model( self, @@ -2337,7 +2369,7 @@ def execute_model( ), record_function_or_nullcontext("Forward"), self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output): - model_output = self.model( + model_output = self._model_forward( input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, From eded855d78c72e0d601abd16d8ccc1f02415f3f2 Mon Sep 17 00:00:00 2001 From: Jiangyun Zhu Date: Sun, 28 Sep 2025 00:09:00 +0800 Subject: [PATCH 054/166] [torch.compile]: Add VLLM_DEBUG_DUMP_PATH environment variable (#25651) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: zjy0516 Signed-off-by: Jiangyun Zhu Co-authored-by: Luka Govedič --- vllm/compilation/monitor.py | 10 ++++------ vllm/compilation/vllm_inductor_pass.py | 5 +---- vllm/compilation/wrapper.py | 11 +++++------ vllm/config/__init__.py | 26 ++++++++++++++++++++++++++ vllm/config/compilation.py | 3 ++- vllm/envs.py | 6 ++++++ 6 files changed, 44 insertions(+), 17 deletions(-) diff --git a/vllm/compilation/monitor.py b/vllm/compilation/monitor.py index c46721ab2d74..35658466d66d 100644 --- a/vllm/compilation/monitor.py +++ b/vllm/compilation/monitor.py @@ -1,7 +1,6 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project -import os import time from vllm.config import CompilationConfig, CompilationLevel, VllmConfig @@ -18,13 +17,12 @@ def start_monitoring_torch_compile(vllm_config: VllmConfig): torch_compile_start_time = time.time() compilation_config: CompilationConfig = vllm_config.compilation_config - if compilation_config.level == CompilationLevel.PIECEWISE and \ - compilation_config.debug_dump_path: + path = vllm_config.compile_debug_dump_path() + if compilation_config.level == CompilationLevel.PIECEWISE and path: import depyf - path = os.path.join(compilation_config.debug_dump_path, - f"rank_{vllm_config.parallel_config.rank}") + path.mkdir(parents=True, exist_ok=True) global context_manager - context_manager = depyf.prepare_debug(path) + context_manager = depyf.prepare_debug(path.as_posix()) context_manager.__enter__() diff --git a/vllm/compilation/vllm_inductor_pass.py b/vllm/compilation/vllm_inductor_pass.py index 837770d18199..59019d74cb80 100644 --- a/vllm/compilation/vllm_inductor_pass.py +++ b/vllm/compilation/vllm_inductor_pass.py @@ -3,7 +3,6 @@ import functools import operator import time -from pathlib import Path from typing import ClassVar, Optional import regex as re @@ -96,12 +95,10 @@ def dump_patterns(self, config: VllmConfig, pm_pass: PatternMatcherPass): TODO(luka): use pattern object to manually produce pattern graph """ - debug_dump_path = config.compilation_config.debug_dump_path + debug_dump_path = config.compile_debug_dump_path() if not debug_dump_path: return - rank = config.parallel_config.rank - debug_dump_path = Path(debug_dump_path) / f"rank_{rank}" debug_dump_path.mkdir(parents=True, exist_ok=True) from vllm.utils import unique_filepath diff --git a/vllm/compilation/wrapper.py b/vllm/compilation/wrapper.py index 930e4d27b410..062c9dc27017 100644 --- a/vllm/compilation/wrapper.py +++ b/vllm/compilation/wrapper.py @@ -92,12 +92,11 @@ def bytecode_hook(self, old_code: CodeType, new_code: CodeType): return self.compiled_codes.append(new_code) - debug_dump_dir = self.vllm_config.compilation_config.debug_dump_path - if isinstance(debug_dump_dir, str) and debug_dump_dir != "": - rank = self.vllm_config.parallel_config.rank - decompiled_file = os.path.join(debug_dump_dir, f"rank_{rank}", - "transformed_code.py") - if not os.path.exists(decompiled_file): + + path = self.vllm_config.compile_debug_dump_path() + if path: + decompiled_file = path / "transformed_code.py" + if not decompiled_file.exists(): try: # usually the decompilation will succeed for most models, # as we guarantee a full-graph compilation in Dynamo. diff --git a/vllm/config/__init__.py b/vllm/config/__init__.py index ecea90988ebc..ccb91999d370 100644 --- a/vllm/config/__init__.py +++ b/vllm/config/__init__.py @@ -12,6 +12,7 @@ from contextlib import contextmanager from dataclasses import field, fields, is_dataclass, replace from functools import cached_property, lru_cache +from pathlib import Path from typing import (TYPE_CHECKING, Any, Literal, Optional, Protocol, TypeVar, Union, cast) @@ -541,6 +542,17 @@ def __post_init__(self): # local attention. self.scheduler_config.disable_hybrid_kv_cache_manager = True + if self.compilation_config.debug_dump_path: + self.compilation_config.debug_dump_path = \ + self.compilation_config.debug_dump_path.absolute().expanduser() + if envs.VLLM_DEBUG_DUMP_PATH is not None: + env_path = Path(envs.VLLM_DEBUG_DUMP_PATH).absolute().expanduser() + if self.compilation_config.debug_dump_path: + logger.warning( + "Config-specified debug dump path is overridden" + " by VLLM_DEBUG_DUMP_PATH to %s", env_path) + self.compilation_config.debug_dump_path = env_path + def update_sizes_for_sequence_parallelism(self, possible_sizes: list) -> list: # remove the sizes that not multiple of tp_size when @@ -672,6 +684,20 @@ def try_verify_and_update_config(self): f"but got '{self.load_config.load_format}'. " f"Model: {self.model_config.model}") + def compile_debug_dump_path(self) -> Optional[Path]: + """Returns a rank-aware path for dumping + torch.compile debug information. + """ + if self.compilation_config.debug_dump_path is None: + return None + tp_rank = self.parallel_config.rank + dp_rank = self.parallel_config.data_parallel_rank + data_parallel_size = self.parallel_config.data_parallel_size + append_path = f"rank_{tp_rank}" if data_parallel_size == 1 \ + else f"rank_{tp_rank}_dp_{dp_rank}" + path = self.compilation_config.debug_dump_path / append_path + return path + def __str__(self): return ( f"model={self.model_config.model!r}, " diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py index 9735db98567d..825de7d26191 100644 --- a/vllm/config/compilation.py +++ b/vllm/config/compilation.py @@ -5,6 +5,7 @@ import hashlib from collections import Counter from dataclasses import asdict, field +from pathlib import Path from typing import TYPE_CHECKING, Any, Callable, ClassVar, Optional, Union from pydantic import TypeAdapter, field_validator @@ -169,7 +170,7 @@ class CompilationConfig: - 1: dynamo as is. - 2: dynamo once. - 3: piecewise compilation.""" - debug_dump_path: str = "" + debug_dump_path: Optional[Path] = None """The path to dump the debug information.""" cache_dir: str = "" """The directory to store the compiled graph, to accelerate Inductor diff --git a/vllm/envs.py b/vllm/envs.py index 94b0dece9655..854328044304 100755 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -199,6 +199,7 @@ VLLM_DBO_COMM_SMS: int = 20 GPT_OSS_SYSTEM_TOOL_MCP_LABELS: list[str] = [] VLLM_PATTERN_MATCH_DEBUG: Optional[str] = None + VLLM_DEBUG_DUMP_PATH: Optional[str] = None VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True VLLM_USE_NCCL_SYMM_MEM: bool = False @@ -513,6 +514,11 @@ def get_vllm_port() -> Optional[int]: "VLLM_PATTERN_MATCH_DEBUG": lambda: os.environ.get("VLLM_PATTERN_MATCH_DEBUG", None), + # Dump fx graphs to the given directory. + # It will override CompilationConfig.debug_dump_path if set. + "VLLM_DEBUG_DUMP_PATH": + lambda: os.environ.get("VLLM_DEBUG_DUMP_PATH", None), + # local rank of the process in the distributed setting, used to determine # the GPU device id "LOCAL_RANK": From 0488225d034f7370a80f89e57d475774db615ca5 Mon Sep 17 00:00:00 2001 From: Clayton Coleman Date: Sat, 27 Sep 2025 13:36:28 -0400 Subject: [PATCH 055/166] [Bug]: Set LD_LIBRARY_PATH to include the 'standard' CUDA location (#25766) Signed-off-by: Clayton Coleman --- docker/Dockerfile | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/docker/Dockerfile b/docker/Dockerfile index c2b855be4403..62be0a12e988 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -474,6 +474,12 @@ ENV CUDA_HOME=/usr/local/cuda RUN export TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-9.0a+PTX}" \ && bash install_python_libraries.sh +# CUDA image changed from /usr/local/nvidia to /usr/local/cuda in 12.8 but will +# return to /usr/local/nvidia in 13.0 to allow container providers to mount drivers +# consistently from the host (see https://github.com/vllm-project/vllm/issues/18859). +# Until then, add /usr/local/nvidia/lib64 before the image cuda path to allow override. +ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:${LD_LIBRARY_PATH} + #################### vLLM installation IMAGE #################### #################### TEST IMAGE #################### From b81791e5068ca2ba6c7a44afe19f95ecd3a39894 Mon Sep 17 00:00:00 2001 From: Jialin Ouyang Date: Sat, 27 Sep 2025 10:53:31 -0700 Subject: [PATCH 056/166] [Core] GC Debug callback (#24829) Signed-off-by: Jialin Ouyang Signed-off-by: Jialin Ouyang Co-authored-by: Jialin Ouyang --- docs/contributing/profiling.md | 8 +++ tests/utils_/test_gc_utils.py | 69 ++++++++++++++++++ vllm/envs.py | 8 +++ vllm/utils/gc_utils.py | 128 +++++++++++++++++++++++++++++++++ vllm/v1/engine/core.py | 4 ++ 5 files changed, 217 insertions(+) create mode 100644 tests/utils_/test_gc_utils.py create mode 100644 vllm/utils/gc_utils.py diff --git a/docs/contributing/profiling.md b/docs/contributing/profiling.md index 5b83d93274f0..a1b7927a95d1 100644 --- a/docs/contributing/profiling.md +++ b/docs/contributing/profiling.md @@ -208,3 +208,11 @@ One example is [snakeviz](https://jiffyclub.github.io/snakeviz/). pip install snakeviz snakeviz expensive_function.prof ``` + +### Analyzing Garbage Collection Costs + +Leverage VLLM_GC_DEBUG environment variable to debug GC costs. + +- VLLM_GC_DEBUG=1: enable GC debugger with gc.collect elpased times +- VLLM_GC_DEBUG='{"top_objects":5}': enable GC debugger to log top 5 + collected objects for each gc.collect diff --git a/tests/utils_/test_gc_utils.py b/tests/utils_/test_gc_utils.py new file mode 100644 index 000000000000..265761b069ca --- /dev/null +++ b/tests/utils_/test_gc_utils.py @@ -0,0 +1,69 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +from dataclasses import dataclass +from typing import Any + +from vllm.utils.gc_utils import (GCDebugConfig, _compute_detailed_type, + _compute_top_gc_collected_objects) + + +@dataclass +class Normal: + v: int + + +@dataclass +class ListWrapper: + vs: list[int] + + def __len__(self) -> int: + return len(self.vs) + + +def test_compute_detailed_type(): + assert _compute_detailed_type( + Normal(v=8)) == "" + + assert _compute_detailed_type([1, 2, 3]) == "(size:3)" + assert _compute_detailed_type({4, 5}) == "(size:2)" + assert _compute_detailed_type({6: 7}) == "(size:1)" + assert _compute_detailed_type(ListWrapper( + vs=[])) == "(size:0)" + + +def test_compute_top_gc_collected_objects(): + objects: list[Any] = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], + {13, 14}, { + 15: 16, + 17: 18 + }, + Normal(v=19), + Normal(v=20), + Normal(v=21)] + assert _compute_top_gc_collected_objects(objects, top=-1) == "" + assert _compute_top_gc_collected_objects(objects, top=0) == "" + assert _compute_top_gc_collected_objects( + objects, top=1) == " 4:(size:3)" + assert _compute_top_gc_collected_objects(objects, top=2) == "\n".join([ + " 4:(size:3)", + " 3:" + ]) + assert _compute_top_gc_collected_objects(objects, top=3) == "\n".join([ + " 4:(size:3)", + " 3:", + " 1:(size:2)" + ]) + + +def test_gc_debug_config(): + assert not GCDebugConfig(None).enabled + assert not GCDebugConfig("").enabled + assert not GCDebugConfig("0").enabled + + config = GCDebugConfig("1") + assert config.enabled + assert config.top_objects == -1 + + config = GCDebugConfig("{\"top_objects\":5}") + assert config.enabled + assert config.top_objects == 5 diff --git a/vllm/envs.py b/vllm/envs.py index 854328044304..f06c860b8297 100755 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -205,6 +205,7 @@ VLLM_USE_NCCL_SYMM_MEM: bool = False VLLM_NCCL_INCLUDE_PATH: Optional[str] = None VLLM_USE_FBGEMM: bool = False + VLLM_GC_DEBUG: str = "" def get_default_cache_root(): @@ -1475,6 +1476,13 @@ def get_vllm_port() -> Optional[int]: lambda: os.environ.get("VLLM_NCCL_INCLUDE_PATH", None), # Flag to enable FBGemm kernels on model execution "VLLM_USE_FBGEMM": lambda: bool(int(os.getenv("VLLM_USE_FBGEMM", "0"))), + + # GC debug config + # - VLLM_GC_DEBUG=0: disable GC debugger + # - VLLM_GC_DEBUG=1: enable GC debugger with gc.collect elpased times + # - VLLM_GC_DEBUG='{"top_objects":5}': enable GC debugger with + # top 5 collected objects + "VLLM_GC_DEBUG": lambda: os.getenv("VLLM_GC_DEBUG", ""), } # --8<-- [end:env-vars-definition] diff --git a/vllm/utils/gc_utils.py b/vllm/utils/gc_utils.py new file mode 100644 index 000000000000..8ce2c200e299 --- /dev/null +++ b/vllm/utils/gc_utils.py @@ -0,0 +1,128 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import gc +import json +import time +from collections import Counter +from contextlib import suppress +from typing import Any, Optional + +from vllm.envs import VLLM_GC_DEBUG +from vllm.logger import init_logger + +logger = init_logger(__name__) + + +class GCDebugConfig: + """ + Config for GC Debugger. + - 0: disable GC debugger + - 1: enable GC debugger with gc.collect elpased times + - '{"top_objects":5}': enable GC debugger with top 5 collected objects + """ + + def __init__(self, gc_debug_conf: Optional[str] = None) -> None: + self.enabled: bool = False + self.top_objects: int = -1 + + if not gc_debug_conf or gc_debug_conf == "0": + pass + elif gc_debug_conf == "1": + self.enabled = True + else: + try: + json_conf = json.loads(gc_debug_conf) + self.enabled = True + self.top_objects = json_conf.get("top_objects", -1) + except Exception: + self.enabled = False + logger.error("Failed to parse VLLM_GC_DEBUG(%s)", + VLLM_GC_DEBUG) + logger.info("GC Debug Config. %s", str(self)) + + def __repr__(self) -> str: + return f"enabled:{self.enabled},top_objects:{self.top_objects}" + + +class GCDebugger: + """ + Debugger for GC which logs helpful information for GC understanding. + To enable, you should call maybe_attach_gc_debug_callback in the process. + """ + + def __init__(self, config: GCDebugConfig) -> None: + self.config = config + # Start time in micro second of this GC cycle + self.start_time_ns: int = time.monotonic_ns() + # If config.top_objects is positive, + # compute top collected objects by object types + self.gc_top_collected_objects: str = "" + + def handle(self, phase: str, info: dict[str, int]) -> None: + """ + Handles a GC event (e.g. GC start or GC finish) + """ + generation = info.get("generation") + if generation is None: + return + if phase == "start": + # Before GC started, record GC start time + # and top collected objects + self.start_time_ns = time.monotonic_ns() + self.gc_top_collected_objects = _compute_top_gc_collected_objects( + gc.get_objects(generation), self.config.top_objects) + elif phase == "stop": + # After GC finished, Record GC elapsed time and + # optionally top collected objects + elpased_ms = (time.monotonic_ns() - self.start_time_ns) / 1e6 + logger.info( + "GC took %.3fms to complete. " + "Collected %s objects in GC generation %d.%s", + elpased_ms, + str(info.get("collected", "?")), + generation, + (f" Top collected objects: \n{self.gc_top_collected_objects}" + if self.gc_top_collected_objects else ""), + ) + + +def maybe_attach_gc_debug_callback() -> None: + """ + Attached a callback for GC debug when VLLM_GC_DEBUG is enabled. + """ + config = GCDebugConfig(VLLM_GC_DEBUG) + if config.enabled: + debugger: GCDebugger = GCDebugger(config) + + def gc_callback(phase: str, info: dict[str, int]) -> None: + debugger.handle(phase, info) + + gc.callbacks.append(gc_callback) + + +def _compute_detailed_type(o: Any) -> str: + """ + Detailed object type. + + TODO(Jialin): Further enhance the detailed type with element types for + easier debugging. We tried but occasionally it would run into signals + which kills the engine. + """ + size_str: str = "" + # Object doesn't support len() - this can happen with type objects + # or other objects that don't implement __len__ properly + with suppress(Exception): + size_str = f"(size:{len(o)})" + return f"{str(type(o))}{size_str}" + + +def _compute_top_gc_collected_objects(objects: list[Any], top: int) -> str: + """ + Group collected objects by types. + """ + if top <= 0: + return "" + object_types = [_compute_detailed_type(o) for o in objects] + return "\n".join( + f"{count:>5}:{object_type}" + for object_type, count in Counter(object_types).most_common(top)) diff --git a/vllm/v1/engine/core.py b/vllm/v1/engine/core.py index a43042a5510a..3ee804f10c17 100644 --- a/vllm/v1/engine/core.py +++ b/vllm/v1/engine/core.py @@ -29,6 +29,7 @@ maybe_register_config_serialize_by_value) from vllm.utils import (decorate_logs, get_hash_fn_by_name, make_zmq_socket, resolve_obj_by_qualname, set_process_title) +from vllm.utils.gc_utils import maybe_attach_gc_debug_callback from vllm.v1.core.kv_cache_utils import (BlockHash, generate_scheduler_kv_cache_config, get_kv_cache_configs, @@ -532,6 +533,9 @@ def __init__( gc.collect() gc.freeze() + # If enable, attach GC debugger after static variable freeze. + maybe_attach_gc_debug_callback() + @contextmanager def _perform_handshakes( self, From 0c412f07a88116a21091cf449ea26f75de018097 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nicol=C3=B2=20Lucchesi?= Date: Sat, 27 Sep 2025 21:17:35 +0200 Subject: [PATCH 057/166] [Bugfix][NIXL] Fix Async Scheduler timeout issue (#25808) Signed-off-by: NickLucche --- .../kv_connector/v1/nixl_connector.py | 31 +++++++++++++++++-- 1 file changed, 28 insertions(+), 3 deletions(-) diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py index c205501e6c98..5af2b33f029c 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py @@ -105,6 +105,7 @@ def __init__(self): self.reqs_to_recv: dict[ReqId, ReqMeta] = {} self.reqs_to_save: dict[ReqId, ReqMeta] = {} self.reqs_to_send: dict[ReqId, float] = {} + self.reqs_in_batch: set[ReqId] = set() def add_new_req( self, @@ -278,6 +279,7 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str): self._reqs_need_save: dict[ReqId, tuple[Request, list[int]]] = {} # Reqs to send and their expiration time self._reqs_need_send: dict[ReqId, float] = {} + self._reqs_in_batch: set[ReqId] = set() def get_num_new_matched_tokens( self, request: "Request", @@ -324,6 +326,9 @@ def update_state_after_alloc(self, request: "Request", if not params: return + + if params.get("do_remote_decode"): + self._reqs_in_batch.add(request.request_id) if self.use_host_buffer and params.get("do_remote_decode"): # NOTE: when accelerator is not directly supported by Nixl, # prefilled blocks need to be saved to host memory before transfer. @@ -373,6 +378,8 @@ def build_connector_meta( request_id=req_id, local_block_ids=block_ids, kv_transfer_params=req.kv_transfer_params, + load_remote_cache=True, + save_to_host=False, ) for req_id, (req, block_ids) in self._reqs_need_save.items(): @@ -386,10 +393,12 @@ def build_connector_meta( ) meta.reqs_to_send = self._reqs_need_send + meta.reqs_in_batch = self._reqs_in_batch # Clear the list once workers start the transfers self._reqs_need_recv.clear() self._reqs_need_save.clear() + self._reqs_in_batch = set() self._reqs_need_send = {} return meta @@ -546,6 +555,8 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str): self._recving_transfers = defaultdict[ReqId, list[Transfer]](list) # Track the expiration time of requests that are waiting to be sent. self._reqs_to_send: dict[ReqId, float] = {} + # Set of requests that have been part of a batch, regardless of status. + self._reqs_to_process: set[ReqId] = set() # Background thread for handling new handshake requests. self._nixl_handshake_listener_t: Optional[threading.Thread] = None @@ -1082,6 +1093,7 @@ def get_finished(self) -> tuple[set[str], set[str]]: "Releasing expired KV blocks for request %s which were " "retrieved by %d decode worker(s) within %d seconds.", req_id, count, envs.VLLM_NIXL_ABORT_REQUEST_TIMEOUT) + self._reqs_to_process.remove(req_id) del self._reqs_to_send[req_id] done_sending.add(req_id) @@ -1097,7 +1109,8 @@ def _get_new_notifs(self) -> set[str]: for notifs in self.nixl_wrapper.get_new_notifs().values(): for notif in notifs: req_id, tp_ratio = notif.decode("utf-8").rsplit(":", 1) - if req_id not in self._reqs_to_send: + if (req_id not in self._reqs_to_send + and req_id not in self._reqs_to_process): logger.error( "Potentially invalid KV blocks for " "unrecognized request %s were retrieved by " @@ -1110,7 +1123,8 @@ def _get_new_notifs(self) -> set[str]: tp_ratio): notified_req_ids.add(req_id) del self.consumer_notification_counts_by_req[req_id] - del self._reqs_to_send[req_id] + self._reqs_to_process.remove(req_id) + self._reqs_to_send.pop(req_id, None) return notified_req_ids def _pop_done_transfers( @@ -1171,8 +1185,19 @@ def start_load_kv(self, metadata: NixlConnectorMetadata): while not self._ready_requests.empty(): self._read_blocks_for_req(*self._ready_requests.get_nowait()) + # Keep around the requests that have been part of a batch. This is + # needed because async scheduling pushes the misalignment between the + # moment in which requests expiration is set (P side) and the moment in + # which blocks are read from D. As P can now more easily lag behind D + # while processing the next batch, we make sure to only set an + # expiration for requests that have not been read from D yet. + for req_id in metadata.reqs_in_batch: + self._reqs_to_process.add(req_id) + # Add to requests that are waiting to be read and track expiration. - self._reqs_to_send.update(metadata.reqs_to_send) + for req_id, expiration_time in metadata.reqs_to_send.items(): + if req_id in self._reqs_to_process: + self._reqs_to_send[req_id] = expiration_time def _read_blocks_for_req(self, req_id: str, meta: ReqMeta): logger.debug( From 150982ea331ac05e116ff494145a05a7efc4712e Mon Sep 17 00:00:00 2001 From: Roger Wang Date: Sat, 27 Sep 2025 19:17:58 -0700 Subject: [PATCH 058/166] [MM] Optimize memory profiling for scattered multimodal embeddings (#25810) Signed-off-by: Roger Wang --- vllm/v1/worker/gpu_model_runner.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 2354e8222e7a..0960fe3a25fb 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -3429,6 +3429,23 @@ def profile_run(self) -> None: expected_num_items=max_mm_items_per_batch, ) + # NOTE: This happens when encoder cache needs to store + # the embeddings that encoder outputs are scattered onto. + # In this case we create dummy embeddings of size + # (encode_budget, hidden_size) and scatter encoder + # output into it. + encoder_output_shape = dummy_encoder_outputs[0].shape + if encoder_output_shape[0] < encoder_budget: + expanded_outputs = [] + for output in dummy_encoder_outputs: + expanded = output.new_zeros( + (encoder_budget, encoder_output_shape[-1])) + num_tokens = output.shape[0] + expanded[:num_tokens].copy_(output) + expanded_outputs.append(expanded) + + dummy_encoder_outputs = expanded_outputs + # Cache the dummy encoder outputs. self.encoder_cache["tmp"] = dict( enumerate(dummy_encoder_outputs)) From a0ccd4735c283c1a15dda0fccdb5bc2a6a14a786 Mon Sep 17 00:00:00 2001 From: Roger Wang Date: Sat, 27 Sep 2025 20:21:09 -0700 Subject: [PATCH 059/166] [Bugfix] Fix Qwen3-VL regression from #24982 (#25814) Signed-off-by: Roger Wang --- vllm/model_executor/models/qwen3_moe.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/models/qwen3_moe.py b/vllm/model_executor/models/qwen3_moe.py index 45b9c656a4bb..61f1abad72b6 100644 --- a/vllm/model_executor/models/qwen3_moe.py +++ b/vllm/model_executor/models/qwen3_moe.py @@ -107,7 +107,7 @@ def __init__( ): super().__init__() - config = vllm_config.model_config.hf_config + config = vllm_config.model_config.hf_text_config parallel_config = vllm_config.parallel_config quant_config = vllm_config.quant_config @@ -293,7 +293,7 @@ class Qwen3MoeDecoderLayer(nn.Module): def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None: super().__init__() - config = vllm_config.model_config.hf_config + config = vllm_config.model_config.hf_text_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config @@ -372,7 +372,7 @@ class Qwen3MoeModel(nn.Module): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() - config = vllm_config.model_config.hf_config.get_text_config() + config = vllm_config.model_config.hf_text_config quant_config = vllm_config.quant_config parallel_config = vllm_config.parallel_config eplb_config = parallel_config.eplb_config @@ -586,7 +586,7 @@ class Qwen3MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() - config = vllm_config.model_config.hf_config + config = vllm_config.model_config.hf_text_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config From b23facaf726ea3f04468cf2c09cfdff5cd0f52dc Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Sun, 28 Sep 2025 12:21:01 +0800 Subject: [PATCH 060/166] [VLM] Update Qwen3-VL max_num_video_tokens calculation for configurable video profiling (#25557) Signed-off-by: Isotr0py Signed-off-by: Roger Wang Co-authored-by: Roger Wang --- vllm/model_executor/models/qwen2_vl.py | 13 +++-- vllm/model_executor/models/qwen3_vl.py | 70 ++++++++++++++++++++++++-- 2 files changed, 74 insertions(+), 9 deletions(-) diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index 8192c3ce05dd..6ef01f333554 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -79,7 +79,7 @@ logger = init_logger(__name__) # For profile run -_MAX_FRAMES_PER_VIDEO = 32 +_MAX_FRAMES_PER_VIDEO = 14 # === Vision Inputs === # @@ -932,6 +932,7 @@ def get_num_image_tokens( _, num_image_tokens = self._get_vision_info( image_width=image_width, image_height=image_height, + num_frames=1, image_processor=image_processor, ) return num_image_tokens @@ -956,6 +957,7 @@ def get_image_size_with_most_features(self) -> ImageSize: max_image_size, _ = self._get_vision_info( image_width=9999999, image_height=9999999, + num_frames=1, image_processor=None, ) return max_image_size @@ -969,10 +971,12 @@ def get_max_image_tokens(self) -> int: image_processor=None, ) - def _get_max_video_frames(self, max_tokens: int) -> int: + def _get_max_video_frames(self, + max_tokens: int, + start_num_frames: int = 1) -> int: target_width, target_height = self.get_image_size_with_most_features() - num_frames = 0 + num_frames = start_num_frames while True: next_num_frames = num_frames + 1 @@ -994,12 +998,13 @@ def get_num_frames_with_most_features( self, seq_len: int, mm_counts: Mapping[str, int], + max_frames_per_video: int = _MAX_FRAMES_PER_VIDEO, ) -> int: max_videos = mm_counts.get("video", 0) max_total_frames = self._get_max_video_frames(seq_len) max_frames_per_video = min(max_total_frames // max(max_videos, 1), - _MAX_FRAMES_PER_VIDEO) + max_frames_per_video) return max(max_frames_per_video, 1) diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index 5d0b66f91ace..c8f91dd48969 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -33,11 +33,14 @@ import torch.nn.functional as F from transformers import BatchFeature from transformers.models.qwen2_vl import Qwen2VLImageProcessorFast -from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize +from transformers.models.qwen2_vl.image_processing_qwen2_vl import ( + smart_resize as image_smart_resize) from transformers.models.qwen3_vl import (Qwen3VLProcessor, Qwen3VLVideoProcessor) from transformers.models.qwen3_vl.configuration_qwen3_vl import ( Qwen3VLConfig, Qwen3VLVisionConfig) +from transformers.models.qwen3_vl.video_processing_qwen3_vl import ( + smart_resize as video_smart_resize) from transformers.video_utils import VideoMetadata from vllm.attention.layer import check_upstream_fa_availability @@ -85,6 +88,9 @@ logger = init_logger(__name__) +# Official recommended max pixels is 24576 * 32 * 32 +_MAX_FRAMES_PER_VIDEO = 24576 + class Qwen3_VisionPatchEmbed(nn.Module): @@ -593,11 +599,16 @@ def _get_vision_info( image_height: int, num_frames: int = 2, do_resize: bool = True, - image_processor: Optional[Qwen2VLImageProcessorFast], + image_processor: Optional[Union[Qwen2VLImageProcessorFast, + Qwen3VLVideoProcessor]], ) -> tuple[ImageSize, int]: - if image_processor is None: + if image_processor is None and num_frames > 1: + image_processor = self.get_video_processor() + elif image_processor is None: image_processor = self.get_image_processor() + is_video = isinstance(image_processor, Qwen3VLVideoProcessor) + hf_config = self.get_hf_config() vision_config = hf_config.vision_config patch_size = vision_config.patch_size @@ -605,12 +616,22 @@ def _get_vision_info( temporal_patch_size = vision_config.temporal_patch_size if do_resize: + if is_video: + smart_resize = video_smart_resize + extra_kwargs = { + "num_frames": num_frames, + "temporal_factor": temporal_patch_size + } + else: + smart_resize = image_smart_resize + extra_kwargs = {} resized_height, resized_width = smart_resize( height=image_height, width=image_width, factor=patch_size * merge_size, min_pixels=image_processor.size["shortest_edge"], max_pixels=image_processor.size["longest_edge"], + **extra_kwargs, ) preprocessed_size = ImageSize(width=resized_width, height=resized_height) @@ -629,6 +650,39 @@ def _get_vision_info( return preprocessed_size, num_vision_tokens + def _get_max_video_frames(self, + max_tokens: int, + start_num_frames: int = 2) -> int: + return super()._get_max_video_frames(max_tokens, + start_num_frames=start_num_frames) + + def get_num_frames_with_most_features( + self, + seq_len: int, + mm_counts: Mapping[str, int], + ) -> int: + return super().get_num_frames_with_most_features( + seq_len, mm_counts, max_frames_per_video=_MAX_FRAMES_PER_VIDEO) + + def get_max_video_tokens( + self, + seq_len: int, + mm_counts: Mapping[str, int], + ) -> int: + target_width, target_height = self.get_image_size_with_most_features() + video_soft_tokens = self.get_num_video_tokens( + image_width=target_width, + image_height=target_height, + num_frames=self.get_num_frames_with_most_features( + seq_len, mm_counts), + image_processor=None, + ) + + # NOTE: By default in Qwen3-VL, one video token is converted to + # "<{timestamp} seconds>" (on average 9.5 tokens) + vision_start_token + video_token + vision_end_token # noqa: E501 + formatted_video_soft_tokens = video_soft_tokens * 12.5 + return int(formatted_video_soft_tokens) + def _calculate_timestamps(self, indices: list[int] | torch.Tensor, video_fps: float, merge_size: int): if not isinstance(indices, list): @@ -698,6 +752,12 @@ def get_dummy_mm_data( self.info.get_image_size_with_most_features()) target_num_frames = self.info.get_num_frames_with_most_features( seq_len, mm_counts) + target_video_size, _ = self.info._get_vision_info( + image_width=target_width, + image_height=target_height, + num_frames=target_num_frames, + image_processor=self.info.get_video_processor(), + ) return { "image": self._get_dummy_images(width=target_width, @@ -705,8 +765,8 @@ def get_dummy_mm_data( num_images=num_images), "video": self._get_dummy_videos( - width=target_width, - height=target_height, + width=target_video_size.width, + height=target_video_size.height, num_frames=target_num_frames, num_videos=num_videos, ), From 453971a17875eb46ad04aca3581d613a0d9e7bb0 Mon Sep 17 00:00:00 2001 From: weiliang Date: Sun, 28 Sep 2025 16:23:44 +0800 Subject: [PATCH 061/166] Fix random dataset mismatched token length with config. (#24937) Signed-off-by: Weiliang Liu Signed-off-by: Roger Wang Co-authored-by: Roger Wang --- vllm/benchmarks/datasets.py | 145 +++++++++++++++++++++++++++++------- 1 file changed, 118 insertions(+), 27 deletions(-) diff --git a/vllm/benchmarks/datasets.py b/vllm/benchmarks/datasets.py index f0c0d829a393..807e543dc0cd 100644 --- a/vllm/benchmarks/datasets.py +++ b/vllm/benchmarks/datasets.py @@ -366,11 +366,67 @@ def process_video(video: Any) -> Mapping[str, Any]: f"Invalid video input {video}. Must be a string of local path/remote url, or a dictionary with raw video bytes in the form of `{{'bytes': raw_video_bytes}}`." # noqa: E501 ) + +def gen_prompt_decode_to_target_len( + tokenizer: PreTrainedTokenizerBase, + token_sequence: list[int], + target_token_len: int, + max_retry: int = 10, + add_special_tokens: bool = False, + rng: Optional[np.random.Generator] = None, +) -> tuple[str, list[int]]: + """ + Ensure decoded-then-encoded prompt length matches the target token length. + + This function decodes an initial token sequence to text and re-encodes it + , iteratively adjusting the token sequence length to match a target. + This is necessary because some tokenizers do not guarantee a 1:1 mapping + between consecutive tokens and the decoded-then-encoded sequence length. + For example, for GPT2Tokenizer: + [6880, 6881] -> ['Ġcalls', 'here'] -> + [1650, 939, 486] -> ['Ġcall', 'sh', 'ere'] + + Returns a tuple of the final prompt string and the adjusted token sequence. + """ + remain_num_try = max_retry + token_mismatch = 0 + while True: + prompt = tokenizer.decode(token_sequence) + token_sequence = tokenizer.encode( + prompt, add_special_tokens=add_special_tokens + ) + if remain_num_try <= 0: + if len(token_sequence) != target_token_len: + token_mismatch = len(token_sequence) - target_token_len + break + + if len(token_sequence) == target_token_len: + break + elif len(token_sequence) < target_token_len: + if rng is not None: + extra_tokens = rng.integers( + 0, + tokenizer.vocab_size, + size=target_token_len - len(token_sequence), + ).tolist() + else: + extra_tokens = np.random.randint( + 0, + tokenizer.vocab_size, + size=target_token_len - len(token_sequence), + ).tolist() + token_sequence.extend(extra_tokens) + elif len(token_sequence) > target_token_len: + token_sequence = token_sequence[:target_token_len] + + remain_num_try -= 1 + + return prompt, token_sequence, token_mismatch + # ----------------------------------------------------------------------------- # Random Dataset Implementation (Synthetic Data) # ----------------------------------------------------------------------------- - class RandomDataset(BenchmarkDataset): """ Synthetic text-only dataset for serving/throughput benchmarks. @@ -420,8 +476,9 @@ def sample( vocab_size = tokenizer.vocab_size requests = [] + token_mismatch_total = 0 for i in range(num_requests): - prompt, total_input_len = self.generate_token_sequence( + prompt, total_input_len, token_mismatch = self.generate_token_sequence( # noqa: E501 tokenizer=tokenizer, prefix_token_ids=prefix_token_ids, prefix_len=prefix_len, @@ -430,6 +487,7 @@ def sample( offset=int(offsets[i]), index=i, ) + token_mismatch_total += token_mismatch requests.append( SampleRequest( prompt=prompt, @@ -453,6 +511,18 @@ def sample( ) ) requests = batch_requests + + if token_mismatch_total != 0: + sign = "more" if token_mismatch_total > 0 else "fewer" + logger.warning( + "Across all generated prompts, there were %d %s tokens " + "than expected after decoding and re-encoding. This is " + "expected due to the imperfect nature of the sampling " + "procedure.", + abs(token_mismatch_total), + sign, + ) + return requests def get_prefix( @@ -530,7 +600,7 @@ def generate_token_sequence( input_len: int, offset: int, index: int, - ) -> tuple[str, int]: + ) -> tuple[str, int, int]: """ Returns (prompt, total_input_len). @@ -549,15 +619,16 @@ def generate_token_sequence( token_sequence = prefix_token_ids + inner_seq # Decode, then re-encode and truncate to preserve token count invariants - prompt = tokenizer.decode(token_sequence) total_input_len = prefix_len + int(input_len) - - re_encoded_sequence = tokenizer.encode( - prompt, add_special_tokens=False)[:total_input_len] - prompt = tokenizer.decode(re_encoded_sequence) - total_input_len = len(re_encoded_sequence) - - return prompt, total_input_len + prompt, adjusted_token_sequence, token_mismatch = gen_prompt_decode_to_target_len( # noqa: E501 + tokenizer=tokenizer, + token_sequence=token_sequence, + target_token_len=total_input_len, + add_special_tokens=False, + rng=self._rng, + ) + total_input_len = len(adjusted_token_sequence) + return prompt, total_input_len, token_mismatch # ----------------------------------------------------------------------------- @@ -873,8 +944,9 @@ def sample( vocab_size = tokenizer.vocab_size # Add synthetic multimodal items to each request mm_requests = [] + token_mismatch_total = 0 for i in range(num_requests): - prompt, total_input_len = self.generate_token_sequence( + prompt, total_input_len, token_mismatch = self.generate_token_sequence( # noqa: E501 tokenizer=tokenizer, prefix_token_ids=prefix_token_ids, prefix_len=prefix_len, @@ -883,6 +955,7 @@ def sample( offset=int(offsets[i]), index=i, ) + token_mismatch_total += token_mismatch # Get multimodal item iterator for a given request mm_item_iterator = self.get_mm_item_iterator( min_num_mm_items, @@ -918,6 +991,18 @@ def sample( request_id=request_id_prefix + str(i), ) mm_requests.append(sample_request) + + if token_mismatch_total != 0: + sign = "more" if token_mismatch_total > 0 else "fewer" + logger.warning( + "Across all generated prompts, there were %d %s tokens " + "than expected after decoding and re-encoding. This is " + "expected due to the imperfect nature of the sampling " + "procedure.", + abs(token_mismatch_total), + sign, + ) + return mm_requests # ----------------------------------------------------------------------------- @@ -2694,27 +2779,23 @@ def _generate_exact_length_tokens(target_length: int) -> list[int]: # Generate random tokens tokens = np.random.randint( 0, vocab_size, size=target_length).tolist() - text = tokenizer.decode(tokens) - re_encoded = tokenizer.encode(text, add_special_tokens=False) - - if len(re_encoded) == target_length: - return re_encoded - elif len(re_encoded) < target_length: - # Recursively generate additional consistent tokens - needed = target_length - len(re_encoded) - extra_tokens = _generate_exact_length_tokens(needed) - return re_encoded + extra_tokens - else: - # Truncate to target length - return re_encoded[:target_length] + + _, adjusted_tokens, token_mismatch = gen_prompt_decode_to_target_len( # noqa: E501 + tokenizer=tokenizer, + token_sequence=tokens, + target_token_len=target_length, + add_special_tokens=False, + ) + return adjusted_tokens, token_mismatch requests = [] + token_mismatch_total = 0 for _ in range(num_prefixes): prefix_tokens = _generate_exact_length_tokens(prefix_len) for _ in range(prompts_per_prefix): - suffix_tokens = _generate_exact_length_tokens(suffix_len) - + suffix_tokens, token_mistmatch = _generate_exact_length_tokens(suffix_len) # noqa: E501 + token_mismatch_total += token_mistmatch combined_tokens = prefix_tokens + suffix_tokens prompt = tokenizer.decode(combined_tokens) prompt_len = len(combined_tokens) @@ -2726,6 +2807,16 @@ def _generate_exact_length_tokens(target_length: int) -> list[int]: ) ) + if token_mismatch_total != 0: + sign = "more" if token_mismatch_total > 0 else "fewer" + logger.warning( + "Across all generated prompts, there were %d %s tokens " + "than expected after decoding and re-encoding. This is " + "expected due to the imperfect nature of the sampling " + "procedure.", + abs(token_mismatch_total), + sign, + ) random.shuffle(requests) return requests From 58d2117263d12363f52c331ea05a7fa6e5c88bbc Mon Sep 17 00:00:00 2001 From: Yuxuan Zhang <2448370773@qq.com> Date: Sun, 28 Sep 2025 20:05:51 +0800 Subject: [PATCH 062/166] Update GLM-4.5 Doc transformers version (#25830) Signed-off-by: zRzRzRzRzRzRzR <2448370773@qq.com> --- docs/features/tool_calling.md | 6 ++++-- docs/models/supported_models.md | 2 +- tests/models/registry.py | 2 +- vllm/model_executor/models/glm4_moe.py | 2 +- 4 files changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/features/tool_calling.md b/docs/features/tool_calling.md index 16693db7255c..241438ae5578 100644 --- a/docs/features/tool_calling.md +++ b/docs/features/tool_calling.md @@ -323,8 +323,10 @@ Flags: `--tool-call-parser longcat` Supported models: -* `ZhipuAI/GLM-4.5` -* `ZhipuAI/GLM-4.5-Air` +* `zai-org/GLM-4.5` +* `zai-org/GLM-4.5-Air` +* `zai-org/GLM-4.6` +* `zai-org/GLM-4.6-Air` Flags: `--tool-call-parser glm45` diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index 650f62492282..3ee5a7d0ffc5 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -367,7 +367,7 @@ th { | `Gemma3nForCausalLM` | Gemma 3n | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | | ✅︎ | | `GlmForCausalLM` | GLM-4 | `zai-org/glm-4-9b-chat-hf`, etc. | ✅︎ | ✅︎ | ✅︎ | | `Glm4ForCausalLM` | GLM-4-0414 | `zai-org/GLM-4-32B-0414`, etc. | ✅︎ | ✅︎ | ✅︎ | -| `Glm4MoeForCausalLM` | GLM-4.5 | `zai-org/GLM-4.5`, etc. | ✅︎ | ✅︎ | ✅︎ | +| `Glm4MoeForCausalLM` | GLM-4.5, GLM-4.6 | `zai-org/GLM-4.5`, etc. | ✅︎ | ✅︎ | ✅︎ | | `GPT2LMHeadModel` | GPT-2 | `gpt2`, `gpt2-xl`, etc. | | ✅︎ | ✅︎ | | `GPTBigCodeForCausalLM` | StarCoder, SantaCoder, WizardCoder | `bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, `WizardLM/WizardCoder-15B-V1.0`, etc. | ✅︎ | ✅︎ | ✅︎ | | `GPTJForCausalLM` | GPT-J | `EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc. | | ✅︎ | ✅︎ | diff --git a/tests/models/registry.py b/tests/models/registry.py index e321acc873c6..124a97ed0c89 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -642,7 +642,7 @@ def check_available_online( speculative_model="baidu/ERNIE-4.5-21B-A3B-PT"), "Glm4MoeMTPModel": _HfExamplesInfo("zai-org/GLM-4.5", speculative_model="zai-org/GLM-4.5", - min_transformers_version="4.54", + min_transformers_version="4.56", is_available_online=False), "LongCatFlashMTPModel": _HfExamplesInfo( "meituan-longcat/LongCat-Flash-Chat", diff --git a/vllm/model_executor/models/glm4_moe.py b/vllm/model_executor/models/glm4_moe.py index 947c6ce62f55..2557748b7faa 100644 --- a/vllm/model_executor/models/glm4_moe.py +++ b/vllm/model_executor/models/glm4_moe.py @@ -21,7 +21,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Inference-only GLM-4.5 model compatible with HuggingFace weights.""" +"""Inference-only GLM-4.5, GLM-4.6 model compatible with HuggingFace weights.""" import typing from collections.abc import Callable, Iterable from itertools import islice From a6a2b4d1fbb16ace7ed249d3784ad82fe71289b9 Mon Sep 17 00:00:00 2001 From: JJJYmmm <92386084+JJJYmmm@users.noreply.github.com> Date: Mon, 29 Sep 2025 01:56:12 +0800 Subject: [PATCH 063/166] [Bugfix] fix Qwen3VLMoe load when pp > 1 (#25838) Signed-off-by: liuye.hj Co-authored-by: liuye.hj --- vllm/model_executor/models/qwen3_vl_moe.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vllm/model_executor/models/qwen3_vl_moe.py b/vllm/model_executor/models/qwen3_vl_moe.py index 52ea652b3765..02cc5d6d66d1 100644 --- a/vllm/model_executor/models/qwen3_vl_moe.py +++ b/vllm/model_executor/models/qwen3_vl_moe.py @@ -212,6 +212,8 @@ def load_weights(self, weights: Iterable[tuple[str, # attempted to load as other weights later is_expert_weight = True name_mapped = name.replace(weight_name, param_name) + if is_pp_missing_parameter(name_mapped, self): + continue if is_fused_expert: loaded_weight = loaded_weight.transpose(-1, -2) # no bias @@ -230,8 +232,6 @@ def load_weights(self, weights: Iterable[tuple[str, name_mapped, params_dict, loaded_weight, shard_id, num_experts) else: - if is_pp_missing_parameter(name_mapped, self): - continue # Skip loading extra parameters for GPTQ/modelopt models if name_mapped.endswith( ignore_suffixes From 2512056190d48435a8a00276c82b4060c421e208 Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Sun, 28 Sep 2025 17:12:42 -0400 Subject: [PATCH 064/166] Remove redundant cudagraph dispatcher warning (#25841) --- vllm/v1/cudagraph_dispatcher.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/vllm/v1/cudagraph_dispatcher.py b/vllm/v1/cudagraph_dispatcher.py index 2dbe2bfb8082..29bb220760c0 100644 --- a/vllm/v1/cudagraph_dispatcher.py +++ b/vllm/v1/cudagraph_dispatcher.py @@ -4,9 +4,6 @@ from vllm.config import CUDAGraphMode, VllmConfig from vllm.forward_context import BatchDescriptor -from vllm.logger import init_logger - -logger = init_logger(__name__) class CudagraphDispatcher: @@ -102,8 +99,6 @@ def dispatch( """ # if not initialized, just skip dispatching. if not self.keys_initialized: - logger.warning_once("cudagraph dispatching keys are not " - "initialized. No cudagraph will be used.") return CUDAGraphMode.NONE, None non_uniform_key = batch_descriptor.non_uniform From 7b326f71e183914d61fcf6e6f49281abeaaf615f Mon Sep 17 00:00:00 2001 From: Juechen Liu Date: Sun, 28 Sep 2025 21:18:57 -0700 Subject: [PATCH 065/166] [Misc] fix tests failure by using current_platform (#25825) Signed-off-by: Juechen Liu --- vllm/attention/ops/triton_reshape_and_cache_flash.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/attention/ops/triton_reshape_and_cache_flash.py b/vllm/attention/ops/triton_reshape_and_cache_flash.py index cc9e1bb23b9b..0d82935bb418 100644 --- a/vllm/attention/ops/triton_reshape_and_cache_flash.py +++ b/vllm/attention/ops/triton_reshape_and_cache_flash.py @@ -137,7 +137,7 @@ def triton_reshape_and_cache_flash( # heuristics instead of autotuning TILE_SIZE = min(2048, triton.next_power_of_2(n)) - if torch.version.hip or torch.version.xpu: + if current_platform.is_rocm() or current_platform.is_xpu(): num_stages = 4 num_warps = 8 else: # cuda From ccbfaab574703cd2c8d6ca164d9c00010cca3507 Mon Sep 17 00:00:00 2001 From: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com> Date: Mon, 29 Sep 2025 00:46:30 -0400 Subject: [PATCH 066/166] [P/D] NIXL Updates (#25844) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Sage Moore Signed-off-by: simon-mo Signed-off-by: rentianyue-jk Signed-off-by: Russell Bryant Signed-off-by: Isotr0py Signed-off-by: Chenheli Hua Signed-off-by: mgoin Signed-off-by: Tyler Michael Smith Signed-off-by: NickLucche Signed-off-by: Roger Wang Signed-off-by: Robert Shaw Co-authored-by: Sage Moore Co-authored-by: Russell Bryant Co-authored-by: rentianyue-jk Co-authored-by: Isotr0py Co-authored-by: Chenheli Hua Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Co-authored-by: Michael Goin Co-authored-by: Tyler Michael Smith Co-authored-by: Nicolò Lucchesi Co-authored-by: Roger Wang Co-authored-by: Robert Shaw --- .../kv_transfer/kv_connector/v1/nixl_connector.py | 7 +++++-- vllm/v1/core/sched/scheduler.py | 7 ++++++- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py index 5af2b33f029c..c11189d7ec10 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py @@ -474,8 +474,11 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str): "backends", ["UCX"]) # Agent. non_ucx_backends = [b for b in self.nixl_backends if b != "UCX"] - config = nixl_agent_config(backends=self.nixl_backends) if len( - non_ucx_backends) > 0 and nixl_agent_config is not None else None + if nixl_agent_config is None: + config = None + else: + config = nixl_agent_config(backends=self.nixl_backends) if len( + non_ucx_backends) > 0 else nixl_agent_config(num_threads=8) self.nixl_wrapper = NixlWrapper(str(uuid.uuid4()), config) # Map of engine_id -> {rank0: agent_name0, rank1: agent_name1..}. diff --git a/vllm/v1/core/sched/scheduler.py b/vllm/v1/core/sched/scheduler.py index 10d8f6bbda5c..8d6ea887142d 100644 --- a/vllm/v1/core/sched/scheduler.py +++ b/vllm/v1/core/sched/scheduler.py @@ -1290,4 +1290,9 @@ def _update_from_kv_xfer_finished(self, self.finished_recving_kv_req_ids.add(req_id) for req_id in (kv_connector_output.finished_sending or ()): logger.debug("Finished sending KV transfer for request %s", req_id) - self._free_blocks(self.requests[req_id]) + if req_id not in self.requests: + logger.warning( + "Got finished sending KV transfer for request %s," + "but the request is already freed.", req_id) + else: + self._free_blocks(self.requests[req_id]) From 29312d6f7806c9cccb83f529e82bde5d2e68c419 Mon Sep 17 00:00:00 2001 From: Thomas Parnell Date: Mon, 29 Sep 2025 07:08:17 +0200 Subject: [PATCH 067/166] Add Phi4FlashForCausalLM to _PREVIOUSLY_SUPPORTED_MODELS (#25832) Signed-off-by: Thomas Parnell --- vllm/model_executor/models/registry.py | 1 + 1 file changed, 1 insertion(+) diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 0471164ab8a6..a68012d8a8c9 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -333,6 +333,7 @@ _PREVIOUSLY_SUPPORTED_MODELS = { "Phi3SmallForCausalLM": "0.9.2", + "Phi4FlashForCausalLM": "0.10.2", # encoder-decoder models except whisper # have been removed for V0 deprecation. "BartModel": "0.10.2", From e37c80ddcafecb80c09037a211e9cde1ce2cda0d Mon Sep 17 00:00:00 2001 From: Kunshang Ji Date: Mon, 29 Sep 2025 13:15:10 +0800 Subject: [PATCH 068/166] [XPU]Fix xpu spec decoding UTs, avoid using cuda graph (#25847) Signed-off-by: Kunshang Ji --- .buildkite/scripts/hardware_ci/run-xpu-test.sh | 2 +- tests/utils.py | 2 ++ vllm/v1/spec_decode/eagle.py | 7 ++++--- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/.buildkite/scripts/hardware_ci/run-xpu-test.sh b/.buildkite/scripts/hardware_ci/run-xpu-test.sh index 6b9c0121c4aa..2fd7265fa536 100644 --- a/.buildkite/scripts/hardware_ci/run-xpu-test.sh +++ b/.buildkite/scripts/hardware_ci/run-xpu-test.sh @@ -42,7 +42,7 @@ docker run \ pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py pytest -v -s v1/structured_output - pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py --ignore=v1/spec_decode/test_tree_attention.py + pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py --ignore=v1/kv_connector/unit/test_shared_storage_connector.py pytest -v -s v1/test_metrics pytest -v -s v1/test_serial_utils.py diff --git a/tests/utils.py b/tests/utils.py index ab6ccc7ad9f9..ffdc0f732543 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1143,6 +1143,8 @@ def get_attn_backend_list_based_on_platform() -> list[str]: print("Skip FLASH_ATTN on ROCm as aiter is not installed") return attn_backend_list + elif current_platform.is_xpu(): + return ["FLASH_ATTN", "TRITON_ATTN"] else: raise ValueError("Unsupported platform") diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index 1b5bafb9ca1b..a2f7dbe5703f 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -72,12 +72,13 @@ def __init__( self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None - self.use_cuda_graph = (self.vllm_config.compilation_config.level + self.use_cuda_graph = (not current_platform.is_xpu() + and self.vllm_config.compilation_config.level == CompilationLevel.PIECEWISE and not self.vllm_config.model_config.enforce_eager) self.cudagraph_batch_sizes = list( - reversed( - self.vllm_config.compilation_config.cudagraph_capture_sizes)) + reversed(self.vllm_config.compilation_config. + cudagraph_capture_sizes)) if self.use_cuda_graph else [] # persistent buffers for cuda graph self.input_ids = torch.zeros(self.max_num_tokens, From 9e7482910088c2bf5cc9abc4590f341b9e80e41f Mon Sep 17 00:00:00 2001 From: Roger Wang Date: Sun, 28 Sep 2025 23:03:51 -0700 Subject: [PATCH 069/166] [Bugfix] Fallback ViT attn backend to SDPA for blackwell (#25851) Signed-off-by: Roger Wang --- vllm/model_executor/models/qwen3_vl.py | 10 +--------- vllm/platforms/cuda.py | 6 ++++++ 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index c8f91dd48969..6d2a6019ef6f 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -66,7 +66,7 @@ PromptReplacement, PromptUpdate, PromptUpdateDetails) from vllm.multimodal.profiling import BaseDummyInputsBuilder -from vllm.platforms import _Backend, current_platform +from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import uses_mrope from vllm.utils import is_list_of @@ -336,14 +336,6 @@ def __init__( }: raise RuntimeError( f"Qwen3-VL does not support {self.attn_backend} backend now.") - if current_platform.is_device_capability( - 100) and self.attn_backend != _Backend.TORCH_SDPA: - # TODO(Roger/Wentao): remove this after FA - # or XFORMERS's issue fixed on Blackwell - logger.info_once("Qwen3-VL vision attention does not support " - f"{self.attn_backend} backend on Blackwell now. " - "Vision attention backend is set to TORCH_SDPA.") - self.attn_backend = _Backend.TORCH_SDPA self.blocks = nn.ModuleList([ Qwen3_VisionBlock( diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 58ba08101bc9..8b9f9f569206 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -205,6 +205,12 @@ def get_current_memory_usage(cls, @classmethod def get_vit_attn_backend(cls, head_size: int, dtype: torch.dtype) -> _Backend: + + # For Blackwell GPUs, force TORCH_SDPA for now. + # See https://github.com/facebookresearch/xformers/issues/1317#issuecomment-3199392579 # noqa: E501 + if cls.has_device_capability(100): + return _Backend.TORCH_SDPA + if dtype not in (torch.float16, torch.bfloat16): return _Backend.XFORMERS From b948798fb4903612e22b182f0cc0426bb25abcd3 Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Mon, 29 Sep 2025 14:09:18 +0800 Subject: [PATCH 070/166] [V0 Deprecation][Models] Remove all V0 condition for mm embeddings merge (#25331) Signed-off-by: Isotr0py Signed-off-by: isotr0py <2037008807@qq.com> --- vllm/model_executor/models/aya_vision.py | 11 --- vllm/model_executor/models/blip2.py | 11 --- vllm/model_executor/models/chameleon.py | 12 --- vllm/model_executor/models/cohere2_vision.py | 11 --- vllm/model_executor/models/deepseek_vl2.py | 11 --- vllm/model_executor/models/fuyu.py | 11 --- vllm/model_executor/models/gemma3_mm.py | 19 ---- vllm/model_executor/models/glm4_1v.py | 50 +--------- vllm/model_executor/models/glm4v.py | 17 +--- vllm/model_executor/models/granite_speech.py | 11 --- .../models/hyperclovax_vision.py | 15 +-- vllm/model_executor/models/idefics3.py | 11 --- vllm/model_executor/models/interns1.py | 18 +--- vllm/model_executor/models/internvl.py | 18 +--- vllm/model_executor/models/kimi_vl.py | 16 ---- vllm/model_executor/models/llava.py | 11 --- vllm/model_executor/models/llava_next.py | 11 --- .../model_executor/models/llava_next_video.py | 11 --- vllm/model_executor/models/llava_onevision.py | 46 +-------- vllm/model_executor/models/minicpmv.py | 15 +-- vllm/model_executor/models/mistral3.py | 11 --- vllm/model_executor/models/mllama4.py | 11 --- vllm/model_executor/models/molmo.py | 11 --- .../model_executor/models/nano_nemotron_vl.py | 18 +--- vllm/model_executor/models/nemotron_vl.py | 11 --- vllm/model_executor/models/ovis.py | 11 --- vllm/model_executor/models/ovis2_5.py | 12 --- vllm/model_executor/models/paligemma.py | 11 --- vllm/model_executor/models/phi3v.py | 11 --- vllm/model_executor/models/phi4_multimodal.py | 16 ---- vllm/model_executor/models/phi4mm.py | 48 +--------- vllm/model_executor/models/pixtral.py | 11 --- .../models/qwen2_5_omni_thinker.py | 31 +------ vllm/model_executor/models/qwen2_5_vl.py | 58 +----------- vllm/model_executor/models/qwen2_audio.py | 11 --- vllm/model_executor/models/qwen2_vl.py | 50 +--------- vllm/model_executor/models/qwen3_vl.py | 93 +------------------ vllm/model_executor/models/qwen_vl.py | 12 --- vllm/model_executor/models/skyworkr1v.py | 11 --- vllm/model_executor/models/transformers.py | 13 --- vllm/model_executor/models/ultravox.py | 12 --- vllm/model_executor/models/voxtral.py | 13 --- 42 files changed, 13 insertions(+), 809 deletions(-) diff --git a/vllm/model_executor/models/aya_vision.py b/vllm/model_executor/models/aya_vision.py index eab996e9ba22..f6dfa435ddd4 100644 --- a/vllm/model_executor/models/aya_vision.py +++ b/vllm/model_executor/models/aya_vision.py @@ -427,17 +427,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - input_ids = None - hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py index 4d1850d07b28..334743a7358c 100644 --- a/vllm/model_executor/models/blip2.py +++ b/vllm/model_executor/models/blip2.py @@ -672,17 +672,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == _IMAGE_TOKEN_ID, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/chameleon.py b/vllm/model_executor/models/chameleon.py index f9740adb151b..86dbf63fa5df 100644 --- a/vllm/model_executor/models/chameleon.py +++ b/vllm/model_executor/models/chameleon.py @@ -1014,18 +1014,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - image_token_id = self.model.vocabulary_mapping.image_token_id - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == image_token_id, - ) - input_ids = None - hidden_states = self.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/cohere2_vision.py b/vllm/model_executor/models/cohere2_vision.py index 99edcba4d874..7162571c08d9 100644 --- a/vllm/model_executor/models/cohere2_vision.py +++ b/vllm/model_executor/models/cohere2_vision.py @@ -440,17 +440,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_id, - ) - input_ids = None - hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/deepseek_vl2.py b/vllm/model_executor/models/deepseek_vl2.py index b98008c83bdc..0f87fb34bf32 100644 --- a/vllm/model_executor/models/deepseek_vl2.py +++ b/vllm/model_executor/models/deepseek_vl2.py @@ -614,17 +614,6 @@ def forward(self, if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.image_token_id, - ) - input_ids = None - hidden_states = self.language_model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py index b99fe33a1dcc..9e491c0b50d2 100644 --- a/vllm/model_executor/models/fuyu.py +++ b/vllm/model_executor/models/fuyu.py @@ -352,17 +352,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == _IMAGE_TOKEN_ID, - ) - input_ids = None - hidden_states = self.language_model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/gemma3_mm.py b/vllm/model_executor/models/gemma3_mm.py index be75e36fe23b..36f8651371ba 100644 --- a/vllm/model_executor/models/gemma3_mm.py +++ b/vllm/model_executor/models/gemma3_mm.py @@ -596,25 +596,6 @@ def forward(self, if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - if (vision_embeddings is not None) and len(vision_embeddings) != 0: - kwargs = self.prepare_attn_masks( - input_ids, - positions, - mask_dtype=self.dtype, - **kwargs, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/glm4_1v.py b/vllm/model_executor/models/glm4_1v.py index dbb5431ae491..722f1e428be7 100644 --- a/vllm/model_executor/models/glm4_1v.py +++ b/vllm/model_executor/models/glm4_1v.py @@ -71,7 +71,6 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors -from vllm.transformers_utils.config import uses_mrope from vllm.utils.tensor_schema import TensorSchema, TensorShape from ..layers.activation import SiluAndMul @@ -80,8 +79,7 @@ from .qwen2_vl import (_create_qwen2vl_field_factory, apply_rotary_pos_emb_vision) from .utils import (AutoWeightsLoader, WeightsMapper, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model logger = init_logger(__name__) @@ -1552,32 +1550,6 @@ def get_multimodal_embeddings( multimodal_embeddings += video_embeddings return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Glm4vImageInputs] = None, - video_input: Optional[Glm4vVideoInputs] = None, - ) -> torch.Tensor: - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=self.config.image_token_id, - ) - - if video_input is not None: - video_embeds = self._process_video_input(video_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - video_embeds, - placeholder_token_id=self.config.video_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1604,26 +1576,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - video_input = self._parse_and_validate_video_input(**kwargs) - - if image_input is None and video_input is None: - inputs_embeds = None - else: - if uses_mrope(self.config): - assert positions.ndim == 2 and positions.size(0) == 3, ( - "multimodal section rotary embedding requires " - f"(3, seq_len) positions, but got {positions.size()}") - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - video_input=video_input) - input_ids = None - hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/glm4v.py b/vllm/model_executor/models/glm4v.py index ace9c05daf15..22ddb1d75160 100644 --- a/vllm/model_executor/models/glm4v.py +++ b/vllm/model_executor/models/glm4v.py @@ -43,7 +43,7 @@ from .chatglm import ChatGLMBaseModel, ChatGLMModel from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) -from .utils import flatten_bn, isin_list +from .utils import flatten_bn class GLMVImagePixelInputs(TensorSchema): @@ -618,21 +618,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=isin_list(input_ids, [ - self.config.boi_token_id, - self.config.pad_token_id, - self.config.eoi_token_id, - ]), - ) - input_ids = None - hidden_states = self.transformer(input_ids, positions, intermediate_tensors, inputs_embeds) diff --git a/vllm/model_executor/models/granite_speech.py b/vllm/model_executor/models/granite_speech.py index 8a02da58ea0b..0ec451356f5e 100644 --- a/vllm/model_executor/models/granite_speech.py +++ b/vllm/model_executor/models/granite_speech.py @@ -765,17 +765,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - audio_embeds = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - audio_embeds, - is_multimodal=input_ids == self.config.audio_token_index, - ) - input_ids = None - model_output = self.language_model(input_ids, positions, intermediate_tensors, inputs_embeds) return model_output diff --git a/vllm/model_executor/models/hyperclovax_vision.py b/vllm/model_executor/models/hyperclovax_vision.py index f851688bf7ba..b0f9d5e2657e 100644 --- a/vllm/model_executor/models/hyperclovax_vision.py +++ b/vllm/model_executor/models/hyperclovax_vision.py @@ -45,8 +45,7 @@ from .clip import CLIPVisionModel from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel -from .utils import (AutoWeightsLoader, init_vllm_registered_model, isin_list, - maybe_prefix) +from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix from .vision import get_vision_encoder_info EOT = "<|endofturn|>" @@ -747,18 +746,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - multimodal_embeddings, - is_multimodal=isin_list( - input_ids, - [self.config.image_token_id, self.config.video_token_id]), - ) - input_ids = None hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/idefics3.py b/vllm/model_executor/models/idefics3.py index 3334ee224253..dddf1c6fb626 100644 --- a/vllm/model_executor/models/idefics3.py +++ b/vllm/model_executor/models/idefics3.py @@ -702,17 +702,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_id, - ) - input_ids = None - hidden_states = self.model.text_model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/interns1.py b/vllm/model_executor/models/interns1.py index 545dad1a96f5..0292845f819c 100644 --- a/vllm/model_executor/models/interns1.py +++ b/vllm/model_executor/models/interns1.py @@ -40,7 +40,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, isin_list, maybe_prefix) + init_vllm_registered_model, maybe_prefix) class InternS1MultiModalProjector(nn.Module): @@ -798,22 +798,6 @@ def forward( input_ids = None inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - context_token_ids = [ - token_id for token_id in (self.img_context_token_id, - self.video_context_token_id) - if token_id is not None - ] - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=isin_list(input_ids, context_token_ids), - ) - input_ids = None - forward_kwargs = { "input_ids": input_ids, "positions": positions, diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py index 78aac8541434..0c95c49f90b1 100644 --- a/vllm/model_executor/models/internvl.py +++ b/vllm/model_executor/models/internvl.py @@ -43,7 +43,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - isin_list, maybe_prefix) + maybe_prefix) IMG_START = '' IMG_END = '' @@ -1371,22 +1371,6 @@ def forward( input_ids = None inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - context_token_ids = [ - token_id for token_id in (self.img_context_token_id, - self.video_context_token_id) - if token_id is not None - ] - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=isin_list(input_ids, context_token_ids), - ) - input_ids = None - forward_kwargs = { "input_ids": input_ids, "positions": positions, diff --git a/vllm/model_executor/models/kimi_vl.py b/vllm/model_executor/models/kimi_vl.py index db032736f914..30ec9029f74f 100644 --- a/vllm/model_executor/models/kimi_vl.py +++ b/vllm/model_executor/models/kimi_vl.py @@ -433,22 +433,6 @@ def forward( ) -> IntermediateTensors: if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - if image_input is None: - inputs_embeds = None - else: - image_embeds = self._process_image_input(image_input) - inputs_embeds = self.get_input_embeddings( - input_ids, - image_embeds, - is_multimodal=input_ids == - self.config.media_placeholder_token_id, - ) - input_ids = None hidden_states = self.language_model( input_ids=input_ids, diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 6f3cfd88aee2..46cf93be191e 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -723,17 +723,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index e132389c4f06..c4f1daaab9bf 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -547,17 +547,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/llava_next_video.py b/vllm/model_executor/models/llava_next_video.py index 2642d8c77cf3..aebc661d53f8 100644 --- a/vllm/model_executor/models/llava_next_video.py +++ b/vllm/model_executor/models/llava_next_video.py @@ -431,17 +431,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.video_token_index, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/llava_onevision.py b/vllm/model_executor/models/llava_onevision.py index 906858f4e2f4..6088195c91d5 100644 --- a/vllm/model_executor/models/llava_onevision.py +++ b/vllm/model_executor/models/llava_onevision.py @@ -30,8 +30,7 @@ LlavaNextProcessingInfo) from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) # For profile run _MAX_FRAMES_PER_VIDEO = 16 @@ -850,33 +849,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[LlavaOnevisionImagePixelInputs] = None, - video_input: Optional[LlavaOnevisionVideoPixelInputs] = None, - ) -> torch.Tensor: - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=self.config.image_token_index, - ) - - if video_input is not None: - video_embeds = self._process_video_pixels(video_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - video_embeds, - placeholder_token_id=self.config.video_token_index, - ) - - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -894,22 +866,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - video_input = self._parse_and_validate_video_input(**kwargs) - - if image_input is None and video_input is None: - inputs_embeds = None - else: - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - video_input=video_input) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/minicpmv.py b/vllm/model_executor/models/minicpmv.py index bffc9a0c125e..eaa3839af37b 100644 --- a/vllm/model_executor/models/minicpmv.py +++ b/vllm/model_executor/models/minicpmv.py @@ -71,7 +71,7 @@ from .idefics2_vision_model import Idefics2VisionTransformer from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) -from .utils import AutoWeightsLoader, flatten_bn, isin_list, maybe_prefix +from .utils import AutoWeightsLoader, flatten_bn, maybe_prefix # For profile run _MAX_FRAMES_PER_VIDEO = 16 @@ -1154,19 +1154,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=isin_list(input_ids, list(self.mm_token_ids)), - ) - input_ids = None - hidden_states = self.llm.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/mistral3.py b/vllm/model_executor/models/mistral3.py index 31571ce962d1..e932f7f007f5 100644 --- a/vllm/model_executor/models/mistral3.py +++ b/vllm/model_executor/models/mistral3.py @@ -571,17 +571,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/mllama4.py b/vllm/model_executor/models/mllama4.py index 3af5267928cd..db5a9fbc6a33 100644 --- a/vllm/model_executor/models/mllama4.py +++ b/vllm/model_executor/models/mllama4.py @@ -823,17 +823,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, - # this condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - input_ids = None - return self.language_model(input_ids, positions, intermediate_tensors, inputs_embeds) diff --git a/vllm/model_executor/models/molmo.py b/vllm/model_executor/models/molmo.py index 054caee9e8a4..0227a83a1f55 100644 --- a/vllm/model_executor/models/molmo.py +++ b/vllm/model_executor/models/molmo.py @@ -1490,17 +1490,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.img_patch_id, - ) - input_ids = None - hidden_states = self.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/nano_nemotron_vl.py b/vllm/model_executor/models/nano_nemotron_vl.py index 505806a15c89..2d0ebdc90277 100644 --- a/vllm/model_executor/models/nano_nemotron_vl.py +++ b/vllm/model_executor/models/nano_nemotron_vl.py @@ -35,7 +35,7 @@ from vllm.model_executor.models.radio import RadioModel from vllm.model_executor.models.utils import (flatten_bn, init_vllm_registered_model, - isin_list, maybe_prefix) + maybe_prefix) from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, MultiModalKwargs, MultiModalKwargsItems, @@ -1135,22 +1135,6 @@ def forward( input_ids = None inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - context_token_ids = [ - token_id for token_id in (self.img_context_token_id, - self.video_context_token_id) - if token_id is not None - ] - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=isin_list(input_ids, context_token_ids), - ) - input_ids = None - hidden_states = self.language_model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/nemotron_vl.py b/vllm/model_executor/models/nemotron_vl.py index 2627a262e958..0e7ec8e458cf 100644 --- a/vllm/model_executor/models/nemotron_vl.py +++ b/vllm/model_executor/models/nemotron_vl.py @@ -608,17 +608,6 @@ def forward( input_ids = None inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.img_context_token_id, - ) - input_ids = None - forward_kwargs = { "input_ids": input_ids, "positions": positions, diff --git a/vllm/model_executor/models/ovis.py b/vllm/model_executor/models/ovis.py index 8503d3f71d1c..2f9c6ddfc661 100644 --- a/vllm/model_executor/models/ovis.py +++ b/vllm/model_executor/models/ovis.py @@ -511,17 +511,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.image_pad_token_id, - ) - input_ids = None - # up until here we have an inputs_embeds 100% numerical identity # between the OG HF Transformers implementation and ours hidden_states = self.llm( diff --git a/vllm/model_executor/models/ovis2_5.py b/vllm/model_executor/models/ovis2_5.py index 2ecc7bff07e0..86ce7e9eab27 100644 --- a/vllm/model_executor/models/ovis2_5.py +++ b/vllm/model_executor/models/ovis2_5.py @@ -596,18 +596,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.image_pad_token_id, - ) - input_ids = None - # up until here we have a inputs_embeds 100% numerical identity # between the OG HF Transformers implementation and ours hidden_states = self.llm( diff --git a/vllm/model_executor/models/paligemma.py b/vllm/model_executor/models/paligemma.py index f07f444819f4..d118e6c89ab5 100644 --- a/vllm/model_executor/models/paligemma.py +++ b/vllm/model_executor/models/paligemma.py @@ -370,17 +370,6 @@ def forward(self, if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.config.image_token_index, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/phi3v.py b/vllm/model_executor/models/phi3v.py index ea34c8d92f13..59977796e2af 100644 --- a/vllm/model_executor/models/phi3v.py +++ b/vllm/model_executor/models/phi3v.py @@ -679,17 +679,6 @@ def forward(self, if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=self.image_token_id, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/phi4_multimodal.py b/vllm/model_executor/models/phi4_multimodal.py index e8b79717d75d..3dbb67d28065 100644 --- a/vllm/model_executor/models/phi4_multimodal.py +++ b/vllm/model_executor/models/phi4_multimodal.py @@ -1411,22 +1411,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - audio_input = self._parse_and_validate_audio_input(**kwargs) - - if image_input is None and audio_input is None: - inputs_embeds = None - else: - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - audio_input=audio_input) - input_ids = None - hidden_states = self.language_model( input_ids, positions, diff --git a/vllm/model_executor/models/phi4mm.py b/vllm/model_executor/models/phi4mm.py index 15b09c7ae2bc..8ccc7129ddb2 100644 --- a/vllm/model_executor/models/phi4mm.py +++ b/vllm/model_executor/models/phi4mm.py @@ -35,8 +35,7 @@ from .idefics2_vision_model import Idefics2VisionTransformer from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal from .phi4mm_audio import AudioEmbedding -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, maybe_prefix, - merge_multimodal_embeddings) +from .utils import AutoWeightsLoader, WeightsMapper, flatten_bn, maybe_prefix # <|endoftext10|> (see vocab.json in hf model) _IMAGE_PLACEHOLDER_TOKEN_ID = 200010 @@ -1174,35 +1173,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Phi4MMImagePixelInputs] = None, - audio_input: Optional[Phi4MMAudioFeatureInputs] = None, - ) -> torch.Tensor: - audio_projection_mode = 'speech' - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=_IMAGE_PLACEHOLDER_TOKEN_ID, - ) - audio_projection_mode = 'vision' - - if audio_input is not None: - audio_embeds = self._process_audio_input( - audio_input, audio_projection_mode=audio_projection_mode) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - audio_embeds, - placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN_ID, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1214,22 +1184,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - audio_input = self._parse_and_validate_audio_input(**kwargs) - - if image_input is None and audio_input is None: - inputs_embeds = None - else: - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - audio_input=audio_input) - input_ids = None - hidden_states = self.model( input_ids, positions, diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index 2c04b6f0f4f9..6344fc394833 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -444,17 +444,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.vision_args.image_token_id, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/qwen2_5_omni_thinker.py b/vllm/model_executor/models/qwen2_5_omni_thinker.py index bfa398ee43b5..8f069710b0f9 100644 --- a/vllm/model_executor/models/qwen2_5_omni_thinker.py +++ b/vllm/model_executor/models/qwen2_5_omni_thinker.py @@ -69,8 +69,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, WeightsMapper, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) try: import flash_attn @@ -908,26 +907,6 @@ def get_multimodal_embeddings_v0( multimodal_embeddings.append((video_embeds, "video")) return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - multimodal_embeddings: Optional[NestedTensors] = None, - ) -> torch.Tensor: - inputs_embeds = self.language_model.get_input_embeddings(input_ids) - if multimodal_embeddings is None or len(multimodal_embeddings) == 0: - return inputs_embeds - - for embeddings, modality in multimodal_embeddings: - if modality == "audio": - placeholder_token_id = self.config.audio_token_index - if modality == "image": - placeholder_token_id = self.config.image_token_index - if modality == "video": - placeholder_token_id = self.config.video_token_index - inputs_embeds = merge_multimodal_embeddings( - input_ids, inputs_embeds, embeddings, placeholder_token_id) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -939,14 +918,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - multimodal_embeddings = self.get_multimodal_embeddings_v0(**kwargs) - inputs_embeds = self.get_input_embeddings_v0( - input_ids, multimodal_embeddings) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py index 5b092b42205f..da3889d31a7d 100644 --- a/vllm/model_executor/models/qwen2_5_vl.py +++ b/vllm/model_executor/models/qwen2_5_vl.py @@ -64,7 +64,6 @@ from vllm.multimodal.processing import PromptReplacement, PromptUpdate from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors -from vllm.transformers_utils.config import uses_mrope from vllm.utils import is_pin_memory_available from vllm.utils.tensor_schema import TensorSchema, TensorShape @@ -75,8 +74,7 @@ from .qwen2_vl import (Qwen2VLMultiModalProcessor, Qwen2VLProcessingInfo, apply_rotary_pos_emb_vision) from .utils import (AutoWeightsLoader, WeightsMapper, cast_overflow_tensors, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model logger = init_logger(__name__) @@ -1365,40 +1363,6 @@ def get_multimodal_embeddings(self, multimodal_embeddings += video_embeddings return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Qwen2_5_VLImageInputs] = None, - video_input: Optional[Qwen2_5_VLVideoInputs] = None, - ) -> torch.Tensor: - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - if self.is_multimodal_pruning_enabled: - image_embeds = self._postprocess_image_embeds_evs( - image_embeds, image_input - ) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=self.config.image_token_id, - ) - - if video_input is not None: - video_embeds = self._process_video_input(video_input) - if self.is_multimodal_pruning_enabled: - video_embeds = self._postprocess_video_embeds_evs( - video_embeds, video_input - ) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - video_embeds, - placeholder_token_id=self.config.video_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1421,26 +1385,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - video_input = self._parse_and_validate_video_input(**kwargs) - - if image_input is None and video_input is None: - inputs_embeds = None - else: - if uses_mrope(self.config): - assert positions.ndim == 2 and positions.size(0) == 3, ( - "multimodal section rotary embedding requires " - f"(3, seq_len) positions, but got {positions.size()}") - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - video_input=video_input) - input_ids = None - hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/qwen2_audio.py b/vllm/model_executor/models/qwen2_audio.py index 9dfa29eef5ce..f9136863b8d6 100644 --- a/vllm/model_executor/models/qwen2_audio.py +++ b/vllm/model_executor/models/qwen2_audio.py @@ -449,17 +449,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - multimodal_embeddings, - is_multimodal=input_ids == self.config.audio_token_index, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index 6ef01f333554..f83a411459cc 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -65,15 +65,13 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.platforms import _Backend, current_platform from vllm.sequence import IntermediateTensors -from vllm.transformers_utils.config import uses_mrope from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, WeightsMapper, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model logger = init_logger(__name__) @@ -1464,32 +1462,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Qwen2VLImagePixelInputs] = None, - video_input: Optional[Qwen2VLVideoPixelInputs] = None, - ) -> torch.Tensor: - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=self.config.image_token_id, - ) - - if video_input is not None: - video_embeds = self._process_video_input(video_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - video_embeds, - placeholder_token_id=self.config.video_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1515,26 +1487,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - video_input = self._parse_and_validate_video_input(**kwargs) - - if image_input is None and video_input is None: - inputs_embeds = None - else: - if uses_mrope(self.config): - assert positions.ndim == 2 and positions.size(0) == 3, ( - "multimodal section rotary embedding requires " - f"(3, seq_len) positions, but got {positions.size()}") - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - video_input=video_input) - input_ids = None - hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index 6d2a6019ef6f..ce92557d6424 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -68,7 +68,6 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors -from vllm.transformers_utils.config import uses_mrope from vllm.utils import is_list_of from .interfaces import (MultiModalEmbeddings, SupportsLoRA, @@ -82,8 +81,7 @@ from .qwen2_vl import Qwen2VLProcessingInfo from .qwen3 import Qwen3ForCausalLM, Qwen3Model from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper, - _merge_multimodal_embeddings, maybe_prefix, - merge_multimodal_embeddings) + _merge_multimodal_embeddings, maybe_prefix) from .vision import get_vit_attn_backend, run_dp_sharded_mrope_vision_model logger = init_logger(__name__) @@ -1464,75 +1462,6 @@ def get_input_embeddings( return inputs_embeds - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Qwen2_5_VLImageInputs] = None, - video_input: Optional[Qwen2_5_VLVideoInputs] = None, - ) -> torch.Tensor: - inputs_embeds = self.get_input_embeddings(input_ids) - - if self.use_deepstack: - visual_dim = inputs_embeds.shape[-1] - deepstack_input_embeds = None - if image_input is not None or video_input is not None: - deepstack_input_embeds = torch.zeros_like( - inputs_embeds).unsqueeze(1).repeat( - 1, self.deepstack_num_level, 1).flatten(1) - - if image_input is not None: - image_embeds = self._process_image_input(image_input) - if self.use_deepstack: - image_embeds = torch.cat(image_embeds) - - image_embeds, image_embeds_multiscale = image_embeds.split( - [visual_dim, visual_dim * self.deepstack_num_level], - dim=-1) - - deepstack_input_embeds = merge_multimodal_embeddings( - input_ids, - deepstack_input_embeds, - image_embeds_multiscale, - placeholder_token_id=self.config.image_token_id, - ) - - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=self.config.image_token_id, - ) - - if video_input is not None: - video_embeds = self._process_video_input(video_input) - if self.use_deepstack: - video_embeds = torch.cat(video_embeds) - - video_embeds, video_embeds_multiscale = video_embeds.split( - [visual_dim, visual_dim * self.deepstack_num_level], - dim=-1) - - deepstack_input_embeds = merge_multimodal_embeddings( - input_ids, - deepstack_input_embeds, - video_embeds_multiscale, - placeholder_token_id=self.config.video_token_id, - ) - - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - video_embeds, - placeholder_token_id=self.config.video_token_id, - ) - - if self.use_deepstack and deepstack_input_embeds is not None: - deepstack_input_embeds = deepstack_input_embeds.view( - inputs_embeds.shape[0], self.deepstack_num_level, - visual_dim).permute(1, 0, 2).contiguous() - self._set_deepstack_input_embeds(deepstack_input_embeds) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1568,26 +1497,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - video_input = self._parse_and_validate_video_input(**kwargs) - - if image_input is None and video_input is None: - inputs_embeds = None - else: - if uses_mrope(self.config): - assert positions.ndim == 2 and positions.size(0) == 3, ( - "multimodal section rotary embedding requires " - f"(3, seq_len) positions, but got {positions.size()}") - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - video_input=video_input) - input_ids = None - if self.use_deepstack and inputs_embeds is not None and get_pp_group( ).is_first_rank: deepstack_input_embeds = self._get_deepstack_input_embeds( diff --git a/vllm/model_executor/models/qwen_vl.py b/vllm/model_executor/models/qwen_vl.py index dc11b60604a9..924119ed63ab 100644 --- a/vllm/model_executor/models/qwen_vl.py +++ b/vllm/model_executor/models/qwen_vl.py @@ -767,18 +767,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == - self.transformer.visual.image_pad_id, - ) - input_ids = None - hidden_states = self.transformer(input_ids, positions, intermediate_tensors, inputs_embeds) return hidden_states diff --git a/vllm/model_executor/models/skyworkr1v.py b/vllm/model_executor/models/skyworkr1v.py index f9a107c06085..f03022aa719c 100644 --- a/vllm/model_executor/models/skyworkr1v.py +++ b/vllm/model_executor/models/skyworkr1v.py @@ -874,17 +874,6 @@ def forward( input_ids = None inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - vision_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - vision_embeddings, - is_multimodal=input_ids == self.img_context_token_id, - ) - input_ids = None - forward_kwargs = { "input_ids": input_ids, "positions": positions, diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py index 7cfb639f675d..00d87f560e70 100644 --- a/vllm/model_executor/models/transformers.py +++ b/vllm/model_executor/models/transformers.py @@ -881,19 +881,6 @@ def forward( inputs_embeds: Optional[torch.Tensor] = None, **kwargs: object, ) -> Union[torch.Tensor, IntermediateTensors]: - # NOTE: In v1, inputs_embeds is always generated at model runner from - # `get_multimodal_embeddings` and `get_input_embeddings`, this - # condition is only for v0 compatibility. - if inputs_embeds is None: - multimodal_embeds = self.get_multimodal_embeddings(**kwargs) - if multimodal_embeds is not None: - inputs_embeds = self.get_input_embeddings( - input_ids, - multimodal_embeds, - is_multimodal=input_ids == self.config.image_token_id, - ) - input_ids = None - model_output = super().forward(input_ids, positions, intermediate_tensors, inputs_embeds) return model_output diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py index 77e886c22e63..70aabf6dfe78 100644 --- a/vllm/model_executor/models/ultravox.py +++ b/vllm/model_executor/models/ultravox.py @@ -597,18 +597,6 @@ def forward(self, if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) - - inputs_embeds = self.get_input_embeddings( - input_ids, - multimodal_embeddings, - is_multimodal=input_ids == self.config.audio_token_index, - ) - input_ids = None - language_model = self.language_model if hasattr(language_model, "language_model"): language_model = language_model.language_model diff --git a/vllm/model_executor/models/voxtral.py b/vllm/model_executor/models/voxtral.py index f93e7ccfd06f..1edeaeb0f319 100644 --- a/vllm/model_executor/models/voxtral.py +++ b/vllm/model_executor/models/voxtral.py @@ -371,19 +371,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - # NOTE: In v1, inputs_embeds is always generated at model runner, this - # condition is for v0 compatibility. - elif inputs_embeds is None: - audio_encoder = self.tokenizer.instruct.audio_encoder - audio_tok_id = audio_encoder.audio_token - audio_embeddings = self.get_multimodal_embeddings(**kwargs) - inputs_embeds = self.get_input_embeddings( - input_ids, - audio_embeddings, - is_multimodal=input_ids == audio_tok_id, - ) - input_ids = None - hidden_states = self.language_model.model(input_ids, positions, intermediate_tensors, From fd2fcf3406c938216cffc94d2287f0f5524c17cd Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Mon, 29 Sep 2025 16:03:37 +0800 Subject: [PATCH 071/166] [Misc] Remove more `get_input_embeddings_v0` (#25857) Signed-off-by: DarkLight1337 --- vllm/model_executor/models/gemma3n_mm.py | 3 +- vllm/model_executor/models/keye.py | 46 +------------------ vllm/model_executor/models/phi4_multimodal.py | 37 +-------------- vllm/model_executor/models/utils.py | 4 ++ 4 files changed, 7 insertions(+), 83 deletions(-) diff --git a/vllm/model_executor/models/gemma3n_mm.py b/vllm/model_executor/models/gemma3n_mm.py index b23437a08e5a..101e083ac123 100644 --- a/vllm/model_executor/models/gemma3n_mm.py +++ b/vllm/model_executor/models/gemma3n_mm.py @@ -45,8 +45,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsTranscription) from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) + init_vllm_registered_model, maybe_prefix) logger = init_logger(__name__) diff --git a/vllm/model_executor/models/keye.py b/vllm/model_executor/models/keye.py index 62a71b7b1fa8..10b5c45169f4 100644 --- a/vllm/model_executor/models/keye.py +++ b/vllm/model_executor/models/keye.py @@ -41,7 +41,6 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors -from vllm.transformers_utils.config import uses_mrope from vllm.utils import is_list_of from vllm.utils.tensor_schema import TensorSchema, TensorShape @@ -50,7 +49,7 @@ from .siglip import SiglipMLP from .utils import (AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, is_pp_missing_parameter, - maybe_prefix, merge_multimodal_embeddings) + maybe_prefix) from .vision import get_vit_attn_backend logger = init_logger(__name__) @@ -1450,32 +1449,6 @@ def get_multimodal_embeddings( multimodal_embeddings += video_embeddings return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Any] = None, - video_input: Optional[Any] = None, - ) -> torch.Tensor: - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=self.config.image_token_id, - ) - - if video_input is not None: - video_embeds = self._process_video_input(video_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - video_embeds, - placeholder_token_id=self.config.video_token_id, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, @@ -1500,23 +1473,6 @@ def forward( if intermediate_tensors is not None: inputs_embeds = None - elif inputs_embeds is None: - image_input = self._parse_and_validate_image_input(**kwargs) - video_input = self._parse_and_validate_video_input(**kwargs) - if image_input is None and video_input is None: - inputs_embeds = None - else: - if uses_mrope(self.config): - assert positions.ndim == 2 and positions.size(0) == 3, ( - "multimodal section rotary embedding requires " - f"(3, seq_len) positions, but got {positions.size()}") - inputs_embeds = self.get_input_embeddings_v0( - input_ids, - image_input=image_input, - video_input=video_input, - ) - input_ids = None - hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, diff --git a/vllm/model_executor/models/phi4_multimodal.py b/vllm/model_executor/models/phi4_multimodal.py index 3dbb67d28065..a4f9f96cb951 100644 --- a/vllm/model_executor/models/phi4_multimodal.py +++ b/vllm/model_executor/models/phi4_multimodal.py @@ -44,13 +44,7 @@ from .idefics2_vision_model import Idefics2VisionTransformer from .interfaces import MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - init_vllm_registered_model, maybe_prefix, - merge_multimodal_embeddings) - -# <|endoftext10|> (see vocab.json in hf model) -_IMAGE_PLACEHOLDER_TOKEN_ID = 200010 -# <|endoftext11|> -_AUDIO_PLACEHOLDER_TOKEN_ID = 200011 + init_vllm_registered_model, maybe_prefix) _AUDIO_MAX_SOUNDFILE_SIZE = 241_000 @@ -1371,35 +1365,6 @@ def get_multimodal_embeddings(self, return multimodal_embeddings - def get_input_embeddings_v0( - self, - input_ids: torch.Tensor, - image_input: Optional[Phi4MMImagePixelInputs] = None, - audio_input: Optional[Phi4MMAudioFeatureInputs] = None, - ) -> torch.Tensor: - audio_projection_mode = 'speech' - inputs_embeds = self.get_input_embeddings(input_ids) - if image_input is not None: - image_embeds = self._process_image_input(image_input) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - image_embeds, - placeholder_token_id=_IMAGE_PLACEHOLDER_TOKEN_ID, - ) - audio_projection_mode = 'vision' - - if audio_input is not None: - audio_embeds = self._process_audio_input( - audio_input, audio_projection_mode=audio_projection_mode) - inputs_embeds = merge_multimodal_embeddings( - input_ids, - inputs_embeds, - audio_embeds, - placeholder_token_id=_AUDIO_PLACEHOLDER_TOKEN_ID, - ) - return inputs_embeds - def forward( self, input_ids: torch.Tensor, diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py index 4bf151fbf62d..d6fa88f06e56 100644 --- a/vllm/model_executor/models/utils.py +++ b/vllm/model_executor/models/utils.py @@ -10,6 +10,7 @@ import torch.nn as nn from torch.func import functional_call from transformers import PretrainedConfig +from typing_extensions import deprecated import vllm.envs as envs from vllm.config import VllmConfig @@ -439,6 +440,9 @@ def _merge_multimodal_embeddings( return inputs_embeds +@deprecated("`merge_multimodal_embeddings` has been replaced with " + "`SupportsMultiModal.get_input_embeddings` and will be " + "removed in v0.12.") def merge_multimodal_embeddings( input_ids: torch.Tensor, inputs_embeds: torch.Tensor, From f44292bf4ad5c20cf30b191f44a7c50e25a41421 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Mon, 29 Sep 2025 17:51:43 +0800 Subject: [PATCH 072/166] update to latest deepgemm for dsv3.2 (#25871) Signed-off-by: youkaichao --- docker/Dockerfile | 2 +- tools/install_deepgemm.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 62be0a12e988..a6b24f69f64e 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -456,7 +456,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ ARG DEEPGEMM_GIT_REF COPY tools/install_deepgemm.sh /tmp/install_deepgemm.sh RUN --mount=type=cache,target=/root/.cache/uv \ - VLLM_DOCKER_BUILD_CONTEXT=1 /tmp/install_deepgemm.sh --cuda-version "${CUDA_VERSION}" ${DEEPGEMM_GIT_REF:+--ref "$DEEPGEMM_GIT_REF"} + VLLM_DOCKER_BUILD_CONTEXT=1 TORCH_CUDA_ARCH_LIST="9.0a 10.0a" /tmp/install_deepgemm.sh --cuda-version "${CUDA_VERSION}" ${DEEPGEMM_GIT_REF:+--ref "$DEEPGEMM_GIT_REF"} COPY tools/install_gdrcopy.sh install_gdrcopy.sh RUN set -eux; \ diff --git a/tools/install_deepgemm.sh b/tools/install_deepgemm.sh index 98427f1835ec..4f2cd302c3ef 100755 --- a/tools/install_deepgemm.sh +++ b/tools/install_deepgemm.sh @@ -6,7 +6,7 @@ set -e # Default values DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git" -DEEPGEMM_GIT_REF="ea9c5d9270226c5dd7a577c212e9ea385f6ef048" +DEEPGEMM_GIT_REF="594953acce41793ae00a1233eb516044d604bcb6" # Parse command line arguments while [[ $# -gt 0 ]]; do From e8a4abc6291cc4b676286f79c10483d0fc2d3760 Mon Sep 17 00:00:00 2001 From: Yingjun Mou Date: Mon, 29 Sep 2025 03:49:35 -0700 Subject: [PATCH 073/166] [Bugfix] Fix requirements paths in install instructions (#25827) Signed-off-by: yingjun-mou --- docs/getting_started/installation/cpu/s390x.inc.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/getting_started/installation/cpu/s390x.inc.md b/docs/getting_started/installation/cpu/s390x.inc.md index f9c4ccb942fa..442c2b4ec64e 100644 --- a/docs/getting_started/installation/cpu/s390x.inc.md +++ b/docs/getting_started/installation/cpu/s390x.inc.md @@ -46,22 +46,22 @@ Execute the following commands to build and install vLLM from source. Please build the following dependencies, `torchvision`, `pyarrow` from source before building vLLM. ```bash - sed -i '/^torch/d' requirements-build.txt # remove torch from requirements-build.txt since we use nightly builds + sed -i '/^torch/d' requirements/build.txt # remove torch from requirements/build.txt since we use nightly builds uv pip install -v \ --torch-backend auto \ - -r requirements-build.txt \ - -r requirements-cpu.txt \ + -r requirements/build.txt \ + -r requirements/cpu.txt \ VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \ uv pip install dist/*.whl ``` ??? console "pip" ```bash - sed -i '/^torch/d' requirements-build.txt # remove torch from requirements-build.txt since we use nightly builds + sed -i '/^torch/d' requirements/build.txt # remove torch from requirements/build.txt since we use nightly builds pip install -v \ --extra-index-url https://download.pytorch.org/whl/nightly/cpu \ - -r requirements-build.txt \ - -r requirements-cpu.txt \ + -r requirements/build.txt \ + -r requirements/cpu.txt \ VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \ pip install dist/*.whl ``` From 864ba4ded851752370148a4f48c54579873bac21 Mon Sep 17 00:00:00 2001 From: Zhou Jiahao Date: Mon, 29 Sep 2025 18:59:04 +0800 Subject: [PATCH 074/166] [Model][Bugfix] Fix issues in MiDashengLM implementation for quantized models (#25854) Signed-off-by: zhoukz --- vllm/model_executor/models/midashenglm.py | 193 ++++++++++++++-------- 1 file changed, 122 insertions(+), 71 deletions(-) diff --git a/vllm/model_executor/models/midashenglm.py b/vllm/model_executor/models/midashenglm.py index 0bf04e0e7e2f..0b62fbd40b07 100644 --- a/vllm/model_executor/models/midashenglm.py +++ b/vllm/model_executor/models/midashenglm.py @@ -22,6 +22,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """Inference-only MiDashengLM model compatible with HuggingFace weights.""" + import collections import collections.abc from collections.abc import Iterable, Mapping, Sequence @@ -30,10 +31,10 @@ import numpy as np import torch import torch.nn as nn -import torchaudio.transforms as audio_transforms +import torchaudio.functional as F +from torch.nn.functional import scaled_dot_product_attention from transformers import BatchFeature -from vllm.attention.layer import MultiHeadAttention from vllm.config import VllmConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn @@ -41,7 +42,6 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, MultiModalKwargsItems) @@ -147,15 +147,19 @@ def __init__( super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features - self.fc1 = ColumnParallelLinear(input_size=in_features, - output_size=hidden_features, - quant_config=quant_config, - prefix=f"{prefix}.fc1") + self.fc1 = ColumnParallelLinear( + input_size=in_features, + output_size=hidden_features, + quant_config=quant_config, + prefix=f"{prefix}.fc1", + ) self.act = get_act_fn("gelu") - self.fc2 = RowParallelLinear(input_size=hidden_features, - output_size=out_features, - quant_config=quant_config, - prefix=f"{prefix}.fc2") + self.fc2 = RowParallelLinear( + input_size=hidden_features, + output_size=out_features, + quant_config=quant_config, + prefix=f"{prefix}.fc2", + ) def forward(self, x: torch.Tensor) -> torch.Tensor: x, _ = self.fc1(x) @@ -171,7 +175,6 @@ def __init__( dim: int, num_heads: int = 8, qkv_bias: bool = False, - causal: bool = False, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): @@ -205,33 +208,30 @@ def __init__( quant_config=quant_config, prefix=f"{prefix}.qkv", ) - self.attn = MultiHeadAttention( - self.num_heads, - self.head_dim, - self.scale, - num_kv_heads=self.num_kv_heads, - ) self.proj = RowParallelLinear( input_size=dim, output_size=dim, quant_config=quant_config, prefix=f"{prefix}.proj", ) - self.causal = causal def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None): B, N, C = x.shape - qkv_out, _ = self.qkv(x) - q, k, v = qkv_out.split([self.q_size, self.kv_size, self.kv_size], - dim=-1) + qkv, _ = self.qkv(x) + qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads) + qkv = qkv.permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) - attn_out = self.attn(q, k, v) - C_local = attn_out.numel() // (B * N) # C_local for parallel - attn_out = attn_out.view(B, N, C_local) - - x, _ = self.proj(attn_out) + x = scaled_dot_product_attention( + q, + k, + v, + attn_mask=mask[:, None, None, :] if mask is not None else None, + ) + x = x.transpose(1, 2).reshape(B, N, C) + x, _ = self.proj(x) return x @@ -280,6 +280,63 @@ def forward( return x +class DashengFrontend(nn.Module): + + def __init__(self, config: DashengConfig): + super().__init__() + self.config = config + + spectrogram_window = torch.hann_window(self.config.win_length) + self.register_buffer( + "spectrogram_window", + spectrogram_window, + persistent=False, + ) + self.spectrogram_window: torch.Tensor + + melscale_fbanks = F.melscale_fbanks( + n_freqs=self.config.n_fft // 2 + 1, + f_min=self.config.f_min, + f_max=self.config.f_max, + n_mels=self.config.n_mels, + sample_rate=self.config.sample_rate, + ) + self.register_buffer("melscale_fbanks", + melscale_fbanks, + persistent=False) + self.melscale_fbanks: torch.Tensor + + def forward(self, waveform: torch.Tensor) -> torch.Tensor: + spectrogram = F.spectrogram( + waveform=waveform.to(torch.float32), + pad=0, + window=self.spectrogram_window, + n_fft=self.config.n_fft, + hop_length=self.config.hop_length, + win_length=self.config.win_length, + power=2, + normalized=False, + center=self.config.center, + ) + mel_spectrogram = ( + spectrogram.mT @ self.melscale_fbanks.to(torch.float32)).mT + # x has shape [batch, freq, time]. + # F.amplitude_to_DB accepts inputs shaped as: + # - [freq, time] + # - [channel, freq, time] + # - [..., channel, freq, time] + # Here we insert a channel dimension of size 1 before calling it, + # then remove that extra dimension afterward. + log_mel_spectrogram = F.amplitude_to_DB( + mel_spectrogram.unsqueeze(1), + multiplier=10, + amin=1e-10, + db_multiplier=0, + top_db=120, + ).squeeze(1) + return log_mel_spectrogram.to(waveform.dtype) + + class DashengAudioTransformer(nn.Module): def __init__( @@ -293,7 +350,7 @@ def __init__( self.target_length = config.target_length self.hop_length = config.hop_length - self._init_front_end(config) + self.front_end = DashengFrontend(config) self.init_bn = nn.BatchNorm2d(config.n_mels, momentum=0.01) @@ -318,34 +375,10 @@ def __init__( qkv_bias=config.qkv_bias, init_values=config.init_values, quant_config=quant_config, - prefix=f"{prefix}.block{i}", + prefix=f"{prefix}.blocks.{i}", ) for i in range(config.depth)) self.norm = nn.LayerNorm(config.embed_dim, eps=1e-6) - def _init_front_end(self, config): - with set_default_torch_dtype(torch.float32): - self.front_end = nn.Sequential( - audio_transforms.MelSpectrogram( - f_min=config.f_min, - f_max=config.f_max, - center=config.center, - win_length=config.win_length, - hop_length=config.hop_length, - sample_rate=config.sample_rate, - n_fft=config.n_fft, - n_mels=config.n_mels, - ), - audio_transforms.AmplitudeToDB(top_db=120), - ) - - mel_spectrogram = self.front_end[0] - fb = mel_spectrogram.mel_scale.fb - win = mel_spectrogram.spectrogram.window - mel_spectrogram.mel_scale.fb = fb.to(torch.bfloat16).to( - torch.float32) - mel_spectrogram.spectrogram.window = win.to(torch.bfloat16).to( - torch.float32) - def forward_features( self, x: torch.Tensor, @@ -430,14 +463,16 @@ def __init__( quant_config=quant_config, prefix=f"{prefix}.net.0", return_bias=False, - ), get_act_fn("gelu"), + ), + get_act_fn("gelu"), RowParallelLinear( input_size=out_dim, output_size=out_dim, quant_config=quant_config, prefix=f"{prefix}.net.2", return_bias=False, - )) + ), + ) def forward(self, x, mask=None): batch_size, seq_len, dim = x.shape @@ -534,9 +569,12 @@ def _call_hf_processor( # + Padding min_audio_len = self.info.get_min_audio_len() processed_audios = [ - np.pad(audio, (0, min_audio_len - audio.shape[-1]), - mode='constant', - constant_values=0) if isinstance(audio, np.ndarray) + np.pad( + audio, + (0, min_audio_len - audio.shape[-1]), + mode="constant", + constant_values=0, + ) if isinstance(audio, np.ndarray) and audio.shape[-1] < min_audio_len else audio for audio in audios ] @@ -585,8 +623,8 @@ def _get_prompt_updates( if audio_length is None: audio_output_lengths = [] else: - audio_length_np = audio_length.cpu().numpy() if isinstance( - audio_length, torch.Tensor) else audio_length + audio_length_np = (audio_length.cpu().numpy() if isinstance( + audio_length, torch.Tensor) else audio_length) audio_output_lengths = [ max(1, calculate_mel_frames_dasheng( int(length))) # at least one frame @@ -617,6 +655,17 @@ def get_replacement_midashenglm(item_idx: int): dummy_inputs=MiDashengLMDummyInputsBuilder, ) class MiDashengLMModel(nn.Module, SupportsMultiModal, SupportsPP): + packed_modules_mapping = { + "qkv_proj": [ + "q_proj", + "k_proj", + "v_proj", + ], + "gate_up_proj": [ + "gate_proj", + "up_proj", + ], + } @classmethod def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]: @@ -660,8 +709,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): def _validate_and_reshape_mm_tensor(self, mm_input: object, name: str) -> torch.Tensor: if not isinstance(mm_input, (torch.Tensor, list)): - raise ValueError(f"Incorrect type of {name}. " - f"Got type: {type(mm_input)}") + raise ValueError( + f"Incorrect type of {name}. Got type: {type(mm_input)}") if isinstance(mm_input, torch.Tensor): return mm_input.reshape(-1, *mm_input.shape[2:]) @@ -710,8 +759,8 @@ def _process_audio_input( audio_input["input_values"].dtype) batch_size, max_audio_tokens, embed_dim = audio_embeddings.shape - audio_length_np = audio_length.cpu().numpy() if isinstance( - audio_length, torch.Tensor) else audio_length + audio_length_np = (audio_length.cpu().numpy() if isinstance( + audio_length, torch.Tensor) else audio_length) audio_output_lengths = [ max(1, calculate_mel_frames_dasheng( int(length))) # at least one frame @@ -720,11 +769,11 @@ def _process_audio_input( audio_output_lengths = torch.tensor(audio_output_lengths).to( audio_embeddings.device) - audio_feature_mask = (torch.arange( + audio_feature_mask = torch.arange( max_audio_tokens, device=audio_embeddings.device).unsqueeze(0).expand( - batch_size, max_audio_tokens) - < audio_output_lengths.unsqueeze(1)) + batch_size, + max_audio_tokens) < audio_output_lengths.unsqueeze(1) masked_audio_features = audio_embeddings[audio_feature_mask].view( -1, embed_dim) @@ -762,10 +811,12 @@ def forward( ) input_ids = None - return self.decoder.model(input_ids, - positions, - intermediate_tensors, - inputs_embeds=inputs_embeds) + return self.decoder.model( + input_ids, + positions, + intermediate_tensors, + inputs_embeds=inputs_embeds, + ) def compute_logits( self, From 68c275d7b3c048b84aeda6dd552ef17d7a01e889 Mon Sep 17 00:00:00 2001 From: Jiangyun Zhu Date: Mon, 29 Sep 2025 21:54:52 +0800 Subject: [PATCH 075/166] [torch.compile] serialize cudagraph_mode as its enum name instead of value (#25868) Signed-off-by: zjy0516 --- vllm/config/compilation.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py index 825de7d26191..df08fa20e0cf 100644 --- a/vllm/config/compilation.py +++ b/vllm/config/compilation.py @@ -73,6 +73,9 @@ def valid_runtime_modes(self) -> bool: CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL ] + def __str__(self) -> str: + return self.name + @config @dataclass @@ -417,10 +420,11 @@ def __repr__(self) -> str: if pass_config_exclude: exclude["pass_config"] = pass_config_exclude - return TypeAdapter(CompilationConfig).dump_json( - self, - exclude=exclude, # type: ignore[arg-type] - exclude_unset=True).decode() + config = TypeAdapter(CompilationConfig).dump_python(self, + exclude=exclude, + exclude_unset=True) + + return str(config) __str__ = __repr__ From 23ea7465968c6c0641b4093d3ef1cf4b67bbf992 Mon Sep 17 00:00:00 2001 From: Chenxi Yang Date: Mon, 29 Sep 2025 07:31:51 -0700 Subject: [PATCH 076/166] [Nixl][P/D] Add cuda2cpu support (HD->DH transfer) (#24690) Signed-off-by: Chenxi Yang Co-authored-by: Chenxi Yang --- .../nixl_integration/run_accuracy_test.sh | 31 ++++++++++++++-- .../nixl_integration/run_edge_case_test.sh | 37 ++++++++++++++++--- vllm/config/kv_transfer.py | 4 +- .../kv_connector/v1/nixl_connector.py | 8 +++- vllm/platforms/cuda.py | 24 ++++++++++++ vllm/v1/worker/gpu_model_runner.py | 7 ++-- 6 files changed, 96 insertions(+), 15 deletions(-) mode change 100644 => 100755 tests/v1/kv_connector/nixl_integration/run_edge_case_test.sh diff --git a/tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh b/tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh index bc8837079109..3b0f2d102c1f 100755 --- a/tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh +++ b/tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh @@ -1,6 +1,31 @@ #!/bin/bash set -xe +# Parse command line arguments +KV_BUFFER_DEVICE="cuda" # Default to cuda +while [[ $# -gt 0 ]]; do + case $1 in + --kv_buffer_device) + KV_BUFFER_DEVICE="$2" + shift 2 + ;; + *) + echo "Unknown option $1" + echo "Usage: $0 [--kv_buffer_device ]" + exit 1 + ;; + esac +done + +echo "Running accuracy tests with kv_buffer_device=$KV_BUFFER_DEVICE" + +# Build the kv-transfer-config once +if [[ "$KV_BUFFER_DEVICE" == "cuda" ]]; then + KV_CONFIG='{"kv_connector":"NixlConnector","kv_role":"kv_both"}' +else + KV_CONFIG="{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\",\"kv_buffer_device\":\"$KV_BUFFER_DEVICE\"}" +fi + # Models to run MODELS=( "Qwen/Qwen3-0.6B" @@ -79,7 +104,7 @@ run_tests_for_model() { # Calculate port number (base port + instance number) PORT=$((8100 + i)) - # Calculate side channel port. Avoid clash with with TP workers. + # Calculate side channel port. Avoid clash with with TP workers. SIDE_CHANNEL_PORT=$((5559 + i)) echo "Starting prefill instance $i on GPU $GPU_ID, port $PORT" @@ -93,7 +118,7 @@ run_tests_for_model() { --enforce-eager \ --gpu-memory-utilization 0.2 \ --tensor-parallel-size $PREFILLER_TP_SIZE \ - --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'" + --kv-transfer-config '$KV_CONFIG'" if [ -n "$model_args" ]; then FULL_CMD="$BASE_CMD $model_args" @@ -128,7 +153,7 @@ run_tests_for_model() { --enforce-eager \ --gpu-memory-utilization 0.2 \ --tensor-parallel-size $DECODER_TP_SIZE \ - --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'" + --kv-transfer-config '$KV_CONFIG'" if [ -n "$model_args" ]; then FULL_CMD="$BASE_CMD $model_args" diff --git a/tests/v1/kv_connector/nixl_integration/run_edge_case_test.sh b/tests/v1/kv_connector/nixl_integration/run_edge_case_test.sh old mode 100644 new mode 100755 index b64461292910..c48b452e24cd --- a/tests/v1/kv_connector/nixl_integration/run_edge_case_test.sh +++ b/tests/v1/kv_connector/nixl_integration/run_edge_case_test.sh @@ -1,6 +1,33 @@ #!/bin/bash set -xe +# Parse command line arguments +KV_BUFFER_DEVICE="cuda" # Default to cuda +PREFILL_GPU_ID=4 # Default GPU IDs +DECODE_GPU_ID=5 +while [[ $# -gt 0 ]]; do + case $1 in + --kv_buffer_device) + KV_BUFFER_DEVICE="$2" + shift 2 + ;; + *) + echo "Unknown option $1" + echo "Usage: $0 [--kv_buffer_device ]" + exit 1 + ;; + esac +done + +echo "Running edge case tests with kv_buffer_device=$KV_BUFFER_DEVICE (GPUs: $PREFILL_GPU_ID, $DECODE_GPU_ID)" + +# Build the kv-transfer-config once +if [[ "$KV_BUFFER_DEVICE" == "cuda" ]]; then + KV_CONFIG='{"kv_connector":"NixlConnector","kv_role":"kv_both"}' +else + KV_CONFIG="{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\",\"kv_buffer_device\":\"$KV_BUFFER_DEVICE\"}" +fi + # Models to run MODELS=( "Qwen/Qwen3-0.6B" @@ -50,15 +77,15 @@ run_tests_for_model() { # Get model-specific arguments local model_args=$(get_model_args "$model_name") - + # Start prefill instance PREFILL_PORT=8001 - BASE_CMD="CUDA_VISIBLE_DEVICES=0 VLLM_NIXL_SIDE_CHANNEL_PORT=5559 vllm serve $model_name \ + BASE_CMD="CUDA_VISIBLE_DEVICES=$PREFILL_GPU_ID VLLM_NIXL_SIDE_CHANNEL_PORT=5559 vllm serve $model_name \ --port $PREFILL_PORT \ --enforce-eager \ --gpu-memory-utilization 0.2 \ - --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'" + --kv-transfer-config '$KV_CONFIG'" if [ -n "$model_args" ]; then FULL_CMD="$BASE_CMD $model_args" @@ -72,11 +99,11 @@ run_tests_for_model() { DECODE_PORT=8002 # Build the command with or without model-specific args - BASE_CMD="CUDA_VISIBLE_DEVICES=1 VLLM_NIXL_SIDE_CHANNEL_PORT=6000 vllm serve $model_name \ + BASE_CMD="CUDA_VISIBLE_DEVICES=$DECODE_GPU_ID VLLM_NIXL_SIDE_CHANNEL_PORT=6000 vllm serve $model_name \ --port $DECODE_PORT \ --enforce-eager \ --gpu-memory-utilization 0.2 \ - --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_both\"}'" + --kv-transfer-config '$KV_CONFIG'" if [ -n "$model_args" ]; then FULL_CMD="$BASE_CMD $model_args" diff --git a/vllm/config/kv_transfer.py b/vllm/config/kv_transfer.py index 9abf4acacfe8..c3d9a3309eb3 100644 --- a/vllm/config/kv_transfer.py +++ b/vllm/config/kv_transfer.py @@ -28,8 +28,8 @@ class KVTransferConfig: """The engine id for KV transfers.""" kv_buffer_device: Optional[str] = "cuda" - """The device used by kv connector to buffer the KV cache. - Currently only support 'cuda'.""" + """The device used by kv connector to buffer the KV cache. Choices are + 'cuda' and 'cpu'.""" kv_buffer_size: float = 1e9 """The buffer size for TorchDistributedConnector. Measured in number of diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py index c11189d7ec10..1c7569515dec 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py @@ -67,7 +67,10 @@ # Supported platforms and types of kv transfer buffer. # {device: tuple of supported kv buffer types} _NIXL_SUPPORTED_DEVICE = { - "cuda": ("cuda", ), + "cuda": ( + "cuda", + "cpu", + ), "tpu": ("cpu", ), "xpu": ("cpu", ), } @@ -701,6 +704,9 @@ def initialize_host_xfer_buffer( def set_host_xfer_buffer_ops(self, copy_operation: CopyBlocksOp): """Assign copy (d2h, h2d) operations when host buffer is used.""" + # Set a no-op if the host buffer is not cpu. + if self.kv_buffer_device != "cpu": + return assert self.use_host_buffer self.copy_blocks = copy_operation diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 8b9f9f569206..6738d3dec286 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -500,6 +500,30 @@ def check_if_supports_dtype(cls, torch_dtype: torch.dtype): "You can use float16 instead by explicitly setting the " "`dtype` flag in CLI, for example: --dtype=half.") + @classmethod + def insert_blocks_to_device( + cls, + src_cache: torch.Tensor, + dst_cache: torch.Tensor, + src_block_indices: torch.Tensor, + dst_block_indices: torch.Tensor, + ) -> None: + """Copy blocks from src_cache to dst_cache on GPU.""" + _src_cache = src_cache[:, src_block_indices] + dst_cache[:, dst_block_indices] = _src_cache.to(dst_cache.device) + + @classmethod + def swap_out_blocks_to_host( + cls, + src_cache: torch.Tensor, + dst_cache: torch.Tensor, + src_block_indices: torch.Tensor, + dst_block_indices: torch.Tensor, + ) -> None: + """Copy blocks from GPU to host (CPU).""" + _src_cache = src_cache[:, src_block_indices] + dst_cache[:, dst_block_indices] = _src_cache.cpu() + @classmethod def support_hybrid_kv_cache(cls) -> bool: return True diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 0960fe3a25fb..f8b0b9cba1bc 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -4059,10 +4059,9 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None: self.drafter.validate_same_kv_cache_group(kv_cache_config) if has_kv_transfer_group(): - get_kv_transfer_group().register_kv_caches(kv_caches) - if self.device.type == 'xpu': - get_kv_transfer_group().set_host_xfer_buffer_ops( - copy_kv_blocks) + kv_transfer_group = get_kv_transfer_group() + kv_transfer_group.register_kv_caches(kv_caches) + kv_transfer_group.set_host_xfer_buffer_ops(copy_kv_blocks) if self.dcp_world_size > 1: layer_names = self.attn_groups[0][0].layer_names From da931fc1cdeaf89c6dd604051e8abe2ee3648a2d Mon Sep 17 00:00:00 2001 From: Rahul Tuli Date: Mon, 29 Sep 2025 21:07:20 +0530 Subject: [PATCH 077/166] [Bugfix][Speculative Decoding] Fix Eagle3 quantization config issue (#25883) Signed-off-by: Rahul Tuli --- .../speculators/test_eagle3.py | 3 +++ vllm/model_executor/models/llama.py | 7 ++++++- vllm/model_executor/models/llama_eagle3.py | 14 +++++++++++++- 3 files changed, 22 insertions(+), 2 deletions(-) diff --git a/tests/speculative_decoding/speculators/test_eagle3.py b/tests/speculative_decoding/speculators/test_eagle3.py index 368238b3a720..87d799a5fed7 100644 --- a/tests/speculative_decoding/speculators/test_eagle3.py +++ b/tests/speculative_decoding/speculators/test_eagle3.py @@ -14,6 +14,9 @@ pytest.param( "nm-testing/Speculator-Qwen3-8B-Eagle3-converted-071-quantized", id="qwen3-eagle3-speculator"), + pytest.param( + "nm-testing/Speculator-Qwen3-8B-Eagle3-converted-071-quantized-w4a16", + id="qwen3-eagle3-speculator-w4a16-verifier"), ]) def test_eagle3_speculators_model(vllm_runner, example_prompts, model_path, monkeypatch): diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py index c7dd134ea47e..a6081d331511 100644 --- a/vllm/model_executor/models/llama.py +++ b/vllm/model_executor/models/llama.py @@ -248,7 +248,7 @@ def __init__(self, config = config or vllm_config.model_config.hf_config cache_config = vllm_config.cache_config - quant_config = vllm_config.quant_config + quant_config = self.get_quant_config(vllm_config) self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) @@ -328,6 +328,11 @@ def forward( hidden_states = self.mlp(hidden_states) return hidden_states, residual + def get_quant_config( + self, vllm_config: VllmConfig) -> Optional[QuantizationConfig]: + """Get quantization config for this layer. Override in subclasses.""" + return vllm_config.quant_config + @support_torch_compile class LlamaModel(nn.Module): diff --git a/vllm/model_executor/models/llama_eagle3.py b/vllm/model_executor/models/llama_eagle3.py index 7192a76c8749..3fb6f2f8d5ec 100644 --- a/vllm/model_executor/models/llama_eagle3.py +++ b/vllm/model_executor/models/llama_eagle3.py @@ -13,6 +13,8 @@ from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import QKVParallelLinear from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader @@ -33,7 +35,7 @@ def __init__(self, super().__init__(vllm_config, prefix=prefix, config=config) config = config or vllm_config.model_config.hf_config - quant_config = vllm_config.quant_config + quant_config = self.get_quant_config(vllm_config) # override qkv self.self_attn.qkv_proj = QKVParallelLinear( @@ -53,6 +55,16 @@ def __init__(self, else: self._residual_norm = self._norm_after_residual + def get_quant_config( + self, vllm_config: VllmConfig) -> Optional[QuantizationConfig]: + """Use drafter's quantization config instead of verifier's.""" + draft_model_config = vllm_config.speculative_config.draft_model_config + draft_load_config = vllm_config.load_config + + return VllmConfig.get_quantization_config( + draft_model_config, + draft_load_config) if draft_model_config else None + def _norm_before_residual( self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: From 11f34dcd58d5b9afac2dd704b2c5aa0fa9a7e73b Mon Sep 17 00:00:00 2001 From: Isotr0py Date: Tue, 30 Sep 2025 00:33:39 +0800 Subject: [PATCH 078/166] [CI/Build] Include Transformers backend test in nightly transformers test (#25885) Signed-off-by: Isotr0py --- .buildkite/test-pipeline.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index e603c1582e1f..460f0afb6f67 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -752,6 +752,7 @@ steps: commands: - pip install --upgrade git+https://github.com/huggingface/transformers - pytest -v -s tests/models/test_initialization.py + - pytest -v -s tests/models/test_transformers.py - pytest -v -s tests/models/multimodal/processing/ - pytest -v -s tests/models/multimodal/test_mapping.py - python3 examples/offline_inference/basic/chat.py From 9f91bc26fb6c2d916ec79864314f2535dc2bb3d9 Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Tue, 30 Sep 2025 00:36:30 +0800 Subject: [PATCH 079/166] [Model] Remove MotifForCausalLM (#25866) Signed-off-by: Jee Jee Li --- docs/models/supported_models.md | 2 - tests/models/registry.py | 3 - tests/models/test_initialization.py | 4 - vllm/model_executor/models/motif.py | 345 ------------------------- vllm/model_executor/models/registry.py | 2 +- 5 files changed, 1 insertion(+), 355 deletions(-) delete mode 100644 vllm/model_executor/models/motif.py diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index 3ee5a7d0ffc5..8e87a98e3d51 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -396,7 +396,6 @@ th { | `MiniCPM3ForCausalLM` | MiniCPM3 | `openbmb/MiniCPM3-4B`, etc. | ✅︎ | ✅︎ | ✅︎ | | `MistralForCausalLM` | Mistral, Mistral-Instruct | `mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc. | ✅︎ | ✅︎ | ✅︎ | | `MixtralForCausalLM` | Mixtral-8x7B, Mixtral-8x7B-Instruct | `mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, `mistral-community/Mixtral-8x22B-v0.1`, etc. | ✅︎ | ✅︎ | ✅︎ | -| `MotifForCausalLM` | Motif-1-Tiny | `Motif-Technologies/Motif-2.6B`, `Motif-Technologies/Motif-2.6b-v1.1-LC`, etc. | ✅︎ | ✅︎ | | | `MPTForCausalLM` | MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter | `mosaicml/mpt-7b`, `mosaicml/mpt-7b-storywriter`, `mosaicml/mpt-30b`, etc. | | ✅︎ | ✅︎ | | `NemotronForCausalLM` | Nemotron-3, Nemotron-4, Minitron | `nvidia/Minitron-8B-Base`, `mgoin/Nemotron-4-340B-Base-hf-FP8`, etc. | ✅︎ | ✅︎ | ✅︎ | | `NemotronHForCausalLM` | Nemotron-H | `nvidia/Nemotron-H-8B-Base-8K`, `nvidia/Nemotron-H-47B-Base-8K`, `nvidia/Nemotron-H-56B-Base-8K`, etc. | ✅︎ | ✅︎ | ✅︎ | @@ -409,7 +408,6 @@ th { | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ | ✅︎ | | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ | | `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ | -| `Phi4FlashForCausalLM` | Phi-4-mini-flash-reasoning | `microsoft/microsoft/Phi-4-mini-instruct`, etc. | | | | | `PersimmonForCausalLM` | Persimmon | `adept/persimmon-8b-base`, `adept/persimmon-8b-chat`, etc. | | ✅︎ | ✅︎ | | `Plamo2ForCausalLM` | PLaMo2 | `pfnet/plamo-2-1b`, `pfnet/plamo-2-8b`, etc. | | ✅︎ | ✅︎ | | `QWenLMHeadModel` | Qwen | `Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc. | ✅︎ | ✅︎ | ✅︎ | diff --git a/tests/models/registry.py b/tests/models/registry.py index 124a97ed0c89..37ee474d3ecb 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -298,9 +298,6 @@ def check_available_online( "MistralForCausalLM": _HfExamplesInfo("mistralai/Mistral-7B-Instruct-v0.1"), "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1", # noqa: E501 {"tiny": "TitanML/tiny-mixtral"}), # noqa: E501 - "MotifForCausalLM": _HfExamplesInfo("Motif-Technologies/Motif-2.6B", - trust_remote_code=True, - v0_only=True), "MptForCausalLM": _HfExamplesInfo("mpt", is_available_online=False), "MPTForCausalLM": _HfExamplesInfo("mosaicml/mpt-7b"), "NemotronForCausalLM": _HfExamplesInfo("nvidia/Minitron-8B-Base"), diff --git a/tests/models/test_initialization.py b/tests/models/test_initialization.py index 42d69367042d..e818b908e8a8 100644 --- a/tests/models/test_initialization.py +++ b/tests/models/test_initialization.py @@ -76,10 +76,6 @@ def _initialize_kv_caches_v1(self, vllm_config): if model_info.v0_only: # NOTE(woosuk): skip the test for V0-only models return - - if model_arch in ("Phi4FlashForCausalLM", "MotifForCausalLM"): - pytest.skip( - "Differential Flash Attention backend has been removed.") if model_arch == "GptOssForCausalLM": # FIXME: A hack to bypass FA3 assertion because our CI's L4 GPU # has cc==8.9 which hasn't supported FA3 yet. Remove this hack when diff --git a/vllm/model_executor/models/motif.py b/vllm/model_executor/models/motif.py deleted file mode 100644 index 153f36dcf1f5..000000000000 --- a/vllm/model_executor/models/motif.py +++ /dev/null @@ -1,345 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the vLLM project - -# Adapted from -# https://huggingface.co/Motif-Technologies/Motif-2.6B/blob/main/modeling_motif.py -# Copyright (c) Alibaba Cloud. -# LICENSE: https://huggingface.co/Motif-Technologies/Motif-2.6B/blob/main/LICENSE -"""Inference-only Motif model compatible with HuggingFace weights.""" -import math -from typing import Any, Optional - -import torch -from torch import nn -from transformers import PretrainedConfig - -from vllm.attention import Attention, AttentionType -from vllm.attention.selector import _Backend -from vllm.config import CacheConfig, VllmConfig -from vllm.distributed import get_tensor_model_parallel_world_size -from vllm.model_executor.layers.layernorm import PolyNorm, RMSNorm -from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, - QKVParallelLinear, - RowParallelLinear) -from vllm.model_executor.layers.quantization import QuantizationConfig -from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.models.llama import LlamaForCausalLM - -from .adapters import as_seq_cls_model -from .interfaces import SupportsV0Only -from .utils import extract_layer_index - - -class MotifMLP(nn.Module): - """MLP for the language component of the Motif model, which contains a - MergedColumnParallelLinear merging 2 outputs via PolyNorm activation.""" - - def __init__( - self, - hidden_size: int, - intermediate_size: int, - hidden_act: str = "poly_norm", - quant_config: Optional[QuantizationConfig] = None, - bias: bool = False, - prefix: str = "", - reduce_results: bool = True, - ): - super().__init__() - self.gate_up_proj = MergedColumnParallelLinear( - input_size=hidden_size, - output_sizes=[intermediate_size] * 2, - bias=bias, - quant_config=quant_config, - prefix=f"{prefix}.gate_up_proj", - ) - self.down_proj = RowParallelLinear( - input_size=intermediate_size, - output_size=hidden_size, - bias=bias, - quant_config=quant_config, - reduce_results=reduce_results, - prefix=f"{prefix}.down_proj", - ) - if hidden_act != "poly_norm": - raise NotImplementedError(f"Unsupported activation: {hidden_act}. " - "Only poly_norm is supported for now.") - self.act_fn = PolyNorm() - self.intermediate_size = intermediate_size - tp_size = get_tensor_model_parallel_world_size() - if hidden_act == "poly_norm" and tp_size > 1: - raise NotImplementedError( - "Tensor parallelism for poly_norm is not supported yet. " - "Support will be added in the future.") - - def forward(self, x): - x, _ = self.gate_up_proj(x) - x = self.act_fn( - x[..., :self.intermediate_size]) * x[..., self.intermediate_size:] - x, _ = self.down_proj(x) - return x - - -class MotifAttention(nn.Module): - - def __init__( - self, - config: PretrainedConfig, - hidden_size: int, - num_heads: int, - num_kv_heads: int, - rope_theta: float = 10000, - rope_scaling: Optional[dict[str, Any]] = None, - max_position_embeddings: int = 8192, - quant_config: Optional[QuantizationConfig] = None, - bias: bool = False, - bias_o_proj: bool = False, - cache_config: Optional[CacheConfig] = None, - prefix: str = "", - attn_type: str = AttentionType.DECODER, - ) -> None: - super().__init__() - layer_idx = extract_layer_index(prefix) - self.hidden_size = hidden_size - tp_size = get_tensor_model_parallel_world_size() - self.total_num_heads = num_heads - assert self.total_num_heads % tp_size == 0 - self.num_heads = self.total_num_heads // tp_size - self.total_num_kv_heads = num_kv_heads - if self.total_num_kv_heads >= tp_size: - # Number of KV heads is greater than TP size, so we partition - # the KV heads across multiple tensor parallel GPUs. - assert self.total_num_kv_heads % tp_size == 0 - else: - # Number of KV heads is less than TP size, so we replicate - # the KV heads across multiple tensor parallel GPUs. - assert tp_size % self.total_num_kv_heads == 0 - self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) - # MistralConfig has an optional head_dim introduced by Mistral-Nemo - head_dim = getattr(config, "head_dim", None) - if head_dim is None: - head_dim = self.hidden_size // self.total_num_heads - self.head_dim = head_dim - # Phi models introduced a partial_rotary_factor parameter in the config - self.partial_rotary_factor = getattr(config, "partial_rotary_factor", - 1) - self.q_size = self.num_heads * self.head_dim - self.kv_size = self.num_kv_heads * self.head_dim - self.scaling = self.head_dim**-0.5 - self.rope_theta = rope_theta - self.max_position_embeddings = max_position_embeddings - - assert self.num_heads % 2 == 0, 'num_heads should be even' - assert self.num_kv_heads % 2 == 0, 'num_heads should be even' - - self.qkv_proj = QKVParallelLinear( - hidden_size=hidden_size, - head_size=self.head_dim, - total_num_heads=self.total_num_heads, - total_num_kv_heads=self.total_num_kv_heads, - bias=bias, - quant_config=quant_config, - prefix=f"{prefix}.qkv_proj", - ) - - self.o_proj = RowParallelLinear( - input_size=self.total_num_heads * self.head_dim, - output_size=hidden_size, - bias=bias_o_proj, - quant_config=quant_config, - prefix=f"{prefix}.o_proj", - ) - - self._init_rotary_emb(config, - rope_scaling=rope_scaling, - quant_config=quant_config) - sliding_window = None - - self.lambda_init = self.lambda_init_fn(layer_idx) - self.lambda_q1 = nn.Parameter( - torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, - std=0.1)) - self.lambda_k1 = nn.Parameter( - torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, - std=0.1)) - self.lambda_q2 = nn.Parameter( - torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, - std=0.1)) - self.lambda_k2 = nn.Parameter( - torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, - std=0.1)) - self.subln = RMSNorm(2 * self.head_dim, eps=config.attn_rms_norm_eps) - - params = { - 'differential_flash_attention_config': { - 'lambda_init': self.lambda_init, - 'lambda_q1': self.lambda_q1, - 'lambda_k1': self.lambda_k1, - 'lambda_q2': self.lambda_q2, - 'lambda_k2': self.lambda_k2, - "subln": self.subln, - } - } - - diff_attn_err_msg = ( - 'Set VLLM_ATTENTION_BACKEND="DIFFERENTIAL_FLASH_ATTN" ' - 'to enable Differential Flash Attention.') - try: - self.attn = Attention( - self.num_heads, - self.head_dim, - self.scaling, - num_kv_heads=self.num_kv_heads, - cache_config=cache_config, - quant_config=quant_config, - per_layer_sliding_window=sliding_window, - attn_type=attn_type, - prefix=f"{prefix}.attn", - **params, - ) - except TypeError as e: - raise ValueError(diff_attn_err_msg) from e - assert (self.attn.backend == _Backend.DIFFERENTIAL_FLASH_ATTN - ), diff_attn_err_msg - - def lambda_init_fn(self, depth): - return 0.8 - 0.6 * math.exp(-0.3 * (depth - 1)) - - def forward( - self, - positions: torch.Tensor, - hidden_states: torch.Tensor, - ) -> torch.Tensor: - qkv, _ = self.qkv_proj(hidden_states) - q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) - q, k = self.rotary_emb(positions, q, k) - attn_output = self.attn(q, k, v) - output, _ = self.o_proj(attn_output) - return output - - def _init_rotary_emb(self, config: PretrainedConfig, - rope_scaling: Optional[dict[str, Any]], - quant_config: Optional[QuantizationConfig]) -> None: - is_neox_style = True - is_gguf = quant_config and quant_config.get_name() == "gguf" - if is_gguf and config.model_type == "llama": - is_neox_style = False - - self.rotary_emb = get_rope( - self.head_dim, - rotary_dim=self.head_dim, - max_position=self.max_position_embeddings, - base=self.rope_theta, - rope_scaling=rope_scaling, - is_neox_style=is_neox_style, - partial_rotary_factor=self.partial_rotary_factor, - ) - - -class MotifDecoderLayer(nn.Module): - - def __init__( - self, - config: PretrainedConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - prefix: str = "", - ) -> None: - super().__init__() - self.hidden_size = config.hidden_size - rope_theta = getattr(config, "rope_theta", 10000) - rope_scaling = getattr(config, "rope_scaling", None) - if rope_scaling is not None and getattr( - config, "original_max_position_embeddings", None): - rope_scaling["original_max_position_embeddings"] = ( - config.original_max_position_embeddings) - max_position_embeddings = getattr(config, "max_position_embeddings", - 8192) - attention_bias = getattr(config, "attention_bias", False) or getattr( - config, "use_bias", False) - bias_o_proj = attention_bias - if hasattr(config, 'qkv_bias'): - attention_bias = config.qkv_bias - - # By default, Motif uses causal attention as it is a decoder-only model. - # You can override the HF config with `is_causal=False` to enable - # bidirectional attention, which is used in some embedding models - # (e.g. parasail-ai/GritLM-7B-vllm) - if getattr(config, "is_causal", True): - attn_type = AttentionType.DECODER - else: - attn_type = AttentionType.ENCODER_ONLY - - self.self_attn = MotifAttention( - config=config, - hidden_size=self.hidden_size, - num_heads=config.num_attention_heads, - num_kv_heads=getattr(config, "num_key_value_heads", - config.num_attention_heads), - rope_theta=rope_theta, - rope_scaling=rope_scaling, - max_position_embeddings=max_position_embeddings, - quant_config=quant_config, - bias=attention_bias, - bias_o_proj=bias_o_proj, - cache_config=cache_config, - prefix=f"{prefix}.self_attn", - attn_type=attn_type, - ) - self.mlp = MotifMLP( - hidden_size=self.hidden_size, - intermediate_size=config.intermediate_size, - hidden_act=config.hidden_act, - quant_config=quant_config, - bias=getattr(config, "use_bias", False), - prefix=f"{prefix}.mlp", - ) - self.input_layernorm = RMSNorm(config.hidden_size, - eps=config.rms_norm_eps) - self.post_attention_layernorm = RMSNorm(config.hidden_size, - eps=config.rms_norm_eps) - - def forward( - self, - positions: torch.Tensor, - hidden_states: torch.Tensor, - residual: Optional[torch.Tensor], - ) -> tuple[torch.Tensor, torch.Tensor]: - # Self Attention - if residual is None: - residual = hidden_states - hidden_states = self.input_layernorm(hidden_states) - else: - hidden_states, residual = self.input_layernorm( - hidden_states, residual) - hidden_states = self.self_attn(positions=positions, - hidden_states=hidden_states) - - # Fully Connected - hidden_states, residual = self.post_attention_layernorm( - hidden_states, residual) - hidden_states = self.mlp(hidden_states) - return hidden_states, residual - - -# Motif model uses differential attention -# Only supported in v0 (no chunked prefill support) -class MotifForCausalLM(LlamaForCausalLM, SupportsV0Only): - - def __init__(self, - *, - vllm_config: VllmConfig, - prefix: str = "", - layer_type: type[nn.Module] = MotifDecoderLayer): - - # Prefix caching and chunked prefill is not supported for this model. - assert not vllm_config.cache_config.enable_prefix_caching, \ - "Motif currently does not support prefix caching" - assert not vllm_config.scheduler_config.chunked_prefill_enabled, \ - "Motif currently does not support chunked prefill" - - super().__init__(vllm_config=vllm_config, - prefix=prefix, - layer_type=layer_type) - - -MotifForSequenceClassification = as_seq_cls_model(MotifForCausalLM) diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index a68012d8a8c9..62aa5af9fb14 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -119,7 +119,6 @@ "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"), "MistralForCausalLM": ("llama", "LlamaForCausalLM"), "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"), - "MotifForCausalLM": ("motif", "MotifForCausalLM"), # transformers's mpt class has lower case "MptForCausalLM": ("mpt", "MPTForCausalLM"), "MPTForCausalLM": ("mpt", "MPTForCausalLM"), @@ -332,6 +331,7 @@ ] _PREVIOUSLY_SUPPORTED_MODELS = { + "MotifForCausalLM": "0.10.2", "Phi3SmallForCausalLM": "0.9.2", "Phi4FlashForCausalLM": "0.10.2", # encoder-decoder models except whisper From 3f22bc9b1b32c04e0cd4a9b9a48cca097f0bd186 Mon Sep 17 00:00:00 2001 From: Lee Nau Date: Mon, 29 Sep 2025 12:07:29 -0700 Subject: [PATCH 080/166] [Bugfix] Use correct key "ignore" for config.json non-quantized layers (#25706) Signed-off-by: Lee Nau --- vllm/model_executor/layers/quantization/modelopt.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/vllm/model_executor/layers/quantization/modelopt.py b/vllm/model_executor/layers/quantization/modelopt.py index 0be43da00b53..20704439eaa9 100644 --- a/vllm/model_executor/layers/quantization/modelopt.py +++ b/vllm/model_executor/layers/quantization/modelopt.py @@ -138,13 +138,15 @@ def from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config": if not quant_method: raise ValueError("Missing 'quant_algo' in quantization config") kv_cache_quant_method = quant_config.get("kv_cache_quant_algo") + # "exclude_modules" is the key in the legacy hf_quant_config.json exclude_modules = quant_config.get("exclude_modules") else: # Compressed-tensors style format: # {"quant_algo": "...", "quant_method": "modelopt"} quant_method = config.get("quant_algo", "") kv_cache_quant_method = config.get("kv_cache_quant_algo") - exclude_modules = config.get("exclude_modules") + # "ignore" is the key in config.json + exclude_modules = config.get("ignore") if quant_method not in QUANT_ALGOS: raise ValueError( @@ -723,6 +725,7 @@ def from_config(cls, config: dict[str, Any]) -> "ModelOptNvFp4Config": raise ValueError(f"group_size must be an integer, got " f"{type(group_size_raw)}") from None + # "exclude_modules" is the key in the legacy hf_quant_config.json exclude_modules = quant_config.get("exclude_modules", []) if not isinstance(exclude_modules, list): raise ValueError(f"exclude_modules must be a list, got " @@ -756,7 +759,8 @@ def from_config(cls, config: dict[str, Any]) -> "ModelOptNvFp4Config": raise ValueError(f"group_size must be an integer, got " f"{type(group_size_raw)}") from None - exclude_modules = config.get("exclude_modules", []) + # "ignore" is the key in config.json + exclude_modules = config.get("ignore", []) if not isinstance(exclude_modules, list): raise ValueError(f"exclude_modules must be a list, got " f"{type(exclude_modules)}") From 9280e9543ab04f63a08f75a037b5d5fc19758081 Mon Sep 17 00:00:00 2001 From: Adrian Abeyta Date: Mon, 29 Sep 2025 14:52:04 -0500 Subject: [PATCH 081/166] [BugFix][torch.compile] KV scale calculation issues with FP8 quantization (#25513) Signed-off-by: adabeyta --- tests/compile/test_full_graph.py | 15 +++++++++++ vllm/attention/layer.py | 43 +++++++++++++++++++++++++++--- vllm/v1/worker/gpu_model_runner.py | 9 +++++++ 3 files changed, 64 insertions(+), 3 deletions(-) diff --git a/tests/compile/test_full_graph.py b/tests/compile/test_full_graph.py index 870aa553ca62..f9f146810924 100644 --- a/tests/compile/test_full_graph.py +++ b/tests/compile/test_full_graph.py @@ -139,6 +139,21 @@ def test_custom_compile_config( run_model(compilation_config, model, model_kwargs) +@pytest.mark.parametrize( + "optimization_level", + [CompilationLevel.NO_COMPILATION, CompilationLevel.PIECEWISE], +) +def test_fp8_kv_scale_compile(optimization_level: int): + model = "Qwen/Qwen2-0.5B" + model_kwargs = { + "quantization": "fp8", + "kv_cache_dtype": "fp8_e4m3", + "calculate_kv_scales": True, + "max_model_len": 512, + } + run_model(optimization_level, model, model_kwargs) + + def test_inductor_graph_partition_attn_fusion(caplog_vllm): if not is_torch_equal_or_newer("2.9.0.dev"): pytest.skip("inductor graph partition is only available " diff --git a/vllm/attention/layer.py b/vllm/attention/layer.py index 326fe6dd048a..d97c87d96e99 100644 --- a/vllm/attention/layer.py +++ b/vllm/attention/layer.py @@ -277,9 +277,8 @@ def forward( `vllm.forward_context.get_forward_context().attn_metadata`. """ if self.calculate_kv_scales: - attn_metadata = get_forward_context().attn_metadata - if attn_metadata.enable_kv_scales_calculation: - self.calc_kv_scales(query, key, value) + torch.ops.vllm.maybe_calc_kv_scales(query, key, value, + self.layer_name) output_dtype = query.dtype if self.query_quant is not None: @@ -554,6 +553,44 @@ def maybe_save_kv_layer_to_connector( attn_metadata[layer_name]) +def maybe_calc_kv_scales( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + layer_name: str, +) -> None: + + forward_context: ForwardContext = get_forward_context() + attn_metadata = forward_context.attn_metadata + + if isinstance(attn_metadata, dict): + attn_metadata = attn_metadata[layer_name] + + if attn_metadata is None or not getattr( + attn_metadata, 'enable_kv_scales_calculation', False): + return + + self = forward_context.no_compile_layers[layer_name] + self.calc_kv_scales(query, key, value) + + +def maybe_calc_kv_scales_fake( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + layer_name: str, +) -> None: + return + + +direct_register_custom_op( + op_name="maybe_calc_kv_scales", + op_func=maybe_calc_kv_scales, + mutates_args=["query", "key", "value"], + fake_impl=maybe_calc_kv_scales_fake, +) + + def unified_attention( query: torch.Tensor, key: torch.Tensor, diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index f8b0b9cba1bc..9e7d6eb0387b 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -2351,6 +2351,15 @@ def execute_model( self.cudagraph_dispatcher.dispatch(batch_descriptor, use_cascade_attn) + # Set cudagraph mode to none if calc_kv_scales is true. + if attn_metadata is not None: + metadata_list = (attn_metadata.values() if isinstance( + attn_metadata, dict) else [attn_metadata]) + if any( + getattr(m, 'enable_kv_scales_calculation', False) + for m in metadata_list): + cudagraph_runtime_mode = CUDAGraphMode.NONE + # This is currently to get around the assert in the DPMetadata # where it wants `num_tokens_across_dp` to align with `num_tokens` if ubatch_slices is not None: From 2c2a7710148d9cb9c37236f8adc98f60032b39dc Mon Sep 17 00:00:00 2001 From: Naman Lalit Date: Mon, 29 Sep 2025 13:49:49 -0700 Subject: [PATCH 082/166] [Doc] Add documentation for vLLM continuous benchmarking and profiling (#25819) Signed-off-by: Naman Lalit --- docs/contributing/benchmarks.md | 24 ++++++++++++++++++++++++ docs/contributing/profiling.md | 16 ++++++++++++++++ 2 files changed, 40 insertions(+) diff --git a/docs/contributing/benchmarks.md b/docs/contributing/benchmarks.md index a97d1fa6a3a5..cf14770c01a6 100644 --- a/docs/contributing/benchmarks.md +++ b/docs/contributing/benchmarks.md @@ -823,6 +823,30 @@ The latest performance results are hosted on the public [vLLM Performance Dashbo More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md). +### Continuous Benchmarking + +The continuous benchmarking provides automated performance monitoring for vLLM across different models and GPU devices. This helps track vLLM's performance characteristics over time and identify any performance regressions or improvements. + +#### How It Works + +The continuous benchmarking is triggered via a [GitHub workflow CI](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) in the PyTorch infrastructure repository, which runs automatically every 4 hours. The workflow executes three types of performance tests: + +- **Serving tests**: Measure request handling and API performance +- **Throughput tests**: Evaluate token generation rates +- **Latency tests**: Assess response time characteristics + +#### Benchmark Configuration + +The benchmarking currently runs on a predefined set of models configured in the [vllm-benchmarks directory](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks). To add new models for benchmarking: + +1. Navigate to the appropriate GPU directory in the benchmarks configuration +2. Add your model specifications to the corresponding configuration files +3. The new models will be included in the next scheduled benchmark run + +#### Viewing Results + +All continuous benchmarking results are automatically published to the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm). + [](){ #nightly-benchmarks } ## Nightly Benchmarks diff --git a/docs/contributing/profiling.md b/docs/contributing/profiling.md index a1b7927a95d1..b62560a58748 100644 --- a/docs/contributing/profiling.md +++ b/docs/contributing/profiling.md @@ -160,6 +160,22 @@ GUI example: Screenshot 2025-03-05 at 11 48 42 AM +## Continuous Profiling + +There is a [GitHub CI workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-profiling.yml) in the PyTorch infrastructure repository that provides continuous profiling for different models on vLLM. This automated profiling helps track performance characteristics over time and across different model configurations. + +### How It Works + +The workflow currently runs weekly profiling sessions for selected models, generating detailed performance traces that can be analyzed using different tools to identify performance regressions or optimization opportunities. But, it can be triggered manually as well, using the Github Action tool. + +### Adding New Models + +To extend the continuous profiling to additional models, you can modify the [profiling-tests.json](https://github.com/pytorch/pytorch-integration-testing/blob/main/vllm-profiling/cuda/profiling-tests.json) configuration file in the PyTorch integration testing repository. Simply add your model specifications to this file to include them in the automated profiling runs. + +### Viewing Profiling Results + +The profiling traces generated by the continuous profiling workflow are publicly available on the [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm). Look for the **Profiling traces** table to access and download the traces for different models and runs. + ## Profiling vLLM Python Code The Python standard library includes From b67583daeb31fc5e0d491d5c2f6fc97198c16ff8 Mon Sep 17 00:00:00 2001 From: Gregory Shtrasberg <156009573+gshtras@users.noreply.github.com> Date: Mon, 29 Sep 2025 17:01:50 -0400 Subject: [PATCH 083/166] [Bugfix][ROCm] Fixing trying to import non-existent symbols from libnccl.so (#25605) Signed-off-by: Gregory Shtrasberg --- .../device_communicators/pynccl_wrapper.py | 27 ++++++++++++++++--- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/vllm/distributed/device_communicators/pynccl_wrapper.py b/vllm/distributed/device_communicators/pynccl_wrapper.py index c3e99e177e2d..2e9a4e024de4 100644 --- a/vllm/distributed/device_communicators/pynccl_wrapper.py +++ b/vllm/distributed/device_communicators/pynccl_wrapper.py @@ -30,7 +30,9 @@ import torch from torch.distributed import ReduceOp +from vllm import envs from vllm.logger import init_logger +from vllm.platforms import current_platform from vllm.utils import find_nccl_library logger = init_logger(__name__) @@ -275,10 +277,27 @@ def __init__(self, so_file: Optional[str] = None): if so_file not in NCCLLibrary.path_to_dict_mapping: _funcs: dict[str, Any] = {} for func in NCCLLibrary.exported_functions: - f = getattr(self.lib, func.name) - f.restype = func.restype - f.argtypes = func.argtypes - _funcs[func.name] = f + try: + f = getattr(self.lib, func.name) + f.restype = func.restype + f.argtypes = func.argtypes + _funcs[func.name] = f + except AttributeError: + if func.name in [ + "ncclCommWindowRegister", + "ncclCommWindowDeregister" + ]: + if envs.VLLM_USE_NCCL_SYMM_MEM: + logger.warning_once( + "The symbol %s is not found in the NCCL " + "library %s. To enable VLLM_USE_NCCL_SYMM_MEM " + " please update your NCCL version to >= " + "2.27.03.", func.name, so_file) + if current_platform.is_rocm(): + # Having an exception here on ROCm platform is + # not allowed during graph capturing + continue + raise NCCLLibrary.path_to_dict_mapping[so_file] = _funcs self._funcs = NCCLLibrary.path_to_dict_mapping[so_file] From 4142c7799dd4f7dc10dc008cca7e6f0750b49e8c Mon Sep 17 00:00:00 2001 From: Thomas Parnell Date: Mon, 29 Sep 2025 23:18:25 +0200 Subject: [PATCH 084/166] [Kernel] Chunk-aligned mamba2 (#24683) --- .../layers/mamba/mamba_mixer2.py | 8 +- .../layers/mamba/ops/ssd_bmm.py | 42 ++-- .../layers/mamba/ops/ssd_chunk_scan.py | 236 ++++++------------ .../layers/mamba/ops/ssd_chunk_state.py | 48 ++-- .../layers/mamba/ops/ssd_combined.py | 56 ++--- .../layers/mamba/ops/ssd_state_passing.py | 97 ++----- vllm/model_executor/models/plamo2.py | 8 +- vllm/v1/attention/backends/mamba2_attn.py | 189 ++++++-------- 8 files changed, 250 insertions(+), 434 deletions(-) diff --git a/vllm/model_executor/layers/mamba/mamba_mixer2.py b/vllm/model_executor/layers/mamba/mamba_mixer2.py index 6dd09fad7a90..bfb0666d361f 100644 --- a/vllm/model_executor/layers/mamba/mamba_mixer2.py +++ b/vllm/model_executor/layers/mamba/mamba_mixer2.py @@ -502,9 +502,9 @@ def forward_cuda( prep_initial_states = attn_metadata.prep_initial_states chunk_size = attn_metadata.chunk_size seq_idx_p = attn_metadata.seq_idx_p - chunk_indices_p = attn_metadata.chunk_indices_p - chunk_offsets_p = attn_metadata.chunk_offsets_p query_start_loc_p = attn_metadata.query_start_loc_p + cu_chunk_seqlen_p = attn_metadata.cu_chunk_seqlen_p + last_chunk_indices_p = attn_metadata.last_chunk_indices_p # 1. Gated MLP's linear projection projected_states, _ = self.in_proj(hidden_states) @@ -634,9 +634,9 @@ def forward_cuda( z=None, dt_bias=self.dt_bias, seq_idx=seq_idx_p, - chunk_indices=chunk_indices_p, - chunk_offsets=chunk_offsets_p, cu_seqlens=query_start_loc_p, + cu_chunk_seqlens=cu_chunk_seqlen_p, + last_chunk_indices=last_chunk_indices_p, initial_states=initial_states, dt_softplus=True, dt_limit=(0.0, float("inf")), diff --git a/vllm/model_executor/layers/mamba/ops/ssd_bmm.py b/vllm/model_executor/layers/mamba/ops/ssd_bmm.py index 601b71ab2a51..15a72fc61261 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_bmm.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_bmm.py @@ -6,8 +6,6 @@ # ruff: noqa: E501,SIM102 -import math - import torch from vllm.triton_utils import tl, triton @@ -96,7 +94,7 @@ def _bmm_chunk_fwd_kernel( a_ptr, b_ptr, out_ptr, - seq_idx_ptr, + cu_chunk_seqlens_ptr, # Matrix dimensions seqlen, chunk_size: tl.constexpr, @@ -112,7 +110,6 @@ def _bmm_chunk_fwd_kernel( stride_out_head: tl.int64, stride_outm: tl.int64, stride_outn: tl.constexpr, - stride_seq_idx_seqlen: tl.constexpr, # Meta-parameters IS_CAUSAL: tl.constexpr, dot_dtype: tl.constexpr, @@ -129,10 +126,12 @@ def _bmm_chunk_fwd_kernel( if IS_CAUSAL: if pid_n * BLOCK_SIZE_N >= (pid_m + 1) * BLOCK_SIZE_M: return - a_ptr += pid_c * chunk_size * stride_a_seqlen + pid_h * stride_a_head - b_ptr += pid_c * chunk_size * stride_b_seqlen + pid_h * stride_b_head - seq_idx_ptr += pid_c * chunk_size * stride_seq_idx_seqlen + chunk_seqlen_start = tl.load(cu_chunk_seqlens_ptr + pid_c) + chunk_seqlen_end = tl.load(cu_chunk_seqlens_ptr + pid_c + 1) + + a_ptr += chunk_seqlen_start * stride_a_seqlen + pid_h * stride_a_head + b_ptr += chunk_seqlen_start * stride_b_seqlen + pid_h * stride_b_head offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) @@ -141,7 +140,7 @@ def _bmm_chunk_fwd_kernel( offs_k[None, :] * stride_ak) b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_n[None, :] * stride_b_seqlen) - chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) + chunk_size_limit = chunk_seqlen_end - chunk_seqlen_start acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) @@ -162,16 +161,6 @@ def _bmm_chunk_fwd_kernel( offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - # Zero out the results that are not from the same request - # in the varlen batch - seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen, - mask=offs_m < chunk_size_limit, - other=-1) - seq_idx_n = tl.load(seq_idx_ptr + offs_n * stride_seq_idx_seqlen, - mask=offs_n < chunk_size_limit, - other=-2) - acc = tl.where(seq_idx_m[:, None] == seq_idx_n[None, :], acc, 0.0) - out = acc.to(out_ptr.dtype.element_ty) out_ptr += pid_c * stride_out_chunk + pid_h * stride_out_head out_ptrs = out_ptr + (stride_outm * offs_m[:, None] + @@ -182,12 +171,18 @@ def _bmm_chunk_fwd_kernel( (offs_n[None, :] < chunk_size)) -def _bmm_chunk_fwd(a, b, chunk_size, seq_idx, causal=False, output_dtype=None): +def _bmm_chunk_fwd(a, + b, + chunk_size, + cu_chunk_seqlens, + causal=False, + output_dtype=None): """ Argument: a: (seqlen, ngroups, k) b: (seqlen, ngroups, k) - seq_idx: (seqlen,). out[i, j] for seq_idx[i] != seq_idx[j] will be zeroed out. + chunk_size: int + cu_chunk_seq_lens: (nchunks+1,) causal: if True, then out[i, j] for i > j will be arbitrary, only out[i, j] for i <= j are guaranteed to be correct. Return: @@ -195,14 +190,12 @@ def _bmm_chunk_fwd(a, b, chunk_size, seq_idx, causal=False, output_dtype=None): """ seqlen, ngroups, k = a.shape assert b.shape == a.shape - assert seq_idx is not None - assert seq_idx.shape == (seqlen, ) if a.stride(-1) != 1 and a.stride(0) != 1: a = a.contiguous() if b.stride(-1) != 1 and b.stride(0) != 1: b = b.contiguous() - nchunks = math.ceil(seqlen / chunk_size) + nchunks = len(cu_chunk_seqlens) - 1 # Allocates output. out_dtype = a.dtype if output_dtype is None else output_dtype out = torch.empty((nchunks, ngroups, chunk_size, chunk_size), @@ -220,7 +213,7 @@ def _bmm_chunk_fwd(a, b, chunk_size, seq_idx, causal=False, output_dtype=None): a_ptr=a, b_ptr=b, out_ptr=out, - seq_idx_ptr=seq_idx, + cu_chunk_seqlens_ptr=cu_chunk_seqlens, seqlen=seqlen, chunk_size=chunk_size, K=k, @@ -235,7 +228,6 @@ def _bmm_chunk_fwd(a, b, chunk_size, seq_idx, causal=False, output_dtype=None): stride_out_head=out.stride(1), stride_outm=out.stride(-2), stride_outn=out.stride(-1), - stride_seq_idx_seqlen=seq_idx.stride(0), IS_CAUSAL=causal, dot_dtype=dot_dtype, ) diff --git a/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py b/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py index add72617fcea..e1e77e14f69d 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py @@ -120,9 +120,7 @@ def _chunk_scan_fwd_kernel( states_ptr, D_ptr, initstates_ptr, - chunk_indices_ptr, - chunk_offsets_ptr, - chunk_meta_num, + cu_chunk_seqlens_ptr, # Matrix dimensions chunk_size: tl.constexpr, hdim: tl.constexpr, @@ -149,7 +147,7 @@ def _chunk_scan_fwd_kernel( stride_dA_cs_chunk: tl.int64, stride_dA_cs_head: tl.int64, stride_dA_cs_csize: tl.constexpr, - stride_seq_idx_seqlen: tl.constexpr, + stride_seq_idx_chunk: tl.constexpr, stride_C_seqlen: tl.int64, stride_C_head: tl.int64, stride_C_dstate: tl.constexpr, @@ -175,170 +173,107 @@ def _chunk_scan_fwd_kernel( HAS_INITSTATES: tl.constexpr, ): pid_c = tl.program_id(axis=1).to(tl.int64) - if not HAS_INITSTATES: - c_idx = pid_c - c_off = 0 - else: - c_idx = tl.load(chunk_indices_ptr + pid_c, mask=pid_c > -1, other=0) - c_off = tl.load(chunk_offsets_ptr + pid_c, mask=pid_c > -1, other=0) - pid_h = tl.program_id(axis=2) num_pid_n = tl.cdiv(hdim, BLOCK_SIZE_N) pid_m = tl.program_id(axis=0) // num_pid_n pid_n = tl.program_id(axis=0) % num_pid_n - cb_ptr += c_idx * stride_cb_chunk + (pid_h // + cb_ptr += pid_c * stride_cb_chunk + (pid_h // nheads_ngroups_ratio) * stride_cb_head - x_ptr += c_idx * chunk_size * stride_x_seqlen + pid_h * stride_x_head - dt_ptr += c_idx * stride_dt_chunk + pid_h * stride_dt_head - dA_cumsum_ptr += c_idx * stride_dA_cs_chunk + pid_h * stride_dA_cs_head - C_ptr += c_idx * chunk_size * stride_C_seqlen + ( + chunk_seqlen_start = tl.load(cu_chunk_seqlens_ptr + pid_c) + chunk_seqlen_end = tl.load(cu_chunk_seqlens_ptr + pid_c + 1) + x_ptr += chunk_seqlen_start * stride_x_seqlen + pid_h * stride_x_head + dt_ptr += pid_c * stride_dt_chunk + pid_h * stride_dt_head + dA_cumsum_ptr += pid_c * stride_dA_cs_chunk + pid_h * stride_dA_cs_head + C_ptr += chunk_seqlen_start * stride_C_seqlen + ( pid_h // nheads_ngroups_ratio) * stride_C_head # M-block offsets and prev states # - logic in next block may override these if there is an active offset - offs_m = pid_m * BLOCK_SIZE_M + c_off + tl.arange(0, BLOCK_SIZE_M) - prev_states_ptr = states_ptr + c_idx * stride_states_chunk + pid_h * stride_states_head - prev_states_hdim = stride_states_hdim - prev_states_dstate = stride_states_dstate - - chunk_size_limit = min(chunk_size, seqlen - c_idx * chunk_size) - - seq_idx_ptr += c_idx * chunk_size * stride_seq_idx_seqlen - # - we only need seq_idx_prev to be aligned to chunk boundary - seq_idx_prev = tl.load(seq_idx_ptr - stride_seq_idx_seqlen, - mask=c_idx >= 1, - other=0) - - if HAS_INITSTATES: - # if there are init states, we only need seq_idx_m to point - # what is the current seq_idx - - # get current seq idx - if (pid_m * BLOCK_SIZE_M + c_off) < chunk_size_limit: - seq_idx_m = tl.load( - seq_idx_ptr + - (pid_m * BLOCK_SIZE_M + c_off) * stride_seq_idx_seqlen, ) - - # - recall that in ssd_state_passing, for the case c_off == 0 - # i.e., the very first sequence, we made states_ptr hold its initial state - # so this edge case is taken care of - if ((c_off == 0) and (seq_idx_prev != seq_idx_m - ) # if a seq is changed exactly on boundary - or (c_off > 0) # implies a new example (pseudo chunk) - ): + offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + + seq_idx_ptr += pid_c * stride_seq_idx_chunk + seq_idx = tl.load(seq_idx_ptr) + seq_idx_prev = tl.load(seq_idx_ptr - stride_seq_idx_chunk, + mask=pid_c >= 1, + other=-1) + + if HAS_INITSTATES and (seq_idx != seq_idx_prev): + prev_states_ptr = initstates_ptr + seq_idx * stride_init_states_batch + pid_h * stride_init_states_head + prev_states_hdim = stride_init_states_hdim + prev_states_dstate = stride_init_states_dstate + else: + prev_states_ptr = states_ptr + ( + pid_c - 1) * stride_states_chunk + pid_h * stride_states_head + prev_states_hdim = stride_states_hdim + prev_states_dstate = stride_states_dstate - # - replace prev_states_ptr with init_states - prev_states_ptr = initstates_ptr + seq_idx_m * stride_init_states_batch + pid_h * stride_init_states_head - prev_states_hdim = stride_init_states_hdim # override strides - prev_states_dstate = stride_init_states_dstate + chunk_size_limit = chunk_seqlen_end - chunk_seqlen_start offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) dA_cs_m = tl.load(dA_cumsum_ptr + offs_m * stride_dA_cs_csize, mask=offs_m < chunk_size, other=0.0).to(tl.float32) - # - handle chunk state limit - if HAS_INITSTATES: - # have to split this if otherwise compilation will have problems - dA_cs_m_boundary = 0.0 - - # get the c_idx for the next (logica) chunk - c_idx_n = tl.load( - chunk_indices_ptr + (pid_c + 1), - mask=pid_c > -1 and (pid_c + 1) < chunk_meta_num, - other=-1 # to trigger different chunk - ) - - # - there are things to consider - # A. if c_off > 0 then we need to move the dA_cs boundary to ensure correct - # contribution of past states - # B. if c_off_n < chunk_size_limit, then we need to adjust this so as not to - # encroach into the next sequence, where c_off_n is the offset of the next - # (logical) chunk. - # An equivalent check for B is c_idx == c_idx_n, where there is repetition in - # (logical) chunk indices. - - if (c_idx == c_idx_n) or c_off > 0: - - # get the next offset - c_off_n = tl.load(chunk_offsets_ptr + (pid_c + 1), - mask=pid_c > -1 and (pid_c + 1) < chunk_meta_num, - other=chunk_size) - - # in this case, adjust down the chunk_size_limit - if c_idx == c_idx_n: - chunk_size_limit = min(c_off_n, chunk_size_limit) - - # get the cs at the offset boundary - # - c_off == 0 is a passthrough - # - We need dA_cs at the boundary, defined by c_off - no need - # to increase pointer by pid_m (it is a constant offset, - # i.e. the same for all blocks) - dA_cs_m_boundary = tl.load( - dA_cumsum_ptr + (c_off - 1) * stride_dA_cs_csize, - mask=(((c_off - 1) > -1) and ((c_off) < chunk_size)), - other=0.0).to(tl.float32) - else: - # - handle seq idx when HAS_INITSTATES==False - seq_idx_m = tl.load(seq_idx_ptr + offs_m * stride_seq_idx_seqlen, - mask=offs_m < chunk_size_limit, - other=-1) - acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) - # Without the if (pid_c > -1), with Triton 2.1.0, I get - # Assertion `!(srcMmaLayout && dstMmaLayout) && "Unexpected mma -> mm a layout conversion"' failed. - # With Triton 2.2.0, this works - if IS_TRITON_22 or c_idx > -1: - # Faster to just do 1 iteration with larger BLOCK_SIZE_K, up to block size 128 - offs_k_dstate = tl.arange( - 0, BLOCK_SIZE_DSTATE if BLOCK_SIZE_DSTATE <= 128 else BLOCK_SIZE_K) - C_ptrs = C_ptr + (offs_m[:, None] * stride_C_seqlen + - offs_k_dstate[None, :] * stride_C_dstate) + offs_out_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_out_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) - prev_states_ptrs = prev_states_ptr + ( - offs_n[None, :] * prev_states_hdim + - offs_k_dstate[:, None] * prev_states_dstate) + # Faster to just do 1 iteration with larger BLOCK_SIZE_K, up to block size 128 + offs_k_dstate = tl.arange( + 0, BLOCK_SIZE_DSTATE if BLOCK_SIZE_DSTATE <= 128 else BLOCK_SIZE_K) + C_ptrs = C_ptr + (offs_m[:, None] * stride_C_seqlen + + offs_k_dstate[None, :] * stride_C_dstate) + + scale_m = tl.exp(dA_cs_m) + if BLOCK_SIZE_DSTATE <= 128: + C = tl.load(C_ptrs, + mask=(offs_m[:, None] < chunk_size_limit) & + (offs_k_dstate[None, :] < dstate), + other=0.0) - if not HAS_INITSTATES: - # - this is for continuous batching where there is no init states - scale_m = tl.where(seq_idx_m == seq_idx_prev, tl.exp(dA_cs_m), 0.0) + if not HAS_INITSTATES and (seq_idx != seq_idx_prev): + # if no init states AND starting a new sequence, we need zeros + prev_states = tl.zeros((BLOCK_SIZE_DSTATE, BLOCK_SIZE_N), + dtype=C_ptr.dtype.element_ty) else: - # - if there is initstates, we will rely on prev_states, no zeroing - # required. - scale_m = tl.exp(dA_cs_m - dA_cs_m_boundary) - - if BLOCK_SIZE_DSTATE <= 128: - C = tl.load(C_ptrs, - mask=(offs_m[:, None] < chunk_size_limit) & - (offs_k_dstate[None, :] < dstate), - other=0.0) - + # otherwise read the previous state + prev_states_ptrs = prev_states_ptr \ + + offs_n[None, :] * prev_states_hdim \ + + offs_k_dstate[:, None] * prev_states_dstate prev_states = tl.load(prev_states_ptrs, mask=(offs_k_dstate[:, None] < dstate) & (offs_n[None, :] < hdim), other=0.0) prev_states = prev_states.to(C_ptr.dtype.element_ty) - acc = tl.dot(C, prev_states) * scale_m[:, None] - else: - for k in range(0, dstate, BLOCK_SIZE_K): - C = tl.load(C_ptrs, - mask=(offs_m[:, None] < chunk_size_limit) & - (offs_k_dstate[None, :] < dstate - k), - other=0.0) - # C = (C * scale_m[:, None]).to(C_ptr.dtype.element_ty) + + acc = tl.dot(C, prev_states) * scale_m[:, None] + + else: + prev_states_ptrs = prev_states_ptr \ + + offs_n[None, :] * prev_states_hdim \ + + offs_k_dstate[:, None] * prev_states_dstate + for k in range(0, dstate, BLOCK_SIZE_K): + C = tl.load(C_ptrs, + mask=(offs_m[:, None] < chunk_size_limit) & + (offs_k_dstate[None, :] < dstate - k), + other=0.0) + if not HAS_INITSTATES and (seq_idx != seq_idx_prev): + prev_states = tl.zeros((BLOCK_SIZE_DSTATE, BLOCK_SIZE_K), + dtype=C_ptr.dtype.element_ty) + else: prev_states = tl.load( prev_states_ptrs, mask=(offs_k_dstate[:, None] < dstate - k) & (offs_n[None, :] < hdim), other=0.0) prev_states = prev_states.to(C_ptr.dtype.element_ty) - acc += tl.dot(C, prev_states) - C_ptrs += BLOCK_SIZE_K - prev_states_ptrs += BLOCK_SIZE_K - acc *= scale_m[:, None] + acc += tl.dot(C, prev_states) + C_ptrs += BLOCK_SIZE_K + prev_states_ptrs += BLOCK_SIZE_K + acc *= scale_m[:, None] - offs_k = tl.arange(0, BLOCK_SIZE_K) + c_off + offs_k = tl.arange(0, BLOCK_SIZE_K) cb_ptrs = cb_ptr + (offs_m[:, None] * stride_cb_csize_m + offs_k[None, :] * stride_cb_csize_k) x_ptrs = x_ptr + (offs_k[:, None] * stride_x_seqlen + @@ -375,7 +310,7 @@ def _chunk_scan_fwd_kernel( dt_ptrs += BLOCK_SIZE_K * stride_dt_csize dA_cumsum_ptrs += BLOCK_SIZE_K * stride_dA_cs_csize - offs_out_m = pid_m * BLOCK_SIZE_M + c_off + tl.arange(0, BLOCK_SIZE_M) + offs_out_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_out_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) if HAS_D: @@ -393,7 +328,7 @@ def _chunk_scan_fwd_kernel( acc += x_residual * D if HAS_Z: - z_ptr += c_idx * chunk_size * stride_z_seqlen + pid_h * stride_z_head + z_ptr += chunk_seqlen_start * stride_z_seqlen + pid_h * stride_z_head z_ptrs = z_ptr + (stride_z_seqlen * offs_out_m[:, None] + stride_z_hdim * offs_out_n[None, :]) z = tl.load(z_ptrs, @@ -402,7 +337,7 @@ def _chunk_scan_fwd_kernel( other=0.0).to(tl.float32) acc *= z * tl.sigmoid(z) - out_ptr += c_idx * chunk_size * stride_out_seqlen + pid_h * stride_out_head + out_ptr += chunk_seqlen_start * stride_out_seqlen + pid_h * stride_out_head out_ptrs = out_ptr + (stride_out_seqlen * offs_out_m[:, None] + offs_out_n[None, :] * stride_out_hdim) tl.store(out_ptrs, @@ -418,12 +353,11 @@ def _chunk_scan_fwd( dA_cumsum, C, states, + cu_chunk_seqlens, out, seq_idx, D=None, z=None, - chunk_indices=None, - chunk_offsets=None, initial_states=None, ): assert seq_idx is not None, "this implementation requires seq_idx" @@ -441,20 +375,10 @@ def _chunk_scan_fwd( assert dt.shape == (nheads, nchunks, chunk_size) assert dA_cumsum.shape == (nheads, nchunks, chunk_size) assert states.shape == (nchunks, nheads, headdim, dstate) - assert seq_idx.shape == (seqlen, ) - - if initial_states is not None: - # with initial states, we need to take care of how - # seq_idx crosses the boundaries - assert chunk_indices is not None and chunk_offsets is not None, \ - "chunk_indices and chunk_offsets should have been set" - else: - chunk_indices, chunk_offsets = None, None + assert seq_idx.shape == (nchunks, ) - grid = lambda META: ( - triton.cdiv(chunk_size, META['BLOCK_SIZE_M']) * triton.cdiv( - headdim, META['BLOCK_SIZE_N']), nchunks - if chunk_offsets is None else len(chunk_offsets), nheads) + grid = lambda META: (triton.cdiv(chunk_size, META['BLOCK_SIZE_M']) * triton + .cdiv(headdim, META['BLOCK_SIZE_N']), nchunks, nheads) z_strides = ((z.stride(0), z.stride(1), z.stride(2)) if z is not None else (0, 0, 0)) @@ -476,9 +400,7 @@ def _chunk_scan_fwd( states_ptr=states, D_ptr=D, initstates_ptr=initial_states, - chunk_indices_ptr=chunk_indices, - chunk_offsets_ptr=chunk_offsets, - chunk_meta_num=len(chunk_indices) if chunk_indices is not None else 0, + cu_chunk_seqlens_ptr=cu_chunk_seqlens, chunk_size=chunk_size, hdim=headdim, dstate=dstate, @@ -503,7 +425,7 @@ def _chunk_scan_fwd( stride_dA_cs_chunk=dA_cumsum.stride(1), stride_dA_cs_head=dA_cumsum.stride(0), stride_dA_cs_csize=dA_cumsum.stride(2), - stride_seq_idx_seqlen=seq_idx.stride(0), + stride_seq_idx_chunk=seq_idx.stride(0), stride_C_seqlen=C.stride(0), stride_C_head=C.stride(1), stride_C_dstate=C.stride(2), diff --git a/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py b/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py index 8ee41f2cbc1b..3a3e0f293459 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py @@ -6,8 +6,6 @@ # ruff: noqa: E501 -import math - import torch from vllm.triton_utils import tl, triton @@ -34,6 +32,7 @@ def _chunk_cumsum_fwd_kernel( dt_bias_ptr, dt_out_ptr, dA_cumsum_ptr, + cu_chunk_seqlens_ptr, # Matrix dimension seqlen, nheads: tl.constexpr, @@ -61,7 +60,11 @@ def _chunk_cumsum_fwd_kernel( # https://github.com/triton-lang/triton/issues/1058 pid_c = tl.program_id(axis=0).to(tl.int64) pid_h = tl.program_id(axis=1) - dt_ptr += pid_c * chunk_size * stride_dt_seqlen + + chunk_seqlen_start = tl.load(cu_chunk_seqlens_ptr + pid_c) + chunk_seqlen_end = tl.load(cu_chunk_seqlens_ptr + pid_c + 1) + + dt_ptr += chunk_seqlen_start * stride_dt_seqlen dt_out_ptr += pid_c * stride_dt_out_chunk dA_cumsum_ptr += pid_c * stride_dA_cs_chunk @@ -74,7 +77,7 @@ def _chunk_cumsum_fwd_kernel( offs_c[None, :] * stride_dt_out_csize) dA_cs_ptrs = dA_cumsum_ptr + (offs_h[:, None] * stride_dA_cs_head + offs_c[None, :] * stride_dA_cs_csize) - chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) + chunk_size_limit = chunk_seqlen_end - chunk_seqlen_start dt = tl.load(dt_ptrs, mask=(offs_h[:, None] < nheads) & @@ -188,7 +191,7 @@ def _chunk_state_fwd_kernel( states_ptr, dt_ptr, dA_cumsum_ptr, - seq_idx_ptr, + cu_chunk_seqlens_ptr, # Matrix dimensions hdim: tl.constexpr, dstate: tl.constexpr, @@ -212,7 +215,6 @@ def _chunk_state_fwd_kernel( stride_dA_cs_head: tl.int64, stride_dA_cs_chunk: tl.int64, stride_dA_cs_csize: tl.constexpr, - stride_seq_idx_seqlen: tl.constexpr, # Meta-parameters BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, @@ -223,14 +225,14 @@ def _chunk_state_fwd_kernel( num_pid_n = tl.cdiv(dstate, BLOCK_SIZE_N) pid_m = tl.program_id(axis=0) // num_pid_n pid_n = tl.program_id(axis=0) % num_pid_n - b_ptr += pid_c * chunk_size * stride_b_seqlen + ( + chunk_seqlen_start = tl.load(cu_chunk_seqlens_ptr + pid_c) + chunk_seqlen_end = tl.load(cu_chunk_seqlens_ptr + pid_c + 1) + b_ptr += chunk_seqlen_start * stride_b_seqlen + ( pid_h // nheads_ngroups_ratio) * stride_b_head - x_ptr += pid_c * chunk_size * stride_x_seqlen + pid_h * stride_x_head + x_ptr += chunk_seqlen_start * stride_x_seqlen + pid_h * stride_x_head dt_ptr += pid_c * stride_dt_chunk + pid_h * stride_dt_head dA_cumsum_ptr += pid_c * stride_dA_cs_chunk + pid_h * stride_dA_cs_head - seq_idx_ptr += pid_c * chunk_size * stride_seq_idx_seqlen - offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_k = tl.arange(0, BLOCK_SIZE_K) @@ -243,10 +245,7 @@ def _chunk_state_fwd_kernel( (chunk_size - 1) * stride_dA_cs_csize).to(tl.float32) dA_cumsum_ptrs = dA_cumsum_ptr + offs_k * stride_dA_cs_csize - seq_idx_ptrs = seq_idx_ptr + offs_k * stride_seq_idx_seqlen - chunk_size_limit = min(chunk_size, seqlen - pid_c * chunk_size) - seq_idx_last = tl.load(seq_idx_ptr + - (chunk_size_limit - 1) * stride_seq_idx_seqlen) + chunk_size_limit = chunk_seqlen_end - chunk_seqlen_start acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) for k in range(0, chunk_size_limit, BLOCK_SIZE_K): @@ -261,15 +260,9 @@ def _chunk_state_fwd_kernel( dA_cs_k = tl.load(dA_cumsum_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0).to(tl.float32) - - seq_idx_k = tl.load(seq_idx_ptrs, - mask=offs_k < chunk_size_limit - k, - other=-1) dt_k = tl.load(dt_ptrs, mask=offs_k < chunk_size_limit - k, other=0.0).to(tl.float32) - - scale = tl.where(seq_idx_k == seq_idx_last, - tl.exp(dA_cs_last - dA_cs_k) * dt_k, 0.0) + scale = tl.exp(dA_cs_last - dA_cs_k) * dt_k b *= scale[:, None] b = b.to(x_ptr.dtype.element_ty) acc += tl.dot(x, b) @@ -278,7 +271,6 @@ def _chunk_state_fwd_kernel( b_ptrs += BLOCK_SIZE_K * stride_b_seqlen dt_ptrs += BLOCK_SIZE_K * stride_dt_csize dA_cumsum_ptrs += BLOCK_SIZE_K * stride_dA_cs_csize - seq_idx_ptrs += BLOCK_SIZE_K * stride_seq_idx_seqlen states = acc.to(states_ptr.dtype.element_ty) @@ -534,6 +526,7 @@ def _chunk_state_varlen_kernel( def _chunk_cumsum_fwd(dt, A, chunk_size, + cu_chunk_seqlens, dt_bias=None, dt_softplus=False, dt_limit=(0.0, float("inf"))): @@ -541,7 +534,7 @@ def _chunk_cumsum_fwd(dt, assert A.shape == (nheads, ) if dt_bias is not None: assert dt_bias.shape == (nheads, ) - nchunks = math.ceil(seqlen / chunk_size) + nchunks = cu_chunk_seqlens.shape[0] - 1 dt_out = torch.empty(nheads, nchunks, chunk_size, @@ -561,6 +554,7 @@ def _chunk_cumsum_fwd(dt, dt_bias_ptr=dt_bias, dt_out_ptr=dt_out, dA_cumsum_ptr=dA_cumsum, + cu_chunk_seqlens_ptr=cu_chunk_seqlens, seqlen=seqlen, nheads=nheads, chunk_size=chunk_size, @@ -588,7 +582,7 @@ def _chunk_state_fwd(B, x, dt, dA_cumsum, - seq_idx=None, + cu_chunk_seqlens, states=None, states_in_fp32=True): seqlen, nheads, headdim = x.shape @@ -599,9 +593,6 @@ def _chunk_state_fwd(B, assert dt.shape == (nheads, nchunks, chunk_size) assert dA_cumsum.shape == dt.shape - assert seq_idx is not None - assert seq_idx.shape == (seqlen, ) - if states is not None: assert states.shape == (nchunks, nheads, headdim, dstate) else: @@ -619,7 +610,7 @@ def _chunk_state_fwd(B, states_ptr=states, dt_ptr=dt, dA_cumsum_ptr=dA_cumsum, - seq_idx_ptr=seq_idx, + cu_chunk_seqlens_ptr=cu_chunk_seqlens, hdim=headdim, dstate=dstate, chunk_size=chunk_size, @@ -641,7 +632,6 @@ def _chunk_state_fwd(B, stride_dA_cs_head=dA_cumsum.stride(0), stride_dA_cs_chunk=dA_cumsum.stride(1), stride_dA_cs_csize=dA_cumsum.stride(2), - stride_seq_idx_seqlen=seq_idx.stride(0), ) return states diff --git a/vllm/model_executor/layers/mamba/ops/ssd_combined.py b/vllm/model_executor/layers/mamba/ops/ssd_combined.py index 37d6c2870812..f3eb61d5840e 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_combined.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_combined.py @@ -14,8 +14,7 @@ from .ssd_bmm import _bmm_chunk_fwd from .ssd_chunk_scan import _chunk_scan_fwd -from .ssd_chunk_state import (_chunk_cumsum_fwd, _chunk_state_fwd, - chunk_state_varlen) +from .ssd_chunk_state import _chunk_cumsum_fwd, _chunk_state_fwd from .ssd_state_passing import _state_passing_fwd TRITON_22 = version.parse(triton.__version__) >= version.parse('2.2.0') @@ -37,9 +36,9 @@ def _mamba_chunk_scan_combined_fwd(x, dt_bias=None, initial_states=None, seq_idx=None, - chunk_indices=None, - chunk_offsets=None, cu_seqlens=None, + cu_chunk_seqlens=None, + last_chunk_indices=None, dt_softplus=False, dt_limit=(0.0, float("inf")), state_dtype=None): @@ -56,7 +55,7 @@ def _mamba_chunk_scan_combined_fwd(x, if D is not None: assert D.shape == (nheads, headdim) or D.shape == (nheads, ) if seq_idx is not None: - assert seq_idx.shape == (seqlen, ) + assert seq_idx.shape == (cu_chunk_seqlens.shape[0] - 1, ) if B.stride(-1) != 1: B = B.contiguous() if C.stride(-1) != 1: @@ -89,6 +88,7 @@ def _mamba_chunk_scan_combined_fwd(x, dA_cumsum, dt = _chunk_cumsum_fwd(dt, A, chunk_size, + cu_chunk_seqlens, dt_bias=dt_bias, dt_softplus=dt_softplus, dt_limit=dt_limit) @@ -99,36 +99,31 @@ def _mamba_chunk_scan_combined_fwd(x, x, dt, dA_cumsum, - seq_idx=seq_idx, + cu_chunk_seqlens, states_in_fp32=True) # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries # (middle term of factorization of off-diag blocks; A terms) - # - for handling chunked prefill, this requires i) initial_states - # ii) seq_idx iii) is_cont_batched and (iv) chunk_offsets to be all specified. + # - for handling chunked prefill, this requires i) initial_states and + # ii) seq_idx to be all specified. # - When a new seq_idx is detected, we will stop passing the prev_state # and switch accordingly to the init_state corresponding to the new seq_idx. - # - We will also make sure that the dA_cumsum is taken only from the start of the - # sequence (hence we need the full dA_cumsum tensor and not just the values at chunk boundaries) - # - this will ensure that states will be updated with the rightmost flushed seq_idx - # of the previous chunk. This implies that the first chunk of states is either 0 - # or equal to init_states of the first example. states = _state_passing_fwd( rearrange(states, "... p n -> ... (p n)"), dA_cumsum, # (nheads, nchunks, chunk_size) + cu_chunk_seqlens, initial_states=rearrange(initial_states, "... p n -> ... (p n)") if initial_states is not None else None, # (batch, nheads, headdim*dstate) seq_idx=seq_idx, - out_dtype=state_dtype if state_dtype is not None else C.dtype, - chunk_offsets=chunk_offsets) + out_dtype=state_dtype if state_dtype is not None else C.dtype) states = rearrange(states, "... (p n) -> ... p n", n=dstate) # 4. Compute batched matrix multiply for C_j^T B_i terms CB = _bmm_chunk_fwd(C, B, chunk_size, - seq_idx=seq_idx, + cu_chunk_seqlens, output_dtype=torch.float32) # 5. Scan and compute the diagonal blocks, taking into @@ -148,26 +143,15 @@ def _mamba_chunk_scan_combined_fwd(x, dA_cumsum, C, states, + cu_chunk_seqlens, out, # in-place update seq_idx, D=D, z=z, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, initial_states=initial_states, ) - varlen_states = chunk_state_varlen( - B, - x, - dt, - dA_cumsum, - cu_seqlens, - states, - initial_states=initial_states, - ) - - return varlen_states + return states[last_chunk_indices] def mamba_chunk_scan_combined_varlen( @@ -178,14 +162,14 @@ def mamba_chunk_scan_combined_varlen( C, chunk_size, cu_seqlens, + cu_chunk_seqlens, + last_chunk_indices, seq_idx, out, D=None, z=None, dt_bias=None, initial_states=None, - chunk_indices=None, - chunk_offsets=None, dt_softplus=False, dt_limit=(0.0, float("inf")), state_dtype=None, @@ -198,8 +182,10 @@ def mamba_chunk_scan_combined_varlen( B: (seqlen, ngroups, dstate) C: (seqlen, ngroups, dstate) chunk_size: int - seq_idx: (seqlen) - cu_seqlens: (batch + 1) + cu_seqlens: (batch + 1,) + cu_chunk_seqlens: (nchunks + 1,) + last_chunk_indices: (batch,) + seq_idx: (nchunks,) out: (seqlen, nheads, headdim) preallocated output tensor D: (nheads, headdim) or (nheads,) z: (seqlen, nheads, headdim) @@ -228,9 +214,9 @@ def mamba_chunk_scan_combined_varlen( dt_bias=dt_bias, initial_states=initial_states, seq_idx=seq_idx, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, cu_seqlens=cu_seqlens, + cu_chunk_seqlens=cu_chunk_seqlens, + last_chunk_indices=last_chunk_indices, dt_softplus=dt_softplus, dt_limit=dt_limit, state_dtype=state_dtype) diff --git a/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py b/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py index 71a8a4b0a1c8..f09af262cfc2 100644 --- a/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py +++ b/vllm/model_executor/layers/mamba/ops/ssd_state_passing.py @@ -30,8 +30,7 @@ def _state_passing_fwd_kernel( dA_cs_ptr, initstates_ptr, seq_idx_ptr, - chunk_offsets_ptr, - chunk_meta_num, + cu_chunk_seqlens_ptr, # Matrix dimensions dim: tl.constexpr, nchunks, @@ -50,94 +49,52 @@ def _state_passing_fwd_kernel( stride_initstates_batch: tl.int64, stride_initstates_head: tl.int64, stride_initstates_dim: tl.constexpr, - stride_seq_idx_seqlen: tl.constexpr, + stride_seq_idx_chunk: tl.constexpr, # Meta-parameters HAS_INITSTATES: tl.constexpr, BLOCK_SIZE: tl.constexpr, ): pid_h = tl.program_id(axis=1) pid_m = tl.program_id(axis=0) + states_ptr += pid_h * stride_states_head dA_cs_ptr += pid_h * stride_dA_cs_head + (chunk_size - 1) * stride_dA_cs_csize out_ptr += pid_h * stride_out_head - if HAS_INITSTATES: - initstates_ptr += pid_h * stride_initstates_head offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) states_ptrs = states_ptr + offs_m * stride_states_dim out_ptrs = out_ptr + offs_m * stride_out_dim - # - states will be the past state of the sequence that continues on the current check - if not HAS_INITSTATES: - states = tl.zeros((BLOCK_SIZE, ), dtype=tl.float32) - else: - initstates_ptr += offs_m * stride_initstates_dim - initstates_ptrs = initstates_ptr - # - for cont batches, for the first chunk mean it will be the first batch's - # init state + if HAS_INITSTATES: + initstates_ptrs = initstates_ptr \ + + pid_h * stride_initstates_head \ + + offs_m * stride_initstates_dim + states = tl.load(initstates_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) + else: + states = tl.zeros((BLOCK_SIZE, ), dtype=tl.float32) - tl.store(out_ptrs, states, mask=offs_m < dim) - out_ptrs += stride_out_chunk - prev_seq_idx_chunk_end = 0 - logical_chunk_idx = 0 - for c in range(nchunks - 1): + prev_seq_idx = 0 + for c in range(nchunks): new_states = tl.load(states_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) dA_cs = tl.load(dA_cs_ptr).to(tl.float32) - scale_mask = True - # - the seq to pass forward is the one that is flushed to the right - # boundary. - # - that is given by seq_idx_chunk_end below: the sequence index at the end of the chunk. - seq_idx_chunk_end = tl.load(seq_idx_ptr + - (min((c + 1) * chunk_size, seqlen) - 1) * - stride_seq_idx_seqlen) - - if HAS_INITSTATES: - if prev_seq_idx_chunk_end != seq_idx_chunk_end: - # this means in the current chunk the rightmost flushed seq - # has changed. - # - so we do not propagate the state from previous chunk - # - but rather we load that sequence's init state - initstates_ptrs = initstates_ptr + seq_idx_chunk_end * stride_initstates_batch - - # - update state with seq_idx_new's init state + seq_idx = tl.load(seq_idx_ptr + c * stride_seq_idx_chunk) + # we have started a new sequence + if prev_seq_idx != seq_idx: + if HAS_INITSTATES: + initstates_ptrs = initstates_ptr + seq_idx * stride_initstates_batch \ + + pid_h * stride_initstates_head \ + + offs_m * stride_initstates_dim states = tl.load(initstates_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) + else: + states = tl.zeros((BLOCK_SIZE, ), dtype=tl.float32) - # - we need to consider the cumsum only of the last sequence in the chunk - # - find its starting position (given by c_off of the logical chunk index) - # - and subtract the cumsum just before that position from the total cumsum - # - first, update the logical chunk index (add the number of sequences in the current physical chunk): - # sequence index at the start of the current chunk - seq_idx_chunk_start = tl.load(seq_idx_ptr + - min(c * chunk_size, seqlen) * - stride_seq_idx_seqlen) - logical_chunk_idx += seq_idx_chunk_end - seq_idx_chunk_start - # - load the chunk offset: - c_off = tl.load(chunk_offsets_ptr + logical_chunk_idx, - mask=logical_chunk_idx < chunk_meta_num, - other=0) - # - if offset is 0, then the sequence starts at the beginning of the chunk, and we don't need to subtract anything - if c_off > 0: - # - dA_cs_ptr currently points to the cumsum at the end of the chunk - subtract the chunk size and add the offset - dA_cs_boundary = tl.load( - dA_cs_ptr - (chunk_size - 1) * stride_dA_cs_csize + - (c_off - 1) * stride_dA_cs_csize, - mask=(c_off - 1) > -1 and c_off < chunk_size, - other=0.0) - dA_cs -= dA_cs_boundary - - # - increment logical chunk index for every physical chunk - logical_chunk_idx += 1 - else: - scale_mask = seq_idx_chunk_end == prev_seq_idx_chunk_end - prev_seq_idx_chunk_end = seq_idx_chunk_end - - scale = tl.where(scale_mask, tl.exp(dA_cs), 0.0) - states = scale * states + new_states + prev_seq_idx = seq_idx + states = tl.exp(dA_cs) * states + new_states tl.store(out_ptrs, states, mask=offs_m < dim) states_ptrs += stride_states_chunk @@ -148,8 +105,8 @@ def _state_passing_fwd_kernel( def _state_passing_fwd( states, dA_cumsum, + cu_chunk_seqlens, seq_idx, - chunk_offsets, initial_states=None, out_dtype=None, ): @@ -175,9 +132,7 @@ def _state_passing_fwd( dA_cs_ptr=dA_cumsum, initstates_ptr=initial_states, seq_idx_ptr=seq_idx, - chunk_offsets_ptr=chunk_offsets, - chunk_meta_num=len(chunk_offsets) - if chunk_offsets is not None else 0, + cu_chunk_seqlens_ptr=cu_chunk_seqlens, dim=dim, nchunks=nchunks, seqlen=seqlen if seq_idx is not None else 0, @@ -194,7 +149,7 @@ def _state_passing_fwd( stride_initstates_batch=initial_states_strides[0], stride_initstates_head=initial_states_strides[1], stride_initstates_dim=initial_states_strides[2], - stride_seq_idx_seqlen=seq_idx.stride(0), + stride_seq_idx_chunk=seq_idx.stride(0), HAS_INITSTATES=initial_states is not None, ) return out diff --git a/vllm/model_executor/models/plamo2.py b/vllm/model_executor/models/plamo2.py index 03265b13de50..8234d40e94ab 100644 --- a/vllm/model_executor/models/plamo2.py +++ b/vllm/model_executor/models/plamo2.py @@ -260,9 +260,9 @@ def forward_cuda( prep_initial_states = attn_metadata.prep_initial_states chunk_size = attn_metadata.chunk_size seq_idx_p = attn_metadata.seq_idx_p - chunk_indices_p = attn_metadata.chunk_indices_p - chunk_offsets_p = attn_metadata.chunk_offsets_p query_start_loc_p = attn_metadata.query_start_loc_p + cu_chunk_seqlen_p = attn_metadata.cu_chunk_seqlen_p + last_chunk_indices_p = attn_metadata.last_chunk_indices_p # 1. Gated MLP's linear projection projected_states = self.in_proj(hidden_states) @@ -368,9 +368,9 @@ def forward_cuda( self.num_heads // self.tp_size, self.head_dim), dt_bias=self.dt_bias, seq_idx=seq_idx_p, - chunk_indices=chunk_indices_p, - chunk_offsets=chunk_offsets_p, cu_seqlens=query_start_loc_p, + cu_chunk_seqlens=cu_chunk_seqlen_p, + last_chunk_indices=last_chunk_indices_p, initial_states=initial_states, dt_softplus=True, dt_limit=(0.0, float("inf")), diff --git a/vllm/v1/attention/backends/mamba2_attn.py b/vllm/v1/attention/backends/mamba2_attn.py index 6f16fda962ae..e4f16f37a430 100644 --- a/vllm/v1/attention/backends/mamba2_attn.py +++ b/vllm/v1/attention/backends/mamba2_attn.py @@ -1,6 +1,5 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project -import math from dataclasses import dataclass from typing import Optional @@ -8,6 +7,7 @@ from vllm.attention.backends.abstract import AttentionBackend from vllm.config import VllmConfig +from vllm.utils import cdiv from vllm.v1.attention.backends.mamba_attn import ( BaseMambaAttentionMetadataBuilder) from vllm.v1.attention.backends.utils import (PAD_SLOT_ID, @@ -17,91 +17,6 @@ from vllm.v1.kv_cache_interface import AttentionSpec -def _query_start_loc_to_chunk_indices_offsets( - query_start_loc: torch.Tensor, chunk_size: int, - total_seqlens: int) -> tuple[torch.Tensor, torch.Tensor]: - """ - Args: - query_start_loc (torch.Tensor): 1D tensor of cumulative sequence - lengths, shape (num_seqs + 1,). - The first element should be 0. Each entry represents the starting - index of a sequence in the flattened token array. - chunk_size (int): The size of each physical mamba chunk - (number of tokens per chunk). - total_seqlens (int): The total number of tokens in the batch. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: A tuple containing: - - chunk_indices (torch.Tensor): 1D tensor of indices - indicating the physical chunk for each logical chunk. - - chunk_offsets (torch.Tensor): 1D tensor of offsets - indicating the starting index of each logical chunk within - its physical chunk. - - This function computes the chunk indices and offsets for the given - query_start_loc and chunk_size. Both are tensors of integers with length N, - where N is the number of logical (pseudo) chunks. - A logical chunk is a sequence of tokens that are all part of the same - sequence and are all in the same physical mamba chunk. - In other words, a logical chunk changes every time we cross a sequence - boundary or a physical mamba chunk boundary. - Logical chunks are needed to handle batched requests with initial states - (see _state_passing_fwd and _chunk_scan_fwd). - The chunk_indices tensor contains the index of the physical chunk for each - logical chunk. - The chunk_offsets tensor contains the offset (AKA starting index) of the - logical chunk in the physical chunk. - - Example: - query_start_loc = [0, 5, 10] - chunk_size = 8 - total_seqlens = 10 - -> chunk_indices = [0, 0, 1] - -> chunk_offsets = [0, 5, 0] - - In this example, we have 2 sequences, each with 5 tokens. The physical - chunk size is 8 tokens. - We have three logical chunks: - - the first logical chunk starts at token 0 in the first physical chunk - and contains all 5 tokens from the first sequence - - the second logical chunk starts at token 5 in the first physical chunk - and contains first 3 tokens from the second sequence - - the third logical chunk starts at token 0 in the second physical chunk - and contains the remaining 2 tokens from the second sequence - """ - - cu_seqlens = query_start_loc[1:] # remove prepended 0 - - # outputs will have length expansion of chunks that do not divide - # chunk_size - N = math.ceil(total_seqlens / chunk_size) + (cu_seqlens[:-1] % chunk_size - > 0).sum() - chunk_indices = torch.arange(N, - dtype=torch.int, - device=query_start_loc.device) - chunk_offsets = torch.zeros((N, ), - dtype=torch.int, - device=query_start_loc.device) - - p = 0 # num of insertions - for s, e in zip(cu_seqlens[:-1], cu_seqlens[1:]): - - # if does not divide chunk_size, then there is one chunk insertion - p += (s % chunk_size > 0) - - # get the dimensions - # - the + 1 for _e is to shift the boundary by one chunk - # - this shifting is not needed if chunk_size divides e - _s, _e = s // chunk_size + p, e // chunk_size + p + (e % chunk_size - > 0) - - # adjust indices and offsets - chunk_indices[_s:_e] -= p - chunk_offsets[_s] = s % chunk_size - - return chunk_indices, chunk_offsets - - class Mamba2AttentionBackend(AttentionBackend): @staticmethod @@ -125,8 +40,16 @@ class Mamba2AttentionMetadata: # the batch has no prefill request. has_initial_states_p: Optional[torch.Tensor] seq_idx_p: Optional[torch.Tensor] - chunk_indices_p: Optional[torch.Tensor] - chunk_offsets_p: Optional[torch.Tensor] + + # cu_chunk_seqlen_p is a tensor of shape (nchunks+1,) that contains, for + # each chunk, its offests into the varlen sequence dimension. It is defined + # such that the i-th chunk contains tokens from cu_chunk_seqlen_p[i] to + # cu_chunk_seqlen_p[i+1]. + cu_chunk_seqlen_p: Optional[torch.Tensor] + + # last_chunk_indices_p is a tensor of shape (batch,) that contains the + # index of the last chunk for every sequence in the (prefill) batch. + last_chunk_indices_p: Optional[torch.Tensor] state_indices_tensor: torch.Tensor # shape: [batch,] @@ -151,13 +74,14 @@ def build(self, common_attn_metadata: CommonAttentionMetadata, fast_build: bool = False) -> Mamba2AttentionMetadata: num_reqs = common_attn_metadata.num_reqs - query_start_loc_p = None seq_lens = common_attn_metadata.seq_lens + query_start_loc_p = None seq_idx_p = None - chunk_indices_p, chunk_offsets_p = None, None + cu_chunk_seqlen_p = None + last_chunk_indices_p = None + # Need flags to indicate if there are initial states - # currently we really only support the FlashAttention backend has_initial_states_p = None prep_initial_states = False @@ -171,7 +95,7 @@ def build(self, common_attn_metadata, decode_threshold=self.reorder_batch_threshold)) - # Compute seq_idx, chunk_indices and chunk_offsets for prefill only + # Compute seq_idx for prefill only if num_prefills > 0: #[batch,] has_initial_states_cpu = ( @@ -184,21 +108,68 @@ def build(self, query_start_loc_p = common_attn_metadata.query_start_loc[ -num_prefills - 1:] - num_decode_tokens - seq_idx_p = torch.repeat_interleave(torch.arange( - num_prefills, - dtype=torch.int32, - device=query_start_loc_p.device), - query_start_loc_p.diff(), - output_size=num_prefill_tokens) - - # We compute metadata for chunked prefill once at the top level - # model forward and reuse them in mamba layers. If not needed, - # they will be ignored inside mamba kernels. - if prep_initial_states: - chunk_indices_p, chunk_offsets_p = ( - _query_start_loc_to_chunk_indices_offsets( - query_start_loc_p, self.chunk_size, - num_prefill_tokens)) + num_computed_tokens_p = \ + common_attn_metadata.num_computed_tokens_cpu[ + num_reqs - num_prefills:num_reqs] + query_start_loc_p_cpu = common_attn_metadata.query_start_loc_cpu[ + -num_prefills - 1:] - num_decode_tokens + + # The code below carefully constructs the chunks such that: + # 1. Chunks contain tokens from a *single* sequence only. + # 2. For every sequence, we are guaranteed that we can + # retrieve the mamba state *every* chunk_size tokens. + # Constraint (1) dramatically simplifies the mamba2 kernels. + # Constraint (2) dramatically simplifies the implementation + # of prefix caching for mamba2 (wip). We need to take care + # of the interaction with chunked prefill in order to + # satisfy constraint (2). + # TODO (tdoublep): This code could probably be optimized. + cu_chunk_seqlen = [] + seq_idx = [] + last_chunk_indices = [] + seqlen_pos = 0 + for req_idx in range(num_prefills): + this_num_computed = num_computed_tokens_p[req_idx].item() + this_new_tokens = query_start_loc_p_cpu[req_idx + 1].item( + ) - query_start_loc_p_cpu[req_idx].item() + + # if computed tokens are not chunk-aligned, use the first + # chunk to finish it off + if this_num_computed % self.chunk_size != 0: + seq_idx.append(req_idx) + cu_chunk_seqlen.append(seqlen_pos) + # how many tokens to finish the chunk? + chunk_len = cdiv(this_num_computed, self.chunk_size + ) * self.chunk_size - this_num_computed + # we can only use at most this_new_tokens + chunk_len = min(chunk_len, this_new_tokens) + seqlen_pos += chunk_len + this_new_tokens -= chunk_len + + n_chunks = cdiv(this_new_tokens, self.chunk_size) + for chunk in range(n_chunks): + seq_idx.append(req_idx) + cu_chunk_seqlen.append(seqlen_pos) + chunk_len = min(self.chunk_size, this_new_tokens) + seqlen_pos += chunk_len + this_new_tokens -= chunk_len + + assert this_new_tokens == 0 + last_chunk_indices.append(len(cu_chunk_seqlen) - 1) + + cu_chunk_seqlen.append(seqlen_pos) + + seq_idx_p = torch.as_tensor(seq_idx, + device=query_start_loc_p.device, + dtype=torch.int32) + cu_chunk_seqlen_p = torch.as_tensor( + cu_chunk_seqlen, + device=query_start_loc_p.device, + dtype=torch.int32) + last_chunk_indices_p = torch.as_tensor( + last_chunk_indices, + device=query_start_loc_p.device, + dtype=torch.int32) nums_dict, batch_ptr, token_chunk_offset_ptr = \ compute_causal_conv1d_metadata(query_start_loc_p) @@ -222,9 +193,9 @@ def build(self, chunk_size=self.chunk_size, has_initial_states_p=has_initial_states_p, seq_idx_p=seq_idx_p, - chunk_indices_p=chunk_indices_p, - chunk_offsets_p=chunk_offsets_p, state_indices_tensor=state_indices_tensor, + cu_chunk_seqlen_p=cu_chunk_seqlen_p, + last_chunk_indices_p=last_chunk_indices_p, nums_dict=nums_dict, batch_ptr=batch_ptr, token_chunk_offset_ptr=token_chunk_offset_ptr, From 7375eee14c7b7be00b456becf1a889d4d20221d0 Mon Sep 17 00:00:00 2001 From: Zhuohan Li Date: Mon, 29 Sep 2025 14:31:34 -0700 Subject: [PATCH 085/166] [Doc] Polish example for torchrun dp (#25899) --- .../offline_inference/torchrun_dp_example.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/examples/offline_inference/torchrun_dp_example.py b/examples/offline_inference/torchrun_dp_example.py index 8e888a100254..295d1637528c 100644 --- a/examples/offline_inference/torchrun_dp_example.py +++ b/examples/offline_inference/torchrun_dp_example.py @@ -4,6 +4,11 @@ experimental support for data-parallel inference with torchrun Note the data load balancing and distribution is done out of the vllm engine, no internal lb supported in external_launcher mode. + +To run this example: +```bash +$ torchrun --nproc-per-node=2 examples/offline_inference/torchrun_dp_example.py +``` """ from vllm import LLM, SamplingParams @@ -14,7 +19,7 @@ "The president of the United States is", "The capital of France is", "The future of AI is", -] * 50 +] # Create sampling parameters, the same across all ranks sampling_params = SamplingParams(temperature=0.8, top_p=0.95) @@ -45,14 +50,13 @@ outputs = llm.generate(prompts, sampling_params) - -# all ranks will have the same outputs -print("-" * 50) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text - print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}\n") - print("-" * 50) + print( + f"DP Rank: {dp_rank} Prompt: {prompt!r}\nGenerated text: {generated_text!r}\n" + ) + """ Further tips: From fbcf37f11153774974eeefced7fadc8d271ecf4c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nicol=C3=B2=20Lucchesi?= Date: Mon, 29 Sep 2025 23:35:14 +0200 Subject: [PATCH 086/166] [NIXL] Increase default KV block eviction timeout on P (#25897) Signed-off-by: NickLucche --- docs/features/nixl_connector_usage.md | 2 +- vllm/envs.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/features/nixl_connector_usage.md b/docs/features/nixl_connector_usage.md index afecbc82947b..5e273af05dc5 100644 --- a/docs/features/nixl_connector_usage.md +++ b/docs/features/nixl_connector_usage.md @@ -84,7 +84,7 @@ python tests/v1/kv_connector/nixl_integration/toy_proxy_server.py \ - Connection info is passed via KVTransferParams from prefiller to decoder for handshake - `VLLM_NIXL_ABORT_REQUEST_TIMEOUT`: Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request. (Optional) - - Default: 120 + - Default: 480 - If a request is aborted and the decoder has not yet read the KV-cache blocks through the nixl channel, the prefill instance will release its KV-cache blocks after this timeout to avoid holding them indefinitely. ## Multi-Instance Setup diff --git a/vllm/envs.py b/vllm/envs.py index f06c860b8297..ffa7ed5c3aa5 100755 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -174,7 +174,7 @@ "NONE"] = "NONE" VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: Optional[int] = None - VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 120 + VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 480 VLLM_USE_CUDNN_PREFILL: bool = False VLLM_ENABLE_CUDAGRAPH_GC: bool = False VLLM_LOOPBACK_IP: str = "" @@ -1330,7 +1330,7 @@ def get_vllm_port() -> Optional[int]: # consumer. This is only applicable when using NixlConnector in a # disaggregated decode-prefill setup. "VLLM_NIXL_ABORT_REQUEST_TIMEOUT": - lambda: int(os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "120")), + lambda: int(os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "480")), # Controls whether or not to use cudnn prefill "VLLM_USE_CUDNN_PREFILL": From 75ed63e367e5801eff6c39fd10730f075f23c5cf Mon Sep 17 00:00:00 2001 From: Aaron Pham Date: Mon, 29 Sep 2025 19:26:11 -0400 Subject: [PATCH 087/166] [V0 Deprecation] Remove `vllm.worker` and update according imports (#25901) --- .../tensorizer_loader/conftest.py | 2 +- tools/pre_commit/check_pickle_imports.py | 1 - vllm/executor/executor_base.py | 10 +- vllm/executor/ray_utils.py | 2 +- vllm/executor/uniproc_executor.py | 10 +- vllm/platforms/cuda.py | 12 +- vllm/platforms/rocm.py | 12 +- vllm/v1/executor/multiproc_executor.py | 4 +- vllm/v1/worker/worker_base.py | 271 ++++++++++++++++- vllm/worker/__init__.py | 0 vllm/worker/worker_base.py | 279 ------------------ 11 files changed, 276 insertions(+), 327 deletions(-) delete mode 100644 vllm/worker/__init__.py delete mode 100644 vllm/worker/worker_base.py diff --git a/tests/model_executor/model_loader/tensorizer_loader/conftest.py b/tests/model_executor/model_loader/tensorizer_loader/conftest.py index 571dc2e0eb50..cc02d7ecf20b 100644 --- a/tests/model_executor/model_loader/tensorizer_loader/conftest.py +++ b/tests/model_executor/model_loader/tensorizer_loader/conftest.py @@ -10,7 +10,7 @@ from vllm.model_executor.model_loader.tensorizer import TensorizerConfig from vllm.utils import get_distributed_init_method, get_ip, get_open_port from vllm.v1.executor.abstract import UniProcExecutor -from vllm.worker.worker_base import WorkerWrapperBase +from vllm.v1.worker.worker_base import WorkerWrapperBase MODEL_REF = "facebook/opt-125m" diff --git a/tools/pre_commit/check_pickle_imports.py b/tools/pre_commit/check_pickle_imports.py index acbbc1f181d6..c97a5b0b6c71 100644 --- a/tools/pre_commit/check_pickle_imports.py +++ b/tools/pre_commit/check_pickle_imports.py @@ -36,7 +36,6 @@ 'benchmarks/cutlass_benchmarks/w8a8_benchmarks.py', 'benchmarks/cutlass_benchmarks/sparse_benchmarks.py', # cloudpickle - 'vllm/worker/worker_base.py', 'vllm/executor/mp_distributed_executor.py', 'vllm/executor/ray_distributed_executor.py', 'vllm/entrypoints/llm.py', diff --git a/vllm/executor/executor_base.py b/vllm/executor/executor_base.py index e3063ec2b8ab..fe80be61410c 100644 --- a/vllm/executor/executor_base.py +++ b/vllm/executor/executor_base.py @@ -19,7 +19,7 @@ from vllm.tasks import SupportedTask from vllm.utils import make_async from vllm.v1.outputs import PoolerOutput, SamplerOutput -from vllm.worker.worker_base import WorkerBase +from vllm.v1.worker.worker_base import WorkerBase logger = init_logger(__name__) @@ -30,7 +30,7 @@ class ExecutorBase(ABC): """Base class for all executors. An executor is responsible for executing the model on one device, - or it can be a distributed executor + or it can be a distributed executor that can execute the model on multiple devices. """ @@ -83,7 +83,7 @@ def collective_rpc(self, Returns: A list containing the results from each worker. - + Note: It is recommended to use this API to only pass control messages, and set up data-plane communication to pass data. @@ -100,7 +100,7 @@ def determine_num_available_blocks(self) -> tuple[int, int]: Returns a tuple `(num_gpu_blocks, num_cpu_blocks)`, where `num_gpu_blocks` are blocks that are "active" on the device and can be - appended to. + appended to. `num_cpu_blocks` refers to "swapped" blocks in CPU memory and cannot be appended to. """ @@ -327,7 +327,7 @@ def _run_workers( run only in the remote TP workers, not the driver worker. It will also be run asynchronously and return a list of futures rather than blocking on the results. - + # TODO: simplify and merge with collective_rpc """ raise NotImplementedError diff --git a/vllm/executor/ray_utils.py b/vllm/executor/ray_utils.py index 0bdeb2856989..d8eb7977dbde 100644 --- a/vllm/executor/ray_utils.py +++ b/vllm/executor/ray_utils.py @@ -16,7 +16,7 @@ from vllm.platforms import current_platform from vllm.sequence import ExecuteModelRequest, IntermediateTensors from vllm.utils import get_ip -from vllm.worker.worker_base import WorkerWrapperBase +from vllm.v1.worker.worker_base import WorkerWrapperBase if TYPE_CHECKING: from vllm.v1.core.sched.output import SchedulerOutput diff --git a/vllm/executor/uniproc_executor.py b/vllm/executor/uniproc_executor.py index 7a753d608a43..d669592e75f1 100644 --- a/vllm/executor/uniproc_executor.py +++ b/vllm/executor/uniproc_executor.py @@ -19,7 +19,7 @@ from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType from vllm.v1.executor.utils import get_and_update_mm_cache from vllm.v1.outputs import AsyncModelRunnerOutput -from vllm.worker.worker_base import WorkerWrapperBase +from vllm.v1.worker.worker_base import WorkerWrapperBase logger = init_logger(__name__) @@ -160,10 +160,10 @@ def determine_num_available_blocks(self) -> Tuple[int, int]: """ Determine the number of available KV blocks. Add an additional all_reduce to get the min across all ranks. - Note that even if we have the same `gpu_memory_utilization` and - `swap_space`, the available memory in every rank might still - differ because NCCL can take different amounts of memory in - different ranks. Therefore, it is necessary to test if all ranks + Note that even if we have the same `gpu_memory_utilization` and + `swap_space`, the available memory in every rank might still + differ because NCCL can take different amounts of memory in + different ranks. Therefore, it is necessary to test if all ranks agree on the same KV cache configuration. """ a, b = super().determine_num_available_blocks() diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 6738d3dec286..1463fe34fc75 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -110,17 +110,7 @@ def check_and_update_config(cls, vllm_config: "VllmConfig") -> None: model_config = vllm_config.model_config if parallel_config.worker_cls == "auto": - if vllm_config.speculative_config: - if not envs.VLLM_USE_V1: - raise NotImplementedError( - "Speculative decoding is not supported on vLLM V0.") - parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker" - else: - if envs.VLLM_USE_V1: - parallel_config.worker_cls = \ - "vllm.v1.worker.gpu_worker.Worker" - else: - parallel_config.worker_cls = "vllm.worker.worker.Worker" + parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker" cache_config = vllm_config.cache_config if cache_config and cache_config.block_size is None: diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py index 1dacd026b667..f67568bf07c1 100644 --- a/vllm/platforms/rocm.py +++ b/vllm/platforms/rocm.py @@ -327,17 +327,7 @@ def check_and_update_config(cls, vllm_config: "VllmConfig") -> None: cache_config.block_size = 16 if parallel_config.worker_cls == "auto": - if vllm_config.speculative_config: - if not use_v1: - raise NotImplementedError( - "Speculative decoding is not supported on vLLM V0.") - parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker" - else: - if use_v1: - parallel_config.worker_cls = \ - "vllm.v1.worker.gpu_worker.Worker" - else: - parallel_config.worker_cls = "vllm.worker.worker.Worker" + parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker" # Aiter rms norm perform best when CUDA Graph capture is enabled. if (use_v1 and use_aiter_rms_norm and not is_eager_execution and "-rms_norm" not in compilation_config.custom_ops): diff --git a/vllm/v1/executor/multiproc_executor.py b/vllm/v1/executor/multiproc_executor.py index ef90af263664..eecdf8def6de 100644 --- a/vllm/v1/executor/multiproc_executor.py +++ b/vllm/v1/executor/multiproc_executor.py @@ -41,7 +41,7 @@ from vllm.v1.executor.utils import get_and_update_mm_cache from vllm.v1.outputs import (AsyncModelRunnerOutput, DraftTokenIds, ModelRunnerOutput) -from vllm.worker.worker_base import WorkerWrapperBase +from vllm.v1.worker.worker_base import WorkerWrapperBase logger = init_logger(__name__) @@ -702,7 +702,7 @@ def setup_proc_title_and_log_prefix(enable_ep: bool) -> None: def set_multiprocessing_worker_envs(): """ Set up environment variables that should be used when there are workers - in a multiprocessing environment. This should be called by the parent + in a multiprocessing environment. This should be called by the parent process before worker processes are created""" _maybe_force_spawn() diff --git a/vllm/v1/worker/worker_base.py b/vllm/v1/worker/worker_base.py index 038ce4b54f96..5b393ee6bf3e 100644 --- a/vllm/v1/worker/worker_base.py +++ b/vllm/v1/worker/worker_base.py @@ -1,23 +1,35 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project -from typing import Optional +from __future__ import annotations + +import os +from typing import Any, Callable, Optional, TypeVar, Union import torch import torch.nn as nn -from vllm.config import VllmConfig +from vllm.config import VllmConfig, set_current_vllm_config from vllm.logger import init_logger +from vllm.lora.request import LoRARequest +from vllm.sequence import ExecuteModelRequest +from vllm.utils import (enable_trace_function_call_for_thread, + resolve_obj_by_qualname, run_method, + update_environment_variables, + warn_for_unimplemented_methods) from vllm.v1.kv_cache_interface import KVCacheSpec -from vllm.worker.worker_base import WorkerBase as WorkerBaseV0 +from vllm.v1.outputs import SamplerOutput logger = init_logger(__name__) +_R = TypeVar("_R") -class WorkerBase(WorkerBaseV0): - """ - Abstract class for v1 worker, mainly define some methods for v1. - For methods shared by v0 and v1, define them in v0 WorkerBase + +@warn_for_unimplemented_methods +class WorkerBase: + """Worker interface that allows vLLM to cleanly separate implementations for + different hardware. Also abstracts control plane communication, e.g., to + communicate request metadata to other workers. """ def __init__( @@ -27,10 +39,10 @@ def __init__( rank: int, distributed_init_method: str, is_driver_worker: bool = False, - ): + ) -> None: """ Initialize common worker components. - + Args: vllm_config: Complete vLLM configuration local_rank: Local device index @@ -39,8 +51,21 @@ def __init__( is_driver_worker: Whether this worker handles driver responsibilities """ - # Configuration storage - super().__init__(vllm_config=vllm_config) + self.vllm_config = vllm_config + self.model_config = vllm_config.model_config + self.cache_config = vllm_config.cache_config + self.lora_config = vllm_config.lora_config + self.load_config = vllm_config.load_config + self.parallel_config = vllm_config.parallel_config + self.scheduler_config = vllm_config.scheduler_config + self.device_config = vllm_config.device_config + self.speculative_config = vllm_config.speculative_config + self.observability_config = vllm_config.observability_config + self.kv_transfer_config = vllm_config.kv_transfer_config + self.compilation_config = vllm_config.compilation_config + + from vllm.platforms import current_platform + self.current_platform = current_platform self.parallel_config.rank = rank self.local_rank = local_rank @@ -63,3 +88,227 @@ def compile_or_warm_up_model(self) -> None: def check_health(self) -> None: """Basic health check (override for device-specific checks).""" return + + def init_device(self) -> None: + """Initialize device state, such as loading the model or other on-device + memory allocations. + """ + raise NotImplementedError + + def initialize_cache(self, num_gpu_blocks: int, + num_cpu_blocks: int) -> None: + """Initialize the KV cache with the given size in blocks. + """ + raise NotImplementedError + + def get_model(self) -> nn.Module: + raise NotImplementedError + + def apply_model(self, fn: Callable[[nn.Module], _R]) -> _R: + """Apply a function on the model inside this worker.""" + return fn(self.get_model()) + + def load_model(self) -> None: + """Load model onto target device.""" + raise NotImplementedError + + def execute_model( + self, + execute_model_req: Optional[ExecuteModelRequest] = None + ) -> Optional[list[SamplerOutput]]: + raise NotImplementedError + + def start_worker_execution_loop(self) -> None: + """Execute model loop in parallel worker. + + You can stop the loop by executing a driver worker with an empty output. + See `stop_remote_worker_execution_loop` for more details. + """ + with self.current_platform.inference_mode(): + while True: + output = self.execute_model(execute_model_req=None) + if output is None: + return None + + def determine_num_available_blocks(self) -> tuple[int, int]: + """Determine the number of available blocks for the GPU KV cache and + swappable CPU KV cache. + + The implementation may run profiling or other heuristics to determine + the size of caches. + + Returns a tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks + are blocks that are "active" on the device and can be appended to. + num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be + appended to. + """ + raise NotImplementedError + + def get_cache_block_size_bytes(self) -> int: + """Return the size of a single cache block, in bytes. Used in + speculative decoding. + """ + raise NotImplementedError + + def add_lora(self, lora_request: LoRARequest) -> bool: + raise NotImplementedError + + def remove_lora(self, lora_id: int) -> bool: + raise NotImplementedError + + def pin_lora(self, lora_id: int) -> bool: + raise NotImplementedError + + def list_loras(self) -> set[int]: + raise NotImplementedError + + @property + def vocab_size(self) -> int: + """Get vocabulary size from model configuration.""" + return self.model_config.get_vocab_size() + + def shutdown(self) -> None: + """Clean up resources held by the worker.""" + return + + +class WorkerWrapperBase: + """ + This class represents one process in an executor/engine. It is responsible + for lazily initializing the worker and handling the worker's lifecycle. + We first instantiate the WorkerWrapper, which remembers the worker module + and class name. Then, when we call `update_environment_variables`, and the + real initialization happens in `init_worker`. + """ + + def __init__( + self, + vllm_config: VllmConfig, + rpc_rank: int = 0, + ) -> None: + """ + Initialize the worker wrapper with the given vllm_config and rpc_rank. + Note: rpc_rank is the rank of the worker in the executor. In most cases, + it is also the rank of the worker in the distributed group. However, + when multiple executors work together, they can be different. + e.g. in the case of SPMD-style offline inference with TP=2, + users can launch 2 engines/executors, each with only 1 worker. + All workers have rpc_rank=0, but they have different ranks in the TP + group. + """ + self.rpc_rank = rpc_rank + self.worker: Optional[WorkerBase] = None + self.vllm_config: Optional[VllmConfig] = None + # do not store this `vllm_config`, `init_worker` will set the final + # one. TODO: investigate if we can remove this field in + # `WorkerWrapperBase`, `init_cached_hf_modules` should be + # unnecessary now. + if vllm_config.model_config is not None: + # it can be None in tests + trust_remote_code = vllm_config.model_config.trust_remote_code + if trust_remote_code: + # note: lazy import to avoid importing torch before initializing + from vllm.utils import init_cached_hf_modules + init_cached_hf_modules() + + def shutdown(self) -> None: + if self.worker is not None: + self.worker.shutdown() + + def adjust_rank(self, rank_mapping: dict[int, int]) -> None: + """ + Adjust the rpc_rank based on the given mapping. + It is only used during the initialization of the executor, + to adjust the rpc_rank of workers after we create all workers. + """ + if self.rpc_rank in rank_mapping: + self.rpc_rank = rank_mapping[self.rpc_rank] + + def update_environment_variables( + self, + envs_list: list[dict[str, str]], + ) -> None: + envs = envs_list[self.rpc_rank] + key = 'CUDA_VISIBLE_DEVICES' + if key in envs and key in os.environ: + # overwriting CUDA_VISIBLE_DEVICES is desired behavior + # suppress the warning in `update_environment_variables` + del os.environ[key] + update_environment_variables(envs) + + def init_worker(self, all_kwargs: list[dict[str, Any]]) -> None: + """ + Here we inject some common logic before initializing the worker. + Arguments are passed to the worker class constructor. + """ + kwargs = all_kwargs[self.rpc_rank] + self.vllm_config = kwargs.get("vllm_config") + assert self.vllm_config is not None, ( + "vllm_config is required to initialize the worker") + enable_trace_function_call_for_thread(self.vllm_config) + + from vllm.plugins import load_general_plugins + load_general_plugins() + + if isinstance(self.vllm_config.parallel_config.worker_cls, str): + worker_class = resolve_obj_by_qualname( + self.vllm_config.parallel_config.worker_cls) + else: + raise ValueError( + "passing worker_cls is no longer supported. Please pass keep the class in a separate module and pass the qualified name of the class as a string." # noqa: E501 + ) + if self.vllm_config.parallel_config.worker_extension_cls: + worker_extension_cls = resolve_obj_by_qualname( + self.vllm_config.parallel_config.worker_extension_cls) + extended_calls = [] + if worker_extension_cls not in worker_class.__bases__: + # check any conflicts between worker and worker_extension_cls + for attr in dir(worker_extension_cls): + if attr.startswith("__"): + continue + assert not hasattr(worker_class, attr), ( + f"Worker class {worker_class} already has an attribute" + f" {attr}, which conflicts with the worker" + f" extension class {worker_extension_cls}.") + if callable(getattr(worker_extension_cls, attr)): + extended_calls.append(attr) + # dynamically inherit the worker extension class + worker_class.__bases__ = worker_class.__bases__ + ( + worker_extension_cls, ) + logger.info( + "Injected %s into %s for extended collective_rpc calls %s", + worker_extension_cls, worker_class, extended_calls) + with set_current_vllm_config(self.vllm_config): + # To make vLLM config available during worker initialization + self.worker = worker_class(**kwargs) + assert self.worker is not None + + def initialize_from_config(self, kv_cache_configs: list[Any]) -> None: + kv_cache_config = kv_cache_configs[self.rpc_rank] + with set_current_vllm_config(self.vllm_config): + self.worker.initialize_from_config(kv_cache_config) # type: ignore + + def init_device(self): + with set_current_vllm_config(self.vllm_config): + # To make vLLM config available during device initialization + self.worker.init_device() # type: ignore + + def execute_method(self, method: Union[str, bytes], *args, **kwargs): + try: + # method resolution order: + # if a method is defined in this class, it will be called directly. + # otherwise, since we define `__getattr__` and redirect attribute + # query to `self.worker`, the method will be called on the worker. + return run_method(self, method, args, kwargs) + except Exception as e: + # if the driver worker also execute methods, + # exceptions in the rest worker may cause deadlock in rpc like ray + # see https://github.com/vllm-project/vllm/issues/3455 + # print the error and inform the user to solve the error + msg = (f"Error executing method {method!r}. " + "This might cause deadlock in distributed execution.") + logger.exception(msg) + raise e + + def __getattr__(self, attr): + return getattr(self.worker, attr) diff --git a/vllm/worker/__init__.py b/vllm/worker/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/vllm/worker/worker_base.py b/vllm/worker/worker_base.py deleted file mode 100644 index 20fabef4f19b..000000000000 --- a/vllm/worker/worker_base.py +++ /dev/null @@ -1,279 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 -# SPDX-FileCopyrightText: Copyright contributors to the vLLM project - -import os -from typing import (Any, Callable, Dict, List, Optional, Set, Tuple, TypeVar, - Union) - -import cloudpickle -import torch.nn as nn - -from vllm.config import VllmConfig, set_current_vllm_config -from vllm.logger import init_logger -from vllm.lora.request import LoRARequest -from vllm.sequence import ExecuteModelRequest -from vllm.utils import (enable_trace_function_call_for_thread, - resolve_obj_by_qualname, run_method, - update_environment_variables, - warn_for_unimplemented_methods) -from vllm.v1.outputs import SamplerOutput - -logger = init_logger(__name__) - -_R = TypeVar("_R") - - -@warn_for_unimplemented_methods -class WorkerBase: - """Worker interface that allows vLLM to cleanly separate implementations for - different hardware. Also abstracts control plane communication, e.g., to - communicate request metadata to other workers. - """ - - def __init__( - self, - vllm_config: VllmConfig, - ) -> None: - self.vllm_config = vllm_config - self.model_config = vllm_config.model_config - self.cache_config = vllm_config.cache_config - self.lora_config = vllm_config.lora_config - self.load_config = vllm_config.load_config - self.parallel_config = vllm_config.parallel_config - self.scheduler_config = vllm_config.scheduler_config - self.device_config = vllm_config.device_config - self.speculative_config = vllm_config.speculative_config - self.observability_config = vllm_config.observability_config - self.kv_transfer_config = vllm_config.kv_transfer_config - self.compilation_config = vllm_config.compilation_config - from vllm.platforms import current_platform - self.current_platform = current_platform - - def init_device(self) -> None: - """Initialize device state, such as loading the model or other on-device - memory allocations. - """ - raise NotImplementedError - - def initialize_cache(self, num_gpu_blocks: int, - num_cpu_blocks: int) -> None: - """Initialize the KV cache with the given size in blocks. - """ - raise NotImplementedError - - def get_model(self) -> nn.Module: - raise NotImplementedError - - def apply_model(self, fn: Callable[[nn.Module], _R]) -> _R: - """Apply a function on the model inside this worker.""" - return fn(self.get_model()) - - def load_model(self) -> None: - """Load model onto target device.""" - raise NotImplementedError - - def execute_model( - self, - execute_model_req: Optional[ExecuteModelRequest] = None - ) -> Optional[List[SamplerOutput]]: - raise NotImplementedError - - def start_worker_execution_loop(self) -> None: - """Execute model loop in parallel worker. - - You can stop the loop by executing a driver worker with an empty output. - See `stop_remote_worker_execution_loop` for more details. - """ - with self.current_platform.inference_mode(): - while True: - output = self.execute_model(execute_model_req=None) - if output is None: - return None - - def determine_num_available_blocks(self) -> Tuple[int, int]: - """Determine the number of available blocks for the GPU KV cache and - swappable CPU KV cache. - - The implementation may run profiling or other heuristics to determine - the size of caches. - - Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks - are blocks that are "active" on the device and can be appended to. - num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be - appended to. - """ - raise NotImplementedError - - def get_cache_block_size_bytes(self) -> int: - """Return the size of a single cache block, in bytes. Used in - speculative decoding. - """ - raise NotImplementedError - - def add_lora(self, lora_request: LoRARequest) -> bool: - raise NotImplementedError - - def remove_lora(self, lora_id: int) -> bool: - raise NotImplementedError - - def pin_lora(self, lora_id: int) -> bool: - raise NotImplementedError - - def list_loras(self) -> Set[int]: - raise NotImplementedError - - @property - def vocab_size(self) -> int: - """Get vocabulary size from model configuration.""" - return self.model_config.get_vocab_size() - - def shutdown(self) -> None: - """Clean up resources held by the worker.""" - return - - -class WorkerWrapperBase: - """ - This class represents one process in an executor/engine. It is responsible - for lazily initializing the worker and handling the worker's lifecycle. - We first instantiate the WorkerWrapper, which remembers the worker module - and class name. Then, when we call `update_environment_variables`, and the - real initialization happens in `init_worker`. - """ - - def __init__( - self, - vllm_config: VllmConfig, - rpc_rank: int = 0, - ) -> None: - """ - Initialize the worker wrapper with the given vllm_config and rpc_rank. - Note: rpc_rank is the rank of the worker in the executor. In most cases, - it is also the rank of the worker in the distributed group. However, - when multiple executors work together, they can be different. - e.g. in the case of SPMD-style offline inference with TP=2, - users can launch 2 engines/executors, each with only 1 worker. - All workers have rpc_rank=0, but they have different ranks in the TP - group. - """ - self.rpc_rank = rpc_rank - self.worker: Optional[WorkerBase] = None - self.vllm_config: Optional[VllmConfig] = None - # do not store this `vllm_config`, `init_worker` will set the final - # one. TODO: investigate if we can remove this field in - # `WorkerWrapperBase`, `init_cached_hf_modules` should be - # unnecessary now. - if vllm_config.model_config is not None: - # it can be None in tests - trust_remote_code = vllm_config.model_config.trust_remote_code - if trust_remote_code: - # note: lazy import to avoid importing torch before initializing - from vllm.utils import init_cached_hf_modules - init_cached_hf_modules() - - def shutdown(self) -> None: - if self.worker is not None: - self.worker.shutdown() - - def adjust_rank(self, rank_mapping: Dict[int, int]) -> None: - """ - Adjust the rpc_rank based on the given mapping. - It is only used during the initialization of the executor, - to adjust the rpc_rank of workers after we create all workers. - """ - if self.rpc_rank in rank_mapping: - self.rpc_rank = rank_mapping[self.rpc_rank] - - def update_environment_variables(self, envs_list: List[Dict[str, - str]]) -> None: - envs = envs_list[self.rpc_rank] - key = 'CUDA_VISIBLE_DEVICES' - if key in envs and key in os.environ: - # overwriting CUDA_VISIBLE_DEVICES is desired behavior - # suppress the warning in `update_environment_variables` - del os.environ[key] - update_environment_variables(envs) - - def init_worker(self, all_kwargs: List[Dict[str, Any]]) -> None: - """ - Here we inject some common logic before initializing the worker. - Arguments are passed to the worker class constructor. - """ - kwargs = all_kwargs[self.rpc_rank] - self.vllm_config = kwargs.get("vllm_config") - assert self.vllm_config is not None, ( - "vllm_config is required to initialize the worker") - enable_trace_function_call_for_thread(self.vllm_config) - - from vllm.plugins import load_general_plugins - load_general_plugins() - - if isinstance(self.vllm_config.parallel_config.worker_cls, str): - worker_class = resolve_obj_by_qualname( - self.vllm_config.parallel_config.worker_cls) - else: - logger.warning( - "passing worker_cls as a class object is strongly deprecated," - " as the serialization of class objects can be tricky and" - " error-prone. To be safe, please keep the class in a separate" - " module and pass the qualified name of the class as a string." - ) - assert isinstance(self.vllm_config.parallel_config.worker_cls, - bytes) - worker_class = cloudpickle.loads( - self.vllm_config.parallel_config.worker_cls) - if self.vllm_config.parallel_config.worker_extension_cls: - worker_extension_cls = resolve_obj_by_qualname( - self.vllm_config.parallel_config.worker_extension_cls) - extended_calls = [] - if worker_extension_cls not in worker_class.__bases__: - # check any conflicts between worker and worker_extension_cls - for attr in dir(worker_extension_cls): - if attr.startswith("__"): - continue - assert not hasattr(worker_class, attr), ( - f"Worker class {worker_class} already has an attribute" - f" {attr}, which conflicts with the worker" - f" extension class {worker_extension_cls}.") - if callable(getattr(worker_extension_cls, attr)): - extended_calls.append(attr) - # dynamically inherit the worker extension class - worker_class.__bases__ = worker_class.__bases__ + ( - worker_extension_cls, ) - logger.info( - "Injected %s into %s for extended collective_rpc calls %s", - worker_extension_cls, worker_class, extended_calls) - with set_current_vllm_config(self.vllm_config): - # To make vLLM config available during worker initialization - self.worker = worker_class(**kwargs) - assert self.worker is not None - - def initialize_from_config(self, kv_cache_configs: List[Any]) -> None: - kv_cache_config = kv_cache_configs[self.rpc_rank] - with set_current_vllm_config(self.vllm_config): - self.worker.initialize_from_config(kv_cache_config) # type: ignore - - def init_device(self): - with set_current_vllm_config(self.vllm_config): - # To make vLLM config available during device initialization - self.worker.init_device() # type: ignore - - def execute_method(self, method: Union[str, bytes], *args, **kwargs): - try: - # method resolution order: - # if a method is defined in this class, it will be called directly. - # otherwise, since we define `__getattr__` and redirect attribute - # query to `self.worker`, the method will be called on the worker. - return run_method(self, method, args, kwargs) - except Exception as e: - # if the driver worker also execute methods, - # exceptions in the rest worker may cause deadlock in rpc like ray - # see https://github.com/vllm-project/vllm/issues/3455 - # print the error and inform the user to solve the error - msg = (f"Error executing method {method!r}. " - "This might cause deadlock in distributed execution.") - logger.exception(msg) - raise e - - def __getattr__(self, attr): - return getattr(self.worker, attr) From 5b84d22f8dc1a80cf88fb82bd2e675dc2b37141f Mon Sep 17 00:00:00 2001 From: Andrew Sansom Date: Mon, 29 Sep 2025 19:10:58 -0500 Subject: [PATCH 088/166] Test Prompt Embeds/LoRA compatibility and Enable LoRA Support for OPT Models (#25717) Signed-off-by: Andrew Sansom --- docs/features/README.md | 2 +- docs/models/supported_models.md | 2 +- tests/entrypoints/conftest.py | 8 +++++ .../test_completion_with_prompt_embeds.py | 34 +++++++++++++++---- vllm/model_executor/models/opt.py | 5 ++- 5 files changed, 40 insertions(+), 11 deletions(-) diff --git a/docs/features/README.md b/docs/features/README.md index 10cc448cc2ee..05ce0b57a9fc 100644 --- a/docs/features/README.md +++ b/docs/features/README.md @@ -52,7 +52,7 @@ th:not(:first-child) { | [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](gh-pr:4194)^ | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | | | | best-of | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | | | | beam-search | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ | | -| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](gh-issue:25096) | ? | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ? | ? | ❌ | ? | ? | ✅ | +| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](gh-issue:25096) | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❔ | ❔ | ❌ | ❔ | ❔ | ✅ | \* Chunked prefill and prefix caching are only applicable to last-token pooling. ^ LoRA is only applicable to the language backbone of multimodal models. diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index 8e87a98e3d51..d720fa2458e1 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -403,7 +403,7 @@ th { | `OLMo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ | ✅︎ | | `OLMo3ForCausalLM` | OLMo3 | TBA | ✅︎ | ✅︎ | ✅︎ | | `OLMoEForCausalLM` | OLMoE | `allenai/OLMoE-1B-7B-0924`, `allenai/OLMoE-1B-7B-0924-Instruct`, etc. | | ✅︎ | ✅︎ | -| `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | | ✅︎ | ✅︎ | +| `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | ✅︎ | ✅︎ | ✅︎ | | `OrionForCausalLM` | Orion | `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc. | | ✅︎ | ✅︎ | | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ | ✅︎ | | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ | diff --git a/tests/entrypoints/conftest.py b/tests/entrypoints/conftest.py index da75806ccf4d..7daf62595b1b 100644 --- a/tests/entrypoints/conftest.py +++ b/tests/entrypoints/conftest.py @@ -208,3 +208,11 @@ def zephyr_lora_files(): """Download zephyr LoRA files once per test session.""" from huggingface_hub import snapshot_download return snapshot_download(repo_id="typeof/zephyr-7b-beta-lora") + + +@pytest.fixture(scope="session") +def opt125_lora_files() -> str: + """Download opt-125m LoRA files once per test session.""" + from huggingface_hub import snapshot_download + return snapshot_download( + repo_id="peft-internal-testing/opt-125m-dummy-lora") diff --git a/tests/entrypoints/openai/test_completion_with_prompt_embeds.py b/tests/entrypoints/openai/test_completion_with_prompt_embeds.py index ae51025455b1..cad914282306 100644 --- a/tests/entrypoints/openai/test_completion_with_prompt_embeds.py +++ b/tests/entrypoints/openai/test_completion_with_prompt_embeds.py @@ -3,6 +3,7 @@ import base64 import io +import json import openai # use the official client for correctness check import pytest @@ -16,13 +17,15 @@ # any model with a chat template should work here MODEL_NAME = "facebook/opt-125m" +LORA_SERVING_MODEL_NAME = "opt125m-lora" CONFIG = AutoConfig.from_pretrained(MODEL_NAME) -@pytest.fixture(scope="module") -def default_server_args() -> list[str]: - return [ +@pytest.fixture(scope="module", params=["use-lora"]) +def default_server_args(request: pytest.FixtureRequest, + opt125_lora_files: str) -> list[str]: + args = [ # use half precision for speed and memory savings in CI environment "--dtype", "bfloat16", @@ -35,6 +38,25 @@ def default_server_args() -> list[str]: "--enable-prompt-embeds", ] + if request.param == "use-lora": + lora_module_1 = { + "name": LORA_SERVING_MODEL_NAME, + "path": opt125_lora_files, + "base_model_name": MODEL_NAME + } + + args.extend([ + "--enable-lora", + "--lora-module", + json.dumps(lora_module_1), + "--max-lora-rank", + "64", + "--max-cpu-loras", + "2", + ]) + + return args + EXAMPLE_PROMPTS = [ "Hello, my name is", @@ -74,7 +96,7 @@ async def client_with_prompt_embeds(server_with_prompt_embeds): @pytest.mark.asyncio -@pytest.mark.parametrize("model_name", [MODEL_NAME]) +@pytest.mark.parametrize("model_name", [MODEL_NAME, LORA_SERVING_MODEL_NAME]) async def test_completions_with_prompt_embeds( example_prompt_embeds, client_with_prompt_embeds: openai.AsyncOpenAI, @@ -179,7 +201,7 @@ async def test_completions_with_prompt_embeds( @pytest.mark.asyncio -@pytest.mark.parametrize("model_name", [MODEL_NAME]) +@pytest.mark.parametrize("model_name", [MODEL_NAME, LORA_SERVING_MODEL_NAME]) async def test_completions_errors_with_prompt_embeds( client_with_prompt_embeds: openai.AsyncOpenAI, model_name: str): # Test error case: invalid prompt_embeds @@ -194,7 +216,7 @@ async def test_completions_errors_with_prompt_embeds( @pytest.mark.asyncio @pytest.mark.parametrize("logprobs_arg", [1, 0]) -@pytest.mark.parametrize("model_name", [MODEL_NAME]) +@pytest.mark.parametrize("model_name", [MODEL_NAME, LORA_SERVING_MODEL_NAME]) async def test_completions_with_logprobs_and_prompt_embeds( example_prompt_embeds, client_with_prompt_embeds: openai.AsyncOpenAI, diff --git a/vllm/model_executor/models/opt.py b/vllm/model_executor/models/opt.py index 4c3ce9f61efb..c4746166471c 100644 --- a/vllm/model_executor/models/opt.py +++ b/vllm/model_executor/models/opt.py @@ -43,7 +43,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.sequence import IntermediateTensors -from .interfaces import SupportsPP +from .interfaces import SupportsLoRA, SupportsPP from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) @@ -352,10 +352,9 @@ def load_weights(self, weights: Iterable[tuple[str, return loaded_params -class OPTForCausalLM(nn.Module, SupportsPP): +class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA): packed_modules_mapping = { "qkv_proj": ["q_proj", "k_proj", "v_proj"], - "gate_up_proj": ["gate_proj", "up_proj"] } hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={ From a9b6fc992b114eea676939eff5b29bfb153c5d53 Mon Sep 17 00:00:00 2001 From: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Date: Mon, 29 Sep 2025 21:15:19 -0400 Subject: [PATCH 089/166] [Bug] Fix Weight Loading for Block FP8 Cutlass SM90 (#25909) Signed-off-by: yewentao256 Signed-off-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- .../model_executor/layers/quantization/utils/fp8_utils.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py index b2548e66827d..828111dc299e 100644 --- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py +++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py @@ -911,15 +911,15 @@ def maybe_post_process_fp8_weight_block(layer: torch.nn.Module, # On Blackwell or Hopper, if E8M0 for DeepGemm is used, we need to # requantize the weight and input to the specific scale # at the same time. - if is_deep_gemm_e8m0_used(): + should_use_deepgemm = should_use_deepgemm_for_fp8_linear( + layer.orig_dtype, layer.weight) + if is_deep_gemm_e8m0_used() and should_use_deepgemm: block_sz = tuple(layer.weight_block_size) requant_weight_ue8m0_inplace(layer.weight.data, layer.weight_scale.data, block_sz) # SM90 Block FP8 CUTLASS requires row-major weight scales elif (current_platform.is_device_capability(90) - and cutlass_block_fp8_supported - and not should_use_deepgemm_for_fp8_linear(torch.bfloat16, - layer.weight)): + and cutlass_block_fp8_supported and not should_use_deepgemm): layer.weight_scale = torch.nn.Parameter( layer.weight_scale.data.T.contiguous(), requires_grad=False) From dfb8e202fe771753a96bbb664b9fa2a502fc431e Mon Sep 17 00:00:00 2001 From: Zhuohan Li Date: Mon, 29 Sep 2025 18:43:57 -0700 Subject: [PATCH 090/166] [Benchmark] Support benchmark throughput for external launcher DP (#25913) Signed-off-by: Zhuohan Li --- vllm/benchmarks/throughput.py | 33 +++++++++++++++++++++++++++------ 1 file changed, 27 insertions(+), 6 deletions(-) diff --git a/vllm/benchmarks/throughput.py b/vllm/benchmarks/throughput.py index 96e39fd92eba..9e38e63a0883 100644 --- a/vllm/benchmarks/throughput.py +++ b/vllm/benchmarks/throughput.py @@ -358,7 +358,23 @@ def get_requests(args, tokenizer): raise ValueError(f"Unknown dataset name: {args.dataset_name}") # Remove None values sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None} - return dataset_cls(**common_kwargs).sample(**sample_kwargs) + requests = dataset_cls(**common_kwargs).sample(**sample_kwargs) + requests = filter_requests_for_dp(requests, args.data_parallel_size) + return requests + + +def filter_requests_for_dp(requests, data_parallel_size): + # Note(zhuohan): The way we get data_parallel_rank is hacky and only + # works for external launcher mode. Should be cleaned up and deprecated + # in the future with a better vLLM distributed process design. + if data_parallel_size == 1: + return requests + + global_rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + data_parallel_rank = global_rank // (world_size // data_parallel_size) + return [r for i, r in enumerate(requests) + if i % data_parallel_size == data_parallel_rank] def validate_args(args): @@ -453,12 +469,17 @@ def validate_args(args): if args.backend == "mii" and args.tokenizer != args.model: raise ValueError( "Tokenizer must be the same as the model for MII backend.") - - # --data-parallel is not supported currently. - # https://github.com/vllm-project/vllm/issues/16222 - if args.data_parallel_size > 1: + + if args.data_parallel_size > 1 and ( + args.distributed_executor_backend != "external_launcher" + or args.async_engine): + # --data-parallel is not supported fully. + # Old issue: https://github.com/vllm-project/vllm/issues/16222 + # Currently we only support data parallel with external launcher + # mode (i.e., launch with toruchrun). raise ValueError( - "Data parallel is not supported in offline benchmark, " + "Data parallel is only supported with external launcher mode " + "with synchronous engine in offline benchmark, " "please use benchmark serving instead" ) From 4b1cf5eaf04ee3dbf2ceeef2ecd44e62a4c9ce87 Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Tue, 30 Sep 2025 03:49:49 +0100 Subject: [PATCH 091/166] Move`VllmConfig` from `config/__init__.py` to `config/vllm.py` (#25271) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- vllm/attention/layer.py | 3 +- .../layers/chunked_local_attention.py | 3 +- vllm/config/__init__.py | 905 ++---------------- vllm/config/utils.py | 42 +- vllm/config/vllm.py | 789 +++++++++++++++ .../layers/mamba/linear_attn.py | 3 +- .../layers/quantization/auto_round.py | 5 +- .../layers/quantization/bitblas.py | 5 +- .../layers/quantization/bitsandbytes.py | 5 +- .../layers/quantization/deepspeedfp.py | 5 +- .../layers/quantization/gptq.py | 8 +- .../layers/quantization/gptq_bitblas.py | 5 +- .../layers/quantization/gptq_marlin.py | 2 +- .../layers/quantization/gptq_marlin_24.py | 5 +- .../layers/quantization/ipex_quant.py | 5 +- .../kernels/mixed_precision/bitblas.py | 3 +- .../layers/quantization/tpu_int8.py | 5 +- .../layers/quantization/utils/gptq_utils.py | 18 +- vllm/model_executor/models/aimv2.py | 3 +- vllm/model_executor/models/aria.py | 3 +- vllm/model_executor/models/bailing_moe.py | 3 +- vllm/model_executor/models/granite.py | 3 +- vllm/model_executor/models/granitemoe.py | 3 +- .../model_executor/models/granitemoeshared.py | 3 +- vllm/model_executor/models/hunyuan_v1.py | 3 +- vllm/model_executor/models/interfaces.py | 3 +- vllm/model_executor/models/llama4_eagle.py | 3 +- vllm/model_executor/models/mamba.py | 3 +- vllm/model_executor/models/mamba2.py | 3 +- vllm/model_executor/models/minimax_text_01.py | 3 +- vllm/model_executor/models/ovis.py | 3 +- vllm/model_executor/models/ovis2_5.py | 3 +- vllm/model_executor/models/phimoe.py | 3 +- vllm/model_executor/models/siglip2navit.py | 2 +- vllm/model_executor/models/step3_text.py | 3 +- vllm/model_executor/models/whisper.py | 3 +- 36 files changed, 964 insertions(+), 905 deletions(-) create mode 100644 vllm/config/vllm.py diff --git a/vllm/attention/layer.py b/vllm/attention/layer.py index d97c87d96e99..8cab8330a6cd 100644 --- a/vllm/attention/layer.py +++ b/vllm/attention/layer.py @@ -20,8 +20,7 @@ from vllm.logger import init_logger from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase from vllm.model_executor.layers.linear import UnquantizedLinearMethod -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod from vllm.model_executor.layers.quantization.utils.quant_utils import ( diff --git a/vllm/attention/layers/chunked_local_attention.py b/vllm/attention/layers/chunked_local_attention.py index 087c5004bde0..3964eca7d36b 100644 --- a/vllm/attention/layers/chunked_local_attention.py +++ b/vllm/attention/layers/chunked_local_attention.py @@ -9,7 +9,8 @@ from vllm.attention.backends.abstract import (AttentionBackend, AttentionMetadata) from vllm.attention.selector import get_attn_backend -from vllm.config import CacheConfig, QuantizationConfig +from vllm.config import CacheConfig +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.v1.attention.backends.utils import ( CommonAttentionMetadata, make_local_attention_virtual_batches, subclass_attention_backend) diff --git a/vllm/config/__init__.py b/vllm/config/__init__.py index ccb91999d370..c909265c071d 100644 --- a/vllm/config/__init__.py +++ b/vllm/config/__init__.py @@ -1,29 +1,6 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project -# ruff: noqa: F401 -import ast -import copy -import hashlib -import inspect -import json -import os -import textwrap -from contextlib import contextmanager -from dataclasses import field, fields, is_dataclass, replace -from functools import cached_property, lru_cache -from pathlib import Path -from typing import (TYPE_CHECKING, Any, Literal, Optional, Protocol, TypeVar, - Union, cast) - -import regex as re -import torch -from pydantic import ConfigDict, SkipValidation -from pydantic.dataclasses import dataclass -from typing_extensions import runtime_checkable - -import vllm.envs as envs -from vllm import version from vllm.config.cache import (BlockSize, CacheConfig, CacheDType, MambaDType, PrefixCachingHashAlgo) from vllm.config.compilation import (CompilationConfig, CompilationLevel, @@ -48,806 +25,82 @@ from vllm.config.speculative import SpeculativeConfig from vllm.config.speech_to_text import SpeechToTextConfig from vllm.config.structured_outputs import StructuredOutputsConfig -from vllm.config.utils import ConfigType, config, get_attr_docs, is_init_field -from vllm.logger import init_logger -from vllm.multimodal import MULTIMODAL_REGISTRY -from vllm.transformers_utils.runai_utils import is_runai_obj_uri -from vllm.utils import random_uuid - -if TYPE_CHECKING: - from _typeshed import DataclassInstance - from transformers.configuration_utils import PretrainedConfig - - from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) -else: - DataclassInstance = Any - PretrainedConfig = Any - QuantizationConfig = Any - QuantizationMethods = Any - BaseModelLoader = Any - LogitsProcessor = Any - -logger = init_logger(__name__) -DataclassInstanceT = TypeVar("DataclassInstanceT", bound=DataclassInstance) - - -@runtime_checkable -class SupportsHash(Protocol): - - def compute_hash(self) -> str: - ... - - -class SupportsMetricsInfo(Protocol): - - def metrics_info(self) -> dict[str, str]: - ... - - -@config -@dataclass(config=ConfigDict(arbitrary_types_allowed=True)) -class VllmConfig: - """Dataclass which contains all vllm-related configuration. This - simplifies passing around the distinct configurations in the codebase. - """ - - # TODO: use default_factory once default constructing ModelConfig doesn't - # try to download a model - model_config: ModelConfig = None # type: ignore - """Model configuration.""" - cache_config: CacheConfig = field(default_factory=CacheConfig) - """Cache configuration.""" - parallel_config: ParallelConfig = field(default_factory=ParallelConfig) - """Parallel configuration.""" - scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig) - """Scheduler configuration.""" - device_config: DeviceConfig = field(default_factory=DeviceConfig) - """Device configuration.""" - load_config: LoadConfig = field(default_factory=LoadConfig) - """Load configuration.""" - lora_config: Optional[LoRAConfig] = None - """LoRA configuration.""" - speculative_config: Optional[SpeculativeConfig] = None - """Speculative decoding configuration.""" - structured_outputs_config: StructuredOutputsConfig = field( - default_factory=StructuredOutputsConfig) - """Structured outputs configuration.""" - observability_config: Optional[ObservabilityConfig] = None - """Observability configuration.""" - quant_config: Optional[QuantizationConfig] = None - """Quantization configuration.""" - compilation_config: CompilationConfig = field( - default_factory=CompilationConfig) - """`torch.compile` and cudagraph capture configuration for the model. - - As a shorthand, `-O` can be used to directly specify the compilation - level `n`: `-O3` is equivalent to `-O.level=3` (same as `-O='{"level":3}'`). - Currently, -O and -O= are supported as well but this will likely be - removed in favor of clearer -O syntax in the future. - - NOTE: level 0 is the default level without any optimization. level 1 and 2 - are for internal testing only. level 3 is the recommended level for - production, also default in V1. - - You can specify the full compilation config like so: - `{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}` - """ - kv_transfer_config: Optional[KVTransferConfig] = None - """The configurations for distributed KV cache transfer.""" - kv_events_config: Optional[KVEventsConfig] = None - """The configurations for event publishing.""" - # some opaque config, only used to provide additional information - # for the hash computation, mainly used for testing, debugging or out of - # tree config registration. - additional_config: Union[dict, SupportsHash] = field(default_factory=dict) - """Additional config for specified platform. Different platforms may - support different configs. Make sure the configs are valid for the platform - you are using. Contents must be hashable.""" - instance_id: str = "" - """The ID of the vLLM instance.""" - - def compute_hash(self) -> str: - """ - WARNING: Whenever a new field is added to this config, - ensure that it is included in the factors list if - it affects the computation graph. - - Provide a hash that uniquely identifies all the configs - that affect the structure of the computation - graph from input ids/embeddings to the final hidden states, - excluding anything before input ids/embeddings and after - the final hidden states. - """ - factors: list[Any] = [] - - # summarize vllm config - vllm_factors: list[Any] = [] - from vllm import __version__ - vllm_factors.append(__version__) - vllm_factors.append(envs.VLLM_USE_V1) - if self.model_config: - vllm_factors.append(self.model_config.compute_hash()) - else: - vllm_factors.append("None") - if self.cache_config: - vllm_factors.append(self.cache_config.compute_hash()) - else: - vllm_factors.append("None") - if self.parallel_config: - vllm_factors.append(self.parallel_config.compute_hash()) - else: - vllm_factors.append("None") - if self.scheduler_config: - vllm_factors.append(self.scheduler_config.compute_hash()) - else: - vllm_factors.append("None") - if self.device_config: - vllm_factors.append(self.device_config.compute_hash()) - else: - vllm_factors.append("None") - if self.load_config: - vllm_factors.append(self.load_config.compute_hash()) - else: - vllm_factors.append("None") - if self.lora_config: - vllm_factors.append(self.lora_config.compute_hash()) - # LoRA creates static buffers based on max_num_batched_tokens. - # The tensor sizes and strides get captured in the torch.compile - # graph explicitly. - vllm_factors.append( - str(self.scheduler_config.max_num_batched_tokens)) - else: - vllm_factors.append("None") - if self.speculative_config: - vllm_factors.append(self.speculative_config.compute_hash()) - else: - vllm_factors.append("None") - if self.structured_outputs_config: - vllm_factors.append(self.structured_outputs_config.compute_hash()) - else: - vllm_factors.append("None") - if self.observability_config: - vllm_factors.append(self.observability_config.compute_hash()) - else: - vllm_factors.append("None") - if self.quant_config: - pass # should be captured by model_config.quantization - if self.compilation_config: - vllm_factors.append(self.compilation_config.compute_hash()) - else: - vllm_factors.append("None") - if self.kv_transfer_config: - vllm_factors.append(self.kv_transfer_config.compute_hash()) - else: - vllm_factors.append("None") - if self.additional_config: - if isinstance(additional_config := self.additional_config, dict): - additional_config_hash = hashlib.md5( - json.dumps(additional_config, sort_keys=True).encode(), - usedforsecurity=False, - ).hexdigest() - else: - additional_config_hash = additional_config.compute_hash() - vllm_factors.append(additional_config_hash) - else: - vllm_factors.append("None") - factors.append(vllm_factors) - - hash_str = hashlib.md5(str(factors).encode(), - usedforsecurity=False).hexdigest()[:10] - return hash_str - - def pad_for_cudagraph(self, batch_size: int) -> int: - # if batch_size > self.compilation_config.max_capture_size, - # it should raise an IndexError. - # the caller should make sure the batch_size is within the range, - # i.e., batch_size <= self.compilation_config.max_capture_size - return self.compilation_config.bs_to_padded_graph_size[batch_size] - - @staticmethod - def _get_quantization_config( - model_config: ModelConfig, - load_config: LoadConfig) -> Optional[QuantizationConfig]: - """Get the quantization config.""" - from vllm.platforms import current_platform - if model_config.quantization is not None: - from vllm.model_executor.model_loader.weight_utils import ( - get_quant_config) - quant_config = get_quant_config(model_config, load_config) - capability_tuple = current_platform.get_device_capability() - - if capability_tuple is not None: - capability = capability_tuple.to_int() - if capability < quant_config.get_min_capability(): - raise ValueError( - f"The quantization method {model_config.quantization} " - "is not supported for the current GPU. Minimum " - f"capability: {quant_config.get_min_capability()}. " - f"Current capability: {capability}.") - supported_dtypes = quant_config.get_supported_act_dtypes() - if model_config.dtype not in supported_dtypes: - raise ValueError( - f"{model_config.dtype} is not supported for quantization " - f"method {model_config.quantization}. Supported dtypes: " - f"{supported_dtypes}") - quant_config.maybe_update_config(model_config.model) - return quant_config - return None - - @staticmethod - def get_quantization_config( - model_config: ModelConfig, - load_config: LoadConfig) -> Optional[QuantizationConfig]: - import copy - - # For some reason, the _ version of this modifies the model_config - # object, so using deepcopy to avoid this problem. - return VllmConfig._get_quantization_config(copy.deepcopy(model_config), - load_config) - - def with_hf_config( - self, - hf_config: PretrainedConfig, - architectures: Optional[list[str]] = None, - ) -> "VllmConfig": - if architectures is not None: - hf_config = copy.deepcopy(hf_config) - hf_config.architectures = architectures - - model_config = copy.deepcopy(self.model_config) - model_config.hf_config = hf_config - - return replace(self, model_config=model_config) - - def __post_init__(self): - """Verify configs are valid & consistent with each other. - """ - - self.try_verify_and_update_config() - - if self.model_config is not None: - self.model_config.verify_with_parallel_config(self.parallel_config) - self.model_config.verify_dual_chunk_attention_config( - self.load_config) - - self.cache_config.verify_with_parallel_config(self.parallel_config) - - if self.lora_config is not None: - self.lora_config.verify_with_cache_config(self.cache_config) - self.lora_config.verify_with_model_config(self.model_config) - - if self.quant_config is None and self.model_config is not None: - self.quant_config = VllmConfig._get_quantization_config( - self.model_config, self.load_config) - - from vllm.platforms import current_platform - if self.model_config is not None and \ - self.scheduler_config.chunked_prefill_enabled and \ - self.model_config.dtype == torch.float32 and \ - current_platform.get_device_capability() == (7, 5): - logger.warning_once( - "Turing devices tensor cores do not support float32 matmul. " - "To workaround this limitation, vLLM will set 'ieee' input " - "precision for chunked prefill triton kernels.") - - # If the user does not explicitly set a compilation level, then - # we use the default level. The default level depends on other - # settings (see the below code). - if self.compilation_config.level is None: - if envs.VLLM_USE_V1: - if (self.model_config is not None - and not self.model_config.enforce_eager): - self.compilation_config.level = CompilationLevel.PIECEWISE - else: - self.compilation_config.level = \ - CompilationLevel.NO_COMPILATION - - else: - # NB: Passing both --enforce-eager and a compilation level - # in V0 means the compilation level wins out. - self.compilation_config.level = CompilationLevel.NO_COMPILATION - - # async tp is built on top of sequence parallelism - # and requires it to be enabled. - if self.compilation_config.pass_config.enable_async_tp: - self.compilation_config.pass_config.enable_sequence_parallelism = \ - True - if self.compilation_config.pass_config.enable_sequence_parallelism: - self.compilation_config.custom_ops.append("+rms_norm") - - if current_platform.support_static_graph_mode(): - # if cudagraph_mode is not explicitly set by users, set default - # value - if self.compilation_config.cudagraph_mode is None: - if envs.VLLM_USE_V1 and self.compilation_config.level \ - == CompilationLevel.PIECEWISE: - # default to full and piecewise for most models - self.compilation_config.cudagraph_mode = \ - CUDAGraphMode.FULL_AND_PIECEWISE - - # pooling models and encoder-decoder models - # do not support full cudagraphs - if self.model_config is not None and \ - (self.model_config.pooler_config is not None - or self.model_config.is_encoder_decoder): - self.compilation_config.cudagraph_mode = \ - CUDAGraphMode.PIECEWISE - else: - self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE - - # disable cudagraph when enforce eager execution - if self.model_config is not None and \ - self.model_config.enforce_eager: - logger.info("Cudagraph is disabled under eager mode") - self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE - elif envs.VLLM_USE_V1: - self.compilation_config.cudagraph_num_of_warmups = 1 - - self._set_cudagraph_sizes() - else: - self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE - - if self.cache_config.kv_sharing_fast_prefill: - - if self.speculative_config is not None and \ - self.speculative_config.use_eagle(): - raise NotImplementedError( - "Fast prefill optimization for KV sharing is not " - "compatible with EAGLE as EAGLE requires correct logits " - "for all tokens while fast prefill gives incorrect logits " - "for prompt tokens.") - - logger.warning_once( - "--kv-sharing-fast-prefill requires changes on model side for " - "correctness and to realize prefill savings. ") - - disable_chunked_prefill_reasons: list[str] = [] - - if self.model_config: - if self.model_config.pooler_config: - pooling_type = self.model_config.pooler_config.pooling_type - if pooling_type is None or pooling_type.lower() != "last": - disable_chunked_prefill_reasons.append( - "Only \"last\" pooling supports chunked " - "prefill and prefix caching; disabling both.") - if not getattr(self.model_config.hf_config, "is_causal", True): - disable_chunked_prefill_reasons.append( - "Only models using causal attention supports chunked " - "prefill and prefix caching; disabling both.") - elif self.model_config.is_encoder_decoder: - self.scheduler_config.max_num_encoder_input_tokens = \ - MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(self.model_config) - logger.debug( - "Encoder-decoder model detected: setting " - "`max_num_encoder_input_tokens` to encoder length (%s)", - self.scheduler_config.max_num_encoder_input_tokens) - self.scheduler_config.disable_chunked_mm_input = True - disable_chunked_prefill_reasons.append( - "Encoder-decoder models do not support chunked prefill nor" - " prefix caching; disabling both.") - if (self.model_config.architecture - == "WhisperForConditionalGeneration" - and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") - != "spawn"): - logger.warning( - "Whisper is known to have issues with " - "forked workers. If startup is hanging, " - "try setting 'VLLM_WORKER_MULTIPROC_METHOD' " - "to 'spawn'.") - - if disable_chunked_prefill_reasons: - for reason in disable_chunked_prefill_reasons: - logger.info(reason) - self.scheduler_config.chunked_prefill_enabled = False - self.scheduler_config.long_prefill_token_threshold = 0 - - if self.cache_config is not None: - self.cache_config.enable_prefix_caching = False - - if (self.kv_events_config is not None - and self.kv_events_config.enable_kv_cache_events - and not self.cache_config.enable_prefix_caching): - logger.warning( - "KV cache events are on, but prefix caching is not enabled." - "Use --enable-prefix-caching to enable.") - if (self.kv_events_config is not None - and self.kv_events_config.publisher != "null" - and not self.kv_events_config.enable_kv_cache_events): - logger.warning("KV cache events are disabled," - "but the scheduler is configured to publish them." - "Modify KVEventsConfig.enable_kv_cache_events" - "to True to enable.") - current_platform.check_and_update_config(self) - - # Do this after all the updates to compilation_config.level - if envs.VLLM_USE_V1 and \ - self.compilation_config.level == CompilationLevel.PIECEWISE: - self.compilation_config.set_splitting_ops_for_v1() - - # final check of cudagraph mode after all possible updates - if envs.VLLM_USE_V1 and current_platform.is_cuda_alike(): - if self.compilation_config.cudagraph_mode.has_full_cudagraphs()\ - and self.model_config is not None and \ - not self.model_config.disable_cascade_attn and\ - not self.compilation_config.cudagraph_mode.\ - has_piecewise_cudagraphs(): - logger.warning_once( - "No piecewise cudagraph for executing cascade attention." - " Will fall back to eager execution if a batch runs " - "into cascade attentions") - - if self.compilation_config.cudagraph_mode\ - .requires_piecewise_compilation(): - assert self.compilation_config.level == \ - CompilationLevel.PIECEWISE, \ - "Compilation level should be CompilationLevel.PIECEWISE "\ - "when cudagraph_mode piecewise cudagraphs is used, "\ - f"cudagraph_mode={self.compilation_config.cudagraph_mode}" - - # final migrate the deprecated flags - self.compilation_config.use_cudagraph = self.compilation_config.\ - cudagraph_mode!= CUDAGraphMode.NONE - self.compilation_config.full_cuda_graph = self.compilation_config.\ - cudagraph_mode.has_full_cudagraphs() - - if self.parallel_config.enable_dbo: - a2a_backend = envs.VLLM_ALL2ALL_BACKEND - assert a2a_backend in \ - ["deepep_low_latency", "deepep_high_throughput"], \ - "Microbatching currently only supports the deepep_low_latency and "\ - f"deepep_high_throughput all2all backend. {a2a_backend} is not "\ - "supported. To fix set the VLLM_ALL2ALL_BACKEND environment "\ - "variable to deepep_low_latency or deepep_high_throughput and "\ - "install the DeepEP kernels." - - if not self.model_config.disable_cascade_attn: - self.model_config.disable_cascade_attn = True - logger.warning_once( - "Disabling cascade attention when DBO is enabled.") - - if not self.instance_id: - self.instance_id = random_uuid()[:5] - - if (envs.VLLM_USE_V1 - and not self.scheduler_config.disable_hybrid_kv_cache_manager): - # logger should only print warning message for hybrid models. As we - # can't know whether the model is hybrid or not now, so we don't log - # warning message here and will log it later. - if not current_platform.support_hybrid_kv_cache(): - # Hybrid KV cache manager is not supported on non-GPU platforms. - self.scheduler_config.disable_hybrid_kv_cache_manager = True - if self.kv_transfer_config is not None: - # Hybrid KV cache manager is not compatible with KV transfer. - self.scheduler_config.disable_hybrid_kv_cache_manager = True - if self.kv_events_config is not None: - # Hybrid KV cache manager is not compatible with KV events. - self.scheduler_config.disable_hybrid_kv_cache_manager = True - if self.model_config is not None and \ - self.model_config.attention_chunk_size is not None: - if self.speculative_config is not None and \ - self.speculative_config.use_eagle(): - # Hybrid KV cache manager is not yet supported with chunked - # local attention + eagle. - self.scheduler_config.disable_hybrid_kv_cache_manager = True - elif \ - not envs.VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: - logger.warning( - "There is a latency regression when using chunked local" - " attention with the hybrid KV cache manager. Disabling" - " it, by default. To enable it, set the environment " - "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1." - ) - # Hybrid KV cache manager is not yet supported with chunked - # local attention. - self.scheduler_config.disable_hybrid_kv_cache_manager = True - - if self.compilation_config.debug_dump_path: - self.compilation_config.debug_dump_path = \ - self.compilation_config.debug_dump_path.absolute().expanduser() - if envs.VLLM_DEBUG_DUMP_PATH is not None: - env_path = Path(envs.VLLM_DEBUG_DUMP_PATH).absolute().expanduser() - if self.compilation_config.debug_dump_path: - logger.warning( - "Config-specified debug dump path is overridden" - " by VLLM_DEBUG_DUMP_PATH to %s", env_path) - self.compilation_config.debug_dump_path = env_path - - def update_sizes_for_sequence_parallelism(self, - possible_sizes: list) -> list: - # remove the sizes that not multiple of tp_size when - # enable sequence parallelism - removed_sizes = [ - size for size in possible_sizes - if size % self.parallel_config.tensor_parallel_size != 0 - ] - if removed_sizes: - logger.warning( - "Batch sizes %s are removed because they are not " - "multiple of tp_size %d when " - "sequence parallelism is enabled", removed_sizes, - self.parallel_config.tensor_parallel_size) - - return [ - size for size in possible_sizes - if size % self.parallel_config.tensor_parallel_size == 0 - ] - - def _set_cudagraph_sizes(self): - """ - vLLM defines the default candidate list of batch sizes for CUDA graph - capture as: - - ```python - max_graph_size = min(max_num_seqs * 2, 512) - # 1, 2, 4, then multiples of 8 up to max_graph_size - cuda_graph_sizes = [1, 2, 4, 8, 16, 24, 32, 40, ..., max_graph_size] - - In the end, `vllm_config.compilation_config.cudagraph_capture_sizes` - will be the final sizes to capture cudagraph (in descending order). - - These sizes are used to capture and reuse CUDA graphs for - performance-critical paths (e.g., decoding). Capturing enables - significantly faster kernel dispatch by avoiding Python overhead. The - list is then filtered based on `max_num_batched_tokens` (e.g., 8192 on - most GPUs), which controls the total allowed number of tokens in a - batch. Since each sequence may have a variable number of tokens, the - maximum usable batch size will depend on actual sequence lengths. - - Example: - With `max_num_batched_tokens = 8192`, and typical sequences - averaging ~32 tokens, most practical batch sizes fall below 256. - However, the system will still allow capture sizes up to 512 if - shape and memory permit. - - Note: - If users explicitly specify cudagraph capture sizes in the - compilation config, those will override this default logic. - At runtime: - - - If batch size <= one of the `cudagraph_capture_sizes`, the closest - padded CUDA graph will be used. - - If batch size > largest `cudagraph_capture_sizes`, cudagraph will - not be used. - """ - - # calculate the default `batch_size_capture_list` - batch_size_capture_list = [] - if self.model_config is not None and \ - not self.model_config.enforce_eager: - cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes - if len(cuda_graph_sizes) == 1: - batch_size_capture_list = [1, 2, 4] + [ - i for i in range(8, cuda_graph_sizes[0] + 1, 8) - ] - elif len(cuda_graph_sizes) > 1: - batch_size_capture_list = sorted(cuda_graph_sizes) - else: - raise TypeError(f"Invalid value for {cuda_graph_sizes=}.") - if self.parallel_config.tensor_parallel_size > 1 and \ - self.compilation_config.pass_config.enable_sequence_parallelism: - batch_size_capture_list = \ - self.update_sizes_for_sequence_parallelism(batch_size_capture_list) - max_num_tokens = self.scheduler_config.max_num_batched_tokens - batch_size_capture_list = [ - size for size in batch_size_capture_list - if size <= max_num_tokens - ] - - self.compilation_config.init_with_cudagraph_sizes( - batch_size_capture_list) - - def recalculate_max_model_len(self, max_model_len: int): - # Can only be called in try_verify_and_update_config - model_config = self.model_config - max_model_len = model_config.get_and_verify_max_len(max_model_len) - self.model_config.max_model_len = max_model_len - self.scheduler_config.max_model_len = max_model_len - - def try_verify_and_update_config(self): - if self.model_config is None: - return - - # Avoid running try_verify_and_update_config multiple times - if getattr(self.model_config, "config_updated", False): - return - self.model_config.config_updated = True - - architecture = self.model_config.architecture - if architecture is None: - return - - from vllm.model_executor.models.config import ( - MODELS_CONFIG_MAP, HybridAttentionMambaModelConfig) - cls = MODELS_CONFIG_MAP.get(architecture, None) - if cls is not None: - cls.verify_and_update_config(self) - - if self.model_config.is_hybrid: - HybridAttentionMambaModelConfig.verify_and_update_config(self) - - if self.model_config.convert_type == "classify": - # Maybe convert ForCausalLM into ForSequenceClassification model. - from vllm.model_executor.models.adapters import ( - SequenceClassificationConfig) - SequenceClassificationConfig.verify_and_update_config(self) - - if hasattr(self.model_config, "model_weights") and is_runai_obj_uri( - self.model_config.model_weights): - if self.load_config.load_format == "auto": - logger.info("Detected Run:ai model config. " - "Overriding `load_format` to 'runai_streamer'") - self.load_config.load_format = "runai_streamer" - elif self.load_config.load_format != "runai_streamer": - raise ValueError(f"To load a model from S3, 'load_format' " - f"must be 'runai_streamer', " - f"but got '{self.load_config.load_format}'. " - f"Model: {self.model_config.model}") - - def compile_debug_dump_path(self) -> Optional[Path]: - """Returns a rank-aware path for dumping - torch.compile debug information. - """ - if self.compilation_config.debug_dump_path is None: - return None - tp_rank = self.parallel_config.rank - dp_rank = self.parallel_config.data_parallel_rank - data_parallel_size = self.parallel_config.data_parallel_size - append_path = f"rank_{tp_rank}" if data_parallel_size == 1 \ - else f"rank_{tp_rank}_dp_{dp_rank}" - path = self.compilation_config.debug_dump_path / append_path - return path - - def __str__(self): - return ( - f"model={self.model_config.model!r}, " - f"speculative_config={self.speculative_config!r}, " - f"tokenizer={self.model_config.tokenizer!r}, " - f"skip_tokenizer_init={self.model_config.skip_tokenizer_init}, " - f"tokenizer_mode={self.model_config.tokenizer_mode}, " - f"revision={self.model_config.revision}, " - f"tokenizer_revision={self.model_config.tokenizer_revision}, " - f"trust_remote_code={self.model_config.trust_remote_code}, " - f"dtype={self.model_config.dtype}, " - f"max_seq_len={self.model_config.max_model_len}, " - f"download_dir={self.load_config.download_dir!r}, " - f"load_format={self.load_config.load_format}, " - f"tensor_parallel_size={self.parallel_config.tensor_parallel_size}, " # noqa - f"pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, " # noqa - f"data_parallel_size={self.parallel_config.data_parallel_size}, " # noqa - f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, " # noqa - f"quantization={self.model_config.quantization}, " - f"enforce_eager={self.model_config.enforce_eager}, " - f"kv_cache_dtype={self.cache_config.cache_dtype}, " - f"device_config={self.device_config.device}, " - f"structured_outputs_config={self.structured_outputs_config!r}, " - f"observability_config={self.observability_config!r}, " - f"seed={self.model_config.seed}, " - f"served_model_name={self.model_config.served_model_name}, " - f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, " - f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, " # noqa - f"pooler_config={self.model_config.pooler_config!r}, " - f"compilation_config={self.compilation_config!r}") - - -_current_vllm_config: Optional[VllmConfig] = None -_current_prefix: Optional[str] = None - - -@contextmanager -def set_current_vllm_config(vllm_config: VllmConfig, - check_compile=False, - prefix: Optional[str] = None): - """ - Temporarily set the current vLLM config. - Used during model initialization. - We save the current vLLM config in a global variable, - so that all modules can access it, e.g. custom ops - can access the vLLM config to determine how to dispatch. - """ - global _current_vllm_config, _current_prefix - old_vllm_config = _current_vllm_config - old_prefix = _current_prefix - from vllm.compilation.counter import compilation_counter - num_models_seen = compilation_counter.num_models_seen - try: - _current_vllm_config = vllm_config - _current_prefix = prefix - yield - except Exception: - raise - else: - if check_compile: - vllm_config.compilation_config.custom_op_log_check() - - if check_compile and \ - vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \ - and compilation_counter.num_models_seen == num_models_seen: - # If the model supports compilation, - # compilation_counter.num_models_seen should be increased - # by at least 1. - # If it is not increased, it means the model does not support - # compilation (does not have @support_torch_compile decorator). - logger.warning( - "`torch.compile` is turned on, but the model %s" - " does not support it. Please open an issue on GitHub" - " if you want it to be supported.", - vllm_config.model_config.model) - finally: - _current_vllm_config = old_vllm_config - _current_prefix = old_prefix - # Clear the compilation config cache when context changes - get_cached_compilation_config.cache_clear() - - -@lru_cache(maxsize=1) -def get_cached_compilation_config(): - """Cache config to avoid repeated calls to get_current_vllm_config()""" - return get_current_vllm_config().compilation_config - - -def get_current_vllm_config() -> VllmConfig: - if _current_vllm_config is None: - # in ci, usually when we test custom ops/modules directly, - # we don't set the vllm config. In that case, we set a default - # config. - logger.warning("Current vLLM config is not set.") - from vllm.config import VllmConfig - return VllmConfig() - return _current_vllm_config - - -def get_current_model_prefix() -> str: - """ - Get the prefix of the model that's currently being initialized. - """ - assert _current_prefix is not None, \ - "Current model prefix is not set. " - return _current_prefix - - -T = TypeVar("T") - - -def get_layers_from_vllm_config( - vllm_config: VllmConfig, - layer_type: type[T], - layer_names: Optional[list[str]] = None) -> dict[str, T]: - """ - Get layers from the vLLM config. - - Args: - vllm_config: The vLLM config. - layer_type: The type of the layer to get. - layer_names: The names of the layers to get. If None, return all layers. - """ - - if layer_names is None: - layer_names = list( - vllm_config.compilation_config.static_forward_context.keys()) - - forward_context = vllm_config.compilation_config.static_forward_context - - return { - layer_name: forward_context[layer_name] - for layer_name in layer_names - if isinstance(forward_context[layer_name], layer_type) - } - - -def update_config(config: DataclassInstanceT, - overrides: dict[str, Any]) -> DataclassInstanceT: - processed_overrides = {} - for field_name, value in overrides.items(): - assert hasattr( - config, field_name), f"{type(config)} has no field `{field_name}`" - current_value = getattr(config, field_name) - if is_dataclass(current_value) and not is_dataclass(value): - assert isinstance(value, dict), ( - f"Overrides to {type(config)}.{field_name} must be a dict" - f" or {type(current_value)}, but got {type(value)}") - value = update_config( - current_value, # type: ignore[type-var] - value) - processed_overrides[field_name] = value - return replace(config, **processed_overrides) +from vllm.config.utils import (ConfigType, SupportsMetricsInfo, config, + get_attr_docs, is_init_field, update_config) +from vllm.config.vllm import (VllmConfig, get_cached_compilation_config, + get_current_vllm_config, + get_layers_from_vllm_config, + set_current_vllm_config) + +__all__ = [ + # From vllm.config.cache + "BlockSize", + "CacheConfig", + "CacheDType", + "MambaDType", + "PrefixCachingHashAlgo", + # From vllm.config.compilation + "CompilationConfig", + "CompilationLevel", + "CUDAGraphMode", + "PassConfig", + # From vllm.config.device + "Device", + "DeviceConfig", + # From vllm.config.kv_events + "KVEventsConfig", + # From vllm.config.kv_transfer + "KVTransferConfig", + # From vllm.config.load + "LoadConfig", + # From vllm.config.lora + "LoRAConfig", + # From vllm.config.model + "ConvertOption", + "HfOverrides", + "LogprobsMode", + "ModelConfig", + "ModelDType", + "ModelImpl", + "RunnerOption", + "TaskOption", + "TokenizerMode", + "iter_architecture_defaults", + "try_match_architecture_defaults", + # From vllm.config.multimodal + "MMCacheType", + "MMEncoderTPMode", + "MultiModalConfig", + # From vllm.config.observability + "DetailedTraceModules", + "ObservabilityConfig", + # From vllm.config.parallel + "DistributedExecutorBackend", + "EPLBConfig", + "ParallelConfig", + # From vllm.config.pooler + "PoolerConfig", + # From vllm.config.scheduler + "RunnerType", + "SchedulerConfig", + "SchedulerPolicy", + # From vllm.config.speculative + "SpeculativeConfig", + # From vllm.config.speech_to_text + "SpeechToTextConfig", + # From vllm.config.structured_outputs + "StructuredOutputsConfig", + # From vllm.config.utils + "ConfigType", + "SupportsMetricsInfo", + "config", + "get_attr_docs", + "is_init_field", + "update_config", + # From vllm.config.vllm + "VllmConfig", + "get_cached_compilation_config", + "get_current_vllm_config", + "set_current_vllm_config", + "get_layers_from_vllm_config", +] diff --git a/vllm/config/utils.py b/vllm/config/utils.py index 91e61b330273..2da30cbf149c 100644 --- a/vllm/config/utils.py +++ b/vllm/config/utils.py @@ -1,21 +1,21 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project - +"""Utility functions for vLLM config dataclasses.""" import ast import inspect import textwrap -from dataclasses import MISSING, Field, field, fields, is_dataclass -from typing import TYPE_CHECKING, Any, TypeVar +from dataclasses import MISSING, Field, field, fields, is_dataclass, replace +from typing import TYPE_CHECKING, Any, Protocol, TypeVar import regex as re +from typing_extensions import runtime_checkable if TYPE_CHECKING: from _typeshed import DataclassInstance - - ConfigType = type[DataclassInstance] else: - ConfigType = type + DataclassInstance = Any +ConfigType = type[DataclassInstance] ConfigT = TypeVar("ConfigT", bound=ConfigType) @@ -143,3 +143,33 @@ def pairwise(iterable): def is_init_field(cls: ConfigType, name: str) -> bool: return next(f for f in fields(cls) if f.name == name).init + + +@runtime_checkable +class SupportsHash(Protocol): + + def compute_hash(self) -> str: + ... + + +class SupportsMetricsInfo(Protocol): + + def metrics_info(self) -> dict[str, str]: + ... + + +def update_config(config: ConfigT, overrides: dict[str, Any]) -> ConfigT: + processed_overrides = {} + for field_name, value in overrides.items(): + assert hasattr( + config, field_name), f"{type(config)} has no field `{field_name}`" + current_value = getattr(config, field_name) + if is_dataclass(current_value) and not is_dataclass(value): + assert isinstance(value, dict), ( + f"Overrides to {type(config)}.{field_name} must be a dict" + f" or {type(current_value)}, but got {type(value)}") + value = update_config( + current_value, # type: ignore[type-var] + value) + processed_overrides[field_name] = value + return replace(config, **processed_overrides) diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py new file mode 100644 index 000000000000..7336f5756527 --- /dev/null +++ b/vllm/config/vllm.py @@ -0,0 +1,789 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import copy +import hashlib +import json +import os +from contextlib import contextmanager +from dataclasses import field, replace +from functools import lru_cache +from pathlib import Path +from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union + +import torch +from pydantic import ConfigDict +from pydantic.dataclasses import dataclass + +import vllm.envs as envs +from vllm.logger import init_logger +from vllm.transformers_utils.runai_utils import is_runai_obj_uri +from vllm.utils import random_uuid + +from .cache import CacheConfig +from .compilation import CompilationConfig, CompilationLevel, CUDAGraphMode +from .device import DeviceConfig +from .kv_events import KVEventsConfig +from .kv_transfer import KVTransferConfig +from .load import LoadConfig +from .lora import LoRAConfig +from .model import ModelConfig +from .observability import ObservabilityConfig +from .parallel import ParallelConfig +from .scheduler import SchedulerConfig +from .speculative import SpeculativeConfig +from .structured_outputs import StructuredOutputsConfig +from .utils import SupportsHash, config + +if TYPE_CHECKING: + from transformers import PretrainedConfig + + from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) +else: + PretrainedConfig = Any + + QuantizationConfig = Any + +logger = init_logger(__name__) + + +@config +@dataclass(config=ConfigDict(arbitrary_types_allowed=True)) +class VllmConfig: + """Dataclass which contains all vllm-related configuration. This + simplifies passing around the distinct configurations in the codebase. + """ + + # TODO: use default_factory once default constructing ModelConfig doesn't + # try to download a model + model_config: ModelConfig = None # type: ignore + """Model configuration.""" + cache_config: CacheConfig = field(default_factory=CacheConfig) + """Cache configuration.""" + parallel_config: ParallelConfig = field(default_factory=ParallelConfig) + """Parallel configuration.""" + scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig) + """Scheduler configuration.""" + device_config: DeviceConfig = field(default_factory=DeviceConfig) + """Device configuration.""" + load_config: LoadConfig = field(default_factory=LoadConfig) + """Load configuration.""" + lora_config: Optional[LoRAConfig] = None + """LoRA configuration.""" + speculative_config: Optional[SpeculativeConfig] = None + """Speculative decoding configuration.""" + structured_outputs_config: StructuredOutputsConfig = field( + default_factory=StructuredOutputsConfig) + """Structured outputs configuration.""" + observability_config: Optional[ObservabilityConfig] = None + """Observability configuration.""" + quant_config: Optional[QuantizationConfig] = None + """Quantization configuration.""" + compilation_config: CompilationConfig = field( + default_factory=CompilationConfig) + """`torch.compile` and cudagraph capture configuration for the model. + + As a shorthand, `-O` can be used to directly specify the compilation + level `n`: `-O3` is equivalent to `-O.level=3` (same as `-O='{"level":3}'`). + Currently, -O and -O= are supported as well but this will likely be + removed in favor of clearer -O syntax in the future. + + NOTE: level 0 is the default level without any optimization. level 1 and 2 + are for internal testing only. level 3 is the recommended level for + production, also default in V1. + + You can specify the full compilation config like so: + `{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}` + """ + kv_transfer_config: Optional[KVTransferConfig] = None + """The configurations for distributed KV cache transfer.""" + kv_events_config: Optional[KVEventsConfig] = None + """The configurations for event publishing.""" + # some opaque config, only used to provide additional information + # for the hash computation, mainly used for testing, debugging or out of + # tree config registration. + additional_config: Union[dict, SupportsHash] = field(default_factory=dict) + """Additional config for specified platform. Different platforms may + support different configs. Make sure the configs are valid for the platform + you are using. Contents must be hashable.""" + instance_id: str = "" + """The ID of the vLLM instance.""" + + def compute_hash(self) -> str: + """ + WARNING: Whenever a new field is added to this config, + ensure that it is included in the factors list if + it affects the computation graph. + + Provide a hash that uniquely identifies all the configs + that affect the structure of the computation + graph from input ids/embeddings to the final hidden states, + excluding anything before input ids/embeddings and after + the final hidden states. + """ + factors: list[Any] = [] + + # summarize vllm config + vllm_factors: list[Any] = [] + from vllm import __version__ + vllm_factors.append(__version__) + vllm_factors.append(envs.VLLM_USE_V1) + if self.model_config: + vllm_factors.append(self.model_config.compute_hash()) + else: + vllm_factors.append("None") + if self.cache_config: + vllm_factors.append(self.cache_config.compute_hash()) + else: + vllm_factors.append("None") + if self.parallel_config: + vllm_factors.append(self.parallel_config.compute_hash()) + else: + vllm_factors.append("None") + if self.scheduler_config: + vllm_factors.append(self.scheduler_config.compute_hash()) + else: + vllm_factors.append("None") + if self.device_config: + vllm_factors.append(self.device_config.compute_hash()) + else: + vllm_factors.append("None") + if self.load_config: + vllm_factors.append(self.load_config.compute_hash()) + else: + vllm_factors.append("None") + if self.lora_config: + vllm_factors.append(self.lora_config.compute_hash()) + # LoRA creates static buffers based on max_num_batched_tokens. + # The tensor sizes and strides get captured in the torch.compile + # graph explicitly. + vllm_factors.append( + str(self.scheduler_config.max_num_batched_tokens)) + else: + vllm_factors.append("None") + if self.speculative_config: + vllm_factors.append(self.speculative_config.compute_hash()) + else: + vllm_factors.append("None") + if self.structured_outputs_config: + vllm_factors.append(self.structured_outputs_config.compute_hash()) + else: + vllm_factors.append("None") + if self.observability_config: + vllm_factors.append(self.observability_config.compute_hash()) + else: + vllm_factors.append("None") + if self.quant_config: + pass # should be captured by model_config.quantization + if self.compilation_config: + vllm_factors.append(self.compilation_config.compute_hash()) + else: + vllm_factors.append("None") + if self.kv_transfer_config: + vllm_factors.append(self.kv_transfer_config.compute_hash()) + else: + vllm_factors.append("None") + if self.additional_config: + if isinstance(additional_config := self.additional_config, dict): + additional_config_hash = hashlib.md5( + json.dumps(additional_config, sort_keys=True).encode(), + usedforsecurity=False, + ).hexdigest() + else: + additional_config_hash = additional_config.compute_hash() + vllm_factors.append(additional_config_hash) + else: + vllm_factors.append("None") + factors.append(vllm_factors) + + hash_str = hashlib.md5(str(factors).encode(), + usedforsecurity=False).hexdigest()[:10] + return hash_str + + def pad_for_cudagraph(self, batch_size: int) -> int: + # if batch_size > self.compilation_config.max_capture_size, + # it should raise an IndexError. + # the caller should make sure the batch_size is within the range, + # i.e., batch_size <= self.compilation_config.max_capture_size + return self.compilation_config.bs_to_padded_graph_size[batch_size] + + @staticmethod + def _get_quantization_config( + model_config: ModelConfig, + load_config: LoadConfig) -> Optional[QuantizationConfig]: + """Get the quantization config.""" + from vllm.platforms import current_platform + if model_config.quantization is not None: + from vllm.model_executor.model_loader.weight_utils import ( + get_quant_config) + quant_config = get_quant_config(model_config, load_config) + capability_tuple = current_platform.get_device_capability() + + if capability_tuple is not None: + capability = capability_tuple.to_int() + if capability < quant_config.get_min_capability(): + raise ValueError( + f"The quantization method {model_config.quantization} " + "is not supported for the current GPU. Minimum " + f"capability: {quant_config.get_min_capability()}. " + f"Current capability: {capability}.") + supported_dtypes = quant_config.get_supported_act_dtypes() + if model_config.dtype not in supported_dtypes: + raise ValueError( + f"{model_config.dtype} is not supported for quantization " + f"method {model_config.quantization}. Supported dtypes: " + f"{supported_dtypes}") + quant_config.maybe_update_config(model_config.model) + return quant_config + return None + + @staticmethod + def get_quantization_config( + model_config: ModelConfig, + load_config: LoadConfig) -> Optional[QuantizationConfig]: + import copy + + # For some reason, the _ version of this modifies the model_config + # object, so using deepcopy to avoid this problem. + return VllmConfig._get_quantization_config(copy.deepcopy(model_config), + load_config) + + def with_hf_config( + self, + hf_config: PretrainedConfig, + architectures: Optional[list[str]] = None, + ) -> "VllmConfig": + if architectures is not None: + hf_config = copy.deepcopy(hf_config) + hf_config.architectures = architectures + + model_config = copy.deepcopy(self.model_config) + model_config.hf_config = hf_config + + return replace(self, model_config=model_config) + + def __post_init__(self): + """Verify configs are valid & consistent with each other. + """ + + self.try_verify_and_update_config() + + if self.model_config is not None: + self.model_config.verify_with_parallel_config(self.parallel_config) + self.model_config.verify_dual_chunk_attention_config( + self.load_config) + + self.cache_config.verify_with_parallel_config(self.parallel_config) + + if self.lora_config is not None: + self.lora_config.verify_with_cache_config(self.cache_config) + self.lora_config.verify_with_model_config(self.model_config) + + if self.quant_config is None and self.model_config is not None: + self.quant_config = VllmConfig._get_quantization_config( + self.model_config, self.load_config) + + from vllm.platforms import current_platform + if self.model_config is not None and \ + self.scheduler_config.chunked_prefill_enabled and \ + self.model_config.dtype == torch.float32 and \ + current_platform.get_device_capability() == (7, 5): + logger.warning_once( + "Turing devices tensor cores do not support float32 matmul. " + "To workaround this limitation, vLLM will set 'ieee' input " + "precision for chunked prefill triton kernels.") + + # If the user does not explicitly set a compilation level, then + # we use the default level. The default level depends on other + # settings (see the below code). + if self.compilation_config.level is None: + if envs.VLLM_USE_V1: + if (self.model_config is not None + and not self.model_config.enforce_eager): + self.compilation_config.level = CompilationLevel.PIECEWISE + else: + self.compilation_config.level = \ + CompilationLevel.NO_COMPILATION + + else: + # NB: Passing both --enforce-eager and a compilation level + # in V0 means the compilation level wins out. + self.compilation_config.level = CompilationLevel.NO_COMPILATION + + # async tp is built on top of sequence parallelism + # and requires it to be enabled. + if self.compilation_config.pass_config.enable_async_tp: + self.compilation_config.pass_config.enable_sequence_parallelism = \ + True + if self.compilation_config.pass_config.enable_sequence_parallelism: + self.compilation_config.custom_ops.append("+rms_norm") + + if current_platform.support_static_graph_mode(): + # if cudagraph_mode is not explicitly set by users, set default + # value + if self.compilation_config.cudagraph_mode is None: + if envs.VLLM_USE_V1 and self.compilation_config.level \ + == CompilationLevel.PIECEWISE: + # default to full and piecewise for most models + self.compilation_config.cudagraph_mode = \ + CUDAGraphMode.FULL_AND_PIECEWISE + + # pooling models and encoder-decoder models + # do not support full cudagraphs + if self.model_config is not None and \ + (self.model_config.pooler_config is not None + or self.model_config.is_encoder_decoder): + self.compilation_config.cudagraph_mode = \ + CUDAGraphMode.PIECEWISE + else: + self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE + + # disable cudagraph when enforce eager execution + if self.model_config is not None and \ + self.model_config.enforce_eager: + logger.info("Cudagraph is disabled under eager mode") + self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE + elif envs.VLLM_USE_V1: + self.compilation_config.cudagraph_num_of_warmups = 1 + + self._set_cudagraph_sizes() + else: + self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE + + if self.cache_config.kv_sharing_fast_prefill: + + if self.speculative_config is not None and \ + self.speculative_config.use_eagle(): + raise NotImplementedError( + "Fast prefill optimization for KV sharing is not " + "compatible with EAGLE as EAGLE requires correct logits " + "for all tokens while fast prefill gives incorrect logits " + "for prompt tokens.") + + logger.warning_once( + "--kv-sharing-fast-prefill requires changes on model side for " + "correctness and to realize prefill savings. ") + + disable_chunked_prefill_reasons: list[str] = [] + + if self.model_config: + if self.model_config.pooler_config: + pooling_type = self.model_config.pooler_config.pooling_type + if pooling_type is None or pooling_type.lower() != "last": + disable_chunked_prefill_reasons.append( + "Only \"last\" pooling supports chunked " + "prefill and prefix caching; disabling both.") + if not getattr(self.model_config.hf_config, "is_causal", True): + disable_chunked_prefill_reasons.append( + "Only models using causal attention supports chunked " + "prefill and prefix caching; disabling both.") + elif self.model_config.is_encoder_decoder: + from vllm.multimodal import MULTIMODAL_REGISTRY + self.scheduler_config.max_num_encoder_input_tokens = \ + MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(self.model_config) + logger.debug( + "Encoder-decoder model detected: setting " + "`max_num_encoder_input_tokens` to encoder length (%s)", + self.scheduler_config.max_num_encoder_input_tokens) + self.scheduler_config.disable_chunked_mm_input = True + disable_chunked_prefill_reasons.append( + "Encoder-decoder models do not support chunked prefill nor" + " prefix caching; disabling both.") + if (self.model_config.architecture + == "WhisperForConditionalGeneration" + and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") + != "spawn"): + logger.warning( + "Whisper is known to have issues with " + "forked workers. If startup is hanging, " + "try setting 'VLLM_WORKER_MULTIPROC_METHOD' " + "to 'spawn'.") + + if disable_chunked_prefill_reasons: + for reason in disable_chunked_prefill_reasons: + logger.info(reason) + self.scheduler_config.chunked_prefill_enabled = False + self.scheduler_config.long_prefill_token_threshold = 0 + + if self.cache_config is not None: + self.cache_config.enable_prefix_caching = False + + if (self.kv_events_config is not None + and self.kv_events_config.enable_kv_cache_events + and not self.cache_config.enable_prefix_caching): + logger.warning( + "KV cache events are on, but prefix caching is not enabled." + "Use --enable-prefix-caching to enable.") + if (self.kv_events_config is not None + and self.kv_events_config.publisher != "null" + and not self.kv_events_config.enable_kv_cache_events): + logger.warning("KV cache events are disabled," + "but the scheduler is configured to publish them." + "Modify KVEventsConfig.enable_kv_cache_events" + "to True to enable.") + current_platform.check_and_update_config(self) + + # Do this after all the updates to compilation_config.level + if envs.VLLM_USE_V1 and \ + self.compilation_config.level == CompilationLevel.PIECEWISE: + self.compilation_config.set_splitting_ops_for_v1() + + # final check of cudagraph mode after all possible updates + if envs.VLLM_USE_V1 and current_platform.is_cuda_alike(): + if self.compilation_config.cudagraph_mode.has_full_cudagraphs()\ + and self.model_config is not None and \ + not self.model_config.disable_cascade_attn and\ + not self.compilation_config.cudagraph_mode.\ + has_piecewise_cudagraphs(): + logger.warning_once( + "No piecewise cudagraph for executing cascade attention." + " Will fall back to eager execution if a batch runs " + "into cascade attentions") + + if self.compilation_config.cudagraph_mode\ + .requires_piecewise_compilation(): + assert self.compilation_config.level == \ + CompilationLevel.PIECEWISE, \ + "Compilation level should be CompilationLevel.PIECEWISE "\ + "when cudagraph_mode piecewise cudagraphs is used, "\ + f"cudagraph_mode={self.compilation_config.cudagraph_mode}" + + # final migrate the deprecated flags + self.compilation_config.use_cudagraph = self.compilation_config.\ + cudagraph_mode!= CUDAGraphMode.NONE + self.compilation_config.full_cuda_graph = self.compilation_config.\ + cudagraph_mode.has_full_cudagraphs() + + if self.parallel_config.enable_dbo: + a2a_backend = envs.VLLM_ALL2ALL_BACKEND + assert a2a_backend in \ + ["deepep_low_latency", "deepep_high_throughput"], \ + "Microbatching currently only supports the deepep_low_latency and "\ + f"deepep_high_throughput all2all backend. {a2a_backend} is not "\ + "supported. To fix set the VLLM_ALL2ALL_BACKEND environment "\ + "variable to deepep_low_latency or deepep_high_throughput and "\ + "install the DeepEP kernels." + + if not self.model_config.disable_cascade_attn: + self.model_config.disable_cascade_attn = True + logger.warning_once( + "Disabling cascade attention when DBO is enabled.") + + if not self.instance_id: + self.instance_id = random_uuid()[:5] + + if (envs.VLLM_USE_V1 + and not self.scheduler_config.disable_hybrid_kv_cache_manager): + # logger should only print warning message for hybrid models. As we + # can't know whether the model is hybrid or not now, so we don't log + # warning message here and will log it later. + if not current_platform.support_hybrid_kv_cache(): + # Hybrid KV cache manager is not supported on non-GPU platforms. + self.scheduler_config.disable_hybrid_kv_cache_manager = True + if self.kv_transfer_config is not None: + # Hybrid KV cache manager is not compatible with KV transfer. + self.scheduler_config.disable_hybrid_kv_cache_manager = True + if self.kv_events_config is not None: + # Hybrid KV cache manager is not compatible with KV events. + self.scheduler_config.disable_hybrid_kv_cache_manager = True + if self.model_config is not None and \ + self.model_config.attention_chunk_size is not None: + if self.speculative_config is not None and \ + self.speculative_config.use_eagle(): + # Hybrid KV cache manager is not yet supported with chunked + # local attention + eagle. + self.scheduler_config.disable_hybrid_kv_cache_manager = True + elif \ + not envs.VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: + logger.warning( + "There is a latency regression when using chunked local" + " attention with the hybrid KV cache manager. Disabling" + " it, by default. To enable it, set the environment " + "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1." + ) + # Hybrid KV cache manager is not yet supported with chunked + # local attention. + self.scheduler_config.disable_hybrid_kv_cache_manager = True + + if self.compilation_config.debug_dump_path: + self.compilation_config.debug_dump_path = \ + self.compilation_config.debug_dump_path.absolute().expanduser() + if envs.VLLM_DEBUG_DUMP_PATH is not None: + env_path = Path(envs.VLLM_DEBUG_DUMP_PATH).absolute().expanduser() + if self.compilation_config.debug_dump_path: + logger.warning( + "Config-specified debug dump path is overridden" + " by VLLM_DEBUG_DUMP_PATH to %s", env_path) + self.compilation_config.debug_dump_path = env_path + + def update_sizes_for_sequence_parallelism(self, + possible_sizes: list) -> list: + # remove the sizes that not multiple of tp_size when + # enable sequence parallelism + removed_sizes = [ + size for size in possible_sizes + if size % self.parallel_config.tensor_parallel_size != 0 + ] + if removed_sizes: + logger.warning( + "Batch sizes %s are removed because they are not " + "multiple of tp_size %d when " + "sequence parallelism is enabled", removed_sizes, + self.parallel_config.tensor_parallel_size) + + return [ + size for size in possible_sizes + if size % self.parallel_config.tensor_parallel_size == 0 + ] + + def _set_cudagraph_sizes(self): + """ + vLLM defines the default candidate list of batch sizes for CUDA graph + capture as: + + ```python + max_graph_size = min(max_num_seqs * 2, 512) + # 1, 2, 4, then multiples of 8 up to max_graph_size + cuda_graph_sizes = [1, 2, 4, 8, 16, 24, 32, 40, ..., max_graph_size] + + In the end, `vllm_config.compilation_config.cudagraph_capture_sizes` + will be the final sizes to capture cudagraph (in descending order). + + These sizes are used to capture and reuse CUDA graphs for + performance-critical paths (e.g., decoding). Capturing enables + significantly faster kernel dispatch by avoiding Python overhead. The + list is then filtered based on `max_num_batched_tokens` (e.g., 8192 on + most GPUs), which controls the total allowed number of tokens in a + batch. Since each sequence may have a variable number of tokens, the + maximum usable batch size will depend on actual sequence lengths. + + Example: + With `max_num_batched_tokens = 8192`, and typical sequences + averaging ~32 tokens, most practical batch sizes fall below 256. + However, the system will still allow capture sizes up to 512 if + shape and memory permit. + + Note: + If users explicitly specify cudagraph capture sizes in the + compilation config, those will override this default logic. + At runtime: + + - If batch size <= one of the `cudagraph_capture_sizes`, the closest + padded CUDA graph will be used. + - If batch size > largest `cudagraph_capture_sizes`, cudagraph will + not be used. + """ + + # calculate the default `batch_size_capture_list` + batch_size_capture_list = [] + if self.model_config is not None and \ + not self.model_config.enforce_eager: + cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes + if len(cuda_graph_sizes) == 1: + batch_size_capture_list = [1, 2, 4] + [ + i for i in range(8, cuda_graph_sizes[0] + 1, 8) + ] + elif len(cuda_graph_sizes) > 1: + batch_size_capture_list = sorted(cuda_graph_sizes) + else: + raise TypeError(f"Invalid value for {cuda_graph_sizes=}.") + if self.parallel_config.tensor_parallel_size > 1 and \ + self.compilation_config.pass_config.enable_sequence_parallelism: + batch_size_capture_list = \ + self.update_sizes_for_sequence_parallelism(batch_size_capture_list) + max_num_tokens = self.scheduler_config.max_num_batched_tokens + batch_size_capture_list = [ + size for size in batch_size_capture_list + if size <= max_num_tokens + ] + + self.compilation_config.init_with_cudagraph_sizes( + batch_size_capture_list) + + def recalculate_max_model_len(self, max_model_len: int): + # Can only be called in try_verify_and_update_config + model_config = self.model_config + max_model_len = model_config.get_and_verify_max_len(max_model_len) + self.model_config.max_model_len = max_model_len + self.scheduler_config.max_model_len = max_model_len + + def try_verify_and_update_config(self): + if self.model_config is None: + return + + # Avoid running try_verify_and_update_config multiple times + if getattr(self.model_config, "config_updated", False): + return + self.model_config.config_updated = True + + architecture = self.model_config.architecture + if architecture is None: + return + + from vllm.model_executor.models.config import ( + MODELS_CONFIG_MAP, HybridAttentionMambaModelConfig) + cls = MODELS_CONFIG_MAP.get(architecture, None) + if cls is not None: + cls.verify_and_update_config(self) + + if self.model_config.is_hybrid: + HybridAttentionMambaModelConfig.verify_and_update_config(self) + + if self.model_config.convert_type == "classify": + # Maybe convert ForCausalLM into ForSequenceClassification model. + from vllm.model_executor.models.adapters import ( + SequenceClassificationConfig) + SequenceClassificationConfig.verify_and_update_config(self) + + if hasattr(self.model_config, "model_weights") and is_runai_obj_uri( + self.model_config.model_weights): + if self.load_config.load_format == "auto": + logger.info("Detected Run:ai model config. " + "Overriding `load_format` to 'runai_streamer'") + self.load_config.load_format = "runai_streamer" + elif self.load_config.load_format != "runai_streamer": + raise ValueError(f"To load a model from S3, 'load_format' " + f"must be 'runai_streamer', " + f"but got '{self.load_config.load_format}'. " + f"Model: {self.model_config.model}") + + def compile_debug_dump_path(self) -> Optional[Path]: + """Returns a rank-aware path for dumping + torch.compile debug information. + """ + if self.compilation_config.debug_dump_path is None: + return None + tp_rank = self.parallel_config.rank + dp_rank = self.parallel_config.data_parallel_rank + data_parallel_size = self.parallel_config.data_parallel_size + append_path = f"rank_{tp_rank}" if data_parallel_size == 1 \ + else f"rank_{tp_rank}_dp_{dp_rank}" + path = self.compilation_config.debug_dump_path / append_path + return path + + def __str__(self): + return ( + f"model={self.model_config.model!r}, " + f"speculative_config={self.speculative_config!r}, " + f"tokenizer={self.model_config.tokenizer!r}, " + f"skip_tokenizer_init={self.model_config.skip_tokenizer_init}, " + f"tokenizer_mode={self.model_config.tokenizer_mode}, " + f"revision={self.model_config.revision}, " + f"tokenizer_revision={self.model_config.tokenizer_revision}, " + f"trust_remote_code={self.model_config.trust_remote_code}, " + f"dtype={self.model_config.dtype}, " + f"max_seq_len={self.model_config.max_model_len}, " + f"download_dir={self.load_config.download_dir!r}, " + f"load_format={self.load_config.load_format}, " + f"tensor_parallel_size={self.parallel_config.tensor_parallel_size}, " # noqa + f"pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, " # noqa + f"data_parallel_size={self.parallel_config.data_parallel_size}, " # noqa + f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, " # noqa + f"quantization={self.model_config.quantization}, " + f"enforce_eager={self.model_config.enforce_eager}, " + f"kv_cache_dtype={self.cache_config.cache_dtype}, " + f"device_config={self.device_config.device}, " + f"structured_outputs_config={self.structured_outputs_config!r}, " + f"observability_config={self.observability_config!r}, " + f"seed={self.model_config.seed}, " + f"served_model_name={self.model_config.served_model_name}, " + f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, " + f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, " # noqa + f"pooler_config={self.model_config.pooler_config!r}, " + f"compilation_config={self.compilation_config!r}") + + +_current_vllm_config: Optional[VllmConfig] = None +_current_prefix: Optional[str] = None + + +@contextmanager +def set_current_vllm_config(vllm_config: VllmConfig, + check_compile=False, + prefix: Optional[str] = None): + """ + Temporarily set the current vLLM config. + Used during model initialization. + We save the current vLLM config in a global variable, + so that all modules can access it, e.g. custom ops + can access the vLLM config to determine how to dispatch. + """ + global _current_vllm_config, _current_prefix + old_vllm_config = _current_vllm_config + old_prefix = _current_prefix + from vllm.compilation.counter import compilation_counter + num_models_seen = compilation_counter.num_models_seen + try: + _current_vllm_config = vllm_config + _current_prefix = prefix + yield + except Exception: + raise + else: + if check_compile: + vllm_config.compilation_config.custom_op_log_check() + + if check_compile and \ + vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \ + and compilation_counter.num_models_seen == num_models_seen: + # If the model supports compilation, + # compilation_counter.num_models_seen should be increased + # by at least 1. + # If it is not increased, it means the model does not support + # compilation (does not have @support_torch_compile decorator). + logger.warning( + "`torch.compile` is turned on, but the model %s" + " does not support it. Please open an issue on GitHub" + " if you want it to be supported.", + vllm_config.model_config.model) + finally: + _current_vllm_config = old_vllm_config + _current_prefix = old_prefix + # Clear the compilation config cache when context changes + get_cached_compilation_config.cache_clear() + + +@lru_cache(maxsize=1) +def get_cached_compilation_config(): + """Cache config to avoid repeated calls to get_current_vllm_config()""" + return get_current_vllm_config().compilation_config + + +def get_current_vllm_config() -> VllmConfig: + if _current_vllm_config is None: + # in ci, usually when we test custom ops/modules directly, + # we don't set the vllm config. In that case, we set a default + # config. + logger.warning("Current vLLM config is not set.") + return VllmConfig() + return _current_vllm_config + + +T = TypeVar("T") + + +def get_layers_from_vllm_config( + vllm_config: VllmConfig, + layer_type: type[T], + layer_names: Optional[list[str]] = None) -> dict[str, T]: + """ + Get layers from the vLLM config. + + Args: + vllm_config: The vLLM config. + layer_type: The type of the layer to get. + layer_names: The names of the layers to get. If None, return all layers. + """ + + if layer_names is None: + layer_names = list( + vllm_config.compilation_config.static_forward_context.keys()) + + forward_context = vllm_config.compilation_config.static_forward_context + + return { + layer_name: forward_context[layer_name] + for layer_name in layer_names + if isinstance(forward_context[layer_name], layer_type) + } diff --git a/vllm/model_executor/layers/mamba/linear_attn.py b/vllm/model_executor/layers/mamba/linear_attn.py index 410cbef4f6bc..319133777992 100644 --- a/vllm/model_executor/layers/mamba/linear_attn.py +++ b/vllm/model_executor/layers/mamba/linear_attn.py @@ -29,8 +29,7 @@ from vllm.model_executor.layers.mamba.abstract import MambaBase from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateDtypeCalculator, MambaStateShapeCalculator) -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.utils import direct_register_custom_op from vllm.v1.attention.backends.linear_attn import LinearAttentionMetadata diff --git a/vllm/model_executor/layers/quantization/auto_round.py b/vllm/model_executor/layers/quantization/auto_round.py index bf5141fa4894..eb7600af3371 100644 --- a/vllm/model_executor/layers/quantization/auto_round.py +++ b/vllm/model_executor/layers/quantization/auto_round.py @@ -9,9 +9,8 @@ from vllm.logger import init_logger from vllm.model_executor.layers.linear import (LinearBase, UnquantizedLinearMethod) -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.platforms import current_platform from vllm.scalar_type import scalar_types diff --git a/vllm/model_executor/layers/quantization/bitblas.py b/vllm/model_executor/layers/quantization/bitblas.py index d05c0c0d5473..81e51f4a4358 100644 --- a/vllm/model_executor/layers/quantization/bitblas.py +++ b/vllm/model_executor/layers/quantization/bitblas.py @@ -7,9 +7,8 @@ from vllm.logger import init_logger from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.layers.quantization.utils.bitblas_utils import ( BITBLAS_OPTIMIZE_FEATURES, BITBLAS_SUPPORTED_NUM_BITS, BITBLAS_SUPPORTED_SYM, MINIMUM_BITBLAS_VERSION) diff --git a/vllm/model_executor/layers/quantization/bitsandbytes.py b/vllm/model_executor/layers/quantization/bitsandbytes.py index 29584188630f..7b7011cb06d3 100644 --- a/vllm/model_executor/layers/quantization/bitsandbytes.py +++ b/vllm/model_executor/layers/quantization/bitsandbytes.py @@ -13,9 +13,8 @@ from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, UnquantizedLinearMethod, set_weight_attrs) -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.platforms import current_platform from vllm.utils import direct_register_custom_op diff --git a/vllm/model_executor/layers/quantization/deepspeedfp.py b/vllm/model_executor/layers/quantization/deepspeedfp.py index 2922aef32939..4a189ab4a171 100644 --- a/vllm/model_executor/layers/quantization/deepspeedfp.py +++ b/vllm/model_executor/layers/quantization/deepspeedfp.py @@ -9,9 +9,8 @@ from packaging import version from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.utils import set_weight_attrs diff --git a/vllm/model_executor/layers/quantization/gptq.py b/vllm/model_executor/layers/quantization/gptq.py index 0335b9c46b4d..842ce92333c9 100644 --- a/vllm/model_executor/layers/quantization/gptq.py +++ b/vllm/model_executor/layers/quantization/gptq.py @@ -4,7 +4,7 @@ import enum from enum import Enum from fractions import Fraction -from typing import Any, Optional, Union +from typing import TYPE_CHECKING, Any, Optional, Union import torch from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE @@ -13,7 +13,6 @@ from vllm import _custom_ops as ops from vllm.model_executor.layers.fused_moe.layer import FusedMoE from vllm.model_executor.layers.linear import LinearMethodBase -from vllm.model_executor.layers.quantization import QuantizationMethods from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) from vllm.model_executor.layers.quantization.utils.gptq_utils import ( @@ -26,6 +25,11 @@ from vllm.transformers_utils.config import get_safetensors_params_metadata from vllm.utils import is_list_of +if TYPE_CHECKING: + from vllm.model_executor.layers.quantization import QuantizationMethods +else: + QuantizationMethods = str + class GPTQConfig(QuantizationConfig): """Config class for GPTQ. diff --git a/vllm/model_executor/layers/quantization/gptq_bitblas.py b/vllm/model_executor/layers/quantization/gptq_bitblas.py index 646229258648..c193dd85e32f 100644 --- a/vllm/model_executor/layers/quantization/gptq_bitblas.py +++ b/vllm/model_executor/layers/quantization/gptq_bitblas.py @@ -9,9 +9,8 @@ from vllm.logger import init_logger from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, set_weight_attrs) -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.layers.quantization.kernels.mixed_precision import ( BitBLASLinearKernel, MPLinearLayerConfig) from vllm.model_executor.layers.quantization.utils.bitblas_utils import ( diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 967e46c24378..253675e25f34 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -43,7 +43,7 @@ def get_moe_quant_method( - config: QuantizationConfig, + config: "GPTQMarlinConfig", layer: torch.nn.Module, prefix: str, moe_method_cls: type, diff --git a/vllm/model_executor/layers/quantization/gptq_marlin_24.py b/vllm/model_executor/layers/quantization/gptq_marlin_24.py index eba917d85411..6b9e3effc29d 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin_24.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin_24.py @@ -9,9 +9,8 @@ from vllm import _custom_ops as ops from vllm.logger import init_logger from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.parameter import (BasevLLMParameter, ChannelQuantScaleParameter, GroupQuantScaleParameter, diff --git a/vllm/model_executor/layers/quantization/ipex_quant.py b/vllm/model_executor/layers/quantization/ipex_quant.py index c83b0b47a4b7..353942cdd591 100644 --- a/vllm/model_executor/layers/quantization/ipex_quant.py +++ b/vllm/model_executor/layers/quantization/ipex_quant.py @@ -14,11 +14,10 @@ from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase, UnquantizedLinearMethod) -from vllm.model_executor.layers.quantization import QuantizationMethods +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.layers.quantization.awq import (AWQLinearMethod, is_layer_skipped_awq) -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) from vllm.model_executor.layers.quantization.fp8 import (Fp8Config, Fp8LinearMethod) from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod diff --git a/vllm/model_executor/layers/quantization/kernels/mixed_precision/bitblas.py b/vllm/model_executor/layers/quantization/kernels/mixed_precision/bitblas.py index 0eca3b4c024e..fe72910659e2 100644 --- a/vllm/model_executor/layers/quantization/kernels/mixed_precision/bitblas.py +++ b/vllm/model_executor/layers/quantization/kernels/mixed_precision/bitblas.py @@ -7,8 +7,7 @@ from packaging import version from vllm.logger import init_logger -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.utils import replace_parameter from vllm.model_executor.layers.quantization.utils.bitblas_utils import ( BITBLAS_OPTIMIZE_FEATURES, BITBLAS_SUPPORTED_GROUP_SIZES, diff --git a/vllm/model_executor/layers/quantization/tpu_int8.py b/vllm/model_executor/layers/quantization/tpu_int8.py index 38de4b54fb19..7f738d170db4 100644 --- a/vllm/model_executor/layers/quantization/tpu_int8.py +++ b/vllm/model_executor/layers/quantization/tpu_int8.py @@ -8,9 +8,8 @@ from torch.nn.parameter import Parameter from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase -from vllm.model_executor.layers.quantization import QuantizationMethods -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import (QuantizationConfig, + QuantizationMethods) from vllm.model_executor.parameter import ModelWeightParameter ACTIVATION_SCHEMES = ["none", "dynamic"] diff --git a/vllm/model_executor/layers/quantization/utils/gptq_utils.py b/vllm/model_executor/layers/quantization/utils/gptq_utils.py index 41b833725b30..fd76af230620 100644 --- a/vllm/model_executor/layers/quantization/utils/gptq_utils.py +++ b/vllm/model_executor/layers/quantization/utils/gptq_utils.py @@ -4,21 +4,27 @@ from copy import deepcopy from fractions import Fraction from types import MappingProxyType -from typing import Optional, Union +from typing import TYPE_CHECKING, Optional, Union import regex as re import torch -from vllm.config import QuantizationConfig from vllm.model_executor.layers.linear import (LinearBase, UnquantizedLinearMethod) from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, UnquantizedEmbeddingMethod) +if TYPE_CHECKING: + from ..gptq import GPTQConfig + from ..gptq_marlin import GPTQMarlinConfig +else: + GPTQConfig = object + GPTQMarlinConfig = object + # Match dynamic rules with module name (prefix) and override quantize # config if module (prefix) matches a rule -def override_config(config: QuantizationConfig, prefix: str): +def override_config(config: Union[GPTQConfig, GPTQMarlinConfig], prefix: str): weight_bits = get_dynamic_override(config, prefix, "bits", config.weight_bits) if isinstance(weight_bits, int): @@ -34,6 +40,7 @@ def override_config(config: QuantizationConfig, prefix: str): config.pack_factor = Fraction(32, config.weight_bits) # packed into int32 if config.get_name() == "gptq_marlin": + assert isinstance(config, GPTQMarlinConfig) is_sym = get_dynamic_override(config, prefix, "sym", config.is_sym) if isinstance(is_sym, bool): config.is_sym = is_sym @@ -45,6 +52,7 @@ def override_config(config: QuantizationConfig, prefix: str): config.quant_type = config.TYPE_MAP[(config.weight_bits, config.is_sym)] elif config.get_name() == "gptq": + assert isinstance(config, GPTQConfig) if config.weight_bits not in [2, 3, 4, 8]: raise ValueError( "Currently, only 2/3/4/8-bit weight quantization is " @@ -52,7 +60,7 @@ def override_config(config: QuantizationConfig, prefix: str): def get_dynamic_override( - config: QuantizationConfig, + config: Union[GPTQConfig, GPTQMarlinConfig], layer_name: str, key: Optional[str] = None, default_value: Union[int, bool, @@ -116,7 +124,7 @@ def is_layer_gptq_quantized( def get_linear_quant_method( - config: QuantizationConfig, + config: Union[GPTQConfig, GPTQMarlinConfig], layer: torch.nn.Module, prefix: str, linear_method_cls: type, diff --git a/vllm/model_executor/models/aimv2.py b/vllm/model_executor/models/aimv2.py index b13d863ebb74..419f8a5ae2c7 100644 --- a/vllm/model_executor/models/aimv2.py +++ b/vllm/model_executor/models/aimv2.py @@ -17,8 +17,7 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.transformers_utils.configs.ovis import AIMv2Config diff --git a/vllm/model_executor/models/aria.py b/vllm/model_executor/models/aria.py index e0d7af0b1c3e..82f35d889605 100644 --- a/vllm/model_executor/models/aria.py +++ b/vllm/model_executor/models/aria.py @@ -9,13 +9,14 @@ from transformers.models.aria.modeling_aria import AriaCrossAttention from transformers.models.aria.processing_aria import AriaProcessor -from vllm.config import QuantizationConfig, VllmConfig +from vllm.config import VllmConfig from vllm.distributed import get_tensor_model_parallel_rank from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.linear import (ColumnParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) diff --git a/vllm/model_executor/models/bailing_moe.py b/vllm/model_executor/models/bailing_moe.py index 82cd4a26a1ba..6e470378cb60 100644 --- a/vllm/model_executor/models/bailing_moe.py +++ b/vllm/model_executor/models/bailing_moe.py @@ -45,8 +45,7 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/granite.py b/vllm/model_executor/models/granite.py index 2c619396e6c0..893cc8a41455 100644 --- a/vllm/model_executor/models/granite.py +++ b/vllm/model_executor/models/granite.py @@ -41,8 +41,7 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/granitemoe.py b/vllm/model_executor/models/granitemoe.py index 76a5745a4f51..489c0bb3d3af 100644 --- a/vllm/model_executor/models/granitemoe.py +++ b/vllm/model_executor/models/granitemoe.py @@ -42,8 +42,7 @@ ReplicatedLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/granitemoeshared.py b/vllm/model_executor/models/granitemoeshared.py index b434822bff0a..c864856db654 100644 --- a/vllm/model_executor/models/granitemoeshared.py +++ b/vllm/model_executor/models/granitemoeshared.py @@ -21,8 +21,7 @@ from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.sequence import IntermediateTensors diff --git a/vllm/model_executor/models/hunyuan_v1.py b/vllm/model_executor/models/hunyuan_v1.py index d28c97116790..085e740ce226 100644 --- a/vllm/model_executor/models/hunyuan_v1.py +++ b/vllm/model_executor/models/hunyuan_v1.py @@ -47,8 +47,7 @@ ReplicatedLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/interfaces.py b/vllm/model_executor/models/interfaces.py index d40df9b43dd4..c95c63cd8534 100644 --- a/vllm/model_executor/models/interfaces.py +++ b/vllm/model_executor/models/interfaces.py @@ -16,8 +16,7 @@ from vllm.inputs import TokensPrompt from vllm.inputs.data import PromptType from vllm.logger import init_logger -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.utils import supports_kw from .interfaces_base import VllmModel, is_pooling_model diff --git a/vllm/model_executor/models/llama4_eagle.py b/vllm/model_executor/models/llama4_eagle.py index 0768edd08315..572eca344e0a 100644 --- a/vllm/model_executor/models/llama4_eagle.py +++ b/vllm/model_executor/models/llama4_eagle.py @@ -28,8 +28,7 @@ from vllm.logger import init_logger from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.torchao import TorchAOConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) diff --git a/vllm/model_executor/models/mamba.py b/vllm/model_executor/models/mamba.py index 5bd268291c7d..d810701c50b4 100644 --- a/vllm/model_executor/models/mamba.py +++ b/vllm/model_executor/models/mamba.py @@ -16,8 +16,7 @@ from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateDtypeCalculator, MambaStateShapeCalculator) -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader diff --git a/vllm/model_executor/models/mamba2.py b/vllm/model_executor/models/mamba2.py index 97e9c5785e72..f8a5a8f6081b 100644 --- a/vllm/model_executor/models/mamba2.py +++ b/vllm/model_executor/models/mamba2.py @@ -16,8 +16,7 @@ from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2 from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateDtypeCalculator, MambaStateShapeCalculator) -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader diff --git a/vllm/model_executor/models/minimax_text_01.py b/vllm/model_executor/models/minimax_text_01.py index a92890c9f7b5..45228aa0bb93 100644 --- a/vllm/model_executor/models/minimax_text_01.py +++ b/vllm/model_executor/models/minimax_text_01.py @@ -33,8 +33,7 @@ MiniMaxText01LinearAttention) from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateDtypeCalculator, MambaStateShapeCalculator) -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/ovis.py b/vllm/model_executor/models/ovis.py index 2f9c6ddfc661..2e8e4a44102f 100644 --- a/vllm/model_executor/models/ovis.py +++ b/vllm/model_executor/models/ovis.py @@ -29,8 +29,7 @@ from vllm.config import VllmConfig from vllm.model_executor.layers.linear import ReplicatedLinear -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.models.aimv2 import AIMv2Model from vllm.model_executor.models.siglip import SiglipVisionModel from vllm.model_executor.models.utils import (AutoWeightsLoader, flatten_bn, diff --git a/vllm/model_executor/models/ovis2_5.py b/vllm/model_executor/models/ovis2_5.py index 86ce7e9eab27..9c8adb617310 100644 --- a/vllm/model_executor/models/ovis2_5.py +++ b/vllm/model_executor/models/ovis2_5.py @@ -11,8 +11,7 @@ from vllm.config import VllmConfig from vllm.model_executor.layers.linear import ReplicatedLinear -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.models.ovis import (OvisImagePatchInputs, VisualEmbedding) from vllm.model_executor.models.siglip2navit import Siglip2NavitModel diff --git a/vllm/model_executor/models/phimoe.py b/vllm/model_executor/models/phimoe.py index 3ce67ce37a7a..7308fef092b5 100644 --- a/vllm/model_executor/models/phimoe.py +++ b/vllm/model_executor/models/phimoe.py @@ -40,8 +40,7 @@ ReplicatedLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/siglip2navit.py b/vllm/model_executor/models/siglip2navit.py index 7d90d3a7ef12..18de4b576c49 100644 --- a/vllm/model_executor/models/siglip2navit.py +++ b/vllm/model_executor/models/siglip2navit.py @@ -14,13 +14,13 @@ from transformers.configuration_utils import PretrainedConfig from vllm.attention.layer import check_upstream_fa_availability -from vllm.config import QuantizationConfig from vllm.distributed import divide, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.linear import (ColumnParallelLinear, LinearBase, QKVParallelLinear, ReplicatedLinear, RowParallelLinear) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.platforms import _Backend diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py index 0fe723d59483..960813822139 100644 --- a/vllm/model_executor/models/step3_text.py +++ b/vllm/model_executor/models/step3_text.py @@ -23,8 +23,7 @@ ReplicatedLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) diff --git a/vllm/model_executor/models/whisper.py b/vllm/model_executor/models/whisper.py index 7beeeddf988f..1eecac7ed76b 100644 --- a/vllm/model_executor/models/whisper.py +++ b/vllm/model_executor/models/whisper.py @@ -26,8 +26,7 @@ QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) +from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.model_executor.model_loader.weight_utils import default_weight_loader From 9fe29b111e93c8c2364a1c96e6c33dcf1456ff1e Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Tue, 30 Sep 2025 00:18:59 -0400 Subject: [PATCH 092/166] [BugFix] Fix DP/EP hang (#25906) Signed-off-by: Lucas Wilkinson --- vllm/v1/worker/gpu_model_runner.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 9e7d6eb0387b..98e00f6d98a9 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -3075,13 +3075,19 @@ def _dummy_run( # We currently only microbatch if the number of tokens is # over a certain threshold. if self.parallel_config.enable_dbo and allow_microbatching: - ubatch_slices, num_tokens_after_padding = ubatch_split( + ubatch_slices, ubatch_num_tokens_after_padding = ubatch_split( num_scheduled_tokens, total_num_scheduled_tokens, total_num_scheduled_tokens, uniform_decode=uniform_decode, vllm_config=self.vllm_config, ) + # Currently when DBO is enabled `ubatch_split` returns + # the num_tokens_after_padding for a single ubatch, but we have 2 + # TODO(sage,lucas): this is cruft that should be addressed in the + # padding refactor. + if ubatch_num_tokens_after_padding is not None: + num_tokens_after_padding = ubatch_num_tokens_after_padding * 2 # If we failed to microbatch, currently need to resynchronize # TODO(lucas,sage): we should be able to avoid this second sync by @@ -3198,7 +3204,7 @@ def _dummy_run( # filter out the valid batch descriptor _cg_mode, batch_descriptor = self.cudagraph_dispatcher.dispatch( - BatchDescriptor(num_tokens=num_tokens, + BatchDescriptor(num_tokens=num_tokens_after_padding, uniform_decode=uniform_decode)) \ if not is_profile else (CUDAGraphMode.NONE, None) if cudagraph_runtime_mode is not None: @@ -3212,7 +3218,13 @@ def _dummy_run( cudagraph_runtime_mode = _cg_mode if ubatch_slices is not None: - num_tokens = num_tokens // 2 + # Adjust values to reflect a single ubatch. + # TODO(sage,lucas): this is cruft that should be addressed in + # the padding refactor. + num_tokens_after_padding = ubatch_slices[0].num_tokens + if num_tokens_across_dp is not None: + num_tokens_across_dp[:] = num_tokens_after_padding + with self.maybe_randomize_inputs(input_ids), set_forward_context( attn_metadata, self.vllm_config, From f7d6b13106de040f8c975bba2206ed99182ad243 Mon Sep 17 00:00:00 2001 From: acisseJZhong <40467976+acisseJZhong@users.noreply.github.com> Date: Mon, 29 Sep 2025 22:09:50 -0700 Subject: [PATCH 093/166] [BugFix] Pass config_format via try_get_generation_config (#25912) --- vllm/config/model.py | 2 ++ vllm/transformers_utils/config.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/vllm/config/model.py b/vllm/config/model.py index 3fb448ebbf36..28af82de7722 100644 --- a/vllm/config/model.py +++ b/vllm/config/model.py @@ -1334,11 +1334,13 @@ def try_get_generation_config(self) -> dict[str, Any]: self.hf_config_path or self.model, trust_remote_code=self.trust_remote_code, revision=self.revision, + config_format=self.config_format, ) else: config = try_get_generation_config( self.generation_config, trust_remote_code=self.trust_remote_code, + config_format=self.config_format, ) if config is None: diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index b0816cfb0702..4f5e5c01e5cb 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -949,6 +949,7 @@ def try_get_generation_config( model: str, trust_remote_code: bool, revision: Optional[str] = None, + config_format: Union[str, ConfigFormat] = "auto", ) -> Optional[GenerationConfig]: try: return GenerationConfig.from_pretrained( @@ -961,6 +962,7 @@ def try_get_generation_config( model, trust_remote_code=trust_remote_code, revision=revision, + config_format=config_format, ) return GenerationConfig.from_model_config(config) except OSError: # Not found From e78363fd40288c7234ae99af6155ac37e085217f Mon Sep 17 00:00:00 2001 From: Zhou Jiahao Date: Tue, 30 Sep 2025 16:15:23 +0800 Subject: [PATCH 094/166] [Model][Bugfix] Fix MiDashengLM audio encoder mask by removing incorrect `logical_not` (#25925) Signed-off-by: zhoukz --- vllm/model_executor/models/midashenglm.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/vllm/model_executor/models/midashenglm.py b/vllm/model_executor/models/midashenglm.py index 0b62fbd40b07..33bd64df5b53 100644 --- a/vllm/model_executor/models/midashenglm.py +++ b/vllm/model_executor/models/midashenglm.py @@ -426,8 +426,7 @@ def forward( assert x_length.ndim == 1, "Lengths are of size (B,)" scaled_lengths = (x_length / (self.hop_length * 4)).long() mask = self._to_mask(max_length=t, lengths=scaled_lengths) - split_masks = mask.logical_not().split(target_length_in_patches, - dim=-1) + split_masks = mask.split(target_length_in_patches, dim=-1) else: mask = None split_masks = [None] * len(input_splits) From 687f42d7168a3f1499d4394b42f1ff55f30ce8ad Mon Sep 17 00:00:00 2001 From: Simon Danielsson <70206058+simondanielsson@users.noreply.github.com> Date: Tue, 30 Sep 2025 10:17:49 +0200 Subject: [PATCH 095/166] [Bugfix]: Clean up chunked prefill logging when using whisper (#25075) Signed-off-by: simondanielsson --- tests/v1/core/test_scheduler.py | 52 ++++++++++++++++++++++++++++++++- vllm/config/scheduler.py | 21 +++++++++++-- vllm/config/vllm.py | 9 +++--- vllm/engine/arg_utils.py | 1 + 4 files changed, 75 insertions(+), 8 deletions(-) diff --git a/tests/v1/core/test_scheduler.py b/tests/v1/core/test_scheduler.py index f6fc1e6d37d1..5e2bdaa75d3f 100644 --- a/tests/v1/core/test_scheduler.py +++ b/tests/v1/core/test_scheduler.py @@ -1,5 +1,6 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import dataclasses from typing import Optional from unittest.mock import Mock @@ -1899,4 +1900,53 @@ def test_priority_scheduling_preemption_when_out_of_kv(): assert output.scheduled_cached_reqs.num_reqs == 1 assert output.scheduled_cached_reqs.req_ids[0] == request_high.request_id assert len(scheduler.waiting) == 1 - assert len(scheduler.running) == 1 \ No newline at end of file + assert len(scheduler.running) == 1 + + +@pytest.mark.parametrize( + ("enable_chunked_prefill", "is_encoder_decoder", "expect_enabled"), + [ + (True, False, True), + (False, False, False), + # Encoder-decoder models should always have it disabled + (False, True, False), + (True, True, False), + ]) +def test_chunked_prefill_disabled_for_encoder_decoder( + enable_chunked_prefill: bool, is_encoder_decoder: bool, + expect_enabled: bool) -> None: + """Validate that chunked prefill is appropriately disabled for + encoder-decoder models.""" + scheduler_config = SchedulerConfig( + enable_chunked_prefill=enable_chunked_prefill, + is_encoder_decoder=is_encoder_decoder, + ) + + # `is_encoder_decoder` should only be used during construction + # of the config, and otherwise stored in the model config. + assert "is_encoder_decoder" not in vars(scheduler_config) + assert "is_encoder_decoder" not in [ + f.name for f in dataclasses.fields(scheduler_config) + ] + _validate_chunked_prefill_settings_for_encoder_decoder( + scheduler_config, is_encoder_decoder, expect_enabled) + + # Ensure it is retained in VllmConfig, even after its post-init. + vllm_config = VllmConfig(scheduler_config=scheduler_config) + _validate_chunked_prefill_settings_for_encoder_decoder( + vllm_config.scheduler_config, is_encoder_decoder, expect_enabled) + + +def _validate_chunked_prefill_settings_for_encoder_decoder( + scheduler_config: SchedulerConfig, is_encoder_decoder: bool, + expect_enabled: bool) -> None: + """Validate chunked prefill settings in the scheduler config for + encoder-decoder models.""" + assert scheduler_config.chunked_prefill_enabled is expect_enabled + assert scheduler_config.enable_chunked_prefill is expect_enabled + if is_encoder_decoder: + # Encoder-decoder models should automatically disable chunked multimodal + # inputs as well + assert scheduler_config.disable_chunked_mm_input is not expect_enabled + if is_encoder_decoder and not expect_enabled: + assert scheduler_config.long_prefill_token_threshold == 0 diff --git a/vllm/config/scheduler.py b/vllm/config/scheduler.py index daf094d2df5c..1b0a10d3a069 100644 --- a/vllm/config/scheduler.py +++ b/vllm/config/scheduler.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import hashlib -from dataclasses import field +from dataclasses import InitVar, field from typing import Any, Literal, Union from pydantic import SkipValidation, model_validator @@ -84,6 +84,13 @@ class SchedulerConfig: is_multimodal_model: bool = False """True if the model is multimodal.""" + is_encoder_decoder: InitVar[bool] = False + """True if the model is an encoder-decoder model. + + Note: This is stored in the ModelConfig, and is used only here to + disable chunked prefill and prefix caching for encoder-decoder models. + """ + # TODO (ywang96): Make this configurable. max_num_encoder_input_tokens: int = field(init=False) """Multimodal encoder compute budget, only used in V1. @@ -161,13 +168,23 @@ def compute_hash(self) -> str: usedforsecurity=False).hexdigest() return hash_str - def __post_init__(self) -> None: + def __post_init__(self, is_encoder_decoder: bool) -> None: if self.max_model_len is None: self.max_model_len = 8192 if self.max_num_seqs is None: self.max_num_seqs = 128 + if is_encoder_decoder: + # Chunked prefill should be disabled for encoder-decoder models. + self.disable_chunked_mm_input = True + self.chunked_prefill_enabled = False + self.enable_chunked_prefill = False + self.long_prefill_token_threshold = 0 + logger.info( + "Encoder-decoder models do not support chunked prefill nor" + " prefix caching; disabling both.") + if self.max_num_batched_tokens is None: if self.enable_chunked_prefill: self.max_num_batched_tokens = DEFAULT_MAX_NUM_BATCHED_TOKENS diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py index 7336f5756527..585d3997cc3a 100644 --- a/vllm/config/vllm.py +++ b/vllm/config/vllm.py @@ -386,10 +386,6 @@ def __post_init__(self): "Encoder-decoder model detected: setting " "`max_num_encoder_input_tokens` to encoder length (%s)", self.scheduler_config.max_num_encoder_input_tokens) - self.scheduler_config.disable_chunked_mm_input = True - disable_chunked_prefill_reasons.append( - "Encoder-decoder models do not support chunked prefill nor" - " prefix caching; disabling both.") if (self.model_config.architecture == "WhisperForConditionalGeneration" and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") @@ -400,7 +396,10 @@ def __post_init__(self): "try setting 'VLLM_WORKER_MULTIPROC_METHOD' " "to 'spawn'.") - if disable_chunked_prefill_reasons: + # Disable prefix caching only if chunked prefill is explicitly disabled + # (and not merely unset) + if (self.scheduler_config.chunked_prefill_enabled is False + or disable_chunked_prefill_reasons): for reason in disable_chunked_prefill_reasons: logger.info(reason) self.scheduler_config.chunked_prefill_enabled = False diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index 6bb794177db8..ce0f1708235f 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1367,6 +1367,7 @@ def create_engine_config( enable_chunked_prefill=self.enable_chunked_prefill, disable_chunked_mm_input=self.disable_chunked_mm_input, is_multimodal_model=model_config.is_multimodal_model, + is_encoder_decoder=model_config.is_encoder_decoder, send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER and parallel_config.use_ray), policy=self.scheduling_policy, From 886bd1239b5d3db8d004e6ecff2d4f94a5e2d549 Mon Sep 17 00:00:00 2001 From: Yongye Zhu Date: Tue, 30 Sep 2025 05:14:41 -0400 Subject: [PATCH 096/166] [New Model] DeepSeek-V3.2 (Rebased to Main) (#25896) Signed-off-by: Chen Zhang Signed-off-by: youkaichao Signed-off-by: Lucas Wilkinson Signed-off-by: mgoin Signed-off-by: NickLucche Signed-off-by: Yongye Zhu Signed-off-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com> Signed-off-by: Lucia Fang Co-authored-by: Chen Zhang Co-authored-by: youkaichao Co-authored-by: Lucas Wilkinson Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com> Co-authored-by: Lucas Wilkinson Co-authored-by: yewentao256 Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Co-authored-by: mgoin Co-authored-by: Lucia Fang <116399278+luccafong@users.noreply.github.com> Co-authored-by: Lucia Fang Co-authored-by: NickLucche Co-authored-by: Siyuan Fu Co-authored-by: Matthew Bonanni Co-authored-by: Xiaozhu Meng Co-authored-by: Barry Kang <43644113+Barry-Delaney@users.noreply.github.com> --- cmake/external_projects/flashmla.cmake | 87 ++- csrc/cache.h | 8 + csrc/cache_kernels.cu | 258 ++++++++- csrc/quantization/fp8/nvidia/quant_utils.cuh | 11 + csrc/torch_bindings.cpp | 7 + setup.py | 4 + tests/compile/test_fusion_attn.py | 1 - tests/kernels/attention/test_cache.py | 113 ++++ .../attention/test_deepgemm_attention.py | 279 +++++++++ tests/kernels/attention/test_flashmla.py | 22 +- .../kernels/attention/test_flashmla_sparse.py | 119 ++++ .../attention/test_pack_unpack_triton.py | 245 ++++++++ tests/models/registry.py | 1 + tests/models/test_initialization.py | 9 +- .../vllm_add_dummy_platform/dummy_platform.py | 2 +- tests/v1/attention/test_mla_backends.py | 69 ++- .../v1/attention/test_sparse_mla_backends.py | 426 ++++++++++++++ tests/v1/attention/utils.py | 1 - tests/v1/core/test_kv_cache_utils.py | 56 +- tests/v1/core/test_prefix_caching.py | 7 +- .../core/test_single_type_kv_cache_manager.py | 6 - tests/v1/engine/test_engine_core_client.py | 3 +- tests/v1/worker/test_gpu_model_runner.py | 1 - vllm/_custom_ops.py | 9 + vllm/attention/backends/abstract.py | 1 + vllm/attention/layer.py | 5 +- vllm/attention/ops/common.py | 205 +++++++ vllm/attention/ops/flashmla.py | 165 ++++-- vllm/attention/ops/paged_attn.py | 1 + vllm/attention/selector.py | 5 +- vllm/config/cache.py | 20 +- vllm/config/compilation.py | 1 + vllm/config/model.py | 4 +- vllm/config/speculative.py | 2 +- vllm/model_executor/layers/layernorm.py | 18 + vllm/model_executor/layers/mla.py | 16 + vllm/model_executor/models/config.py | 29 +- vllm/model_executor/models/deepseek_mtp.py | 14 +- vllm/model_executor/models/deepseek_v2.py | 449 ++++++++++++++- vllm/model_executor/models/longcat_flash.py | 3 + vllm/model_executor/models/registry.py | 1 + vllm/platforms/cpu.py | 5 +- vllm/platforms/cuda.py | 15 +- vllm/platforms/interface.py | 2 +- vllm/platforms/rocm.py | 5 +- vllm/platforms/tpu.py | 5 +- vllm/platforms/xpu.py | 5 +- vllm/transformers_utils/config.py | 2 + vllm/transformers_utils/configs/__init__.py | 2 + .../transformers_utils/configs/deepseek_v3.py | 101 ++++ vllm/utils/__init__.py | 7 + vllm/utils/deep_gemm.py | 124 +++- vllm/v1/attention/backends/cpu_attn.py | 1 + vllm/v1/attention/backends/flash_attn.py | 1 + vllm/v1/attention/backends/flashinfer.py | 1 + vllm/v1/attention/backends/flex_attention.py | 1 + vllm/v1/attention/backends/mla/common.py | 170 +++++- vllm/v1/attention/backends/mla/flashmla.py | 1 + .../attention/backends/mla/flashmla_sparse.py | 544 ++++++++++++++++++ vllm/v1/attention/backends/mla/indexer.py | 293 ++++++++++ vllm/v1/attention/backends/pallas.py | 1 + vllm/v1/attention/backends/rocm_aiter_fa.py | 1 + vllm/v1/attention/backends/tree_attn.py | 1 + vllm/v1/attention/backends/triton_attn.py | 1 + vllm/v1/attention/backends/xformers.py | 1 + vllm/v1/core/kv_cache_utils.py | 9 +- vllm/v1/core/single_type_kv_cache_manager.py | 3 +- vllm/v1/kv_cache_interface.py | 51 +- vllm/v1/spec_decode/eagle.py | 56 +- vllm/v1/worker/gpu_model_runner.py | 45 +- vllm/v1/worker/tpu_model_runner.py | 2 - 71 files changed, 3918 insertions(+), 221 deletions(-) create mode 100644 tests/kernels/attention/test_deepgemm_attention.py create mode 100644 tests/kernels/attention/test_flashmla_sparse.py create mode 100644 tests/kernels/attention/test_pack_unpack_triton.py create mode 100644 tests/v1/attention/test_sparse_mla_backends.py create mode 100644 vllm/transformers_utils/configs/deepseek_v3.py create mode 100644 vllm/v1/attention/backends/mla/flashmla_sparse.py create mode 100644 vllm/v1/attention/backends/mla/indexer.py diff --git a/cmake/external_projects/flashmla.cmake b/cmake/external_projects/flashmla.cmake index 02224cfe3ee8..c9e7aec880b9 100644 --- a/cmake/external_projects/flashmla.cmake +++ b/cmake/external_projects/flashmla.cmake @@ -18,8 +18,8 @@ if(FLASH_MLA_SRC_DIR) else() FetchContent_Declare( flashmla - GIT_REPOSITORY https://github.com/vllm-project/FlashMLA.git - GIT_TAG a757314c04eedd166e329e846c820eb1bdd702de + GIT_REPOSITORY https://github.com/vllm-project/FlashMLA + GIT_TAG 5f65b85703c7ed75fda01e06495077caad207c3f GIT_PROGRESS TRUE CONFIGURE_COMMAND "" BUILD_COMMAND "" @@ -33,23 +33,64 @@ message(STATUS "FlashMLA is available at ${flashmla_SOURCE_DIR}") # The FlashMLA kernels only work on hopper and require CUDA 12.3 or later. # Only build FlashMLA kernels if we are building for something compatible with # sm90a -cuda_archs_loose_intersection(FLASH_MLA_ARCHS "9.0a" "${CUDA_ARCHS}") -if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.3 AND FLASH_MLA_ARCHS) + +set(SUPPORT_ARCHS) +if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.3) + list(APPEND SUPPORT_ARCHS 9.0a) +endif() +if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8) + list(APPEND SUPPORT_ARCHS 10.0a) +endif() + + +cuda_archs_loose_intersection(FLASH_MLA_ARCHS "${SUPPORT_ARCHS}" "${CUDA_ARCHS}") +if(FLASH_MLA_ARCHS) + set(VLLM_FLASHMLA_GPU_FLAGS ${VLLM_GPU_FLAGS}) + list(APPEND VLLM_FLASHMLA_GPU_FLAGS "--expt-relaxed-constexpr" "--expt-extended-lambda" "--use_fast_math") + set(FlashMLA_SOURCES - ${flashmla_SOURCE_DIR}/csrc/flash_api.cpp - ${flashmla_SOURCE_DIR}/csrc/kernels/get_mla_metadata.cu - ${flashmla_SOURCE_DIR}/csrc/kernels/mla_combine.cu - ${flashmla_SOURCE_DIR}/csrc/kernels/splitkv_mla.cu - ${flashmla_SOURCE_DIR}/csrc/kernels_fp8/flash_fwd_mla_fp8_sm90.cu) + ${flashmla_SOURCE_DIR}/csrc/torch_api.cpp + ${flashmla_SOURCE_DIR}/csrc/pybind.cpp + ${flashmla_SOURCE_DIR}/csrc/smxx/get_mla_metadata.cu + ${flashmla_SOURCE_DIR}/csrc/smxx/mla_combine.cu + ${flashmla_SOURCE_DIR}/csrc/sm90/decode/dense/splitkv_mla.cu + ${flashmla_SOURCE_DIR}/csrc/sm90/decode/sparse_fp8/splitkv_mla.cu + ${flashmla_SOURCE_DIR}/csrc/sm90/prefill/sparse/fwd.cu + ${flashmla_SOURCE_DIR}/csrc/sm100/decode/sparse_fp8/splitkv_mla.cu + ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/dense/fmha_cutlass_fwd_sm100.cu + ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/dense/fmha_cutlass_bwd_sm100.cu + ${flashmla_SOURCE_DIR}/csrc/sm100/prefill/sparse/fwd.cu + ) + + set(FlashMLA_Extension_SOURCES + ${flashmla_SOURCE_DIR}/csrc/extension/torch_api.cpp + ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/pybind.cpp + ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/flash_fwd_mla_fp8_sm90.cu + ) set(FlashMLA_INCLUDES + ${flashmla_SOURCE_DIR}/csrc + ${flashmla_SOURCE_DIR}/csrc/sm90 + ${flashmla_SOURCE_DIR}/csrc/cutlass/include + ${flashmla_SOURCE_DIR}/csrc/cutlass/tools/util/include + ) + + set(FlashMLA_Extension_INCLUDES + ${flashmla_SOURCE_DIR}/csrc + ${flashmla_SOURCE_DIR}/csrc/sm90 + ${flashmla_SOURCE_DIR}/csrc/extension/sm90/dense_fp8/ ${flashmla_SOURCE_DIR}/csrc/cutlass/include - ${flashmla_SOURCE_DIR}/csrc) + ${flashmla_SOURCE_DIR}/csrc/cutlass/tools/util/include + ) set_gencode_flags_for_srcs( SRCS "${FlashMLA_SOURCES}" CUDA_ARCHS "${FLASH_MLA_ARCHS}") + set_gencode_flags_for_srcs( + SRCS "${FlashMLA_Extension_SOURCES}" + CUDA_ARCHS "${FLASH_MLA_ARCHS}") + define_gpu_extension_target( _flashmla_C DESTINATION vllm @@ -60,8 +101,32 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.3 AND FLASH_MLA_ARCHS) INCLUDE_DIRECTORIES ${FlashMLA_INCLUDES} USE_SABI 3 WITH_SOABI) + + # Keep Stable ABI for the module, but *not* for CUDA/C++ files. + # This prevents Py_LIMITED_API from affecting nvcc and C++ compiles. + target_compile_options(_flashmla_C PRIVATE + $<$:-UPy_LIMITED_API> + $<$:-UPy_LIMITED_API>) + + define_gpu_extension_target( + _flashmla_extension_C + DESTINATION vllm + LANGUAGE ${VLLM_GPU_LANG} + SOURCES ${FlashMLA_Extension_SOURCES} + COMPILE_FLAGS ${VLLM_FLASHMLA_GPU_FLAGS} + ARCHITECTURES ${VLLM_GPU_ARCHES} + INCLUDE_DIRECTORIES ${FlashMLA_Extension_INCLUDES} + USE_SABI 3 + WITH_SOABI) + + # Keep Stable ABI for the module, but *not* for CUDA/C++ files. + # This prevents Py_LIMITED_API from affecting nvcc and C++ compiles. + target_compile_options(_flashmla_extension_C PRIVATE + $<$:-UPy_LIMITED_API> + $<$:-UPy_LIMITED_API>) else() - # Create an empty target for setup.py when not targeting sm90a systems + # Create empty targets for setup.py when not targeting sm90a systems add_custom_target(_flashmla_C) + add_custom_target(_flashmla_extension_C) endif() diff --git a/csrc/cache.h b/csrc/cache.h index fd230bec27fc..427bd0d54fac 100644 --- a/csrc/cache.h +++ b/csrc/cache.h @@ -56,3 +56,11 @@ void cp_gather_cache( torch::Tensor const& block_table, // [BATCH, BLOCK_INDICES] torch::Tensor const& cu_seq_lens, // [BATCH+1] int64_t batch_size, std::optional seq_starts = std::nullopt); + +// Indexer K quantization and cache function +void indexer_k_quant_and_cache( + torch::Tensor& k, // [num_tokens, head_dim] + torch::Tensor& kv_cache, // [num_blocks, block_size, cache_stride] + torch::Tensor& slot_mapping, // [num_tokens] + int64_t quant_block_size, // quantization block size + const std::string& scale_fmt); diff --git a/csrc/cache_kernels.cu b/csrc/cache_kernels.cu index 80b4c47c5547..b1c43163c6a5 100644 --- a/csrc/cache_kernels.cu +++ b/csrc/cache_kernels.cu @@ -16,6 +16,7 @@ #include #include +#include // FLT_MIN #include #include @@ -396,6 +397,176 @@ __global__ void concat_and_cache_mla_kernel( copy(k_pe, kv_cache, k_pe_stride, block_stride, pe_dim, kv_lora_rank); } +template +__global__ void concat_and_cache_ds_mla_kernel( + const scalar_t* __restrict__ kv_c, // [num_tokens, kv_lora_rank] + const scalar_t* __restrict__ k_pe, // [num_tokens, pe_dim] + cache_t* __restrict__ kv_cache, // [num_blocks, block_size, (kv_lora_rank + // + pe_dim)] + const int64_t* __restrict__ slot_mapping, // [num_tokens] + const int block_stride, // + const int entry_stride, // + const int kv_c_stride, // + const int k_pe_stride, // + const int kv_lora_rank, // + const int pe_dim, // + const int block_size, // + const float* scale // +) { + const int64_t token_idx = blockIdx.x; + const int64_t slot_idx = slot_mapping[token_idx]; + // NOTE: slot_idx can be -1 if the token is padded + if (slot_idx < 0) { + return; + } + const int64_t block_idx = slot_idx / block_size; + const int64_t block_offset = slot_idx % block_size; + const int64_t dst_idx_start = + block_idx * block_stride + block_offset * entry_stride; + + // Create 4 tile scales in shared memory + __shared__ float smem[20]; + float* shard_abs_max = smem; + float* tile_scales = smem + 16; + + // For the NoPE part, each tile of 128 elements is handled by 4 warps + // (128 threads). There are 4 total tiles, so 16 warps (512 threads). + // The first thread of the first warp in each tile writes the scale + // value for the tile. The RoPE part (last 64 elements) is handled + // by another 2 warps (64 threads). + // So in total, we use 18 warps (576 threads) per block. + + // Cast kv_cache to 16_bit for RoPE values + scalar_t* kv_cache_16bit = + reinterpret_cast(&kv_cache[dst_idx_start]); + + // The last 64 threads handle the RoPE part + if (threadIdx.x >= kv_lora_rank) { + const int8_t pe_idx = threadIdx.x - kv_lora_rank; + const int64_t src_idx = token_idx * k_pe_stride + pe_idx; + // RoPE values start after the packed 8-bit NoPE values and the + // 32-bit scales + const int64_t dst_idx = kv_lora_rank / 2 + 8 + pe_idx; + kv_cache_16bit[dst_idx] = k_pe[src_idx]; + return; + } + + // Determine the scale for each chunk of NoPE + const int16_t tile_idx = threadIdx.x >> 7; + const int16_t warp_idx = (threadIdx.x & 127) >> 5; + const int16_t lane_idx = threadIdx.x & 31; + + // Load the NoPE element for this thread into registers + const int64_t src_idx = token_idx * kv_c_stride + threadIdx.x; + const scalar_t src_val = kv_c[src_idx]; + + // Warp-level reduction to find the max absolute value in the warp + float max_abs = fabsf(src_val); +#pragma unroll + for (int offset = 16; offset > 0; offset /= 2) { +#ifdef USE_ROCM + max_abs = fmaxf(max_abs, __shfl_down_sync(UINT64_MAX, max_abs, offset)); +#else + max_abs = fmaxf(max_abs, __shfl_down_sync(0xFFFFFFFF, max_abs, offset)); +#endif + } + + // The first lane of each warp in each tile writes the max_abs of this part + // of the tile to shared memory + if (lane_idx == 0) { + shard_abs_max[tile_idx * 4 + warp_idx] = max_abs; + } + __syncthreads(); + + // The first lane of the first warp in each tile computes the scale for the + // tile and writes it to shared memory and to kv_cache + if (warp_idx == 0 && lane_idx == 0) { + float4 shard_abs_max_vec = + reinterpret_cast(shard_abs_max)[tile_idx]; + float tile_scale = fmaxf(fmaxf(shard_abs_max_vec.x, shard_abs_max_vec.y), + fmaxf(shard_abs_max_vec.z, shard_abs_max_vec.w)) / + 448.f; + + // Avoid division by zero in `scaled_convert` + tile_scales[tile_idx] = fmaxf(tile_scale, FLT_MIN); + float* kv_cache_32bit = reinterpret_cast(&kv_cache[dst_idx_start]); + const uint64_t dst_idx = kv_lora_rank / 4 + tile_idx; + kv_cache_32bit[dst_idx] = tile_scales[tile_idx]; + } + + __syncthreads(); + + // Now all threads in the block scale and write their element + const float scale_val = tile_scales[tile_idx]; + const int64_t dst_idx = dst_idx_start + threadIdx.x; + kv_cache[dst_idx] = + fp8::scaled_convert( + src_val, scale_val); +} + +template +__global__ void indexer_k_quant_and_cache_kernel( + const scalar_t* __restrict__ k, // [num_tokens, head_dim] + cache_t* __restrict__ kv_cache, // [num_blocks, block_size, cache_stride] + const int64_t* __restrict__ slot_mapping, // [num_tokens] + const int head_dim, // dimension of each head + const int quant_block_size, // quantization block size + const int cache_block_size, // cache block size + const int cache_stride, // stride for each token in kv_cache + const bool use_ue8m0 // use ue8m0 scale format +) { + constexpr int VEC_SIZE = 4; + const int64_t token_idx = blockIdx.x; + const int64_t head_dim_idx = (blockIdx.y * blockDim.y * blockDim.x + + threadIdx.y * blockDim.x + threadIdx.x) * + VEC_SIZE; + const int64_t slot_idx = slot_mapping[token_idx]; + const int64_t block_idx = slot_idx / cache_block_size; + const int64_t block_offset = slot_idx % cache_block_size; + + // NOTE: slot_idx can be -1 if the token is padded + if (slot_idx < 0 || (head_dim_idx >= head_dim)) { + return; + } + + float2 k_val = (reinterpret_cast( + k))[(token_idx * head_dim + head_dim_idx) / VEC_SIZE]; + scalar_t* k_val_ptr = reinterpret_cast(&k_val); + float amax = 0.0f; + for (int i = 0; i < VEC_SIZE; i++) { + amax = fmaxf(amax, fabsf(float(k_val_ptr[i]))); + } + __syncwarp(); + + // Reduced amax + for (int mask = 16; mask > 0; mask /= 2) { +#ifdef USE_ROCM + amax = fmaxf(amax, __shfl_xor_sync(uint64_t(-1), amax, mask)); +#else + amax = fmaxf(amax, __shfl_xor_sync(unsigned(-1), amax, mask)); +#endif + } + __syncwarp(); + float scale = fmaxf(amax, 1e-4) / 448.0f; + if (use_ue8m0) { + scale = exp2f(ceilf(log2f(scale))); + } + + const int64_t dst_offset = block_idx * cache_block_size * cache_stride + + block_offset * head_dim + head_dim_idx; + for (int i = 0; i < VEC_SIZE; i++) { + kv_cache[dst_offset + i] = + fp8::scaled_convert(k_val_ptr[i], scale); + } + if (threadIdx.x == 0) { + const int64_t dst_scale_idx = + block_idx * cache_block_size * cache_stride + + cache_block_size * head_dim + + (block_offset * head_dim + head_dim_idx) * 4 / quant_block_size; + reinterpret_cast(kv_cache)[dst_scale_idx / 4] = scale; + } +} + } // namespace vllm // KV_T is the data type of key and value tensors. @@ -438,7 +609,7 @@ void reshape_and_cache( const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); DISPATCH_BY_KV_CACHE_DTYPE(key.dtype(), kv_cache_dtype, - CALL_RESHAPE_AND_CACHE) + CALL_RESHAPE_AND_CACHE); } // KV_T is the data type of key and value tensors. @@ -509,6 +680,18 @@ void reshape_and_cache_flash( kv_c_stride, k_pe_stride, kv_lora_rank, pe_dim, block_size, \ reinterpret_cast(scale.data_ptr())); +// KV_T is the data type of key and value tensors. +// CACHE_T is the stored data type of kv-cache. +#define CALL_CONCAT_AND_CACHE_DS_MLA(KV_T, CACHE_T, KV_DTYPE) \ + vllm::concat_and_cache_ds_mla_kernel \ + <<>>( \ + reinterpret_cast(kv_c.data_ptr()), \ + reinterpret_cast(k_pe.data_ptr()), \ + reinterpret_cast(kv_cache.data_ptr()), \ + slot_mapping.data_ptr(), block_stride, entry_stride, \ + kv_c_stride, k_pe_stride, kv_lora_rank, pe_dim, block_size, \ + reinterpret_cast(scale.data_ptr())); + void concat_and_cache_mla( torch::Tensor& kv_c, // [num_tokens, kv_lora_rank] torch::Tensor& k_pe, // [num_tokens, pe_dim] @@ -531,20 +714,44 @@ void concat_and_cache_mla( int pe_dim = k_pe.size(1); int block_size = kv_cache.size(1); - TORCH_CHECK(kv_cache.size(2) == kv_lora_rank + pe_dim); + if (kv_cache_dtype == "fp8_ds_mla") { + TORCH_CHECK(kv_lora_rank == 512, "kv_lora_rank must be 512 for fp8_ds_mla"); + TORCH_CHECK(pe_dim == 64, "pe_dim must be 64 for fp8_ds_mla"); + TORCH_CHECK(kv_cache.size(2) == 656 / kv_cache.itemsize(), + "kv_cache.size(2) must be 656 bytes for fp8_ds_mla"); + TORCH_CHECK(kv_c.itemsize() == 2, + "kv_c.itemsize() must be 2 for fp8_ds_mla"); + TORCH_CHECK(k_pe.itemsize() == 2, + "k_pe.itemsize() must be 2 for fp8_ds_mla"); + } else { + TORCH_CHECK(kv_cache.size(2) == kv_lora_rank + pe_dim); + } int kv_c_stride = kv_c.stride(0); int k_pe_stride = k_pe.stride(0); int block_stride = kv_cache.stride(0); int entry_stride = kv_cache.stride(1); - dim3 grid(num_tokens); - dim3 block(std::min(kv_lora_rank, 512)); const at::cuda::OptionalCUDAGuard device_guard(device_of(kv_c)); const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype, - CALL_CONCAT_AND_CACHE_MLA); + if (kv_cache_dtype == "fp8_ds_mla") { + dim3 grid(num_tokens); + // For the NoPE part, each tile of 128 elements is handled by 4 warps + // (128 threads). There are 4 total tiles, so 16 warps (512 threads). + // The first thread of the first warp in each tile writes the scale + // value for the tile. The RoPE part (last 64 elements) is handled + // by another 2 warps (64 threads). + // So in total, we use 18 warps (576 threads) per block. + dim3 block(576); + DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype, + CALL_CONCAT_AND_CACHE_DS_MLA); + } else { + dim3 grid(num_tokens); + dim3 block(std::min(kv_lora_rank, 512)); + DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype, + CALL_CONCAT_AND_CACHE_MLA); + } } namespace vllm { @@ -922,3 +1129,42 @@ void cp_gather_cache( TORCH_CHECK(false, "Unsupported data type width: ", dtype_bits); } } + +// Macro to dispatch the kernel based on the data type. +#define CALL_INDEXER_K_QUANT_AND_CACHE(KV_T, CACHE_T, KV_DTYPE) \ + vllm::indexer_k_quant_and_cache_kernel \ + <<>>( \ + reinterpret_cast(k.data_ptr()), \ + reinterpret_cast(kv_cache.data_ptr()), \ + slot_mapping.data_ptr(), head_dim, quant_block_size, \ + cache_block_size, cache_stride, use_ue8m0); + +void indexer_k_quant_and_cache( + torch::Tensor& k, // [num_tokens, head_dim] + torch::Tensor& kv_cache, // [num_blocks, block_size, cache_stride] + torch::Tensor& slot_mapping, // [num_tokens] + int64_t quant_block_size, // quantization block size + const std::string& scale_fmt) { + int num_tokens = k.size(0); + int head_dim = k.size(1); + int cache_block_size = kv_cache.size(1); + int cache_stride = kv_cache.size(2); + bool use_ue8m0 = scale_fmt == "ue8m0"; + + TORCH_CHECK(k.device() == kv_cache.device(), + "k and kv_cache must be on the same device"); + TORCH_CHECK(k.device() == slot_mapping.device(), + "k and slot_mapping must be on the same device"); + TORCH_CHECK(head_dim % quant_block_size == 0, + "head_dim must be divisible by quant_block_size"); + + constexpr int vec_size = 4; + dim3 grid(num_tokens, (head_dim + quant_block_size * vec_size - 1) / + (quant_block_size * vec_size)); + dim3 block(32, vec_size); + const at::cuda::OptionalCUDAGuard device_guard(device_of(k)); + const cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + DISPATCH_BY_KV_CACHE_DTYPE(k.dtype(), "fp8_e4m3", + CALL_INDEXER_K_QUANT_AND_CACHE); +} \ No newline at end of file diff --git a/csrc/quantization/fp8/nvidia/quant_utils.cuh b/csrc/quantization/fp8/nvidia/quant_utils.cuh index 5b9c2df8468c..5361a8b1a598 100644 --- a/csrc/quantization/fp8/nvidia/quant_utils.cuh +++ b/csrc/quantization/fp8/nvidia/quant_utils.cuh @@ -576,6 +576,17 @@ __inline__ __device__ Tout scaled_convert(const Tin& x, const float scale) { TORCH_CHECK(false, \ "Unsupported input type of kv cache: ", SRC_DTYPE); \ } \ + } else if (KV_DTYPE == "fp8_ds_mla") { \ + if (SRC_DTYPE == at::ScalarType::Float) { \ + FN(float, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3); \ + } else if (SRC_DTYPE == at::ScalarType::Half) { \ + FN(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3); \ + } else if (SRC_DTYPE == at::ScalarType::BFloat16) { \ + FN(__nv_bfloat16, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3); \ + } else { \ + TORCH_CHECK(false, \ + "Unsupported input type of kv cache: ", SRC_DTYPE); \ + } \ } else { \ TORCH_CHECK(false, "Unsupported data type of kv cache: ", KV_DTYPE); \ } \ diff --git a/csrc/torch_bindings.cpp b/csrc/torch_bindings.cpp index bc096406c51a..ebd28e735088 100644 --- a/csrc/torch_bindings.cpp +++ b/csrc/torch_bindings.cpp @@ -713,6 +713,13 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) { "cp_gather_cache(Tensor src_cache, Tensor! dst, Tensor block_table, " "Tensor cu_seq_lens, int batch_size, Tensor? seq_starts) -> ()"); cache_ops.impl("cp_gather_cache", torch::kCUDA, &cp_gather_cache); + + cache_ops.def( + "indexer_k_quant_and_cache(Tensor k, Tensor! kv_cache, Tensor " + "slot_mapping, " + "int quant_block_size, str kv_cache_dtype) -> ()"); + cache_ops.impl("indexer_k_quant_and_cache", torch::kCUDA, + &indexer_k_quant_and_cache); } TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cuda_utils), cuda_utils) { diff --git a/setup.py b/setup.py index a8fec8a028d0..5491046991ca 100644 --- a/setup.py +++ b/setup.py @@ -322,6 +322,8 @@ def extract_precompiled_and_patch_package(wheel_url_or_path: str) -> dict: "vllm/_C.abi3.so", "vllm/_moe_C.abi3.so", "vllm/_flashmla_C.abi3.so", + "vllm/_flashmla_extension_C.abi3.so", + "vllm/_sparse_flashmla_C.abi3.so", "vllm/vllm_flash_attn/_vllm_fa2_C.abi3.so", "vllm/vllm_flash_attn/_vllm_fa3_C.abi3.so", "vllm/cumem_allocator.abi3.so", @@ -589,6 +591,8 @@ def _read_requirements(filename: str) -> list[str]: # not targeting a hopper system ext_modules.append( CMakeExtension(name="vllm._flashmla_C", optional=True)) + ext_modules.append( + CMakeExtension(name="vllm._flashmla_extension_C", optional=True)) ext_modules.append(CMakeExtension(name="vllm.cumem_allocator")) if _build_custom_ops(): diff --git a/tests/compile/test_fusion_attn.py b/tests/compile/test_fusion_attn.py index 6c2679ccfc81..eb8c49135428 100644 --- a/tests/compile/test_fusion_attn.py +++ b/tests/compile/test_fusion_attn.py @@ -191,7 +191,6 @@ def __init__(self, num_qo_heads: int, num_kv_heads: int, head_size: int, num_kv_heads=self.num_kv_heads, head_size=self.head_size, dtype=self.kv_cache_dtype, - use_mla=False, ), layer_names=[self.attn.layer_name], vllm_config=self.vllm_config, diff --git a/tests/kernels/attention/test_cache.py b/tests/kernels/attention/test_cache.py index 1325e6883132..6e096a4c3999 100644 --- a/tests/kernels/attention/test_cache.py +++ b/tests/kernels/attention/test_cache.py @@ -593,6 +593,119 @@ def test_concat_and_cache_mla( torch.testing.assert_close(kv_cache, ref_kv_cache) +@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS) +@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS) +@pytest.mark.parametrize("num_tokens", NUM_TOKENS_MLA) +@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA) +@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA) +@pytest.mark.parametrize("dtype", DTYPES) +@pytest.mark.parametrize("seed", SEEDS) +@pytest.mark.parametrize("device", CUDA_DEVICES) +@torch.inference_mode() +def test_concat_and_cache_ds_mla( + kv_lora_rank: int, + qk_rope_head_dim: int, + num_tokens: int, + block_size: int, + num_blocks: int, + dtype: torch.dtype, + seed: int, + device: str, +) -> None: + if dtype.itemsize != 2: + pytest.skip("ds_mla only supports 16-bit input") + kv_cache_dtype = "fp8_ds_mla" + current_platform.seed_everything(seed) + torch.set_default_device(device) + + total_slots = num_blocks * block_size + slot_mapping_lst = random.sample(range(total_slots), num_tokens) + slot_mapping = torch.tensor(slot_mapping_lst, + dtype=torch.long, + device=device) + + kv_c = torch.randn(num_tokens, kv_lora_rank, dtype=dtype, device=device) + k_pe = torch.randn(num_tokens, + qk_rope_head_dim, + dtype=dtype, + device=device) + entry_size = kv_lora_rank + (4 * 4) + (2 * qk_rope_head_dim) + + scale = torch.tensor(1.0, dtype=torch.float32, device=device) + kv_cache = _create_mla_cache(num_blocks, + block_size, + entry_size, + dtype=torch.uint8, + kv_cache_dtype=kv_cache_dtype, + device=device) + + ref_cache = torch.zeros_like(kv_cache, dtype=kv_cache.dtype) + tile_data = torch.zeros(128, dtype=dtype, device=device) + + for i in range(num_tokens): + slot = slot_mapping[i].item() + block_idx = slot // block_size + block_offset = slot % block_size + + ref_cache_slice = ref_cache[block_idx, block_offset] + ref_cache_16bit = ref_cache_slice.view(dtype) + ref_cache_32bit = ref_cache_slice.view(torch.float32) + + kv_c_data = kv_c[i] + for tile_idx in range(4): + tile_start = tile_idx * 128 + tile_end = (tile_idx + 1) * 128 + tile_data[:] = kv_c_data[tile_start:tile_end] + + # tile_scale = tile_data.amax().to(torch.float32) / 448. + # NOTE: Using torch's amax() gives different results, + # so this must be manually computed. + tile_data_float = tile_data.to(torch.float32) + manual_max = abs(tile_data_float[0]) + for j in range(1, 128): + manual_max = max(manual_max, abs(tile_data_float[j])) + tile_scale = manual_max / 448. + + ref_cache_32bit[kv_lora_rank // 4 + tile_idx] = tile_scale + + ops.convert_fp8(ref_cache_slice[tile_start:tile_end], + tile_data, + tile_scale.item(), + kv_dtype="fp8") + + for j in range(qk_rope_head_dim): + ref_cache_16bit[kv_lora_rank // 2 + 8 + j] = k_pe[i, j] + + opcheck( + torch.ops._C_cache_ops.concat_and_cache_mla, + (kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale), + test_utils=DEFAULT_OPCHECK_TEST_UTILS, + ) + + ops.concat_and_cache_mla(kv_c, k_pe, kv_cache, slot_mapping, + kv_cache_dtype, scale) + + for i in range(num_tokens): + slot = slot_mapping[i].item() + block_idx = slot // block_size + block_offset = slot % block_size + kv_cache_slice = kv_cache[block_idx, block_offset] + ref_cache_slice = ref_cache[block_idx, block_offset] + + kv_nope = kv_cache_slice[:kv_lora_rank] + ref_nope = ref_cache_slice[:kv_lora_rank] + kv_scales = kv_cache_slice.view(torch.float32)[kv_lora_rank // + 4:kv_lora_rank // 4 + 4] + ref_scales = ref_cache_slice.view( + torch.float32)[kv_lora_rank // 4:kv_lora_rank // 4 + 4] + kv_rope = kv_cache_slice.view(dtype)[kv_lora_rank // 2 + 8:] + ref_rope = ref_cache_slice.view(dtype)[kv_lora_rank // 2 + 8:] + + torch.testing.assert_close(kv_nope, ref_nope, atol=0.001, rtol=0.1) + torch.testing.assert_close(kv_scales, ref_scales, atol=0.001, rtol=0.1) + torch.testing.assert_close(kv_rope, ref_rope, atol=0.001, rtol=0.1) + + @pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS) @pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS) @pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA) diff --git a/tests/kernels/attention/test_deepgemm_attention.py b/tests/kernels/attention/test_deepgemm_attention.py new file mode 100644 index 000000000000..2d901e408b27 --- /dev/null +++ b/tests/kernels/attention/test_deepgemm_attention.py @@ -0,0 +1,279 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import random + +import pytest +import torch + +from vllm.platforms import current_platform +from vllm.utils import cdiv, has_deep_gemm +from vllm.utils.deep_gemm import (_ceil_to_ue8m0, calc_diff, fp8_mqa_logits, + fp8_paged_mqa_logits, get_num_sms, + get_paged_mqa_logits_metadata) + + +def kv_cache_cast_to_fp8(x: torch.Tensor) -> torch.Tensor: + # x: (num_blocks, block_size, 1, head_dim) + num_blocks, block_size, num_heads, head_dim = x.shape + assert num_heads == 1 + x_amax = x.abs().float().amax(dim=3, keepdim=True).clamp(1e-4) + sf = x_amax / 448.0 + x_scaled = (x * (1.0 / sf)).to(torch.float8_e4m3fn) + x_fp8 = torch.empty( + (num_blocks, block_size * (head_dim + 4)), + device=x.device, + dtype=torch.uint8, + ) + x_fp8[:, :block_size * head_dim] = x_scaled.view( + num_blocks, block_size * head_dim).view(dtype=torch.uint8) + x_fp8[:, + block_size * head_dim:] = sf.view(num_blocks, + block_size).view(dtype=torch.uint8) + return x_fp8.view(num_blocks, block_size, num_heads, head_dim + 4) + + +def per_custom_dims_cast_to_fp8( + x: torch.Tensor, dims: tuple, + use_ue8m0: bool) -> tuple[torch.Tensor, torch.Tensor]: + excluded_dims = tuple([i for i in range(x.dim()) if i not in set(dims)]) + x_amax = x.abs().float().amax(dim=excluded_dims, keepdim=True).clamp(1e-4) + sf = x_amax / 448.0 + sf = _ceil_to_ue8m0(sf) if use_ue8m0 else sf + x_scaled = (x * (1.0 / sf)).to(torch.float8_e4m3fn) + return x_scaled, sf.squeeze() + + +def _generate_cp_test_data(seq_len: int, seq_len_kv: int): + assert seq_len_kv % seq_len == 0 and seq_len % 2 == 0 + chunk_size = seq_len // 2 + cp_size = seq_len_kv // seq_len + cp_id = cp_size // 3 + ks = torch.zeros(seq_len, dtype=torch.int, device="cuda") + ke = torch.zeros(seq_len, dtype=torch.int, device="cuda") + for i in range(chunk_size): + ke[i] = cp_id * chunk_size + i + ke[i + chunk_size] = (cp_size * 2 - 1 - cp_id) * chunk_size + i + return ks, ke + + +def _ref_fp8_mqa_logits( + q: torch.Tensor, + kv: torch.Tensor, + weights: torch.Tensor, + cu_seqlen_ks: torch.Tensor, + cu_seqlen_ke: torch.Tensor, +): + seq_len_kv = kv.shape[0] + + k = kv + q = q.float() + k = k.float() + + mask_lo = (torch.arange(0, seq_len_kv, device="cuda")[None, :] + >= cu_seqlen_ks[:, None]) + mask_hi = (torch.arange(0, seq_len_kv, device="cuda")[None, :] + < cu_seqlen_ke[:, None]) + mask = mask_lo & mask_hi + + score = torch.einsum("mhd,and->hmn", q, k) + logits = (score.relu() * weights.unsqueeze(-1).transpose(0, 1)).sum(dim=0) + logits = logits.masked_fill(~mask, float("-inf")) + + return logits + + +@pytest.mark.skipif(not current_platform.is_cuda(), reason="CUDA only") +@pytest.mark.skipif(not has_deep_gemm(), reason="DeepGEMM not available") +@pytest.mark.skipif(not current_platform.has_device_capability(90), + reason="SM90 and SM100 only") +def test_deepgemm_fp8_mqa_logits(): + torch.manual_seed(0) + random.seed(0) + num_heads, head_dim = 32, 128 + for seq_len in (512, ): + for seq_len_kv in (1024, ): + for disable_cp in (False, True): + q = torch.randn( + seq_len, + num_heads, + head_dim, + device="cuda", + dtype=torch.bfloat16, + ) + kv = torch.randn(seq_len_kv, + head_dim, + device="cuda", + dtype=torch.bfloat16) + weights = torch.randn(seq_len, + num_heads, + device="cuda", + dtype=torch.float32) + + if disable_cp: + ks = torch.zeros(seq_len, dtype=torch.int, device="cuda") + ke = torch.arange(seq_len, dtype=torch.int, + device="cuda") + (seq_len_kv - seq_len) + else: + ks, ke = _generate_cp_test_data(seq_len, seq_len_kv) + + q_fp8 = q.to(torch.float8_e4m3fn) + kv_fp8 = per_custom_dims_cast_to_fp8(kv, (0, ), False) + logits = fp8_mqa_logits(q_fp8, kv_fp8, weights, ks, ke) + + ref_logits = _ref_fp8_mqa_logits( + q=q, + kv=kv, + weights=weights, + cu_seqlen_ks=ks, + cu_seqlen_ke=ke, + ) + + ref_neginf_mask = ref_logits == float("-inf") + neginf_mask = logits == float("-inf") + assert torch.equal(neginf_mask, ref_neginf_mask) + + ref_logits = ref_logits.masked_fill(ref_neginf_mask, 0) + logits = logits.masked_fill(neginf_mask, 0) + diff = calc_diff(logits, ref_logits) + assert diff < 1e-3, f"{diff=}" + + +def _ref_fp8_paged_mqa_logits( + q: torch.Tensor, + kv_cache: torch.Tensor, + weights: torch.Tensor, + context_lens: torch.Tensor, + block_tables: torch.Tensor, + max_model_len: int, +): + batch_size, next_n, _, _ = q.size() + _, block_size, _, _ = kv_cache.size() + logits = torch.full( + [batch_size * next_n, max_model_len], + float("-inf"), + device=q.device, + dtype=torch.float32, + ) + context_lens_list = context_lens.tolist() + for i in range(batch_size): + context_len = context_lens_list[i] + q_offsets = torch.arange(context_len - next_n, + context_len, + device="cuda") + weight_slice = (weights[i * next_n:(i + 1) * next_n, :].transpose( + 0, 1).contiguous()) + for block_rk in range(cdiv(context_len, block_size)): + block_idx = block_tables[i][block_rk] + qx, kx = q[i], kv_cache[block_idx] + k_offsets = torch.arange( + block_rk * block_size, + (block_rk + 1) * block_size, + device="cuda", + ) + mask = (k_offsets[None, :] < context_len) & (k_offsets[None, :] + <= q_offsets[:, None]) + s = torch.where( + mask[None, :, :], + (qx.transpose(0, 1) @ kx.transpose(0, 1).transpose(1, 2)).to( + logits.dtype), + float("-inf"), + ) + s = torch.relu(s) * weight_slice[..., None] + s = s.sum(dim=0) + logits[ + i * next_n:(i + 1) * next_n, + block_rk * block_size:(block_rk + 1) * block_size, + ] = torch.where(k_offsets[None, :] <= q_offsets[:, None], s, + float("-inf")) + return logits + + +@pytest.mark.skipif(not current_platform.is_cuda(), reason="CUDA only") +@pytest.mark.skipif(not has_deep_gemm(), reason="DeepGEMM not available") +@pytest.mark.skipif(not current_platform.has_device_capability(90), + reason="SM90 and SM100 only") +def test_deepgemm_fp8_paged_mqa_logits(): + torch.manual_seed(0) + random.seed(0) + + max_model_len = 4096 + for batch_size, next_n in [(4, 1), (2, 2)]: + for heads, index_dim in [(32, 128)]: + for avg_kv in (2048, ): + num_blocks, blocksize = max_model_len * 2, 64 + + q = torch.randn( + (batch_size, next_n, heads, index_dim), + device="cuda", + dtype=torch.bfloat16, + ) + kv_cache = torch.randn( + (num_blocks, blocksize, 1, index_dim), + device="cuda", + dtype=torch.bfloat16, + ) + weights = torch.randn( + (batch_size * next_n, heads), + device="cuda", + dtype=torch.float32, + ) + + context_lens = (torch.randint(int(0.8 * avg_kv), + int(1.2 * avg_kv), + (batch_size, )).cuda().to( + torch.int32)) + max_block_len = ((context_lens.max().item() + blocksize - 1) // + blocksize * blocksize) + block_tables = torch.zeros( + (batch_size, max_block_len), + device="cuda", + dtype=torch.int32, + ) + + counter = 0 + block_idx_pool = list(range(num_blocks)) + random.shuffle(block_idx_pool) + for i in range(batch_size): + ctx_len = int(context_lens[i].item()) + for j in range((ctx_len + blocksize - 1) // blocksize): + block_tables[i][j] = block_idx_pool[counter] + counter += 1 + + q_fp8 = q.to(torch.float8_e4m3fn) + kv_cache_fp8 = kv_cache_cast_to_fp8(kv_cache) + + schedule_metadata = get_paged_mqa_logits_metadata( + context_lens, blocksize, get_num_sms()) + logits = fp8_paged_mqa_logits( + q_fp8, + kv_cache_fp8, + weights, + context_lens, + block_tables, + schedule_metadata, + max_model_len, + ) + + ref_logits = _ref_fp8_paged_mqa_logits( + q, + kv_cache, + weights, + context_lens, + block_tables, + max_model_len, + ) + + positions = (torch.arange(max_model_len, + device="cuda").unsqueeze(0).expand( + batch_size * next_n, -1)) + row_indices = ( + torch.arange(batch_size * next_n, device="cuda") // next_n) + next_n_offset = ( + torch.arange(batch_size * next_n, device="cuda") % next_n) + mask = positions <= (context_lens[row_indices] - next_n + + next_n_offset).unsqueeze(1) + + logits = logits.masked_fill(~mask, 0) + ref_logits = ref_logits.masked_fill(~mask, 0) + diff = calc_diff(logits, ref_logits) + assert diff < 1e-3, f"{diff=}" diff --git a/tests/kernels/attention/test_flashmla.py b/tests/kernels/attention/test_flashmla.py index abcfe828d5ac..bddd7e5c50ed 100644 --- a/tests/kernels/attention/test_flashmla.py +++ b/tests/kernels/attention/test_flashmla.py @@ -97,18 +97,16 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal, descale_k = None def flash_mla(): - return flash_mla_with_kvcache( - q, - blocked_k, - block_table, - cache_seqlens, - dv, - tile_scheduler_metadata, - num_splits, - causal=causal, - descale_q=descale_q, - descale_k=descale_k, - ) + return flash_mla_with_kvcache(q, + blocked_k, + block_table, + cache_seqlens, + dv, + tile_scheduler_metadata, + num_splits, + causal=causal, + descale_q=descale_q, + descale_k=descale_k) def scaled_dot_product_attention(query, key, value, is_causal=False): query = query.float() diff --git a/tests/kernels/attention/test_flashmla_sparse.py b/tests/kernels/attention/test_flashmla_sparse.py new file mode 100644 index 000000000000..9036e4e7800b --- /dev/null +++ b/tests/kernels/attention/test_flashmla_sparse.py @@ -0,0 +1,119 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import pytest +import torch + + +def _cuda_sm90_available() -> bool: + if not torch.cuda.is_available(): + return False + major, _ = torch.cuda.get_device_capability() + return major == 9 + + +def test_sparse_flashmla_metadata_smoke(): + import vllm.attention.ops.flashmla as fm + ok, reason = fm.is_flashmla_supported() + if not ok or not _cuda_sm90_available(): + pytest.skip(reason or "SM90 not available") + + device = torch.device("cuda") + batch_size = 1 + seqlen_q = 1 + num_heads_q = 128 + num_heads_k = 1 + q_seq_per_hk = seqlen_q * num_heads_q // num_heads_k + topk = 128 + + cache_seqlens = torch.zeros(batch_size, dtype=torch.int32, device=device) + + tile_md, num_splits = fm.get_mla_metadata(cache_seqlens, + q_seq_per_hk, + num_heads_k, + num_heads_q=num_heads_q, + topk=topk, + is_fp8_kvcache=True) + assert tile_md.dtype == torch.int32 + assert num_splits.dtype == torch.int32 + + +def test_sparse_flashmla_decode_smoke(): + import vllm.attention.ops.flashmla as fm + ok, reason = fm.is_flashmla_supported() + if not ok or not _cuda_sm90_available(): + pytest.skip(reason or "SM90 not available") + + device = torch.device("cuda") + batch_size = 1 + seqlen_q = 1 + num_heads_q = 1 + head_dim_k = 576 + head_dim_v = 512 + num_heads_k = 1 + page_block_size = 64 + bytes_per_token = 656 + topk = 128 + + # Metadata + q_seq_per_hk = seqlen_q * num_heads_q // num_heads_k + # q_heads_per_hk = num_heads_q // num_heads_k + cache_seqlens = torch.zeros(batch_size, dtype=torch.int32, device=device) + tile_md, num_splits = fm.get_mla_metadata(cache_seqlens, + q_seq_per_hk, + num_heads_k, + num_heads_q=num_heads_q, + topk=topk, + is_fp8_kvcache=True) + + # Inputs + q = torch.zeros((batch_size, seqlen_q, num_heads_q, head_dim_k), + dtype=torch.bfloat16, + device=device) + k_cache = torch.zeros((1, page_block_size, num_heads_k, bytes_per_token), + dtype=torch.uint8, + device=device) + indices = torch.zeros((batch_size, seqlen_q, topk), + dtype=torch.int32, + device=device) + + block_table = torch.zeros((batch_size, 128), + dtype=torch.int32, + device=device) + out, lse = fm.flash_mla_with_kvcache(q, + k_cache, + block_table, + cache_seqlens, + head_dim_v, + tile_md, + num_splits, + indices=indices, + is_fp8_kvcache=True) + assert out.shape[0] == batch_size + assert out.shape[-1] == head_dim_v + assert lse.shape[0] == batch_size + + +def test_sparse_flashmla_prefill_smoke(): + import vllm.attention.ops.flashmla as fm + ok, reason = fm.is_flashmla_supported() + if not ok or not _cuda_sm90_available(): + pytest.skip(reason or "SM90 not available") + + device = torch.device("cuda") + s_q = 1 + s_kv = 1 + h_q = 64 # kernel expects multiple of 64 + h_kv = 1 + d_qk = 576 + d_v = 512 + topk = 128 + + q = torch.zeros((s_q, h_q, d_qk), dtype=torch.bfloat16, device=device) + kv = torch.zeros((s_kv, h_kv, d_qk), dtype=torch.bfloat16, device=device) + indices = torch.zeros((s_q, h_kv, topk), dtype=torch.int32, device=device) + + out, max_logits, lse = fm.flash_mla_sparse_prefill(q, kv, indices, 1.0, + d_v) + assert out.shape == (s_q, h_q, d_v) + assert max_logits.shape == (s_q, h_q) + assert lse.shape == (s_q, h_q) diff --git a/tests/kernels/attention/test_pack_unpack_triton.py b/tests/kernels/attention/test_pack_unpack_triton.py new file mode 100644 index 000000000000..20c0b262b479 --- /dev/null +++ b/tests/kernels/attention/test_pack_unpack_triton.py @@ -0,0 +1,245 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import torch +from torch.testing import assert_close + +from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton + + +def test_pack_seq_basic_fp8(): + """Test basic functionality of pack_seq_triton with fp8 and 3D tensors.""" + device = "cuda" + dtype = torch.float8_e4m3fn + + # Test cases with 3D tensors (N, H, D) + test_cases = [ + (6, 8, 4, 2, [3, 3]), # (6, 8, 4) -> (2, 3, 8, 4) + (10, 4, 8, 3, [2, 4, 4]), # (10, 4, 8) -> (3, 4, 4, 8) + (20, 16, 32, 4, [5, 5, 5, 5]), # (20, 16, 32) -> (4, 5, 16, 32) + ] + + for N, H, D, B, lengths_list in test_cases: + # Create input tensor with small values for fp8 + x = torch.randn(N, H, D, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor(lengths_list, device=device) + + # Pack the data + packed = pack_seq_triton(x, lengths) + + # Check output shape and properties + expected_shape = (B, max(lengths_list), H, D) + assert packed.shape == expected_shape + assert packed.dtype == dtype + assert packed.device == x.device + + # Check that valid data is preserved (within fp8 precision) + for b in range(B): + start_idx = sum(lengths_list[:b]) + seq_len = lengths_list[b] + + expected_data = x[start_idx:start_idx + seq_len].to(torch.float32) + actual_data = packed[b, :seq_len].to(torch.float32) + + assert_close(actual_data, expected_data, rtol=1e-1, atol=1e-2) + + +def test_pack_seq_custom_padding_fp8(): + """Test pack_seq_triton with custom padding values for fp8.""" + device = "cuda" + dtype = torch.float8_e4m3fn + N, H, D, B = 20, 8, 16, 2 + lengths = torch.tensor([10, 10], device=device) + + x = torch.randn(N, H, D, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + + # Test with different padding values + for pad_value in [-100.0, -10.0, 0.0, 10.0, 100.0]: + result = pack_seq_triton(x, lengths, pad_value=pad_value) + + # Check valid data + for b in range(B): + start_idx = b * 10 + expected_data = x[start_idx:start_idx + 10].to(torch.float32) + actual_data = result[b, :10].to(torch.float32) + assert_close(actual_data, expected_data, rtol=1e-1, atol=1e-2) + + # Check padding (fp8 has limited range, so check for large values) + padded_data = result[:, 10:].to(torch.float32) + if pad_value < 0: + assert torch.all(padded_data < -50) # Large negative values + elif pad_value > 0: + assert torch.all(padded_data > 50) # Large positive values + else: + assert torch.allclose(padded_data, + torch.zeros_like(padded_data), + atol=1e-2) + + +def test_pack_seq_default_negative_inf_padding_fp8(): + """Test that pack_seq_triton uses -inf padding by default for fp8.""" + device = "cuda" + dtype = torch.float8_e4m3fn + # B = 2 + N, H, D = 20, 8, 16 + lengths = torch.tensor([10, 10], device=device) + + x = torch.randn(N, H, D, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + result = pack_seq_triton(x, lengths) + + # Check that padding is large negative values (fp8 representation of -inf) + padded_data = result[:, 10:].to(torch.float32) + assert torch.all( + padded_data < -100) # fp8 -inf is represented as large negative number + + +def test_pack_seq_edge_cases_fp8(): + """Test pack_seq_triton with edge cases for fp8.""" + device = "cuda" + dtype = torch.float8_e4m3fn + + # Test with single batch element + x = torch.randn(10, 8, 16, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor([10], device=device) + result = pack_seq_triton(x, lengths) + assert result.shape == (1, 10, 8, 16) + + # Test with very short sequences + x = torch.randn(20, 4, 8, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor([1, 1, 1], device=device) + result = pack_seq_triton(x, lengths) + assert result.shape == (3, 1, 4, 8) + + # Test with different sequence lengths + x = torch.randn(15, 8, 16, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor([5, 7, 3], device=device) + result = pack_seq_triton(x, lengths) + assert result.shape == (3, 7, 8, 16) + + +def test_pack_seq_different_block_sizes_fp8(): + """Test pack_seq_triton with different block sizes for fp8.""" + device = "cuda" + dtype = torch.float8_e4m3fn + N, H, D, B = 100, 16, 32, 4 + lengths = torch.tensor([25, 25, 25, 25], device=device) + + x = torch.randn(N, H, D, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + + # Test different block sizes + for block_t, block_d in [(32, 32), (64, 64), (128, 128)]: + result = pack_seq_triton(x, lengths, block_t=block_t, block_d=block_d) + + assert result.shape == (B, 25, H, D) + + # Check that valid data is preserved (within fp8 precision) + for b in range(B): + start_idx = b * 25 + expected_data = x[start_idx:start_idx + 25].to(torch.float32) + actual_data = result[b, :25].to(torch.float32) + assert_close(actual_data, expected_data, rtol=1e-1, atol=1e-2) + + +def test_pack_seq_shape_consistency(): + """Test that pack_seq_triton maintains shape consistency.""" + device = "cuda" + dtype = torch.float8_e4m3fn + N, H, D, B = 20, 8, 16, 2 + lengths = torch.tensor([10, 10], device=device) + + x = torch.randn(N, H, D, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + + result = pack_seq_triton(x, lengths) + + # Check shape consistency + assert result.shape[0] == B # Batch dimension + assert result.shape[1] == lengths.max().item() # Max sequence length + assert result.shape[2:] == x.shape[1:] # Feature dimensions preserved + + +def test_pack_unpack_roundtrip_fp8(): + """Test that pack -> unpack gives us back the original data for fp8.""" + device = "cuda" + dtype = torch.float8_e4m3fn + + # Test cases with 3D tensors + test_cases = [ + (6, 8, 4, 2, [3, 3]), + (10, 4, 8, 3, [2, 4, 4]), + (20, 16, 32, 4, [5, 5, 5, 5]), + (15, 8, 16, 3, [7, 5, 3]), + ] + + for N, H, D, B, lengths_list in test_cases: + # Create input tensor with small values for fp8 + x = torch.randn(N, H, D, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor(lengths_list, device=device) + + # Pack the data + packed = pack_seq_triton(x, lengths) + + # Unpack the data + unpacked = unpack_seq_triton(packed, lengths) + + # Check that we get back the original data (within fp8 precision) + assert unpacked.shape == x.shape + x_f32 = x.to(torch.float32) + unpacked_f32 = unpacked.to(torch.float32) + assert_close(x_f32, unpacked_f32, rtol=1e-3, atol=1e-3) + + # Unpack without explicit start locations (computed in kernel) + unpacked_with_loc = unpack_seq_triton(packed, lengths) + assert_close(x_f32, + unpacked_with_loc.to(torch.float32), + rtol=1e-3, + atol=1e-2) + + +def test_unpack_seq_triton_edge_cases_fp8(): + """Test unpack function with edge cases for fp8.""" + device = "cuda" + dtype = torch.float8_e4m3fn + + # Test with single batch element + x = torch.randn(10, 8, 16, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor([10], device=device) + packed = pack_seq_triton(x, lengths) + unpacked = unpack_seq_triton(packed, lengths) + assert unpacked.shape == x.shape + assert_close(x.to(torch.float32), + unpacked.to(torch.float32), + rtol=1e-1, + atol=1e-2) + + # Test with very short sequences + x = torch.randn(20, 4, 8, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor([1, 1, 1], device=device) + packed = pack_seq_triton(x, lengths) + unpacked = unpack_seq_triton(packed, lengths) + # Only compare the first 3 elements that were actually packed + assert_close(x[:3].to(torch.float32), + unpacked.to(torch.float32), + rtol=1e-1, + atol=1e-2) + + x = torch.randn(15, 8, 16, dtype=torch.float32, device=device) * 0.1 + x = x.to(dtype=dtype) + lengths = torch.tensor([5, 7, 3], device=device) + packed = pack_seq_triton(x, lengths) + unpacked = unpack_seq_triton(packed, lengths) + assert unpacked.shape == x.shape + assert_close(x.to(torch.float32), + unpacked.to(torch.float32), + rtol=1e-1, + atol=1e-2) diff --git a/tests/models/registry.py b/tests/models/registry.py index 37ee474d3ecb..b7a2514d8bc0 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -207,6 +207,7 @@ def check_available_online( trust_remote_code=True), "DeepseekV3ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3", # noqa: E501 trust_remote_code=True), + "DeepseekV32ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3.2-Exp"), "Ernie4_5ForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-0.3B-PT", min_transformers_version="4.54"), "Ernie4_5_MoeForCausalLM": _HfExamplesInfo("baidu/ERNIE-4.5-21B-A3B-PT", diff --git a/tests/models/test_initialization.py b/tests/models/test_initialization.py index e818b908e8a8..1db0dc3da922 100644 --- a/tests/models/test_initialization.py +++ b/tests/models/test_initialization.py @@ -8,7 +8,8 @@ from vllm import LLM from vllm.utils import GiB_bytes -from vllm.v1.core.kv_cache_utils import get_kv_cache_configs +from vllm.v1.core.kv_cache_utils import (generate_scheduler_kv_cache_config, + get_kv_cache_configs) from vllm.v1.engine.core import EngineCore as V1EngineCore from ..utils import create_new_process_for_each_test @@ -62,11 +63,13 @@ def can_initialize(model_arch: str, monkeypatch: pytest.MonkeyPatch, # Avoid calling model.forward() def _initialize_kv_caches_v1(self, vllm_config): kv_cache_specs = self.model_executor.get_kv_cache_specs() - scheduler_kv_cache_config = get_kv_cache_configs( + kv_cache_configs = get_kv_cache_configs( vllm_config, kv_cache_specs, [10 * GiB_bytes], - )[0] + ) + scheduler_kv_cache_config = generate_scheduler_kv_cache_config( + kv_cache_configs) # gpu_blocks (> 0), cpu_blocks, scheduler_kv_cache_config return 1, 0, scheduler_kv_cache_config diff --git a/tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_platform.py b/tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_platform.py index 8d0687b49bb4..30d721304b5c 100644 --- a/tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_platform.py +++ b/tests/plugins/vllm_add_dummy_platform/vllm_add_dummy_platform/dummy_platform.py @@ -26,5 +26,5 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None: def get_attn_backend_cls(self, backend_name, head_size, dtype, kv_cache_dtype, block_size, use_v1, use_mla, - has_sink): + has_sink, use_sparse): return "vllm_add_dummy_platform.dummy_attention_backend.DummyAttentionBackend" # noqa E501 diff --git a/tests/v1/attention/test_mla_backends.py b/tests/v1/attention/test_mla_backends.py index d4829c64b5c6..228551573ba8 100644 --- a/tests/v1/attention/test_mla_backends.py +++ b/tests/v1/attention/test_mla_backends.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Tests for v1 MLA backends without GPUModelRunner dependency.""" +from typing import Optional, Union import pytest import torch @@ -10,6 +11,7 @@ create_standard_kv_cache_spec, create_vllm_config, get_attention_backend) +from vllm import _custom_ops as ops from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv from vllm.v1.attention.backends.utils import CommonAttentionMetadata from vllm.v1.kv_cache_interface import FullAttentionSpec @@ -78,7 +80,9 @@ def create_and_prepopulate_kv_cache( device: torch.device, num_blocks: int, common_attn_metadata: CommonAttentionMetadata, - randomize_blocks: bool = True) -> torch.Tensor: + randomize_blocks: bool = True, + kv_cache_dtype: Optional[str] = None, + scale: Union[float, torch.Tensor] = 1.0) -> torch.Tensor: """Create and prepopulate an MLA KV cache with context data. Args: @@ -93,6 +97,11 @@ def create_and_prepopulate_kv_cache( common_attn_metadata: Common attention metadata randomize_blocks: Whether to randomly permute blocks or use sequential order + kv_cache_dtype: Optional kv cache dtype string. When set to + "fp8_ds_mla" the cache is populated using the + fp8 DeepSeek MLA layout via concat_and_cache_mla. + scale: Scaling factor forwarded to concat_and_cache_mla when the + fp8 cache layout is requested. Returns: MLA KV cache tensor @@ -105,23 +114,61 @@ def create_and_prepopulate_kv_cache( block_table = common_attn_metadata.block_table_tensor slot_mapping = common_attn_metadata.slot_mapping - # Create MLA KV cache: (num_blocks, block_size, head_size) - kv_cache = torch.empty(num_blocks, - block_size, - head_size, - dtype=dtype, - device=device) - kv_cache_flat = kv_cache.view(-1, head_size) + use_fp8_ds_mla = kv_cache_dtype == "fp8_ds_mla" + + if use_fp8_ds_mla: + if not kv_c_contexts: + raise ValueError("kv_c_contexts cannot be empty when using" + " fp8_ds_mla cache dtype") + kv_lora_rank = kv_c_contexts[0].shape[-1] + rope_dim = k_pe_contexts[0].shape[-1] + entry_size = kv_lora_rank + 4 * 4 + 2 * rope_dim + kv_cache = torch.zeros(num_blocks, + block_size, + entry_size, + dtype=torch.uint8, + device=device) + scale_tensor = (scale + if isinstance(scale, torch.Tensor) else torch.tensor( + scale, dtype=torch.float32, device=device)) + scale_tensor = scale_tensor.to(device=device, dtype=torch.float32) + else: + # Create MLA KV cache: (num_blocks, block_size, head_size) + kv_cache = torch.empty(num_blocks, + block_size, + head_size, + dtype=dtype, + device=device) + kv_cache_flat = kv_cache.view(-1, head_size) # Populate the cache with the context tokens # Start from block_id=1 since block_id=0 is considered the null block start_block_idx = 1 for i in range(batch_size): kv_c_context, k_pe_context = kv_c_contexts[i], k_pe_contexts[i] - kv_context = torch.cat([kv_c_context, k_pe_context.squeeze(1)], dim=-1) + context_len = kv_c_context.shape[0] + if context_len == 0: + start_block_idx += cdiv(int(seq_lens[i]), block_size) + continue + start = start_block_idx * block_size - end = start + kv_context.shape[0] - kv_cache_flat[start:end, ...] = kv_context + + if use_fp8_ds_mla: + slots = torch.arange(context_len, device=device, + dtype=torch.long) + start + ops.concat_and_cache_mla( + kv_c_context, + k_pe_context.squeeze(1), + kv_cache, + slots, + kv_cache_dtype="fp8_ds_mla", + scale=scale_tensor, + ) + else: + kv_context = torch.cat( + [kv_c_context, k_pe_context.squeeze(1)], dim=-1) + end = start + kv_context.shape[0] + kv_cache_flat[start:end, ...] = kv_context # Stay block aligned and allocate enough blocks for the new tokens start_block_idx += cdiv(int(seq_lens[i]), block_size) diff --git a/tests/v1/attention/test_sparse_mla_backends.py b/tests/v1/attention/test_sparse_mla_backends.py new file mode 100644 index 000000000000..74eea6f716fe --- /dev/null +++ b/tests/v1/attention/test_sparse_mla_backends.py @@ -0,0 +1,426 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +"""Unit tests for the FlashMLA sparse backend utilities.""" + +import math +from types import MethodType, SimpleNamespace + +import numpy as np +import pytest +import torch + +from tests.v1.attention.test_mla_backends import ( + BATCH_SPECS, BatchSpec, MockAttentionLayer, + create_and_prepopulate_kv_cache) +from tests.v1.attention.utils import (create_common_attn_metadata, + create_standard_kv_cache_spec, + create_vllm_config) +from vllm import _custom_ops as ops +from vllm.attention.ops import flashmla +from vllm.model_executor.layers.linear import ColumnParallelLinear +from vllm.utils import cdiv +from vllm.v1.attention.backends.mla.flashmla_sparse import ( + FlashMLASparseBackend, FlashMLASparseDecodeAndContextMetadata, + FlashMLASparseImpl, FlashMLASparseMetadata) + +SPARSE_BACKEND_BATCH_SPECS = { + name: BATCH_SPECS[name] + for name in [ + "mixed_small", + "mixed_medium", + "small_prefill", + "medium_prefill", + "single_prefill", + ] +} + +SPARSE_BACKEND_BATCH_SPECS["large_q_prefill"] = BatchSpec(seq_lens=[1024] * 2, + query_lens=[256] * 2) +SPARSE_BACKEND_BATCH_SPECS["large_q_pure_prefill"] = BatchSpec( + seq_lens=[256] * 2, query_lens=[256] * 2) + + +def _dequantize_fp8_ds_mla_entry( + cache_slice: torch.Tensor, kv_lora_rank: int, rope_dim: int, + dtype: torch.dtype) -> tuple[torch.Tensor, torch.Tensor]: + """Dequantize a single fp8_ds_mla cache entry back to latent + rope.""" + + # The first kv_lora_rank bytes store FP8 latent values with one scale per + # 128 element tile written as float32 right after the latent payload. + scales = cache_slice.view(torch.float32)[kv_lora_rank // + 4:kv_lora_rank // 4 + 4] + latent = torch.empty(kv_lora_rank, + dtype=torch.float16, + device=cache_slice.device) + for tile_idx in range(4): + tile_start = tile_idx * 128 + tile_end = tile_start + 128 + ops.convert_fp8(latent[tile_start:tile_end], + cache_slice[tile_start:tile_end], + float(scales[tile_idx].item()), + kv_dtype="fp8") + latent = latent.to(dtype) + + rope_offset = kv_lora_rank // 2 + 8 + rope_vals = cache_slice.view(dtype)[rope_offset:rope_offset + rope_dim] + return latent, rope_vals.clone() + + +def _quantize_dequantize_fp8_ds_mla( + kv_c: torch.Tensor, k_pe: torch.Tensor, block_size: int, + scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Round-trip kv_c/k_pe though the fp8_ds_mla cache layout.""" + + if kv_c.numel() == 0: + return kv_c.clone(), k_pe.clone() + + kv_lora_rank = kv_c.shape[-1] + rope_dim = k_pe.shape[-1] + num_tokens = kv_c.shape[0] + num_blocks = max(1, math.ceil(num_tokens / block_size)) + entry_size = kv_lora_rank + 4 * 4 + 2 * rope_dim + + tmp_cache = torch.zeros(num_blocks, + block_size, + entry_size, + dtype=torch.uint8, + device=kv_c.device) + slot_mapping = torch.arange(num_tokens, + dtype=torch.long, + device=kv_c.device) + + ops.concat_and_cache_mla(kv_c, + k_pe, + tmp_cache, + slot_mapping, + kv_cache_dtype="fp8_ds_mla", + scale=scale) + + dequant_kv_c = torch.empty_like(kv_c) + dequant_k_pe = torch.empty_like(k_pe) + + for token_idx in range(num_tokens): + slot = slot_mapping[token_idx].item() + block_idx = slot // block_size + block_offset = slot % block_size + cache_slice = tmp_cache[block_idx, block_offset] + latent, rope_vals = _dequantize_fp8_ds_mla_entry( + cache_slice, kv_lora_rank, rope_dim, kv_c.dtype) + dequant_kv_c[token_idx] = latent + dequant_k_pe[token_idx] = rope_vals + + return dequant_kv_c, dequant_k_pe + + +def test_sparse_backend_metadata_registration(): + backend = FlashMLASparseBackend + + assert backend.get_name() == "FLASHMLA_SPARSE_VLLM_V1" + assert backend.get_metadata_cls() is FlashMLASparseMetadata + assert backend.get_impl_cls() is FlashMLASparseImpl + + dtype_list = backend.get_supported_dtypes() + assert torch.bfloat16 in dtype_list + + shape = backend.get_kv_cache_shape(num_blocks=2, + block_size=64, + num_kv_heads=1, + head_size=576) + assert shape == (2, 64, 576) + + +def test_sparse_decode_metadata_filters_prefill_indices(): + prefill_context_lengths = torch.tensor([4, 2], dtype=torch.int32) + metadata = FlashMLASparseDecodeAndContextMetadata( + scheduler_metadata=torch.tensor([[0]], dtype=torch.int32), + num_splits=torch.tensor([1, 1], dtype=torch.int32), + cache_lens=torch.tensor([10, 12], dtype=torch.int32), + prefill_context_lengths=prefill_context_lengths, + ) + + indices = torch.tensor([[0, 3, 5], [1, 2, 4]], dtype=torch.int32) + + context_indices, new_token_indices = metadata.filter_prefill_indices( + indices) + + expected_context = torch.tensor([[-1, -1, 5], [-1, -1, 4]], + dtype=torch.int32) + expected_new_tokens = torch.tensor([[-1, -1, 1], [-1, 0, 2]], + dtype=torch.int32) + + assert torch.equal(context_indices, expected_context) + assert torch.equal(new_token_indices, expected_new_tokens) + + +def test_sparse_impl_zero_fills_when_metadata_missing(): + impl = FlashMLASparseImpl.__new__(FlashMLASparseImpl) + dummy_layer = object() + q = torch.zeros((2, 1, 3)) + k_c = torch.zeros((2, 3)) + k_pe = torch.zeros((2, 1, 1)) + kv_cache = torch.zeros((1, 1, 1)) + output = torch.ones((2, 4)) + + result = FlashMLASparseImpl.forward(impl, + dummy_layer, + q, + k_c, + k_pe, + kv_cache, + attn_metadata=None, + output=output) + + assert result is output + assert torch.all(result == 0) + + +@pytest.mark.parametrize("batch_name", list(SPARSE_BACKEND_BATCH_SPECS.keys())) +@pytest.mark.parametrize("kv_cache_dtype", ["fp8_ds_mla", "auto"]) +def test_sparse_backend_decode_correctness(dist_init, batch_name, + kv_cache_dtype): + if not torch.cuda.is_available(): + pytest.skip("CUDA is required for sparse MLA decode test") + + device = torch.device("cuda") + dtype = torch.bfloat16 + + batch_spec = SPARSE_BACKEND_BATCH_SPECS[batch_name] + + # Model hyper-parameters (kept intentionally small for the unit test) + num_heads = 128 + kv_lora_rank = 512 + qk_nope_head_dim = 128 + qk_rope_head_dim = 64 + v_head_dim = 128 + head_size = kv_lora_rank + qk_rope_head_dim + topk_tokens = 2048 + + max_seqlen = max(batch_spec.seq_lens) + total_cache_tokens = sum(batch_spec.seq_lens) + block_size = 64 + + vllm_config = create_vllm_config( + model_name="deepseek-ai/DeepSeek-V2-Lite-Chat", + max_model_len=max_seqlen, + num_gpu_blocks=max(2048, + cdiv(total_cache_tokens, block_size) + 1), + block_size=block_size) + model_config = vllm_config.model_config + model_config.hf_config = SimpleNamespace( + attn_module_list_cfg=[{ + "topk_tokens": topk_tokens + }]) + model_config.hf_text_config = SimpleNamespace( + q_lora_rank=None, + kv_lora_rank=kv_lora_rank, + qk_nope_head_dim=qk_nope_head_dim, + qk_rope_head_dim=qk_rope_head_dim, + v_head_dim=v_head_dim, + model_type="deepseek_v2", + ) + model_config.dtype = dtype + model_config.get_num_attention_heads = MethodType( + lambda self, parallel_config: num_heads, model_config) + model_config.get_num_kv_heads = MethodType(lambda self, parallel_config: 1, + model_config) + model_config.get_head_size = MethodType(lambda self: head_size, + model_config) + model_config.get_sliding_window = MethodType(lambda self: None, + model_config) + + kv_cache_spec = create_standard_kv_cache_spec(vllm_config) + + torch.manual_seed(0) + + scale = 1.0 / math.sqrt(head_size) + + # Shared MLA projection weights to keep reference and backend in sync + W_UK = torch.randn(kv_lora_rank, + num_heads, + qk_nope_head_dim, + dtype=dtype, + device=device) + W_UV = torch.randn(kv_lora_rank, + num_heads, + v_head_dim, + dtype=dtype, + device=device) + + # Build synthetic decode-only workload + seq_lens = batch_spec.seq_lens + query_lens = batch_spec.query_lens + + all_q_vllm, all_kv_c_vllm, all_k_pe_vllm = [], [], [] + kv_c_contexts, k_pe_contexts = [], [] + reference_outputs = [] + + kv_cache_scale = torch.tensor(1.0, dtype=torch.float32, device=device) + + for i in range(batch_spec.batch_size): + s_len = seq_lens[i] + q_len = query_lens[i] + ctx_len = s_len - q_len + + q_c = torch.rand(q_len, + num_heads, + qk_nope_head_dim + qk_rope_head_dim, + dtype=dtype, + device=device) + kv_c_full = torch.rand(s_len, kv_lora_rank, dtype=dtype, device=device) + k_pe_full = torch.rand(s_len, + 1, + qk_rope_head_dim, + dtype=dtype, + device=device) + + kv_c_full, k_pe_full = _quantize_dequantize_fp8_ds_mla( + kv_c_full, + k_pe_full.squeeze(1), + block_size=vllm_config.cache_config.block_size, + scale=kv_cache_scale, + ) + + q_nope, q_pe = q_c.split([qk_nope_head_dim, qk_rope_head_dim], dim=-1) + ql_nope = torch.einsum("qnh,lnh->qnl", q_nope, W_UK) + q_mqa = torch.cat([ql_nope, q_pe], dim=-1) + + k_mqa = torch.cat([kv_c_full, k_pe_full], dim=-1) + k_mqa = k_mqa.unsqueeze(1).expand(-1, num_heads, -1) + v_mqa = kv_c_full.unsqueeze(1).expand(-1, num_heads, -1) + + attn_mask = torch.ones(q_len, s_len, dtype=torch.bool, device=device) + causal_mask = torch.tril(torch.ones(q_len, q_len, device=device)) + attn_mask[:, ctx_len:] = causal_mask + + q_sdpa_in = q_mqa.unsqueeze(0).transpose(1, 2) + k_sdpa_in = k_mqa.unsqueeze(0).transpose(1, 2) + v_sdpa_in = v_mqa.unsqueeze(0).transpose(1, 2) + + sdpa_out = torch.nn.functional.scaled_dot_product_attention( + q_sdpa_in, k_sdpa_in, v_sdpa_in, attn_mask=attn_mask, scale=scale) + sdpa_out = sdpa_out.transpose(1, 2).squeeze(0) + + sdpa_out = torch.einsum("qnl,lnv->qnv", sdpa_out, W_UV) + reference_outputs.append(sdpa_out.flatten(start_dim=-2)) + + all_q_vllm.append(q_c) + all_kv_c_vllm.append(kv_c_full[ctx_len:]) + all_k_pe_vllm.append(k_pe_full[ctx_len:]) + kv_c_contexts.append(kv_c_full[:ctx_len + 1]) + k_pe_contexts.append(k_pe_full[:ctx_len + 1]) + + query_vllm = torch.cat(all_q_vllm, dim=0) + kv_c_vllm = torch.cat(all_kv_c_vllm, dim=0) + k_pe_vllm = torch.cat(all_k_pe_vllm, dim=0) + sdpa_reference = torch.cat(reference_outputs, dim=0) + + vllm_config.cache_config.cache_dtype = kv_cache_dtype + + common_attn_metadata = create_common_attn_metadata( + batch_spec, + vllm_config.cache_config.block_size, + device, + arange_block_indices=True) + + kv_cache = create_and_prepopulate_kv_cache( + kv_c_contexts=kv_c_contexts, + k_pe_contexts=k_pe_contexts, + block_size=vllm_config.cache_config.block_size, + head_size=head_size, + dtype=dtype, + device=device, + num_blocks=vllm_config.cache_config.num_gpu_blocks, + common_attn_metadata=common_attn_metadata, + randomize_blocks=False, + kv_cache_dtype=vllm_config.cache_config.cache_dtype, + scale=kv_cache_scale, + ) + + builder_cls = FlashMLASparseBackend.get_builder_cls() + builder = builder_cls(kv_cache_spec, ["placeholder"], vllm_config, device) + metadata = builder.build(common_prefix_len=0, + common_attn_metadata=common_attn_metadata) + + starts = np.asarray(common_attn_metadata.query_start_loc_cpu, + dtype=np.int32) + seg_lengths = np.diff(starts) + positions = np.arange(starts[-1], dtype=np.int32) - np.repeat( + starts[:-1], seg_lengths) + seq_lengths = np.asarray(common_attn_metadata.seq_lens_cpu, dtype=np.int32) + prefix_lengths = seq_lengths - seg_lengths + positions += np.repeat(prefix_lengths, seg_lengths) + + pos_gpu = torch.as_tensor(positions, device=device, dtype=torch.int32) + topk = metadata.topk_tokens + debug_indices = torch.arange(topk, device=device, + dtype=torch.int32).unsqueeze(0) + token_positions = pos_gpu.unsqueeze(1) + causal_mask = (debug_indices <= token_positions) + debug_indices = torch.where(causal_mask, debug_indices, + torch.full_like(debug_indices, -1)) + + # FlashMLASparseImpl now reads top-k indices from the indexer-provided + # buffer, so emulate that contract with a simple namespace mock. + debug_indices = debug_indices.expand(metadata.num_actual_tokens, + -1).clone() + mock_indexer = SimpleNamespace(topk_indices_buffer=debug_indices) + + ok, reason = flashmla.is_flashmla_supported() + if not ok: + pytest.skip(reason) + + kv_b_proj_weight = torch.cat([W_UK, W_UV], dim=-1) + kv_b_proj_weight = kv_b_proj_weight.view( + kv_lora_rank, num_heads * (qk_nope_head_dim + v_head_dim)) + + mock_kv_b_proj = ColumnParallelLinear(input_size=kv_lora_rank, + output_size=num_heads * + (qk_nope_head_dim + v_head_dim), + bias=False).to(device=device, + dtype=dtype) + mock_kv_b_proj.weight = torch.nn.Parameter(kv_b_proj_weight.T.contiguous()) + + impl_cls = FlashMLASparseBackend.get_impl_cls() + impl = impl_cls(num_heads=num_heads, + head_size=head_size, + scale=scale, + num_kv_heads=1, + alibi_slopes=None, + sliding_window=None, + kv_cache_dtype=vllm_config.cache_config.cache_dtype, + logits_soft_cap=None, + attn_type="decoder", + kv_sharing_target_layer_name=None, + q_lora_rank=None, + kv_lora_rank=kv_lora_rank, + qk_nope_head_dim=qk_nope_head_dim, + qk_rope_head_dim=qk_rope_head_dim, + qk_head_dim=qk_nope_head_dim + qk_rope_head_dim, + v_head_dim=v_head_dim, + kv_b_proj=mock_kv_b_proj, + indexer=mock_indexer) + + impl.process_weights_after_loading(dtype) + + layer = MockAttentionLayer(device) + out_buffer = torch.empty(metadata.num_actual_tokens, + num_heads * v_head_dim, + dtype=dtype, + device=device) + + backend_output = impl.forward(layer, + query_vllm, + kv_c_vllm, + k_pe_vllm, + kv_cache, + metadata, + output=out_buffer) + + assert backend_output.shape == sdpa_reference.shape + assert backend_output.dtype == sdpa_reference.dtype + assert torch.isfinite(backend_output).all() + + torch.testing.assert_close(backend_output, + sdpa_reference, + rtol=0.5, + atol=0.5) diff --git a/tests/v1/attention/utils.py b/tests/v1/attention/utils.py index 01b5de83a59a..adfe2b2db040 100644 --- a/tests/v1/attention/utils.py +++ b/tests/v1/attention/utils.py @@ -168,7 +168,6 @@ def create_standard_kv_cache_spec( vllm_config.parallel_config), head_size=vllm_config.model_config.get_head_size(), dtype=vllm_config.model_config.dtype, - use_mla=vllm_config.model_config.use_mla, sliding_window=vllm_config.model_config.get_sliding_window(), ) diff --git a/tests/v1/core/test_kv_cache_utils.py b/tests/v1/core/test_kv_cache_utils.py index 4cb7ed6ce382..452b16ef4a91 100644 --- a/tests/v1/core/test_kv_cache_utils.py +++ b/tests/v1/core/test_kv_cache_utils.py @@ -24,7 +24,8 @@ make_block_hash_with_group_id) from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig, KVCacheGroupSpec, KVCacheSpec, - KVCacheTensor, SlidingWindowSpec, + KVCacheTensor, MLAAttentionSpec, + SlidingWindowSpec, UniformTypeKVCacheSpecs) from vllm.v1.metrics.stats import PrefixCacheStats from vllm.v1.request import Request @@ -77,13 +78,11 @@ def new_kv_cache_spec(block_size=16, num_kv_heads=2, head_size=64, dtype=torch.float32, - use_mla=False, sliding_window=None): return FullAttentionSpec(block_size=block_size, num_kv_heads=num_kv_heads, head_size=head_size, dtype=dtype, - use_mla=use_mla, sliding_window=sliding_window) @@ -91,13 +90,11 @@ def new_sliding_window_spec(block_size=16, num_kv_heads=2, head_size=64, dtype=torch.float32, - use_mla=False, sliding_window=1): return SlidingWindowSpec(block_size=block_size, num_kv_heads=num_kv_heads, head_size=head_size, dtype=dtype, - use_mla=use_mla, sliding_window=sliding_window) @@ -894,7 +891,6 @@ def test_merge_kv_cache_spec(): num_kv_heads=full_spec.num_kv_heads, head_size=full_spec.head_size, dtype=full_spec.dtype, - use_mla=full_spec.use_mla, sliding_window=1, ), ] @@ -991,7 +987,6 @@ def test_estimate_max_model_len(model_id, max_model_len, num_kv_heads=32, head_size=128, dtype=torch.float16, - use_mla=False, ) # Estimate the maximum model length, 16384 model_len need 8GB estimated_max_len = estimate_max_model_len(vllm_config, kv_cache_spec, @@ -1022,7 +1017,6 @@ def test_get_max_concurrency_for_kv_cache_config(): num_kv_heads=32, head_size=128, dtype=torch.float16, - use_mla=False, ) sliding_window_spec = SlidingWindowSpec( @@ -1030,7 +1024,6 @@ def test_get_max_concurrency_for_kv_cache_config(): num_kv_heads=32, head_size=128, dtype=torch.float16, - use_mla=False, sliding_window=1024, ) @@ -1412,3 +1405,48 @@ def test_generate_scheduler_kv_cache_config(): KVCacheGroupSpec(['layer_1', 'layer_2'], new_kv_cache_spec()) ], ) + + +def new_mla_spec(cache_dtype_str=None): + return MLAAttentionSpec(block_size=16, + num_kv_heads=16, + head_size=64, + dtype=torch.float32, + cache_dtype_str=cache_dtype_str) + + +def test_merge_mla_spec(): + kv_cache_specs = [ + new_mla_spec(), + new_mla_spec(), + ] + mla_spec = kv_cache_specs[0].merge(kv_cache_specs) + assert mla_spec == new_mla_spec() + + kv_cache_specs = [ + new_mla_spec(cache_dtype_str="fp8_ds_mla"), + new_mla_spec(cache_dtype_str="fp8_ds_mla"), + ] + mla_spec = kv_cache_specs[0].merge(kv_cache_specs) + assert mla_spec == new_mla_spec(cache_dtype_str="fp8_ds_mla") + + kv_cache_specs = [ + new_mla_spec(cache_dtype_str="fp8_ds_mla"), + new_mla_spec(cache_dtype_str=None), + ] + with pytest.raises(AssertionError): + kv_cache_specs[0].merge(kv_cache_specs) + + kv_cache_specs = [ + new_kv_cache_spec(), + new_mla_spec(), + ] + with pytest.raises(AssertionError): + kv_cache_specs[0].merge(kv_cache_specs) + + kv_cache_specs = [ + new_mla_spec(cache_dtype_str="fp8_ds_mla"), + new_kv_cache_spec(), + ] + with pytest.raises(AssertionError): + kv_cache_specs[0].merge(kv_cache_specs) diff --git a/tests/v1/core/test_prefix_caching.py b/tests/v1/core/test_prefix_caching.py index 37b4f9a08e40..5769099e0af1 100644 --- a/tests/v1/core/test_prefix_caching.py +++ b/tests/v1/core/test_prefix_caching.py @@ -76,7 +76,7 @@ def make_kv_cache_config(block_size: int, num_blocks: int) -> KVCacheConfig: kv_cache_groups=[ KVCacheGroupSpec( ["layer"], - FullAttentionSpec(block_size, 1, 1, torch.float32, False), + FullAttentionSpec(block_size, 1, 1, torch.float32), ) ], ) @@ -90,7 +90,7 @@ def make_kv_cache_config_hybrid_model(block_size: int, kv_cache_groups=[ KVCacheGroupSpec( ["layer1"], - FullAttentionSpec(block_size, 1, 1, torch.float32, False), + FullAttentionSpec(block_size, 1, 1, torch.float32), ), KVCacheGroupSpec( ["layer2"], @@ -98,7 +98,6 @@ def make_kv_cache_config_hybrid_model(block_size: int, 1, 1, torch.float32, - False, sliding_window=2 * block_size), ), KVCacheGroupSpec( @@ -107,7 +106,6 @@ def make_kv_cache_config_hybrid_model(block_size: int, 1, 1, torch.float32, - False, sliding_window=2 * block_size), ), ], @@ -1338,7 +1336,6 @@ def test_eagle_with_sliding_window(): head_size=1, dtype=torch.float32, sliding_window=block_size, - use_mla=False, ) manager = KVCacheManager( KVCacheConfig( diff --git a/tests/v1/core/test_single_type_kv_cache_manager.py b/tests/v1/core/test_single_type_kv_cache_manager.py index 01b54ae56e90..1f6825b6d24e 100644 --- a/tests/v1/core/test_single_type_kv_cache_manager.py +++ b/tests/v1/core/test_single_type_kv_cache_manager.py @@ -35,7 +35,6 @@ def test_chunked_local_attention_possible_cached_prefix(): head_size=1, dtype=torch.float32, attention_chunk_size=4, - use_mla=False, ) block_pool = BlockPool(num_gpu_blocks=100, enable_caching=True) @@ -100,7 +99,6 @@ def test_sliding_window_possible_cached_prefix(): head_size=1, dtype=torch.float32, sliding_window=4, - use_mla=False, ) block_pool = BlockPool(num_gpu_blocks=100, enable_caching=True) @@ -165,7 +163,6 @@ def test_chunked_local_attention_remove_skipped_blocks(): head_size=1, dtype=torch.float32, attention_chunk_size=4, - use_mla=False, ) block_pool = BlockPool(num_gpu_blocks=2000, enable_caching=True) @@ -217,7 +214,6 @@ def test_sliding_window_remove_skipped_blocks(): head_size=1, dtype=torch.float32, sliding_window=4, - use_mla=False, ) block_pool = BlockPool(num_gpu_blocks=2000, enable_caching=True) @@ -285,7 +281,6 @@ def test_get_num_blocks_to_allocate(): head_size=1, dtype=torch.float32, sliding_window=4, # Placeholder value, not related to test result - use_mla=False, ) block_pool = BlockPool(num_gpu_blocks=100, enable_caching=True) @@ -308,7 +303,6 @@ def test_chunked_local_attention_get_num_blocks_to_allocate(): head_size=1, dtype=torch.float32, attention_chunk_size=4, # Placeholder value, not related to test result - use_mla=False, ) block_pool = BlockPool(num_gpu_blocks=100, enable_caching=True) diff --git a/tests/v1/engine/test_engine_core_client.py b/tests/v1/engine/test_engine_core_client.py index 992c4e01386e..10adac9bab5f 100644 --- a/tests/v1/engine/test_engine_core_client.py +++ b/tests/v1/engine/test_engine_core_client.py @@ -836,8 +836,7 @@ def create_mock_executor(vllm_config): mock_spec = FullAttentionSpec(block_size=16, num_kv_heads=1, head_size=64, - dtype=torch.float16, - use_mla=False) + dtype=torch.float16) mock_executor.get_kv_cache_specs.return_value = [{ "default": mock_spec diff --git a/tests/v1/worker/test_gpu_model_runner.py b/tests/v1/worker/test_gpu_model_runner.py index 8b571f95c5ec..49a7a61e1889 100644 --- a/tests/v1/worker/test_gpu_model_runner.py +++ b/tests/v1/worker/test_gpu_model_runner.py @@ -39,7 +39,6 @@ def initialize_kv_cache(runner: GPUModelRunner): runner.parallel_config), head_size=runner.model_config.get_head_size(), dtype=runner.kv_cache_dtype, - use_mla=False, ) tensor_size = attn_spec.page_size_bytes * NUM_BLOCKS kv_cache_config = KVCacheConfig( diff --git a/vllm/_custom_ops.py b/vllm/_custom_ops.py index a108542e1436..f07fa1e4e7be 100644 --- a/vllm/_custom_ops.py +++ b/vllm/_custom_ops.py @@ -1678,6 +1678,15 @@ def cp_gather_cache(src_cache: torch.Tensor, cu_seq_lens, batch_size, seq_starts) +def indexer_k_quant_and_cache(k: torch.Tensor, kv_cache: torch.Tensor, + slot_mapping: torch.Tensor, + quant_block_size: int, + kv_cache_dtype: str) -> None: + torch.ops._C_cache_ops.indexer_k_quant_and_cache(k, kv_cache, slot_mapping, + quant_block_size, + kv_cache_dtype) + + def get_device_attribute(attribute: int, device: int) -> int: return torch.ops._C_cuda_utils.get_device_attribute(attribute, device) diff --git a/vllm/attention/backends/abstract.py b/vllm/attention/backends/abstract.py index 629e42a8b902..b49e1c007c57 100644 --- a/vllm/attention/backends/abstract.py +++ b/vllm/attention/backends/abstract.py @@ -70,6 +70,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> Tuple[int, ...]: raise NotImplementedError diff --git a/vllm/attention/layer.py b/vllm/attention/layer.py index 8cab8330a6cd..4ce6a864d7ad 100644 --- a/vllm/attention/layer.py +++ b/vllm/attention/layer.py @@ -94,6 +94,7 @@ def __init__( logits_soft_cap: Optional[float] = None, per_layer_sliding_window: Optional[int] = None, use_mla: bool = False, + use_sparse: bool = False, prefix: str = "", attn_type: str = AttentionType.DECODER, kv_sharing_target_layer_name: Optional[str] = None, @@ -154,6 +155,7 @@ def __init__( self._o_scale_float: Optional[float] = None self.use_mla = use_mla + self.use_sparse = use_sparse self.num_heads = num_heads self.head_size = head_size self.num_kv_heads = num_kv_heads @@ -186,7 +188,8 @@ def __init__( kv_cache_dtype, block_size, use_mla=use_mla, - has_sink=self.has_sink) + has_sink=self.has_sink, + use_sparse=use_sparse) else: self.attn_backend = attn_backend diff --git a/vllm/attention/ops/common.py b/vllm/attention/ops/common.py index c8efa6e63a2e..e659f1f3eae9 100644 --- a/vllm/attention/ops/common.py +++ b/vllm/attention/ops/common.py @@ -138,3 +138,208 @@ def cp_lse_ag_out_rs(cp_attn_out: torch.Tensor, out, _ = correct_attn_out(cp_attn_out, lses, cp_group.rank_in_group, ctx) out = cp_group.reduce_scatter(out, dim=1) return out + + +@triton.jit +def _pack_seq_kernel( + x_ptr, # [N, D] + out_ptr, # [B, Lmax, D] + lengths_ptr, # *i32, [B] + N: tl.constexpr, + D: tl.constexpr, + Lmax: tl.constexpr, + PAD_VALUE: tl.constexpr, + BLOCK_T: tl.constexpr, # timesteps per program + BLOCK_D: tl.constexpr # features per program +): + pid_b = tl.program_id(0) # batch id + pid_t = tl.program_id(1) # block over time dimension + pid_d = tl.program_id(2) # block over feature dimension + off_t = pid_t * BLOCK_T + tl.arange(0, BLOCK_T) # [BLOCK_T] + off_d = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) # [BLOCK_D] + + # Compute start index and sequence length from cumulative lengths + in_start = 0 + for i in range(pid_b): + in_start += tl.load(lengths_ptr + i) + seq_len = tl.load(lengths_ptr + pid_b) + + # valid time positions for this block + t_mask = off_t < Lmax + + # compute input row indices for valid (b, t) + in_row = in_start + off_t + valid_row = (off_t < seq_len) & t_mask + + # Pointers + # x_ptr: row-major [N, D] + x_row_ptr = x_ptr + in_row[:, None] * D + off_d[None, :] + + # out_ptr: row-major [B, Lmax, D] + out_row_ptr = out_ptr + (pid_b * Lmax + off_t)[:, + None] * D + off_d[None, :] + + # Initialize with PAD (cast will occur as needed based on out_ptr dtype) + d_mask = off_d[None, :] < D + pad_vals = tl.full([BLOCK_T, BLOCK_D], PAD_VALUE, tl.float32) + tl.store(out_row_ptr, pad_vals, mask=t_mask[:, None] & d_mask) + + # Load & write only where within seq_len + x_vals = tl.load(x_row_ptr, mask=valid_row[:, None] & d_mask) + tl.store(out_row_ptr, x_vals, mask=valid_row[:, None] & d_mask) + + +def pack_seq_triton(x: torch.Tensor, + lengths: torch.Tensor, + pad_value: float = -float('inf'), + block_t: int = 64, + block_d: int = 64) -> torch.Tensor: + """ + Pack sequences of different lengths into a batched tensor. + + Args: + x: [N, ...] - input tensor where N is total number of tokens + lengths: [B] - sequence lengths for each batch + pad_value: value to use for padding + block_t: block size for time dimension + block_d: block size for feature dimension + + Returns: + packed: [B, Lmax, ...] - packed tensor + """ + + # Handle multi-dimensional input by reshaping to (N, -1) + original_shape = x.shape + if len(original_shape) > 2: + N = original_shape[0] + x_reshaped = x.reshape(N, -1) + D = x_reshaped.shape[1] + else: + N, D = x.shape + x_reshaped = x + + B = lengths.numel() + Lmax = int(lengths.max().item()) + + # Starts are computed inside the kernel from lengths + + out = torch.empty((B, Lmax, D), device=x.device, dtype=x.dtype) + + grid = (B, triton.cdiv(Lmax, block_t), triton.cdiv(D, block_d)) + _pack_seq_kernel[grid](x_reshaped, + out, + lengths.int(), + N, + D, + Lmax, + PAD_VALUE=float(pad_value), + BLOCK_T=block_t, + BLOCK_D=block_d, + num_warps=4, + num_stages=2) + + # Reshape output back to original dimensions (except first dimension) + if len(original_shape) > 2: + output_shape = (B, Lmax) + original_shape[1:] + out = out.reshape(output_shape) + + return out + + +@triton.jit +def _unpack_seq_triton_kernel( + packed_ptr, # [B, Lmax, D] + out_ptr, # [N, D] + lengths_ptr, # *i32, [B] + B: tl.constexpr, + Lmax: tl.constexpr, + D: tl.constexpr, + BLOCK_T: tl.constexpr, # timesteps per program + BLOCK_D: tl.constexpr # features per program +): + pid_b = tl.program_id(0) # batch id + pid_t = tl.program_id(1) # block over time dimension + pid_d = tl.program_id(2) # block over feature dimension + off_t = pid_t * BLOCK_T + tl.arange(0, BLOCK_T) # [BLOCK_T] + off_d = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) # [BLOCK_D] + + # bounds: compute start from cumulative lengths + in_start = 0 + for i in range(pid_b): + in_start += tl.load(lengths_ptr + i) + seq_len = tl.load(lengths_ptr + pid_b) + + # valid time positions for this block + t_mask = off_t < Lmax + valid_row = (off_t < seq_len) & t_mask + + # compute output row indices for valid (b, t) + out_row = in_start + off_t + + # Pointers + # packed_ptr: row-major [B, Lmax, D] + packed_row_ptr = packed_ptr + (pid_b * Lmax + + off_t)[:, None] * D + off_d[None, :] + + # out_ptr: row-major [N, D] + out_row_ptr = out_ptr + out_row[:, None] * D + off_d[None, :] + + # Load from packed tensor and store to output + d_mask = off_d[None, :] < D + packed_vals = tl.load(packed_row_ptr, mask=valid_row[:, None] & d_mask) + tl.store(out_row_ptr, packed_vals, mask=valid_row[:, None] & d_mask) + + +def unpack_seq_triton(packed_tensor: torch.Tensor, + lengths: torch.Tensor, + block_t: int = 64, + block_d: int = 64) -> torch.Tensor: + """ + Unpack a packed decode query tensor back to the original format. + Efficient Triton implementation. + + Args: + packed_tensor: [B, Lmax, ...] - packed tensor from pack_seq_triton + lengths: [B] - sequence lengths for each batch + block_t: block size for time dimension + block_d: block size for feature dimension + + Returns: + unpacked_tensor: [N, ...] where N = sum(lengths) + """ + + # Handle multi-dimensional input by reshaping to (B, Lmax, -1) + original_shape = packed_tensor.shape + if len(original_shape) > 3: + B, Lmax = original_shape[:2] + packed_reshaped = packed_tensor.reshape(B, Lmax, -1) + D = packed_reshaped.shape[2] + else: + B, Lmax, D = packed_tensor.shape + packed_reshaped = packed_tensor + + # Calculate total number of elements + N = int(lengths.sum().item()) + + out = torch.empty((N, D), + device=packed_tensor.device, + dtype=packed_tensor.dtype) + + grid = (B, triton.cdiv(Lmax, block_t), triton.cdiv(D, block_d)) + _unpack_seq_triton_kernel[grid](packed_reshaped, + out, + lengths.int(), + B, + Lmax, + D, + BLOCK_T=block_t, + BLOCK_D=block_d, + num_warps=4, + num_stages=2) + + # Reshape output back to original dimensions (except first dimension) + if len(original_shape) > 3: + output_shape = (N, ) + original_shape[2:] + out = out.reshape(output_shape) + + return out diff --git a/vllm/attention/ops/flashmla.py b/vllm/attention/ops/flashmla.py index 2c3e8c42400c..3cc0e4adfa0a 100644 --- a/vllm/attention/ops/flashmla.py +++ b/vllm/attention/ops/flashmla.py @@ -19,6 +19,15 @@ else: _flashmla_C_AVAILABLE = False +if current_platform.is_cuda(): + try: + import vllm._flashmla_extension_C # noqa: F401 + _flashmla_extension_C_AVAILABLE = True + except ImportError: + _flashmla_extension_C_AVAILABLE = False +else: + _flashmla_extension_C_AVAILABLE = False + def is_flashmla_supported() -> Tuple[bool, Optional[str]]: """ @@ -37,24 +46,34 @@ def is_flashmla_supported() -> Tuple[bool, Optional[str]]: def get_mla_metadata( - cache_seqlens: torch.Tensor, - num_heads_per_head_k: int, - num_heads_k: int, -) -> Tuple[torch.Tensor, torch.Tensor]: + cache_seqlens: torch.Tensor, + num_q_tokens_per_head_k: int, + num_heads_k: int, + num_heads_q: Optional[int] = None, + is_fp8_kvcache: bool = False, + topk: Optional[int] = None) -> Tuple[torch.Tensor, torch.Tensor]: """ Arguments: - cache_seqlens: (batch_size), dtype torch.int32. - num_heads_per_head_k: Equals to seq_len_q * num_heads_q // num_heads_k. - num_heads_k: num_heads_k. - - Return: - tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), - dtype torch.int32. - num_splits: (batch_size + 1), dtype torch.int32. + - cache_seqlens: (batch_size), dtype torch.int32. + - num_q_tokens_per_head_k: + Equals to num_q_tokens_per_q_seq * num_heads_q // num_heads_k. + - num_heads_k: The number of k heads. + - num_heads_q: + The number of q heads. + This argument is optional when sparse attention is not enabled + - is_fp8_kvcache: Whether the k_cache and v_cache are in fp8 format. + - topk: If not None, sparse attention will be enabled, + and only tokens in the `indices` array + passed to `flash_mla_with_kvcache_sm90` will be attended to. + + Returns: + - tile_scheduler_metadata: + (num_sm_parts, TileSchedulerMetaDataSize), dtype torch.int32. + - num_splits: (batch_size + 1), dtype torch.int32. """ - return torch.ops._flashmla_C.get_mla_metadata(cache_seqlens, - num_heads_per_head_k, - num_heads_k) + return torch.ops._flashmla_C.get_mla_decoding_metadata( + cache_seqlens, num_q_tokens_per_head_k, num_heads_k, num_heads_q, + is_fp8_kvcache, topk) def flash_mla_with_kvcache( @@ -69,45 +88,95 @@ def flash_mla_with_kvcache( causal: bool = False, descale_q: Optional[torch.Tensor] = None, descale_k: Optional[torch.Tensor] = None, + is_fp8_kvcache: bool = False, + indices: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Arguments: - q: (batch_size, seq_len_q, num_heads_q, head_dim). - k_cache: (num_blocks, page_block_size, num_heads_k, head_dim). - block_table: (batch_size, max_num_blocks_per_seq), torch.int32. - cache_seqlens: (batch_size), torch.int32. - head_dim_v: Head_dim of v. - tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), - torch.int32, return by get_mla_metadata. - num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata. - softmax_scale: float. The scaling of QK^T before applying softmax. - Default to 1 / sqrt(head_dim). - causal: bool. Whether to apply causal attention mask. - descale_q: (batch_size), torch.float32. Descaling factors for Q. - descale_k: (batch_size), torch.float32. Descaling factors for K. - - Return: - out: (batch_size, seq_len_q, num_heads_q, head_dim_v). - softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32. + - q: (batch_size, seq_len_q, num_heads_q, head_dim). + - k_cache: (num_blocks, page_block_size, num_heads_k, head_dim). + - block_table: (batch_size, max_num_blocks_per_seq), torch.int32. + - cache_seqlens: (batch_size), torch.int32. + - head_dim_v: Head dimension of v. + - tile_scheduler_metadata: + (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, + returned by get_mla_metadata. + - num_splits: + (batch_size + 1), torch.int32, returned by get_mla_metadata. + - softmax_scale: float. + The scale of QK^T before applying softmax. + Default to 1 / sqrt(head_dim). + - causal: bool. Whether to apply causal attention mask. + - descale_q: (batch_size), + torch.float32. Descaling factors for Q, used for fp8 quantization. + - descale_k: (batch_size), + torch.float32. Descaling factors for K, used for fp8 quantization. + - is_fp8_kvcache: bool. + Whether the k_cache and v_cache are in fp8 format. + For the format of FP8 KV cache, please refer to README.md + - indices: (batch_size, seq_len_q, topk), torch.int32. + If not None, sparse attention will be enabled, + and only tokens in the `indices` array will be attended to. + Invalid indices should be set to -1 or numbers >= total_seq_len_kv. + For details about how to set up `indices`, please refer to README.md. + + Returns: + - out: (batch_size, seq_len_q, num_heads_q, head_dim_v). + - softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32. """ if softmax_scale is None: softmax_scale = q.shape[-1]**(-0.5) - out, softmax_lse = torch.ops._flashmla_C.fwd_kvcache_mla( - q, - k_cache, - head_dim_v, - cache_seqlens, - block_table, - softmax_scale, - causal, - tile_scheduler_metadata, - num_splits, - descale_q, - descale_k, - ) - - # Note(hc): need revisit when we support DCP with decode query_len > 1. - return out.squeeze(1), softmax_lse.squeeze(-1) + if indices is not None: + # NOTE (zyongye): sparse attention is also causal + # since it only attend to the tokens before + # but here `causal` should not be specified + assert not causal, \ + "causal must be `false` if sparse attention is enabled." + assert (descale_q is None) == ( + descale_k is None + ), "descale_q and descale_k should be both None or both not None" + + if (descale_q is not None) and (descale_k is not None): + out, softmax_lse = torch.ops._flashmla_extension_C.fwd_kvcache_mla_fp8( + q, k_cache, head_dim_v, cache_seqlens, block_table, softmax_scale, + causal, tile_scheduler_metadata, num_splits, descale_q, descale_k) + else: + out, softmax_lse = torch.ops._flashmla_C.fwd_kvcache_mla( + q, k_cache, head_dim_v, cache_seqlens, block_table, softmax_scale, + causal, tile_scheduler_metadata, num_splits, is_fp8_kvcache, + indices) + return out, softmax_lse + + +def flash_mla_sparse_prefill( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + sm_scale: float, + d_v: int = 512, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Sparse attention prefill kernel + + Args: + - q: [s_q, h_q, d_qk], bfloat16 + - kv: [s_kv, h_kv, d_qk], bfloat16 + - indices: [s_q, h_kv, topk], int32. + Invalid indices should be set to -1 or numbers >= s_kv + - sm_scale: float + - d_v: The dimension of value vectors. Can only be 512 + + Returns: + - (output, max_logits, lse) + About the definition of output, + max_logits and lse, please refer to README.md + - output: [s_q, h_q, d_v], bfloat16 + - max_logits: [s_q, h_q], float + - lse: [s_q, h_q], float, 2-based log-sum-exp + """ + results = torch.ops._flashmla_C.sparse_prefill_fwd(q, kv, indices, + sm_scale, d_v) + return results # diff --git a/vllm/attention/ops/paged_attn.py b/vllm/attention/ops/paged_attn.py index 4d870a45e580..539b57e41de7 100644 --- a/vllm/attention/ops/paged_attn.py +++ b/vllm/attention/ops/paged_attn.py @@ -50,6 +50,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> Tuple[int, ...]: return (2, num_blocks, block_size * num_kv_heads * head_size) diff --git a/vllm/attention/selector.py b/vllm/attention/selector.py index bd83473db6f3..6f048e589f7f 100644 --- a/vllm/attention/selector.py +++ b/vllm/attention/selector.py @@ -144,6 +144,7 @@ def get_attn_backend( block_size: int, use_mla: bool = False, has_sink: bool = False, + use_sparse: bool = False, ) -> type[AttentionBackend]: """Selects which attention backend to use and lazily imports it.""" # Accessing envs.* behind an @lru_cache decorator can cause the wrong @@ -158,6 +159,7 @@ def get_attn_backend( use_v1=envs.VLLM_USE_V1, use_mla=use_mla, has_sink=has_sink, + use_sparse=use_sparse, ) @@ -170,6 +172,7 @@ def _cached_get_attn_backend( use_v1: bool = False, use_mla: bool = False, has_sink: bool = False, + use_sparse: bool = False, ) -> type[AttentionBackend]: # Check whether a particular choice of backend was @@ -203,7 +206,7 @@ def _cached_get_attn_backend( # get device-specific attn_backend attention_cls = current_platform.get_attn_backend_cls( selected_backend, head_size, dtype, kv_cache_dtype, block_size, use_v1, - use_mla, has_sink) + use_mla, has_sink, use_sparse) if not attention_cls: raise ValueError( f"Invalid attention backend for {current_platform.device_name}") diff --git a/vllm/config/cache.py b/vllm/config/cache.py index 4c4e39c37ee5..58770649a8af 100644 --- a/vllm/config/cache.py +++ b/vllm/config/cache.py @@ -22,7 +22,8 @@ logger = init_logger(__name__) BlockSize = Literal[1, 8, 16, 32, 64, 128] -CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2", "fp8_inc"] +CacheDType = Literal["auto", "bfloat16", "fp8", "fp8_e4m3", "fp8_e5m2", + "fp8_inc"] MambaDType = Literal["auto", "float32"] PrefixCachingHashAlgo = Literal["sha256", "sha256_cbor"] @@ -52,7 +53,11 @@ class CacheConfig: cache_dtype: CacheDType = "auto" """Data type for kv cache storage. If "auto", will use model data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ROCm (AMD GPU) supports - fp8 (=fp8_e4m3). Intel Gaudi (HPU) supports fp8 (using fp8_inc).""" + fp8 (=fp8_e4m3). Intel Gaudi (HPU) supports fp8 (using fp8_inc). + Some models (namely DeepSeekV3.2) default to fp8, set to bfloat16 to use + bfloat16 instead, this is an invalid option for models that do not default + to fp8. + """ is_attention_free: bool = False """Whether the model is attention-free. This is primarily set in `ModelConfig` and that value should be manually duplicated here.""" @@ -171,11 +176,12 @@ def _verify_cache_dtype(self) -> None: if self.cache_dtype == "auto": pass elif self.cache_dtype in get_args(CacheDType): - logger.info( - "Using fp8 data type to store kv cache. It reduces the GPU " - "memory footprint and boosts the performance. " - "Meanwhile, it may cause accuracy drop without a proper " - "scaling factor.") + if self.cache_dtype.startswith("fp8"): + logger.info( + "Using fp8 data type to store kv cache. It reduces the GPU " + "memory footprint and boosts the performance. " + "Meanwhile, it may cause accuracy drop without a proper " + "scaling factor.") else: raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}") diff --git a/vllm/config/compilation.py b/vllm/config/compilation.py index df08fa20e0cf..ce173edb4b94 100644 --- a/vllm/config/compilation.py +++ b/vllm/config/compilation.py @@ -374,6 +374,7 @@ class CompilationConfig: "vllm.linear_attention", "vllm.plamo2_mamba_mixer", "vllm.gdn_attention", + "vllm.sparse_attn_indexer", ] def compute_hash(self) -> str: diff --git a/vllm/config/model.py b/vllm/config/model.py index 28af82de7722..c1392318dd8e 100644 --- a/vllm/config/model.py +++ b/vllm/config/model.py @@ -1082,14 +1082,14 @@ def is_deepseek_mla(self) -> bool: if not hasattr(self.hf_text_config, "model_type"): return False elif self.hf_text_config.model_type in \ - ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp', + ('deepseek_v2', 'deepseek_v3', 'deepseek_v32', 'deepseek_mtp', 'kimi_k2', 'longcat_flash'): return self.hf_text_config.kv_lora_rank is not None elif self.hf_text_config.model_type == 'eagle': # if the model is an EAGLE module, check for the # underlying architecture return self.hf_text_config.model.model_type in \ - ('deepseek_v2', 'deepseek_v3') \ + ('deepseek_v2', 'deepseek_v3', 'deepseek_v32') \ and self.hf_text_config.kv_lora_rank is not None return False diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index cb4f0ae2cee0..f684e4e4ccd4 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -145,7 +145,7 @@ def compute_hash(self) -> str: @staticmethod def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig: - if hf_config.model_type == "deepseek_v3": + if hf_config.model_type in ("deepseek_v3", "deepseek_v32"): hf_config.model_type = "deepseek_mtp" if hf_config.model_type == "deepseek_mtp": n_predict = getattr(hf_config, "num_nextn_predict_layers", None) diff --git a/vllm/model_executor/layers/layernorm.py b/vllm/model_executor/layers/layernorm.py index 8123259d037b..363245daa89d 100644 --- a/vllm/model_executor/layers/layernorm.py +++ b/vllm/model_executor/layers/layernorm.py @@ -5,6 +5,7 @@ import torch import torch.nn as nn +import torch.nn.functional as F import vllm.envs as envs from vllm.model_executor.custom_op import CustomOp @@ -375,3 +376,20 @@ def forward_cuda( x: torch.Tensor, ) -> torch.Tensor: return poly_norm(x, self.weight, self.bias, self.variance_epsilon) + + +class LayerNorm(nn.Module): + """ + Layer Normalization. + """ + + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.dim = dim + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32)) + self.bias = nn.Parameter(torch.zeros(dim, dtype=torch.float32)) + + def forward(self, x: torch.Tensor): + return F.layer_norm(x.float(), (self.dim, ), self.weight, self.bias, + self.eps).type_as(x) diff --git a/vllm/model_executor/layers/mla.py b/vllm/model_executor/layers/mla.py index 9782b37c321f..66bf3823e191 100644 --- a/vllm/model_executor/layers/mla.py +++ b/vllm/model_executor/layers/mla.py @@ -24,6 +24,9 @@ class MLAModules: q_a_layernorm: Optional[torch.nn.Module] q_b_proj: Optional[torch.nn.Module] q_proj: Optional[torch.nn.Module] + indexer: Optional[torch.nn.Module] + is_sparse: bool + topk_indices_buffer: Optional[torch.Tensor] @CustomOp.register("multi_head_latent_attention") @@ -76,6 +79,13 @@ def __init__( self.kv_b_proj = mla_modules.kv_b_proj self.rotary_emb = mla_modules.rotary_emb self.o_proj = mla_modules.o_proj + self.indexer = mla_modules.indexer + self.is_sparse = mla_modules.is_sparse + + if self.indexer is not None: + assert hasattr(self.indexer, "topk_tokens") + self.topk_tokens = self.indexer.topk_tokens + self.topk_indices_buffer = mla_modules.topk_indices_buffer # In the MLA backend, kv_cache includes both k_c and # pe (i.e. decoupled position embeddings). In particular, @@ -92,6 +102,7 @@ def __init__( quant_config=quant_config, prefix=f"{prefix}.attn", use_mla=True, + use_sparse=mla_modules.is_sparse, # MLA Args q_lora_rank=self.q_lora_rank, kv_lora_rank=self.kv_lora_rank, @@ -100,6 +111,7 @@ def __init__( qk_head_dim=self.qk_head_dim, v_head_dim=self.v_head_dim, kv_b_proj=self.kv_b_proj, + indexer=self.indexer, ) self.prefix = prefix @@ -145,6 +157,10 @@ def forward_native( q[..., self.qk_nope_head_dim:], k_pe = self.rotary_emb( positions, q[..., self.qk_nope_head_dim:], k_pe) + if self.indexer and self.is_sparse: + _topk_indices = self.indexer(hidden_states, q_c, positions, + self.rotary_emb) + attn_out = self.mla_attn( q, kv_c_normed, diff --git a/vllm/model_executor/models/config.py b/vllm/model_executor/models/config.py index cab85ea347f4..589ca0069034 100644 --- a/vllm/model_executor/models/config.py +++ b/vllm/model_executor/models/config.py @@ -346,8 +346,7 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: block_size=1, num_kv_heads=model_config.get_num_kv_heads(parallel_config), head_size=model_config.get_head_size(), - dtype=kv_cache_dtype, - use_mla=model_config.use_mla).page_size_bytes + dtype=kv_cache_dtype).page_size_bytes model_cls, _ = ModelRegistry.resolve_model_cls( model_config.architecture, @@ -401,6 +400,31 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: "exactly equal.", mamba_padding_pct) +class DeepseekV3ForCausalLM(VerifyAndUpdateConfig): + + @classmethod + def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: + """ + Updated fp8 cache to custom "fp8_ds_mla" format for DeepSeekV32 + """ + hf_config = vllm_config.model_config.hf_config + + is_v32 = hasattr(hf_config, "index_topk") + + if is_v32: + # For DeepSeekV3.2, we use a custom fp8 format as default (i.e. + # "auto") + cache_config = vllm_config.cache_config + if cache_config.cache_dtype == "auto" or \ + cache_config.cache_dtype.startswith("fp8"): + cache_config.cache_dtype = "fp8_ds_mla" + logger.info( + "Using custom fp8 kv-cache format for DeepSeekV3.2") + if cache_config.cache_dtype == "bfloat16": + cache_config.cache_dtype = "auto" + logger.info("Using bfloat16 kv-cache for DeepSeekV3.2") + + MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = { "GteModel": SnowflakeGteNewModelConfig, "GteNewModel": GteNewModelConfig, @@ -417,4 +441,5 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: "MambaForCausalLM": MambaModelConfig, "Mamba2ForCausalLM": MambaModelConfig, "FalconMambaForCausalLM": MambaModelConfig, + "DeepseekV3ForCausalLM": DeepseekV3ForCausalLM, } diff --git a/vllm/model_executor/models/deepseek_mtp.py b/vllm/model_executor/models/deepseek_mtp.py index a4623ff13cec..788e561ac394 100644 --- a/vllm/model_executor/models/deepseek_mtp.py +++ b/vllm/model_executor/models/deepseek_mtp.py @@ -53,8 +53,20 @@ def __init__(self, vllm_config: VllmConfig, prefix: str) -> None: self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False) + + self.is_v32 = hasattr(config, "index_topk") + if self.is_v32: + topk_tokens = config.index_topk + topk_indices_buffer = torch.empty( + vllm_config.scheduler_config.max_num_batched_tokens, + topk_tokens, + dtype=torch.int32, + device="cuda") + else: + topk_indices_buffer = None self.shared_head = SharedHead(config=config, quant_config=quant_config) - self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix) + self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix, + topk_indices_buffer) def forward( self, diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py index 2e0bcbe5d2e5..03c43654d68f 100644 --- a/vllm/model_executor/models/deepseek_v2.py +++ b/vllm/model_executor/models/deepseek_v2.py @@ -33,15 +33,21 @@ from transformers import DeepseekV2Config, DeepseekV3Config from vllm.attention import Attention +from vllm.attention.backends.abstract import AttentionBackend +from vllm.attention.ops.common import pack_seq_triton, unpack_seq_triton from vllm.compilation.decorators import support_torch_compile -from vllm.config import CacheConfig, ParallelConfig, VllmConfig +from vllm.config import (CacheConfig, ParallelConfig, VllmConfig, + get_current_vllm_config) from vllm.distributed import (get_ep_group, get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather) +from vllm.forward_context import get_forward_context +from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul +from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase from vllm.model_executor.layers.fused_moe import FusedMoE -from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.layernorm import LayerNorm, RMSNorm from vllm.model_executor.layers.linear import (ColumnParallelLinear, MergedColumnParallelLinear, ReplicatedLinear, @@ -49,6 +55,8 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttention from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.quantization.utils.fp8_utils import ( + per_token_group_quant_fp8) from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE from vllm.model_executor.layers.vocab_parallel_embedding import ( @@ -56,13 +64,26 @@ from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) from vllm.model_executor.models.utils import sequence_parallel_chunk +from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors +from vllm.utils import cdiv, direct_register_custom_op +from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits +from vllm.v1.attention.backends.mla.indexer import (DeepseekV32IndexerBackend, + DeepseekV32IndexerMetadata) +from vllm.v1.kv_cache_interface import KVCacheSpec, MLAAttentionSpec from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP from .utils import (PPMissingLayer, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) +if current_platform.is_cuda_alike(): + from vllm import _custom_ops as ops +elif current_platform.is_xpu(): + from vllm._ipex_ops import ipex_ops as ops + +logger = init_logger(__name__) + class DeepseekV2MLP(nn.Module): @@ -276,6 +297,7 @@ class DeepseekV2Attention(nn.Module): def __init__( self, + vllm_config: VllmConfig, config: Union[DeepseekV2Config, DeepseekV3Config], hidden_size: int, num_heads: int, @@ -289,6 +311,7 @@ def __init__( max_position_embeddings: int = 8192, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, + topk_indices_buffer: Optional[torch.Tensor] = None, prefix: str = "", ) -> None: super().__init__() @@ -306,6 +329,8 @@ def __init__( self.scaling = self.qk_head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings + assert topk_indices_buffer is None, "topk_indices_buffer is not \ + supported for DeepseekV2Attention" if self.q_lora_rank is not None: self.q_a_proj = ReplicatedLinear(self.hidden_size, @@ -418,6 +443,391 @@ def forward( return output +class DeepseekV32IndexerCache(torch.nn.Module, AttentionLayerBase): + + def __init__(self, head_dim: int, dtype: torch.dtype, prefix: str, + cache_config: CacheConfig): + super().__init__() + self.kv_cache = [torch.tensor([])] + self.head_dim = head_dim + self.prefix = prefix + self.cache_config = cache_config + self.dtype = dtype + compilation_config = get_current_vllm_config().compilation_config + if prefix in compilation_config.static_forward_context: + raise ValueError(f"Duplicate layer name: {prefix}") + compilation_config.static_forward_context[prefix] = self + + def get_kv_cache_spec(self) -> KVCacheSpec: + return MLAAttentionSpec( # Only has one vector instead of K + V + block_size=self.cache_config.block_size, + num_kv_heads=1, + head_size=self.head_dim, + dtype=self.dtype, + ) + + def forward(self): + ... + + def get_attn_backend(self) -> AttentionBackend: + return DeepseekV32IndexerBackend + + +@torch.inference_mode() +def cp_gather_indexer_k_quant_cache( + kv_cache, # [num_blocks, block_size, head_dim + 1] + dst_value, # [cu_seq_lens[-1], head_dim] + dst_scale, # [cu_seq_lens[-1], 4] + block_table, # [batch_size, num_blocks] + cu_seq_lens, # [batch_size + 1, ] + batch_size, +): + num_blocks, block_size, _ = kv_cache.shape + head_dim = dst_value.shape[-1] + kv_cache = kv_cache.view(num_blocks, -1) + + expected_value = [] + expected_scale = [] + for b in range(batch_size): + s = cu_seq_lens[b + 1] - cu_seq_lens[b] + if s == 0: + continue + tot = cdiv(s, block_size) + blocks = block_table[b, :tot] + + value = [] + scale = [] + full_block = torch.arange(tot - 1, + device=kv_cache.device, + dtype=torch.int32) + non_remaining_value = kv_cache[blocks[full_block], :block_size * + head_dim].view(-1, head_dim) + non_remaining_scale = kv_cache[blocks[full_block], + block_size * head_dim:].view(-1, 4) + + remaining = s - (tot - 1) * block_size + + value = torch.cat([ + non_remaining_value, + kv_cache[blocks[-1], :remaining * head_dim].view(-1, head_dim) + ], + dim=0) + scale = torch.cat([ + non_remaining_scale, + kv_cache[blocks[-1], block_size * head_dim:block_size * head_dim + + remaining * 4].view(-1, 4) + ], + dim=0) + + expected_value.append(value) + expected_scale.append(scale) + + gather_value = torch.cat(expected_value, dim=0).view(-1, head_dim) + gather_scale = torch.cat(expected_scale, dim=0).view(-1, 4) + gather_value = gather_value.view(torch.float8_e4m3fn) + gather_scale = gather_scale.view(torch.float32) + dst_value.copy_(gather_value) + dst_scale.copy_(gather_scale) + + +def sparse_attn_indexer( + hidden_states: torch.Tensor, + k_cache_prefix: str, + kv_cache: torch.Tensor, + q_fp8: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + quant_block_size: int, + scale_fmt: Optional[str], + topk_tokens: int, + head_dim: int, + max_model_len: int, + total_seq_lens: int, + topk_indices_buffer: Optional[torch.Tensor], +) -> torch.Tensor: + + # careful! this will be None in dummy run + attn_metadata = get_forward_context().attn_metadata + # assert isinstance(attn_metadata, dict) + if not isinstance(attn_metadata, dict): + return sparse_attn_indexer_fake( + hidden_states, + k_cache_prefix, + kv_cache, + q_fp8, + k, + weights, + quant_block_size, + scale_fmt, + topk_tokens, + head_dim, + max_model_len, + total_seq_lens, + topk_indices_buffer, + ) + attn_metadata = attn_metadata[k_cache_prefix] + assert isinstance(attn_metadata, DeepseekV32IndexerMetadata) + slot_mapping = attn_metadata.slot_mapping + has_decode = attn_metadata.num_decodes > 0 + has_prefill = attn_metadata.num_prefills > 0 + num_decode_tokens = attn_metadata.num_decode_tokens + + ops.indexer_k_quant_and_cache( + k, + kv_cache, + slot_mapping, + quant_block_size, + scale_fmt, + ) + + topk_indices_buffer[:hidden_states.shape[0]] = -1 + if has_prefill: + prefill_metadata = attn_metadata.prefill + num_prefills = attn_metadata.num_prefills + k_fp8 = torch.empty([prefill_metadata.total_seq_lens, head_dim], + device=k.device, + dtype=torch.float8_e4m3fn) + k_scale = torch.empty([prefill_metadata.total_seq_lens, 1], + device=k.device, + dtype=torch.float32) + cp_gather_indexer_k_quant_cache( + kv_cache, + k_fp8, + k_scale, + prefill_metadata.block_table, + prefill_metadata.cu_seq_lens, + num_prefills, + ) + cu_seqlen_ks = prefill_metadata.cu_seqlen_ks + cu_seqlen_ke = prefill_metadata.cu_seqlen_ke + num_tokens = attn_metadata.num_actual_tokens + logits = fp8_mqa_logits( + q_fp8[num_decode_tokens:num_tokens], + (k_fp8, k_scale), + weights[num_decode_tokens:num_tokens], + cu_seqlen_ks, + cu_seqlen_ke, + ) + topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]), + dim=-1)[1] + topk_indices -= cu_seqlen_ks[:, None] + mask_lo = topk_indices >= 0 + mask_hi = topk_indices - (cu_seqlen_ke - cu_seqlen_ks)[:, None] < 0 + mask = torch.full_like(topk_indices, + False, + dtype=torch.bool, + device=topk_indices.device) + mask = mask_lo & mask_hi + topk_indices = topk_indices.masked_fill(~mask, -1) + topk_indices_buffer[num_decode_tokens:num_tokens, :topk_indices. + shape[-1]] = topk_indices.to(dtype=torch.int32) + + if has_decode: + decode_metadata = attn_metadata.decode + # kv_cache size requirement [num_block, block_size, n_head, head_dim], + # we only have [num_block, block_size, head_dim], + kv_cache = kv_cache.unsqueeze(-2) + decode_lens = decode_metadata.decode_lens + if decode_metadata.requires_padding: + # pad in edge case where we have short chunked prefill length < + # decode_threshold since we unstrictly split + # prefill and decode by decode_threshold + # (currently set to 1 + speculative tokens) + padded_q_fp8_decode_tokens = pack_seq_triton( + q_fp8[:num_decode_tokens], decode_lens) + else: + padded_q_fp8_decode_tokens = q_fp8[:num_decode_tokens].reshape( + decode_lens.shape[0], -1, *q_fp8.shape[1:]) + # TODO: move and optimize below logic with triton kernels + batch_size = padded_q_fp8_decode_tokens.shape[0] + next_n = padded_q_fp8_decode_tokens.shape[1] + assert batch_size == decode_metadata.seq_lens.shape[0] + num_padded_tokens = batch_size * next_n + logits = fp8_paged_mqa_logits( + padded_q_fp8_decode_tokens, + kv_cache, + weights[:num_padded_tokens], + decode_metadata.seq_lens, + decode_metadata.block_table, + decode_metadata.schedule_metadata, + max_model_len=max_model_len, + ) + # padded query len + current_device = padded_q_fp8_decode_tokens.device + padded_num_tokens = batch_size * next_n + positions = torch.arange(max_model_len, + device=current_device).unsqueeze(0).expand( + batch_size * next_n, -1) + row_indices = torch.arange(padded_num_tokens, + device=current_device) // next_n + next_n_offset = torch.arange( + padded_num_tokens, + device=padded_q_fp8_decode_tokens.device) % next_n + index_end_pos = (decode_metadata.seq_lens[row_indices] - next_n + + next_n_offset).unsqueeze(1) + # index_end_pos: [B * N, 1] + mask = positions <= index_end_pos + # mask: [B * N, L] + logits = logits.masked_fill(~mask, float('-inf')) + topk_indices = logits.topk(topk_tokens, + dim=-1)[1].to(torch.int32) # [B * N, K] + # ensure we don't set indices for the top k + # that is out of range(masked already) + # this will happen if context length is shorter than K + topk_indices[topk_indices > index_end_pos] = -1 + if decode_metadata.requires_padding: + # if padded, we need to unpack + # the topk indices removing padded tokens + topk_indices = unpack_seq_triton( + topk_indices.reshape(batch_size, -1, topk_indices.shape[-1]), + decode_lens) + topk_indices_buffer[:num_decode_tokens, :topk_indices. + shape[-1]] = topk_indices.to(dtype=torch.int32) + + return topk_indices_buffer + + +def sparse_attn_indexer_fake( + hidden_states: torch.Tensor, + k_cache_prefix: str, + kv_cache: torch.Tensor, + q_fp8: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + quant_block_size: int, + scale_fmt: Optional[str], + topk_tokens: int, + head_dim: int, + max_model_len: int, + total_seq_lens: int, + topk_indices_buffer: Optional[torch.Tensor], +) -> torch.Tensor: + # profile run + # NOTE(Chen): create the max possible flattened_kv. So that + # profile_run can get correct memory usage. + _flattened_kv = torch.empty([total_seq_lens, head_dim + 4], + device=k.device, + dtype=torch.uint8) + _k_fp8 = _flattened_kv[..., :head_dim].view( + torch.float8_e4m3fn).contiguous() + _k_scale = _flattened_kv[..., head_dim:].view(torch.float32).contiguous() + return topk_indices_buffer + + +direct_register_custom_op( + op_name="sparse_attn_indexer", + op_func=sparse_attn_indexer, + mutates_args=["topk_indices_buffer"], + fake_impl=sparse_attn_indexer_fake, + dispatch_key=current_platform.dispatch_key, +) + + +class Indexer(nn.Module): + + def __init__(self, + vllm_config: VllmConfig, + config: Union[DeepseekV2Config, DeepseekV3Config], + hidden_size: int, + q_lora_rank: int, + quant_config: Optional[QuantizationConfig], + cache_config: Optional[CacheConfig], + topk_indices_buffer: Optional[torch.Tensor], + prefix: str = ""): + super().__init__() + self.vllm_config = vllm_config + self.config = config + # self.indexer_cfg = config.attn_module_list_cfg[0]["attn_index"] + self.topk_tokens = config.index_topk + self.n_head = config.index_n_heads # 64 + self.head_dim = config.index_head_dim # 128 + self.rope_dim = config.qk_rope_head_dim # 64 + self.q_lora_rank = q_lora_rank # 1536 + # no tensor parallel, just replicated + self.wq_b = ReplicatedLinear(self.q_lora_rank, + self.head_dim * self.n_head, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.wq_b") + self.wk = ReplicatedLinear(hidden_size, + self.head_dim, + bias=False, + quant_config=quant_config, + prefix=f"{prefix}.wk") + self.k_norm = LayerNorm(self.head_dim, eps=1e-6) + self.weights_proj = ReplicatedLinear(hidden_size, + self.n_head, + quant_config=None, + prefix=f"{prefix}.weights_proj") + self.softmax_scale = self.head_dim**-0.5 + + self.scale_fmt = "ue8m0" + self.quant_block_size = 128 # TODO: get from config + self.topk_indices_buffer = topk_indices_buffer + + # NOTE: (zyongye) we use fp8 naive cache, + # where we store value in fp8 and scale in fp32 + # per self.quant_block_size element + self.k_cache = DeepseekV32IndexerCache( + head_dim=self.head_dim + + self.head_dim // self.quant_block_size * 4, + dtype=torch.uint8, + prefix=f"{prefix}.k_cache", + cache_config=cache_config) + self.max_model_len = vllm_config.model_config.max_model_len + self.prefix = prefix + from vllm.v1.attention.backends.mla.indexer import ( + get_max_prefill_buffer_size) + self.max_total_seq_len = get_max_prefill_buffer_size(vllm_config) + + def forward(self, hidden_states: torch.Tensor, qr: torch.Tensor, positions, + rotary_emb) -> torch.Tensor: + q, _ = self.wq_b(qr) + q = q.view(-1, self.n_head, self.head_dim) + q_pe, q_nope = torch.split( + q, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1) + + k, _ = self.wk(hidden_states) + k = self.k_norm(k) + k_pe, k_nope = torch.split( + k, [self.rope_dim, self.head_dim - self.rope_dim], dim=-1) + + q_pe, k_pe = rotary_emb(positions, q_pe, k_pe.unsqueeze(1)) + q = torch.cat([q_pe, q_nope], dim=-1) + k = torch.cat([k_pe.squeeze(1), k_nope], dim=-1) + + # we only quant q here since k quant is fused with cache insertion + q = q.view(-1, self.head_dim) + q_fp8, q_scale = per_token_group_quant_fp8(q, + self.quant_block_size, + column_major_scales=False, + use_ue8m0=self.scale_fmt + is not None) + q_fp8 = q_fp8.view(-1, self.n_head, self.head_dim) + q_scale = q_scale.view(-1, self.n_head, 1) + + weights, _ = self.weights_proj(hidden_states) + weights = weights.unsqueeze( + -1) * q_scale * self.softmax_scale * self.n_head**-0.5 + weights = weights.squeeze(-1) + + return torch.ops.vllm.sparse_attn_indexer( + hidden_states, + self.k_cache.prefix, + self.k_cache.kv_cache[0], + q_fp8, + k, + weights, + self.quant_block_size, + self.scale_fmt, + self.topk_tokens, + self.head_dim, + self.max_model_len, + self.max_total_seq_len, + self.topk_indices_buffer, + ) + + class DeepseekV2MLAAttention(nn.Module): """ Main reference: DeepseekV2 paper, and FlashInfer Implementation @@ -429,6 +839,7 @@ class DeepseekV2MLAAttention(nn.Module): def __init__( self, + vllm_config: VllmConfig, config: Union[DeepseekV2Config, DeepseekV3Config], hidden_size: int, num_heads: int, @@ -443,6 +854,7 @@ def __init__( cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", + topk_indices_buffer: Optional[torch.Tensor] = None, ) -> None: super().__init__() self.hidden_size = hidden_size @@ -523,6 +935,15 @@ def __init__( mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim)) self.scaling = self.scaling * mscale * mscale + self.is_v32 = hasattr(config, "index_topk") + + if self.is_v32: + self.indexer = Indexer(vllm_config, config, hidden_size, + q_lora_rank, quant_config, cache_config, + topk_indices_buffer, f"{prefix}.indexer") + else: + self.indexer = None + mla_modules = MLAModules( kv_a_layernorm=self.kv_a_layernorm, kv_b_proj=self.kv_b_proj, @@ -536,7 +957,11 @@ def __init__( if self.q_lora_rank is not None else None, q_b_proj=self.q_b_proj if self.q_lora_rank is not None else None, q_proj=self.q_proj if self.q_lora_rank is None else None, + indexer=self.indexer, + is_sparse=self.is_v32, + topk_indices_buffer=topk_indices_buffer, ) + self.mla_attn = MultiHeadLatentAttention( self.hidden_size, self.num_local_heads, @@ -562,7 +987,10 @@ def forward( class DeepseekV2DecoderLayer(nn.Module): - def __init__(self, vllm_config: VllmConfig, prefix: str) -> None: + def __init__(self, + vllm_config: VllmConfig, + prefix: str, + topk_indices_buffer: Optional[torch.Tensor] = None) -> None: super().__init__() config = vllm_config.model_config.hf_config @@ -585,6 +1013,7 @@ def __init__(self, vllm_config: VllmConfig, prefix: str) -> None: else: attn_cls = DeepseekV2Attention self.self_attn = attn_cls( + vllm_config=vllm_config, config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, @@ -600,6 +1029,7 @@ def __init__(self, vllm_config: VllmConfig, prefix: str) -> None: cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", + topk_indices_buffer=topk_indices_buffer, ) if (config.n_routed_experts is not None @@ -683,6 +1113,16 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.config = config self.vocab_size = config.vocab_size + self.is_v32 = hasattr(config, "index_topk") + if self.is_v32: + topk_tokens = config.index_topk + topk_indices_buffer = torch.empty( + vllm_config.scheduler_config.max_num_batched_tokens, + topk_tokens, + dtype=torch.int32, + device="cuda") + else: + topk_indices_buffer = None if get_pp_group().is_first_rank: self.embed_tokens = VocabParallelEmbedding( @@ -695,7 +1135,8 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, - lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix), + lambda prefix: DeepseekV2DecoderLayer(vllm_config, prefix, + topk_indices_buffer), prefix=f"{prefix}.layers") if get_pp_group().is_last_rank: diff --git a/vllm/model_executor/models/longcat_flash.py b/vllm/model_executor/models/longcat_flash.py index 1a7a64bfd1a4..78e6e3d4b535 100644 --- a/vllm/model_executor/models/longcat_flash.py +++ b/vllm/model_executor/models/longcat_flash.py @@ -308,6 +308,7 @@ class FlashDecoderLayer(nn.Module): def __init__( self, + vllm_config: VllmConfig, config: FlashConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, @@ -329,6 +330,7 @@ def __init__( # Dual attention structure self.self_attn = nn.ModuleList([ DeepseekV2MLAAttention( + vllm_config=vllm_config, config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, @@ -454,6 +456,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: FlashDecoderLayer( + vllm_config, config, cache_config=cache_config, quant_config=quant_config, diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 62aa5af9fb14..732181265a97 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -70,6 +70,7 @@ "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"), "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"), "DeepseekV3ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"), + "DeepseekV32ForCausalLM": ("deepseek_v2", "DeepseekV3ForCausalLM"), "Dots1ForCausalLM": ("dots1", "Dots1ForCausalLM"), "Ernie4_5ForCausalLM": ("ernie45", "Ernie4_5ForCausalLM"), "Ernie4_5_MoeForCausalLM": ("ernie45_moe", "Ernie4_5_MoeForCausalLM"), diff --git a/vllm/platforms/cpu.py b/vllm/platforms/cpu.py index 1e15dc6a91aa..0b26446a87d8 100644 --- a/vllm/platforms/cpu.py +++ b/vllm/platforms/cpu.py @@ -93,11 +93,14 @@ def get_device_name(cls, device_id: int = 0) -> str: def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int, dtype: torch.dtype, kv_cache_dtype: Optional[str], block_size: int, use_v1: bool, use_mla: bool, - has_sink: bool) -> str: + has_sink: bool, use_sparse: bool) -> str: if selected_backend and selected_backend != _Backend.TORCH_SDPA: logger.info("Cannot use %s backend on CPU.", selected_backend) if use_mla: raise NotImplementedError("MLA is not supported on CPU.") + if use_sparse: + raise NotImplementedError( + "Sparse Attention is not supported on CPU.") logger.info("Using Torch SDPA backend.") if not use_v1: raise ValueError("CPU backend only supports V1.") diff --git a/vllm/platforms/cuda.py b/vllm/platforms/cuda.py index 1463fe34fc75..a9a8d9ea2625 100644 --- a/vllm/platforms/cuda.py +++ b/vllm/platforms/cuda.py @@ -119,6 +119,8 @@ def check_and_update_config(cls, vllm_config: "VllmConfig") -> None: # TODO(lucas): handle this more gracefully # Note: model_config may be None during testing if model_config is not None and model_config.use_mla: + use_sparse = hasattr(vllm_config.model_config.hf_config, + "index_topk") # If `VLLM_ATTENTION_BACKEND` is not set and we are using MLA, # then we default to FlashMLA backend for non-blackwell GPUs, # else we default to CutlassMLA. For each case, we force the @@ -165,6 +167,12 @@ def check_and_update_config(cls, vllm_config: "VllmConfig") -> None: "Forcing kv cache block size to 64 for FlashInferMLA " "backend.") + # TODO(Chen): remove this hacky code + if use_sparse and cache_config.block_size != 64: + cache_config.block_size = 64 + logger.info( + "Forcing kv cache block size to 64 for FlashMLASparse " + "backend.") # lazy import to avoid circular import from vllm.config import CUDAGraphMode @@ -221,7 +229,7 @@ def get_vit_attn_backend(cls, head_size: int, @classmethod def get_attn_backend_cls(cls, selected_backend, head_size, dtype, kv_cache_dtype, block_size, use_v1, use_mla, - has_sink) -> str: + has_sink, use_sparse) -> str: if use_mla: if not use_v1: raise RuntimeError( @@ -231,6 +239,11 @@ def get_attn_backend_cls(cls, selected_backend, head_size, dtype, from vllm.attention.ops.flashmla import is_flashmla_supported from vllm.attention.utils.fa_utils import flash_attn_supports_mla + if use_sparse: + logger.info_once("Using Sparse MLA backend on V1 engine.") + return ("vllm.v1.attention.backends.mla.flashmla_sparse." + "FlashMLASparseBackend") + use_cutlassmla = selected_backend == _Backend.CUTLASS_MLA or ( selected_backend is None and cls.is_device_capability(100) and block_size == 128) diff --git a/vllm/platforms/interface.py b/vllm/platforms/interface.py index de23a665d2ea..1691ad62650b 100644 --- a/vllm/platforms/interface.py +++ b/vllm/platforms/interface.py @@ -194,7 +194,7 @@ def get_vit_attn_backend(cls, head_size: int, def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int, dtype: torch.dtype, kv_cache_dtype: Optional[str], block_size: int, use_v1: bool, use_mla: bool, - has_sink: bool) -> str: + has_sink: bool, use_sparse: bool) -> str: """Get the attention backend class of a device.""" return "" diff --git a/vllm/platforms/rocm.py b/vllm/platforms/rocm.py index f67568bf07c1..14762f1b7094 100644 --- a/vllm/platforms/rocm.py +++ b/vllm/platforms/rocm.py @@ -195,7 +195,10 @@ def get_vit_attn_backend(cls, head_size: int, @classmethod def get_attn_backend_cls(cls, selected_backend, head_size, dtype, kv_cache_dtype, block_size, use_v1, use_mla, - has_sink) -> str: + has_sink, use_sparse) -> str: + if use_sparse: + raise NotImplementedError( + "Sparse Attention is not supported on ROCm.") if use_mla: if not use_v1: raise RuntimeError( diff --git a/vllm/platforms/tpu.py b/vllm/platforms/tpu.py index c2ba37224d61..4a4931f7f009 100644 --- a/vllm/platforms/tpu.py +++ b/vllm/platforms/tpu.py @@ -49,7 +49,10 @@ class TpuPlatform(Platform): def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int, dtype: torch.dtype, kv_cache_dtype: Optional[str], block_size: int, use_v1: bool, use_mla: bool, - has_sink) -> str: + has_sink, use_sparse) -> str: + if use_sparse: + raise NotImplementedError( + "Sparse Attention is not supported on TPU.") if selected_backend != _Backend.PALLAS: logger.info("Cannot use %s backend on TPU.", selected_backend) diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py index cf408cc5df04..12d6a2a2d1ba 100644 --- a/vllm/platforms/xpu.py +++ b/vllm/platforms/xpu.py @@ -36,7 +36,10 @@ class XPUPlatform(Platform): def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int, dtype: torch.dtype, kv_cache_dtype: Optional[str], block_size: int, use_v1: bool, use_mla: bool, - has_sink: bool) -> str: + has_sink: bool, use_sparse) -> str: + if use_sparse: + raise NotImplementedError( + "Sparse Attention is not supported on XPU.") use_v1 = envs.VLLM_USE_V1 if not use_v1: raise ValueError("XPU backend only supports V1.") diff --git a/vllm/transformers_utils/config.py b/vllm/transformers_utils/config.py index 4f5e5c01e5cb..86345287f988 100644 --- a/vllm/transformers_utils/config.py +++ b/vllm/transformers_utils/config.py @@ -66,6 +66,8 @@ def __getitem__(self, key): _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict( chatglm="ChatGLMConfig", deepseek_vl_v2="DeepseekVLV2Config", + deepseek_v3="DeepseekV3Config", + deepseek_v32="DeepseekV3Config", kimi_vl="KimiVLConfig", Llama_Nemotron_Nano_VL="Nemotron_Nano_VL_Config", RefinedWeb="RWConfig", # For tiiuae/falcon-40b(-instruct) diff --git a/vllm/transformers_utils/configs/__init__.py b/vllm/transformers_utils/configs/__init__.py index 52fa49ad302b..1b33b5e70e0b 100644 --- a/vllm/transformers_utils/configs/__init__.py +++ b/vllm/transformers_utils/configs/__init__.py @@ -8,6 +8,7 @@ """ from vllm.transformers_utils.configs.chatglm import ChatGLMConfig +from vllm.transformers_utils.configs.deepseek_v3 import DeepseekV3Config from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config from vllm.transformers_utils.configs.dotsocr import DotsOCRConfig from vllm.transformers_utils.configs.eagle import EAGLEConfig @@ -37,6 +38,7 @@ __all__ = [ "ChatGLMConfig", "DeepseekVLV2Config", + "DeepseekV3Config", "DotsOCRConfig", "EAGLEConfig", "RWConfig", diff --git a/vllm/transformers_utils/configs/deepseek_v3.py b/vllm/transformers_utils/configs/deepseek_v3.py new file mode 100644 index 000000000000..4b26cdfd94b5 --- /dev/null +++ b/vllm/transformers_utils/configs/deepseek_v3.py @@ -0,0 +1,101 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + + +class DeepseekV3Config(PretrainedConfig): + + model_type = "deepseek_v3" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=129280, + hidden_size=7168, + intermediate_size=18432, + moe_intermediate_size=2048, + num_hidden_layers=61, + num_nextn_predict_layers=1, + num_attention_heads=128, + num_key_value_heads=128, + n_shared_experts=1, + n_routed_experts=256, + ep_size=1, + routed_scaling_factor=2.5, + kv_lora_rank=512, + q_lora_rank=1536, + qk_rope_head_dim=64, + v_head_dim=128, + qk_nope_head_dim=128, + topk_method='noaux_tc', + n_group=8, + topk_group=4, + num_experts_per_tok=8, + moe_layer_freq=1, + first_k_dense_replace=3, + norm_topk_prob=True, + scoring_func='sigmoid', + hidden_act="silu", + max_position_embeddings=4096, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=None, + bos_token_id=0, + eos_token_id=1, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + attention_bias=False, + attention_dropout=0.0, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.moe_intermediate_size = moe_intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_nextn_predict_layers = num_nextn_predict_layers + self.num_attention_heads = num_attention_heads + self.n_shared_experts = n_shared_experts + self.n_routed_experts = n_routed_experts + self.ep_size = ep_size + self.routed_scaling_factor = routed_scaling_factor + self.kv_lora_rank = kv_lora_rank + self.q_lora_rank = q_lora_rank + self.qk_rope_head_dim = qk_rope_head_dim + self.v_head_dim = v_head_dim + self.qk_nope_head_dim = qk_nope_head_dim + self.topk_method = topk_method + self.n_group = n_group + self.topk_group = topk_group + self.num_experts_per_tok = num_experts_per_tok + self.moe_layer_freq = moe_layer_freq + self.first_k_dense_replace = first_k_dense_replace + self.norm_topk_prob = norm_topk_prob + self.scoring_func = scoring_func + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/vllm/utils/__init__.py b/vllm/utils/__init__.py index ba280d6dbe4a..11d6686009b2 100644 --- a/vllm/utils/__init__.py +++ b/vllm/utils/__init__.py @@ -130,6 +130,7 @@ "fp8_e5m2": torch.uint8, "int8": torch.int8, "fp8_inc": torch.float8_e4m3fn, + "fp8_ds_mla": torch.uint8, } TORCH_DTYPE_TO_NUMPY_DTYPE = { @@ -3433,6 +3434,12 @@ def has_triton_kernels() -> bool: return _has_module("triton_kernels") +def has_tilelang() -> bool: + """Whether the optional `tilelang` package is available.""" + + return _has_module("tilelang") + + def set_process_title(name: str, suffix: str = "", prefix: str = envs.VLLM_PROCESS_NAME_PREFIX) -> None: diff --git a/vllm/utils/deep_gemm.py b/vllm/utils/deep_gemm.py index f955beb92b36..0e3bdaec829e 100644 --- a/vllm/utils/deep_gemm.py +++ b/vllm/utils/deep_gemm.py @@ -70,17 +70,25 @@ def _missing(*_: Any, **__: Any) -> NoReturn: _fp8_gemm_nt_impl: Callable[..., Any] | None = None _grouped_impl: Callable[..., Any] | None = None _grouped_masked_impl: Callable[..., Any] | None = None +_fp8_mqa_logits_impl: Callable[..., Any] | None = None +_fp8_paged_mqa_logits_impl: Callable[..., Any] | None = None +_get_paged_mqa_logits_metadata_impl: Callable[..., Any] | None = None _get_mn_major_tma_aligned_tensor_impl: Callable[..., Any] | None = None def _lazy_init() -> None: """Import deep_gemm and resolve symbols on first use.""" - global _fp8_gemm_nt_impl, _grouped_impl, _grouped_masked_impl,\ - _get_mn_major_tma_aligned_tensor_impl + global _fp8_gemm_nt_impl, _grouped_impl, _grouped_masked_impl + global _fp8_mqa_logits_impl, _fp8_paged_mqa_logits_impl + global _get_paged_mqa_logits_metadata_impl + global _get_mn_major_tma_aligned_tensor_impl # fast path if (_fp8_gemm_nt_impl is not None or _grouped_impl is not None - or _grouped_masked_impl is not None): + or _grouped_masked_impl is not None + or _fp8_mqa_logits_impl is not None + or _fp8_paged_mqa_logits_impl is not None + or _get_paged_mqa_logits_metadata_impl is not None): return if not has_deep_gemm(): @@ -97,10 +105,20 @@ def _lazy_init() -> None: _fp8_gemm_nt_impl = getattr(_dg, "fp8_gemm_nt", None) _grouped_impl = getattr(_dg, "m_grouped_fp8_gemm_nt_contiguous", None) _grouped_masked_impl = getattr(_dg, "fp8_m_grouped_gemm_nt_masked", None) + _fp8_mqa_logits_impl = getattr(_dg, "fp8_mqa_logits", None) + _fp8_paged_mqa_logits_impl = getattr(_dg, "fp8_paged_mqa_logits", None) + _get_paged_mqa_logits_metadata_impl = getattr( + _dg, "get_paged_mqa_logits_metadata", None) _get_mn_major_tma_aligned_tensor_impl = getattr( _dg, "get_mn_major_tma_aligned_tensor", None) +def get_num_sms() -> int: + _lazy_init() + _dg = importlib.import_module("deep_gemm") + return int(_dg.get_num_sms()) + + def get_col_major_tma_aligned_tensor(x: torch.Tensor) -> torch.Tensor: """Wrapper for DeepGEMM's get_mn_major_tma_aligned_tensor""" _lazy_init() @@ -135,6 +153,100 @@ def fp8_m_grouped_gemm_nt_masked(*args, **kwargs): *args, disable_ue8m0_cast=not is_deep_gemm_e8m0_used(), **kwargs) +def fp8_mqa_logits( + q: torch.Tensor, + kv: tuple[torch.Tensor, torch.Tensor], + weights: torch.Tensor, + cu_seqlen_ks: torch.Tensor, + cu_seqlen_ke: torch.Tensor, +) -> torch.Tensor: + """Compute FP8 MQA logits for a single sequence without KV paging. + + Args: + q: Query tensor of shape [M, H, D]. Casted to + `torch.float8_e4m3fn` by caller. + kv: Tuple `(k_fp8, k_scales)` where `k_fp8` has shape [N, D] with + dtype `torch.float8_e4m3fn` and `k_scales` has shape [N] (or + [N, 1]) with dtype `torch.float32`. + weights: weights of shape [M, H], dtype `torch.float32`. + cu_seqlen_ks: Start indices (inclusive) for valid K per query position, + shape [M], dtype int32. + cu_seqlen_ke: End indices (exclusive) for valid K per query position, + shape [M], dtype int32. + + Returns: + Logits tensor of shape [M, N], dtype `torch.float32`. + """ + _lazy_init() + if _fp8_mqa_logits_impl is None: + return _missing() + return _fp8_mqa_logits_impl(q, kv, weights, cu_seqlen_ks, cu_seqlen_ke) + + +def get_paged_mqa_logits_metadata(context_lens: torch.Tensor, block_size: int, + num_sms: int) -> torch.Tensor: + """Build scheduling metadata for paged MQA logits. + + Args: + context_lens: Tensor of shape [B], dtype int32; effective context length + per batch element. + block_size: KV-cache block size in tokens (e.g., 64). + num_sms: Number of SMs available. 132 for Hopper + + Returns: + Backend-specific tensor consumed by `fp8_paged_mqa_logits` to + schedule work across SMs. + """ + _lazy_init() + if _get_paged_mqa_logits_metadata_impl is None: + return _missing() + return _get_paged_mqa_logits_metadata_impl(context_lens, block_size, + num_sms) + + +def fp8_paged_mqa_logits( + q_fp8: torch.Tensor, + kv_cache_fp8: torch.Tensor, + weights: torch.Tensor, + context_lens: torch.Tensor, + block_tables: torch.Tensor, + schedule_metadata: torch.Tensor, + max_model_len: int, +) -> torch.Tensor: + """Compute FP8 MQA logits using paged KV-cache. + + Args: + q_fp8: Query tensor of shape [B, next_n, H, D]. Casted to + `torch.float8_e4m3fn` by caller. + kv_cache_fp8: Paged KV-cache in packed FP8+scale layout with shape + [num_blocks, block_size, 1, D+4], dtype `torch.uint8`. The last + 4 bytes per (block,pos) store the `float` dequant scale. + weights: Tensor of shape [B * next_n, H], dtype `torch.float32`. + context_lens: Tensor of shape [B], dtype int32; effective context length + for each batch element. + block_tables: Tensor of shape [B, max_blocks], dtype int32; maps logical + block indices to physical blocks in the paged cache. + schedule_metadata: Returned by `get_paged_mqa_logits_metadata`; + used to distribute work across SMs. + max_model_len: Maximum sequence length used to size the logits output. + + Returns: + Logits tensor of shape [B * next_n, max_model_len], dtype + `torch.float32`. + """ + _lazy_init() + if _fp8_paged_mqa_logits_impl is None: + return _missing() + return _fp8_paged_mqa_logits_impl(q_fp8, + kv_cache_fp8, + weights, + context_lens, + block_tables, + schedule_metadata, + max_model_len, + clean_logits=True) + + def _ceil_to_ue8m0(x: torch.Tensor): return torch.pow(2.0, torch.ceil(torch.log2(x.abs()))) @@ -195,9 +307,13 @@ def should_use_deepgemm_for_fp8_linear(output_dtype: torch.dtype, "fp8_gemm_nt", "m_grouped_fp8_gemm_nt_contiguous", "fp8_m_grouped_gemm_nt_masked", + "fp8_mqa_logits", + "fp8_paged_mqa_logits", + "get_paged_mqa_logits_metadata", "per_block_cast_to_fp8", "is_deep_gemm_e8m0_used", "is_deep_gemm_supported", + "get_num_sms", "should_use_deepgemm_for_fp8_linear", "get_col_major_tma_aligned_tensor", -] \ No newline at end of file +] diff --git a/vllm/v1/attention/backends/cpu_attn.py b/vllm/v1/attention/backends/cpu_attn.py index ab09ab9f8e0e..6ca0c63f6b59 100644 --- a/vllm/v1/attention/backends/cpu_attn.py +++ b/vllm/v1/attention/backends/cpu_attn.py @@ -74,6 +74,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: return _get_paged_attn_impl().get_kv_cache_shape( num_blocks, block_size, num_kv_heads, head_size) diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py index f284847dd9e9..f0770f744146 100755 --- a/vllm/v1/attention/backends/flash_attn.py +++ b/vllm/v1/attention/backends/flash_attn.py @@ -80,6 +80,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") diff --git a/vllm/v1/attention/backends/flashinfer.py b/vllm/v1/attention/backends/flashinfer.py index a4bf3635bbca..688e681f0591 100755 --- a/vllm/v1/attention/backends/flashinfer.py +++ b/vllm/v1/attention/backends/flashinfer.py @@ -187,6 +187,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: return (num_blocks, 2, block_size, num_kv_heads, head_size) diff --git a/vllm/v1/attention/backends/flex_attention.py b/vllm/v1/attention/backends/flex_attention.py index 807b8d987a2d..e548b51060d8 100644 --- a/vllm/v1/attention/backends/flex_attention.py +++ b/vllm/v1/attention/backends/flex_attention.py @@ -90,6 +90,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: return (2, num_blocks, block_size, num_kv_heads, head_size) diff --git a/vllm/v1/attention/backends/mla/common.py b/vllm/v1/attention/backends/mla/common.py index 1053fde09910..561793b6a377 100755 --- a/vllm/v1/attention/backends/mla/common.py +++ b/vllm/v1/attention/backends/mla/common.py @@ -286,6 +286,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, # assumed to be 1 for MLA head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: return (num_blocks, block_size, head_size) @@ -407,6 +408,7 @@ def __post_init__(self): M = TypeVar("M", bound=MLACommonMetadata) +A = TypeVar("A") def use_flashinfer_prefill() -> bool: @@ -930,7 +932,9 @@ def reorg_kvcache( return reorganized_kv_c_normed, reorganized_k_pe -class MLACommonImpl(MLAAttentionImpl[M], Generic[M]): +# TODO(Lucas): rename MLACommonBaseImpl -> MLACommonImpl, +# and MLACommonImpl -> MLACommonDenseImpl or somthing like that +class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]): """ NOTE: Please read the comment at the top of the file before trying to understand this class @@ -956,6 +960,7 @@ def __init__( qk_head_dim: int, v_head_dim: int, kv_b_proj: ColumnParallelLinear, + indexer=None, q_pad_num_heads: Optional[int] = None, ) -> None: if kv_sharing_target_layer_name is not None: @@ -974,8 +979,140 @@ def __init__( self.qk_head_dim = qk_head_dim self.v_head_dim = v_head_dim self.kv_b_proj = kv_b_proj + self.indexer = indexer self.q_pad_num_heads = q_pad_num_heads + def process_weights_after_loading(self, act_dtype: torch.dtype): + + def get_layer_weight(layer): + WEIGHT_NAMES = ("weight", "qweight", "weight_packed") + for attr in WEIGHT_NAMES: + if hasattr(layer, attr): + return getattr(layer, attr) + raise AttributeError( + f"Layer '{layer}' has no recognized weight attribute:" + f" {WEIGHT_NAMES}.") + + def get_and_maybe_dequant_weights(layer: LinearBase): + if not isinstance(layer.quant_method, UnquantizedLinearMethod): + # NOTE: This should only be used offline, since it's O(N^3) + eye = torch.eye(layer.input_size_per_partition, + dtype=act_dtype, + device=get_layer_weight(layer).device) + dequant_weights = layer.quant_method.apply(layer, + eye, + bias=None) + del eye + # standardize to (output, input) + return dequant_weights.T + return layer.weight + + # we currently do not have quantized bmm's which are needed for + # `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform + # the bmm's in 16-bit, the extra memory overhead of this is fairly low + kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T + assert kv_b_proj_weight.shape == ( + self.kv_lora_rank, + self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), ( + f"{kv_b_proj_weight.shape=}, " + f"{self.kv_lora_rank=}, " + f"{self.num_heads=}, " + f"{self.qk_nope_head_dim=}, " + f"{self.v_head_dim=}") + kv_b_proj_weight = kv_b_proj_weight.view( + self.kv_lora_rank, + self.num_heads, + self.qk_nope_head_dim + self.v_head_dim, + ) + + W_UK, W_UV = kv_b_proj_weight.split( + [self.qk_nope_head_dim, self.v_head_dim], dim=-1) + + if is_rocm_aiter_fp8bmm_enabled(): + W_K = W_UK.transpose(0, 1) # 16 512 128 + W_V = W_UV.permute(1, 2, 0) # 16 128 512 + self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant( + W_K, dtype=current_platform.fp8_dtype()) + self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant( + W_V, dtype=current_platform.fp8_dtype()) + + # The kernel operates on non-padded inputs. Hence, pre-compiling + # triton kernel to avoid runtime compilation for unseen batch sizes + # Pre-compile for batch sizes 1 to 1024 to cover most use-cases. + # On DS-R1, this step adds roughly 50s to the model loading time. + max_batch_size = 1024 # [ToDo] Find the optimal upper limit + pre_compilation_list = list(range(1, max_batch_size + 1)) + if is_global_first_rank(): + pre_compilation_list = tqdm( + pre_compilation_list, + desc="[Aiter Triton] Pre-compiling fp8 BMM kernel", + total=max_batch_size, + ) + + for m in pre_compilation_list: + x = torch.empty((self.W_K.shape[0], m, self.W_K.shape[2]), + dtype=torch.bfloat16, + device=self.W_K.device) + aiter_triton_fp8_bmm(x, + self.W_K, + self.W_K_scale, + group_size=128, + transpose_bm=True) + + x = torch.empty((self.W_V.shape[0], m, self.W_V.shape[2]), + dtype=torch.bfloat16, + device=self.W_V.device) + aiter_triton_fp8_bmm(x, + self.W_V, + self.W_V_scale, + group_size=128, + transpose_bm=True) + else: + # Convert from (L, N, V) to (N, L, V) + self.W_UV = W_UV.transpose(0, 1) + # Convert from (L, N, P) to (N, P, L) + self.W_UK_T = W_UK.permute(1, 2, 0) + + def _v_up_proj(self, x: torch.Tensor, out: torch.Tensor): + # Convert from (B, N, L) to (N, B, L) + x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1) + if is_rocm_aiter_fp8bmm_enabled(): + # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V) + x = aiter_triton_fp8_bmm(x, + self.W_V, + self.W_V_scale, + group_size=128, + transpose_bm=True) + # Convert from (B, N, V) to (B, N * V) + x = x.reshape(-1, self.num_heads * self.v_head_dim) + # Copy result + out.copy_(x) + else: + # Convert from (B, N * V) to (N, B, V) + out = out.view(-1, self.num_heads, self.v_head_dim).transpose(0, 1) + + # Multiply (N, B, L) x (N, L, V) -> (N, B, V) + torch.bmm(x, self.W_UV, out=out) # Reuse "out" to make it "hot" + + # Convert from (N, B, V) to (B, N * V) + out_new = out.transpose(0, 1).reshape( + -1, self.num_heads * self.v_head_dim) + + # Adjust output buffer shape back to the original (B, N * V) + N, B, V = out.shape + out.resize_((B, N * V)) + out.copy_(out_new) # Copy result + + +class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]): + """ + NOTE: Please read the comment at the top of the file before trying to + understand this class + """ + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + if use_flashinfer_prefill(): logger.debug_once("Using FlashInfer prefill for MLA") self._run_prefill_context_chunk = self._run_prefill_context_chunk_fi @@ -1154,36 +1291,6 @@ def _run_prefill_context_chunk_cudnn(self, True, #Indicates actual_seq_lens are on GPU or CPU. ) - def _v_up_proj(self, x: torch.Tensor, out: torch.Tensor): - # Convert from (B, N, L) to (N, B, L) - x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1) - if is_rocm_aiter_fp8bmm_enabled(): - # Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V) - x = aiter_triton_fp8_bmm(x, - self.W_V, - self.W_V_scale, - group_size=128, - transpose_bm=True) - # Convert from (B, N, V) to (B, N * V) - x = x.reshape(-1, self.num_heads * self.v_head_dim) - # Copy result - out.copy_(x) - else: - # Convert from (B, N * V) to (N, B, V) - out = out.view(-1, self.num_heads, self.v_head_dim).transpose(0, 1) - - # Multiply (N, B, L) x (N, L, V) -> (N, B, V) - torch.bmm(x, self.W_UV, out=out) # Reuse "out" to make it "hot" - - # Convert from (N, B, V) to (B, N * V) - out_new = out.transpose(0, 1).reshape( - -1, self.num_heads * self.v_head_dim) - - # Adjust output buffer shape back to the original (B, N * V) - N, B, V = out.shape - out.resize_((B, N * V)) - out.copy_(out_new) # Copy result - def process_weights_after_loading(self, act_dtype: torch.dtype): def get_layer_weight(layer): @@ -1455,6 +1562,7 @@ def _forward_prefill( attn_metadata: MLACommonMetadata, k_scale: torch.Tensor, ) -> torch.Tensor: + # TODO (zyongye): Prefill function here assert attn_metadata.prefill is not None assert self.dcp_world_size is not None diff --git a/vllm/v1/attention/backends/mla/flashmla.py b/vllm/v1/attention/backends/mla/flashmla.py index ac0524ba088b..67c21f83cf5d 100644 --- a/vllm/v1/attention/backends/mla/flashmla.py +++ b/vllm/v1/attention/backends/mla/flashmla.py @@ -177,6 +177,7 @@ def _forward_decode( attn_metadata: FlashMLAMetadata, layer: AttentionLayer, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: + # TODO: (zyongye) decode function for mla here assert kv_c_and_k_pe_cache.numel() > 0 assert attn_metadata.decode is not None diff --git a/vllm/v1/attention/backends/mla/flashmla_sparse.py b/vllm/v1/attention/backends/mla/flashmla_sparse.py new file mode 100644 index 000000000000..36c3c188042c --- /dev/null +++ b/vllm/v1/attention/backends/mla/flashmla_sparse.py @@ -0,0 +1,544 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import math +from dataclasses import dataclass +from typing import TYPE_CHECKING, ClassVar, Optional + +import numpy as np +import torch + +from vllm import _custom_ops as ops +from vllm.attention.backends.abstract import (AttentionBackend, AttentionLayer, + AttentionMetadata) +from vllm.attention.backends.utils import get_mla_dims +from vllm.attention.ops.flashmla import (flash_mla_sparse_prefill, + flash_mla_with_kvcache, + get_mla_metadata) +from vllm.config import VllmConfig +from vllm.logger import init_logger +from vllm.platforms import current_platform +from vllm.triton_utils import tl, triton +from vllm.utils import cdiv +from vllm.v1.attention.backends.mla.common import MLACommonBaseImpl +from vllm.v1.attention.backends.utils import (AttentionCGSupport, + AttentionMetadataBuilder, + CommonAttentionMetadata) +from vllm.v1.kv_cache_interface import AttentionSpec + +if TYPE_CHECKING: + from vllm.model_executor.models.deepseek_v2 import Indexer + +logger = init_logger(__name__) +""" +NOTE: FlashMLA Sparse uses an fp8 cache with the following format + +In the "FP8 with scale" format, each token's KV cache is 656 Bytes, +structured as: +- **First 512 bytes:** The "quantized NoPE" part, containing 512 + `float8_e4m3` values. +- **Next 16 bytes:** Scale factors, containing 4 `float32` values. + The first `float32` is the scale for the first 128 `float8_e4m3` values, + the second for the next 128, and so on. +- **Last 128 bytes:** The "RoPE" part, containing 64 `bfloat16` values. This + part is not quantized for accuracy. +""" + + +def _lse2_to_lse(lse_base2: torch.Tensor) -> torch.Tensor: + # Convert base-2 LSE to natural-log LSE + # Keep FP32 for numerical stability during the merge. + return (lse_base2.to(torch.float32) * math.log(2.0)) + + +class FlashMLASparseBackend(AttentionBackend): + + accept_output_buffer: bool = True + + @staticmethod + def get_name() -> str: + return "FLASHMLA_SPARSE_VLLM_V1" + + @staticmethod + def get_metadata_cls() -> type[AttentionMetadata]: + return FlashMLASparseMetadata + + @staticmethod + def get_builder_cls() -> type["FlashMLASparseMetadataBuilder"]: + return FlashMLASparseMetadataBuilder + + @staticmethod + def get_impl_cls() -> type["FlashMLASparseImpl"]: + return FlashMLASparseImpl + + @staticmethod + def get_kv_cache_shape( + num_blocks: int, + block_size: int, + num_kv_heads: int, # assumed to be 1 for MLA + head_size: int, + cache_dtype_str: str = "auto", + ) -> tuple[int, ...]: + if cache_dtype_str == "fp8_ds_mla": + # custom storage fromat is 656 bytes + # see FlashMLA readme.md for details + return (num_blocks, block_size, 656) + else: + return (num_blocks, block_size, head_size) + + @classmethod + def get_supported_dtypes(cls) -> list[torch.dtype]: + return [torch.bfloat16] + + @classmethod + def get_supported_head_sizes(cls) -> list[int]: + return [576] + + +@dataclass +class MLASparsePrefillMetadata: + # NOTE(Chen): not call it "FlashMLASparsePrefillMetadata" because + # the kernel is not from flashmla + block_table: torch.Tensor + has_context: bool = False + context_lens: Optional[torch.Tensor] = None + + +@dataclass +class FlashMLASparseDecodeAndContextMetadata: + scheduler_metadata: torch.Tensor = None + num_splits: torch.Tensor = None + cache_lens: torch.Tensor = None + prefill_context_lengths: Optional[torch.Tensor] = None + prefill_new_k_start_locs: Optional[torch.Tensor] = None + dummy_block_table: torch.Tensor = None + + def filter_prefill_indices( + self, indices: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + assert self.prefill_context_lengths is not None + prefill_context_lengths = self.prefill_context_lengths.unsqueeze(-1) + context_indices = torch.where(indices < prefill_context_lengths, + indices, -1) + new_token_indices = torch.where(indices >= prefill_context_lengths, + indices - prefill_context_lengths, -1) + return context_indices, new_token_indices + + +@dataclass +class FlashMLASparseMetadata: + num_reqs: int + max_query_len: int + max_seq_len: int + + num_actual_tokens: int # Number of tokens excluding padding. + query_start_loc: torch.Tensor + slot_mapping: torch.Tensor + + block_table: torch.Tensor + req_id_per_token: torch.Tensor + block_size: int = 64 + topk_tokens: int = 2048 + + @dataclass + class FP8KernelMetadata: + scheduler_metadata: Optional[torch.Tensor] + num_splits: torch.Tensor + dummy_block_table: torch.Tensor + cache_lens: torch.Tensor + + fp8_extra_metadata: Optional[FP8KernelMetadata] = None + + +@triton.jit +def _convert_req_index_to_global_index_kernel( + req_id_ptr, # int32 [num_tokens] + block_table_ptr, # int32 [num_requests, max_num_blocks_per_req] + token_indices_ptr, # int32 [num_tokens, NUM_TOPK_TOKENS] + out_ptr, # int32 [num_tokens, NUM_TOPK_TOKENS] + # shapes (compile-time where possible) + max_num_blocks_per_req: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + BLOCK_N: tl.constexpr, # tile width along columns + # strides (in elements) + bt_stride0, + bt_stride1, + ti_stride0, + ti_stride1, + out_stride0, + out_stride1, +): + # program_id(0) -> token_id (row) + # program_id(1) -> tile index along columns + token_id = tl.program_id(0) + tile_id = tl.program_id(1) + + # Each program covers BLOCK_N consecutive columns + indice_id = tile_id * BLOCK_N + tl.arange(0, BLOCK_N) + + # Load request id for this token (no mask: grid is exact) + req = tl.load(req_id_ptr + token_id) + + # Load token indices for this tile + ti_ptr = token_indices_ptr + token_id * ti_stride0 + indice_id * ti_stride1 + tok = tl.load(ti_ptr) # int32 + + # Only token == -1 should propagate as -1 + is_invalid_tok = tok < 0 + + # Compute block id and in-block offset + block_id = tok // BLOCK_SIZE + inblock_off = tok % BLOCK_SIZE + + # Guard block_table access + valid_block = block_id < max_num_blocks_per_req + bt_ptr = block_table_ptr + req * bt_stride0 + block_id * bt_stride1 + base = tl.load(bt_ptr, mask=valid_block, other=0) + + # If token == -1 OR block_id OOB, output -1; else base * BLOCK_SIZE + offset + out_val = tl.where(is_invalid_tok | (~valid_block), -1, + base * BLOCK_SIZE + inblock_off) + + # Store results + out_ptr_ij = out_ptr + token_id * out_stride0 + indice_id * out_stride1 + tl.store(out_ptr_ij, out_val) + + +def triton_convert_req_index_to_global_index( + req_id: torch.Tensor, # int32 [num_tokens] + block_table: torch. + Tensor, # int32 [num_requests, max_num_blocks_per_req] + token_indices: torch.Tensor, # int32 [num_tokens, NUM_TOPK_TOKENS] + BLOCK_SIZE: int = 64, + NUM_TOPK_TOKENS: int = 2048, + BLOCK_N: int = 128, # tile width along columns +): + """ + out[token_id, indice_id] = + block_table[req_id[token_id], + token_indices[token_id, indice_id] // BLOCK_SIZE] * BLOCK_SIZE + + token_indices[token_id, indice_id] % BLOCK_SIZE + + Only when token_indices[token_id, indice_id] == -1 do we output -1. + For safety, we also output -1 if the derived block_id would be + out-of-bounds. + """ + assert req_id.dtype == torch.int32 + assert block_table.dtype == torch.int32 + assert token_indices.dtype == torch.int32 + assert token_indices.shape[1] == NUM_TOPK_TOKENS + assert NUM_TOPK_TOKENS % BLOCK_N == 0, \ + f"NUM_TOPK_TOKENS ({NUM_TOPK_TOKENS}) must be divisible by" \ + f"BLOCK_N ({BLOCK_N})" + + num_tokens = req_id.shape[0] + num_requests, max_num_blocks_per_req = block_table.shape + tiles_per_row = NUM_TOPK_TOKENS // BLOCK_N + + # Ensure contiguous tensors on the same device + req_id_c = req_id.contiguous() + block_table_c = block_table.contiguous() + token_indices_c = token_indices.contiguous() + out = torch.empty_like(token_indices_c) + + # Strides in elements + bt_stride0, bt_stride1 = block_table_c.stride() + ti_stride0, ti_stride1 = token_indices_c.stride() + out_stride0, out_stride1 = out.stride() + + # Exact 2D grid: tokens × column tiles + grid = (num_tokens, tiles_per_row) + + _convert_req_index_to_global_index_kernel[grid]( + req_id_c, + block_table_c, + token_indices_c, + out, + # shapes / constexprs + max_num_blocks_per_req, + BLOCK_SIZE, + BLOCK_N, + # strides + bt_stride0, + bt_stride1, + ti_stride0, + ti_stride1, + out_stride0, + out_stride1, + ) + return out + + +@dataclass +class FlashMLASparseMetadataBuilder( + AttentionMetadataBuilder[FlashMLASparseMetadata]): + cudagraph_support: ClassVar[AttentionCGSupport] = \ + AttentionCGSupport.UNIFORM_BATCH + + def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str], + vllm_config: VllmConfig, device: torch.device): + + cache_config = vllm_config.cache_config + self.kv_cache_spec = kv_cache_spec + self.model_config = vllm_config.model_config + parallel_config = vllm_config.parallel_config + self.device = device + + props = torch.cuda.get_device_properties(device) + sm_count = props.multi_processor_count + + self.num_heads = self.model_config.get_num_attention_heads( + parallel_config) + self.mla_dims = get_mla_dims(self.model_config) + self.topk_tokens = vllm_config.model_config.hf_config.index_topk + self.use_fp8_kv_cache = cache_config.cache_dtype == "fp8_ds_mla" + self.topk_tokens_tensor = torch.tensor([self.topk_tokens], + device=device, + dtype=torch.int32) + self.max_model_len_tensor = torch.tensor( + [self.model_config.max_model_len], + device=device, + dtype=torch.int32) + # this is ignored by `flash_mla_with_kvcache` if indices not None + self.dummy_block_table = torch.empty((1, 1), + dtype=torch.int32, + device=self.device) + + # Equation taken from FlashMLA/csrc/pybind.cpp + h_q, h_k = self.num_heads, 1 + s_q = 1 # inversely proportional to s_q, so s_q = 1 is the largest + max_num_sm_parts = int( + max((sm_count // 2) / h_k // (cdiv(h_q // h_k, 2 * 64) * s_q), 1)) + if current_platform.is_device_capability(100): + max_num_sm_parts *= 2 + self.tile_scheduler_metadata_buffer = torch.empty( + # TileSchedulerMetaDataSize = 8 + # see: FlashMLA/csrc/params.h + (max_num_sm_parts, 8), + dtype=torch.int32, + device=device) + self.num_splits_buffer = torch.empty( + # We pack all the tokens into one batch for sparse attention. + # Otherwise, we can exceed the sm of `get_mla_metadata`. + ( + 2, ), + dtype=torch.int32, + device=device) + self.req_id_per_token_buffer = torch.empty( + (vllm_config.scheduler_config.max_num_batched_tokens, ), + dtype=torch.int32, + device=device) + + def build(self, + common_prefix_len: int, + common_attn_metadata: CommonAttentionMetadata, + fast_build: bool = False) -> FlashMLASparseMetadata: + + num_tokens = common_attn_metadata.num_actual_tokens + starts = np.asarray(common_attn_metadata.query_start_loc_cpu, + dtype=np.int32) + seg_lengths = np.diff(starts) + req_id_per_token = np.repeat( + np.arange(seg_lengths.shape[0], dtype=np.int32), seg_lengths) + # Zero-fill for cudagraphs + self.req_id_per_token_buffer.fill_(0) + self.req_id_per_token_buffer[:req_id_per_token.shape[0]]\ + .copy_(torch.from_numpy(req_id_per_token), non_blocking=True) + req_id_per_token = self.req_id_per_token_buffer[:num_tokens] + + fp8_extra_metadata = None + if self.use_fp8_kv_cache: + tile_scheduler_metadata, num_splits = get_mla_metadata( + cache_seqlens=self.topk_tokens_tensor, + num_q_tokens_per_head_k=num_tokens * self.num_heads, + topk=self.topk_tokens, + num_heads_q=self.num_heads, + num_heads_k=1, + is_fp8_kvcache=True, + ) + + num_sm_parts = tile_scheduler_metadata.size(0) + # Copy to persistent buffer for full-CG support + tile_scheduler_metadata_buffer = \ + self.tile_scheduler_metadata_buffer[:num_sm_parts] + tile_scheduler_metadata_buffer.copy_(tile_scheduler_metadata) + self.num_splits_buffer.copy_(num_splits) + + fp8_extra_metadata = FlashMLASparseMetadata.FP8KernelMetadata( + scheduler_metadata=tile_scheduler_metadata_buffer, + num_splits=self.num_splits_buffer, + # cache_lens and block_table are basically unused in sparse case + # but the decode kernel will treat -1 and indices >= cache_lens + # as invalid so we make sure cache_lens is large enough to not + # accidentally mark indices invalid, we will use -1 exclusively + # to mark invalid indices + cache_lens=self.max_model_len_tensor, + dummy_block_table=self.dummy_block_table) + + metadata = FlashMLASparseMetadata( + num_reqs=common_attn_metadata.num_reqs, + max_query_len=common_attn_metadata.max_query_len, + max_seq_len=common_attn_metadata.max_seq_len, + num_actual_tokens=common_attn_metadata.num_actual_tokens, + query_start_loc=common_attn_metadata.query_start_loc, + slot_mapping=common_attn_metadata.slot_mapping, + block_table=common_attn_metadata.block_table_tensor, + req_id_per_token=req_id_per_token, + block_size=self.kv_cache_spec.block_size, + topk_tokens=self.topk_tokens, + fp8_extra_metadata=fp8_extra_metadata, + ) + return metadata + + +class FlashMLASparseImpl(MLACommonBaseImpl[FlashMLASparseMetadata]): + + def __init__( + self, + num_heads: int, + head_size: int, + scale: float, + num_kv_heads: int, + alibi_slopes: Optional[list[float]], + sliding_window: Optional[int], + kv_cache_dtype: str, + logits_soft_cap: Optional[float], + attn_type: str, + kv_sharing_target_layer_name: Optional[str], + # MLA Specific Arguments + topk_indice_buffer: Optional[torch.Tensor] = None, + indexer: Optional["Indexer"] = None, + **mla_args) -> None: + super().__init__(num_heads, head_size, scale, num_kv_heads, + alibi_slopes, sliding_window, kv_cache_dtype, + logits_soft_cap, attn_type, + kv_sharing_target_layer_name, **mla_args) + self.softmax_scale = scale + assert indexer is not None + self.topk_indices_buffer = indexer.topk_indices_buffer + self.padding = 128 if current_platform.is_device_capability( + 100) else 64 + + def _forward_bf16_kv( + self, q: torch.Tensor, kv_c_and_k_pe_cache: torch.Tensor, + topk_indices: torch.Tensor, + attn_metadata: FlashMLASparseMetadata) -> torch.Tensor: + num_tokens = q.shape[0] + kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.view( + -1, 1, kv_c_and_k_pe_cache.shape[-1]) + + # NOTE(Chen): kernel requires num_local_head to be a multiple of + # 64 on hopper and 128 on blackwell + if self.num_heads % self.padding != 0: + assert self.padding % self.num_heads == 0 + logger.warning_once(f"padding num_heads to {self.padding} \ + due to sparse attn kernel requirement") + q_padded = q.new_empty((q.shape[0], self.padding, q.shape[2])) + q_padded[:, :self.num_heads, :] = q + q = q_padded + + topk_indices = topk_indices.view(num_tokens, 1, -1) + output = flash_mla_sparse_prefill(q, kv_c_and_k_pe_cache, topk_indices, + self.softmax_scale)[0] + output = output[:, :self.num_heads, :] + return output + + def _forward_fp8_kv(self, q: torch.Tensor, + kv_c_and_k_pe_cache: torch.Tensor, + topk_indices: torch.Tensor, + attn_metadata: FlashMLASparseMetadata) -> torch.Tensor: + + assert attn_metadata.fp8_extra_metadata is not None + extra_metadata = attn_metadata.fp8_extra_metadata + + _attn_out, _ = flash_mla_with_kvcache( + q=q.unsqueeze(0), # unsqueeze to add batch_dim + k_cache=kv_c_and_k_pe_cache.view(torch.uint8).unsqueeze(-2), + block_table=extra_metadata.dummy_block_table, + head_dim_v=512, + cache_seqlens=extra_metadata.cache_lens, + tile_scheduler_metadata=extra_metadata.scheduler_metadata, + num_splits=extra_metadata.num_splits, + is_fp8_kvcache=True, + indices=topk_indices.unsqueeze(0), # unsqueeze to add batch_dim + softmax_scale=self.softmax_scale, + ) + + return _attn_out + + def forward( + self, + layer: AttentionLayer, + q: torch.Tensor, + k_c_normed: torch.Tensor, # key in unified attn + k_pe: torch.Tensor, # value in unified attn + kv_cache: torch.Tensor, + attn_metadata: FlashMLASparseMetadata, + output: Optional[torch.Tensor] = None, + output_scale: Optional[torch.Tensor] = None, + output_block_scale: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + # NOTE(lucas): for the sparse FlashMLA kernels the kernels want to use + # MQA 576/512 approach for both prefill and decode + + assert output is not None, "Output tensor must be provided." + + if output_scale is not None or output_block_scale is not None: + raise NotImplementedError( + "fused output quantization is not yet supported" + " for MLACommonImpl") + + if attn_metadata is None: + # The zero fill is required when used with DP + EP + # to ensure all ranks within a DP group compute the + # same expert outputs. + return output.fill_(0) + + num_actual_toks = attn_metadata.num_actual_tokens + + # Inputs and outputs may be padded for CUDA graphs + + q = q[:num_actual_toks, ...] + k_c_normed = k_c_normed[:num_actual_toks, ...] + k_pe = k_pe[:num_actual_toks, ...] + + q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], + dim=-1) + # Convert from (B, N, P) to (N, B, P) + q_nope = q_nope.transpose(0, 1) + # Multiply (N, B, P) x (N, P, L) -> (N, B, L) + ql_nope = torch.bmm(q_nope, self.W_UK_T) + # Convert from (N, B, L) to (B, N, L) + ql_nope = ql_nope.transpose(0, 1) + + topk_indices = self.topk_indices_buffer[:num_actual_toks] + + # TODO: handle index / kv_cache correctly + topk_indices_global = triton_convert_req_index_to_global_index( + attn_metadata.req_id_per_token, + attn_metadata.block_table, + topk_indices, + BLOCK_SIZE=attn_metadata.block_size, + NUM_TOPK_TOKENS=attn_metadata.topk_tokens, + ) + + q = torch.cat([ql_nope, q_pe], dim=-1) + + # write the latent and rope to kv cache + if kv_cache.numel() > 0: + ops.concat_and_cache_mla( + k_c_normed, + k_pe.squeeze(1), + kv_cache, + attn_metadata.slot_mapping.flatten(), + kv_cache_dtype=self.kv_cache_dtype, + scale=layer._k_scale, + ) + + if self.kv_cache_dtype != "fp8_ds_mla": + attn_out = self._forward_bf16_kv(q, kv_cache, topk_indices_global, + attn_metadata) + else: + attn_out = self._forward_fp8_kv(q, kv_cache, topk_indices_global, + attn_metadata) + + self._v_up_proj(attn_out, out=output[:num_actual_toks]) + return output diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py new file mode 100644 index 000000000000..4e6b974ad74d --- /dev/null +++ b/vllm/v1/attention/backends/mla/indexer.py @@ -0,0 +1,293 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +from dataclasses import dataclass +from typing import ClassVar, Optional + +import torch + +from vllm.attention.backends.abstract import (AttentionBackend, + AttentionMetadata) +from vllm.config import VllmConfig +from vllm.logger import init_logger +from vllm.utils.deep_gemm import get_paged_mqa_logits_metadata +from vllm.v1.attention.backends.utils import (AttentionCGSupport, + AttentionMetadataBuilder, + CommonAttentionMetadata, + split_decodes_and_prefills) + +logger = init_logger(__name__) + + +class DeepseekV32IndexerBackend(AttentionBackend): + + @staticmethod + def get_metadata_cls() -> type["AttentionMetadata"]: + return DeepseekV32IndexerMetadata + + @classmethod + def get_supported_head_sizes(cls) -> list[int]: + return [32, 64, 128] + + @staticmethod + def get_builder_cls() -> type["DeepseekV32IndexerMetadataBuilder"]: + return DeepseekV32IndexerMetadataBuilder + + @staticmethod + def get_kv_cache_shape( + num_blocks: int, + block_size: int, + num_kv_heads: int, + head_size: int, + cache_dtype_str: str = "auto", + ) -> tuple[int, ...]: + assert num_kv_heads == 1 + return (num_blocks, block_size, head_size) + + @staticmethod + def get_kv_cache_stride_order() -> tuple[int, ...]: + return (0, 1, 2) + + +@dataclass +class DeepseekV32IndexerPrefillMetadata: + block_table: torch.Tensor + query_start_loc: torch.Tensor + max_query_len: int + cu_seqlen_ks: torch.Tensor + cu_seqlen_ke: torch.Tensor + cu_seq_lens: torch.Tensor + total_seq_lens: int + + +@dataclass +class DeepSeekV32IndexerDecodeMetadata: + block_table: torch.Tensor + seq_lens: torch.Tensor + decode_lens: torch.Tensor + requires_padding: bool + schedule_metadata: torch.Tensor + + +@dataclass +class DeepseekV32IndexerMetadata: + + # FIXME (zyongye) + # hacky way to access the data now, need to be in chunked meta + seq_lens: torch.Tensor + + num_reqs: int + max_query_len: int + max_seq_len: int + + num_actual_tokens: int # Number of tokens excluding padding. + query_start_loc: torch.Tensor + slot_mapping: torch.Tensor + # The dimension of the attention heads + head_dim: int + + # New for MLA (compared to FlashAttention) + # For handling prefill decode split + num_decodes: int + num_decode_tokens: int + num_prefills: int + num_prefill_tokens: int + + decode: Optional[DeepSeekV32IndexerDecodeMetadata] = None + prefill: Optional[DeepseekV32IndexerPrefillMetadata] = None + + +# TODO (zyongye) optimize this, this is now vibe coded +def kv_spans_from_batches( + start_seq_loc: torch.Tensor, + seq_len_per_batch: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """ + Args: + start_seq_loc: 1D long tensor [B+1], cumulative counts of + selected tokens per batch. + Example: [0, 2, 4, 7] -> + batch sizes (selected) [2, 2, 3], N=7 tokens total. + seq_len_per_batch: 1D long tensor [B], + full sequence length (KV length) of each batch. + Example: [5, 9, 4]. + + Returns: + start_tensor: 1D long tensor [N], start offset in the + concatenated KV cache for each token's batch. + end_location: 1D long tensor [N], + **exclusive** end = start + token's local position. + (So the attended KV slice is kv[start:end].) + + Assumes each batch contributes its full `seq_len_per_batch[i]` + keys to the KV cache, andthe selected tokens within a batch + are the **last** `counts[i]` positions of that sequence. + """ + q = start_seq_loc.to(dtype=torch.long) + L = seq_len_per_batch.to(dtype=torch.long, device=q.device) + assert q.dim() == 1 and L.dim() == 1 + assert q.numel() == L.numel() + 1, "start_seq_loc must have length B+1" + + # Selected tokens per batch and totals + counts = q[1:] - q[:-1] # [B] + N = int(q[-1].item()) # total selected tokens + B = L.numel() + device = L.device + + if N == 0: + return (torch.empty(0, dtype=torch.long, device=device), + torch.empty(0, dtype=torch.long, device=device)) + + # KV start offsets per batch in the concatenated KV cache + kv_starts_per_batch = torch.cumsum(L, dim=0) - L # [B] + + # For each selected token, which batch does it belong to? + batch_id = torch.repeat_interleave(torch.arange(B, device=device), + counts) # [N] + + # Map batch KV start to each token + start_tensor = kv_starts_per_batch[batch_id] # [N] + + # End-align local positions inside each batch: + # local_pos = L[b] - counts[b] + (1..counts[b]) for each batch b + L_expand = torch.repeat_interleave(L, counts) # [N] + m_expand = torch.repeat_interleave(counts, counts) # [N] + # position within the selected block: 1..counts[b] + pos_within = (torch.arange(N, device=device, dtype=torch.long) - + torch.repeat_interleave(q[:-1], counts) + 1) + + local_pos = L_expand - m_expand + pos_within # [N], 1-based + end_location = start_tensor + local_pos # exclusive end + + return start_tensor.int(), end_location.int() + + +def get_max_prefill_buffer_size(vllm_config: VllmConfig): + max_model_len = vllm_config.model_config.max_model_len + # max_num_batched_tokens = \ + # vllm_config.scheduler_config.max_num_batched_tokens + max_num_seq = vllm_config.scheduler_config.max_num_seqs + # NOTE(Chen): an estimated max size of flattened_kv. Need to double check. + return max_model_len * max_num_seq + + +class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder): + cudagraph_support: ClassVar[AttentionCGSupport] = \ + AttentionCGSupport.UNIFORM_BATCH + + reorder_batch_threshold: int = 1 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + scheduler_config = self.vllm_config.scheduler_config + #NOTE(Chen):an estimated max size of flattened_kv. Need to double check. + self.max_prefill_buffer_size = get_max_prefill_buffer_size( + self.vllm_config) + self.num_speculative_tokens = ( + self.vllm_config.speculative_config.num_speculative_tokens + if self.vllm_config.speculative_config else 0) + # Now deepgemm fp8_paged_mqa_logits does not support next_n > 2 + self.reorder_batch_threshold += min(self.num_speculative_tokens, 1) + + props = torch.cuda.get_device_properties(self.device) + sm_count = props.multi_processor_count + self.num_sms = sm_count + + self.decode_lens_buffer = torch.empty( + (scheduler_config.max_num_seqs, ), + dtype=torch.int32, + device=self.device) + + # See: DeepGMM/csrc/apis/attention.hpp + self.scheduler_metadata_buffer = torch.empty((self.num_sms + 1, 2), + dtype=torch.int32, + device=self.device) + + def build(self, + common_prefix_len: int, + common_attn_metadata: CommonAttentionMetadata, + fast_build: bool = False) -> DeepseekV32IndexerMetadata: + + num_reqs = common_attn_metadata.num_reqs + num_tokens = common_attn_metadata.num_actual_tokens + + device = self.device + block_table_tensor = common_attn_metadata.block_table_tensor + + query_start_loc = common_attn_metadata.query_start_loc + + num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \ + split_decodes_and_prefills( + common_attn_metadata, + decode_threshold=self.reorder_batch_threshold) + + assert num_decodes + num_prefills == num_reqs + assert num_decode_tokens + num_prefill_tokens == num_tokens + + prefill_metadata = None + if num_prefills > 0: + reqs_start = num_decodes + prefill_query_start_loc = query_start_loc[ + reqs_start:] - query_start_loc[reqs_start] + cu_seqlen_ks, cu_seqlen_ke = kv_spans_from_batches( + prefill_query_start_loc, + common_attn_metadata.seq_lens[reqs_start:]) + total_seq_lens = common_attn_metadata.seq_lens[reqs_start:].sum() + assert total_seq_lens < self.max_prefill_buffer_size + cu_seq_lens = torch.cat([ + torch.zeros(1, dtype=torch.int32, device=device), + common_attn_metadata.seq_lens[reqs_start:].cumsum(dim=0) + ]).to(torch.int32).cuda() + prefill_metadata = DeepseekV32IndexerPrefillMetadata( + block_table=block_table_tensor[reqs_start:, ...], + query_start_loc=prefill_query_start_loc, + max_query_len=common_attn_metadata.max_query_len, + cu_seqlen_ks=cu_seqlen_ks, + cu_seqlen_ke=cu_seqlen_ke, + cu_seq_lens=cu_seq_lens, + total_seq_lens=total_seq_lens, + ) + + decode_metadata = None + if num_decodes > 0: + torch.diff(common_attn_metadata.query_start_loc[:num_decodes + 1], + out=self.decode_lens_buffer[:num_decodes]) + decode_lens = self.decode_lens_buffer[:num_decodes] + decode_lens_cpu = torch.diff( + common_attn_metadata.query_start_loc_cpu[:num_decodes + 1]) + + # Use CPU to avoid GPU sync; breaking async scheduling + requires_padding = (decode_lens_cpu.max() + > decode_lens_cpu.min()).item() + + seq_lens = common_attn_metadata.seq_lens[:num_decodes] + + self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata( + seq_lens, self.kv_cache_spec.block_size, self.num_sms) + decode_metadata = DeepSeekV32IndexerDecodeMetadata( + block_table=common_attn_metadata. + block_table_tensor[:num_decodes, ...], + seq_lens=common_attn_metadata.seq_lens[:num_decodes], + decode_lens=decode_lens, + requires_padding=requires_padding, + schedule_metadata=self.scheduler_metadata_buffer, + ) + + attn_metadata = DeepseekV32IndexerMetadata( + seq_lens=common_attn_metadata.seq_lens, + num_reqs=common_attn_metadata.num_reqs, + max_query_len=common_attn_metadata.max_query_len, + max_seq_len=common_attn_metadata.max_seq_len, + num_actual_tokens=common_attn_metadata.num_actual_tokens, + query_start_loc=common_attn_metadata.query_start_loc, + slot_mapping=common_attn_metadata.slot_mapping, + head_dim=128, + num_decodes=num_decodes, + num_decode_tokens=num_decode_tokens, + num_prefills=num_prefills, + num_prefill_tokens=num_prefill_tokens, + prefill=prefill_metadata, + decode=decode_metadata, + ) + + # if get_tensor_model_parallel_rank() == 0: + # logger.info(f"attn_metadata: {attn_metadata}") + return attn_metadata diff --git a/vllm/v1/attention/backends/pallas.py b/vllm/v1/attention/backends/pallas.py index 4cb4b85956bc..7ac1a063f565 100644 --- a/vllm/v1/attention/backends/pallas.py +++ b/vllm/v1/attention/backends/pallas.py @@ -102,6 +102,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: padded_head_size = cdiv( head_size, TPU_HEAD_SIZE_ALIGNMENT) * TPU_HEAD_SIZE_ALIGNMENT diff --git a/vllm/v1/attention/backends/rocm_aiter_fa.py b/vllm/v1/attention/backends/rocm_aiter_fa.py index 96f8e92a2039..ed63c7b1bda6 100644 --- a/vllm/v1/attention/backends/rocm_aiter_fa.py +++ b/vllm/v1/attention/backends/rocm_aiter_fa.py @@ -360,6 +360,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") diff --git a/vllm/v1/attention/backends/tree_attn.py b/vllm/v1/attention/backends/tree_attn.py index 1d4ab4c96728..583756129a29 100644 --- a/vllm/v1/attention/backends/tree_attn.py +++ b/vllm/v1/attention/backends/tree_attn.py @@ -68,6 +68,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") diff --git a/vllm/v1/attention/backends/triton_attn.py b/vllm/v1/attention/backends/triton_attn.py index fc5ecf6ed3b6..3983c5edc76f 100644 --- a/vllm/v1/attention/backends/triton_attn.py +++ b/vllm/v1/attention/backends/triton_attn.py @@ -171,6 +171,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") diff --git a/vllm/v1/attention/backends/xformers.py b/vllm/v1/attention/backends/xformers.py index f739e6832274..9d667ee04f75 100644 --- a/vllm/v1/attention/backends/xformers.py +++ b/vllm/v1/attention/backends/xformers.py @@ -106,6 +106,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") diff --git a/vllm/v1/core/kv_cache_utils.py b/vllm/v1/core/kv_cache_utils.py index 47a41322c423..2ff1bb681d80 100644 --- a/vllm/v1/core/kv_cache_utils.py +++ b/vllm/v1/core/kv_cache_utils.py @@ -1103,7 +1103,9 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]): kv_cache_spec: The kv cache spec of each attention layer in the model """ - if is_kv_cache_spec_uniform(kv_cache_spec): + if is_kv_cache_spec_uniform( + kv_cache_spec) or UniformTypeKVCacheSpecs.is_uniform_type( + kv_cache_spec): return logger.warning( @@ -1128,7 +1130,6 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]): num_kv_heads=spec.num_kv_heads, head_size=spec.head_size, dtype=spec.dtype, - use_mla=spec.use_mla, sliding_window=spec.sliding_window, ) elif isinstance(spec, ChunkedLocalAttentionSpec): @@ -1137,11 +1138,11 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]): num_kv_heads=spec.num_kv_heads, head_size=spec.head_size, dtype=spec.dtype, - use_mla=spec.use_mla, attention_chunk_size=spec.attention_chunk_size, ) - if not is_kv_cache_spec_uniform(kv_cache_spec): + if not (is_kv_cache_spec_uniform(kv_cache_spec) + or UniformTypeKVCacheSpecs.is_uniform_type(kv_cache_spec)): raise ValueError("Hybrid KV cache manager is disabled but failed to " "convert the KV cache specs to one unified type.") diff --git a/vllm/v1/core/single_type_kv_cache_manager.py b/vllm/v1/core/single_type_kv_cache_manager.py index d27239164b0d..e889f7804e84 100644 --- a/vllm/v1/core/single_type_kv_cache_manager.py +++ b/vllm/v1/core/single_type_kv_cache_manager.py @@ -10,7 +10,7 @@ from vllm.v1.kv_cache_interface import (ChunkedLocalAttentionSpec, CrossAttentionSpec, FullAttentionSpec, KVCacheSpec, MambaSpec, - SlidingWindowSpec) + MLAAttentionSpec, SlidingWindowSpec) from vllm.v1.request import Request @@ -656,6 +656,7 @@ def remove_skipped_blocks(self, request_id: str, spec_manager_map: dict[type[KVCacheSpec], type[SingleTypeKVCacheManager]] = { FullAttentionSpec: FullAttentionManager, + MLAAttentionSpec: FullAttentionManager, SlidingWindowSpec: SlidingWindowManager, ChunkedLocalAttentionSpec: ChunkedLocalAttentionManager, MambaSpec: MambaManager, diff --git a/vllm/v1/kv_cache_interface.py b/vllm/v1/kv_cache_interface.py index f72cc8f93a6c..281816653540 100644 --- a/vllm/v1/kv_cache_interface.py +++ b/vllm/v1/kv_cache_interface.py @@ -59,13 +59,10 @@ class AttentionSpec(KVCacheSpec): num_kv_heads: int head_size: int dtype: torch.dtype - use_mla: bool @property def page_size_bytes(self) -> int: - # For MLA we only store a single latent vector - coef = 1 if self.use_mla else 2 - return coef * self.block_size * self.num_kv_heads * self.head_size \ + return 2 * self.block_size * self.num_kv_heads * self.head_size \ * get_dtype_size(self.dtype) @@ -118,12 +115,13 @@ def merge(cls, specs: list[Self]) -> Self: if spec.sliding_window is not None) attention_chunk_size = set(spec.attention_chunk_size for spec in specs if spec.attention_chunk_size is not None) + assert not any(isinstance(spec, MLAAttentionSpec) for spec in specs), ( + "MLAAttentionSpec should be merged in MLAAttentionSpec.merge") merged_spec = cls( block_size=specs[0].block_size, num_kv_heads=specs[0].num_kv_heads, head_size=specs[0].head_size, dtype=specs[0].dtype, - use_mla=specs[0].use_mla, sliding_window=cls.merge_window_sizes(sliding_window), attention_chunk_size=cls.merge_window_sizes(attention_chunk_size), ) @@ -140,6 +138,38 @@ def merge(cls, specs: list[Self]) -> Self: return merged_spec +@dataclass(frozen=True) +class MLAAttentionSpec(FullAttentionSpec): + # TODO(Lucas/Chen): less hacky way to do this + cache_dtype_str: Optional[str] = None + + @property + def page_size_bytes(self) -> int: + if self.cache_dtype_str == "fp8_ds_mla": + # See `vllm/v1/attention/backends/mla/flashmla_sparse.py` + # for details. + return self.block_size * 656 + return self.block_size * self.num_kv_heads * self.head_size \ + * get_dtype_size(self.dtype) + + @classmethod + def merge(cls, specs: list[Self]) -> Self: + assert all(isinstance(spec, MLAAttentionSpec) for spec in specs), ( + "All attention layers in the same KV cache group must be " + "MLAAttentionSpec.") + cache_dtype_str_set = set(spec.cache_dtype_str for spec in specs) + assert len(cache_dtype_str_set) == 1, ( + "All attention layers in the same KV cache group must use the same " + "quantization method.") + return cls( + block_size=specs[0].block_size, + num_kv_heads=specs[0].num_kv_heads, + head_size=specs[0].head_size, + dtype=specs[0].dtype, + cache_dtype_str=cache_dtype_str_set.pop(), + ) + + @dataclass(frozen=True) class ChunkedLocalAttentionSpec(AttentionSpec): attention_chunk_size: int @@ -163,9 +193,6 @@ def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: class SlidingWindowSpec(AttentionSpec): sliding_window: int - def __post_init__(self): - assert not self.use_mla, "MLA is not supported for sliding window" - def max_memory_usage_bytes(self, vllm_config: VllmConfig) -> int: assert vllm_config.parallel_config.decode_context_parallel_size == 1, \ "DCP not support sliding window." @@ -266,9 +293,13 @@ def is_uniform_type(cls, kv_cache_specs: dict[str, KVCacheSpec]) -> bool: # Different block sizes, not uniform. return False one_spec = next(iter(kv_cache_specs.values())) - if isinstance(one_spec, (FullAttentionSpec, CrossAttentionSpec)): + if isinstance(one_spec, FullAttentionSpec): + return all( + isinstance(spec, FullAttentionSpec) + for spec in kv_cache_specs.values()) + elif isinstance(one_spec, CrossAttentionSpec): return all( - isinstance(spec, type(one_spec)) + isinstance(spec, CrossAttentionSpec) for spec in kv_cache_specs.values()) elif isinstance(one_spec, SlidingWindowSpec): return all( diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index a2f7dbe5703f..bb11a543fd8b 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -17,6 +17,7 @@ from vllm.logger import init_logger from vllm.model_executor.model_loader import get_model from vllm.model_executor.models import supports_multimodal +from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.platforms import current_platform @@ -32,6 +33,7 @@ from vllm.v1.spec_decode.metadata import SpecDecodeMetadata from vllm.v1.utils import CpuGpuBuffer from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch +from vllm.v1.worker.ubatching import dbo_current_ubatch_id logger = init_logger(__name__) @@ -52,6 +54,7 @@ def __init__( self.method = self.speculative_config.method self.runner = runner + self.device = device self.dtype = vllm_config.model_config.dtype self.max_model_len = vllm_config.model_config.max_model_len self.block_size = vllm_config.cache_config.block_size @@ -202,20 +205,30 @@ def propose( assert self.runner is not None - # Select the correct attention metadata builders for EAGLE layers. - # Get the attention metadata builders once and reuse for later. - builder = (self._get_attention_metadata_builder() - if self.attn_metadata_builder is None else - self.attn_metadata_builder) - attn_metadata = builder.build_for_drafting( # type: ignore - common_attn_metadata=common_attn_metadata, - draft_index=0) - + # FIXME: need to consider multiple kv_cache_groups + ubatch_id = dbo_current_ubatch_id() + attn_metadata_builder = \ + self.runner.attn_groups[0][0].metadata_builders[ubatch_id] + attn_metadata = attn_metadata_builder.build_for_drafting( + common_attn_metadata=common_attn_metadata, draft_index=0) + # FIXME: support hybrid kv for draft model (remove separate indexer) + if self.draft_indexer_metadata_builder: + draft_indexer_metadata = ( + self.draft_indexer_metadata_builder.build_for_drafting( + common_attn_metadata=common_attn_metadata, + draft_index=0, + )) + else: + draft_indexer_metadata = None # At this moment, we assume all eagle layers belong to the same KV # cache group, thus using the same attention metadata. per_layer_attn_metadata = {} for layer_name in self.attn_layer_names: per_layer_attn_metadata[layer_name] = attn_metadata + for layer_name in self.indexer_layer_names: + assert draft_indexer_metadata is not None + per_layer_attn_metadata[layer_name] = draft_indexer_metadata + if self.use_cuda_graph and \ num_tokens <= self.cudagraph_batch_sizes[-1]: num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens) @@ -370,7 +383,7 @@ def propose( exceeds_max_model_len, PADDING_SLOT_ID) # Rebuild attention metadata - attn_metadata = builder.build_for_drafting( # type: ignore + attn_metadata = attn_metadata_builder.build_for_drafting( # type: ignore common_attn_metadata=common_attn_metadata, draft_index=token_index + 1) for layer_name in self.attn_layer_names: @@ -846,6 +859,10 @@ def load_model(self, target_model: nn.Module) -> None: self.vllm_config.speculative_config.draft_model_config target_attn_layer_names = set( get_layers_from_vllm_config(self.vllm_config, Attention).keys()) + # FIXME: support hybrid kv for draft model + target_indexer_layer_names = set( + get_layers_from_vllm_config(self.vllm_config, + DeepseekV32IndexerCache).keys()) from vllm.compilation.backends import set_model_tag with set_model_tag("eagle_head"): @@ -855,8 +872,25 @@ def load_model(self, target_model: nn.Module) -> None: draft_attn_layer_names = ( get_layers_from_vllm_config(self.vllm_config, Attention).keys() - target_attn_layer_names) - + indexer_layers = get_layers_from_vllm_config(self.vllm_config, + DeepseekV32IndexerCache) + draft_indexer_layer_names = (indexer_layers.keys() - + target_indexer_layer_names) self.attn_layer_names = list(draft_attn_layer_names) + self.indexer_layer_names = list(draft_indexer_layer_names) + + if self.indexer_layer_names: + first_layer = self.indexer_layer_names[0] + self.draft_indexer_metadata_builder = ( + indexer_layers[first_layer].get_attn_backend().get_builder_cls( + )( + indexer_layers[first_layer].get_kv_cache_spec(), + self.indexer_layer_names, + self.vllm_config, + self.device, + )) + else: + self.draft_indexer_metadata_builder = None if self.supports_mm_inputs: # Even if the target model is multimodal, we can also use diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 98e00f6d98a9..bb5c3ea74293 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -40,6 +40,7 @@ from vllm.model_executor.layers.mamba.abstract import MambaBase from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader +from vllm.model_executor.models.deepseek_v2 import DeepseekV32IndexerCache # yapf conflicts with isort for this block # yapf: disable from vllm.model_executor.models.interfaces import (SupportsMultiModal, @@ -80,7 +81,8 @@ EncoderOnlyAttentionSpec, FullAttentionSpec, KVCacheConfig, KVCacheGroupSpec, KVCacheSpec, - MambaSpec, SlidingWindowSpec, + MambaSpec, MLAAttentionSpec, + SlidingWindowSpec, UniformTypeKVCacheSpecs) # yapf: enable from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput, @@ -3154,7 +3156,7 @@ def _dummy_run( attn_metadata_i = (attn_group\ .get_metadata_builder(ubatch_id=ubid)\ .build_for_cudagraph_capture(common_attn_metadata)) - for layer_name in kv_cache_group_spec.layer_names: + for layer_name in attn_group.layer_names: assert type(attn_metadata) is list attn_metadata[ubid][ layer_name] = attn_metadata_i @@ -3162,7 +3164,7 @@ def _dummy_run( assert type(attn_metadata) is dict attn_metadata_i = attn_group.get_metadata_builder()\ .build_for_cudagraph_capture(common_attn_metadata) - for layer_name in kv_cache_group_spec.layer_names: + for layer_name in attn_group.layer_names: attn_metadata[layer_name] = attn_metadata_i with self.maybe_dummy_run_with_lora(self.lora_config, @@ -3915,8 +3917,11 @@ def _reshape_kv_cache_tensors( if isinstance(kv_cache_spec, AttentionSpec): has_attn = True kv_cache_shape = attn_backend.get_kv_cache_shape( - num_blocks, kv_cache_spec.block_size, - kv_cache_spec.num_kv_heads, kv_cache_spec.head_size) + num_blocks, + kv_cache_spec.block_size, + kv_cache_spec.num_kv_heads, + kv_cache_spec.head_size, + cache_dtype_str=self.cache_config.cache_dtype) dtype = kv_cache_spec.dtype try: kv_cache_stride_order = \ @@ -4101,7 +4106,6 @@ def may_add_encoder_only_layers_to_kv_cache_config(self) -> None: Add encoder-only layers to the KV cache config. """ block_size = self.vllm_config.cache_config.block_size - use_mla = self.vllm_config.model_config.use_mla encoder_only_attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list) attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention) @@ -4111,8 +4115,7 @@ def may_add_encoder_only_layers_to_kv_cache_config(self) -> None: block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, - dtype=self.kv_cache_dtype, - use_mla=use_mla) + dtype=self.kv_cache_dtype) encoder_only_attn_specs[attn_spec].append(layer_name) self.runner_only_attn_layers.add(layer_name) if len(encoder_only_attn_specs) > 0: @@ -4134,6 +4137,7 @@ def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: block_size = self.vllm_config.cache_config.block_size use_mla = self.vllm_config.model_config.use_mla + cache_dtype_str = self.vllm_config.cache_config.cache_dtype kv_cache_spec: dict[str, KVCacheSpec] = {} attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention) for layer_name, attn_module in attn_layers.items(): @@ -4153,13 +4157,21 @@ def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: # the attention backends if attn_module.attn_type == AttentionType.DECODER: if attn_module.sliding_window is not None: + assert not use_mla, "MLA is not supported for sliding" \ + "window" kv_cache_spec[layer_name] = SlidingWindowSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, - sliding_window=attn_module.sliding_window, - use_mla=use_mla) + sliding_window=attn_module.sliding_window) + elif use_mla: + kv_cache_spec[layer_name] = MLAAttentionSpec( + block_size=block_size, + num_kv_heads=attn_module.num_kv_heads, + head_size=attn_module.head_size, + dtype=self.kv_cache_dtype, + cache_dtype_str=cache_dtype_str) elif self.attention_chunk_size is not None \ and isinstance(attn_module, ChunkedLocalAttention): kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec( @@ -4167,22 +4179,19 @@ def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, - attention_chunk_size=self.attention_chunk_size, - use_mla=use_mla) + attention_chunk_size=self.attention_chunk_size) else: kv_cache_spec[layer_name] = FullAttentionSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, - dtype=self.kv_cache_dtype, - use_mla=use_mla) + dtype=self.kv_cache_dtype) elif attn_module.attn_type == AttentionType.ENCODER_DECODER: kv_cache_spec[layer_name] = CrossAttentionSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, - dtype=self.kv_cache_dtype, - use_mla=use_mla) + dtype=self.kv_cache_dtype) elif attn_module.attn_type in (AttentionType.ENCODER, AttentionType.ENCODER_ONLY): # encoder-only attention does not need KV cache. @@ -4219,6 +4228,10 @@ def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: self.speculative_config.num_speculative_tokens if self.speculative_config else 0), ) + ds_indexer_layers = get_layers_from_vllm_config( + self.vllm_config, DeepseekV32IndexerCache) + for layer_name, ds_indexer_module in ds_indexer_layers.items(): + kv_cache_spec[layer_name] = ds_indexer_module.get_kv_cache_spec() return kv_cache_spec diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py index 2405f978ca73..0b1c3d7c0e88 100644 --- a/vllm/v1/worker/tpu_model_runner.py +++ b/vllm/v1/worker/tpu_model_runner.py @@ -537,7 +537,6 @@ def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: head_size=attn_module.head_size, dtype=self.kv_cache_dtype, sliding_window=attn_module.sliding_window, - use_mla=False, ) else: kv_cache_spec[layer_name] = FullAttentionSpec( @@ -545,7 +544,6 @@ def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, - use_mla=False, ) elif attn_module.attn_type in (AttentionType.ENCODER, AttentionType.ENCODER_ONLY): From 049c6bc7d2e9b72941a2fbdfe48f690d083d14a4 Mon Sep 17 00:00:00 2001 From: a120092009 <33205509+a120092009@users.noreply.github.com> Date: Tue, 30 Sep 2025 17:59:47 +0800 Subject: [PATCH 097/166] [Doc] Add Cambricon MLU support (#25942) Signed-off-by: a120092009 --- docs/getting_started/installation/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/getting_started/installation/README.md b/docs/getting_started/installation/README.md index 5e57d23f4a1d..a4e63e426b9b 100644 --- a/docs/getting_started/installation/README.md +++ b/docs/getting_started/installation/README.md @@ -25,3 +25,4 @@ The backends below live **outside** the main `vllm` repository and follow the | MetaX MACA GPU | N/A, install from source | | | Rebellions ATOM / REBEL NPU | `vllm-rbln` | | | IBM Spyre AIU | `vllm-spyre` | | +| Cambricon MLU | `vllm-mlu` | | From af1dec3b11dc1d356cc59fb3ca00f43bef4ff61e Mon Sep 17 00:00:00 2001 From: Sergio Paniego Blanco Date: Tue, 30 Sep 2025 12:10:55 +0200 Subject: [PATCH 098/166] Updated TRL integration docs (#25684) Signed-off-by: sergiopaniego Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Signed-off-by: Sergio Paniego Blanco Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- docs/training/trl.md | 52 +++++++++++++++++++++++++++++++++++++++----- mkdocs.yaml | 1 - 2 files changed, 47 insertions(+), 6 deletions(-) diff --git a/docs/training/trl.md b/docs/training/trl.md index c7c1a5a3bbd1..acf48cc4ecb3 100644 --- a/docs/training/trl.md +++ b/docs/training/trl.md @@ -1,12 +1,54 @@ # Transformers Reinforcement Learning -Transformers Reinforcement Learning (TRL) is a full stack library that provides a set of tools to train transformer language models with methods like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more. The library is integrated with 🤗 transformers. +[Transformers Reinforcement Learning](https://huggingface.co/docs/trl) (TRL) is a full stack library that provides a set of tools to train transformer language models with methods like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more. The library is integrated with 🤗 transformers. Online methods such as GRPO or Online DPO require the model to generate completions. vLLM can be used to generate these completions! -See the guide [vLLM for fast generation in online methods](https://huggingface.co/docs/trl/main/en/speeding_up_training#vllm-for-fast-generation-in-online-methods) in the TRL documentation for more information. +See the [vLLM integration guide](https://huggingface.co/docs/trl/main/en/vllm_integration) in the TRL documentation for more information. + +TRL currently supports the following online trainers with vLLM: + +- [GRPO](https://huggingface.co/docs/trl/main/en/grpo_trainer) +- [Online DPO](https://huggingface.co/docs/trl/main/en/online_dpo_trainer) +- [RLOO](https://huggingface.co/docs/trl/main/en/rloo_trainer) +- [Nash-MD](https://huggingface.co/docs/trl/main/en/nash_md_trainer) +- [XPO](https://huggingface.co/docs/trl/main/en/xpo_trainer) + +To enable vLLM in TRL, set the `use_vllm` flag in the trainer configuration to `True`. + +## Modes of Using vLLM During Training + +TRL supports **two modes** for integrating vLLM during training: **server mode** and **colocate mode**. You can control how vLLM operates during training with the `vllm_mode` parameter. + +### Server mode + +In **server mode**, vLLM runs as an independent process on dedicated GPUs and communicates with the trainer through HTTP requests. This configuration is ideal when you have separate GPUs for inference, as it isolates generation workloads from training, ensuring stable performance and easier scaling. + +```python +from trl import GRPOConfig + +training_args = GRPOConfig( + ..., + use_vllm=True, + vllm_mode="server", # default value, can be omitted +) +``` + +### Colocate mode + +In **colocate mode**, vLLM runs inside the trainer process and shares GPU memory with the training model. This avoids launching a separate server and can improve GPU utilization, but may lead to memory contention on the training GPUs. + +```python +from trl import GRPOConfig + +training_args = GRPOConfig( + ..., + use_vllm=True, + vllm_mode="colocate", +) +``` + +Some trainers also support **vLLM sleep mode**, which offloads parameters and caches to GPU RAM during training, helping reduce memory usage. Learn more in the [memory optimization docs](https://huggingface.co/docs/trl/main/en/reducing_memory_usage#vllm-sleep-mode). !!! info - For more information on the `use_vllm` flag you can provide to the configs of these online methods, see: - - [`trl.GRPOConfig.use_vllm`](https://huggingface.co/docs/trl/main/en/grpo_trainer#trl.GRPOConfig.use_vllm) - - [`trl.OnlineDPOConfig.use_vllm`](https://huggingface.co/docs/trl/main/en/online_dpo_trainer#trl.OnlineDPOConfig.use_vllm) + For detailed configuration options and flags, refer to the documentation of the specific trainer you are using. diff --git a/mkdocs.yaml b/mkdocs.yaml index 1535fcc622cd..6f2be65a18af 100644 --- a/mkdocs.yaml +++ b/mkdocs.yaml @@ -102,7 +102,6 @@ plugins: - https://numpy.org/doc/stable/objects.inv - https://pytorch.org/docs/stable/objects.inv - https://psutil.readthedocs.io/en/stable/objects.inv - - https://huggingface.co/docs/transformers/main/en/objects.inv markdown_extensions: - attr_list From 3a89e8c929877e37d4c9a0828337333b8039fe67 Mon Sep 17 00:00:00 2001 From: CSWYF3634076 Date: Tue, 30 Sep 2025 19:11:21 +0800 Subject: [PATCH 099/166] [Bugfix][Model]fix ernie45 moe gate&bias dtype to float32 (#25936) Signed-off-by: wangyafeng --- vllm/model_executor/models/ernie45_moe.py | 5 +++-- vllm/model_executor/models/ernie45_vl_moe.py | 15 ++++++++++----- 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/vllm/model_executor/models/ernie45_moe.py b/vllm/model_executor/models/ernie45_moe.py index d262e9e9da50..38c5249380c3 100644 --- a/vllm/model_executor/models/ernie45_moe.py +++ b/vllm/model_executor/models/ernie45_moe.py @@ -120,11 +120,12 @@ def __init__( self.gate = ReplicatedLinear(config.hidden_size, config.moe_num_experts, bias=False, + params_dtype=torch.float32, quant_config=None, prefix=f"{prefix}.gate") self.gate.e_score_correction_bias = nn.Parameter( - torch.empty(config.moe_num_experts)) + torch.empty(config.moe_num_experts, dtype=torch.float32)) self.experts = FusedMoE( num_experts=config.moe_num_experts, @@ -157,7 +158,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if self.has_shared_experts: shared_output = self.shared_experts(hidden_states) - router_logits, _ = self.gate(hidden_states) + router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32)) final_hidden_states = self.experts(hidden_states=hidden_states, router_logits=router_logits) diff --git a/vllm/model_executor/models/ernie45_vl_moe.py b/vllm/model_executor/models/ernie45_vl_moe.py index f55016f7ccb3..21772f766b40 100644 --- a/vllm/model_executor/models/ernie45_vl_moe.py +++ b/vllm/model_executor/models/ernie45_vl_moe.py @@ -199,7 +199,7 @@ def __init__( assert config.moe_num_experts[0] == config.moe_num_experts[1] self.e_score_correction_bias = nn.Parameter( - torch.empty(2, config.moe_num_experts[0])) + torch.empty(2, config.moe_num_experts[0], dtype=torch.float32)) assert text_moe_layer_start_index <= text_moe_layer_end_index @@ -209,6 +209,7 @@ def __init__( config.hidden_size, config.moe_num_experts[0], bias=False, + params_dtype=torch.float32, quant_config=quant_config, prefix=f"{prefix}.text_experts_gate") @@ -238,6 +239,7 @@ def __init__( config.hidden_size, config.moe_num_experts[1], bias=False, + params_dtype=torch.float32, quant_config=quant_config, prefix=f"{prefix}.vision_experts_gate") @@ -288,7 +290,8 @@ def forward( if visual_token_mask is not None and visual_token_mask.all(): # only vision modal input - router_logits, _ = self.vision_experts_gate(hidden_states) + router_logits, _ = self.vision_experts_gate( + hidden_states.to(dtype=torch.float32)) final_hidden_states = self.vision_experts( hidden_states=hidden_states, router_logits=router_logits) elif visual_token_mask is not None and visual_token_mask.any(): @@ -303,19 +306,21 @@ def forward( vision_hidden_states = hidden_states[visual_token_mask].reshape( -1, self.hidden_size) - text_router_logits, _ = self.text_experts_gate(text_hidden_states) + text_router_logits, _ = self.text_experts_gate( + text_hidden_states.to(dtype=torch.float32)) final_hidden_states[text_token_mask] = self.text_experts( hidden_states=text_hidden_states, router_logits=text_router_logits).flatten() vision_router_logits, _ = self.vision_experts_gate( - vision_hidden_states) + vision_hidden_states.to(dtype=torch.float32)) final_hidden_states[visual_token_mask] = self.vision_experts( hidden_states=vision_hidden_states, router_logits=vision_router_logits).flatten() else: # only text modal input - text_router_logits, _ = self.text_experts_gate(hidden_states) + text_router_logits, _ = self.text_experts_gate( + hidden_states.to(dtype=torch.float32)) final_hidden_states = self.text_experts( hidden_states=hidden_states, router_logits=text_router_logits) From 226073e2fb48e8080d9be5bf33f6f2a1d4c142c2 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Tue, 30 Sep 2025 19:24:57 +0800 Subject: [PATCH 100/166] [Model] Move `vision_feature_select_strategy` into `resolve_visual_encoder_outputs` (#25938) Signed-off-by: DarkLight1337 --- tests/models/test_vision.py | 11 +-- vllm/model_executor/models/aya_vision.py | 27 ++----- vllm/model_executor/models/clip.py | 31 +++++--- vllm/model_executor/models/llava.py | 25 ++----- vllm/model_executor/models/llava_next.py | 25 ++----- .../model_executor/models/llava_next_video.py | 17 +---- vllm/model_executor/models/llava_onevision.py | 25 ++----- vllm/model_executor/models/minimax_vl_01.py | 24 ++----- vllm/model_executor/models/pixtral.py | 22 ++++-- vllm/model_executor/models/siglip.py | 34 +++++---- vllm/model_executor/models/tarsier.py | 23 ++---- vllm/model_executor/models/vision.py | 70 ++++++++++++++++--- 12 files changed, 155 insertions(+), 179 deletions(-) diff --git a/tests/models/test_vision.py b/tests/models/test_vision.py index 8744bcbd3a2a..a30a856a81cf 100644 --- a/tests/models/test_vision.py +++ b/tests/models/test_vision.py @@ -18,7 +18,7 @@ @pytest.mark.parametrize( - ("feature_sample_layers", "num_layers_loaded", "max_possible_layers", + ("select_layers", "num_layers_loaded", "max_possible_layers", "expected_features"), [ # All layers loaded @@ -28,8 +28,8 @@ ([1, 10], 10, 20, [1, 10]), ([-20, -11], 10, 20, [1, 10]), ]) -def test_resolve_visual_encoder_outputs(feature_sample_layers, - num_layers_loaded, max_possible_layers, +def test_resolve_visual_encoder_outputs(select_layers, num_layers_loaded, + max_possible_layers, expected_features): """ Test that offsets are correctly handled for vision feature layers. @@ -39,9 +39,10 @@ def test_resolve_visual_encoder_outputs(feature_sample_layers, ] output_tensor = resolve_visual_encoder_outputs( encoder_outputs=encoder_outputs, - feature_sample_layers=feature_sample_layers, post_layer_norm=None, - max_possible_layers=max_possible_layers) + select_layers=select_layers, + max_possible_layers=max_possible_layers, + ) assert torch.equal(torch.tensor(expected_features), output_tensor) diff --git a/vllm/model_executor/models/aya_vision.py b/vllm/model_executor/models/aya_vision.py index f6dfa435ddd4..81bab5b34bc6 100644 --- a/vllm/model_executor/models/aya_vision.py +++ b/vllm/model_executor/models/aya_vision.py @@ -27,7 +27,6 @@ PromptUpdateDetails) from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.sequence import IntermediateTensors -from vllm.utils.jsontree import json_map_leaves from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP @@ -350,29 +349,11 @@ def _image_pixels_to_features( self, vision_tower: SiglipVisionModel, pixel_values: torch.Tensor, - **kwargs, ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: - target_dtype: torch.dtype = \ - vision_tower.get_input_embeddings().weight.dtype - image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \ - vision_tower(pixel_values.to(dtype=target_dtype), **kwargs) - - def select_features(leaf: torch.Tensor): - return self._select_image_features( - leaf, - strategy=self.config.vision_feature_select_strategy, - ) - - return json_map_leaves(select_features, image_features) - - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - - raise ValueError(f"Unexpected select feature strategy: {strategy}") + return vision_tower( + pixel_values.to(dtype=vision_tower.dtype), + feature_select_strategy=self.config.vision_feature_select_strategy, + ) def _process_image_input(self, image_input: AyaVisionImagePixelInputs, **kwargs) -> list[torch.Tensor]: diff --git a/vllm/model_executor/models/clip.py b/vllm/model_executor/models/clip.py index dcab00822870..451da2120048 100644 --- a/vllm/model_executor/models/clip.py +++ b/vllm/model_executor/models/clip.py @@ -19,7 +19,8 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.interfaces import SupportsQuant -from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs +from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy, + resolve_visual_encoder_outputs) class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]): @@ -308,24 +309,29 @@ def __init__( def forward( self, pixel_values: torch.Tensor, - feature_sample_layers: Optional[list[int]] = None, + *, + select_layers: Optional[list[int]] = None, + feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None, ) -> torch.Tensor: hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) - return_all_hidden_states = feature_sample_layers is not None - # Produces either the last layer output or all of the hidden states, - # depending on if we have feature_sample_layers or not + # depending on if we have select_layers or not encoder_outputs = self.encoder( inputs_embeds=hidden_states, - return_all_hidden_states=return_all_hidden_states) + return_all_hidden_states=select_layers is not None, + ) # Handle post-norm (if applicable) and stacks feature layers if needed encoder_outputs = resolve_visual_encoder_outputs( - encoder_outputs, feature_sample_layers, self.post_layernorm, - self.config.num_hidden_layers) + encoder_outputs, + self.post_layernorm, + select_layers=select_layers, + max_possible_layers=self.config.num_hidden_layers, + feature_select_strategy=feature_select_strategy, + ) return encoder_outputs @@ -355,9 +361,14 @@ def __init__( def forward( self, pixel_values: torch.Tensor, - feature_sample_layers: Optional[list[int]] = None, + select_layers: Optional[list[int]] = None, + feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None, ) -> torch.Tensor: - return self.vision_model(pixel_values, feature_sample_layers) + return self.vision_model( + pixel_values, + select_layers=select_layers, + feature_select_strategy=feature_select_strategy, + ) @property def device(self): diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index 46cf93be191e..d823e5cb58d2 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -33,7 +33,6 @@ PromptUpdateDetails) from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.sequence import IntermediateTensors -from vllm.utils.jsontree import json_map_leaves from vllm.utils.tensor_schema import TensorSchema, TensorShape from .clip import CLIPVisionModel @@ -604,16 +603,6 @@ def _parse_and_validate_image_input( raise AssertionError("This line should be unreachable.") - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421 # noqa - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - - raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features( self, vision_tower: Union[CLIPVisionModel, SiglipVisionModel, @@ -622,16 +611,10 @@ def _image_pixels_to_features( ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower - image_features: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \ - vision_tower(pixel_values) - - def select_features(leaf: torch.Tensor): - return self._select_image_features( - leaf, - strategy=self.config.vision_feature_select_strategy, - ) - - return json_map_leaves(select_features, image_features) + return vision_tower( + pixel_values, + feature_select_strategy=self.config.vision_feature_select_strategy, + ) def _process_image_pixels( self, diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index c4f1daaab9bf..3f7e39c02061 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -235,12 +235,12 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None: # Determine the layer up to which we will initialize the vision tower if isinstance(vision_feature_layer, int): vision_hidden_size = config.vision_config.hidden_size - self.feature_sample_layers = None + self.select_layers = None # Used for multimodal granite models to control encoder outputs elif isinstance(vision_feature_layer, (list, tuple)): vision_hidden_size = config.vision_config.hidden_size * len( vision_feature_layer) - self.feature_sample_layers = vision_feature_layer + self.select_layers = vision_feature_layer else: raise TypeError( f"vision_layer_feature type: {type(vision_feature_layer)}" @@ -312,30 +312,17 @@ def _parse_and_validate_image_input( raise AssertionError("This line should be unreachable.") - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421 # noqa - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - - raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features( self, vision_tower: Union[CLIPVisionModel, SiglipVisionModel], pixel_values: torch.Tensor, ) -> torch.Tensor: - # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower - image_features = vision_tower( - pixel_values, feature_sample_layers=self.feature_sample_layers) - - return self._select_image_features( - image_features, - strategy=self.config.vision_feature_select_strategy, + return vision_tower( + pixel_values, + select_layers=self.select_layers, + feature_select_strategy=self.config.vision_feature_select_strategy, ) # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py diff --git a/vllm/model_executor/models/llava_next_video.py b/vllm/model_executor/models/llava_next_video.py index aebc661d53f8..697b8e819707 100644 --- a/vllm/model_executor/models/llava_next_video.py +++ b/vllm/model_executor/models/llava_next_video.py @@ -349,27 +349,16 @@ def _parse_and_validate_video_input( "w": expected_w, }) - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - - raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _video_pixels_to_features( self, vision_tower: Union[CLIPVisionModel, SiglipVisionModel], pixel_values: torch.Tensor, ) -> torch.Tensor: - # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower - image_features = vision_tower(pixel_values) - image_features = self._select_image_features( - image_features, - strategy=self.config.vision_feature_select_strategy, + image_features = vision_tower( + pixel_values, + feature_select_strategy=self.config.vision_feature_select_strategy, ) image_features = self.vision_resampler(image_features) image_features = self.multi_modal_projector(image_features) diff --git a/vllm/model_executor/models/llava_onevision.py b/vllm/model_executor/models/llava_onevision.py index 6088195c91d5..924f8ba3585f 100644 --- a/vllm/model_executor/models/llava_onevision.py +++ b/vllm/model_executor/models/llava_onevision.py @@ -577,27 +577,16 @@ def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict: return mm_input_by_modality - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - - raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features( self, vision_tower: Union[CLIPVisionModel, SiglipVisionModel], pixel_values: torch.Tensor, ) -> torch.Tensor: - # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower - image_features = vision_tower(pixel_values) - return self._select_image_features( - image_features, - strategy=self.config.vision_feature_select_strategy, + return vision_tower( + pixel_values, + feature_select_strategy=self.config.vision_feature_select_strategy, ) # Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py @@ -750,13 +739,11 @@ def _video_pixels_to_features( vision_tower: Union[CLIPVisionModel, SiglipVisionModel], pixel_values: torch.Tensor, ) -> torch.Tensor: - # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower - video_features = vision_tower(pixel_values) - video_features = self._select_image_features( - video_features, - strategy=self.config.vision_feature_select_strategy, + video_features = vision_tower( + pixel_values, + feature_select_strategy=self.config.vision_feature_select_strategy, ) video_features = self.multi_modal_projector(video_features) video_features = self.apply_pooling(video_features) diff --git a/vllm/model_executor/models/minimax_vl_01.py b/vllm/model_executor/models/minimax_vl_01.py index d41b9d3f14fe..938c9a689fcf 100644 --- a/vllm/model_executor/models/minimax_vl_01.py +++ b/vllm/model_executor/models/minimax_vl_01.py @@ -17,7 +17,6 @@ from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import MultiModalFieldConfig from vllm.sequence import IntermediateTensors -from vllm.utils.jsontree import json_map_leaves from vllm.utils.tensor_schema import TensorSchema, TensorShape from .clip import CLIPVisionModel @@ -221,15 +220,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None: def get_language_model(self) -> torch.nn.Module: return self.language_model - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - - raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features( self, vision_tower: Union[CLIPVisionModel, SiglipVisionModel, @@ -238,16 +228,10 @@ def _image_pixels_to_features( ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: # NOTE: we skip the step to select the vision feature layer since # this is already done inside the vision tower - image_features: tuple[torch.Tensor, ...] = \ - tuple(vision_tower(p) for p in pixel_values) - - def select_features(leaf: torch.Tensor): - return self._select_image_features( - leaf, - strategy=self.config.vision_feature_select_strategy, - ) - - return json_map_leaves(select_features, image_features) + feature_select_strategy = self.config.vision_feature_select_strategy + return tuple( + vision_tower(p, feature_select_strategy=feature_select_strategy) + for p in pixel_values) # adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631 def pack_image_features(self, image_features: list[torch.Tensor], diff --git a/vllm/model_executor/models/pixtral.py b/vllm/model_executor/models/pixtral.py index 6344fc394833..bf451c5005b7 100644 --- a/vllm/model_executor/models/pixtral.py +++ b/vllm/model_executor/models/pixtral.py @@ -51,7 +51,8 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .utils import flatten_bn, init_vllm_registered_model, maybe_prefix -from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs +from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy, + resolve_visual_encoder_outputs) try: from xformers import ops as xops @@ -1218,7 +1219,9 @@ def __init__( def forward( self, pixel_values: list[torch.Tensor], - feature_sample_layers: Optional[list[int]] = None, + *, + select_layers: Optional[list[int]] = None, + feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None, ) -> tuple[torch.Tensor, ...]: """ Args: @@ -1226,7 +1229,7 @@ def forward( in pixel_values. This means it will be a list of tensors because multiple requests batched can have multiple images, each with their own shape potentially - feature_sample_layers: Layer indices whose features should be + select_layers: Layer indices whose features should be concatenated and used as the visual encoder output. If none are provided, the last layer is used. @@ -1267,15 +1270,20 @@ def forward( [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds) - return_all_hidden_states = feature_sample_layers is not None out = self.transformer( patch_embeds, attention_mask, position_embedding, - return_all_hidden_states=return_all_hidden_states) + return_all_hidden_states=select_layers is not None, + ) - out = resolve_visual_encoder_outputs(out, feature_sample_layers, None, - self.config.num_hidden_layers) + out = resolve_visual_encoder_outputs( + out, + None, + select_layers=select_layers, + max_possible_layers=self.config.num_hidden_layers, + feature_select_strategy=feature_select_strategy, + ) # squeeze dim 0 and split into separate tensors for each image return torch.split(out.squeeze(0), embed_sizes) diff --git a/vllm/model_executor/models/siglip.py b/vllm/model_executor/models/siglip.py index eb49d6d2c335..4c60d96c77d7 100644 --- a/vllm/model_executor/models/siglip.py +++ b/vllm/model_executor/models/siglip.py @@ -23,7 +23,8 @@ from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name) -from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs +from .vision import (VisionEncoderInfo, VisionFeatureSelectStrategy, + resolve_visual_encoder_outputs) class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]): @@ -415,28 +416,31 @@ def __init__( def forward( self, pixel_values: torch.Tensor, - interpolate_pos_encoding: bool = True, - feature_sample_layers: Optional[list[int]] = None, + *, + interpolate_pos_encoding: bool = False, + select_layers: Optional[list[int]] = None, + feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None, ) -> torch.Tensor: - hidden_states = self.embeddings( pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, ) - return_all_hidden_states = feature_sample_layers is not None - # Produces either the last layer output or all of the hidden states, - # depending on if we have feature_sample_layers or not + # depending on if we have select_layers or not encoder_outputs = self.encoder( inputs_embeds=hidden_states, - return_all_hidden_states=return_all_hidden_states, + return_all_hidden_states=select_layers is not None, ) # Handle post-norm (if applicable) and stacks feature layers if needed encoder_outputs = resolve_visual_encoder_outputs( - encoder_outputs, feature_sample_layers, self.post_layernorm, - self.config.num_hidden_layers) + encoder_outputs, + self.post_layernorm, + select_layers=select_layers, + max_possible_layers=self.config.num_hidden_layers, + feature_select_strategy=feature_select_strategy, + ) # TODO: add this back when pooled_output is used in inference. # if self.use_head: @@ -471,16 +475,22 @@ def __init__( def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding + @property + def dtype(self): + return self.get_input_embeddings().weight.dtype + def forward( self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False, - feature_sample_layers: Optional[list[int]] = None, + select_layers: Optional[list[int]] = None, + feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None, ) -> torch.Tensor: return self.vision_model( pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, - feature_sample_layers=feature_sample_layers, + select_layers=select_layers, + feature_select_strategy=feature_select_strategy, ) def load_weights(self, weights: Iterable[tuple[str, diff --git a/vllm/model_executor/models/tarsier.py b/vllm/model_executor/models/tarsier.py index 1145bea41480..ed02fe2c389f 100644 --- a/vllm/model_executor/models/tarsier.py +++ b/vllm/model_executor/models/tarsier.py @@ -33,7 +33,6 @@ PromptReplacement, PromptUpdate) from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.sequence import IntermediateTensors -from vllm.utils.jsontree import json_map_leaves from vllm.utils.tensor_schema import TensorSchema, TensorShape from .clip import CLIPVisionModel @@ -476,30 +475,16 @@ def _parse_and_validate_image_input( raise AssertionError("This line should be unreachable.") - def _select_image_features(self, image_features: torch.Tensor, *, - strategy: str) -> torch.Tensor: - if strategy == "default": - return image_features[:, 1:] - elif strategy == "full": - return image_features - raise ValueError(f"Unexpected select feature strategy: {strategy}") - def _image_pixels_to_features( self, vision_tower: Union[CLIPVisionModel, SiglipVisionModel], pixel_values: Union[torch.Tensor, list[torch.Tensor]], ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: # From vLLM LLaVA, vision tower output handling - image_hidden_states: Union[torch.Tensor, tuple[torch.Tensor, ...]] = \ - vision_tower(pixel_values) - - def select_features_fn(leaf: torch.Tensor): - return self._select_image_features( - leaf, - strategy=self.config.vision_feature_select_strategy, - ) - - return json_map_leaves(select_features_fn, image_hidden_states) + return vision_tower( + pixel_values, + feature_select_strategy=self.config.vision_feature_select_strategy, + ) def _add_tarsier_split_tokens( self, projected_image_features: torch.Tensor) -> torch.Tensor: diff --git a/vllm/model_executor/models/vision.py b/vllm/model_executor/models/vision.py index 08ad8fbeb424..e077691fcec2 100644 --- a/vllm/model_executor/models/vision.py +++ b/vllm/model_executor/models/vision.py @@ -4,10 +4,12 @@ import itertools import math from abc import ABC, abstractmethod -from typing import Final, Generic, Literal, Optional, Protocol, TypeVar, Union +from typing import (Callable, Final, Generic, Literal, Optional, Protocol, + TypeVar, Union) import torch from transformers import PretrainedConfig +from typing_extensions import assert_never from vllm.distributed import (get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, @@ -86,11 +88,39 @@ def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend: return current_platform.get_vit_attn_backend(head_size, dtype) +VisionFeatureSelectStrategy = Union[ + Literal["class", "default", "full"], + Callable[[torch.Tensor], torch.Tensor], +] + + +def _get_vision_feature_selector( + strategy: VisionFeatureSelectStrategy, +) -> Callable[[torch.Tensor], torch.Tensor]: + if callable(strategy): + return strategy + + # https://github.com/huggingface/transformers/blob/cd74917ffc3e8f84e4a886052c5ab32b7ac623cc/src/transformers/models/clip/modeling_clip.py#L762 + if strategy == "class": + return lambda feats: feats[:, 0, :] + + # https://github.com/huggingface/transformers/blob/4a02bc7004285bdb12cc033e87ad2578ce2fa900/src/transformers/models/llava/modeling_llava.py#L196 + if strategy == "default": + return lambda feats: feats[:, 1:, :] + + if strategy == "full": + return lambda feats: feats + + assert_never(strategy) + + def resolve_visual_encoder_outputs( encoder_outputs: Union[torch.Tensor, list[torch.Tensor]], - feature_sample_layers: Optional[list[int]], post_layer_norm: Optional[torch.nn.LayerNorm], - max_possible_layers: int, + *, + select_layers: Optional[list[int]] = None, + max_possible_layers: Optional[int] = None, + feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None, ) -> torch.Tensor: """Given the outputs a visual encoder module that may correspond to the output of the last layer, or a list of hidden states to be stacked, @@ -98,17 +128,32 @@ def resolve_visual_encoder_outputs( Args: encoder_outputs: Output of encoder's last layer or all hidden states. - feature_sample_layers: Optional layer indices to grab from the encoder - outputs; if provided, encoder outputs must be a list. post_layer_norm: Post norm to apply to the output of the encoder. + select_layers: Optional layer indices to grab from the encoder + outputs; if provided, encoder outputs must be a list. max_possible_layers: Total layers in the fully loaded visual encoder. - + feature_select_strategy: Defines how to select the hidden states + from each layer. """ - if feature_sample_layers is None: + if select_layers is None: + if not isinstance(encoder_outputs, torch.Tensor): + raise ValueError("Expected only a single encoder output when " + "`select_layers` is not provided") + + if feature_select_strategy is not None: + select_features = _get_vision_feature_selector( + feature_select_strategy) + encoder_outputs = select_features(encoder_outputs) + if post_layer_norm is not None: return post_layer_norm(encoder_outputs) + return encoder_outputs + if max_possible_layers is None: + raise ValueError("`max_possible_layers` must be provided " + "alongside `select_layers`") + # Get the hidden states corresponding to the layer indices. # Negative values are relative to the full visual encoder, # so offset them depending on how many layers were loaded. @@ -120,13 +165,18 @@ def resolve_visual_encoder_outputs( hs_pool = [ encoder_outputs[layer_idx] if layer_idx >= 0 else encoder_outputs[layer_idx + offset] - for layer_idx in feature_sample_layers + for layer_idx in select_layers ] + if feature_select_strategy is not None: + select_features = _get_vision_feature_selector(feature_select_strategy) + hs_pool = [select_features(hs) for hs in hs_pool] + # Apply post-norm on the final hidden state if we are using it - uses_last_layer = feature_sample_layers[-1] in (len(hs_pool) - 1, -1) + uses_last_layer = select_layers[-1] in (max_possible_layers - 1, -1) if post_layer_norm is not None and uses_last_layer: - hs_pool[-1] = post_layer_norm(encoder_outputs) + hs_pool[-1] = post_layer_norm(hs_pool[-1]) + return torch.cat(hs_pool, dim=-1) From 1116b82c3b5f21844fcafac809b10363855e7dac Mon Sep 17 00:00:00 2001 From: Lehua Ding Date: Tue, 30 Sep 2025 19:51:16 +0800 Subject: [PATCH 101/166] [perf] Use CPU tensor to reduce GPU->CPU sync (#25884) Signed-off-by: Lehua Ding --- vllm/v1/worker/gpu_model_runner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index bb5c3ea74293..f4c28dc24d70 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -2478,7 +2478,7 @@ def propose_draft_token_ids(sampled_token_ids): effective_drafter_max_model_len = ( self.speculative_config.draft_model_config.max_model_len) input_fits_in_drafter = spec_decode_common_attn_metadata and ( - spec_decode_common_attn_metadata.seq_lens.max() + + spec_decode_common_attn_metadata.max_seq_len + self.speculative_config.num_speculative_tokens <= effective_drafter_max_model_len) if use_padded_batch_for_eagle and input_fits_in_drafter: From beee5de44419a71fabd90e26c844530d470e4cae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nicol=C3=B2=20Lucchesi?= Date: Tue, 30 Sep 2025 14:18:29 +0200 Subject: [PATCH 102/166] [NIXL] Add support for MLA caches with different latent dim (#25902) Signed-off-by: NickLucche Signed-off-by: Chen Zhang Co-authored-by: Chen Zhang --- .../kv_connector/unit/test_nixl_connector.py | 13 +-- .../kv_connector/v1/nixl_connector.py | 95 ++++++++++++------- 2 files changed, 66 insertions(+), 42 deletions(-) diff --git a/tests/v1/kv_connector/unit/test_nixl_connector.py b/tests/v1/kv_connector/unit/test_nixl_connector.py index 6b4bd29f18a5..578bf02eb519 100644 --- a/tests/v1/kv_connector/unit/test_nixl_connector.py +++ b/tests/v1/kv_connector/unit/test_nixl_connector.py @@ -255,8 +255,9 @@ def _nixl_handshake(self, host: str, port: int, remote_tp_size: int, time.sleep(self._hand_shake_latency) # These should've been done in register_kv_caches(), called by # gpu_model_runner. Here we just hardcode some dummy values. - self.slot_size_bytes = 4096 - self.block_len = self.slot_size_bytes * self.block_size + slot_size_bytes = 4096 + self.slot_size_per_layer = [slot_size_bytes] + self.block_len_per_layer = [slot_size_bytes * self.block_size] self.num_blocks = 1 self.dst_num_blocks[self.engine_id] = self.num_blocks @@ -268,7 +269,7 @@ def _nixl_handshake(self, host: str, port: int, remote_tp_size: int, agent_metadata=FakeNixlWrapper.AGENT_METADATA, kv_caches_base_addr=[0], num_blocks=1, - block_len=self.block_len, + block_lens=self.block_len_per_layer, attn_backend_name=self.backend_name, # `self.kv_cache_layout` is only forced to HND when vllm engine # is started. We mock HND here. @@ -485,8 +486,8 @@ def test_handshake_fails_on_kv_cache_layout_mismatch(self, dist_init): worker = connector.connector_worker # Minimal local registration params used by add_remote_agent - worker.slot_size_bytes = 4096 - worker.block_len = worker.slot_size_bytes * worker.block_size + worker.slot_size_per_layer = [4096] + worker.block_len_per_layer = [4096 * worker.block_size] worker.num_blocks = 1 worker.dst_num_blocks[worker.engine_id] = worker.num_blocks @@ -498,7 +499,7 @@ def test_handshake_fails_on_kv_cache_layout_mismatch(self, dist_init): agent_metadata=FakeNixlWrapper.AGENT_METADATA, kv_caches_base_addr=[0], num_blocks=1, - block_len=worker.block_len, + block_lens=worker.block_len_per_layer, attn_backend_name=worker.backend_name, kv_cache_layout=mismatched_layout, ) diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py index 1c7569515dec..55d87ea994b5 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py @@ -87,7 +87,7 @@ class NixlAgentMetadata( agent_metadata: bytes kv_caches_base_addr: list[int] num_blocks: int - block_len: int + block_lens: list[int] attn_backend_name: str kv_cache_layout: str @@ -772,6 +772,9 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): split_k_and_v = not (self.use_mla or self._use_pallas or self._use_flashinfer) tensor_size_bytes = None + # Enable different block lengths for different layers when MLA is used. + self.block_len_per_layer = list[int]() + self.slot_size_per_layer = list[int]() # HD bytes in kv terms for layer_name, cache_or_caches in xfer_buffers.items(): cache_list = cache_or_caches if split_k_and_v else [ cache_or_caches @@ -789,10 +792,25 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): tensor_size_bytes = curr_tensor_size_bytes self.num_blocks = cache.shape[0] - assert tensor_size_bytes == curr_tensor_size_bytes, \ - "All kv cache tensors must have the same size" + assert cache.shape[0] == self.num_blocks, \ + "All kv cache tensors must have the same number of blocks" + + self.block_len_per_layer.append(curr_tensor_size_bytes // + self.num_blocks) + self.slot_size_per_layer.append(self.block_len_per_layer[-1] // + self.block_size) + + if not self.use_mla: + # Different kv cache shape is not supported by HeteroTP + assert tensor_size_bytes == curr_tensor_size_bytes, \ + "All kv cache tensors must have the same size" caches_data.append( - (base_addr, tensor_size_bytes, self.tp_rank, "")) + (base_addr, curr_tensor_size_bytes, self.tp_rank, "")) + + logger.debug("Different block lengths collected: %s", + set(self.block_len_per_layer)) + assert len(self.block_len_per_layer) == len(seen_base_addresses) + assert self.num_blocks != 0 self.kv_caches_base_addr[self.engine_id] = seen_base_addresses self.num_regions = len(caches_data) @@ -805,16 +823,12 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): logger.debug("Done registering descs") self._registered_descs.append(descs) - assert tensor_size_bytes is not None - assert self.num_blocks != 0 - assert tensor_size_bytes % self.num_blocks == 0 - self.block_len = tensor_size_bytes // self.num_blocks - self.slot_size_bytes = self.block_len // self.block_size self.device_kv_caches = kv_caches self.dst_num_blocks[self.engine_id] = self.num_blocks if self._use_flashinfer: - assert self.slot_size_bytes % 2 == 0 - self.slot_size_bytes /= 2 + for i in range(len(self.slot_size_per_layer)): + assert self.slot_size_per_layer[i] % 2 == 0 + self.slot_size_per_layer[i] //= 2 # NOTE (NickLucche) When FlashInfer is used, memory is registered # with joint KV for each block. This minimizes the overhead in @@ -824,17 +838,17 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): # of 'virtual' regions here and halve `block_len` below. self.num_regions *= 2 - kv_block_len = self.get_backend_aware_kv_block_len() # Register local/src descr for NIXL xfer. blocks_data = [] - for base_addr in seen_base_addresses: + for i, base_addr in enumerate(seen_base_addresses): + kv_block_len = self.get_backend_aware_kv_block_len(layer_idx=i) # NOTE With heter-TP, more blocks are prepared than what are # needed as self.num_blocks >= nixl_agent_meta.num_blocks. We # could create fewer, but then _get_block_descs_ids needs to # select agent_meta.num_blocks instead of self.num_blocks for # local descr, and that makes handling regular flow less clean. for block_id in range(self.num_blocks): - block_offset = block_id * self.block_len + block_offset = block_id * self.block_len_per_layer[i] addr = base_addr + block_offset # (addr, len, device id) blocks_data.append((addr, kv_block_len, self.tp_rank)) @@ -844,7 +858,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): # descs ordering. This is needed for selecting contiguous heads # when split across TP ranks. for block_id in range(self.num_blocks): - block_offset = block_id * self.block_len + block_offset = block_id * self.block_len_per_layer[i] addr = base_addr + block_offset # Register addresses for V cache (K registered first). v_addr = addr + kv_block_len @@ -884,7 +898,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): agent_metadata=self.nixl_wrapper.get_agent_metadata(), kv_caches_base_addr=self.kv_caches_base_addr[self.engine_id], num_blocks=self.num_blocks, - block_len=self.block_len, + block_lens=self.block_len_per_layer, attn_backend_name=self.backend_name, kv_cache_layout=self.kv_cache_layout) ready_event = threading.Event() @@ -909,7 +923,7 @@ def add_remote_agent(self, The latter, assuming D.world_size > P.world_size, requires that two or more local TP worker share the xfer from a single TP worker. - Here's an example: + Here's an example (non-MLA case): rank_offset p_remote_tp_rank (kv split no) @@ -965,14 +979,20 @@ def add_remote_agent(self, total_num_kv_heads = self.model_config.get_total_num_kv_heads() is_kv_replicated = self._tp_size[engine_id] // total_num_kv_heads >= 1 + remote_block_len = nixl_agent_meta.block_lens[0] if self.use_mla or is_kv_replicated: - # With MLA the only difference is in the number of blocks. - remote_block_size = nixl_agent_meta.block_len // ( - self.slot_size_bytes) - assert self.block_len == nixl_agent_meta.block_len + # With replicated KV cache, only the number of blocks can differ. + assert self.block_len_per_layer == nixl_agent_meta.block_lens, \ + "KV cache sizes must match between P and D when replicated" + remote_block_size = remote_block_len // ( + self.slot_size_per_layer[0]) else: - remote_block_size = nixl_agent_meta.block_len // ( - self.slot_size_bytes * tp_ratio) + # When MLA is not used, this is a list of the same block length + for block_len in nixl_agent_meta.block_lens: + assert block_len == remote_block_len, \ + "All remote layers must have the same block size" + remote_block_size = remote_block_len // ( + self.slot_size_per_layer[0] * tp_ratio) if self._use_flashinfer: # With flashinfer, KV are sent in the same message. remote_block_size //= 2 @@ -983,14 +1003,14 @@ def add_remote_agent(self, raise ValueError( "Heterogeneous TP is not supported on XPU") - assert nixl_agent_meta.block_len == self.block_len * tp_ratio, ( + assert remote_block_len == self.block_len_per_layer[0] * tp_ratio, ( "Remote P worker KV layer cache must be of shape [2, N, " "local_kv_heads*tp_ratio, block_size, head_dim] and same dtype." ) assert self.block_size == remote_block_size, ( - "Remote P worker with different block size is not supported " - f"{self.block_size=} {remote_block_size=}") + "Remote P worker with different page/block size is not supported " + f"{self.block_size=}, {remote_block_size=}") # Create dst descs and xfer side handles. TP workers have same #blocks. if engine_id in self.dst_num_blocks: @@ -1005,13 +1025,16 @@ def add_remote_agent(self, # Eg. PTP1 DTP2 => P0 KV:[block0-KV_0 | block0-KV_1..]. self.kv_caches_base_addr[ engine_id] = nixl_agent_meta.kv_caches_base_addr - kv_block_len = self.get_backend_aware_kv_block_len() - rank_offset = self.tp_rank % tp_ratio * kv_block_len \ - if not (self.use_mla or is_kv_replicated) else 0 + + assert len(nixl_agent_meta.kv_caches_base_addr) == len( + self.block_len_per_layer) # Register all remote blocks, but only the corresponding kv heads. - for base_addr in nixl_agent_meta.kv_caches_base_addr: + for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr): + kv_block_len = self.get_backend_aware_kv_block_len(layer_idx=i) + rank_offset = self.tp_rank % tp_ratio * kv_block_len \ + if not (self.use_mla or is_kv_replicated) else 0 for block_id in range(nixl_agent_meta.num_blocks): - block_offset = block_id * nixl_agent_meta.block_len + block_offset = block_id * nixl_agent_meta.block_lens[i] # For each block, grab the heads chunk belonging to rank_i # of size remote_nheads // tp_ratio, which correspond to # self.block_len == remote_block_len//tp_ratio bytes. @@ -1022,9 +1045,9 @@ def add_remote_agent(self, if self._use_flashinfer: # With FlashInfer index V separately to allow head splitting. for block_id in range(nixl_agent_meta.num_blocks): - block_offset = block_id * nixl_agent_meta.block_len + block_offset = block_id * nixl_agent_meta.block_lens[i] addr = base_addr + block_offset + rank_offset - v_addr = addr + nixl_agent_meta.block_len // 2 + v_addr = addr + nixl_agent_meta.block_lens[i] // 2 blocks_data.append((v_addr, kv_block_len, remote_tp_rank)) logger.debug( @@ -1351,7 +1374,7 @@ def _get_block_descs_ids(self, descs_ids = region_ids * num_blocks + block_ids return descs_ids.flatten() - def get_backend_aware_kv_block_len(self): + def get_backend_aware_kv_block_len(self, layer_idx: int): """ Get the block length for one K/V element (K and V have the same size). @@ -1362,9 +1385,9 @@ def get_backend_aware_kv_block_len(self): """ if self._use_flashinfer: # For indexing only half (either just the K or V part). - block_len = self.block_len // 2 + block_len = self.block_len_per_layer[layer_idx] // 2 else: - block_len = self.block_len + block_len = self.block_len_per_layer[layer_idx] return block_len def get_kv_connector_stats(self) -> Optional[KVConnectorStats]: From 8291c88ab08d31259cbe534f244416f62f68b848 Mon Sep 17 00:00:00 2001 From: Reza Barazesh <3146276+rzabarazesh@users.noreply.github.com> Date: Tue, 30 Sep 2025 09:45:20 -0400 Subject: [PATCH 103/166] [CI] Move applicable tests to CPU (#24080) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- .buildkite/test-pipeline.yaml | 65 ++++++++++++++----- .github/mergify.yml | 1 - docker/Dockerfile.cpu | 17 ++--- pyproject.toml | 1 + tests/models/test_utils.py | 3 + tests/models/test_vision.py | 2 + tests/multimodal/test_cache.py | 2 + tests/multimodal/test_hasher.py | 2 + tests/multimodal/test_image.py | 2 + tests/multimodal/test_inputs.py | 3 + tests/multimodal/test_processing.py | 2 + tests/multimodal/test_registry.py | 2 + tests/multimodal/test_video.py | 2 + tests/test_inputs.py | 2 + tests/test_outputs.py | 4 ++ .../mistral}/__init__.py | 0 .../mistral}/conftest.py | 4 +- .../mistral}/test_mistral_tool_calls.py | 0 .../mistral}/utils.py | 0 tests/tool_use/test_glm4_moe_tool_parser.py | 2 + tests/tool_use/test_jamba_tool_parser.py | 2 + tests/tool_use/test_kimi_k2_tool_parser.py | 2 + tests/tool_use/test_minimax_tool_parser.py | 2 + tests/tool_use/test_qwen3coder_tool_parser.py | 2 + tests/tool_use/test_seed_oss_tool_parser.py | 2 + tests/tool_use/test_tool_choice_required.py | 2 + tests/tool_use/test_xlam_tool_parser.py | 2 + tests/v1/core/test_async_scheduler.py | 2 + tests/v1/core/test_encoder_cache_manager.py | 3 + tests/v1/core/test_kv_cache_utils.py | 2 + tests/v1/core/test_prefix_caching.py | 4 +- tests/v1/core/test_scheduler.py | 2 + .../core/test_single_type_kv_cache_manager.py | 3 + .../unit/test_output_aggreagator.py | 4 ++ .../unit/test_remote_decode_lifecycle.py | 4 ++ .../unit/test_remote_prefill_lifecycle.py | 4 ++ tests/v1/metrics/test_metrics_reader.py | 2 + tests/v1/structured_output/test_utils.py | 2 + tests/v1/test_serial_utils.py | 2 + 39 files changed, 136 insertions(+), 28 deletions(-) rename tests/{mistral_tool_use => tool_use/mistral}/__init__.py (100%) rename tests/{mistral_tool_use => tool_use/mistral}/conftest.py (93%) rename tests/{mistral_tool_use => tool_use/mistral}/test_mistral_tool_calls.py (100%) rename tests/{mistral_tool_use => tool_use/mistral}/utils.py (100%) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index 460f0afb6f67..b3d10f75ab50 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -50,19 +50,28 @@ steps: mirror_hardwares: [amdexperimental] source_file_dependencies: - vllm/ + - tests/multimodal + - tests/utils_ + commands: + - pytest -v -s -m 'not cpu_test' multimodal + - pytest -v -s utils_ + +- label: Async Engine, Inputs, Utils, Worker Test (CPU) # 4 mins + timeout_in_minutes: 10 + source_file_dependencies: + - vllm/ - tests/test_inputs.py - tests/test_outputs.py - tests/multimodal - - tests/utils_ - tests/standalone_tests/lazy_imports.py - tests/transformers_utils + no_gpu: true commands: - python3 standalone_tests/lazy_imports.py - pytest -v -s test_inputs.py - pytest -v -s test_outputs.py - - pytest -v -s multimodal - - pytest -v -s utils_ # Utils - - pytest -v -s transformers_utils # transformers_utils + - pytest -v -s -m 'cpu_test' multimodal + - pytest -v -s transformers_utils - label: Python-only Installation Test # 10min timeout_in_minutes: 20 @@ -287,23 +296,34 @@ steps: - tests/v1 commands: # split the test to avoid interference - - pytest -v -s v1/core - pytest -v -s v1/executor - pytest -v -s v1/kv_offload - pytest -v -s v1/sample - pytest -v -s v1/logits_processors - pytest -v -s v1/worker - - pytest -v -s v1/structured_output - pytest -v -s v1/spec_decode - - pytest -v -s v1/kv_connector/unit - - pytest -v -s v1/metrics + - pytest -v -s -m 'not cpu_test' v1/kv_connector/unit + - pytest -v -s -m 'not cpu_test' v1/metrics - pytest -v -s v1/test_oracle.py - pytest -v -s v1/test_request.py - - pytest -v -s v1/test_serial_utils.py # Integration test for streaming correctness (requires special branch). - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine +- label: V1 Test others (CPU) # 5 mins + source_file_dependencies: + - vllm/ + - tests/v1 + no_gpu: true + commands: + # split the test to avoid interference + - pytest -v -s v1/core + - pytest -v -s v1/structured_output + - pytest -v -s v1/test_serial_utils.py + - pytest -v -s -m 'cpu_test' v1/kv_connector/unit + - pytest -v -s -m 'cpu_test' v1/metrics + + - label: Examples Test # 30min timeout_in_minutes: 45 mirror_hardwares: [amdexperimental] @@ -533,10 +553,17 @@ steps: source_file_dependencies: - vllm/ - tests/tool_use - - tests/mistral_tool_use commands: - - pytest -v -s tool_use - - pytest -v -s mistral_tool_use + - pytest -v -s -m 'not cpu_test' tool_use + +- label: OpenAI-Compatible Tool Use (CPU) # 5 mins + timeout_in_minutes: 10 + source_file_dependencies: + - vllm/ + - tests/tool_use + no_gpu: true + commands: + - pytest -v -s -m 'cpu_test' tool_use ##### models test ##### @@ -576,13 +603,19 @@ steps: - vllm/ - tests/models/test_transformers.py - tests/models/test_registry.py + commands: + - pytest -v -s models/test_transformers.py models/test_registry.py + +- label: Basic Models Test (Other CPU) # 5min + timeout_in_minutes: 10 + torch_nightly: true + source_file_dependencies: + - vllm/ - tests/models/test_utils.py - tests/models/test_vision.py + no_gpu: true commands: - - pytest -v -s models/test_transformers.py \ - models/test_registry.py \ - models/test_utils.py \ - models/test_vision.py + - pytest -v -s models/test_utils.py models/test_vision.py - label: Language Models Tests (Standard) timeout_in_minutes: 25 diff --git a/.github/mergify.yml b/.github/mergify.yml index 923f708ea10c..04ad6a5be8df 100644 --- a/.github/mergify.yml +++ b/.github/mergify.yml @@ -239,7 +239,6 @@ pull_request_rules: conditions: - or: - files~=^tests/tool_use/ - - files~=^tests/mistral_tool_use/ - files~=^tests/entrypoints/openai/tool_parsers/ - files=tests/entrypoints/openai/test_chat_with_tool_reasoning.py - files~=^vllm/entrypoints/openai/tool_parsers/ diff --git a/docker/Dockerfile.cpu b/docker/Dockerfile.cpu index b80157d2fd75..388596efd21c 100644 --- a/docker/Dockerfile.cpu +++ b/docker/Dockerfile.cpu @@ -47,7 +47,7 @@ ENV PATH="$VIRTUAL_ENV/bin:$PATH" ENV UV_HTTP_TIMEOUT=500 -# Install Python dependencies +# Install Python dependencies ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL} ENV UV_INDEX_STRATEGY="unsafe-best-match" @@ -104,7 +104,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=cache,target=/root/.cache/ccache \ --mount=type=cache,target=/workspace/vllm/.deps,sharing=locked \ --mount=type=bind,source=.git,target=.git \ - VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel + VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel ######################### TEST DEPS ######################### FROM base AS vllm-test-deps @@ -117,7 +117,7 @@ RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \ uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu RUN --mount=type=cache,target=/root/.cache/uv \ - uv pip install -r requirements/cpu-test.txt + uv pip install -r requirements/cpu-test.txt ######################### DEV IMAGE ######################### FROM vllm-build AS vllm-dev @@ -130,12 +130,12 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ # install development dependencies (for testing) RUN --mount=type=cache,target=/root/.cache/uv \ - uv pip install -e tests/vllm_test_utils + uv pip install -e tests/vllm_test_utils RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=cache,target=/root/.cache/ccache \ --mount=type=bind,source=.git,target=.git \ - VLLM_TARGET_DEVICE=cpu python3 setup.py develop + VLLM_TARGET_DEVICE=cpu python3 setup.py develop COPY --from=vllm-test-deps /workspace/vllm/requirements/cpu-test.txt requirements/test.txt @@ -160,11 +160,12 @@ ADD ./benchmarks/ ./benchmarks/ ADD ./vllm/collect_env.py . ADD ./.buildkite/ ./.buildkite/ +# Create symlink for vllm-workspace to maintain CI compatibility +RUN ln -sf /workspace /vllm-workspace + # install development dependencies (for testing) RUN --mount=type=cache,target=/root/.cache/uv \ - uv pip install -e tests/vllm_test_utils - -ENTRYPOINT ["bash"] + uv pip install -e tests/vllm_test_utils ######################### RELEASE IMAGE ######################### FROM base AS vllm-openai diff --git a/pyproject.toml b/pyproject.toml index 88c5c4067f5a..034a21f1c12b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -126,6 +126,7 @@ markers = [ "core_model: enable this model test in each PR instead of only nightly", "hybrid_model: models that contain mamba layers (including pure SSM and hybrid architectures)", "cpu_model: enable this model test in CPU tests", + "cpu_test: mark test as CPU-only test", "split: run this test as part of a split", "distributed: run this test only in distributed GPU tests", "skip_v1: do not run this test with v1", diff --git a/tests/models/test_utils.py b/tests/models/test_utils.py index b52327a1844f..9b87b1a9d46c 100644 --- a/tests/models/test_utils.py +++ b/tests/models/test_utils.py @@ -1,10 +1,13 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import pytest import torch from vllm.model_executor.models.utils import AutoWeightsLoader +pytestmark = pytest.mark.cpu_test + class ModuleWithBatchNorm(torch.nn.Module): diff --git a/tests/models/test_vision.py b/tests/models/test_vision.py index a30a856a81cf..60ea2447e984 100644 --- a/tests/models/test_vision.py +++ b/tests/models/test_vision.py @@ -16,6 +16,8 @@ from vllm.platforms import current_platform from vllm.utils import get_open_port, update_environment_variables +pytestmark = pytest.mark.cpu_test + @pytest.mark.parametrize( ("select_layers", "num_layers_loaded", "max_possible_layers", diff --git a/tests/multimodal/test_cache.py b/tests/multimodal/test_cache.py index 3c737acfbfe2..48e88e7c0175 100644 --- a/tests/multimodal/test_cache.py +++ b/tests/multimodal/test_cache.py @@ -19,6 +19,8 @@ MultiModalSharedField) from vllm.multimodal.processing import PromptInsertion +pytestmark = pytest.mark.cpu_test + def _dummy_elem( modality: str, diff --git a/tests/multimodal/test_hasher.py b/tests/multimodal/test_hasher.py index 2751e38760e1..46aba1b75f77 100644 --- a/tests/multimodal/test_hasher.py +++ b/tests/multimodal/test_hasher.py @@ -10,6 +10,8 @@ from vllm.multimodal.hasher import MultiModalHasher +pytestmark = pytest.mark.cpu_test + ASSETS_DIR = Path(__file__).parent / "assets" assert ASSETS_DIR.exists() diff --git a/tests/multimodal/test_image.py b/tests/multimodal/test_image.py index 271a85f1195e..2f21ad969e74 100644 --- a/tests/multimodal/test_image.py +++ b/tests/multimodal/test_image.py @@ -8,6 +8,8 @@ from vllm.multimodal.image import ImageMediaIO, convert_image_mode +pytestmark = pytest.mark.cpu_test + ASSETS_DIR = Path(__file__).parent / "assets" assert ASSETS_DIR.exists() diff --git a/tests/multimodal/test_inputs.py b/tests/multimodal/test_inputs.py index ffb3a6fe86b4..f35935d14ff2 100644 --- a/tests/multimodal/test_inputs.py +++ b/tests/multimodal/test_inputs.py @@ -1,10 +1,13 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import pytest import torch from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors +pytestmark = pytest.mark.cpu_test + def assert_nested_tensors_equal(expected: NestedTensors, actual: NestedTensors): diff --git a/tests/multimodal/test_processing.py b/tests/multimodal/test_processing.py index 352b5b5b4fd4..7aa51acff350 100644 --- a/tests/multimodal/test_processing.py +++ b/tests/multimodal/test_processing.py @@ -25,6 +25,8 @@ from .utils import random_image +pytestmark = pytest.mark.cpu_test + # yapf: disable @pytest.mark.parametrize( diff --git a/tests/multimodal/test_registry.py b/tests/multimodal/test_registry.py index d31e75bc279f..01fbe9a52b77 100644 --- a/tests/multimodal/test_registry.py +++ b/tests/multimodal/test_registry.py @@ -11,6 +11,8 @@ from ..models.utils import build_model_context +pytestmark = pytest.mark.cpu_test + @pytest.mark.parametrize( "model_id,limit_mm_per_prompt,expected", diff --git a/tests/multimodal/test_video.py b/tests/multimodal/test_video.py index 05b7b84be7f3..1bdbb5a10a6d 100644 --- a/tests/multimodal/test_video.py +++ b/tests/multimodal/test_video.py @@ -17,6 +17,8 @@ from .utils import cosine_similarity, create_video_from_image, normalize_image +pytestmark = pytest.mark.cpu_test + NUM_FRAMES = 10 FAKE_OUTPUT_1 = np.random.rand(NUM_FRAMES, 1280, 720, 3) FAKE_OUTPUT_2 = np.random.rand(NUM_FRAMES, 1280, 720, 3) diff --git a/tests/test_inputs.py b/tests/test_inputs.py index e549834faf6f..b61b95bc4333 100644 --- a/tests/test_inputs.py +++ b/tests/test_inputs.py @@ -6,6 +6,8 @@ from vllm.inputs import zip_enc_dec_prompts from vllm.inputs.parse import parse_and_batch_prompt +pytestmark = pytest.mark.cpu_test + STRING_INPUTS = [ '', 'foo', diff --git a/tests/test_outputs.py b/tests/test_outputs.py index 4bb1c20f77f1..46da83a428e5 100644 --- a/tests/test_outputs.py +++ b/tests/test_outputs.py @@ -1,8 +1,12 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import pytest + from vllm.outputs import RequestOutput +pytestmark = pytest.mark.cpu_test + def test_request_output_forward_compatible(): output = RequestOutput(request_id="test_request_id", diff --git a/tests/mistral_tool_use/__init__.py b/tests/tool_use/mistral/__init__.py similarity index 100% rename from tests/mistral_tool_use/__init__.py rename to tests/tool_use/mistral/__init__.py diff --git a/tests/mistral_tool_use/conftest.py b/tests/tool_use/mistral/conftest.py similarity index 93% rename from tests/mistral_tool_use/conftest.py rename to tests/tool_use/mistral/conftest.py index e89e60c5a02e..e9dddccdc8c0 100644 --- a/tests/mistral_tool_use/conftest.py +++ b/tests/tool_use/mistral/conftest.py @@ -12,7 +12,7 @@ # for each server config, download the model and return the config -@pytest.fixture(scope="session", params=CONFIGS.keys()) +@pytest.fixture(scope="package", params=CONFIGS.keys()) def server_config(request): config = CONFIGS[request.param] @@ -26,7 +26,7 @@ def server_config(request): # run this for each server config -@pytest.fixture(scope="session") +@pytest.fixture(scope="package") def server(request, server_config: ServerConfig): model = server_config["model"] args_for_model = server_config["arguments"] diff --git a/tests/mistral_tool_use/test_mistral_tool_calls.py b/tests/tool_use/mistral/test_mistral_tool_calls.py similarity index 100% rename from tests/mistral_tool_use/test_mistral_tool_calls.py rename to tests/tool_use/mistral/test_mistral_tool_calls.py diff --git a/tests/mistral_tool_use/utils.py b/tests/tool_use/mistral/utils.py similarity index 100% rename from tests/mistral_tool_use/utils.py rename to tests/tool_use/mistral/utils.py diff --git a/tests/tool_use/test_glm4_moe_tool_parser.py b/tests/tool_use/test_glm4_moe_tool_parser.py index 91913c933184..bb8c36fb13ad 100644 --- a/tests/tool_use/test_glm4_moe_tool_parser.py +++ b/tests/tool_use/test_glm4_moe_tool_parser.py @@ -10,6 +10,8 @@ from vllm.entrypoints.openai.tool_parsers import Glm4MoeModelToolParser from vllm.transformers_utils.tokenizer import get_tokenizer +pytestmark = pytest.mark.cpu_test + pytest.skip("skip glm4_moe parser test", allow_module_level=True) # Use a common model that is likely to be available MODEL = "zai-org/GLM-4.5" diff --git a/tests/tool_use/test_jamba_tool_parser.py b/tests/tool_use/test_jamba_tool_parser.py index 57ace1fa22ac..8f819301e264 100644 --- a/tests/tool_use/test_jamba_tool_parser.py +++ b/tests/tool_use/test_jamba_tool_parser.py @@ -15,6 +15,8 @@ from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer +pytestmark = pytest.mark.cpu_test + MODEL = "ai21labs/Jamba-tiny-dev" diff --git a/tests/tool_use/test_kimi_k2_tool_parser.py b/tests/tool_use/test_kimi_k2_tool_parser.py index bd030632f167..ad9af6361802 100644 --- a/tests/tool_use/test_kimi_k2_tool_parser.py +++ b/tests/tool_use/test_kimi_k2_tool_parser.py @@ -10,6 +10,8 @@ from vllm.entrypoints.openai.tool_parsers import KimiK2ToolParser from vllm.transformers_utils.tokenizer import get_tokenizer +pytestmark = pytest.mark.cpu_test + # Use a common model that is likely to be available MODEL = "moonshotai/Kimi-K2-Instruct" diff --git a/tests/tool_use/test_minimax_tool_parser.py b/tests/tool_use/test_minimax_tool_parser.py index ddf26007121e..7aa19c9a51c9 100644 --- a/tests/tool_use/test_minimax_tool_parser.py +++ b/tests/tool_use/test_minimax_tool_parser.py @@ -12,6 +12,8 @@ from vllm.entrypoints.openai.tool_parsers import MinimaxToolParser from vllm.transformers_utils.tokenizer import get_tokenizer +pytestmark = pytest.mark.cpu_test + # Use a common model that is likely to be available MODEL = "MiniMaxAi/MiniMax-M1-40k" diff --git a/tests/tool_use/test_qwen3coder_tool_parser.py b/tests/tool_use/test_qwen3coder_tool_parser.py index 57eaf84d36f2..ade089e8246e 100644 --- a/tests/tool_use/test_qwen3coder_tool_parser.py +++ b/tests/tool_use/test_qwen3coder_tool_parser.py @@ -18,6 +18,8 @@ from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer +pytestmark = pytest.mark.cpu_test + MODEL = "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8" diff --git a/tests/tool_use/test_seed_oss_tool_parser.py b/tests/tool_use/test_seed_oss_tool_parser.py index 118c7534622e..5100b5ac120b 100644 --- a/tests/tool_use/test_seed_oss_tool_parser.py +++ b/tests/tool_use/test_seed_oss_tool_parser.py @@ -16,6 +16,8 @@ from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer +pytestmark = pytest.mark.cpu_test + # Use a common model that is likely to be available MODEL = "ByteDance-Seed/Seed-OSS-36B-Instruct" diff --git a/tests/tool_use/test_tool_choice_required.py b/tests/tool_use/test_tool_choice_required.py index 130e9547bdcc..7c63816cd6f5 100644 --- a/tests/tool_use/test_tool_choice_required.py +++ b/tests/tool_use/test_tool_choice_required.py @@ -12,6 +12,8 @@ ChatCompletionToolsParam) from vllm.entrypoints.openai.serving_chat import OpenAIServingChat +pytestmark = pytest.mark.cpu_test + EXAMPLE_TOOLS = [ { "type": "function", diff --git a/tests/tool_use/test_xlam_tool_parser.py b/tests/tool_use/test_xlam_tool_parser.py index c07ca0f56d6b..94e2a37cbf63 100644 --- a/tests/tool_use/test_xlam_tool_parser.py +++ b/tests/tool_use/test_xlam_tool_parser.py @@ -14,6 +14,8 @@ from vllm.transformers_utils.detokenizer_utils import detokenize_incrementally from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer +pytestmark = pytest.mark.cpu_test + # Use a common model that is likely to be available MODEL = "Salesforce/Llama-xLAM-2-8B-fc-r" diff --git a/tests/v1/core/test_async_scheduler.py b/tests/v1/core/test_async_scheduler.py index c153e38fe3df..8ffe2e57b532 100644 --- a/tests/v1/core/test_async_scheduler.py +++ b/tests/v1/core/test_async_scheduler.py @@ -11,6 +11,8 @@ from .utils import create_requests, create_scheduler +pytestmark = pytest.mark.cpu_test + def _make_model_runner_output( scheduler_output: SchedulerOutput, ) -> ModelRunnerOutput: diff --git a/tests/v1/core/test_encoder_cache_manager.py b/tests/v1/core/test_encoder_cache_manager.py index 4e3cace86be6..6ef15b337ef0 100644 --- a/tests/v1/core/test_encoder_cache_manager.py +++ b/tests/v1/core/test_encoder_cache_manager.py @@ -1,9 +1,12 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import pytest from vllm.multimodal.inputs import MultiModalFeatureSpec, PlaceholderRange from vllm.v1.core.encoder_cache_manager import EncoderCacheManager +pytestmark = pytest.mark.cpu_test + # ------------------ Mock Classes ------------------ # class MockRequest: diff --git a/tests/v1/core/test_kv_cache_utils.py b/tests/v1/core/test_kv_cache_utils.py index 452b16ef4a91..09f43a793db2 100644 --- a/tests/v1/core/test_kv_cache_utils.py +++ b/tests/v1/core/test_kv_cache_utils.py @@ -32,6 +32,8 @@ # yapf: enable +pytestmark = pytest.mark.cpu_test + @pytest.fixture(autouse=True) def _auto_init_hash_fn(request): diff --git a/tests/v1/core/test_prefix_caching.py b/tests/v1/core/test_prefix_caching.py index 5769099e0af1..93ad4d8080e6 100644 --- a/tests/v1/core/test_prefix_caching.py +++ b/tests/v1/core/test_prefix_caching.py @@ -25,6 +25,8 @@ from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig, KVCacheGroupSpec, SlidingWindowSpec) +pytestmark = pytest.mark.cpu_test + @pytest.fixture(autouse=True) def _auto_init_hash_fn(request): @@ -1267,7 +1269,7 @@ def test_kv_cache_events(blocks_to_cache: int): def test_eagle_enabled_removes_last_block(): - """Verify Eagle does NOT remove blocks when request + """Verify Eagle does NOT remove blocks when request length is divisible by block size.""" block_size = 16 manager = KVCacheManager( diff --git a/tests/v1/core/test_scheduler.py b/tests/v1/core/test_scheduler.py index 5e2bdaa75d3f..3de6dffc3395 100644 --- a/tests/v1/core/test_scheduler.py +++ b/tests/v1/core/test_scheduler.py @@ -23,6 +23,8 @@ from .utils import EOS_TOKEN_ID, create_requests, create_scheduler +pytestmark = pytest.mark.cpu_test + def test_add_requests(): scheduler = create_scheduler() diff --git a/tests/v1/core/test_single_type_kv_cache_manager.py b/tests/v1/core/test_single_type_kv_cache_manager.py index 1f6825b6d24e..166be8bda05e 100644 --- a/tests/v1/core/test_single_type_kv_cache_manager.py +++ b/tests/v1/core/test_single_type_kv_cache_manager.py @@ -3,6 +3,7 @@ import random +import pytest import torch from vllm.v1.core.block_pool import BlockPool @@ -13,6 +14,8 @@ from vllm.v1.kv_cache_interface import (ChunkedLocalAttentionSpec, SlidingWindowSpec) +pytestmark = pytest.mark.cpu_test + def get_sliding_window_manager(sliding_window_spec, block_pool): return SlidingWindowManager(sliding_window_spec, diff --git a/tests/v1/kv_connector/unit/test_output_aggreagator.py b/tests/v1/kv_connector/unit/test_output_aggreagator.py index 5d2b27a9eb4d..607da6425a31 100644 --- a/tests/v1/kv_connector/unit/test_output_aggreagator.py +++ b/tests/v1/kv_connector/unit/test_output_aggreagator.py @@ -3,9 +3,13 @@ from concurrent.futures import Future from typing import Optional +import pytest + from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator from vllm.v1.outputs import KVConnectorOutput, ModelRunnerOutput +pytestmark = pytest.mark.cpu_test + class DummyModelRunnerOutput(ModelRunnerOutput): diff --git a/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py b/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py index 380e72a15633..79b26cf57acd 100644 --- a/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py +++ b/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py @@ -2,12 +2,16 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import copy +import pytest + from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, KVConnectorOutput from vllm.v1.request import FinishReason, RequestStatus from .utils import (assert_scheduler_empty, create_model_runner_output, create_request, create_scheduler, create_vllm_config) +pytestmark = pytest.mark.cpu_test + def test_basic_lifecycle(): """Test lifecycle of a Remote Decode request.""" diff --git a/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py b/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py index 21fec5344255..207cf64f4e3e 100644 --- a/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py +++ b/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py @@ -2,12 +2,16 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import copy +import pytest + from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, KVConnectorOutput from vllm.v1.request import FinishReason, RequestStatus from .utils import (assert_scheduler_empty, create_model_runner_output, create_request, create_scheduler, create_vllm_config) +pytestmark = pytest.mark.cpu_test + def test_basic_lifecycle(): """Test lifecycle of a remote prefill.""" diff --git a/tests/v1/metrics/test_metrics_reader.py b/tests/v1/metrics/test_metrics_reader.py index c05de5e4cb64..16bca359fc2f 100644 --- a/tests/v1/metrics/test_metrics_reader.py +++ b/tests/v1/metrics/test_metrics_reader.py @@ -7,6 +7,8 @@ from vllm.v1.metrics.reader import (Counter, Gauge, Histogram, Vector, get_metrics_snapshot) +pytestmark = pytest.mark.cpu_test + @pytest.fixture(autouse=True) def test_registry(monkeypatch): diff --git a/tests/v1/structured_output/test_utils.py b/tests/v1/structured_output/test_utils.py index 4e7c4b33e8c4..0e2658304d12 100644 --- a/tests/v1/structured_output/test_utils.py +++ b/tests/v1/structured_output/test_utils.py @@ -6,6 +6,8 @@ from vllm.v1.structured_output.backend_xgrammar import ( has_xgrammar_unsupported_json_features) +pytestmark = pytest.mark.cpu_test + @pytest.fixture def unsupported_string_schemas(): diff --git a/tests/v1/test_serial_utils.py b/tests/v1/test_serial_utils.py index 118b40d0ef41..5d467687c308 100644 --- a/tests/v1/test_serial_utils.py +++ b/tests/v1/test_serial_utils.py @@ -16,6 +16,8 @@ MultiModalSharedField, NestedTensors) from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder +pytestmark = pytest.mark.cpu_test + class UnrecognizedType(UserDict): From 2b16dad45a1d903eabb04338d85edf26144115a6 Mon Sep 17 00:00:00 2001 From: ihb2032 <40718643+ihb2032@users.noreply.github.com> Date: Tue, 30 Sep 2025 21:48:07 +0800 Subject: [PATCH 104/166] [Fix] Improve CPU backend compatibility for RISC-V (#25816) Signed-off-by: lyd1992 Signed-off-by: ihb2032 <1355790728@qq.com> --- vllm/engine/arg_utils.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/vllm/engine/arg_utils.py b/vllm/engine/arg_utils.py index ce0f1708235f..ec61fc4b9b06 100644 --- a/vllm/engine/arg_utils.py +++ b/vllm/engine/arg_utils.py @@ -1160,11 +1160,12 @@ def create_engine_config( # Set default arguments for V1 Engine. self._set_default_args(usage_context, model_config) - # Disable chunked prefill for POWER (ppc64le)/ARM/s390x CPUs in V1 + # Disable chunked prefill for POWER (ppc64le)/ARM/s390x/RISCV CPUs in V1 if current_platform.is_cpu() and current_platform.get_cpu_architecture( - ) in (CpuArchEnum.POWERPC, CpuArchEnum.S390X, CpuArchEnum.ARM): - logger.info("Chunked prefill is not supported for ARM and POWER " - "and S390X CPUs; " + ) in (CpuArchEnum.POWERPC, CpuArchEnum.S390X, CpuArchEnum.ARM, + CpuArchEnum.RISCV): + logger.info("Chunked prefill is not supported for ARM and POWER, " + "S390X and RISC-V CPUs; " "disabling it for V1 backend.") self.enable_chunked_prefill = False assert self.enable_chunked_prefill is not None From c13967c09882b8572dfddbc216804dcb8185da9d Mon Sep 17 00:00:00 2001 From: Asaf Joseph Gardin <39553475+Josephasafg@users.noreply.github.com> Date: Tue, 30 Sep 2025 17:30:44 +0300 Subject: [PATCH 105/166] [Kernel][Moe Configs] Add more tuned triton configs for ExpertsInt8 and FP8 (#25858) Signed-off-by: asafg <39553475+Josephasafg@users.noreply.github.com> --- ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 198 ++++++------------ ...072,device_name=NVIDIA_H100_80GB_HBM3.json | 92 ++++---- ...ice_name=NVIDIA_H200,dtype=int8_w8a16.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 146 +++++++++++++ ...792,device_name=NVIDIA_H100_80GB_HBM3.json | 146 +++++++++++++ ...me=NVIDIA_H100_80GB_HBM3,dtype=float8.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 104 ++++----- ...ice_name=NVIDIA_H200,dtype=int8_w8a16.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 146 +++++++++++++ ...me=NVIDIA_H100_80GB_HBM3,dtype=float8.json | 146 +++++++++++++ ...VIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json | 82 ++++---- 15 files changed, 1808 insertions(+), 274 deletions(-) create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=1,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=1,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=1,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=14336,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json create mode 100644 vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json diff --git a/vllm/model_executor/layers/fused_moe/configs/E=1,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=1,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json new file mode 100644 index 000000000000..99501df6f176 --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=1,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "32": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "48": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 4 + }, + "64": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "96": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "128": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "256": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + } + } \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json index 2c78bfaba789..2e0dd7a4b950 100644 --- a/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json +++ b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -1,218 +1,146 @@ { "1": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 32, + "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 3 + "num_stages": 5 }, "2": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 8, + "num_warps": 4, "num_stages": 5 }, "4": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 8, + "num_warps": 4, "num_stages": 5 }, "8": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 4 + "num_warps": 4, + "num_stages": 5 }, "16": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 5 + "num_warps": 4, + "num_stages": 3 }, "24": { - "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "GROUP_SIZE_M": 16, + "num_warps": 4, "num_stages": 3 }, "32": { - "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 4, - "num_stages": 5 + "num_warps": 8, + "num_stages": 3 }, "48": { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 256, + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 3 + "num_stages": 4 }, "64": { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 256, + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 3 + "num_stages": 4 }, "96": { "BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 256, + "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 2 + "num_stages": 5 }, "128": { - "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 1, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, "num_warps": 4, - "num_stages": 2 + "num_stages": 4 }, "256": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 64, - "num_warps": 8, + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, "num_stages": 3 }, "512": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 + "num_warps": 4, + "num_stages": 4 }, "1024": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, "num_stages": 3 }, "1536": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, "num_stages": 3 }, "2048": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, "num_stages": 3 }, "3072": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, "num_stages": 3 }, "4096": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "5120": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "9216": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "13312": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "17408": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "25600": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "33792": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "41984": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "50176": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 - }, - "58368": { - "BLOCK_SIZE_M": 256, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, "num_stages": 3 } } \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3.json b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3.json index 4da841e74a79..4ea86340c324 100644 --- a/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3.json +++ b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3.json @@ -5,7 +5,7 @@ "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 4 + "num_stages": 5 }, "2": { "BLOCK_SIZE_M": 16, @@ -13,7 +13,7 @@ "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 3 + "num_stages": 5 }, "4": { "BLOCK_SIZE_M": 16, @@ -21,7 +21,7 @@ "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 3 + "num_stages": 5 }, "8": { "BLOCK_SIZE_M": 16, @@ -29,7 +29,7 @@ "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 3 + "num_stages": 5 }, "16": { "BLOCK_SIZE_M": 16, @@ -37,52 +37,52 @@ "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 5 + "num_stages": 3 }, "24": { - "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 4 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 }, "32": { "BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 4, - "num_stages": 5 + "num_warps": 8, + "num_stages": 3 }, "48": { - "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 5 + "num_stages": 4 }, "64": { - "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 5 + "num_stages": 4 }, "96": { "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, "num_warps": 4, - "num_stages": 4 + "num_stages": 5 }, "128": { - "BLOCK_SIZE_M": 64, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 16, "num_warps": 4, "num_stages": 4 @@ -91,57 +91,57 @@ "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 64, - "num_warps": 8, - "num_stages": 5 + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 }, "512": { "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 16, - "num_warps": 8, + "GROUP_SIZE_M": 1, + "num_warps": 4, "num_stages": 4 }, "1024": { "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 32, - "num_warps": 8, - "num_stages": 4 + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 }, "1536": { "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 32, - "num_warps": 8, - "num_stages": 4 + "num_warps": 4, + "num_stages": 3 }, "2048": { "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 16, - "num_warps": 8, + "num_warps": 4, "num_stages": 3 }, "3072": { "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 64, - "num_warps": 8, - "num_stages": 4 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 }, "4096": { "BLOCK_SIZE_M": 128, - "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 32, - "num_warps": 8, - "num_stages": 4 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 }, "5120": { "BLOCK_SIZE_M": 128, diff --git a/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json new file mode 100644 index 000000000000..f3f1a562710b --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "24": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "32": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "48": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "64": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "96": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "128": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=1,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json new file mode 100644 index 000000000000..19046fcf1d6a --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=1,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "24": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "32": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "48": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "64": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "96": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "128": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "256": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=1,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=1,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json new file mode 100644 index 000000000000..5f9422fe6f7c --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=1,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "32": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "48": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "64": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "96": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "128": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "256": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=14336,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=14336,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json new file mode 100644 index 000000000000..6d0cdfd27429 --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=14336,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 2 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 5 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 4 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 5 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 4 + }, + "1536": { + "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 4 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + } +} diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json new file mode 100644 index 000000000000..de8eec366eca --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 8, + "num_stages": 3 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + } + } \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3.json new file mode 100644 index 000000000000..80fce79fb64c --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_H100_80GB_HBM3.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 8, + "num_stages": 3 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 8, + "num_stages": 2 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "96": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "256": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 4 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 8, + "num_stages": 4 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 4 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 8, + "num_stages": 4 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 4 + } + } \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json new file mode 100644 index 000000000000..54d3bf190ebe --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 5 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 2 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 5 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "512": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 5 + }, + "1024": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 2 + }, + "1536": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "2048": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 2 + }, + "3072": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "4096": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json index 26f9abd6b789..6a4018195603 100644 --- a/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -2,73 +2,73 @@ "1": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 256, + "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 1, "num_warps": 4, "num_stages": 5 }, "2": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 1, + "GROUP_SIZE_M": 16, "num_warps": 4, - "num_stages": 4 + "num_stages": 3 }, "4": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, - "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 1, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, "num_warps": 8, - "num_stages": 4 + "num_stages": 3 }, "8": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, "num_warps": 4, "num_stages": 5 }, "16": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 64, + "GROUP_SIZE_M": 32, "num_warps": 4, - "num_stages": 4 + "num_stages": 3 }, "24": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 32, - "num_warps": 4, + "GROUP_SIZE_M": 1, + "num_warps": 8, "num_stages": 3 }, "32": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 32, + "GROUP_SIZE_M": 16, "num_warps": 4, - "num_stages": 4 + "num_stages": 3 }, "48": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 64, - "num_warps": 4, - "num_stages": 4 + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 }, "64": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 32, - "num_warps": 4, + "GROUP_SIZE_M": 1, + "num_warps": 8, "num_stages": 3 }, "96": { @@ -77,22 +77,22 @@ "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 32, "num_warps": 4, - "num_stages": 4 + "num_stages": 3 }, "128": { "BLOCK_SIZE_M": 32, - "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 4, - "num_stages": 4 + "num_warps": 8, + "num_stages": 3 }, "256": { - "BLOCK_SIZE_M": 32, - "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 16, - "num_warps": 4, + "GROUP_SIZE_M": 32, + "num_warps": 8, "num_stages": 4 }, "512": { @@ -100,47 +100,47 @@ "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 1, - "num_warps": 8, + "num_warps": 4, "num_stages": 4 }, "1024": { "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 64, - "num_warps": 8, - "num_stages": 5 + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 }, "1536": { "BLOCK_SIZE_M": 256, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, "num_warps": 8, - "num_stages": 3 + "num_stages": 4 }, "2048": { "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 64, - "num_warps": 8, - "num_stages": 5 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 }, "3072": { "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 64, - "num_warps": 8, - "num_stages": 5 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 4 }, "4096": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, "num_stages": 3 } } \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json new file mode 100644 index 000000000000..4f500d487c56 --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H200,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 4 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 5 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "512": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "1536": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 5 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json new file mode 100644 index 000000000000..ed8afa6b6db8 --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=3584,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 4 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 2 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 5 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 2 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 8, + "num_stages": 3 + }, + "256": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 + }, + "512": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 5 + }, + "1024": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "1536": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "2048": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 4 + }, + "3072": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "4096": { + "BLOCK_SIZE_M": 128, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json new file mode 100644 index 000000000000..5fea55a8000f --- /dev/null +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=float8.json @@ -0,0 +1,146 @@ +{ + "1": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 5 + }, + "2": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 5 + }, + "4": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 4 + }, + "8": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "16": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 3 + }, + "24": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "32": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 3 + }, + "48": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "64": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 2 + }, + "96": { + "BLOCK_SIZE_M": 16, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 256, + "GROUP_SIZE_M": 1, + "num_warps": 4, + "num_stages": 2 + }, + "128": { + "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 2 + }, + "256": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 2 + }, + "512": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 + }, + "1024": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 2 + }, + "1536": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 2 + }, + "2048": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 2 + }, + "3072": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 2 + }, + "4096": { + "BLOCK_SIZE_M": 64, + "BLOCK_SIZE_N": 256, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 32, + "num_warps": 4, + "num_stages": 2 + } +} \ No newline at end of file diff --git a/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json b/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json index bbb2386046b1..1e3f46e0ba84 100644 --- a/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json +++ b/vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json @@ -2,7 +2,7 @@ "1": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 32, - "BLOCK_SIZE_K": 256, + "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 1, "num_warps": 4, "num_stages": 4 @@ -20,78 +20,78 @@ "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 4, - "num_stages": 4 + "num_warps": 8, + "num_stages": 3 }, "8": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 1, + "GROUP_SIZE_M": 16, "num_warps": 4, - "num_stages": 3 + "num_stages": 5 }, "16": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 32, + "GROUP_SIZE_M": 64, "num_warps": 4, - "num_stages": 3 + "num_stages": 5 }, "24": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 32, + "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 64, - "num_warps": 4, + "GROUP_SIZE_M": 1, + "num_warps": 8, "num_stages": 3 }, "32": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 64, - "num_warps": 4, + "GROUP_SIZE_M": 1, + "num_warps": 8, "num_stages": 3 }, "48": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 4, + "num_warps": 8, "num_stages": 3 }, "64": { "BLOCK_SIZE_M": 16, - "BLOCK_SIZE_N": 64, + "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 16, - "num_warps": 4, + "GROUP_SIZE_M": 1, + "num_warps": 8, "num_stages": 3 }, "96": { "BLOCK_SIZE_M": 16, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 256, - "GROUP_SIZE_M": 32, + "GROUP_SIZE_M": 64, "num_warps": 4, "num_stages": 4 }, "128": { "BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 256, "GROUP_SIZE_M": 1, - "num_warps": 4, + "num_warps": 8, "num_stages": 3 }, "256": { - "BLOCK_SIZE_M": 32, + "BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 64, + "GROUP_SIZE_M": 1, "num_warps": 4, "num_stages": 3 }, @@ -100,47 +100,47 @@ "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 3 + "num_warps": 4, + "num_stages": 4 }, "1024": { "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 1, - "num_warps": 8, - "num_stages": 5 + "GROUP_SIZE_M": 64, + "num_warps": 4, + "num_stages": 3 }, "1536": { "BLOCK_SIZE_M": 256, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, + "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 1, "num_warps": 8, - "num_stages": 3 + "num_stages": 4 }, "2048": { "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 64, - "num_warps": 8, - "num_stages": 3 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 4 }, "3072": { "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 64, - "GROUP_SIZE_M": 32, - "num_warps": 8, - "num_stages": 4 + "GROUP_SIZE_M": 16, + "num_warps": 4, + "num_stages": 3 }, "4096": { - "BLOCK_SIZE_M": 256, + "BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, - "BLOCK_SIZE_K": 128, - "GROUP_SIZE_M": 1, - "num_warps": 8, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 16, + "num_warps": 4, "num_stages": 3 } } \ No newline at end of file From caaa5a6645013fbe618dd9a412eaef84340b8bc7 Mon Sep 17 00:00:00 2001 From: Sergio Paniego Blanco Date: Tue, 30 Sep 2025 16:35:06 +0200 Subject: [PATCH 106/166] Add Hugging Face Inference Endpoints guide to Deployment docs (#25886) Signed-off-by: sergiopaniego Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- .../hf-inference-endpoints-catalog.png | Bin 0 -> 641771 bytes .../hf-inference-endpoints-choose-infra.png | Bin 0 -> 358264 bytes ...nference-endpoints-click-deploy-button.png | Bin 0 -> 833513 bytes ...nference-endpoints-configure-container.png | Bin 0 -> 273739 bytes ...hf-inference-endpoints-create-endpoint.png | Bin 0 -> 362703 bytes ...ference-endpoints-locate-deploy-button.png | Bin 0 -> 799424 bytes .../hf-inference-endpoints-new-endpoint.png | Bin 0 -> 52361 bytes ...hf-inference-endpoints-select-hardware.png | Bin 0 -> 367803 bytes .../hf-inference-endpoints-select-model.png | Bin 0 -> 83638 bytes .../frameworks/hf_inference_endpoints.md | 170 ++++++++++++++++++ 10 files changed, 170 insertions(+) create mode 100644 docs/assets/deployment/hf-inference-endpoints-catalog.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-choose-infra.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-click-deploy-button.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-configure-container.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-create-endpoint.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-locate-deploy-button.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-new-endpoint.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-select-hardware.png create mode 100644 docs/assets/deployment/hf-inference-endpoints-select-model.png create mode 100644 docs/deployment/frameworks/hf_inference_endpoints.md diff --git a/docs/assets/deployment/hf-inference-endpoints-catalog.png b/docs/assets/deployment/hf-inference-endpoints-catalog.png new file mode 100644 index 0000000000000000000000000000000000000000..a26681eec7b33bcdfddb335d31e92910062b82c1 GIT binary patch literal 641771 zcmbrlcR&+O*Eb9zqH+nMfFex+rAig)AOg~RuL0>b(rZ8j1*It+Y0`V|H3HH@FQJ1# zXrTuPAtd>t-uF|!_xgDy@;cp`= zlOFOD^ME-q$MEIb3oLI-2FK%FC5J{8Vy9%cM}3Q?5fIf`}@Q+R>GB z`8|QrMbxr4YH?8t=f58`f@noX6TMyPPEvB$R3$iOK~>LuCYWAdr=%_-`5Y|y@Ovf^ z;9-(mb`CQER~Ke^5bj8zd6q;UZ-k@6Rmdr`#gY=d$bO`DZ-ChMHtssX64U2_W5S0@ 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zsl~|1SY4^+YpKSNdQ%|kF@;$Xd26qEbKcUz9(DFEC@go8>&DSZjGQx}avX4n&r;T= zzNY3G=(z%~?!=7)cQWNNDZ1;88GG)&2e#%MOH?aPDLZDi-)uyfkjX<~t9&KL&1J4K zcVDxt`fr^Ism!#xquma51}_R`%MlG<6b;Ib>vO-kt#evA%a=0!QlqD@1Jm>@M5h%> z4ZrBGA(}5GT@;l_Ep?l6oQwF?SMM%YhL$_7@%}S=V1Gc7K}CI4shhRqY-)7!4OG#R z|1CuGk&L8oi0zcPefGLB z_dZj_n`!f}y!t%*qN8RXx~SkSU(<-^ke8Cs+S0WXP81emvDQP|&qPZ+KL6B6zgZO& zb4-!`XS61Xu(IS0YyFjcwDnMVP}R`aS2g4*Kce7A<#qJ0Y8jBWdw8VDLjiZEa$gCG)F3C zSS9U_@F_aSHWt~Ayc*lmnk>EKfz>Cb$s9(#M8v_-A<#=ND;*M%?wbtJr z>n}8rNFQjP7_#?E^e4PcG2_o+bU8%{K1`eEDh%+tzZ1 z*tQEgUZQhZy!AU896e_SE(VtDc$~CpLOvlL6FDMxE?mJ&>R)^y*cA{t2us4QA5zDq WCO#B$%4VV **HF Inference Endpoints**. You will be taken to the Inference Endpoints interface to configure the deployment. + + ![Click deploy button](../../assets/deployment/hf-inference-endpoints-click-deploy-button.png) + +4. Select the Hardware (we choose AWS>GPU>T4 for the example) and Container Configuration. Choose `vLLM` as the container type and finalize the deployment pressing **Create Endpoint**. + + ![Select Hardware](../../assets/deployment/hf-inference-endpoints-select-hardware.png) + +5. Use the deployed endpoint. Update the `DEPLOYMENT_URL` with the URL provided in the console (remember to add `/v1` needed). You can then use your endpoint programmatically or via the SDK. + + ```python + # pip install openai + from openai import OpenAI + import os + + client = OpenAI( + base_url = DEPLOYMENT_URL, + api_key = os.environ["HF_TOKEN"] # https://huggingface.co/settings/tokens + ) + + chat_completion = client.chat.completions.create( + model = "ibm-granite/granite-docling-258M", + messages = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "https://huggingface.co/ibm-granite/granite-docling-258M/resolve/main/assets/new_arxiv.png" + } + }, + { + "type": "text", + "text": "Convert this page to docling." + } + ] + } + ], + stream = True + ) + + for message in chat_completion: + print(message.choices[0].delta.content, end = "") + ``` + +!!! note + This method uses best-guess defaults. You may need to adjust the configuration to fit your specific requirements. + +### Method 3: Manual Deployment (Advanced Models) + +Some models require manual deployment because they: + +- Use custom code with the `transformers` tag +- Don't run with standard `transformers` but are supported by `vLLM` + +These models cannot be deployed using the **Deploy** button on the model card. + +In this guide, we demonstrate manual deployment using the [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) model, an OCR model integrated with vLLM (see vLLM [PR](https://github.com/vllm-project/vllm/pull/24645)). + +1. Start a new deployment. Go to [Inference Endpoints](https://endpoints.huggingface.co/) and click `New`. + + ![New Endpoint](../../assets/deployment/hf-inference-endpoints-new-endpoint.png) + +2. Search the model in the Hub. In the dialog, switch to **Hub** and search for the desired model. + + ![Select model](../../assets/deployment/hf-inference-endpoints-select-model.png) + +3. Choosing infrastructure. On the configuration page, select the cloud provider and hardware from the available options. + For this demo, we choose AWS and L4 GPU. Adjust according to your hardware needs. + + ![Choose Infra](../../assets/deployment/hf-inference-endpoints-choose-infra.png) + +4. Configure the container. Scroll to the **Container Configuration** and select `vLLM` as the container type. + + ![Configure Container](../../assets/deployment/hf-inference-endpoints-configure-container.png) + +5. Create the endpoint. Click **Create Endpoint** to deploy the model. + + Once the endpoint is ready, you can use it with the OpenAI Completion API, cURL, or other SDKs. Remember to append `/v1` to the deployment URL if needed. + +!!! note + You can adjust the **container settings** (Container URI, Container Arguments) from the Inference Endpoints UI and press **Update Endpoint**. This redeploys the endpoint with the updated container configuration. Changes to the model itself require creating a new endpoint or redeploying with a different model. For example, for this demo, you may need to update the Container URI to the nightly image (`vllm/vllm-openai:nightly`) and add the `--trust-remote-code` flag in the container arguments. + +## Advanced Deployment Details + +With the [transformers backend integration](https://blog.vllm.ai/2025/04/11/transformers-backend.html), vLLM now offers Day 0 support for any model compatible with `transformers`. This means you can deploy such models immediately, leveraging vLLM’s optimized inference without additional backend modifications. + +Hugging Face Inference Endpoints provides a fully managed environment for serving models via vLLM. You can deploy models without configuring servers, installing dependencies, or managing clusters. Endpoints also support deployment across multiple cloud providers (AWS, Azure, GCP) without the need for separate accounts. + +The platform integrates seamlessly with the Hugging Face Hub, allowing you to deploy any vLLM- or `transformers`-compatible model, track usage, and update the inference engine directly. The vLLM engine comes preconfigured, enabling optimized inference and easy switching between models or engines without modifying your code. This setup simplifies production deployment: endpoints are ready in minutes, include monitoring and logging, and let you focus on serving models rather than maintaining infrastructure. + +## Next Steps + +- Explore the [Inference Endpoints](https://endpoints.huggingface.co/catalog) model catalog +- Read the Inference Endpoints [documentation](https://huggingface.co/docs/inference-endpoints/en/index) +- Learn about [Inference Endpoints engines](https://huggingface.co/docs/inference-endpoints/en/engines/vllm) +- Understand the [transformers backend integration](https://blog.vllm.ai/2025/04/11/transformers-backend.html) From 8721e4878fb0398d8463f97418ae379c5641fb37 Mon Sep 17 00:00:00 2001 From: Anion <123177548+Anionex@users.noreply.github.com> Date: Tue, 30 Sep 2025 22:38:07 +0800 Subject: [PATCH 107/166] [Bugfix][Model] Fix inference for Hunyuan dense models (#25354) Signed-off-by: anion <1005128408@qq.com> Signed-off-by: Anion <123177548+Anionex@users.noreply.github.com> --- vllm/model_executor/models/hunyuan_v1.py | 106 +++++++++++++---------- 1 file changed, 59 insertions(+), 47 deletions(-) diff --git a/vllm/model_executor/models/hunyuan_v1.py b/vllm/model_executor/models/hunyuan_v1.py index 085e740ce226..09f124426fa1 100644 --- a/vllm/model_executor/models/hunyuan_v1.py +++ b/vllm/model_executor/models/hunyuan_v1.py @@ -888,7 +888,7 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): return loaded_params -class HunYuanV1Base(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts): +class HunyuanV1ModelBase(nn.Module, SupportsLoRA, SupportsPP): packed_modules_mapping = { "qkv_proj": [ "q_proj", @@ -930,6 +930,56 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): else: self.lm_head = PPMissingLayer() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + model_output = self.model(input_ids, positions, intermediate_tensors, + inputs_embeds) + return model_output + + def compute_logits( + self, + hidden_states: torch.Tensor, + ) -> Optional[torch.Tensor]: + logits = self.logits_processor(self.lm_head, hidden_states) + return logits + + def make_empty_intermediate_tensors( + self, batch_size: int, dtype: torch.dtype, + device: torch.device) -> IntermediateTensors: + return IntermediateTensors({ + "hidden_states": + torch.zeros((batch_size, self.config.hidden_size), + dtype=dtype, + device=device), + "residual": + torch.zeros((batch_size, self.config.hidden_size), + dtype=dtype, + device=device), + }) + + def load_weights(self, weights: Iterable[tuple[str, + torch.Tensor]]) -> set[str]: + loader = AutoWeightsLoader( + self, + skip_prefixes=(["lm_head."] + if self.config.tie_word_embeddings else None), + ) + return loader.load_weights(weights) + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + + +class HunYuanMoEV1Base(HunyuanV1ModelBase, MixtureOfExperts): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, prefix=prefix) + # Set MoE hyperparameters self.expert_weights = [] self.num_expert_groups = 1 @@ -988,57 +1038,19 @@ def update_physical_experts_metadata( moe.n_redundant_experts = self.num_redundant_experts moe.experts.update_expert_map() - def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: - return self.model.get_input_embeddings(input_ids) + def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: + return self.model.get_expert_mapping() - def forward( - self, - input_ids: torch.Tensor, - positions: torch.Tensor, - intermediate_tensors: Optional[IntermediateTensors] = None, - inputs_embeds: Optional[torch.Tensor] = None, - ) -> Union[torch.Tensor, IntermediateTensors]: - model_output = self.model(input_ids, positions, intermediate_tensors, - inputs_embeds) - return model_output - def compute_logits( - self, - hidden_states: torch.Tensor, - ) -> Optional[torch.Tensor]: - logits = self.logits_processor(self.lm_head, hidden_states) - return logits +class HunYuanDenseV1Base(HunyuanV1ModelBase): - def make_empty_intermediate_tensors( - self, batch_size: int, dtype: torch.dtype, - device: torch.device) -> IntermediateTensors: - return IntermediateTensors({ - "hidden_states": - torch.zeros((batch_size, self.config.hidden_size), - dtype=dtype, - device=device), - "residual": - torch.zeros((batch_size, self.config.hidden_size), - dtype=dtype, - device=device), - }) - - def load_weights(self, weights: Iterable[tuple[str, - torch.Tensor]]) -> set[str]: - loader = AutoWeightsLoader( - self, - skip_prefixes=(["lm_head."] - if self.config.tie_word_embeddings else None), - ) - return loader.load_weights(weights) - - def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: - return self.model.get_expert_mapping() + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, prefix=prefix) -class HunYuanDenseV1ForCausalLM(HunYuanV1Base): +class HunYuanDenseV1ForCausalLM(HunYuanDenseV1Base): pass -class HunYuanMoEV1ForCausalLM(HunYuanV1Base): - pass +class HunYuanMoEV1ForCausalLM(HunYuanMoEV1Base): + pass \ No newline at end of file From 50a6b46d40fda4453866fd082e41b4e386945cb1 Mon Sep 17 00:00:00 2001 From: Pavani Majety Date: Tue, 30 Sep 2025 07:51:31 -0700 Subject: [PATCH 108/166] [Bugfix] Fix accuracy issue of TRTLLM FP8 MOE and improve logging (#25895) Signed-off-by: Pavani Majety --- .../model_executor/layers/quantization/fp8.py | 39 +++++++++++-------- vllm/utils/deep_gemm.py | 7 +++- 2 files changed, 29 insertions(+), 17 deletions(-) diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index fb448de3c234..a0808cb603d0 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -434,14 +434,9 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): self.weight_block_size = self.quant_config.weight_block_size self.block_quant = self.weight_block_size is not None - self.flashinfer_moe_backend: Optional[FlashinferMoeBackend] = None self.fused_experts: Optional[ mk.FusedMoEModularKernel] = None # type: ignore - if envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe(): - self.flashinfer_moe_backend = get_flashinfer_moe_backend() - logger.info_once( - f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels" - ) + # For GPUs that lack FP8 hardware support, we can leverage the Marlin # kernel for fast weight-only FP8 quantization self.use_marlin = (not current_platform.has_device_capability(89) @@ -450,14 +445,27 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): if current_platform.is_rocm(): self.use_marlin = False + # First check for Flashinfer MOE on Blackwell GPUs + self.flashinfer_moe_backend: Optional[FlashinferMoeBackend] = None + if (current_platform.is_cuda() + and current_platform.is_device_capability(100) + and envs.VLLM_USE_FLASHINFER_MOE_FP8 and has_flashinfer_moe()): + self.flashinfer_moe_backend = get_flashinfer_moe_backend() + logger.info_once( + f"Detected Blackwell GPUs, using FlashInfer " + f"{self.flashinfer_moe_backend.value} kernels for FP8 MOE.") + # Check for DeepGemm support. self.allow_deep_gemm = False if envs.VLLM_USE_DEEP_GEMM: if not has_deep_gemm(): logger.warning_once("Failed to import DeepGemm kernels.") elif not self.block_quant: - logger.warning_once("Model is not block quantized. Not using " - "DeepGemm kernels") + logger.warning_once("Model is not block quantized. Not using" + " DeepGemm kernels") + elif self.flashinfer_moe_backend: + logger.info_once("DeepGemm disabled: FlashInfer MOE is" + " enabled.") elif (is_deep_gemm_supported()): logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.") self.allow_deep_gemm = True @@ -471,15 +479,12 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): logger.debug_once("Model is not block quantized. Not using " "CutlassBlockScaledGroupedGemm kernels") elif (current_platform.is_cuda() - and current_platform.is_device_capability(100)): + and current_platform.is_device_capability(100) + and not self.flashinfer_moe_backend): logger.info_once( - "Using CutlassBlockScaledGroupedGemm kernels for Fp8MoEMethod." - ) + "Using CutlassBlockScaledGroupedGemm kernels for Fp8 MOE " + "on SM100.") self.allow_cutlass_block_scaled_grouped_gemm = True - else: - logger.warning_once( - "CutlassBlockScaledGroupedGemm not supported on the current " - "platform.") def create_weights(self, layer: Module, num_experts: int, hidden_size: int, intermediate_size_per_partition: int, @@ -934,7 +939,9 @@ def apply( import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe # noqa: E501, F401 assert (renormalize and use_grouped_topk and custom_routing_function is None) - result = torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8( + e_score_correction_bias = (e_score_correction_bias.to( + x.dtype) if e_score_correction_bias is not None else None) + return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8( routing_logits=router_logits.to(torch.float32), routing_bias=e_score_correction_bias, x=x, diff --git a/vllm/utils/deep_gemm.py b/vllm/utils/deep_gemm.py index 0e3bdaec829e..4f05f0bc35cc 100644 --- a/vllm/utils/deep_gemm.py +++ b/vllm/utils/deep_gemm.py @@ -27,7 +27,8 @@ def is_deep_gemm_supported() -> bool: is_supported_arch = current_platform.is_cuda() and ( current_platform.is_device_capability(90) or current_platform.is_device_capability(100)) - return envs.VLLM_USE_DEEP_GEMM and has_deep_gemm() and is_supported_arch + return (envs.VLLM_USE_DEEP_GEMM and has_deep_gemm() and is_supported_arch + and not envs.VLLM_USE_FLASHINFER_MOE_FP8) @functools.cache @@ -46,6 +47,10 @@ def is_deep_gemm_e8m0_used() -> bool: logger.info_once("DeepGEMM E8M0 disabled: _fp8_gemm_nt_impl not found") return False + if envs.VLLM_USE_FLASHINFER_MOE_FP8: + logger.info_once("DeepGEMM E8M0 disabled: FlashInfer MOE is enabled.") + return False + if current_platform.is_device_capability(100) and \ envs.VLLM_USE_DEEP_GEMM_E8M0: logger.info_once("DeepGEMM E8M0 enabled on Blackwell GPU.") From 2ad110ab01e3936a571e79bb167f9744746efe06 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 1 Oct 2025 00:23:12 +0800 Subject: [PATCH 109/166] [Bugfix] Token type and position embeddings fail to be applied to `inputs_embeds` (#25922) Signed-off-by: DarkLight1337 --- vllm/model_executor/models/bert.py | 17 ++++++++++------- vllm/model_executor/models/roberta.py | 6 ++++-- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/vllm/model_executor/models/bert.py b/vllm/model_executor/models/bert.py index c984845204c4..2ec3edc5a0a7 100644 --- a/vllm/model_executor/models/bert.py +++ b/vllm/model_executor/models/bert.py @@ -61,11 +61,13 @@ def forward( self, input_ids: torch.Tensor, position_ids: torch.Tensor, + inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - token_type_ids = _decode_token_type_ids(input_ids) - inputs_embeds = self.word_embeddings(input_ids) + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) @@ -358,11 +360,12 @@ def forward( intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - if inputs_embeds is not None: - hidden_states = inputs_embeds - else: - hidden_states = self.embeddings(input_ids=input_ids, - position_ids=positions) + hidden_states = self.embeddings( + input_ids=input_ids, + position_ids=positions, + inputs_embeds=inputs_embeds, + ) + return self.encoder(hidden_states) def _load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): diff --git a/vllm/model_executor/models/roberta.py b/vllm/model_executor/models/roberta.py index 53e698c4fa80..a13042a6367c 100644 --- a/vllm/model_executor/models/roberta.py +++ b/vllm/model_executor/models/roberta.py @@ -56,11 +56,13 @@ def forward( self, input_ids: torch.Tensor, position_ids: torch.Tensor, + inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: - token_type_ids = _decode_token_type_ids(input_ids) - inputs_embeds = self.word_embeddings(input_ids) + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) From 9732cbde764b247e2b7f1e14345620bbb8aac723 Mon Sep 17 00:00:00 2001 From: youkaichao Date: Wed, 1 Oct 2025 00:30:36 +0800 Subject: [PATCH 110/166] [bugfix][deepseek] fix flashmla kernel selection (#25956) Signed-off-by: youkaichao --- vllm/attention/ops/flashmla.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/attention/ops/flashmla.py b/vllm/attention/ops/flashmla.py index 3cc0e4adfa0a..9654f9f6775a 100644 --- a/vllm/attention/ops/flashmla.py +++ b/vllm/attention/ops/flashmla.py @@ -136,7 +136,7 @@ def flash_mla_with_kvcache( descale_k is None ), "descale_q and descale_k should be both None or both not None" - if (descale_q is not None) and (descale_k is not None): + if indices is None and q.element_size() == 1: out, softmax_lse = torch.ops._flashmla_extension_C.fwd_kvcache_mla_fp8( q, k_cache, head_dim_v, cache_seqlens, block_table, softmax_scale, causal, tile_scheduler_metadata, num_splits, descale_q, descale_k) From 28793deba416fc756a7ba0f69341fe7095ff7851 Mon Sep 17 00:00:00 2001 From: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Date: Tue, 30 Sep 2025 14:13:03 -0400 Subject: [PATCH 111/166] [Bug] Fix AttributeError: 'QKVParallelLinear' object has no attribute 'orig_dtype' (#25958) Signed-off-by: yewentao256 --- .../compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py | 1 + 1 file changed, 1 insertion(+) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index d42ae22c5139..5ad1b15b7ed5 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -66,6 +66,7 @@ def create_weights(self, layer: torch.nn.Module, output_size_per_partition = sum(output_partition_sizes) layer.logical_widths = output_partition_sizes layer.weight_block_size = None + layer.orig_dtype = params_dtype if self.strategy == QuantizationStrategy.BLOCK: assert self.weight_block_size is not None From eedc4b58dad1f794a3c9af4c3625d17cd33cd36c Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 1 Oct 2025 02:58:29 +0800 Subject: [PATCH 112/166] [Doc] Improve MM Pooling model documentation (#25966) Signed-off-by: DarkLight1337 --- docs/features/multimodal_inputs.md | 2 +- docs/models/supported_models.md | 28 ++- docs/serving/openai_compatible_server.md | 58 ++++-- .../vision_language_pooling.py | 85 +++++++- ...ai_chat_embedding_client_for_multimodal.py | 195 ++++++++++++------ ...vec.jinja => template_vlm2vec_phi3v.jinja} | 0 examples/template_vlm2vec_qwen2vl.jinja | 15 ++ .../pooling/openai/test_vision_embedding.py | 2 +- tests/entrypoints/test_chat_utils.py | 3 +- 9 files changed, 290 insertions(+), 98 deletions(-) rename examples/{template_vlm2vec.jinja => template_vlm2vec_phi3v.jinja} (100%) create mode 100644 examples/template_vlm2vec_qwen2vl.jinja diff --git a/docs/features/multimodal_inputs.md b/docs/features/multimodal_inputs.md index bcc48e756046..b61a54d3ca7f 100644 --- a/docs/features/multimodal_inputs.md +++ b/docs/features/multimodal_inputs.md @@ -428,7 +428,7 @@ Our OpenAI-compatible server accepts multi-modal data via the [Chat Completions If no fallback is available, an error is raised and you have to provide the chat template manually via the `--chat-template` argument. For certain models, we provide alternative chat templates inside . - For example, VLM2Vec uses which is different from the default one for Phi-3-Vision. + For example, VLM2Vec uses which is different from the default one for Phi-3-Vision. ### Image Inputs diff --git a/docs/models/supported_models.md b/docs/models/supported_models.md index d720fa2458e1..18c8b7846cb2 100644 --- a/docs/models/supported_models.md +++ b/docs/models/supported_models.md @@ -626,7 +626,29 @@ See [this page](../features/multimodal_inputs.md) on how to pass multi-modal inp For hybrid-only models such as Llama-4, Step3 and Mistral-3, a text-only mode can be enabled by setting all supported multimodal modalities to 0 (e.g, `--limit-mm-per-prompt '{"image":0}`) so that their multimodal modules will not be loaded to free up more GPU memory for KV cache. !!! note - vLLM currently only supports adding LoRA to the language backbone of multimodal models. + vLLM currently only supports dynamic LoRA adapters on the language backbone of multimodal models. + If you wish to use a model with LoRA in the multi-modal encoder, + please merge the weights into the base model first before running it in vLLM like a regular model. + + ```python + from peft import PeftConfig, PeftModel + from transformers import AutoModelForImageTextToText, AutoProcessor + + def merge_and_save(model_id: str, output_dir: str): + base_model = AutoModelForImageTextToText.from_pretrained(model_id) + lora_model = PeftModel.from_pretrained( + base_model, + model_id, + config=PeftConfig.from_pretrained(model_id), + ) + model = lora_model.merge_and_unload().to(dtype=base_model.dtype) + model._hf_peft_config_loaded = False # Needed to save the merged model + + processor = AutoProcessor.from_pretrained(model_id) + + model.save_pretrained(output_dir) + processor.save_pretrained(output_dir) + ``` ### Generative Models @@ -805,8 +827,8 @@ The following table lists those that are tested in vLLM. | Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) | [V1](gh-issue:8779) | |--------------|--------|--------|-------------------|----------------------|---------------------------|---------------------| -| `LlavaNextForConditionalGeneration`C | LLaVA-NeXT-based | T / I | `royokong/e5-v` | | | | -| `Phi3VForCausalLM`C | Phi-3-Vision-based | T + I | `TIGER-Lab/VLM2Vec-Full` | 🚧 | ✅︎ | | +| `LlavaNextForConditionalGeneration`C | LLaVA-NeXT-based | T / I | `royokong/e5-v` | | ✅︎ | ✅︎ | +| `Phi3VForCausalLM`C | Phi-3-Vision-based | T + I | `TIGER-Lab/VLM2Vec-Full` | | ✅︎ | ✅︎ | | `*ForConditionalGeneration`C, `*ForCausalLM`C, etc. | Generative models | \* | N/A | \* | \* | \* | C Automatically converted into an embedding model via `--convert embed`. ([details](./pooling_models.md#model-conversion)) diff --git a/docs/serving/openai_compatible_server.md b/docs/serving/openai_compatible_server.md index 1ffe9c9ade20..fe0e1e3df378 100644 --- a/docs/serving/openai_compatible_server.md +++ b/docs/serving/openai_compatible_server.md @@ -236,10 +236,32 @@ The following extra parameters are supported: Our Embeddings API is compatible with [OpenAI's Embeddings API](https://platform.openai.com/docs/api-reference/embeddings); you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it. +Code example: + If the model has a [chat template][chat-template], you can replace `inputs` with a list of `messages` (same schema as [Chat API][chat-api]) -which will be treated as a single prompt to the model. +which will be treated as a single prompt to the model. Here is a convenience function for calling the API while retaining OpenAI's type annotations: -Code example: +??? code + + ```python + from openai import OpenAI + from openai._types import NOT_GIVEN, NotGiven + from openai.types.chat import ChatCompletionMessageParam + from openai.types.create_embedding_response import CreateEmbeddingResponse + + def create_chat_embeddings( + client: OpenAI, + *, + messages: list[ChatCompletionMessageParam], + model: str, + encoding_format: Union[Literal["base64", "float"], NotGiven] = NOT_GIVEN, + ) -> CreateEmbeddingResponse: + return client.post( + "/embeddings", + cast_to=CreateEmbeddingResponse, + body={"messages": messages, "model": model, "encoding_format": encoding_format}, + ) + ``` #### Multi-modal inputs @@ -254,7 +276,7 @@ and passing a list of `messages` in the request. Refer to the examples below for vllm serve TIGER-Lab/VLM2Vec-Full --runner pooling \ --trust-remote-code \ --max-model-len 4096 \ - --chat-template examples/template_vlm2vec.jinja + --chat-template examples/template_vlm2vec_phi3v.jinja ``` !!! important @@ -262,34 +284,36 @@ and passing a list of `messages` in the request. Refer to the examples below for to run this model in embedding mode instead of text generation mode. The custom chat template is completely different from the original one for this model, - and can be found here: + and can be found here: Since the request schema is not defined by OpenAI client, we post a request to the server using the lower-level `requests` library: ??? code ```python - import requests - + from openai import OpenAI + client = OpenAI( + base_url="http://localhost:8000/v1", + api_key="EMPTY", + ) image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" - response = requests.post( - "http://localhost:8000/v1/embeddings", - json={ - "model": "TIGER-Lab/VLM2Vec-Full", - "messages": [{ + response = create_chat_embeddings( + client, + model="TIGER-Lab/VLM2Vec-Full", + messages=[ + { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": image_url}}, {"type": "text", "text": "Represent the given image."}, ], - }], - "encoding_format": "float", - }, + } + ], + encoding_format="float", ) - response.raise_for_status() - response_json = response.json() - print("Embedding output:", response_json["data"][0]["embedding"]) + + print("Image embedding output:", response.data[0].embedding) ``` === "DSE-Qwen2-MRL" diff --git a/examples/offline_inference/vision_language_pooling.py b/examples/offline_inference/vision_language_pooling.py index 0cc0c1e708b1..3d1daf4d19ff 100644 --- a/examples/offline_inference/vision_language_pooling.py +++ b/examples/offline_inference/vision_language_pooling.py @@ -10,6 +10,7 @@ from argparse import Namespace from dataclasses import asdict +from pathlib import Path from typing import Literal, NamedTuple, Optional, TypedDict, Union, get_args from PIL.Image import Image @@ -19,6 +20,9 @@ from vllm.multimodal.utils import fetch_image from vllm.utils import FlexibleArgumentParser +ROOT_DIR = Path(__file__).parent.parent.parent +EXAMPLES_DIR = ROOT_DIR / "examples" + class TextQuery(TypedDict): modality: Literal["text"] @@ -82,23 +86,27 @@ def run_e5_v(query: Query) -> ModelRequestData: ) -def run_vlm2vec(query: Query) -> ModelRequestData: +def _get_vlm2vec_prompt_image(query: Query, image_token: str): if query["modality"] == "text": text = query["text"] prompt = f"Find me an everyday image that matches the given caption: {text}" # noqa: E501 image = None elif query["modality"] == "image": - prompt = "<|image_1|> Find a day-to-day image that looks similar to the provided image." # noqa: E501 + prompt = f"{image_token} Find a day-to-day image that looks similar to the provided image." # noqa: E501 image = query["image"] elif query["modality"] == "text+image": text = query["text"] - prompt = ( - f"<|image_1|> Represent the given image with the following question: {text}" # noqa: E501 - ) + prompt = f"{image_token} Represent the given image with the following question: {text}" # noqa: E501 image = query["image"] else: modality = query["modality"] - raise ValueError(f"Unsupported query modality: '{modality}'") + raise ValueError(f"Unsupported query modality: {modality!r}") + + return prompt, image + + +def run_vlm2vec_phi3v(query: Query) -> ModelRequestData: + prompt, image = _get_vlm2vec_prompt_image(query, "<|image_1|>") engine_args = EngineArgs( model="TIGER-Lab/VLM2Vec-Full", @@ -116,6 +124,66 @@ def run_vlm2vec(query: Query) -> ModelRequestData: ) +def run_vlm2vec_qwen2vl(query: Query) -> ModelRequestData: + # vLLM does not support LoRA adapters on multi-modal encoder, + # so we merge the weights first + from huggingface_hub.constants import HF_HUB_CACHE + from peft import PeftConfig, PeftModel + from transformers import AutoModelForImageTextToText, AutoProcessor + + from vllm.entrypoints.chat_utils import load_chat_template + + model_id = "TIGER-Lab/VLM2Vec-Qwen2VL-2B" + + base_model = AutoModelForImageTextToText.from_pretrained(model_id) + lora_model = PeftModel.from_pretrained( + base_model, + model_id, + config=PeftConfig.from_pretrained(model_id), + ) + model = lora_model.merge_and_unload().to(dtype=base_model.dtype) + model._hf_peft_config_loaded = False # Needed to save the merged model + + processor = AutoProcessor.from_pretrained( + model_id, + # `min_pixels` and `max_pixels` are deprecated + size={"shortest_edge": 3136, "longest_edge": 12845056}, + ) + processor.chat_template = load_chat_template( + # The original chat template is not correct + EXAMPLES_DIR / "template_vlm2vec_qwen2vl.jinja", + ) + + merged_path = str( + Path(HF_HUB_CACHE) / ("models--" + model_id.replace("/", "--") + "-vllm") + ) + print(f"Saving merged model to {merged_path}...") + print( + "NOTE: This directory is not tracked by `huggingface_hub` " + "so you have to delete this manually if you don't want it anymore." + ) + model.save_pretrained(merged_path) + processor.save_pretrained(merged_path) + print("Done!") + + prompt, image = _get_vlm2vec_prompt_image(query, "<|image_pad|>") + + engine_args = EngineArgs( + model=merged_path, + runner="pooling", + max_model_len=4096, + trust_remote_code=True, + mm_processor_kwargs={"num_crops": 4}, + limit_mm_per_prompt={"image": 1}, + ) + + return ModelRequestData( + engine_args=engine_args, + prompt=prompt, + image=image, + ) + + def run_jinavl_reranker(query: Query) -> ModelRequestData: if query["modality"] != "text+images": raise ValueError(f"Unsupported query modality: '{query['modality']}'") @@ -232,7 +300,8 @@ def run_score(model: str, modality: QueryModality, seed: Optional[int]): model_example_map = { "e5_v": run_e5_v, - "vlm2vec": run_vlm2vec, + "vlm2vec_phi3v": run_vlm2vec_phi3v, + "vlm2vec_qwen2vl": run_vlm2vec_qwen2vl, "jinavl_reranker": run_jinavl_reranker, } @@ -246,7 +315,7 @@ def parse_args(): "--model-name", "-m", type=str, - default="vlm2vec", + default="vlm2vec_phi3v", choices=model_example_map.keys(), help="The name of the embedding model.", ) diff --git a/examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py b/examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py index 30cb3325b9b1..6e31c3836806 100644 --- a/examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py +++ b/examples/online_serving/pooling/openai_chat_embedding_client_for_multimodal.py @@ -4,69 +4,137 @@ """Example Python client for multimodal embedding API using vLLM API server NOTE: start a supported multimodal embeddings model server with `vllm serve`, e.g. - vllm serve TIGER-Lab/VLM2Vec-Full --runner pooling --trust_remote_code --max_model_len=1024 + vllm serve TIGER-Lab/VLM2Vec-Full \ + --runner pooling \ + --trust-remote-code \ + --max-model-len 4096 \ + --chat-template examples/template_vlm2vec_phi3v.jinja """ import argparse import base64 import io +from typing import Literal, Union -import requests +from openai import OpenAI +from openai._types import NOT_GIVEN, NotGiven +from openai.types.chat import ChatCompletionMessageParam +from openai.types.create_embedding_response import CreateEmbeddingResponse from PIL import Image +# Modify OpenAI's API key and API base to use vLLM's API server. +openai_api_key = "EMPTY" +openai_api_base = "http://localhost:8000/v1" + image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" -def vlm2vec(): - response = requests.post( - "http://localhost:8000/v1/embeddings", - json={ - "model": "TIGER-Lab/VLM2Vec-Full", - "messages": [ - { - "role": "user", - "content": [ - {"type": "image_url", "image_url": {"url": image_url}}, - {"type": "text", "text": "Represent the given image."}, - ], - } - ], - "encoding_format": "float", - }, +def create_chat_embeddings( + client: OpenAI, + *, + messages: list[ChatCompletionMessageParam], + model: str, + encoding_format: Union[Literal["base64", "float"], NotGiven] = NOT_GIVEN, +) -> CreateEmbeddingResponse: + """ + Convenience function for accessing vLLM's Chat Embeddings API, + which is an extension of OpenAI's existing Embeddings API. + """ + return client.post( + "/embeddings", + cast_to=CreateEmbeddingResponse, + body={"messages": messages, "model": model, "encoding_format": encoding_format}, + ) + + +def run_vlm2vec(client: OpenAI, model: str): + response = create_chat_embeddings( + client, + messages=[ + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": image_url}}, + {"type": "text", "text": "Represent the given image."}, + ], + } + ], + model=model, + encoding_format="float", + ) + + print("Image embedding output:", response.data[0].embedding) + + response = create_chat_embeddings( + client, + messages=[ + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": image_url}}, + { + "type": "text", + "text": "Represent the given image with the following question: What is in the image.", + }, + ], + } + ], + model=model, + encoding_format="float", + ) + + print("Image+Text embedding output:", response.data[0].embedding) + + response = create_chat_embeddings( + client, + messages=[ + { + "role": "user", + "content": [ + {"type": "text", "text": "A cat and a dog"}, + ], + } + ], + model=model, + encoding_format="float", ) - response.raise_for_status() - response_json = response.json() - print("Embedding output:", response_json["data"][0]["embedding"]) + print("Text embedding output:", response.data[0].embedding) -def dse_qwen2_vl(inp: dict): - # Embedding an Image - if inp["type"] == "image": - messages = [ +def run_dse_qwen2_vl(client: OpenAI, model: str): + response = create_chat_embeddings( + client, + messages=[ { "role": "user", "content": [ { "type": "image_url", "image_url": { - "url": inp["image_url"], + "url": image_url, }, }, {"type": "text", "text": "What is shown in this image?"}, ], } - ] - # Embedding a Text Query - else: - # MrLight/dse-qwen2-2b-mrl-v1 requires a placeholder image - # of the minimum input size - buffer = io.BytesIO() - image_placeholder = Image.new("RGB", (56, 56)) - image_placeholder.save(buffer, "png") - buffer.seek(0) - image_placeholder = base64.b64encode(buffer.read()).decode("utf-8") - messages = [ + ], + model=model, + encoding_format="float", + ) + + print("Image embedding output:", response.data[0].embedding) + + # MrLight/dse-qwen2-2b-mrl-v1 requires a placeholder image + # of the minimum input size + buffer = io.BytesIO() + image_placeholder = Image.new("RGB", (56, 56)) + image_placeholder.save(buffer, "png") + buffer.seek(0) + image_placeholder = base64.b64encode(buffer.read()).decode("utf-8") + response = create_chat_embeddings( + client, + messages=[ { "role": "user", "content": [ @@ -76,23 +144,21 @@ def dse_qwen2_vl(inp: dict): "url": f"data:image/jpeg;base64,{image_placeholder}", }, }, - {"type": "text", "text": f"Query: {inp['content']}"}, + {"type": "text", "text": "Query: What is the weather like today?"}, ], } - ] - - response = requests.post( - "http://localhost:8000/v1/embeddings", - json={ - "model": "MrLight/dse-qwen2-2b-mrl-v1", - "messages": messages, - "encoding_format": "float", - }, + ], + model=model, + encoding_format="float", ) - response.raise_for_status() - response_json = response.json() - print("Embedding output:", response_json["data"][0]["embedding"]) + print("Text embedding output:", response.data[0].embedding) + + +model_example_map = { + "vlm2vec": run_vlm2vec, + "dse_qwen2_vl": run_dse_qwen2_vl, +} def parse_args(): @@ -103,29 +169,24 @@ def parse_args(): parser.add_argument( "--model", type=str, - choices=["vlm2vec", "dse_qwen2_vl"], + choices=model_example_map.keys(), required=True, - help="Which model to call.", + help="The name of the embedding model.", ) return parser.parse_args() def main(args): - if args.model == "vlm2vec": - vlm2vec() - elif args.model == "dse_qwen2_vl": - dse_qwen2_vl( - { - "type": "image", - "image_url": image_url, - } - ) - dse_qwen2_vl( - { - "type": "text", - "content": "What is the weather like today?", - } - ) + client = OpenAI( + # defaults to os.environ.get("OPENAI_API_KEY") + api_key=openai_api_key, + base_url=openai_api_base, + ) + + models = client.models.list() + model_id = models.data[0].id + + model_example_map[args.model](client, model_id) if __name__ == "__main__": diff --git a/examples/template_vlm2vec.jinja b/examples/template_vlm2vec_phi3v.jinja similarity index 100% rename from examples/template_vlm2vec.jinja rename to examples/template_vlm2vec_phi3v.jinja diff --git a/examples/template_vlm2vec_qwen2vl.jinja b/examples/template_vlm2vec_qwen2vl.jinja new file mode 100644 index 000000000000..3ab099d8f546 --- /dev/null +++ b/examples/template_vlm2vec_qwen2vl.jinja @@ -0,0 +1,15 @@ +{%- if messages | length > 1 -%} + {{ raise_exception('Embedding models should only embed one message at a time') }} +{%- endif -%} + +{% set vars = namespace(parts=[]) %} +{%- for message in messages -%} + {%- for content in message['content'] -%} + {%- if content['type'] == 'text' -%} + {%- set vars.parts = vars.parts + [content['text']] %} + {%- elif content['type'] == 'image' -%} + {%- set vars.parts = vars.parts + ['<|image_pad|>'] %} + {%- endif -%} + {%- endfor -%} +{%- endfor -%} +{{ vars.parts | join(' ') }} diff --git a/tests/entrypoints/pooling/openai/test_vision_embedding.py b/tests/entrypoints/pooling/openai/test_vision_embedding.py index 48434e36eb26..a30413bc3298 100644 --- a/tests/entrypoints/pooling/openai/test_vision_embedding.py +++ b/tests/entrypoints/pooling/openai/test_vision_embedding.py @@ -14,7 +14,7 @@ MODEL_NAME = "TIGER-Lab/VLM2Vec-Full" MAXIMUM_IMAGES = 2 -vlm2vec_jinja_path = VLLM_PATH / "examples/template_vlm2vec.jinja" +vlm2vec_jinja_path = VLLM_PATH / "examples/template_vlm2vec_phi3v.jinja" assert vlm2vec_jinja_path.exists() # Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA) diff --git a/tests/entrypoints/test_chat_utils.py b/tests/entrypoints/test_chat_utils.py index a268f573ef90..85b305c2fa02 100644 --- a/tests/entrypoints/test_chat_utils.py +++ b/tests/entrypoints/test_chat_utils.py @@ -2468,7 +2468,8 @@ def test_resolve_content_format_fallbacks(model, expected_format): ("template_falcon.jinja", "string"), ("template_inkbot.jinja", "string"), ("template_teleflm.jinja", "string"), - ("template_vlm2vec.jinja", "openai"), + ("template_vlm2vec_phi3v.jinja", "openai"), + ("template_vlm2vec_qwen2vl.jinja", "openai"), ("tool_chat_template_granite_20b_fc.jinja", "string"), ("tool_chat_template_hermes.jinja", "string"), ("tool_chat_template_internlm2_tool.jinja", "string"), From d945c6cf7e051d13fca65df9944d4b587f4ca876 Mon Sep 17 00:00:00 2001 From: bnellnm <49004751+bnellnm@users.noreply.github.com> Date: Tue, 30 Sep 2025 15:03:15 -0400 Subject: [PATCH 113/166] [Docs] Add moe kernel features doc (#25297) Signed-off-by: Bill Nell Signed-off-by: bnellnm <49004751+bnellnm@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- docs/design/fused_moe_modular_kernel.md | 26 +----- docs/design/moe_kernel_features.md | 119 ++++++++++++++++++++++++ 2 files changed, 121 insertions(+), 24 deletions(-) create mode 100644 docs/design/moe_kernel_features.md diff --git a/docs/design/fused_moe_modular_kernel.md b/docs/design/fused_moe_modular_kernel.md index cb2037b575e5..ee5701989265 100644 --- a/docs/design/fused_moe_modular_kernel.md +++ b/docs/design/fused_moe_modular_kernel.md @@ -242,30 +242,8 @@ Example: `python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kern ## FusedMoEPrepareAndFinalize Implementations -The following table lists the `FusedMoEPrepareAndFinalize` implementations at the time of writing, - -| Implementation | Type | Comments | -| :--- | :--- | :--- | -| DeepEPHTPrepareAndFinalize | Contiguous / Non-Batched | Uses the DeepEP High-Throughput all2all kernels. | -| DeepEPLLPrepareAndFinalize | Batched | Uses the DeepEP Low-Latency all2all kernels. | -| PplxPrepareAndFinalize | Batched | Uses the Perplexity all2all kernels. | -| FlashInferCutlassMoEPrepareAndFinalize | Contiguous | | -| MoEPrepareAndFinalizeNoEP | Contiguous | This implementation is used when there is no EP. i.e. no all2all kernels are invoked. | -| BatchedPrepareAndFinalize | Batched | A reference prepare/finalize class that reorganizes the tokens into expert batched format, i.e. E x max_num_tokens x K. (Doesn’t use any all2all kernels. This is primarily used in unit testing) | +See [Fused MoE Kernel features](./moe_kernel_features.md#fused-moe-modular-all2all-backends) for a list of all the available modular prepare and finalize subclasses. ## FusedMoEPermuteExpertsUnpermute -The following table lists the `FusedMoEPermuteExpertsUnpermute` implementations at the time of writing, - -| Implementation | Type | Comment | -| :--- | :--- | :--- | -| BatchedDeepGemmExperts | Batched | Uses the DeepGemm’s Masked Grouped Gemm kernels for the fused_moe operation. | -| BatchedTritonExperts | Batched | Uses a Triton Kernel for the Batched matmuls. | -| BatchedTritonOrDeepGemmExperts | Batched | Chooses either the `BatchedDeepGemmExperts` or `BatchedTritonExperts` based on environment settings. | -| DeepGemmExperts | Contiguous / Non-Batched | Uses DeepGemm’s Grouped Gemm kernels for fused_moe operation. | -| TritonExperts | Contiguous / Non-Batched | Uses a Triton Kernel for fused_moe matmuls. | -| TritonOrDeepGemmExperts | Contiguous / Non-Batched | Chooses either the `DeepGemmExperts` or `TritonExperts` based on fused_moe inputs. | -| CutlassExpertsFP8 | Supports both Batched and Contiguous formats | Uses Cutlass Grouped Gemm implementations for the fp8 matmuls. | -| CutlassExpertsFP4 | Supports both Batched and Contiguous formats | Uses Cutlass Grouped Gemm implementations for the fp4 matmuls. | -| FlashInferExperts | Contiguous | Uses fused_moe operation from FlashInfer | -| NaiveBatchedExperts | Batched | Reference Batched Experts implementation. Primarily used in unit tests. | +See [Fused MoE Kernel features](./moe_kernel_features.md#fused-moe-experts-kernels) for a list of all the available modular experts. diff --git a/docs/design/moe_kernel_features.md b/docs/design/moe_kernel_features.md new file mode 100644 index 000000000000..74ed34d0a474 --- /dev/null +++ b/docs/design/moe_kernel_features.md @@ -0,0 +1,119 @@ +# Fused MoE Kernel features + +The purpose of this document is to provide an overview of the various MoE kernels (both modular and non-modular) so it will be easier to select an appropriate set of kernels for any particular situation. This includes information about the all2all backends used by modular kernels. + +## Fused MoE Modular All2All backends + +There are a number of all2all communication backends that are used to implement expert parallelism (EP) for the `FusedMoE` layer. The different `FusedMoEPrepareAndFinalize` sub-classes provide an interface for each all2all backend. + +The following table describes the relevant features of each backend, i.e. activation format, supported quantization schemes and async support. + +The output activation format (standard or batched) corresponds to the output of the prepare step of the `FusedMoEPrepareAndFinalize` subclass, the finalize step requires the same format. All the backend `prepare` methods expect activations in standard format and all the `finalize methods return activations in standard format. More details on the formats can be found in the [Fused MoE Modular Kernel](./fused_moe_modular_kernel.md) document. + +The quantization types and formats enumerate which quantization schemes are supported by each `FusedMoEPrepareAndFinalize` class. The quantization can happen before or after the dispatch based on the format the all2all backend supports. e.g. deepep_high_throughput supports only block-quantized fp8 format, any other format will result in dispatching in higher precision and quantizing afterwards. The output of the prepare step for each backend is the quantized type. The finalize step generally requires the same input type as the original activations, e.g. if the original input is bfloat16 and the quantization scheme is fp8 w/per-tensor scales, `prepare` will return fp8/per-tensor scale activations and `finalize` will take bfloat16 activations. See the diagrams in [Fused MoE Modular Kernel](./fused_moe_modular_kernel.md) for more details on the types and formats of activations at each step of the MoE process. If no quantization type is specified, the kernel operates on float16 and/or bfloat16. + +Async backends support the use of DBO (Dual Batch Overlap) and shared expert overlap (where shared experts are computed during the combine step). + +Certain models require the topk weights to be applied to the input activations rather than the output activations when topk==1, e.g. llama. For modular kernels, this feature is supported by the `FusedMoEPrepareAndFinalize` subclass, for non-modular kernels, it is up to the experts function to deal with this flag. + +unless otherwise specified, backends are controlled via `VLLM_ALL2ALL_BACKEND`. All backends except `flashinfer` only work with EP+DP or EP+TP. `Flashinfer` can work with EP or DP w/o EP. + + + +| Backend | Output act. format | Quant. types | Quant. format | Async | Apply Weight On Input | Sub-class | +|---------------------------------------|--------------------|-----------------|------------------------|-------|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------| +| naive | standard | all1 | G,A,T | N | 6 | [layer.py][vllm.model_executor.layers.fused_moe.layer.FusedMoE.forward_impl] | +| pplx | batched | fp8,int8 | G,A,T | Y | Y | [`PplxPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.pplx_prepare_finalize.PplxPrepareAndFinalize] | +| deepep_high_throughput | standard | fp8 | G(128),A,T2 | Y | Y | [`DeepEPLLPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ll_prepare_finalize.DeepEPLLPrepareAndFinalize] | +| deepep_low_latency | batched | fp8 | G(128),A,T3 | Y | Y | [`DeepEPHTPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize.DeepEPHTPrepareAndFinalize] | +| flashinfer_all2allv | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferAllToAllMoEPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize.FlashInferAllToAllMoEPrepareAndFinalize] | +| flashinfer4 | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferCutlassMoEPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize.FlashInferCutlassMoEPrepareAndFinalize] | +| flashinfer4 | standard | nvfp4,fp8 | G,A,T | N | N | [`FlashInferCutlassMoEPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_prepare_finalize.FlashInferCutlassMoEPrepareAndFinalize] | +| MoEPrepareAndFinalizeNoEP5 | standard | fp8,int8 | G,A,T | N | Y | [`MoEPrepareAndFinalizeNoEP`][vllm.model_executor.layers.fused_moe.prepare_finalize.MoEPrepareAndFinalizeNoEP] | +| BatchedPrepareAndFinalize5 | batched | fp8,int8 | G,A,T | N | Y | [`BatchedPrepareAndFinalize`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedPrepareAndFinalize] | + +!!! info "Table key" + 1. All types: mxfp4, nvfp4, int4, int8, fp8 + 2. A,T quantization occurs after dispatch. + 3. All quantization happens after dispatch. + 4. Controlled by different env vars (`VLLM_FLASHINFER_MOE_BACKEND` "throughput" or "latency") + 5. This is a no-op dispatcher that can be used to pair with any modular experts to produce a modular kernel that runs w/o dispatch or combine. These cannot be selected via environment variable. These are generally use for testing or adapting an expert subclass to the `fused_experts` API. + 6. This depends on the experts implementation. + + --- + + - G - Grouped + - G(N) - Grouped w/block size N + - A - Per activation token + - T - Per tensor + +Modular kernels are supported by the following `FusedMoEMethodBase` classes. + +- [`ModelOptFp8MoEMethod`][vllm.model_executor.layers.quantization.modelopt.ModelOptFp8MoEMethod] +- [`Fp8MoEMethod`][vllm.model_executor.layers.quantization.fp8.Fp8MoEMethod] +- [`CompressedTensorsW4A4MoeMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.CompressedTensorsW4A4MoeMethod] +- [`CompressedTensorsW8A8Fp8MoEMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.CompressedTensorsW8A8Fp8MoEMethod] +- [`Mxfp4MoEMethod`][vllm.model_executor.layers.quantization.mxfp4.Mxfp4MoEMethod] +- [`UnquantizedFusedMoEMethod`][vllm.model_executor.layers.fused_moe.layer.UnquantizedFusedMoEMethod] + +## Fused MoE Experts Kernels + +The are a number of MoE experts kernel implementations for different quantization types and architectures. Most follow the general API of the base Triton [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts] function. Many have modular kernel adatpers so they can be used with compatible all2all backends. This table lists each experts kernel and its particular properties. + +Each kernel must be provided with one of the supported input activation formats. Some flavors of kernels support both standard and batched formats through different entry points, e.g. `TritonExperts` and `BatchedTritonExperts`. Batched format kernels are currently only needed for matching with certain all2all backends, e.g. `pplx`, `DeepEPLLPrepareAndFinalize`. + +Similar to the backend kernels, each experts kernel only supports certain quantization formats. For non-modular experts, the activations will be in the original type and quantized internally by the kernel. Modular experts will expect the activations to already be in the quantized format. Both types of experts will yield outputs in the original activation type. + +Each experts kernel supports one or more activation functions, e.g. silu, gelu that are applied to the intermediate results. + +As with the backends, some experts support applying topk weights on the input activations. The entries in the column in this table only apply to the non-modular experts. + +Most experts flavors include an equivalent modular interface which will be a subclass of `FusedMoEPermuteExpertsUnpermute`. + +To be used with a particular `FusedMoEPrepareAndFinalize` sub-class, MoE kernels must have compatible activation formats, quantization types and quantization formats. + +| Kernel | Input act. format | Quant. types | Quant. format | Activation function | Apply Weight On Input | Modular | Source | +|------------------------------|-----------------------|------------------|---------------|-------------------------------------------------------------|-----------------------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| triton | standard | all1 | G,A,T | silu, gelu,
swigluoai,
silu_no_mul,
gelu_no_mul | Y | Y | [`fused_experts`][vllm.model_executor.layers.fused_moe.fused_moe.fused_experts],
[`TritonExperts`][vllm.model_executor.layers.fused_moe.fused_moe.TritonExperts] | +| triton (batched) | batched | all1 | G,A,T | silu, gelu | 6 | Y | [`BatchedTritonExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.BatchedTritonExperts] | +| deep gemm | standard,
batched | fp8 | G(128),A,T | silu, gelu | 6 | Y | [`deep_gemm_moe_fp8`][vllm.model_executor.layers.fused_moe.deep_gemm_moe.deep_gemm_moe_fp8],
[`DeepGemmExperts`][vllm.model_executor.layers.fused_moe.deep_gemm_moe.DeepGemmExperts],
[`BatchedDeepGemmExperts`][vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe.BatchedDeepGemmExperts] | +| cutlass_fp4 | standard,
batched | nvfp4 | A,T | silu | Y | Y | [`cutlass_moe_fp4`][vllm.model_executor.layers.fused_moe.cutlass_moe.cutlass_moe_fp4],
[`CutlassExpertsFp4`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassExpertsFp4] | +| cutlass_fp8 | standard,
batched | fp8 | A,T | silu, gelu | Y | Y | [`cutlass_moe_fp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.cutlass_moe_fp8],
[`CutlassExpertsFp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassExpertsFp8],
[`CutlasBatchedExpertsFp8`][vllm.model_executor.layers.fused_moe.cutlass_moe.CutlassBatchedExpertsFp8] | +| flashinfer | standard | nvfp4,
fp8 | T | 5 | N | Y | [`flashinfer_cutlass_moe_fp4`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.flashinfer_cutlass_moe_fp4],
[`FlashInferExperts`][vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe.FlashInferExperts] | +| gpt oss triton | standard | N/A | N/A | 5 | Y | Y | [`triton_kernel_fused_experts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.triton_kernel_fused_experts],
[`OAITritonExperts`][vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe.OAITritonExperts] | +| deep gemm+triton2 | standard,
batched | all1 | G(128),A,T | silu, gelu | 6 | Y | [`TritonOrDeepGemmExperts`][vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe.TritonOrDeepGemmExperts],
[`BatchedTritonOrDeepGemmExperts`][vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe.BatchedTritonOrDeepGemmExperts] | +| marlin | standard | 3 | 3 | silu,
swigluoai | Y | N | [`fused_marlin_moe`][vllm.model_executor.layers.fused_moe.fused_marlin_moe.fused_marlin_moe] | +| trtllm | standard | mxfp4,
nvfp4 | G(16),G(32) | 5 | N | Y | [`TrtLlmGenExperts`][vllm.model_executor.layers.fused_moe.trtllm_moe.TrtLlmGenExperts] | +| pallas | standard | N/A | N/A | silu | N | N | [`fused_moe`][vllm.model_executor.layers.fused_moe.moe_pallas.fused_moe] | +| iterative | standard | N/A | N/A | silu | N | N | [`fused_moe`][vllm.model_executor.layers.fused_moe.moe_torch_iterative.fused_moe] | +| rocm aiter moe | standard | fp8 | G(128),A,T | silu, gelu | Y | N | [`rocm_aiter_fused_experts`][vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe.rocm_aiter_fused_moe_impl] | +| cpu_fused_moe | standard | N/A | N/A | silu | N | N | [`CPUFusedMOE`][vllm.model_executor.layers.fused_moe.cpu_fused_moe.CPUFusedMOE] | +| naive batched4 | batched | int8,
fp8 | G,A,T | silu, gelu | 6 | Y | [`NaiveBatchedExperts`][vllm.model_executor.layers.fused_moe.fused_batched_moe.NaiveBatchedExperts] | + +!!! info "Table key" + 1. All types: mxfp4, nvfp4, int4, int8, fp8 + 2. A dispatcher wrapper around triton and deep gemm experts. Will select based on type + shape + quantization params + 3. uint4, uint8, fp8, fp4 + 4. This is a naive implementation of experts that supports batched format. Mainly used for testing. + 5. The `activation` parameter is ignored and SwiGlu is used by default instead. + 6. Only handled by or supported when used with modular kernels. + +## Modular Kernel "families" + +The following table shows "families" of modular kernels that are intended to work together. There are some combinations which may work but have not yet been tested, e.g. flashinfer with other fp8 experts. Note that the "naive" backend will work with any non-modular experts. + +| backend | `FusedMoEPrepareAndFinalize` subclasses | `FusedMoEPermuteExpertsUnpermute` subclasses | +|----------------------------------|------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------| +| deepep_high_throughput,
pplx | `DeepEPHTPrepareAndFinalize`,
`PplxPrepareAndFinalize` | `BatchedDeepGemmExperts`,
`BatchedTritonExperts`,
`BatchedTritonOrDeepGemmExperts`,
`CutlassBatchedExpertsFp8` | +| deepep_low_latency | `DeepEPLLPrepareAndFinalize` | `DeepGemmExperts`,
`TritonExperts`,
`TritonOrDeepGemmExperts`,
`CutlassExpertsFp8` | +| flashinfer | `FlashInferCutlassMoEPrepareAndFinalize` | `FlashInferExperts` | From dc5fa2ea21d59286dec77ab4f01ad01661469a2a Mon Sep 17 00:00:00 2001 From: Or Ozeri Date: Tue, 30 Sep 2025 22:53:04 +0300 Subject: [PATCH 114/166] OffloadingConnector: Fix GPU block tracking bug (#25856) Signed-off-by: Or Ozeri --- .../kv_transfer/kv_connector/v1/offloading_connector.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py index c23efa604544..6936638c7f4e 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py @@ -278,8 +278,9 @@ def _get_reqs_to_store(self, scheduler_output: SchedulerOutput): req, start_idx=start_block_idx, end_idx=num_blocks) store_output = self.manager.prepare_store(new_block_hashes) if store_output is None: - logger.warning("Cannot store %s blocks", num_new_blocks) - break + logger.warning("Request %s: cannot store %s blocks", req_id, + num_new_blocks) + continue self._next_stored_block_idx[req_id] = num_blocks From 6a0a842f5da0f98269c41ccd1a869950f3083857 Mon Sep 17 00:00:00 2001 From: cjackal <44624812+cjackal@users.noreply.github.com> Date: Wed, 1 Oct 2025 05:35:15 +0900 Subject: [PATCH 115/166] [Llama4] [multimodal] Fix misplaced dtype cast of `cos_sin_cache` in `Llama4VisionRotaryEmbedding` (#25889) Signed-off-by: cjackal <44624812+cjackal@users.noreply.github.com> --- .../layers/rotary_embedding/llama4_vision_rope.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/rotary_embedding/llama4_vision_rope.py b/vllm/model_executor/layers/rotary_embedding/llama4_vision_rope.py index 871728035306..c98a426a2a1e 100644 --- a/vllm/model_executor/layers/rotary_embedding/llama4_vision_rope.py +++ b/vllm/model_executor/layers/rotary_embedding/llama4_vision_rope.py @@ -59,7 +59,9 @@ def forward_native( # type: ignore[override] key: Optional[torch.Tensor] = None, ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: assert key is not None - self._match_cos_sin_cache_dtype(query) + # self.cos_sin_cache here is complex tensor so we cannot cast into + # query's dtype directly with self._match_cos_sin_cache_dtype + self.cos_sin_cache: torch.Tensor = self.cos_sin_cache.to(query.device) query_ = torch.view_as_complex(query.float().reshape( *query.shape[:-1], -1, 2)) key_ = torch.view_as_complex(key.float().reshape( From c3954986dc07fa03af24416813e970d1e014ee98 Mon Sep 17 00:00:00 2001 From: Jee Jee Li Date: Wed, 1 Oct 2025 05:13:48 +0800 Subject: [PATCH 116/166] [Bench] Add DeepSeekV32 to MoE benchmark (#25962) Signed-off-by: Jee Jee Li --- benchmarks/kernels/benchmark_moe.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/benchmarks/kernels/benchmark_moe.py b/benchmarks/kernels/benchmark_moe.py index d2beb28f7023..02c2db674d4b 100644 --- a/benchmarks/kernels/benchmark_moe.py +++ b/benchmarks/kernels/benchmark_moe.py @@ -584,8 +584,9 @@ def main(args: argparse.Namespace): topk = config.num_experts_per_tok intermediate_size = config.intermediate_size elif config.architectures[0] in ( - "DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM", + "DeepseekV3ForCausalLM", + "DeepseekV32ForCausalLM", "Glm4MoeForCausalLM", ): E = config.n_routed_experts From 98775a8d946f4932327632e1f495f635cbd0744a Mon Sep 17 00:00:00 2001 From: David Ben-David Date: Wed, 1 Oct 2025 00:57:08 +0300 Subject: [PATCH 117/166] [V1] [P/D] Add Support for KV Load Failure Recovery (#19330) Signed-off-by: David Ben-David Co-authored-by: David Ben-David --- .../kv_load_failure_recovery/README.md | 30 ++ .../decode_example.py | 85 +++++ .../prefill_example.py | 58 +++ .../rogue_shared_storage_connector.py | 145 ++++++++ .../kv_load_failure_recovery/run.sh | 33 ++ .../unit/test_kv_load_failure_recovery.py | 341 ++++++++++++++++++ .../unit/test_offloading_connector.py | 4 +- .../unit/test_output_aggreagator.py | 74 ++-- .../unit/test_remote_decode_lifecycle.py | 6 +- .../unit/test_remote_prefill_lifecycle.py | 14 +- tests/v1/kv_connector/unit/utils.py | 49 ++- tests/v1/worker/test_gpu_model_runner.py | 1 + .../kv_transfer/kv_connector/utils.py | 6 +- .../kv_transfer/kv_connector/v1/base.py | 31 +- .../kv_connector/v1/multi_connector.py | 6 + .../v1/shared_storage_connector.py | 7 +- vllm/v1/core/block_pool.py | 2 +- vllm/v1/core/sched/output.py | 2 + vllm/v1/core/sched/scheduler.py | 188 +++++++++- vllm/v1/core/single_type_kv_cache_manager.py | 3 + vllm/v1/outputs.py | 7 +- vllm/v1/worker/gpu_model_runner.py | 17 + vllm/v1/worker/gpu_worker.py | 3 +- .../worker/kv_connector_model_runner_mixin.py | 5 +- 24 files changed, 1035 insertions(+), 82 deletions(-) create mode 100644 examples/offline_inference/kv_load_failure_recovery/README.md create mode 100644 examples/offline_inference/kv_load_failure_recovery/decode_example.py create mode 100644 examples/offline_inference/kv_load_failure_recovery/prefill_example.py create mode 100644 examples/offline_inference/kv_load_failure_recovery/rogue_shared_storage_connector.py create mode 100755 examples/offline_inference/kv_load_failure_recovery/run.sh create mode 100644 tests/v1/kv_connector/unit/test_kv_load_failure_recovery.py diff --git a/examples/offline_inference/kv_load_failure_recovery/README.md b/examples/offline_inference/kv_load_failure_recovery/README.md new file mode 100644 index 000000000000..230a16812b25 --- /dev/null +++ b/examples/offline_inference/kv_load_failure_recovery/README.md @@ -0,0 +1,30 @@ +# KV Load Failure Recovery Test + +This example builds upon the `disaggregated-prefill-v1` example in `examples/offline_inference`. + +It demonstrates vLLM's ability to recover from KV load failures in both synchronous and asynchronous loading modes. The goal is to verify that vLLM correctly identifies invalid KV blocks, reschedules the affected requests, and ensures successful and consistent output. + +## Files + +- `prefill_example.py` – performs the prefill stage and saves KV data (same as in `disaggregated-prefill-v1`). +- `decode_example.py` – performs the decode stage. Accepts: + - `--simulate-failure`: simulates KV load failure using a custom connector. + - `--async-load`: enables asynchronous KV loading mode. +- `rogue_shared_storage_connector.py` – defines `RogueSharedStorageConnector`, a subclass of `SharedStorageConnector`, that simulates missing or corrupted external KV blocks by failing to load blocks for the first decode request. +- `run.sh` – orchestrates the test: runs the prefill stage, then three decode stages: + 1. Normal decode (baseline). + 2. Decode with simulated sync KV load failure. + 3. Decode with simulated async KV load failure. + + Finally, it compares the output of the baseline with the recovered outputs to verify correctness. + +## How It Works + +- The test dynamically loads `RogueSharedStorageConnector` via `KVTransferConfig.kv_connector_module_path`, enabling controlled simulation of load failures without modifying the original connector. +- The decode stages that simulate failure are expected to trigger recovery logic in vLLM, resulting in the same output as the baseline decode. +- If recovery fails, the script prints a unified diff of the output mismatch and exits with error. + +## Usage + +```bash +./run.sh diff --git a/examples/offline_inference/kv_load_failure_recovery/decode_example.py b/examples/offline_inference/kv_load_failure_recovery/decode_example.py new file mode 100644 index 000000000000..69523f56eace --- /dev/null +++ b/examples/offline_inference/kv_load_failure_recovery/decode_example.py @@ -0,0 +1,85 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import argparse + +from vllm import LLM, SamplingParams +from vllm.config import KVTransferConfig + + +def read_prompts(): + """Read prompts from prefill_output.txt""" + prompts = [] + try: + with open("prefill_output.txt") as f: + for line in f: + prompts.append(line.strip()) + print(f"Loaded {len(prompts)} prompts from prefill_output.txt") + return prompts + except FileNotFoundError: + print("Error: prefill_output.txt file not found") + exit(-1) + + +def main(): + prompts = read_prompts() + sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=10) + + parser = argparse.ArgumentParser() + parser.add_argument( + "--simulate-failure", action="store_true", help="Simulate KV load failure." + ) + parser.add_argument( + "--async-load", action="store_true", help="Simulate async KV load" + ) + args = parser.parse_args() + + if args.simulate_failure: + ktc = KVTransferConfig( + kv_connector="RogueSharedStorageConnector", + kv_role="kv_both", + kv_connector_extra_config={ + "shared_storage_path": "local_storage", + "async_load": args.async_load, + }, + kv_connector_module_path="rogue_shared_storage_connector", + ) + out_file = ( + "async_decode_recovered_output.txt" + if args.async_load + else "sync_decode_recovered_output.txt" + ) + else: + ktc = KVTransferConfig( + kv_connector="SharedStorageConnector", + kv_role="kv_both", + kv_connector_extra_config={ + "shared_storage_path": "local_storage", + }, + ) + out_file = "decode_output.txt" + + llm = LLM( + model="meta-llama/Llama-3.2-1B-Instruct", + enforce_eager=True, + gpu_memory_utilization=0.8, + max_num_batched_tokens=64, + max_num_seqs=16, + kv_transfer_config=ktc, + ) + + outputs = llm.generate(prompts, sampling_params) + + sep_str = "-" * 30 + with open(out_file, "w", encoding="utf-8") as f: + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + out_str = f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}" + print(out_str) + print(sep_str) + f.write(out_str) + f.write(sep_str) + + +if __name__ == "__main__": + main() diff --git a/examples/offline_inference/kv_load_failure_recovery/prefill_example.py b/examples/offline_inference/kv_load_failure_recovery/prefill_example.py new file mode 100644 index 000000000000..047b81c82df5 --- /dev/null +++ b/examples/offline_inference/kv_load_failure_recovery/prefill_example.py @@ -0,0 +1,58 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +from vllm import LLM, SamplingParams +from vllm.config import KVTransferConfig + + +def read_prompts(): + context = "Hi " * 1000 + context2 = "Hey " * 500 + return [ + context + "Hello, my name is", + context + "The capital of France is", + context2 + "Your name is", + context2 + "The capital of China is", + ] + + +def main(): + prompts = read_prompts() + + sampling_params = SamplingParams(temperature=0, top_p=0.95, max_tokens=1) + + llm = LLM( + model="meta-llama/Llama-3.2-1B-Instruct", + enforce_eager=True, + gpu_memory_utilization=0.8, + kv_transfer_config=KVTransferConfig( + kv_connector="SharedStorageConnector", + kv_role="kv_both", + kv_connector_extra_config={"shared_storage_path": "local_storage"}, + ), + ) # , max_model_len=2048, max_num_batched_tokens=2048) + + # 1ST generation (prefill instance) + outputs = llm.generate( + prompts, + sampling_params, + ) + + new_prompts = [] + print("-" * 30) + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + new_prompts.append(prompt + generated_text) + print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}") + print("-" * 30) + + # Write new_prompts to prefill_output.txt + with open("prefill_output.txt", "w") as f: + for prompt in new_prompts: + f.write(prompt + "\n") + print(f"Saved {len(new_prompts)} prompts to prefill_output.txt") + + +if __name__ == "__main__": + main() diff --git a/examples/offline_inference/kv_load_failure_recovery/rogue_shared_storage_connector.py b/examples/offline_inference/kv_load_failure_recovery/rogue_shared_storage_connector.py new file mode 100644 index 000000000000..0abe7d161261 --- /dev/null +++ b/examples/offline_inference/kv_load_failure_recovery/rogue_shared_storage_connector.py @@ -0,0 +1,145 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project +# ruff: noqa: E501 +import logging +from dataclasses import dataclass, field +from typing import TYPE_CHECKING, Optional + +from vllm.config import VllmConfig +from vllm.distributed.kv_transfer.kv_connector.v1.base import ( + KVConnectorMetadata, + KVConnectorRole, +) +from vllm.distributed.kv_transfer.kv_connector.v1.shared_storage_connector import ( + SharedStorageConnector, + SharedStorageConnectorMetadata, +) +from vllm.forward_context import ForwardContext +from vllm.v1.core.kv_cache_manager import KVCacheBlocks +from vllm.v1.request import Request + +if TYPE_CHECKING: + from vllm.v1.core.sched.output import SchedulerOutput + +logger = logging.getLogger() +logging.basicConfig(level=logging.INFO) + + +@dataclass +class RogueSharedStorageConnectorMetadata(SharedStorageConnectorMetadata): + req_to_block_ids: dict[str, set[int]] = field(default_factory=dict) + + @classmethod + def from_base(cls, base: SharedStorageConnectorMetadata): + return cls(requests=base.requests) + + +class RogueSharedStorageConnector(SharedStorageConnector): + def __init__(self, vllm_config: "VllmConfig", role: KVConnectorRole): + super().__init__(vllm_config=vllm_config, role=role) + self._async_load = vllm_config.kv_transfer_config.get_from_extra_config( + "async_load", False + ) + self._invalid_block_ids: set = None + self._seen_requests: set = set() + self._req_to_block_ids: dict[str, list[int]] = dict() + + def bind_connector_metadata(self, connector_metadata: KVConnectorMetadata) -> None: + assert isinstance(connector_metadata, RogueSharedStorageConnectorMetadata) + index, failed_request = next( + ( + (i, x) + for i, x in enumerate(connector_metadata.requests) + if not x.is_store + ), + (None, None), + ) + if index is not None: + del connector_metadata.requests[index] + self._invalid_block_ids = set( + ( + failed_request.slot_mapping[:: self._block_size] // self._block_size + ).tolist() + ) + logger.info( + "Simulating failure to load all KV blocks for the " + "first load request. Total blocks: %d", + len(self._invalid_block_ids), + ) + super().bind_connector_metadata(connector_metadata) + + def clear_connector_metadata(self) -> None: + self._invalid_block_ids = None + super().clear_connector_metadata() + + def start_load_kv(self, forward_context: ForwardContext, **kwargs) -> None: + if self._async_load and forward_context.attn_metadata is None: + # Bypass sanity check in super().start_load_kv + forward_context.attn_metadata = "None" + + super().start_load_kv(forward_context, **kwargs) + + def get_finished( + self, finished_req_ids: set[str] + ) -> tuple[Optional[set[str]], Optional[set[str]]]: + if self._async_load: + meta = self._get_connector_metadata() + assert isinstance(meta, RogueSharedStorageConnectorMetadata) + if meta.req_to_block_ids: + return None, set(meta.req_to_block_ids) + + return None, None + + def get_block_ids_with_load_errors(self) -> set[int]: + return self._invalid_block_ids + + def get_num_new_matched_tokens( + self, + request: Request, + num_computed_tokens: int, + ) -> tuple[int, bool]: + if request.request_id in self._seen_requests: + return 0, False + + self._seen_requests.add(request.request_id) + + num_tokens, _ = super().get_num_new_matched_tokens(request, num_computed_tokens) + return num_tokens, self._async_load and num_tokens > 0 + + def update_state_after_alloc( + self, request: Request, blocks: KVCacheBlocks, num_external_tokens: int + ): + """ + Update KVConnector state after block allocation. + + If blocks were allocated, add to _requests_need_load, + such that we load the KVs in the next forward pass. + """ + super().update_state_after_alloc(request, blocks, num_external_tokens) + + if num_external_tokens > 0: + self._req_to_block_ids[request.request_id] = blocks.get_block_ids()[0] + + def build_connector_meta( + self, + scheduler_output: "SchedulerOutput", + ) -> KVConnectorMetadata: + if not self._async_load: + base = super().build_connector_meta(scheduler_output) + meta = RogueSharedStorageConnectorMetadata.from_base(base) + else: + meta = RogueSharedStorageConnectorMetadata() + if self._requests_need_load: + for req_id, request in self._requests_need_load.items(): + meta.add_request( + token_ids=request.prompt_token_ids, + block_ids=self._req_to_block_ids[req_id], + block_size=self._block_size, + is_store=False, + mm_hashes=[], + ) + # Clear state + self._requests_need_load.clear() + meta.req_to_block_ids = self._req_to_block_ids + self._req_to_block_ids = dict() + return meta diff --git a/examples/offline_inference/kv_load_failure_recovery/run.sh b/examples/offline_inference/kv_load_failure_recovery/run.sh new file mode 100755 index 000000000000..53fe2385d46d --- /dev/null +++ b/examples/offline_inference/kv_load_failure_recovery/run.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# Constants +SHARED_STORAGE_DIR="local_storage" +PREFILL_OUTPUT="prefill_output.txt" +DECODE_OUTPUT="decode_output.txt" +SYNC_DECODE_RECOVERED_OUTPUT="sync_decode_recovered_output.txt" +ASYNC_DECODE_RECOVERED_OUTPUT="async_decode_recovered_output.txt" + +# Cleanup +rm -rf "$SHARED_STORAGE_DIR" +rm -f "$PREFILL_OUTPUT" "$DECODE_OUTPUT" "$SYNC_DECODE_RECOVERED_OUTPUT" "$ASYNC_DECODE_RECOVERED_OUTPUT" + +# Run inference examples +VLLM_ENABLE_V1_MULTIPROCESSING=0 CUDA_VISIBLE_DEVICES=0 python3 prefill_example.py +VLLM_ENABLE_V1_MULTIPROCESSING=0 CUDA_VISIBLE_DEVICES=0 python3 decode_example.py +VLLM_ENABLE_V1_MULTIPROCESSING=0 CUDA_VISIBLE_DEVICES=0 python3 decode_example.py --simulate-failure +VLLM_ENABLE_V1_MULTIPROCESSING=0 CUDA_VISIBLE_DEVICES=0 python3 decode_example.py --simulate-failure --async-load + +# Compare outputs +if ! cmp -s "$DECODE_OUTPUT" "$SYNC_DECODE_RECOVERED_OUTPUT"; then + echo "❌ Outputs differ: sync recovery failed." + diff -u "$DECODE_OUTPUT" "$SYNC_DECODE_RECOVERED_OUTPUT" + exit 1 +fi + +if ! cmp -s "$DECODE_OUTPUT" "$ASYNC_DECODE_RECOVERED_OUTPUT"; then + echo "❌ Outputs differ: async recovery failed." + diff -u "$DECODE_OUTPUT" "$ASYNC_DECODE_RECOVERED_OUTPUT" + exit 1 +fi + +echo "✅ Outputs match: recovery successful." diff --git a/tests/v1/kv_connector/unit/test_kv_load_failure_recovery.py b/tests/v1/kv_connector/unit/test_kv_load_failure_recovery.py new file mode 100644 index 000000000000..549e85875025 --- /dev/null +++ b/tests/v1/kv_connector/unit/test_kv_load_failure_recovery.py @@ -0,0 +1,341 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +from typing import Callable +from unittest.mock import Mock + +import pytest + +from vllm.v1.core.sched.scheduler import Scheduler +from vllm.v1.request import Request, RequestStatus + +from .utils import (create_model_runner_output, create_request, + create_scheduler, create_vllm_config) + + +def _make_get_num_new_matched_tokens( + req_num_new_matched_tokens: dict[str, int], + async_load, +) -> Callable[[Request, int], tuple[int, bool]]: + + def get_num_new_matched_tokens(request: Request, + _: int) -> tuple[int, bool]: + value = req_num_new_matched_tokens.get(request.request_id, 0) + return value, async_load + + return get_num_new_matched_tokens + + +@pytest.fixture +def scheduler(): + vllm_config = create_vllm_config() + return create_scheduler(vllm_config) + + +@pytest.mark.parametrize( + "num_prompt_blocks," + "num_external_computed_blocks," + "invalid_block_idxs", + [ + (100, 99, {0, 98}), + (100, 99, {50, 98}), + (100, 99, {98}), + ], +) +def test_async_load_failure( + scheduler: Scheduler, + num_prompt_blocks: int, + num_external_computed_blocks: int, + invalid_block_idxs: set[int], +): + assert num_prompt_blocks >= num_external_computed_blocks + + num_prompt_tokens = num_prompt_blocks * scheduler.block_size + num_external_computed_tokens = (num_external_computed_blocks * + scheduler.block_size) + + request1 = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request1) + request2 = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request2) + request3 = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request3) + + # Mock KV connector method. + # req_id -> num_external_computed_tokens + req_num_new_matched_tokens = { + request1.request_id: num_external_computed_tokens, + request2.request_id: num_external_computed_tokens, + request3.request_id: num_external_computed_tokens, + } + + scheduler.connector = Mock() + scheduler.connector.get_num_new_matched_tokens.side_effect = ( + _make_get_num_new_matched_tokens(req_num_new_matched_tokens, + async_load=True)) + scheduler.connector.take_events.return_value = () + + scheduler_output = scheduler.schedule() + + assert len(scheduler.waiting) == 3 + for request in scheduler.waiting: + assert request.num_computed_tokens == 0 + assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS + assert scheduler.connector.get_num_new_matched_tokens.call_count == 3 + + # Simulate a failure in loading some of request2 blocks. + (req2_block_ids, ) = scheduler.kv_cache_manager.get_block_ids( + request2.request_id) + invalid_block_ids = {req2_block_ids[i] for i in invalid_block_idxs} + model_runner_output = create_model_runner_output( + reqs=[], + finished_recving={request1.request_id, request3.request_id}, + invalid_block_ids=invalid_block_ids, + use_eos=True) + + scheduler.update_from_output(scheduler_output, model_runner_output) + + min_invalid_block_idx = min(invalid_block_idxs) + + assert len(scheduler.waiting) == 3 + for request in scheduler.waiting: + if request.request_id == request2.request_id: + assert request.num_computed_tokens == (min_invalid_block_idx * + scheduler.block_size) + else: + assert request.num_computed_tokens == 0 + assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS + assert scheduler.failed_recving_kv_req_ids == {request2.request_id} + assert scheduler.connector.get_num_new_matched_tokens.call_count == 3 + + +@pytest.mark.parametrize( + "num_prompt_blocks," + "num_external_computed_blocks," + "invalid_block_idxs", + [ + (100, 99, {0, 98}), + (100, 99, {50, 98}), + (100, 99, {98}), + ], +) +def test_sync_load_failure( + scheduler: Scheduler, + num_prompt_blocks: int, + num_external_computed_blocks: int, + invalid_block_idxs: set[int], +): + assert num_prompt_blocks >= num_external_computed_blocks + + num_prompt_tokens = num_prompt_blocks * scheduler.block_size + num_external_computed_tokens = (num_external_computed_blocks * + scheduler.block_size) + + request1 = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request1) + request2 = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request2) + request3 = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request3) + + # Mock KV connector method. + # req_id -> num_external_computed_tokens + req_num_new_matched_tokens = { + request1.request_id: num_external_computed_tokens, + request2.request_id: num_external_computed_tokens, + request3.request_id: num_external_computed_tokens, + } + + scheduler.connector = Mock() + scheduler.connector.get_num_new_matched_tokens.side_effect = ( + _make_get_num_new_matched_tokens(req_num_new_matched_tokens, + async_load=False)) + scheduler.connector.request_finished.return_value = (False, None) + scheduler.connector.take_events.return_value = () + + scheduler_output = scheduler.schedule() + + # req_id -> num_computed_tokens + expected_computed_tokens = { + request1.request_id: num_external_computed_tokens, + request2.request_id: num_external_computed_tokens, + request3.request_id: num_external_computed_tokens, + } + + assert len(scheduler.running) == 3 + assert len(scheduler_output.scheduled_new_reqs) == 3 + for request in scheduler_output.scheduled_new_reqs: + assert request.num_computed_tokens == expected_computed_tokens[ + request.req_id] + assert scheduler.connector.get_num_new_matched_tokens.call_count == 3 + + # Simulate a failure in loading some of request2 blocks. + req2_block_ids = scheduler_output.scheduled_new_reqs[1].block_ids[0] + invalid_block_ids = {req2_block_ids[i] for i in invalid_block_idxs} + model_runner_output = create_model_runner_output( + [request1, request2, request3], + invalid_block_ids=invalid_block_ids, + use_eos=True) + + scheduler.update_from_output(scheduler_output, model_runner_output) + + assert len(scheduler.running) == 1 + assert scheduler.running[0].request_id == request2.request_id + assert scheduler.running[0].num_computed_tokens == ( + min(invalid_block_idxs) * scheduler.block_size) + assert scheduler.connector.get_num_new_matched_tokens.call_count == 3 + assert scheduler.connector.request_finished.call_count == 2 + + +@pytest.mark.parametrize( + "num_prompt_blocks," + "num_external_computed_blocks," + "num_common_prefix_blocks," + "invalid_block_idxs", + [ + (100, 99, 50, {0, 49}), + (100, 99, 50, {25, 49}), + (100, 99, 50, {49}), + ], +) +def test_sync_load_failure_with_shared_blocks( + scheduler: Scheduler, + num_prompt_blocks: int, + num_external_computed_blocks: int, + num_common_prefix_blocks: int, + invalid_block_idxs: set[int], +): + assert (num_prompt_blocks >= num_external_computed_blocks >= + num_common_prefix_blocks) + + num_prompt_tokens = num_prompt_blocks * scheduler.block_size + num_external_computed_tokens = (num_external_computed_blocks * + scheduler.block_size) + common_prefix_len = num_common_prefix_blocks * scheduler.block_size + + request1 = create_request(num_tokens=num_prompt_tokens, + common_prefix_len=common_prefix_len) + scheduler.add_request(request=request1) + request2 = create_request(num_tokens=num_prompt_tokens, + common_prefix_len=common_prefix_len) + scheduler.add_request(request=request2) + + # Mock KV connector method. + # req_id -> num_external_computed_tokens + req_num_new_matched_tokens = { + request1.request_id: num_external_computed_tokens, + } + + scheduler.connector = Mock() + scheduler.connector.get_num_new_matched_tokens.side_effect = ( + _make_get_num_new_matched_tokens(req_num_new_matched_tokens, + async_load=False)) + scheduler.connector.take_events.return_value = () + + scheduler_output = scheduler.schedule() + + # req_id -> num_computed_tokens + expected_computed_tokens = { + request1.request_id: num_external_computed_tokens, + request2.request_id: common_prefix_len, + } + + assert len(scheduler.running) == 2 + assert len(scheduler_output.scheduled_new_reqs) == 2 + for request in scheduler_output.scheduled_new_reqs: + assert request.num_computed_tokens == expected_computed_tokens[ + request.req_id] + assert scheduler.connector.get_num_new_matched_tokens.call_count == 2 + + # Simulate a failure in loading some of the shared blocks. + req1_block_ids = scheduler_output.scheduled_new_reqs[0].block_ids[0] + invalid_block_ids = {req1_block_ids[i] for i in invalid_block_idxs} + model_runner_output = create_model_runner_output( + [request1, request2], + invalid_block_ids=invalid_block_ids, + use_eos=True) + + scheduler.update_from_output(scheduler_output, model_runner_output) + + # req_id -> num_computed_tokens + # all the common prefix blocks will be computed by request1 + expected_computed_tokens = { + request1.request_id: min(invalid_block_idxs) * scheduler.block_size, + request2.request_id: common_prefix_len, + } + + assert len(scheduler.running) == 2 + for request in scheduler.running: + assert request.num_computed_tokens == expected_computed_tokens[ + request.request_id] + assert scheduler.connector.get_num_new_matched_tokens.call_count == 2 + + +@pytest.mark.parametrize( + "num_prompt_blocks," + "num_external_computed_blocks," + "invalid_block_idxs", + [ + (100, 99, {0, 50, 98}), + (100, 99, {98, 50, 0}), + ], +) +def test_async_progressive_load_failure( + scheduler: Scheduler, + num_prompt_blocks: int, + num_external_computed_blocks: int, + invalid_block_idxs: set[int], +): + assert num_prompt_blocks >= num_external_computed_blocks + + num_prompt_tokens = num_prompt_blocks * scheduler.block_size + num_external_computed_tokens = (num_external_computed_blocks * + scheduler.block_size) + + request = create_request(num_tokens=num_prompt_tokens) + scheduler.add_request(request=request) + + # Mock KV connector method. + # req_id -> num_external_computed_tokens + req_num_new_matched_tokens = { + request.request_id: num_external_computed_tokens, + } + + scheduler.connector = Mock() + scheduler.connector.get_num_new_matched_tokens.side_effect = ( + _make_get_num_new_matched_tokens(req_num_new_matched_tokens, + async_load=True)) + scheduler.connector.take_events.return_value = () + + scheduler_output = scheduler.schedule() + + assert len(scheduler.waiting) == 1 + assert scheduler.waiting.peek_request().request_id == request.request_id + assert request.num_computed_tokens == 0 + assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS + assert scheduler.connector.get_num_new_matched_tokens.call_count == 1 + + min_invalid_block_idx = max(invalid_block_idxs) + 1 + # Simulate failures when progressively loading request blocks. + for invalid_block_idx in invalid_block_idxs: + (req_block_ids, ) = scheduler.kv_cache_manager.get_block_ids( + request.request_id) + invalid_block_ids = {req_block_ids[invalid_block_idx]} + model_runner_output = create_model_runner_output( + reqs=[], + finished_recving=set(), + invalid_block_ids=invalid_block_ids, + use_eos=True) + + scheduler.update_from_output(scheduler_output, model_runner_output) + + min_invalid_block_idx = min(min_invalid_block_idx, invalid_block_idx) + + assert len(scheduler.waiting) == 1 + assert scheduler.waiting.peek_request( + ).request_id == request.request_id + assert request.num_computed_tokens == (min_invalid_block_idx * + scheduler.block_size) + assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS + assert scheduler.failed_recving_kv_req_ids == {request.request_id} + assert scheduler.connector.get_num_new_matched_tokens.call_count == 1 diff --git a/tests/v1/kv_connector/unit/test_offloading_connector.py b/tests/v1/kv_connector/unit/test_offloading_connector.py index f9a4d2fb4de4..f728b25d7834 100644 --- a/tests/v1/kv_connector/unit/test_offloading_connector.py +++ b/tests/v1/kv_connector/unit/test_offloading_connector.py @@ -281,8 +281,8 @@ def _run(self, decoded_tokens: list[int]): model_runner_output = create_model_runner_output( reqs=self.scheduler.running, - finished_sending=list(finished_sending), - finished_recving=list(finished_recving), + finished_sending=finished_sending, + finished_recving=finished_recving, token_id=token_id) if self.scheduler.running: diff --git a/tests/v1/kv_connector/unit/test_output_aggreagator.py b/tests/v1/kv_connector/unit/test_output_aggreagator.py index 607da6425a31..8c85732297f2 100644 --- a/tests/v1/kv_connector/unit/test_output_aggreagator.py +++ b/tests/v1/kv_connector/unit/test_output_aggreagator.py @@ -15,26 +15,38 @@ class DummyModelRunnerOutput(ModelRunnerOutput): def __init__(self, finished_sending: Optional[set[str]] = None, - finished_recving: Optional[set[str]] = None): + finished_recving: Optional[set[str]] = None, + invalid_block_ids: Optional[set[int]] = None): self.kv_connector_output = KVConnectorOutput( finished_sending=finished_sending, finished_recving=finished_recving, - ) + invalid_block_ids=invalid_block_ids or set()) def __repr__(self): return ( f"DummyModelRunnerOutput(" f"finished_sending={self.kv_connector_output.finished_sending}," - f"finished_recving={self.kv_connector_output.finished_recving})") + f"finished_recving={self.kv_connector_output.finished_recving})" + f"invalid_block_ids={self.kv_connector_output.invalid_block_ids})") def test_aggregate_workers_output(): aggregator = KVOutputAggregator(world_size=2) + output1 = DummyModelRunnerOutput() + output2 = DummyModelRunnerOutput() + + aggregated = aggregator.aggregate([output1, output2]) + + assert aggregated is output1 + aggregated = aggregated.kv_connector_output + assert aggregated.finished_sending is None + assert aggregated.finished_recving is None + assert not aggregated.invalid_block_ids + output1 = DummyModelRunnerOutput(finished_sending={'req1'}, finished_recving={'req2'}) - output2 = DummyModelRunnerOutput(finished_sending=None, - finished_recving=None) + output2 = DummyModelRunnerOutput(invalid_block_ids={1}) aggregated = aggregator.aggregate([output1, output2]) @@ -42,11 +54,10 @@ def test_aggregate_workers_output(): aggregated = aggregated.kv_connector_output assert aggregated.finished_sending is None assert aggregated.finished_recving is None + assert aggregated.invalid_block_ids == {1} - output1 = DummyModelRunnerOutput(finished_sending=None, - finished_recving=None) - output2 = DummyModelRunnerOutput(finished_sending={'req1'}, - finished_recving=None) + output1 = DummyModelRunnerOutput(invalid_block_ids={2}) + output2 = DummyModelRunnerOutput(finished_sending={'req1'}) aggregated = aggregator.aggregate([output1, output2]) @@ -54,11 +65,11 @@ def test_aggregate_workers_output(): aggregated = aggregated.kv_connector_output assert aggregated.finished_sending == {'req1'} assert aggregated.finished_recving is None + assert aggregated.invalid_block_ids == {2} - output1 = DummyModelRunnerOutput(finished_sending=None, - finished_recving=None) - output2 = DummyModelRunnerOutput(finished_sending={'req1'}, - finished_recving={'req2'}) + output1 = DummyModelRunnerOutput(invalid_block_ids={3, 4}) + output2 = DummyModelRunnerOutput(finished_recving={'req2'}, + invalid_block_ids={4, 5}) aggregated = aggregator.aggregate([output1, output2]) @@ -66,6 +77,7 @@ def test_aggregate_workers_output(): aggregated = aggregated.kv_connector_output assert aggregated.finished_sending is None assert aggregated.finished_recving == {'req2'} + assert aggregated.invalid_block_ids == {3, 4, 5} def test_async_aggregate_workers_output(): @@ -75,10 +87,26 @@ def test_async_aggregate_workers_output(): future2: Future[DummyModelRunnerOutput] = Future() result_future = aggregator.async_aggregate([future1, future2]) + output1 = DummyModelRunnerOutput() + output2 = DummyModelRunnerOutput() + future1.set_result(output1) + future2.set_result(output2) + + assert result_future.done() + aggregated = result_future.result() + assert aggregated is output1 + aggregated = aggregated.kv_connector_output + assert aggregated.finished_sending is None + assert aggregated.finished_recving is None + assert not aggregated.invalid_block_ids + + future1 = Future() + future2 = Future() + result_future = aggregator.async_aggregate([future1, future2]) + output1 = DummyModelRunnerOutput(finished_sending={'req1'}, finished_recving={'req2'}) - output2 = DummyModelRunnerOutput(finished_sending=None, - finished_recving=None) + output2 = DummyModelRunnerOutput(invalid_block_ids={1}) future1.set_result(output1) future2.set_result(output2) @@ -88,15 +116,14 @@ def test_async_aggregate_workers_output(): aggregated = aggregated.kv_connector_output assert aggregated.finished_sending is None assert aggregated.finished_recving is None + assert aggregated.invalid_block_ids == {1} future1 = Future() future2 = Future() result_future = aggregator.async_aggregate([future1, future2]) - output1 = DummyModelRunnerOutput(finished_sending=None, - finished_recving=None) - output2 = DummyModelRunnerOutput(finished_sending={'req1'}, - finished_recving=None) + output1 = DummyModelRunnerOutput(invalid_block_ids={2}) + output2 = DummyModelRunnerOutput(finished_sending={'req1'}) future1.set_result(output1) future2.set_result(output2) @@ -106,15 +133,15 @@ def test_async_aggregate_workers_output(): aggregated = aggregated.kv_connector_output assert aggregated.finished_sending == {'req1'} assert aggregated.finished_recving is None + assert aggregated.invalid_block_ids == {2} future1 = Future() future2 = Future() result_future = aggregator.async_aggregate([future1, future2]) - output1 = DummyModelRunnerOutput(finished_sending=None, - finished_recving=None) - output2 = DummyModelRunnerOutput(finished_sending={'req1'}, - finished_recving={'req2'}) + output1 = DummyModelRunnerOutput(invalid_block_ids={3, 4}) + output2 = DummyModelRunnerOutput(finished_recving={'req2'}, + invalid_block_ids={4, 5}) future1.set_result(output1) future2.set_result(output2) @@ -124,3 +151,4 @@ def test_async_aggregate_workers_output(): aggregated = aggregated.kv_connector_output assert aggregated.finished_sending is None assert aggregated.finished_recving == {'req2'} + assert aggregated.invalid_block_ids == {3, 4, 5} diff --git a/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py b/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py index 79b26cf57acd..e2c4d05bba71 100644 --- a/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py +++ b/tests/v1/kv_connector/unit/test_remote_decode_lifecycle.py @@ -92,7 +92,7 @@ def test_basic_lifecycle(): # (3b): execute_model() model_runner_output = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT) model_runner_output.kv_connector_output = KVConnectorOutput( - finished_sending=[request_id]) + finished_sending={request_id}) # (3c): update_from_output() scheduler.update_from_output(scheduler_output, model_runner_output) @@ -139,7 +139,7 @@ def test_short_prompt_lifecycle(): scheduler_output = scheduler.schedule() # Use create_model_runner_output to pass kv_connector_output along model_runner_output = create_model_runner_output( - reqs=[request], finished_sending=[request.request_id]) + reqs=[request], finished_sending={request.request_id}) scheduler.update_from_output(scheduler_output, model_runner_output) assert_scheduler_empty(scheduler) @@ -195,6 +195,6 @@ def test_prefix_cache_lifecycle(): scheduler_output = scheduler.schedule() model_runner_output = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT) model_runner_output.kv_connector_output = KVConnectorOutput( - finished_sending=[request_remote.request_id]) + finished_sending={request_remote.request_id}) scheduler.update_from_output(scheduler_output, model_runner_output) assert_scheduler_empty(scheduler) diff --git a/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py b/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py index 207cf64f4e3e..196483d76e87 100644 --- a/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py +++ b/tests/v1/kv_connector/unit/test_remote_prefill_lifecycle.py @@ -78,7 +78,7 @@ def test_basic_lifecycle(): # (2b): forward(): request finishes recv. model_runner_output = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT) model_runner_output.kv_connector_output = KVConnectorOutput( - finished_recving=[request_id]) + finished_recving={request_id}) # (2c): update_from_output(): engine_core_outputs = scheduler.update_from_output(scheduler_output, @@ -197,7 +197,7 @@ def test_interleaved_lifecycle(): model_runner_output = create_model_runner_output( [request_local_a, request_local_b], - finished_recving=[request_remote.request_id]) + finished_recving={request_remote.request_id}) scheduler.update_from_output(scheduler_output, model_runner_output) # STEP 5: RECVed KVs are sent to ModelRunner. @@ -246,16 +246,16 @@ def test_no_spurious_prefix_caching(): request_id=1, block_size=BLOCK_SIZE, num_tokens=NUM_TOKENS, + common_prefix_len=NUM_TOKENS, do_remote_prefill=True, - use_all_1s_for_prompt_tokens=True, ) request_local = create_request( request_id=2, block_size=BLOCK_SIZE, num_tokens=NUM_TOKENS, + common_prefix_len=NUM_TOKENS, do_remote_prefill=False, - use_all_1s_for_prompt_tokens=True, ) # Schedule the remote prefill request. This should not @@ -322,7 +322,7 @@ def test_full_block_prompt(): scheduler_output = scheduler.schedule() model_runner_output = copy.deepcopy(EMPTY_MODEL_RUNNER_OUTPUT) model_runner_output.kv_connector_output = KVConnectorOutput( - finished_recving=[request_id]) + finished_recving={request_id}) scheduler.update_from_output(scheduler_output, model_runner_output) assert len(scheduler.waiting) == 1 assert (request_id in scheduler.finished_recving_kv_req_ids) @@ -402,7 +402,7 @@ def test_cannot_schedule_after_recv(): # Step 3: finish recving (5 blocks in use) scheduler_output = scheduler.schedule() model_runner_output = create_model_runner_output( - reqs=[request_normal], finished_recving=[request_remote.request_id]) + reqs=[request_normal], finished_recving={request_remote.request_id}) scheduler.update_from_output(scheduler_output, model_runner_output) assert len(scheduler.running) == 1 assert len(scheduler.waiting) == 1 @@ -516,7 +516,7 @@ def test_cannot_recv(): # Step 5: finish recving (5 blocks in use) scheduler_output = scheduler.schedule() model_runner_output = create_model_runner_output( - reqs=[], finished_recving=[request_remote.request_id]) + reqs=[], finished_recving={request_remote.request_id}) scheduler.update_from_output(scheduler_output, model_runner_output) assert len(scheduler.running) == 0 assert len(scheduler.waiting) == 1 diff --git a/tests/v1/kv_connector/unit/utils.py b/tests/v1/kv_connector/unit/utils.py index de52668e3dcf..3928cdc37b9d 100644 --- a/tests/v1/kv_connector/unit/utils.py +++ b/tests/v1/kv_connector/unit/utils.py @@ -2,6 +2,7 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import tempfile from collections import defaultdict +from itertools import count from typing import Any, Callable, Optional import torch @@ -61,12 +62,15 @@ def create_vllm_config( max_num_seqs: int = 16, max_num_batched_tokens: int = 64, block_size: int = 16, + max_model_len: int = 10000, + enable_chunked_prefill: bool = True, ) -> VllmConfig: """Initialize VllmConfig For Testing.""" scheduler_config = SchedulerConfig( max_num_seqs=max_num_seqs, max_num_batched_tokens=max_num_batched_tokens, - max_model_len=max_num_batched_tokens, + max_model_len=max_model_len, + enable_chunked_prefill=enable_chunked_prefill, ) model_config = ModelConfig( model=model, @@ -117,19 +121,27 @@ def create_scheduler( ) +_request_count = count(1) _none_hash_initialized = False -def create_request(request_id: int, - num_tokens: int = 10, - max_tokens: int = 16, - do_remote_decode: bool = False, - do_remote_prefill: bool = False, - use_all_1s_for_prompt_tokens: bool = False, - num_remote_blocks: int = 3, - block_size: int = 16, - hash_fn: Callable = sha256) -> Request: +def create_request( + request_id: Optional[int] = None, + num_tokens: int = 10, + common_prefix_len=0, + max_tokens: int = 16, + do_remote_decode: bool = False, + do_remote_prefill: bool = False, + num_remote_blocks: int = 3, + block_size: int = 16, + hash_fn: Callable = sha256, +) -> Request: """Make dummy request for testing.""" + assert num_tokens >= common_prefix_len >= 0 + + if request_id is None: + request_id = next(_request_count) + global _none_hash_initialized if not _none_hash_initialized: init_none_hash(hash_fn) @@ -153,10 +165,9 @@ def create_request(request_id: int, max_tokens = 1 if do_remote_decode else max_tokens sampling_params = SamplingParams(max_tokens=max_tokens) - if use_all_1s_for_prompt_tokens: - prompt_token_ids = [1] * num_tokens - else: - prompt_token_ids = [i * request_id for i in range(num_tokens)] + common_prefix = [1] * common_prefix_len if common_prefix_len > 0 else [] + suffix = [i * request_id for i in range(num_tokens - common_prefix_len)] + prompt_token_ids = common_prefix + suffix req = Request( request_id=f"id-{request_id}", @@ -173,8 +184,9 @@ def create_request(request_id: int, def create_model_runner_output( reqs: list[Request], - finished_sending: Optional[list[str]] = None, - finished_recving: Optional[list[str]] = None, + finished_sending: Optional[set[str]] = None, + finished_recving: Optional[set[str]] = None, + invalid_block_ids: Optional[set[int]] = None, use_eos: bool = False, token_id: int = 0, ) -> ModelRunnerOutput: @@ -189,10 +201,11 @@ def create_model_runner_output( sampled_token_ids = [[sampled_token] for _ in req_ids] kv_connector_output = None if ( - finished_sending is None - and finished_recving is None) else KVConnectorOutput( + finished_sending is None and finished_recving is None + and invalid_block_ids is None) else KVConnectorOutput( finished_sending=finished_sending, finished_recving=finished_recving, + invalid_block_ids=invalid_block_ids or set(), ) # Make output data structure. diff --git a/tests/v1/worker/test_gpu_model_runner.py b/tests/v1/worker/test_gpu_model_runner.py index 49a7a61e1889..23d7ce4cefa3 100644 --- a/tests/v1/worker/test_gpu_model_runner.py +++ b/tests/v1/worker/test_gpu_model_runner.py @@ -250,6 +250,7 @@ def test_update_states_request_resumed(model_runner, dist_init): new_token_ids=[[]], new_block_ids=([[0]], ), num_computed_tokens=[0], + num_output_tokens=[0], ) scheduler_output = SchedulerOutput( diff --git a/vllm/distributed/kv_transfer/kv_connector/utils.py b/vllm/distributed/kv_transfer/kv_connector/utils.py index efa4c9abf47f..103fba41fcb4 100644 --- a/vllm/distributed/kv_transfer/kv_connector/utils.py +++ b/vllm/distributed/kv_transfer/kv_connector/utils.py @@ -117,7 +117,7 @@ def get_kv_connector_cache_layout(): class KVOutputAggregator: - """Utility class to aggregate the output of all workers into a single + """Utility class to aggregate the output of all workers into a single output corresponding to Rank 0 for scheduler.""" def __init__(self, world_size: int): @@ -143,6 +143,7 @@ def update_finished_set(req_ids: Optional[set[str]], finished_sending = set[str]() finished_recving = set[str]() aggregated_kv_connector_stats = None + invalid_block_ids = set[int]() for model_runner_output in outputs: output = model_runner_output.kv_connector_output if not output: @@ -165,6 +166,8 @@ def update_finished_set(req_ids: Optional[set[str]], aggregated_kv_connector_stats = \ aggregated_kv_connector_stats.aggregate(kv_connector_stats) + invalid_block_ids |= output.invalid_block_ids + # select output of the worker specified by output_rank output = outputs[output_rank] @@ -172,6 +175,7 @@ def update_finished_set(req_ids: Optional[set[str]], finished_sending=finished_sending or None, finished_recving=finished_recving or None, kv_connector_stats=aggregated_kv_connector_stats or None, + invalid_block_ids=invalid_block_ids, ) return output diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/base.py b/vllm/distributed/kv_transfer/kv_connector/v1/base.py index 184d0a62f2c3..e3b4bcbfd1e6 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/base.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/base.py @@ -229,6 +229,26 @@ def get_finished( """ return None, None + def get_block_ids_with_load_errors(self) -> set[int]: + """ + Get the set of block IDs that failed to load. + + Returns: + Set of block IDs that encountered load errors. + Empty set if no load errors occurred. + + Notes: + - Applies to both sync- and async-loading requests. + - Async loading: failed blocks may be reported in any forward pass + up to and including the pass where the request ID is returned by + `get_finished()`. Even if failures occur, the request must still + be reported via `get_finished()`, and the failed block IDs must + appear here no later than that same pass. + - Sync loading: failed blocks should be reported in the forward + pass in which they are detected. + """ + return set() + def shutdown(self): """ Shutdown the connector. This is called when the worker process @@ -264,14 +284,21 @@ def get_num_new_matched_tokens( Returns: A tuple with the following elements: - - An optional number of tokens that can be loaded from the - external KV cache beyond what is already computed. + - An optional number of tokens that can be loaded from the + external KV cache beyond what is already computed. If None, it means that the connector needs more time to determine the number of matched tokens, and the scheduler should query for this request again later. - `True` if external KV cache tokens will be loaded asynchronously (between scheduler steps). Must be 'False' if the first element is 0. + + Notes: + The connector should only consider the largest prefix of prompt- + tokens for which KV cache is actually available at the time of the + call. If the cache cannot be loaded for some tokens (e.g., due to + connectivity issues or eviction), those tokens must not be taken + into account. """ pass diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py index 6836a71e58d6..a7713ba326fc 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py @@ -189,6 +189,12 @@ def get_finished( return finished_sending or None, finished_recving or None + def get_block_ids_with_load_errors(self) -> set[int]: + agg_block_ids: set[int] = set() + for c in self._connectors: + agg_block_ids |= c.get_block_ids_with_load_errors() + return agg_block_ids + # ============================== # Scheduler-side methods # ============================== diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py index 48fa1a82c677..c9949d81465c 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import hashlib import os -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, Optional import safetensors @@ -55,10 +55,7 @@ def make_meta(token_ids: list[int], block_ids: list[int], block_size: int, @dataclass class SharedStorageConnectorMetadata(KVConnectorMetadata): - requests: list[ReqMeta] - - def __init__(self): - self.requests = [] + requests: list[ReqMeta] = field(default_factory=list) def add_request( self, diff --git a/vllm/v1/core/block_pool.py b/vllm/v1/core/block_pool.py index 3cc738304821..617a724a1ad2 100644 --- a/vllm/v1/core/block_pool.py +++ b/vllm/v1/core/block_pool.py @@ -211,7 +211,7 @@ def cache_full_blocks( block_size: Number of tokens in each block. kv_cache_group_id: The id of the KV cache group. """ - if num_cached_blocks == num_full_blocks: + if num_cached_blocks >= num_full_blocks: return new_full_blocks = blocks[num_cached_blocks:num_full_blocks] assert len(request.block_hashes) >= num_full_blocks diff --git a/vllm/v1/core/sched/output.py b/vllm/v1/core/sched/output.py index 209fc2a4404f..6874e713aff3 100644 --- a/vllm/v1/core/sched/output.py +++ b/vllm/v1/core/sched/output.py @@ -101,6 +101,7 @@ class CachedRequestData: new_token_ids: list[list[int]] new_block_ids: list[Optional[tuple[list[int], ...]]] num_computed_tokens: list[int] + num_output_tokens: list[int] @property def num_reqs(self) -> int: @@ -114,6 +115,7 @@ def make_empty(cls) -> CachedRequestData: new_token_ids=[], new_block_ids=[], num_computed_tokens=[], + num_output_tokens=[], ) diff --git a/vllm/v1/core/sched/scheduler.py b/vllm/v1/core/sched/scheduler.py index 8d6ea887142d..d4be1b06b3b2 100644 --- a/vllm/v1/core/sched/scheduler.py +++ b/vllm/v1/core/sched/scheduler.py @@ -133,6 +133,7 @@ def __init__( # KV Connector: requests in process of async KV loading or recving self.finished_recving_kv_req_ids: set[str] = set() + self.failed_recving_kv_req_ids: set[str] = set() # Encoder-related. # Calculate encoder cache size if applicable @@ -671,6 +672,7 @@ def _make_cached_request_data( new_token_ids: list[list[int]] = [] new_block_ids: list[Optional[tuple[list[int], ...]]] = [] num_computed_tokens: list[int] = [] + num_output_tokens: list[int] = [] use_connector = self.connector is not None for req in itertools.chain(running_reqs, resumed_reqs): @@ -695,6 +697,7 @@ def _make_cached_request_data( new_block_ids.append( req_to_new_blocks[req_id].get_block_ids(allow_none=True)) num_computed_tokens.append(req.num_computed_tokens) + num_output_tokens.append(len(req.output_token_ids)) # Because resumed_reqs is usually empty, it is more efficient to do # in-place appending so that we don't need to allocate a new list. resumed_from_preemption = [False] * len(running_reqs) @@ -706,6 +709,7 @@ def _make_cached_request_data( new_token_ids=new_token_ids, new_block_ids=new_block_ids, num_computed_tokens=num_computed_tokens, + num_output_tokens=num_output_tokens, ) def _try_schedule_encoder_inputs( @@ -878,6 +882,14 @@ def update_from_output( kv_connector_stats = (kv_connector_output.kv_connector_stats if kv_connector_output else None) + failed_kv_load_req_ids = None + if kv_connector_output and kv_connector_output.invalid_block_ids: + # These blocks contain externally computed tokens that failed to + # load. Identify affected requests and adjust their computed token + # count to trigger recomputation of the invalid blocks. + failed_kv_load_req_ids = self._handle_invalid_blocks( + kv_connector_output.invalid_block_ids) + # NOTE(woosuk): As len(num_scheduled_tokens) can be up to 1K or more, # the below loop can be a performance bottleneck. We should do our best # to avoid expensive operations inside the loop. @@ -885,6 +897,9 @@ def update_from_output( stopped_preempted_reqs: set[Request] = set() for req_id, num_tokens_scheduled in num_scheduled_tokens.items(): assert num_tokens_scheduled > 0 + if failed_kv_load_req_ids and req_id in failed_kv_load_req_ids: + # Skip requests that were recovered from KV load failure + continue request = self.requests.get(req_id) if request is None: # The request is already finished. This can happen if the @@ -988,9 +1003,8 @@ def update_from_output( self.waiting.remove_requests(stopped_preempted_reqs) # KV Connector: update state for finished KV Transfers. - if model_runner_output.kv_connector_output: - self._update_from_kv_xfer_finished( - model_runner_output.kv_connector_output) + if kv_connector_output: + self._update_from_kv_xfer_finished(kv_connector_output) # Create EngineCoreOutputs for all clients that have requests with # outputs in this step. @@ -1252,18 +1266,33 @@ def _update_waiting_for_remote_kv(self, request: Request) -> bool: if request.request_id not in self.finished_recving_kv_req_ids: return False - # Now that the blocks are ready, actually cache them. - (block_ids, ) = self.kv_cache_manager.get_block_ids(request.request_id) - num_computed_tokens = len(block_ids) * self.block_size - # Handle the case where num request tokens less than one block. - num_computed_tokens = min(num_computed_tokens, request.num_tokens) - if num_computed_tokens == request.num_tokens: - num_computed_tokens -= 1 - # This will cache the blocks iff caching is enabled. - self.kv_cache_manager.cache_blocks(request, num_computed_tokens) + if request.request_id in self.failed_recving_kv_req_ids: + # Request had KV load failures; num_computed_tokens was already + # updated in _update_requests_with_invalid_blocks + if request.num_computed_tokens: + # Cache any valid computed tokens. + self.kv_cache_manager.cache_blocks(request, + request.num_computed_tokens) + else: + # No valid computed tokens, release allocated blocks. + # There may be a local cache hit on retry. + self.kv_cache_manager.free(request) - # Update the request state for scheduling. - request.num_computed_tokens = num_computed_tokens + self.failed_recving_kv_req_ids.remove(request.request_id) + else: + # Now that the blocks are ready, actually cache them. + (block_ids, ) = self.kv_cache_manager.get_block_ids( + request.request_id) + num_computed_tokens = len(block_ids) * self.block_size + # Handle the case where num request tokens less than one block. + num_computed_tokens = min(num_computed_tokens, request.num_tokens) + if num_computed_tokens == request.num_tokens: + num_computed_tokens -= 1 + # This will cache the blocks iff caching is enabled. + self.kv_cache_manager.cache_blocks(request, num_computed_tokens) + + # Update the request state for scheduling. + request.num_computed_tokens = num_computed_tokens # Return that we are ready. self.finished_recving_kv_req_ids.remove(request.request_id) @@ -1296,3 +1325,134 @@ def _update_from_kv_xfer_finished(self, "but the request is already freed.", req_id) else: self._free_blocks(self.requests[req_id]) + + def _update_requests_with_invalid_blocks( + self, requests: Iterable[Request], + invalid_block_ids: set[int]) -> tuple[set[str], int]: + """ + Identify and update requests affected by invalid KV cache blocks. + + This method scans the given requests, detects those with invalid blocks + and adjusts their `num_computed_tokens` to the longest valid prefix. + For observability, it also accumulates the total number of tokens that + will need to be recomputed across all affected requests. + + Args: + requests: The set of requests to scan for invalid blocks. + invalid_block_ids: IDs of invalid blocks. + + Returns: + tuple: + - affected_req_ids (set[str]): IDs of requests impacted by + invalid blocks. + - total_affected_tokens (int): Total number of tokens that must + be recomputed across all affected requests (for observability). + """ + affected_req_ids: set[str] = set() + total_affected_tokens = 0 + # If a block is invalid and shared by multiple requests in the batch, + # these requests must be rescheduled, but only the first will recompute + # it. This set tracks blocks already marked for recomputation. + marked_invalid_block_ids: set[int] = set() + for request in requests: + is_affected = False + marked_invalid_block = False + req_id = request.request_id + # TODO (davidb): add support for hybrid memory allocator + (req_block_ids, ) = self.kv_cache_manager.get_block_ids(req_id) + # We iterate only over blocks that may contain externally computed + # tokens + if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS: + # Async loading. If num_computed_tokens is set it implies we + # already processed some block failures for it in a prior step + req_num_computed_tokens = ( + request.num_computed_tokens if req_id + in self.failed_recving_kv_req_ids else len(req_block_ids) * + self.block_size) + else: + # Sync loading. num_computed_tokens includes new tokens + req_num_computed_tokens = request.num_cached_tokens + + req_num_computed_blocks = (req_num_computed_tokens + + self.block_size - 1) // self.block_size + for idx, block_id in zip(range(req_num_computed_blocks), + req_block_ids): + + if block_id not in invalid_block_ids: + continue + + is_affected = True + + if block_id in marked_invalid_block_ids: + # This invalid block is shared with a previous request + # and was already marked for recomputation. + # This means this request can still consider this block + # as computed when rescheduled. + # Currently this only applies to sync loading; Async + # loading does not yet support block sharing + continue + + marked_invalid_block_ids.add(block_id) + + if marked_invalid_block: + # This request has already marked an invalid block for + # recomputation and updated its num_computed_tokens. + continue + + marked_invalid_block = True + # Truncate the computed tokens at the first failed block + request.num_computed_tokens = idx * self.block_size + total_affected_tokens += (req_num_computed_tokens - + request.num_computed_tokens) + + if is_affected: + if not marked_invalid_block: + # All invalid blocks of this request are shared with + # previous requests and will be recomputed by them. + # Revert to considering only cached tokens as computed. + # Currently this only applies to sync loading; Async + # loading does not yet support block sharing + total_affected_tokens += (request.num_computed_tokens - + request.num_cached_tokens) + request.num_computed_tokens = request.num_cached_tokens + + affected_req_ids.add(request.request_id) + + return (affected_req_ids, total_affected_tokens) + + def _handle_invalid_blocks(self, invalid_block_ids: set[int]) -> set[str]: + total_requests_to_reschedule = 0 + total_tokens_to_reschedule = 0 + + # --- Handle async KV loads (WAITING_FOR_REMOTE_KVS) --- + async_load_reqs = ( + req for req in self.waiting + if req.status == RequestStatus.WAITING_FOR_REMOTE_KVS) + async_affected_req_ids, num_tokens_to_reschedule = ( + self._update_requests_with_invalid_blocks(async_load_reqs, + invalid_block_ids)) + + total_requests_to_reschedule += len(async_affected_req_ids) + total_tokens_to_reschedule += num_tokens_to_reschedule + + # Mark requests with async KV load failures; they will be rescheduled + # once loading completes + self.failed_recving_kv_req_ids |= async_affected_req_ids + + # --- Handle sync KV loads (running requests) --- + sync_affected_req_ids, num_tokens_to_reschedule = ( + self._update_requests_with_invalid_blocks(self.running, + invalid_block_ids)) + + total_requests_to_reschedule += len(sync_affected_req_ids) + total_tokens_to_reschedule += num_tokens_to_reschedule + + if total_requests_to_reschedule: + logger.warning( + "Recovered from KV load failure: " + "%d request(s) rescheduled (%d tokens affected).", + total_requests_to_reschedule, total_tokens_to_reschedule) + + # Return the IDs of affected running requests to skip in + # update_from_output. + return sync_affected_req_ids diff --git a/vllm/v1/core/single_type_kv_cache_manager.py b/vllm/v1/core/single_type_kv_cache_manager.py index e889f7804e84..4ecd9c8157e2 100644 --- a/vllm/v1/core/single_type_kv_cache_manager.py +++ b/vllm/v1/core/single_type_kv_cache_manager.py @@ -142,6 +142,9 @@ def cache_blocks(self, request: Request, num_tokens: int) -> None: num_cached_blocks = self.num_cached_block[request.request_id] num_full_blocks = num_tokens // self.block_size + if num_cached_blocks >= num_full_blocks: + return + self.block_pool.cache_full_blocks( request=request, blocks=self.req_to_blocks[request.request_id], diff --git a/vllm/v1/outputs.py b/vllm/v1/outputs.py index 01f3676abd92..d15cdf365962 100644 --- a/vllm/v1/outputs.py +++ b/vllm/v1/outputs.py @@ -2,7 +2,7 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from abc import ABC, abstractmethod -from dataclasses import dataclass +from dataclasses import dataclass, field from typing import TYPE_CHECKING, NamedTuple, Optional, Union import torch @@ -87,10 +87,13 @@ class KVConnectorOutput: finished_sending: Optional[set[str]] = None finished_recving: Optional[set[str]] = None kv_connector_stats: Optional["KVConnectorStats"] = None + # IDs of externally computed KV blocks that failed to load. + # Requests referencing these blocks should be rescheduled to recompute them. + invalid_block_ids: set[int] = field(default_factory=set) def is_empty(self): return (not self.finished_sending and not self.finished_recving - and not self.kv_connector_stats) + and not self.kv_connector_stats and not self.invalid_block_ids) # ModelRunnerOutput is serialized and sent to the scheduler process. diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index f4c28dc24d70..9941cacae8ab 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -634,8 +634,10 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> None: num_computed_tokens = req_data.num_computed_tokens[i] new_block_ids = req_data.new_block_ids[i] resumed_from_preemption = req_data.resumed_from_preemption[i] + num_output_tokens = req_data.num_output_tokens[i] # Update the cached states. + req_state.num_computed_tokens = num_computed_tokens if not is_last_rank: @@ -653,6 +655,21 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> None: elif num_new_tokens > 0: req_state.output_token_ids.extend( new_token_ids[-num_new_tokens:]) + elif num_output_tokens < len(req_state.output_token_ids): + # Some output tokens were discarded due to a sync-KV-load + # failure. Align the cached state. + del req_state.output_token_ids[num_output_tokens:] + + req_index = self.input_batch.req_id_to_index.get(req_id) + if req_index is not None: + old_end_idx = self.input_batch.num_tokens_no_spec[ + req_index] + end_idx = self.input_batch.num_prompt_tokens[ + req_index] + num_output_tokens + self.input_batch.num_tokens[req_index] = end_idx + self.input_batch.num_tokens_no_spec[req_index] = end_idx + self.input_batch.is_token_ids[req_index, + end_idx:old_end_idx] = False # Update the block IDs. if not resumed_from_preemption: diff --git a/vllm/v1/worker/gpu_worker.py b/vllm/v1/worker/gpu_worker.py index 8c75e8914857..a135a594ac6f 100644 --- a/vllm/v1/worker/gpu_worker.py +++ b/vllm/v1/worker/gpu_worker.py @@ -464,8 +464,7 @@ def execute_model( # In case of PP with kv transfer, we need to pass through the # kv_connector_output - if (not kv_connector_output.finished_sending - and not kv_connector_output.finished_recving): + if kv_connector_output.is_empty(): return EMPTY_MODEL_RUNNER_OUTPUT output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT) diff --git a/vllm/v1/worker/kv_connector_model_runner_mixin.py b/vllm/v1/worker/kv_connector_model_runner_mixin.py index 7eaff924ecc1..cdc0d317fffb 100644 --- a/vllm/v1/worker/kv_connector_model_runner_mixin.py +++ b/vllm/v1/worker/kv_connector_model_runner_mixin.py @@ -75,8 +75,7 @@ def kv_connector_no_forward(scheduler_output: "SchedulerOutput", scheduler_output, wait_for_save=False) as kv_connector_output: pass - if (not kv_connector_output.finished_sending - and not kv_connector_output.finished_recving): + if kv_connector_output.is_empty(): return EMPTY_MODEL_RUNNER_OUTPUT output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT) @@ -120,6 +119,8 @@ def _get_kv_connector_output( output.finished_sending, output.finished_recving = ( kv_connector.get_finished(scheduler_output.finished_req_ids)) + output.invalid_block_ids = ( + kv_connector.get_block_ids_with_load_errors()) output.kv_connector_stats = KVConnectorModelRunnerMixin.\ get_kv_connector_stats() From 92e28e74409cb4de060a9dc4e3450887744ffc92 Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Tue, 30 Sep 2025 23:07:06 +0100 Subject: [PATCH 118/166] Add explicit pooling classes for the Transformers backend (#25322) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Signed-off-by: Isotr0py Co-authored-by: Isotr0py --- tests/models/registry.py | 3 +- tests/models/test_transformers.py | 99 ++++----- vllm/config/model.py | 33 ++- vllm/config/utils.py | 13 ++ vllm/model_executor/models/registry.py | 5 +- vllm/model_executor/models/transformers.py | 81 +------ .../models/transformers_pooling.py | 200 ++++++++++++++++++ 7 files changed, 296 insertions(+), 138 deletions(-) create mode 100644 vllm/model_executor/models/transformers_pooling.py diff --git a/tests/models/registry.py b/tests/models/registry.py index b7a2514d8bc0..1068f97cb5a8 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -657,7 +657,8 @@ def check_available_online( } _TRANSFORMERS_BACKEND_MODELS = { - "TransformersModel": _HfExamplesInfo("Qwen/Qwen3-Embedding-0.6B"), + "TransformersEmbeddingModel": _HfExamplesInfo("BAAI/bge-base-en-v1.5", min_transformers_version="4.57.0.dev0"), # noqa: E501 + "TransformersForSequenceClassification": _HfExamplesInfo("papluca/xlm-roberta-base-language-detection", min_transformers_version="4.57.0.dev0"), # noqa: E501 "TransformersForCausalLM": _HfExamplesInfo("hmellor/Ilama-3.2-1B", trust_remote_code=True), # noqa: E501 "TransformersForMultimodalLM": _HfExamplesInfo("BAAI/Emu3-Chat-hf"), } diff --git a/tests/models/test_transformers.py b/tests/models/test_transformers.py index e4b5e7c24453..733ac8de67a3 100644 --- a/tests/models/test_transformers.py +++ b/tests/models/test_transformers.py @@ -9,9 +9,16 @@ from ..conftest import HfRunner, VllmRunner from ..utils import multi_gpu_test, prep_prompts +from .registry import HF_EXAMPLE_MODELS from .utils import check_embeddings_close, check_logprobs_close +def get_model(arch: str) -> str: + model_info = HF_EXAMPLE_MODELS.get_hf_info(arch) + model_info.check_transformers_version(on_fail="skip") + return model_info.default + + def check_implementation( runner_ref: type[Union[HfRunner, VllmRunner]], runner_test: type[VllmRunner], @@ -170,71 +177,47 @@ def test_embed_loading(vllm_runner, model): @pytest.mark.parametrize( - "model", - [ - # Encoder model - "BAAI/bge-base-en-v1.5", - ]) -def test_embed_correctness(hf_runner, vllm_runner, example_prompts, model): - import transformers - from packaging.version import Version - installed = Version(transformers.__version__) - required = Version("4.57.0.dev0") - if installed < required: - pytest.skip("Encoder models with the Transformers backend require " - f"transformers>={required}, but got {installed}") - - with vllm_runner(model, max_model_len=512, - model_impl="transformers") as vllm_model: + "arch", + ["TransformersEmbeddingModel", "TransformersForSequenceClassification"]) +def test_pooling(hf_runner, vllm_runner, example_prompts, arch): + model = get_model(arch) + + vllm_kwargs = dict( + max_model_len=None, + model_impl="transformers", + compilation_config=dict(cudagraph_capture_sizes=[8]), + ) + + hf_kwargs = dict() + if arch == "TransformersEmbeddingModel": + hf_kwargs["is_sentence_transformer"] = True + elif arch == "TransformersForSequenceClassification": + from transformers import AutoModelForSequenceClassification + hf_kwargs["auto_cls"] = AutoModelForSequenceClassification + + # The example_prompts has ending "\n", for example: + # "Write a short story about a robot that dreams for the first time.\n" + # sentence_transformers will strip the input texts, see: + # https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159 + # This makes the input_ids different between hf_model and vllm_model. + # So we need to strip the input texts to avoid test failing. + example_prompts = [str(s).strip() for s in example_prompts] + + with (vllm_runner(model, **vllm_kwargs) as + vllm_model, hf_runner(model, **hf_kwargs) as hf_model): model_config = vllm_model.llm.llm_engine.model_config assert model_config.using_transformers_backend() - vllm_outputs = vllm_model.embed(example_prompts) - - with hf_runner(model, is_sentence_transformer=True) as hf_model: - hf_outputs = hf_model.encode(example_prompts) + if arch == "TransformersEmbeddingModel": + vllm_outputs = vllm_model.embed(example_prompts) + hf_outputs = hf_model.encode(example_prompts) + elif arch == "TransformersForSequenceClassification": + vllm_outputs = vllm_model.classify(example_prompts) + hf_outputs = hf_model.classify(example_prompts) check_embeddings_close( embeddings_0_lst=hf_outputs, embeddings_1_lst=vllm_outputs, name_0="hf", name_1="vllm", - tol=1e-2, ) - - -@pytest.mark.parametrize( - "model", - ["jason9693/Qwen2.5-1.5B-apeach"], -) -@pytest.mark.parametrize("dtype", ["float"]) -def test_classify( - hf_runner, - vllm_runner, - example_prompts, - model: str, - dtype: str, -) -> None: - import torch - from transformers import AutoModelForSequenceClassification - - with vllm_runner(model, - max_model_len=512, - dtype=dtype, - model_impl="transformers") as vllm_model: - model_config = vllm_model.llm.llm_engine.model_config - assert model_config.using_transformers_backend() - - vllm_outputs = vllm_model.classify(example_prompts) - - with hf_runner(model, - dtype=dtype, - auto_cls=AutoModelForSequenceClassification) as hf_model: - hf_outputs = hf_model.classify(example_prompts) - - for hf_output, vllm_output in zip(hf_outputs, vllm_outputs): - hf_output = torch.tensor(hf_output) - vllm_output = torch.tensor(vllm_output) - - assert torch.allclose(hf_output, vllm_output, - 1e-3 if dtype == "float" else 1e-2) diff --git a/vllm/config/model.py b/vllm/config/model.py index c1392318dd8e..e9d5b58ff2c2 100644 --- a/vllm/config/model.py +++ b/vllm/config/model.py @@ -19,6 +19,7 @@ from vllm.config.multimodal import (MMCacheType, MMEncoderTPMode, MultiModalConfig) from vllm.config.pooler import PoolerConfig +from vllm.config.scheduler import RunnerType from vllm.config.utils import assert_hashable, config from vllm.logger import init_logger from vllm.platforms import current_platform @@ -40,7 +41,6 @@ import vllm.model_executor.models as me_models from vllm.config.load import LoadConfig from vllm.config.parallel import ParallelConfig - from vllm.config.scheduler import RunnerType from vllm.model_executor.layers.quantization import QuantizationMethods from vllm.v1.sample.logits_processor import LogitsProcessor else: @@ -52,13 +52,12 @@ "vllm.model_executor.models") LoadConfig = Any ParallelConfig = Any - RunnerType = Any QuantizationMethods = Any LogitsProcessor = Any logger = init_logger(__name__) -RunnerOption = Literal["auto", "generate", "pooling", "draft"] +RunnerOption = Literal["auto", RunnerType] ConvertType = Literal["none", "embed", "classify", "reward"] ConvertOption = Literal["auto", ConvertType] TaskOption = Literal["auto", "generate", "embedding", "embed", "classify", @@ -668,8 +667,28 @@ def validate_model_config_after(self: "ModelConfig") -> "ModelConfig": def _get_transformers_backend_cls(self) -> str: """Determine which Transformers backend class will be used if `model_impl` is set to `transformers` or `auto`.""" - if getattr(self, "runner_type", self.runner) == "pooling": - return "TransformersModel" + # Check if the architecture we're wrapping has defaults + runner = None + convert = None + if defaults := try_match_architecture_defaults(self.architectures[0]): + _, (runner, convert) = defaults + # Overwrite with user-specified values + if self.runner != "auto": + runner = self.runner + if self.convert not in {"auto", "none"}: + convert = self.convert + # Fall back to default values if still not set + if runner is None: + runner = "generate" + if convert in {None, "none"}: + convert = "embed" + # Resolve Transformers backend pooling classes + if runner == "pooling": + if convert == "embed": + return "TransformersEmbeddingModel" + if convert == "classify": + return "TransformersForSequenceClassification" + # Resolve Transformers backend generate classes if self.hf_config != self.hf_text_config: # If 'hf_text_config' is the same as 'hf_config'. If not, it is # probably a composite config, i.e. multimodal @@ -678,7 +697,9 @@ def _get_transformers_backend_cls(self) -> str: def using_transformers_backend(self) -> bool: """Check if the model is using the Transformers backend class.""" - return self.architecture == self._get_transformers_backend_cls() + used_cls = self._model_info.architecture + transformers_backend_cls = self._get_transformers_backend_cls() + return used_cls == transformers_backend_cls @property def registry(self): diff --git a/vllm/config/utils.py b/vllm/config/utils.py index 2da30cbf149c..d355ff3a9023 100644 --- a/vllm/config/utils.py +++ b/vllm/config/utils.py @@ -4,6 +4,7 @@ import ast import inspect import textwrap +from collections.abc import Iterable from dataclasses import MISSING, Field, field, fields, is_dataclass, replace from typing import TYPE_CHECKING, Any, Protocol, TypeVar @@ -52,6 +53,18 @@ def get_field(cls: ConfigType, name: str) -> Field: f"{cls.__name__}.{name} must have a default value or default factory.") +def getattr_iter(object: object, names: Iterable[str], default: Any) -> Any: + """ + A helper function that retrieves an attribute from an object which may + have multiple possible names. This is useful when fetching attributes from + arbitrary `transformers.PretrainedConfig` instances. + """ + for name in names: + if hasattr(object, name): + return getattr(object, name) + return default + + def contains_object_print(text: str) -> bool: """ Check if the text looks like a printed Python object, e.g. diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index 732181265a97..eb572dc30810 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -307,9 +307,10 @@ } _TRANSFORMERS_BACKEND_MODELS = { - "TransformersModel": ("transformers", "TransformersModel"), + "TransformersEmbeddingModel": ("transformers_pooling", "TransformersEmbeddingModel"), # noqa: E501 + "TransformersForSequenceClassification": ("transformers_pooling", "TransformersForSequenceClassification"), # noqa: E501 "TransformersForCausalLM": ("transformers", "TransformersForCausalLM"), - "TransformersForMultimodalLM": ("transformers", "TransformersForMultimodalLM"), # noqa: E501 + "TransformersForMultimodalLM": ("transformers", "TransformersForMultimodalLM"), # noqa: E501 } # yapf: enable diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py index 00d87f560e70..d168398aa182 100644 --- a/vllm/model_executor/models/transformers.py +++ b/vllm/model_executor/models/transformers.py @@ -31,6 +31,7 @@ from vllm.compilation.decorators import support_torch_compile from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, VllmConfig) +from vllm.config.utils import getattr_iter from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.distributed.utils import get_pp_indices from vllm.logger import init_logger @@ -486,10 +487,13 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): # Input embeddings if not isinstance(self.model.get_input_embeddings(), PPMissingLayer): + names = ("embedding_size", "hidden_size") + embedding_dim = getattr_iter(self.text_config, names, None) + assert embedding_dim is not None self.model.set_input_embeddings( VocabParallelEmbedding( self.text_config.vocab_size, - self.text_config.hidden_size, + embedding_dim=embedding_dim, org_num_embeddings=self.text_config.vocab_size, quant_config=self.quant_config, )) @@ -645,7 +649,9 @@ def create_attention_instances( attn_type=attn_type) return attention_instances - def init_parameters(self, module: nn.Module): + def init_parameters(self, + module: nn.Module, + dtype: Optional[torch.dtype] = None): """ If a `parameter` is on the `meta` device, then its parent `module` is the original module created by: @@ -659,11 +665,11 @@ def init_parameters(self, module: nn.Module): if param.device == torch.device("meta"): new_param = nn.Parameter( torch.empty_like(param.data, - dtype=self.model_config.dtype, + dtype=dtype or self.model_config.dtype, device=self.device_config.device)) setattr(module, name, new_param) for child in module.children(): - self.init_parameters(child) + self.init_parameters(child, dtype) def forward( self, @@ -712,73 +718,6 @@ def load_weights(self, weights: Iterable[tuple[str, return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) -@support_torch_compile(enable_if=can_enable_torch_compile) -class TransformersModel(TransformersBase): - hf_to_vllm_mapper = WeightsMapper( - orig_to_new_prefix={ - # Handle BERT-like models - "bert": "model", - # Add `model.` prefix for base model checkpoints - "": "model.", - # Remove `model.` prefix if it was already there - "model.model.": "model.", - # Pooling adapters will be adjacent to `model` - "model.pooler": "pooler", - "model.score": "score", - # Classifier adapter's classifier layer is renamed to score - "model.classifier": "score", - }, - orig_to_new_suffix={ - # Replace legacy suffixes used for norms - ".gamma": ".weight", - ".beta": ".bias", - }) - - def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): - super().__init__(vllm_config=vllm_config, prefix=prefix) - - # After creating a pooling model, `pooler` will be duplicated. - # The one inside `model` comes from the Transformers modelling code. - # The one after `model` is an adapter from vLLM. - # We want to use the adapter so we nullify the original pooler. - if getattr(self.model, "pooler", None) is not None: - self.skip_prefixes.append("pooler.") - self.model.pooler = torch.nn.Identity() - - # Some encoder models have the position_ids buffer in the checkpoint. - # vLLM will always pass position_ids as an argument, so we skip loading - # the buffer if it exists - self.skip_substrs.append("position_ids") - - # Some encoder models have the bias of the final classifier layer - # in the checkpoint. vLLM does not use this bias, so we skip loading - # it if it exists - self.skip_substrs.append("score.bias") - - def create_attention_instances( - self, attn_type: AttentionType = AttentionType.DECODER): - # TODO(hmellor): Better way to detect encoder models - # In encoder models, the attention layers will have `is_causal=False` - is_encoder = lambda m: not getattr(m, "is_causal", True) - # vLLM does not support encoder-decoder models, so if any encoder layer - # is found, we assume the whole model is an encoder model - if any(is_encoder(m) for m in self.model.modules()): - attn_type = AttentionType.ENCODER_ONLY - - # Check minimum transformers version for encoder models support - if attn_type == AttentionType.ENCODER_ONLY: - import transformers - from packaging.version import Version - installed = Version(transformers.__version__) - required = Version("4.57.0.dev0") - if installed < required: - raise ValueError( - "Encoder models with the Transformers backend require " - f"transformers>={required}, but got {installed}") - - return super().create_attention_instances(attn_type) - - @support_torch_compile(enable_if=can_enable_torch_compile) class TransformersForCausalLM(TransformersBase): diff --git a/vllm/model_executor/models/transformers_pooling.py b/vllm/model_executor/models/transformers_pooling.py new file mode 100644 index 000000000000..7e262ade156a --- /dev/null +++ b/vllm/model_executor/models/transformers_pooling.py @@ -0,0 +1,200 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +# Copyright 2024 The vLLM team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Wrapper around `transformers` models for pooling tasks.""" +from typing import Optional, Union + +import torch +from transformers import AutoModelForSequenceClassification + +from vllm.attention import AttentionType +from vllm.compilation.decorators import support_torch_compile +from vllm.config import VllmConfig +from vllm.model_executor.layers.pooler import (ClassifierPooler, CLSPool, + DispatchPooler, Pooler) +from vllm.sequence import IntermediateTensors + +from .interfaces_base import VllmModelForPooling +from .transformers import TransformersBase, can_enable_torch_compile +from .utils import WeightsMapper + + +class TransformersPoolingBase(TransformersBase, VllmModelForPooling): + hf_to_vllm_mapper = WeightsMapper( + # These are applied in order, so the order matters! + orig_to_new_prefix={ + # Handle BERT-like models + "roberta": "model", + "bert": "model", + # Add `model.` prefix for base model checkpoints + "": "model.", + # Remove `model.` prefix if it was already there + "model.model.": "model.", + # Classifier/scoring heads will be adjacent to `model` + "model.score": "classifier", + "model.classifier": "classifier", + }, + orig_to_new_suffix={ + # Replace legacy suffixes used for norms + ".gamma": ".weight", + ".beta": ".bias", + }) + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, prefix=prefix) + + # Skip unsupported/unwanted output embeddings layers + self.skip_prefixes.extend([ + "model.lm_head.", "model.predictions.", "model.qa_outputs.", + "model.embeddings_project.", "model.discriminator_predictions." + ]) + + # Some encoder models have the position_ids buffer in the checkpoint. + # vLLM will always pass position_ids as an argument, so we skip loading + # the buffer if it exists + self.skip_substrs.append("position_ids") + + # Some encoder models have the bias of the final classifier layer + # in the checkpoint. vLLM does not use this bias, so we skip loading + # it if it exists + self.skip_substrs.append("score.bias") + + # roberta-like models an extra padding in positions. + # FIXME(Isotr0py): This is quite hacky for roberta edge case, + # we should find a better way to handle this. + self.is_roberta = "roberta" in self.text_config.model_type + self.padding_idx = self.text_config.pad_token_id + + def create_attention_instances( + self, attn_type: AttentionType = AttentionType.DECODER): + # TODO(hmellor): Better way to detect encoder models + # In encoder models, the attention layers will have `is_causal=False` + is_encoder = lambda m: not getattr(m, "is_causal", True) + # vLLM does not support encoder-decoder models, so if any encoder layer + # is found, we assume the whole model is an encoder model + if any(is_encoder(m) for m in self.model.modules()): + attn_type = AttentionType.ENCODER_ONLY + + # Check minimum transformers version for encoder models support + if attn_type == AttentionType.ENCODER_ONLY: + import transformers + from packaging.version import Version + installed = Version(transformers.__version__) + required = Version("4.57.0.dev0") + if installed < required: + raise ValueError( + "Encoder models with the Transformers backend require " + f"transformers>={required}, but got {installed}") + + return super().create_attention_instances(attn_type) + + def forward( + self, + input_ids: Optional[torch.Tensor], + positions: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + if self.is_roberta: + # RoBERTa-specific positions padding + positions += self.padding_idx + 1 + return super().forward(input_ids=input_ids, + positions=positions, + intermediate_tensors=intermediate_tensors, + inputs_embeds=inputs_embeds) + + +@support_torch_compile(enable_if=can_enable_torch_compile) +class TransformersEmbeddingModel(TransformersPoolingBase): + default_pooling_type = "CLS" + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, prefix=prefix) + + pooler_config = vllm_config.model_config.pooler_config + assert pooler_config is not None + + self.pooler = DispatchPooler({ + "encode": Pooler.for_encode(pooler_config), + "embed": Pooler.for_embed(pooler_config), + }) + + +@support_torch_compile(enable_if=can_enable_torch_compile) +class TransformersForSequenceClassification(TransformersPoolingBase): + default_pooling_type = "CLS" + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__(vllm_config=vllm_config, prefix=prefix) + + pooler_config = vllm_config.model_config.pooler_config + assert pooler_config is not None + + # Certain information about the the model and classifier can only be + # inferred from the `ForSequenceClassification` class. Therefore, we + # instantiate it on the "meta" device to avoid allocating GPU memory. + with torch.device("meta"): + seq_cls_model = AutoModelForSequenceClassification.from_config( + self.config, + torch_dtype=self.model_config.dtype, + trust_remote_code=self.model_config.trust_remote_code, + ) + + # When used for sequence classification, some models have their + # pooling layers removed. Make sure this is reflected in vLLM. + for module in seq_cls_model.modules(): + if hasattr(module, "pooler") and module.pooler is None: + self.model.pooler = None + break + if self.model.pooler is not None: + raise ValueError( + "Sequence classification models with pooling layers are not " + "supported yet in the Transformers backend.") + + # Unlike `lm_head`, `classifier` is not always `nn.Linear`. + self.classifier = seq_cls_model.classifier + self.init_parameters(self.classifier, + dtype=self.model_config.head_dtype) + + class ClassifierWithReshape(self.classifier.__class__): + """CLSPool has already been applied in `pooling`. + Add dim to match expected input shape of `classifier.forward`.""" + + def forward(self, *args, **kwargs): + if len(args) > 0: + args = (args[0].unsqueeze(1), *args[1:]) + return super().forward(*args, **kwargs) + + self.classifier.__class__ = ClassifierWithReshape + + self.pooler = DispatchPooler({ + "encode": + Pooler.for_encode(pooler_config), + "classify": + ClassifierPooler( + pooling=CLSPool(), + classifier=self.classifier, + act_fn=ClassifierPooler.act_fn_for_seq_cls( + vllm_config.model_config), + ), + "score": + ClassifierPooler( + pooling=CLSPool(), + classifier=self.classifier, + act_fn=ClassifierPooler.act_fn_for_cross_encoder( + vllm_config.model_config), + ), + }) From 3b60bdcfa1183254c55e103468315f9731d9473b Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Tue, 30 Sep 2025 23:10:02 +0100 Subject: [PATCH 119/166] [Docs] Remove API Reference from search index (#25949) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- docs/api/vllm/.meta.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api/vllm/.meta.yml b/docs/api/vllm/.meta.yml index c15adfec644c..d105540fee79 100644 --- a/docs/api/vllm/.meta.yml +++ b/docs/api/vllm/.meta.yml @@ -1,2 +1,2 @@ search: - boost: 0.5 + exclude: true From d51f36dfdf66cbf2d5add47f10264902ab98571b Mon Sep 17 00:00:00 2001 From: Andrew Xia Date: Tue, 30 Sep 2025 15:47:07 -0700 Subject: [PATCH 120/166] [gpt-oss] use vLLM instead of openai types for streaming (#25186) Signed-off-by: Andrew Xia Signed-off-by: Andrew Xia Co-authored-by: Andrew Xia --- .../openai/test_response_api_with_harmony.py | 8 +++ vllm/entrypoints/openai/protocol.py | 50 ++++++++++++++----- vllm/entrypoints/openai/serving_responses.py | 23 +++++---- 3 files changed, 59 insertions(+), 22 deletions(-) diff --git a/tests/entrypoints/openai/test_response_api_with_harmony.py b/tests/entrypoints/openai/test_response_api_with_harmony.py index c28970afc731..b882a2f9326e 100644 --- a/tests/entrypoints/openai/test_response_api_with_harmony.py +++ b/tests/entrypoints/openai/test_response_api_with_harmony.py @@ -379,6 +379,14 @@ async def test_streaming(client: OpenAI, model_name: str, background: bool): if event.type == "response.created": resp_id = event.response.id + # test vllm custom types are in the response + if event.type in [ + "response.completed", "response.in_progress", + "response.created" + ]: + assert 'input_messages' in event.response.model_extra + assert 'output_messages' in event.response.model_extra + if current_event_mode != event.type: current_event_mode = event.type print(f"\n[{event.type}] ", end="", flush=True) diff --git a/vllm/entrypoints/openai/protocol.py b/vllm/entrypoints/openai/protocol.py index 8829fa4886f6..9d51372887c2 100644 --- a/vllm/entrypoints/openai/protocol.py +++ b/vllm/entrypoints/openai/protocol.py @@ -17,20 +17,32 @@ ChatCompletionAudio as OpenAIChatCompletionAudio) from openai.types.chat.chat_completion_message import ( Annotation as OpenAIAnnotation) -# yapf: enable from openai.types.responses import ( ResponseCodeInterpreterCallCodeDeltaEvent, ResponseCodeInterpreterCallCodeDoneEvent, ResponseCodeInterpreterCallCompletedEvent, ResponseCodeInterpreterCallInProgressEvent, - ResponseCodeInterpreterCallInterpretingEvent, ResponseCompletedEvent, - ResponseContentPartAddedEvent, ResponseContentPartDoneEvent, - ResponseCreatedEvent, ResponseFunctionToolCall, ResponseInProgressEvent, - ResponseInputItemParam, ResponseOutputItem, ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, ResponsePrompt, ResponseReasoningItem, - ResponseReasoningTextDeltaEvent, ResponseReasoningTextDoneEvent, - ResponseStatus, ResponseWebSearchCallCompletedEvent, - ResponseWebSearchCallInProgressEvent, ResponseWebSearchCallSearchingEvent) + ResponseCodeInterpreterCallInterpretingEvent) +from openai.types.responses import ( + ResponseCompletedEvent as OpenAIResponseCompletedEvent) +from openai.types.responses import (ResponseContentPartAddedEvent, + ResponseContentPartDoneEvent) +from openai.types.responses import ( + ResponseCreatedEvent as OpenAIResponseCreatedEvent) +from openai.types.responses import ResponseFunctionToolCall +from openai.types.responses import ( + ResponseInProgressEvent as OpenAIResponseInProgressEvent) +from openai.types.responses import (ResponseInputItemParam, ResponseOutputItem, + ResponseOutputItemAddedEvent, + ResponseOutputItemDoneEvent, + ResponsePrompt, ResponseReasoningItem, + ResponseReasoningTextDeltaEvent, + ResponseReasoningTextDoneEvent, + ResponseStatus, + ResponseWebSearchCallCompletedEvent, + ResponseWebSearchCallInProgressEvent, + ResponseWebSearchCallSearchingEvent) +# yapf: enable from openai.types.responses.response_reasoning_item import ( Content as ResponseReasoningTextContent) @@ -2077,10 +2089,24 @@ class ResponseReasoningPartAddedEvent(OpenAIBaseModel): """The type of the event. Always `response.reasoning_part.added`.""" +# vLLM Streaming Events +# Note: we override the response type with the vLLM ResponsesResponse type +class ResponseCompletedEvent(OpenAIResponseCompletedEvent): + response: ResponsesResponse # type: ignore[override] + + +class ResponseCreatedEvent(OpenAIResponseCreatedEvent): + response: ResponsesResponse # type: ignore[override] + + +class ResponseInProgressEvent(OpenAIResponseInProgressEvent): + response: ResponsesResponse # type: ignore[override] + + StreamingResponsesResponse: TypeAlias = Union[ - ResponseCreatedEvent, - ResponseInProgressEvent, - ResponseCompletedEvent, + "ResponseCreatedEvent", + "ResponseInProgressEvent", + "ResponseCompletedEvent", ResponseOutputItemAddedEvent, ResponseOutputItemDoneEvent, ResponseContentPartAddedEvent, diff --git a/vllm/entrypoints/openai/serving_responses.py b/vllm/entrypoints/openai/serving_responses.py index c70baba88d43..eaeab3360c60 100644 --- a/vllm/entrypoints/openai/serving_responses.py +++ b/vllm/entrypoints/openai/serving_responses.py @@ -22,16 +22,16 @@ ResponseCodeInterpreterCallCompletedEvent, ResponseCodeInterpreterCallInProgressEvent, ResponseCodeInterpreterCallInterpretingEvent, - ResponseCodeInterpreterToolCallParam, ResponseCompletedEvent, - ResponseContentPartAddedEvent, ResponseContentPartDoneEvent, - ResponseCreatedEvent, ResponseFunctionToolCall, ResponseFunctionWebSearch, - ResponseInProgressEvent, ResponseOutputItem, ResponseOutputItemAddedEvent, - ResponseOutputItemDoneEvent, ResponseOutputMessage, ResponseOutputText, - ResponseReasoningItem, ResponseReasoningTextDeltaEvent, - ResponseReasoningTextDoneEvent, ResponseStatus, ResponseTextDeltaEvent, - ResponseTextDoneEvent, ResponseWebSearchCallCompletedEvent, - ResponseWebSearchCallInProgressEvent, ResponseWebSearchCallSearchingEvent, - response_function_web_search, response_text_delta_event) + ResponseCodeInterpreterToolCallParam, ResponseContentPartAddedEvent, + ResponseContentPartDoneEvent, ResponseFunctionToolCall, + ResponseFunctionWebSearch, ResponseOutputItem, + ResponseOutputItemAddedEvent, ResponseOutputItemDoneEvent, + ResponseOutputMessage, ResponseOutputText, ResponseReasoningItem, + ResponseReasoningTextDeltaEvent, ResponseReasoningTextDoneEvent, + ResponseStatus, ResponseTextDeltaEvent, ResponseTextDoneEvent, + ResponseWebSearchCallCompletedEvent, ResponseWebSearchCallInProgressEvent, + ResponseWebSearchCallSearchingEvent, response_function_web_search, + response_text_delta_event) from openai.types.responses.response_output_text import (Logprob, LogprobTopLogprob) # yapf: enable @@ -58,6 +58,9 @@ InputTokensDetails, OutputTokensDetails, RequestResponseMetadata, + ResponseCompletedEvent, + ResponseCreatedEvent, + ResponseInProgressEvent, ResponseReasoningPartAddedEvent, ResponseReasoningPartDoneEvent, ResponsesRequest, From 645622bc3cc0a9dad9cf9fcb86656082ca236142 Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Tue, 30 Sep 2025 19:38:34 -0400 Subject: [PATCH 121/166] [Misc] Make EP kernels install script support uv (#25785) Signed-off-by: Lucas Wilkinson --- tools/ep_kernels/install_python_libraries.sh | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/tools/ep_kernels/install_python_libraries.sh b/tools/ep_kernels/install_python_libraries.sh index 59bfe69dc0dd..5a3d734190c1 100644 --- a/tools/ep_kernels/install_python_libraries.sh +++ b/tools/ep_kernels/install_python_libraries.sh @@ -10,8 +10,12 @@ if [ ! -d "$WORKSPACE" ]; then mkdir -p $WORKSPACE fi +# configurable pip command (default: pip3) +PIP_CMD=${PIP_CMD:-pip3} +CUDA_HOME=${CUDA_HOME:-/usr/local/cuda} + # install dependencies if not installed -pip3 install cmake torch ninja +$PIP_CMD install cmake torch ninja # build nvshmem pushd $WORKSPACE @@ -110,9 +114,7 @@ clone_repo() { pushd $WORKSPACE clone_repo "https://github.com/ppl-ai/pplx-kernels" "pplx-kernels" "setup.py" "c336faf" cd pplx-kernels -# see https://github.com/pypa/pip/issues/9955#issuecomment-838065925 -# PIP_NO_BUILD_ISOLATION=0 disables build isolation -PIP_NO_BUILD_ISOLATION=0 pip install -vvv -e . +$PIP_CMD install --no-build-isolation -vvv -e . popd # build and install deepep, require pytorch installed @@ -120,5 +122,5 @@ pushd $WORKSPACE clone_repo "https://github.com/deepseek-ai/DeepEP" "DeepEP" "setup.py" "e3908bf" cd DeepEP export NVSHMEM_DIR=$WORKSPACE/nvshmem_install -PIP_NO_BUILD_ISOLATION=0 pip install -vvv -e . +$PIP_CMD install --no-build-isolation -vvv -e . popd From fad6c1a73963558a925c61a7f0dc06109a41cf8b Mon Sep 17 00:00:00 2001 From: Lucia Fang <116399278+luccafong@users.noreply.github.com> Date: Tue, 30 Sep 2025 18:53:22 -0700 Subject: [PATCH 122/166] [Model] MTP fallback to eager for DeepSeek v32 (#25982) Signed-off-by: Lu Fang --- tests/v1/spec_decode/test_eagle.py | 11 ++++++++++- tests/v1/spec_decode/test_mtp.py | 8 +++++++- vllm/config/speculative.py | 8 +++++++- vllm/v1/attention/backends/mla/indexer.py | 2 +- vllm/v1/spec_decode/eagle.py | 8 +++++++- 5 files changed, 32 insertions(+), 5 deletions(-) diff --git a/tests/v1/spec_decode/test_eagle.py b/tests/v1/spec_decode/test_eagle.py index 690732eb1232..49311c0005e7 100644 --- a/tests/v1/spec_decode/test_eagle.py +++ b/tests/v1/spec_decode/test_eagle.py @@ -337,13 +337,19 @@ def test_load_model(mock_get_model, mock_get_layers, mock_get_pp_group, method, "target_attn_1": mock.MagicMock(), "target_attn_2": mock.MagicMock() } + target_indx_layers: dict[str, mock.MagicMock] = {} # Draft model has one extra attention layer compared to target model all_attn_layers = { **target_attn_layers, "draft_extra_attn": mock.MagicMock() } + all_indx_layers: dict[str, mock.MagicMock] = {} + # Make mock_get_layers return different values for each call - mock_get_layers.side_effect = [target_attn_layers, all_attn_layers] + mock_get_layers.side_effect = [ + target_attn_layers, target_indx_layers, all_attn_layers, + all_indx_layers + ] # Setup mock for pp group to return the appropriate value for world size mock_pp_group = mock.MagicMock() @@ -658,6 +664,9 @@ def create_deterministic_logits(token_ids, k: int): # Mock runner for attention metadata building. proposer.runner = mock.MagicMock() proposer.runner.attn_groups.append([mock.MagicMock()]) + proposer.runner.attn_groups[0][0].metadata_builders = [ + attn_metadata_builder + ] proposer.runner.attn_groups[0][0].get_metadata_builder.return_value = \ attn_metadata_builder proposer._get_attention_metadata_builder = mock.MagicMock( diff --git a/tests/v1/spec_decode/test_mtp.py b/tests/v1/spec_decode/test_mtp.py index e4881859ece1..5b9ccfc3f48b 100644 --- a/tests/v1/spec_decode/test_mtp.py +++ b/tests/v1/spec_decode/test_mtp.py @@ -63,7 +63,13 @@ def test_mtp_load_model_unified(mock_get_model, mock_get_layers, target_attn_layers = {"target_attn_1": mock.MagicMock()} all_attn_layers = {**target_attn_layers, "draft_attn_1": mock.MagicMock()} - mock_get_layers.side_effect = [target_attn_layers, all_attn_layers] + target_indexer_layers: dict = {} + all_indexer_layers: dict = {} + + mock_get_layers.side_effect = [ + target_attn_layers, target_indexer_layers, all_attn_layers, + all_indexer_layers + ] mock_pp_group = mock.MagicMock() mock_pp_group.world_size = 1 diff --git a/vllm/config/speculative.py b/vllm/config/speculative.py index f684e4e4ccd4..d5c6d1d4d866 100644 --- a/vllm/config/speculative.py +++ b/vllm/config/speculative.py @@ -41,7 +41,8 @@ @dataclass class SpeculativeConfig: """Configuration for speculative decoding.""" - + enforce_eager: Optional[bool] = None + """Override the default enforce_eager from model_config""" # General speculative decoding control num_speculative_tokens: SkipValidation[int] = None # type: ignore """The number of speculative tokens, if provided. It will default to the @@ -219,6 +220,11 @@ def __post_init__(self): assert ( self.target_model_config is not None), "target_model_config must be present for mtp" + if self.target_model_config.hf_text_config.model_type \ + == "deepseek_v32": + # FIXME(luccafong): cudgraph with v32 MTP is not supported, + # remove this when the issue is fixed. + self.enforce_eager = True # use the draft model from the same model: self.model = self.target_model_config.model # Align the quantization of draft model for cases such as diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py index 4e6b974ad74d..e87b51b15191 100644 --- a/vllm/v1/attention/backends/mla/indexer.py +++ b/vllm/v1/attention/backends/mla/indexer.py @@ -171,7 +171,7 @@ def get_max_prefill_buffer_size(vllm_config: VllmConfig): class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder): cudagraph_support: ClassVar[AttentionCGSupport] = \ - AttentionCGSupport.UNIFORM_BATCH + AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE reorder_batch_threshold: int = 1 diff --git a/vllm/v1/spec_decode/eagle.py b/vllm/v1/spec_decode/eagle.py index bb11a543fd8b..dc6db0138806 100644 --- a/vllm/v1/spec_decode/eagle.py +++ b/vllm/v1/spec_decode/eagle.py @@ -50,6 +50,7 @@ def __init__( ): self.vllm_config = vllm_config self.speculative_config = vllm_config.speculative_config + assert self.speculative_config is not None self.draft_model_config = self.speculative_config.draft_model_config self.method = self.speculative_config.method @@ -74,11 +75,16 @@ def __init__( vllm_config.model_config) self.attn_metadata_builder: Optional[AttentionMetadataBuilder] = None + self.draft_indexer_metadata_builder: Optional[ + AttentionMetadataBuilder] = None + self.attn_layer_names: list[str] = [] + self.indexer_layer_names: list[str] = [] self.use_cuda_graph = (not current_platform.is_xpu() and self.vllm_config.compilation_config.level == CompilationLevel.PIECEWISE and - not self.vllm_config.model_config.enforce_eager) + not self.vllm_config.model_config.enforce_eager + and not self.speculative_config.enforce_eager) self.cudagraph_batch_sizes = list( reversed(self.vllm_config.compilation_config. cudagraph_capture_sizes)) if self.use_cuda_graph else [] From 5b6a7014067cbe5a737556ad2f40df9eb0b74d09 Mon Sep 17 00:00:00 2001 From: Salvatore Cena Date: Wed, 1 Oct 2025 04:18:19 +0200 Subject: [PATCH 123/166] Update launch_bounds_utils.h for correct compile on Multiple Cuda Arch - PTXAS out of range Warning (#25843) Signed-off-by: Salvatore Cena Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --- csrc/launch_bounds_utils.h | 32 +++++++++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/csrc/launch_bounds_utils.h b/csrc/launch_bounds_utils.h index d5a89690111b..92d7ef802f97 100644 --- a/csrc/launch_bounds_utils.h +++ b/csrc/launch_bounds_utils.h @@ -8,11 +8,37 @@ #define VLLM_LAUNCH_BLOCKS_CAP 4 #endif -// compile-time estimate of max threads per SM for launch bounds. +// Compile-time estimate of max threads per SM for launch bounds. +// Families: 1024, 1536, 2048 threads/SM. #ifndef VLLM_MAX_THREADS_PER_SM - #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 300 - #define VLLM_MAX_THREADS_PER_SM 1536 + #ifdef __CUDA_ARCH__ + + /* 1024 thr/SM: Turing (sm_75) */ + #if (__CUDA_ARCH__ == 750) + #define VLLM_MAX_THREADS_PER_SM 1024 + + /* 1536 thr/SM: Ampere GA10x (sm_86/87), Ada (sm_89), + GB20x consumer (sm_120/121), Thor (sm_101 or sm_110) */ + #elif (__CUDA_ARCH__ == 860) || (__CUDA_ARCH__ == 870) || \ + (__CUDA_ARCH__ == 890) || (__CUDA_ARCH__ == 1010) || \ + (__CUDA_ARCH__ == 1100) || (__CUDA_ARCH__ == 1200) || \ + (__CUDA_ARCH__ == 1210) + #define VLLM_MAX_THREADS_PER_SM 1536 + + /* 2048 thr/SM: Volta (sm_70/72), Ampere GA100 (sm_80), + Hopper (sm_90), Blackwell (sm_100/103) */ + #elif (__CUDA_ARCH__ == 700) || (__CUDA_ARCH__ == 720) || \ + (__CUDA_ARCH__ == 800) || (__CUDA_ARCH__ == 900) || \ + (__CUDA_ARCH__ == 1000) || (__CUDA_ARCH__ == 1030) + #define VLLM_MAX_THREADS_PER_SM 2048 + + /* Fallback: use 2048 for unknown future CCs */ + #else + #define VLLM_MAX_THREADS_PER_SM 2048 + #endif + #else + /* Host pass (no __CUDA_ARCH__): neutral default */ #define VLLM_MAX_THREADS_PER_SM 2048 #endif #endif From d6862ab819a318fb0459d6cb550599ca8536e693 Mon Sep 17 00:00:00 2001 From: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Date: Tue, 30 Sep 2025 22:18:43 -0400 Subject: [PATCH 124/166] [Log] Optimize Log for FP8MOE (#25709) Signed-off-by: yewentao256 --- vllm/model_executor/layers/quantization/fp8.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index a0808cb603d0..3ebb20de9996 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -467,7 +467,8 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): logger.info_once("DeepGemm disabled: FlashInfer MOE is" " enabled.") elif (is_deep_gemm_supported()): - logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.") + logger.debug_once( + "DeepGemm kernels available for Fp8MoEMethod.") self.allow_deep_gemm = True else: logger.warning_once( @@ -481,9 +482,8 @@ def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module): elif (current_platform.is_cuda() and current_platform.is_device_capability(100) and not self.flashinfer_moe_backend): - logger.info_once( - "Using CutlassBlockScaledGroupedGemm kernels for Fp8 MOE " - "on SM100.") + logger.debug_once( + "CutlassBlockScaledGroupedGemm available for Fp8MoEMethod.") self.allow_cutlass_block_scaled_grouped_gemm = True def create_weights(self, layer: Module, num_experts: int, hidden_size: int, From 2d3e81d7671b73bf363d8a5682a83b7fea70f5d8 Mon Sep 17 00:00:00 2001 From: Param Date: Tue, 30 Sep 2025 22:19:53 -0400 Subject: [PATCH 125/166] Fix INT8 quantization error on Blackwell GPUs (SM100+) (#25935) Signed-off-by: padg9912 --- csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp | 5 ++++- docs/features/quantization/int8.md | 6 +++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp b/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp index 3af59267bd60..2204a49257b0 100644 --- a/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp +++ b/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_helper.hpp @@ -25,7 +25,10 @@ void dispatch_scaled_mm(torch::Tensor& c, torch::Tensor const& a, if constexpr (!std::is_same_v) { int8_func(c, a, b, a_scales, b_scales, bias); } else { - TORCH_CHECK(false, "Int8 not supported for this architecture"); + int32_t version_num = get_sm_version_num(); + TORCH_CHECK( + false, "Int8 not supported on SM", version_num, + ". Use FP8 quantization instead, or run on older arch (SM < 100)."); } } } else { diff --git a/docs/features/quantization/int8.md b/docs/features/quantization/int8.md index 247d0cbdd3f1..af3650e701ad 100644 --- a/docs/features/quantization/int8.md +++ b/docs/features/quantization/int8.md @@ -6,7 +6,11 @@ This quantization method is particularly useful for reducing model size while ma Please visit the HF collection of [quantized INT8 checkpoints of popular LLMs ready to use with vLLM](https://huggingface.co/collections/neuralmagic/int8-llms-for-vllm-668ec32c049dca0369816415). !!! note - INT8 computation is supported on NVIDIA GPUs with compute capability > 7.5 (Turing, Ampere, Ada Lovelace, Hopper, Blackwell). + INT8 computation is supported on NVIDIA GPUs with compute capability > 7.5 (Turing, Ampere, Ada Lovelace, Hopper). + +!!! warning + **Blackwell GPU Limitation**: INT8 is not supported on compute capability >= 100 (e.g., RTX 6000 Blackwell). + Use [FP8 quantization](fp8.md) instead, or run on Hopper/Ada/Ampere architectures. ## Prerequisites From 35fd9460af72f17315a70db94307de4c45c220e4 Mon Sep 17 00:00:00 2001 From: Roger Wang Date: Tue, 30 Sep 2025 21:13:42 -0700 Subject: [PATCH 126/166] [MM] Add text-only mode for Qwen3-VL (#26000) --- vllm/model_executor/models/qwen3_vl.py | 39 ++++++++++++++-------- vllm/model_executor/models/qwen3_vl_moe.py | 32 +++++++++++------- 2 files changed, 45 insertions(+), 26 deletions(-) diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py index ce92557d6424..00de89811cc7 100644 --- a/vllm/model_executor/models/qwen3_vl.py +++ b/vllm/model_executor/models/qwen3_vl.py @@ -1125,14 +1125,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"): self.config = config self.multimodal_config = multimodal_config self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data" - - self.visual = Qwen3_VisionTransformer( - config.vision_config, - norm_eps=getattr(config, "rms_norm_eps", 1e-6), - quant_config=quant_config, - prefix=maybe_prefix(prefix, "visual"), - use_data_parallel=self.use_data_parallel, - ) + if not multimodal_config.get_limit_per_prompt("image") and \ + not multimodal_config.get_limit_per_prompt("video"): + self.visual = None + else: + self.visual = Qwen3_VisionTransformer( + config.vision_config, + norm_eps=getattr(config, "rms_norm_eps", 1e-6), + quant_config=quant_config, + prefix=maybe_prefix(prefix, "visual"), + use_data_parallel=self.use_data_parallel, + ) self.language_model = Qwen3LLMForCausalLM(vllm_config=vllm_config, prefix=maybe_prefix( @@ -1148,11 +1151,15 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"): config.vision_config.deepstack_visual_indexes ) if self.use_deepstack else 0 # register buffer for deepstack - self.deepstack_input_embeds = [ - torch.zeros(vllm_config.scheduler_config.max_num_batched_tokens, - config.text_config.hidden_size) - for _ in range(self.deepstack_num_level) - ] if self.use_deepstack else None + if self.use_deepstack and self.visual is not None: + self.deepstack_input_embeds = [ + torch.zeros( + vllm_config.scheduler_config.max_num_batched_tokens, + config.text_config.hidden_size) + for _ in range(self.deepstack_num_level) + ] + else: + self.deepstack_input_embeds = None self.visual_dim = config.vision_config.out_hidden_size self.multiscale_dim = self.visual_dim * self.deepstack_num_level @@ -1526,7 +1533,11 @@ def compute_logits( def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: - loader = AutoWeightsLoader(self) + + skip_prefixes = [] + if self.visual is None: + skip_prefixes.extend(["visual."]) + loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) def get_mm_mapping(self) -> MultiModelKeys: diff --git a/vllm/model_executor/models/qwen3_vl_moe.py b/vllm/model_executor/models/qwen3_vl_moe.py index 02cc5d6d66d1..1ed053eb2e96 100644 --- a/vllm/model_executor/models/qwen3_vl_moe.py +++ b/vllm/model_executor/models/qwen3_vl_moe.py @@ -319,13 +319,17 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): self.multimodal_config = multimodal_config self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data" - self.visual = Qwen3_VisionTransformer( - config.vision_config, - norm_eps=getattr(config, "rms_norm_eps", 1e-6), - quant_config=quant_config, - prefix=maybe_prefix(prefix, "visual"), - use_data_parallel=self.use_data_parallel, - ) + if not multimodal_config.get_limit_per_prompt("image") and \ + not multimodal_config.get_limit_per_prompt("video"): + self.visual = None + else: + self.visual = Qwen3_VisionTransformer( + config.vision_config, + norm_eps=getattr(config, "rms_norm_eps", 1e-6), + quant_config=quant_config, + prefix=maybe_prefix(prefix, "visual"), + use_data_parallel=self.use_data_parallel, + ) self.language_model = Qwen3MoeLLMForCausalLM(vllm_config=vllm_config, prefix=maybe_prefix( @@ -341,10 +345,14 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config.vision_config.deepstack_visual_indexes ) if self.use_deepstack else 0 # register buffer for deepstack - self.deepstack_input_embeds = [ - torch.zeros(vllm_config.scheduler_config.max_num_batched_tokens, - config.text_config.hidden_size) - for _ in range(self.deepstack_num_level) - ] if self.use_deepstack else None + if self.use_deepstack and self.visual is not None: + self.deepstack_input_embeds = [ + torch.zeros( + vllm_config.scheduler_config.max_num_batched_tokens, + config.text_config.hidden_size) + for _ in range(self.deepstack_num_level) + ] + else: + self.deepstack_input_embeds = None self.visual_dim = config.vision_config.out_hidden_size self.multiscale_dim = self.visual_dim * self.deepstack_num_level From 3e0baef73882ce073ce4d6a4ad4488c76eb843db Mon Sep 17 00:00:00 2001 From: Zhewen Li Date: Tue, 30 Sep 2025 21:15:11 -0700 Subject: [PATCH 127/166] [Bugfix] Fix `__syncwarp` on ROCM (#25996) --- csrc/cache_kernels.cu | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/csrc/cache_kernels.cu b/csrc/cache_kernels.cu index b1c43163c6a5..1286f5806d4b 100644 --- a/csrc/cache_kernels.cu +++ b/csrc/cache_kernels.cu @@ -536,7 +536,9 @@ __global__ void indexer_k_quant_and_cache_kernel( for (int i = 0; i < VEC_SIZE; i++) { amax = fmaxf(amax, fabsf(float(k_val_ptr[i]))); } +#ifndef USE_ROCM __syncwarp(); +#endif // Reduced amax for (int mask = 16; mask > 0; mask /= 2) { @@ -546,7 +548,9 @@ __global__ void indexer_k_quant_and_cache_kernel( amax = fmaxf(amax, __shfl_xor_sync(unsigned(-1), amax, mask)); #endif } +#ifndef USE_ROCM __syncwarp(); +#endif float scale = fmaxf(amax, 1e-4) / 448.0f; if (use_ue8m0) { scale = exp2f(ceilf(log2f(scale))); @@ -1167,4 +1171,4 @@ void indexer_k_quant_and_cache( DISPATCH_BY_KV_CACHE_DTYPE(k.dtype(), "fp8_e4m3", CALL_INDEXER_K_QUANT_AND_CACHE); -} \ No newline at end of file +} From bff7b71f3398d7dea48238ca76a3a70d64816229 Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Wed, 1 Oct 2025 00:58:31 -0400 Subject: [PATCH 128/166] [BugFix] Fix default kv-cache-dtype default for DeepseekV3.2 (#25988) Signed-off-by: Lucas Wilkinson --- vllm/model_executor/models/config.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/vllm/model_executor/models/config.py b/vllm/model_executor/models/config.py index 589ca0069034..5711b5ebe85e 100644 --- a/vllm/model_executor/models/config.py +++ b/vllm/model_executor/models/config.py @@ -400,7 +400,7 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: "exactly equal.", mamba_padding_pct) -class DeepseekV3ForCausalLM(VerifyAndUpdateConfig): +class DeepseekV32ForCausalLM(VerifyAndUpdateConfig): @classmethod def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: @@ -409,20 +409,20 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: """ hf_config = vllm_config.model_config.hf_config + # Mirror the check in vllm/model_executor/models/deepseek_v2.py is_v32 = hasattr(hf_config, "index_topk") + assert is_v32 - if is_v32: - # For DeepSeekV3.2, we use a custom fp8 format as default (i.e. - # "auto") - cache_config = vllm_config.cache_config - if cache_config.cache_dtype == "auto" or \ - cache_config.cache_dtype.startswith("fp8"): - cache_config.cache_dtype = "fp8_ds_mla" - logger.info( - "Using custom fp8 kv-cache format for DeepSeekV3.2") - if cache_config.cache_dtype == "bfloat16": - cache_config.cache_dtype = "auto" - logger.info("Using bfloat16 kv-cache for DeepSeekV3.2") + # For DeepSeekV3.2, we use a custom fp8 format as default (i.e. + # "auto") + cache_config = vllm_config.cache_config + if cache_config.cache_dtype == "auto" or \ + cache_config.cache_dtype.startswith("fp8"): + cache_config.cache_dtype = "fp8_ds_mla" + logger.info("Using custom fp8 kv-cache format for DeepSeekV3.2") + if cache_config.cache_dtype == "bfloat16": + cache_config.cache_dtype = "auto" + logger.info("Using bfloat16 kv-cache for DeepSeekV3.2") MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = { @@ -441,5 +441,5 @@ def verify_and_update_config(cls, vllm_config: "VllmConfig") -> None: "MambaForCausalLM": MambaModelConfig, "Mamba2ForCausalLM": MambaModelConfig, "FalconMambaForCausalLM": MambaModelConfig, - "DeepseekV3ForCausalLM": DeepseekV3ForCausalLM, + "DeepseekV32ForCausalLM": DeepseekV32ForCausalLM, } From 3b00255841464c9946f1bb7055430d0eccb697ad Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Wed, 1 Oct 2025 06:07:07 +0100 Subject: [PATCH 129/166] Update to Transformers `v4.56.2` (#24638) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Cyrus Leung Co-authored-by: Cyrus Leung Co-authored-by: Isotr0py --- requirements/nightly_torch_test.txt | 4 +- requirements/test.in | 4 +- requirements/test.txt | 4 +- .../multimodal/generation/test_common.py | 4 +- vllm/model_executor/models/transformers.py | 38 ++++++------------- 5 files changed, 20 insertions(+), 34 deletions(-) diff --git a/requirements/nightly_torch_test.txt b/requirements/nightly_torch_test.txt index 790a18f28b7f..67c66a0c03e6 100644 --- a/requirements/nightly_torch_test.txt +++ b/requirements/nightly_torch_test.txt @@ -29,8 +29,8 @@ opencv-python-headless >= 4.11.0 # required for video test datamodel_code_generator # required for minicpm3 test lm-eval[api] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d # required for model evaluation test mteb>=1.38.11, <2 # required for mteb test -transformers==4.52.4 -tokenizers==0.21.1 +transformers==4.56.2 +tokenizers==0.22.0 schemathesis>=3.39.15 # Required for openai schema test. # quantization bitsandbytes>=0.46.1 diff --git a/requirements/test.in b/requirements/test.in index c9496c61a7e4..e0a9311af29d 100644 --- a/requirements/test.in +++ b/requirements/test.in @@ -37,8 +37,8 @@ datamodel_code_generator # required for minicpm3 test # TODO: Use lm-eval[api]==0.4.10 once released lm-eval[api] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d # required for model evaluation test mteb[bm25s]>=1.38.11, <2 # required for mteb test -transformers==4.55.2 -tokenizers==0.21.1 +transformers==4.56.2 +tokenizers==0.22.0 schemathesis>=3.39.15 # Required for openai schema test. # quantization bitsandbytes==0.46.1 diff --git a/requirements/test.txt b/requirements/test.txt index 912e04b2606c..07457e14ccbd 100644 --- a/requirements/test.txt +++ b/requirements/test.txt @@ -1072,7 +1072,7 @@ timm==1.0.17 # segmentation-models-pytorch # terratorch # torchgeo -tokenizers==0.21.1 +tokenizers==0.22.0 # via # -r requirements/test.in # transformers @@ -1153,7 +1153,7 @@ tqdm==4.66.6 # transformers tqdm-multiprocess==0.0.11 # via lm-eval -transformers==4.55.2 +transformers==4.56.2 # via # -r requirements/test.in # genai-perf diff --git a/tests/models/multimodal/generation/test_common.py b/tests/models/multimodal/generation/test_common.py index e76b58e61ec1..c378ef670f91 100644 --- a/tests/models/multimodal/generation/test_common.py +++ b/tests/models/multimodal/generation/test_common.py @@ -214,7 +214,9 @@ vllm_runner_kwargs={ "model_impl": "transformers", }, - marks=[large_gpu_mark(min_gb=32)], + # FIXME: Investigate mrope issue + marks=[large_gpu_mark(min_gb=32), + pytest.mark.skip(reason="Mrope issue")], ), #### Extended model tests "aria": VLMTestInfo( diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py index d168398aa182..070c77073bb0 100644 --- a/vllm/model_executor/models/transformers.py +++ b/vllm/model_executor/models/transformers.py @@ -51,7 +51,6 @@ BaseProcessingInfo) from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.sequence import IntermediateTensors -from vllm.utils import is_list_of from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP, SupportsQuant) @@ -217,9 +216,6 @@ def wrapper(*args, **kwargs): class MultiModalProcessingInfo(BaseProcessingInfo): - def get_hf_config(self): - return self.ctx.model_config.hf_config - def get_supported_mm_limits(self): return {"image": None} @@ -784,6 +780,7 @@ def _can_concat(x: list[torch.Tensor]): }, enable_if=can_enable_torch_compile) class TransformersForMultimodalLM(TransformersForCausalLM, SupportsMultiModal): + merge_by_field_config = True # Backwards compatibility for prev released models. State dicts back then # had different formats and cannot be loaded with `AutoModel` mapping as is hf_to_vllm_mapper = WeightsMapper( @@ -828,40 +825,27 @@ def get_language_model(self) -> torch.nn.Module: return self.model def get_multimodal_embeddings(self, **kwargs): - pixel_values = kwargs.pop("pixel_values", None) - pixel_values = pixel_values if pixel_values is not None else kwargs.pop( - "image_patches", None) - image_embeds = kwargs.pop("image_embeds", None) + pixel_values: Optional[torch.Tensor] = kwargs.pop("pixel_values", None) + image_embeds: Optional[torch.Tensor] = kwargs.pop("image_embeds", None) + # Model might use `image_patches` instead of `pixel_values` + if pixel_values is None: + pixel_values = kwargs.pop("image_patches", None) if image_embeds is not None: return image_embeds - if pixel_values is None and image_embeds is None: + if pixel_values is None: return None num_image_patches = kwargs.pop("num_image_patches") if pixel_values is not None: - if isinstance(pixel_values, torch.Tensor): - pixel_values = flatten_bn(pixel_values).to(self.dtype) - elif is_list_of(pixel_values, torch.Tensor): - pixel_values = flatten_and_concat(pixel_values).to(self.dtype) - else: - raise ValueError( - f"Unsupported pixel_values type {type(pixel_values)}. " - "Expected `torch.Tensor` or list of `torch.Tensor`.") - - if isinstance(num_image_patches, list): - num_image_patches = torch.cat(num_image_patches) - vision_embeddings = self.model.get_image_features( - pixel_values, - **{ - k: v.flatten(0, 1) - for k, v in kwargs.items() - }, - ) + pixel_values, **kwargs) if isinstance(vision_embeddings, torch.Tensor): + if isinstance(num_image_patches, list): + num_image_patches = torch.cat(num_image_patches) + if vision_embeddings.ndim == 2: vision_embeddings = vision_embeddings.unsqueeze(0) From 31c973c1a578c9fcb3e89d6775815c60277b739d Mon Sep 17 00:00:00 2001 From: Lucia Fang <116399278+luccafong@users.noreply.github.com> Date: Tue, 30 Sep 2025 23:04:13 -0700 Subject: [PATCH 130/166] [Misc]allow disable pynccl (#25421) Signed-off-by: Lu Fang Co-authored-by: Lucia (Lu) Fang --- vllm/distributed/device_communicators/cuda_communicator.py | 4 ++++ vllm/distributed/device_communicators/pynccl.py | 3 ++- vllm/envs.py | 6 ++++++ 3 files changed, 12 insertions(+), 1 deletion(-) diff --git a/vllm/distributed/device_communicators/cuda_communicator.py b/vllm/distributed/device_communicators/cuda_communicator.py index 30d1bf10138b..9c2bf51a813e 100644 --- a/vllm/distributed/device_communicators/cuda_communicator.py +++ b/vllm/distributed/device_communicators/cuda_communicator.py @@ -147,6 +147,10 @@ def all_reduce(self, input_): assert out is not None return out pynccl_comm = self.pynccl_comm + if pynccl_comm is None or pynccl_comm.disabled: + out = input_.clone() + torch.distributed.all_reduce(out, group=self.device_group) + return out assert pynccl_comm is not None out = pynccl_comm.all_reduce(input_) if out is None: diff --git a/vllm/distributed/device_communicators/pynccl.py b/vllm/distributed/device_communicators/pynccl.py index 76fe9a93259f..81c02d1899e5 100644 --- a/vllm/distributed/device_communicators/pynccl.py +++ b/vllm/distributed/device_communicators/pynccl.py @@ -8,6 +8,7 @@ import torch.distributed as dist from torch.distributed import ProcessGroup, ReduceOp +import vllm.envs as envs from vllm.distributed.device_communicators.pynccl_wrapper import ( NCCLLibrary, buffer_type, cudaStream_t, ncclComm_t, ncclDataTypeEnum, ncclRedOpTypeEnum, ncclUniqueId) @@ -83,7 +84,7 @@ def __init__( self.group = group # if world_size == 1, no need to create communicator - if self.world_size == 1: + if self.world_size == 1 or envs.VLLM_DISABLE_PYNCCL: self.available = False self.disabled = True return diff --git a/vllm/envs.py b/vllm/envs.py index ffa7ed5c3aa5..03a22e4b2c7e 100755 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -98,6 +98,7 @@ VLLM_SKIP_P2P_CHECK: bool = False VLLM_DISABLED_KERNELS: list[str] = [] VLLM_DISABLE_NCCL_FOR_DP_SYNCHRONIZATION: bool = False + VLLM_DISABLE_PYNCCL: bool = False VLLM_USE_V1: bool = True VLLM_ROCM_USE_AITER: bool = False VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False @@ -897,6 +898,11 @@ def get_vllm_port() -> Optional[int]: (os.getenv("VLLM_DISABLE_NCCL_FOR_DP_SYNCHRONIZATION", "False").lower() in ("true", "1")), + # Disable pynccl (using torch.distributed instead) + "VLLM_DISABLE_PYNCCL": + lambda: + (os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")), + # If set, use the V1 code path. "VLLM_USE_V1": lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))), From 20fecb754c29eec9c5d2eba13663eaa019c11dc8 Mon Sep 17 00:00:00 2001 From: nadathurv Date: Wed, 1 Oct 2025 00:04:56 -0700 Subject: [PATCH 131/166] [Doc] updating torch.compile doc link (#25989) Signed-off-by: nadathurv Signed-off-by: WorldExplored Co-authored-by: Srreyansh Sethi <107075589+WorldExplored@users.noreply.github.com> --- docs/design/torch_compile.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/design/torch_compile.md b/docs/design/torch_compile.md index 47ac4958dbf7..127768b74d7f 100644 --- a/docs/design/torch_compile.md +++ b/docs/design/torch_compile.md @@ -16,7 +16,7 @@ vLLM will take all the available factors into consideration, and decide a direct The factors considered include: -- All the related configs (see the `compute_hash` functions in the [config.py](gh-file:vllm/config.py)) +- All the related configs (see the `compute_hash` functions in their respective configs in the [config folder](gh-file:vllm/config)) - PyTorch configs (see the `compute_hash` functions in the [compiler_interface.py](gh-file:vllm/compilation/compiler_interface.py)) - The model's forward function and the relevant functions called by the forward function (see below) From f18b6ee7377db7d0a6d3401ee6de6a01175e0816 Mon Sep 17 00:00:00 2001 From: Wenlong Wang Date: Wed, 1 Oct 2025 01:03:25 -0700 Subject: [PATCH 132/166] [BugFix][MM] Fix Nonetype error when video is cache in qwen2.5-omni-thinker (#26004) Signed-off-by: wwl2755 --- vllm/model_executor/models/qwen2_5_omni_thinker.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/models/qwen2_5_omni_thinker.py b/vllm/model_executor/models/qwen2_5_omni_thinker.py index 8f069710b0f9..b5e82c9b21cd 100644 --- a/vllm/model_executor/models/qwen2_5_omni_thinker.py +++ b/vllm/model_executor/models/qwen2_5_omni_thinker.py @@ -323,9 +323,15 @@ def _maybe_apply_prompt_updates( mm_item_counts = mm_items.get_all_counts() self._validate_mm_kwargs(mm_kwargs, mm_item_counts) - use_audio_in_video = (all( - item["use_audio_in_video"].data - for item in mm_kwargs["video"]) if "video" in mm_kwargs else False) + use_audio_in_video = False + if "video" in mm_kwargs: + video_items = [ + item for item in mm_kwargs["video"] if item is not None + ] + # only check video items (if there are any) + if video_items: + use_audio_in_video = all(item["use_audio_in_video"].data + for item in video_items) if is_update_applied: mm_placeholders = self._find_mm_placeholders( From b1f4d92ec22e9b6d26ac95229583f63a644d0167 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Wed, 1 Oct 2025 16:31:03 +0800 Subject: [PATCH 133/166] [Misc] Factor out common `_apply_feature_select_strategy` (#26003) Signed-off-by: DarkLight1337 --- vllm/model_executor/models/llava.py | 19 +++----------- vllm/model_executor/models/llava_next.py | 5 ++-- vllm/model_executor/models/tarsier.py | 23 +++++------------ vllm/model_executor/models/vision.py | 32 +++++++++++++++++++++--- 4 files changed, 40 insertions(+), 39 deletions(-) diff --git a/vllm/model_executor/models/llava.py b/vllm/model_executor/models/llava.py index d823e5cb58d2..78c413b77051 100644 --- a/vllm/model_executor/models/llava.py +++ b/vllm/model_executor/models/llava.py @@ -41,7 +41,7 @@ from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, init_vllm_registered_model, maybe_prefix) -from .vision import get_vision_encoder_info +from .vision import get_num_selected_vision_tokens, get_vision_encoder_info class LlavaImagePixelInputs(TensorSchema): @@ -147,19 +147,6 @@ def get_hf_processor(self, **kwargs: object) -> LlavaLikeProcessor: def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]: return {"image": None} - def _apply_feature_select_strategy( - self, - strategy: str, - encoder_num_image_tokens: int, - ) -> int: - if strategy == "default": - return encoder_num_image_tokens - 1 - if strategy == "full": - return encoder_num_image_tokens - - msg = f"Unexpected feature select strategy: {strategy!r}" - raise NotImplementedError(msg) - def get_num_image_tokens( self, *, @@ -169,12 +156,12 @@ def get_num_image_tokens( hf_config = self.get_hf_config() vision_encoder_info = self.get_vision_encoder_info() - return self._apply_feature_select_strategy( - hf_config.vision_feature_select_strategy, + return get_num_selected_vision_tokens( vision_encoder_info.get_num_image_tokens( image_width=image_width, image_height=image_height, ), + hf_config.vision_feature_select_strategy, ) def get_image_size_with_most_features(self) -> ImageSize: diff --git a/vllm/model_executor/models/llava_next.py b/vllm/model_executor/models/llava_next.py index 3f7e39c02061..70fd0b2e5efb 100644 --- a/vllm/model_executor/models/llava_next.py +++ b/vllm/model_executor/models/llava_next.py @@ -27,6 +27,7 @@ from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, init_vllm_registered_model, maybe_prefix) +from .vision import get_num_selected_vision_tokens class LlavaNextImagePixelInputs(TensorSchema): @@ -95,12 +96,12 @@ def get_num_image_tokens( hf_config = self.get_hf_config() vision_encoder_info = self.get_vision_encoder_info() - base_feature_size = self._apply_feature_select_strategy( - hf_config.vision_feature_select_strategy, + base_feature_size = get_num_selected_vision_tokens( vision_encoder_info.get_num_image_tokens( image_width=image_width, image_height=image_height, ), + hf_config.vision_feature_select_strategy, ) num_patch_height, num_patch_width = get_anyres_image_grid_shape( diff --git a/vllm/model_executor/models/tarsier.py b/vllm/model_executor/models/tarsier.py index ed02fe2c389f..8759c4ea4a64 100644 --- a/vllm/model_executor/models/tarsier.py +++ b/vllm/model_executor/models/tarsier.py @@ -40,7 +40,8 @@ from .siglip import SiglipVisionModel from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, maybe_prefix) -from .vision import VisionEncoderInfo, get_vision_encoder_info +from .vision import (VisionEncoderInfo, get_num_selected_vision_tokens, + get_vision_encoder_info) class TarsierImagePixelInputs(TensorSchema): @@ -201,18 +202,6 @@ def get_hf_processor(self, **kwargs: object) -> TarsierProcessor: def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]: return {"image": None} - def _apply_feature_select_strategy( - self, - strategy: str, - encoder_num_image_tokens: int, - ) -> int: - if strategy == "default": - return encoder_num_image_tokens - 1 - if strategy == "full": - return encoder_num_image_tokens - msg = f"Unexpected feature select strategy: {strategy!r}" - raise NotImplementedError(msg) - def get_num_image_tokens( self, *, @@ -221,21 +210,21 @@ def get_num_image_tokens( ) -> int: hf_config = self.get_hf_config() vision_encoder_info = self.get_vision_encoder_info() - num_projected_patches = self._apply_feature_select_strategy( - hf_config.vision_feature_select_strategy, + num_projected_patches = get_num_selected_vision_tokens( vision_encoder_info.get_num_image_tokens( image_width=image_width, image_height=image_height, ), + hf_config.vision_feature_select_strategy, ) if num_projected_patches <= 0: default_size = self.get_image_size_with_most_features() - num_projected_patches_default = self._apply_feature_select_strategy( - hf_config.vision_feature_select_strategy, + num_projected_patches_default = get_num_selected_vision_tokens( vision_encoder_info.get_num_image_tokens( image_width=default_size.width, image_height=default_size.height, ), + hf_config.vision_feature_select_strategy, ) if num_projected_patches_default <= 0: raise ValueError( diff --git a/vllm/model_executor/models/vision.py b/vllm/model_executor/models/vision.py index e077691fcec2..3d16d71e1764 100644 --- a/vllm/model_executor/models/vision.py +++ b/vllm/model_executor/models/vision.py @@ -9,7 +9,6 @@ import torch from transformers import PretrainedConfig -from typing_extensions import assert_never from vllm.distributed import (get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, @@ -22,9 +21,13 @@ _C = TypeVar("_C", bound=PretrainedConfig) +class _RootConfig(Protocol[_C]): + vision_config: _C + + class VisionEncoderInfo(ABC, Generic[_C]): - def __init__(self, hf_config: _C) -> None: + def __init__(self, hf_config: _RootConfig[_C]) -> None: super().__init__() self.hf_config = hf_config @@ -95,7 +98,7 @@ def get_vit_attn_backend(head_size: int, dtype: torch.dtype) -> _Backend: def _get_vision_feature_selector( - strategy: VisionFeatureSelectStrategy, + strategy: Union[VisionFeatureSelectStrategy, str], ) -> Callable[[torch.Tensor], torch.Tensor]: if callable(strategy): return strategy @@ -111,7 +114,28 @@ def _get_vision_feature_selector( if strategy == "full": return lambda feats: feats - assert_never(strategy) + raise ValueError(f"Unexpected feature select strategy: {strategy!r}") + + +def get_num_selected_vision_tokens( + num_vision_tokens: int, + strategy: Union[VisionFeatureSelectStrategy, str], +) -> int: + if callable(strategy): + dummy_features = torch.empty(1, num_vision_tokens, 64) # [B, L, D] + dummy_selected_features = strategy(dummy_features) + return dummy_selected_features.shape[1] + + if strategy == "class": + return 1 + + if strategy == "default": + return num_vision_tokens - 1 + + if strategy == "full": + return num_vision_tokens + + raise ValueError(f"Unexpected feature select strategy: {strategy!r}") def resolve_visual_encoder_outputs( From d4ac9acc865a9e6a635ab3ac5eb4aa2741916ccb Mon Sep 17 00:00:00 2001 From: Harry Mellor <19981378+hmellor@users.noreply.github.com> Date: Wed, 1 Oct 2025 10:03:44 +0100 Subject: [PATCH 134/166] [CI] Only capture a single CUDA graph size in CI by default (#25951) Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> --- tests/conftest.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/conftest.py b/tests/conftest.py index a50985a465e6..e3e412f90af5 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -731,6 +731,9 @@ def __init__( init_ctx = (nullcontext() if default_torch_num_threads is None else set_default_torch_num_threads(default_torch_num_threads)) + if not kwargs.get("compilation_config", None): + kwargs["compilation_config"] = {"cudagraph_capture_sizes": [8]} + with init_ctx: self.llm = LLM( model=model_name, From ac12bfe1f888d75994b8b8b3d3ebf6d100ec06c0 Mon Sep 17 00:00:00 2001 From: billishyahao Date: Wed, 1 Oct 2025 20:39:45 +0800 Subject: [PATCH 135/166] [MISC] Fix misleading batch_size_capture_list when cuda_graph_sizes < 4 (#25829) Signed-off-by: billishyahao Co-authored-by: Luka Govedic --- vllm/config/vllm.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py index 585d3997cc3a..107df502e08e 100644 --- a/vllm/config/vllm.py +++ b/vllm/config/vllm.py @@ -580,9 +580,12 @@ def _set_cudagraph_sizes(self): not self.model_config.enforce_eager: cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes if len(cuda_graph_sizes) == 1: - batch_size_capture_list = [1, 2, 4] + [ - i for i in range(8, cuda_graph_sizes[0] + 1, 8) - ] + max_graph_size = cuda_graph_sizes[0] + assert max_graph_size >= 1, "Maximum cudagraph size should be" \ + " greater than or equal to 1." + batch_size_capture_list = [ + i for i in [1, 2, 4] if i <= max_graph_size + ] + list(range(8, max_graph_size + 1, 8)) elif len(cuda_graph_sizes) > 1: batch_size_capture_list = sorted(cuda_graph_sizes) else: From 065f4b6ffbfc95329ed9b1138dea0ffcb8f18d75 Mon Sep 17 00:00:00 2001 From: Nathan Scott Date: Wed, 1 Oct 2025 22:41:57 +1000 Subject: [PATCH 136/166] [Benchmark] Finish documented v0.11.0 deprecation of --endpoint-type (#26007) Signed-off-by: Nathan Scott --- vllm/benchmarks/serve.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/vllm/benchmarks/serve.py b/vllm/benchmarks/serve.py index 2a042802d0d5..a327e711c208 100644 --- a/vllm/benchmarks/serve.py +++ b/vllm/benchmarks/serve.py @@ -52,21 +52,6 @@ and (shutil.which("gnuplot") is not None)) -# TODO: Remove this in v0.11.0 -class DeprecatedEndpointTypeAction(argparse.Action): - """Argparse action for the deprecated --endpoint-type flag. - """ - - def __call__(self, _, namespace, values, option_string=None): - warnings.warn( - "'--endpoint-type' is deprecated and will be removed in v0.11.0. " - "Please use '--backend' instead or remove this argument if you " - "have already set it.", - stacklevel=1, - ) - setattr(namespace, self.dest, values) - - class TaskType(Enum): GENERATION = "generation" EMBEDDING = "embedding" @@ -883,15 +868,6 @@ def add_cli_args(parser: argparse.ArgumentParser): choices=list(ASYNC_REQUEST_FUNCS.keys()), help="The type of backend or endpoint to use for the benchmark." ) - parser.add_argument( - "--endpoint-type", - type=str, - default=None, - choices=list(ASYNC_REQUEST_FUNCS.keys()), - action=DeprecatedEndpointTypeAction, - help="'--endpoint-type' is deprecated and will be removed in v0.11.0. " - "Please use '--backend' instead.", - ) parser.add_argument( "--base-url", type=str, From 8dad1a0a02ee54bd3bf7a20c0bb3cc960e36e872 Mon Sep 17 00:00:00 2001 From: Kenichi Maehashi <939877+kmaehashi@users.noreply.github.com> Date: Wed, 1 Oct 2025 23:37:35 +0900 Subject: [PATCH 137/166] [Bugfix] Apply same sampling parameters for both `n=1` and `n>1` (#26005) Signed-off-by: Kenichi Maehashi --- vllm/v1/engine/async_llm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index 757baecea9ce..574f8b19c379 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -290,7 +290,7 @@ async def add_request( return queue # Fan out child requests (for n>1). - parent_request = ParentRequest(request_id, params) + parent_request = ParentRequest(request_id, request.sampling_params) for idx in range(params.n): request_id, params = parent_request.get_child_info(idx) child_request = request if idx == params.n - 1 else copy(request) From 4e653480d4bcc1d6c69c941625c66c90d8f2ad9b Mon Sep 17 00:00:00 2001 From: Johnny Date: Wed, 1 Oct 2025 19:50:54 +0200 Subject: [PATCH 138/166] [NVIDIA] Blackwell Family (#24673) Signed-off-by: Johnny Signed-off-by: johnnynunez Signed-off-by: Johnny Signed-off-by: Salvatore Cena Co-authored-by: Aidyn-A <31858918+Aidyn-A@users.noreply.github.com> Co-authored-by: Salvatore Cena --- CMakeLists.txt | 63 ++++++++++++++++--- cmake/utils.cmake | 8 +-- ...scaled_mm_blockwise_sm100_fp8_dispatch.cuh | 10 +-- .../c3x/scaled_mm_sm100_fp8_dispatch.cuh | 2 +- .../cutlass_w8a8/scaled_mm_entry.cu | 5 +- 5 files changed, 66 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 180b896a7aba..f07ca9282563 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -86,6 +86,9 @@ find_package(Torch REQUIRED) # Supported NVIDIA architectures. # This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND + CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0) + set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;11.0;12.0") +elseif(DEFINED CMAKE_CUDA_COMPILER_VERSION AND CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.8) set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0") else() @@ -175,6 +178,15 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA") list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}") endif() +# +# Set compression mode for CUDA >=13.x. +# +if(VLLM_GPU_LANG STREQUAL "CUDA" AND + DEFINED CMAKE_CUDA_COMPILER_VERSION AND + CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0) + list(APPEND VLLM_GPU_FLAGS "--compress-mode=size") +endif() + # # Set CUDA include flags for CXX compiler. # @@ -270,7 +282,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library") # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building. - set(CUTLASS_REVISION "v4.0.0" CACHE STRING "CUTLASS revision to use") + set(CUTLASS_REVISION "v4.2.1" CACHE STRING "CUTLASS revision to use") # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR}) @@ -305,7 +317,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu" "csrc/quantization/fp4/nvfp4_quant_entry.cu" "csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu" - "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu" "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu" "csrc/cutlass_extensions/common.cpp" "csrc/quantization/fp8/per_token_group_quant.cu") @@ -440,7 +451,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") # The cutlass_scaled_mm kernels for Geforce Blackwell SM120 (c3x, i.e. CUTLASS 3.x) require # CUDA 12.8 or later - cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0;12.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS) set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu" @@ -470,7 +485,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") # The cutlass_scaled_mm kernels for Blackwell SM100 (c3x, i.e. CUTLASS 3.x) # require CUDA 12.8 or later - cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS) set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu" @@ -550,7 +569,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") # The nvfp4_scaled_mm_sm120 kernels for Geforce Blackwell SM120 require # CUDA 12.8 or later - cuda_archs_loose_intersection(FP4_ARCHS "12.0;12.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(FP4_ARCHS "12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(FP4_ARCHS "12.0a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS) set(SRCS "csrc/quantization/fp4/nvfp4_quant_kernels.cu" @@ -569,7 +592,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") endif() # FP4 Archs and flags - cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(FP4_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(FP4_ARCHS "10.0a;10.1a;12.0a;12.1a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS) set(SRCS "csrc/quantization/fp4/nvfp4_quant_kernels.cu" @@ -591,7 +618,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") endif() # CUTLASS MLA Archs and flags - cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(MLA_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(MLA_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS) set(SRCS "csrc/attention/mla/sm100_cutlass_mla_kernel.cu") @@ -635,7 +666,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") endif() endif() - cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS) set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.cu") set_gencode_flags_for_srcs( @@ -656,7 +691,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") endif() # moe_data.cu is used by all CUTLASS MoE kernels. - cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0f;11.0f;12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS) set(SRCS "csrc/quantization/cutlass_w8a8/moe/moe_data.cu") set_gencode_flags_for_srcs( @@ -675,7 +714,11 @@ if(VLLM_GPU_LANG STREQUAL "CUDA") endif() endif() - cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}") + if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0) + cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f;12.0f" "${CUDA_ARCHS}") + else() + cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}") + endif() if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS) set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu") set_gencode_flags_for_srcs( diff --git a/cmake/utils.cmake b/cmake/utils.cmake index 8558976e2c39..f6a0d2b75be1 100644 --- a/cmake/utils.cmake +++ b/cmake/utils.cmake @@ -310,13 +310,13 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR list(REMOVE_DUPLICATES _PTX_ARCHS) list(REMOVE_DUPLICATES _SRC_CUDA_ARCHS) - # if x.0a is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should - # remove x.0a from SRC_CUDA_ARCHS and add x.0a to _CUDA_ARCHS + # If x.0a or x.0f is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should + # remove x.0a or x.0f from SRC_CUDA_ARCHS and add x.0a or x.0f to _CUDA_ARCHS set(_CUDA_ARCHS) foreach(_arch ${_SRC_CUDA_ARCHS}) - if(_arch MATCHES "\\a$") + if(_arch MATCHES "[af]$") list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}") - string(REPLACE "a" "" _base "${_arch}") + string(REGEX REPLACE "[af]$" "" _base "${_arch}") if ("${_base}" IN_LIST TGT_CUDA_ARCHS) list(REMOVE_ITEM _TGT_CUDA_ARCHS "${_base}") list(APPEND _CUDA_ARCHS "${_arch}") diff --git a/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh b/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh index dbf79a065115..e7bb061ba024 100644 --- a/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh +++ b/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh @@ -231,7 +231,7 @@ void cutlass_gemm_blockwise_sm100_fp8_dispatch(torch::Tensor& out, } else { cutlass_gemm_caller_blockwise, Int>, - Shape<_1, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized1Sm, + Shape<_1, _1, _1>, cutlass::epilogue::BlockwiseNoSmemWarpSpecialized1Sm, cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>( out, a, b, a_scales, b_scales); } @@ -245,7 +245,7 @@ void cutlass_gemm_blockwise_sm100_fp8_dispatch(torch::Tensor& out, } else { cutlass_gemm_caller_blockwise, Int>, - Shape<_1, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized1Sm, + Shape<_1, _1, _1>, cutlass::epilogue::BlockwiseNoSmemWarpSpecialized1Sm, cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>( out, a, b, a_scales, b_scales); } @@ -259,7 +259,7 @@ void cutlass_gemm_blockwise_sm100_fp8_dispatch(torch::Tensor& out, } else { cutlass_gemm_caller_blockwise, Int>, - Shape<_2, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized2Sm, + Shape<_2, _1, _1>, cutlass::epilogue::BlockwiseNoSmemWarpSpecialized2Sm, cutlass::gemm::KernelTmaWarpSpecializedBlockwise2SmSm100>>( out, a, b, a_scales, b_scales); } @@ -271,10 +271,10 @@ void cutlass_gemm_blockwise_sm100_fp8_dispatch(torch::Tensor& out, // TMA epilogue isn't compatible with Swap A/B cutlass_gemm_caller_blockwise, Int, Int>, - Shape<_1, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized1Sm, + Shape<_1, _1, _1>, cutlass::epilogue::BlockwiseNoSmemWarpSpecialized1Sm, cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100, true>>( out, a, b, a_scales, b_scales); } } -} // namespace vllm +} // namespace vllm \ No newline at end of file diff --git a/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8_dispatch.cuh b/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8_dispatch.cuh index 24564efbd21b..f876b7d9acd8 100644 --- a/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8_dispatch.cuh +++ b/csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8_dispatch.cuh @@ -133,4 +133,4 @@ void cutlass_scaled_mm_sm100_fp8_epilogue(torch::Tensor& out, } } -} // namespace vllm \ No newline at end of file +} // namespace vllm diff --git a/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu b/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu index 84843ee6e094..04b64a35da37 100644 --- a/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu +++ b/csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu @@ -67,8 +67,9 @@ void cutlass_scaled_mm_sm100(torch::Tensor& c, torch::Tensor const& a, std::optional const& bias); #endif -#if defined(ENABLE_SCALED_MM_SM90) && ENABLE_SCALED_MM_SM90 || \ - defined(ENABLE_SCALED_MM_SM100) && ENABLE_SCALED_MM_SM100 +#if defined(ENABLE_SCALED_MM_SM90) && ENABLE_SCALED_MM_SM90 || \ + defined(ENABLE_SCALED_MM_SM100) && ENABLE_SCALED_MM_SM100 || \ + defined(ENABLE_SCALED_MM_SM120) && ENABLE_SCALED_MM_SM120 void get_cutlass_moe_mm_data_caller( const torch::Tensor& topk_ids, torch::Tensor& expert_offsets, torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2, From 83aa1f5c31790f6edc2626cc7571bdd4418704a4 Mon Sep 17 00:00:00 2001 From: Huamin Li <3ericli@gmail.com> Date: Wed, 1 Oct 2025 11:18:36 -0700 Subject: [PATCH 139/166] Fix test_mamba_ssm_ssd.py due to missing _query_start_loc_to_chunk_indices_offsets (#25995) Signed-off-by: Huamin Li <3ericli@gmail.com> --- tests/kernels/mamba/test_mamba_ssm_ssd.py | 111 ++++++++++------------ vllm/v1/attention/backends/mamba2_attn.py | 70 ++++++++++++++ 2 files changed, 118 insertions(+), 63 deletions(-) diff --git a/tests/kernels/mamba/test_mamba_ssm_ssd.py b/tests/kernels/mamba/test_mamba_ssm_ssd.py index 927af32588e6..9798b27cae76 100644 --- a/tests/kernels/mamba/test_mamba_ssm_ssd.py +++ b/tests/kernels/mamba/test_mamba_ssm_ssd.py @@ -10,7 +10,7 @@ mamba_chunk_scan_combined_varlen) from vllm.platforms import current_platform from vllm.v1.attention.backends.mamba2_attn import ( - _query_start_loc_to_chunk_indices_offsets) + compute_varlen_chunk_metadata) # Added by the IBM Team, 2024 @@ -225,13 +225,9 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, Y_min, final_state_min = ssd_minimal_discrete(X * dt.unsqueeze(-1), A * dt, B, C, chunk_size) - cu_seqlens = torch.tensor((0, seqlen), device='cuda').cumsum(dim=0) - seq_idx = torch.zeros(seqlen, dtype=torch.int32, device=cu_seqlens.device) - - chunk_indices, chunk_offsets = \ - _query_start_loc_to_chunk_indices_offsets( - cu_seqlens, chunk_size, cu_seqlens[-1]) - + cu_seqlens = torch.tensor((0, seqlen), device="cuda").cumsum(dim=0) + cu_chunk_seqlens, last_chunk_indices, seq_idx_chunks = ( + compute_varlen_chunk_metadata(cu_seqlens, chunk_size)) # varlen has implicit batch=1 X = X.squeeze(0) dt = dt.squeeze(0) @@ -239,18 +235,20 @@ def test_mamba_chunk_scan_single_example(d_head, n_heads, seq_len_chunk_size, B = B.squeeze(0) C = C.squeeze(0) Y = torch.empty_like(X) - final_state = mamba_chunk_scan_combined_varlen(X, - dt, - A, - B, - C, - chunk_size, - D=None, - cu_seqlens=cu_seqlens, - seq_idx=seq_idx, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, - out=Y) + final_state = mamba_chunk_scan_combined_varlen( + X, + dt, + A, + B, + C, + chunk_size, + cu_seqlens=cu_seqlens.to(torch.int32), + cu_chunk_seqlens=cu_chunk_seqlens, + last_chunk_indices=last_chunk_indices, + seq_idx=seq_idx_chunks, + out=Y, + D=None, + ) # just test the last in sequence torch.testing.assert_close(Y[-1], Y_min[0, -1], atol=atol, rtol=rtol) @@ -312,14 +310,13 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases, exhausted: dict = {} # map: eg -> boolean indicating example is exhausted states = None - for Y_min, cu_seqlens, seq_idx, ( + for Y_min, cu_seqlens, _token_seq_idx, ( A, dt, X, B, C) in generate_continuous_batched_examples( cases, num_examples, seqlen, last_taken, exhausted, n_heads, d_head, itype): - chunk_indices, chunk_offsets = \ - _query_start_loc_to_chunk_indices_offsets( - cu_seqlens, chunk_size, cu_seqlens[-1]) + cu_chunk_seqlens, last_chunk_indices, seq_idx_chunks = ( + compute_varlen_chunk_metadata(cu_seqlens, chunk_size)) Y = torch.empty_like(X) new_states = mamba_chunk_scan_combined_varlen( @@ -329,13 +326,13 @@ def test_mamba_chunk_scan_cont_batch(d_head, n_heads, seq_len_chunk_size_cases, B, C, chunk_size, + cu_seqlens=cu_seqlens.to(torch.int32), + cu_chunk_seqlens=cu_chunk_seqlens, + last_chunk_indices=last_chunk_indices, + seq_idx=seq_idx_chunks, + out=Y, D=None, - cu_seqlens=cu_seqlens, - seq_idx=seq_idx, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, initial_states=states, - out=Y, ) # just test the last in sequence @@ -403,9 +400,8 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): device = X.device ## full seqlen computation - chunk_indices, chunk_offsets = \ - _query_start_loc_to_chunk_indices_offsets( - cu_seqlens, chunk_size, cu_seqlens[-1]) + cu_chunk_seqlens, last_chunk_indices, seq_idx_chunks = ( + compute_varlen_chunk_metadata(cu_seqlens, chunk_size)) Y_ref = torch.empty_like(X) state_ref = mamba_chunk_scan_combined_varlen( X, @@ -414,13 +410,13 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): B, C, chunk_size, + cu_seqlens=cu_seqlens.to(torch.int32), + cu_chunk_seqlens=cu_chunk_seqlens, + last_chunk_indices=last_chunk_indices, + seq_idx=seq_idx_chunks, + out=Y_ref, D=None, - cu_seqlens=cu_seqlens, - seq_idx=seq_idx, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, initial_states=None, - out=Y_ref, ) ## chunked seqlen computation @@ -431,10 +427,6 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): torch.cumsum(chunked_seqlens, dim=0) ], dim=0) - chunked_seq_idx = torch.repeat_interleave( - torch.arange(len(chunked_seqlens), device=device), - chunked_seqlens, - output_size=chunked_cu_seqlens[-1]).to(torch.int32) chunked_input_seq_len = chunked_cu_seqlens[-1] X_chunked = torch.zeros_like(X)[:chunked_input_seq_len, ...] dt_chunked = torch.zeros_like(dt)[:chunked_input_seq_len, ...] @@ -450,9 +442,8 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): C_chunked[chunked_cu_seqlens[i]:chunked_cu_seqlens[i+1], ...] = chunk_f(C, i) # noqa: E501 # fmt: on - chunk_indices, chunk_offsets = \ - _query_start_loc_to_chunk_indices_offsets( - chunked_cu_seqlens, chunk_size, chunked_cu_seqlens[-1]) + cu_chunk_seqlens, last_chunk_indices, seq_idx_chunks = ( + compute_varlen_chunk_metadata(chunked_cu_seqlens, chunk_size)) Y_partial = torch.empty_like(X_chunked) partial_state = mamba_chunk_scan_combined_varlen( X_chunked, @@ -461,13 +452,13 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): B_chunked, C_chunked, chunk_size, + cu_seqlens=chunked_cu_seqlens.to(torch.int32), + cu_chunk_seqlens=cu_chunk_seqlens, + last_chunk_indices=last_chunk_indices, + seq_idx=seq_idx_chunks, + out=Y_partial, D=None, - cu_seqlens=chunked_cu_seqlens, - seq_idx=chunked_seq_idx, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, initial_states=None, - out=Y_partial, ) # remaining chunk @@ -477,10 +468,6 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): torch.cumsum(remaining_chunked_seqlens, dim=0) ], dim=0) - remaining_chunked_seq_idx = torch.repeat_interleave( - torch.arange(len(remaining_chunked_seqlens), device=device), - remaining_chunked_seqlens, - output_size=remaining_chunked_cu_seqlens[-1]).to(torch.int32) remaining_chunked_input_seq_len = remaining_chunked_cu_seqlens[-1] # fmt: off remaining_X_chunked = torch.zeros_like(X)[:remaining_chunked_input_seq_len, ...] # noqa: E501 @@ -509,11 +496,9 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): assert concat_batch_f(B_chunked, remaining_B_chunked).equal(B) assert concat_batch_f(C_chunked, remaining_C_chunked).equal(C) - chunk_indices, chunk_offsets = \ - _query_start_loc_to_chunk_indices_offsets( - remaining_chunked_cu_seqlens, - chunk_size, - remaining_chunked_cu_seqlens[-1]) + cu_chunk_seqlens, last_chunk_indices, seq_idx_chunks = ( + compute_varlen_chunk_metadata(remaining_chunked_cu_seqlens, + chunk_size)) Y_chunked = torch.empty_like(remaining_X_chunked) state_chunked = mamba_chunk_scan_combined_varlen( @@ -523,13 +508,13 @@ def test_mamba_chunk_scan_cont_batch_prefill_chunking(chunk_size, seqlens): remaining_B_chunked, remaining_C_chunked, chunk_size, + cu_seqlens=remaining_chunked_cu_seqlens.to(torch.int32), + cu_chunk_seqlens=cu_chunk_seqlens, + last_chunk_indices=last_chunk_indices, + seq_idx=seq_idx_chunks, + out=Y_chunked, D=None, - cu_seqlens=remaining_chunked_cu_seqlens, - seq_idx=remaining_chunked_seq_idx, - chunk_indices=chunk_indices, - chunk_offsets=chunk_offsets, initial_states=partial_state, - out=Y_chunked, ) Y = concat_batch_f(Y_partial, Y_chunked) diff --git a/vllm/v1/attention/backends/mamba2_attn.py b/vllm/v1/attention/backends/mamba2_attn.py index e4f16f37a430..68b6ff73ba3f 100644 --- a/vllm/v1/attention/backends/mamba2_attn.py +++ b/vllm/v1/attention/backends/mamba2_attn.py @@ -1,5 +1,6 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import itertools from dataclasses import dataclass from typing import Optional @@ -17,6 +18,75 @@ from vllm.v1.kv_cache_interface import AttentionSpec +def compute_varlen_chunk_metadata( + query_start_loc: torch.Tensor, + chunk_size: int, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Build chunk-aligned, variable-length metadata used by Mamba2 SSD kernels. + + Given per-sequence cumulative token starts `query_start_loc` of shape [B+1] + and a physical `chunk_size`, returns three tensors on the same device: + - cu_chunk_seqlens: (nchunks+1,) int32 exclusive prefix-sum of + logical-chunk lengths (each logical chunk never crosses a sequence or + physical-chunk boundary). + - last_chunk_indices: (B,) int32 index of the last logical chunk + for each sequence (=-1 for empty sequences). + - seq_idx_chunks: (nchunks,) int32 sequence index for each logical + chunk in order. + + This is intentionally lightweight and CPU-side; it mirrors the metadata + produced by the V1 Mamba2 meta-data builder and is exported so tests + (and other callers) can avoid duplicating the logic. + """ + assert query_start_loc.ndim == 1, "query_start_loc must be 1-D [B+1]" + assert int(query_start_loc[0].item()) == 0, "query_start_loc[0] must be 0" + device = query_start_loc.device + + qsl64 = query_start_loc.to(torch.int64) + starts = qsl64[:-1].tolist() + ends = qsl64[1:].tolist() + total = int(qsl64[-1].item()) + + chunk_lens: list[int] = [] + seq_idx_chunks: list[int] = [] + last_chunk_indices: list[int] = [-1] * len(starts) + + for b, (s, e) in enumerate(zip(starts, ends)): + if e <= s: + # empty sequence + continue + pos = s + while pos < e: + # split at both sequence boundaries and physical chunk boundaries + room = chunk_size - (pos % chunk_size) + take = min(room, e - pos) + chunk_lens.append(int(take)) + seq_idx_chunks.append(b) + last_chunk_indices[b] = len(chunk_lens) - 1 + pos += take + + # Exclusive prefix sum over logical-chunk lengths + if chunk_lens: + cu_chunk_seqlens = torch.tensor([0] + + list(itertools.accumulate(chunk_lens)), + device=device, + dtype=torch.int32) + # Final boundary must equal total tokens + assert int(cu_chunk_seqlens[-1].item()) == total + else: + cu_chunk_seqlens = torch.tensor([0], device=device, dtype=torch.int32) + + last_chunk_indices_t = (torch.tensor( + last_chunk_indices, device=device, dtype=torch.int32) + if len(starts) > 0 else torch.empty( + (0, ), device=device, dtype=torch.int32)) + seq_idx_chunks_t = torch.tensor(seq_idx_chunks, + device=device, + dtype=torch.int32) + return cu_chunk_seqlens, last_chunk_indices_t, seq_idx_chunks_t + + class Mamba2AttentionBackend(AttentionBackend): @staticmethod From 4b9738db48a5ec5965217fa155ebc9eaeaf3965e Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Wed, 1 Oct 2025 15:02:17 -0400 Subject: [PATCH 140/166] [CI] Tweaks to GPT-OSS Eval (Blackwell) for stability (#26030) Signed-off-by: mgoin --- .buildkite/test-pipeline.yaml | 2 +- tests/evals/gpt_oss/test_gpqa_correctness.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index b3d10f75ab50..c131192c56fc 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -845,7 +845,7 @@ steps: - vllm/v1/attention/backends/flashinfer.py commands: - uv pip install --system 'gpt-oss[eval]==0.0.5' - - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58 --server-args '--tensor-parallel-size 2' + - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58 - label: Blackwell Quantized MoE Test timeout_in_minutes: 60 diff --git a/tests/evals/gpt_oss/test_gpqa_correctness.py b/tests/evals/gpt_oss/test_gpqa_correctness.py index 4cc4041a60ce..07c04f00cd0d 100644 --- a/tests/evals/gpt_oss/test_gpqa_correctness.py +++ b/tests/evals/gpt_oss/test_gpqa_correctness.py @@ -26,7 +26,8 @@ def run_gpqa_eval(model_name: str, base_url: str) -> float: # Build the command to run the evaluation cmd = [ sys.executable, "-m", "gpt_oss.evals", "--eval", "gpqa", "--model", - model_name, "--reasoning-effort", "low", "--base-url", base_url + model_name, "--reasoning-effort", "low", "--base-url", base_url, + "--n-threads", "200" ] try: @@ -72,8 +73,6 @@ def test_gpqa_correctness(request): # Add standard server arguments server_args.extend([ - "--max-model-len", - "32768", "--trust-remote-code", ]) From 30ea5a5643685d5e3a7bb8e56c9a203b4163d53d Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Wed, 1 Oct 2025 15:30:00 -0400 Subject: [PATCH 141/166] [BugFix][DP/EP] Fix CUTLASS MLA hang under load (#26026) Signed-off-by: Lucas Wilkinson Co-authored-by: Robert Shaw Co-authored-by: rshaw@neuralmagic.com --- .../sm100_fmha_mla_tma_warpspecialized.hpp | 64 +++++++++---------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/csrc/attention/mla/cutlass_sm100_mla/kernel/sm100_fmha_mla_tma_warpspecialized.hpp b/csrc/attention/mla/cutlass_sm100_mla/kernel/sm100_fmha_mla_tma_warpspecialized.hpp index 2cbc2379579e..1f62c37ba4b7 100644 --- a/csrc/attention/mla/cutlass_sm100_mla/kernel/sm100_fmha_mla_tma_warpspecialized.hpp +++ b/csrc/attention/mla/cutlass_sm100_mla/kernel/sm100_fmha_mla_tma_warpspecialized.hpp @@ -580,22 +580,22 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { for (; tile_scheduler.is_valid(); ++tile_scheduler) { auto blk_coord = tile_scheduler.get_block_coord(); auto problem_shape = params.problem_shape; - auto local_split_kv = params.split_kv; + auto local_split_kv = params.split_kv; if (params.mainloop.ptr_seq != nullptr) { get<1>(problem_shape) = params.mainloop.ptr_seq[get<2>(blk_coord)]; - if (params.ptr_split_kv != nullptr) { + if (params.ptr_split_kv != nullptr) { local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; } } - if (local_split_kv <= get<3>(blk_coord)) - continue; + if (local_split_kv <= get<3>(blk_coord)) + continue; load_page_table( blk_coord, problem_shape, params.mainloop, shared_storage.tensors, pipeline_page_table, pipeline_pt_producer_state, - local_split_kv + local_split_kv ); } } @@ -604,15 +604,15 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { CUTLASS_PRAGMA_NO_UNROLL for (; tile_scheduler.is_valid(); ++tile_scheduler) { auto blk_coord = tile_scheduler.get_block_coord(); - auto problem_shape = params.problem_shape; - auto local_split_kv = params.split_kv; + auto problem_shape = params.problem_shape; + auto local_split_kv = params.split_kv; if (params.mainloop.ptr_seq != nullptr) { get<1>(problem_shape) = params.mainloop.ptr_seq[get<2>(blk_coord)]; - if (params.ptr_split_kv != nullptr) { + if (params.ptr_split_kv != nullptr) { local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; } } - if (local_split_kv <= get<3>(blk_coord)) + if (local_split_kv <= get<3>(blk_coord)) continue; load_cpasync( blk_coord, @@ -621,7 +621,7 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { params.mainloop_params, shared_storage.tensors, pipeline_load_qk, pipeline_load_qk_producer_state, - local_split_kv, + local_split_kv, /* must be shared pipe */ pipeline_page_table, pipeline_pt_consumer_state ); @@ -633,15 +633,15 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { CUTLASS_PRAGMA_NO_UNROLL for (; tile_scheduler.is_valid(); ++tile_scheduler) { auto blk_coord = tile_scheduler.get_block_coord(); - auto problem_shape = params.problem_shape; - auto local_split_kv = params.split_kv; + auto problem_shape = params.problem_shape; + auto local_split_kv = params.split_kv; if (params.mainloop.ptr_seq != nullptr) { get<1>(problem_shape) = params.mainloop.ptr_seq[get<2>(blk_coord)]; - if (params.ptr_split_kv != nullptr) { - local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; - } + if (params.ptr_split_kv != nullptr) { + local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; + } } - if (local_split_kv <= get<3>(blk_coord)) + if (local_split_kv <= get<3>(blk_coord)) continue; load_tma( blk_coord, @@ -651,7 +651,7 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { shared_storage.tensors, pipeline_load_qk, pipeline_load_qk_producer_state, pipeline_load_qk, pipeline_load_qk_producer_state, - local_split_kv + local_split_kv ); cutlass::arch::NamedBarrier((kNumComputeWarps + kNumLoadWarps) * NumThreadsPerWarp, kNamedBarrierEpilogue).arrive_and_wait(); } @@ -660,15 +660,15 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { CUTLASS_PRAGMA_NO_UNROLL for (; tile_scheduler.is_valid(); ++tile_scheduler) { auto blk_coord = tile_scheduler.get_block_coord(); - auto problem_shape = params.problem_shape; - auto local_split_kv = params.split_kv; + auto problem_shape = params.problem_shape; + auto local_split_kv = params.split_kv; if (params.mainloop.ptr_seq != nullptr) { get<1>(problem_shape) = params.mainloop.ptr_seq[get<2>(blk_coord)]; - if (params.ptr_split_kv != nullptr) { + if (params.ptr_split_kv != nullptr) { local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; - } + } } - if (local_split_kv <= get<3>(blk_coord)) + if (local_split_kv <= get<3>(blk_coord)) continue; load_tma( blk_coord, @@ -678,7 +678,7 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { shared_storage.tensors, pipeline_load_qk, pipeline_load_qk_producer_state, pipeline_load_qk, pipeline_load_qk_producer_state, - local_split_kv + local_split_kv ); cutlass::arch::NamedBarrier((kNumComputeWarps + kNumLoadWarps) * NumThreadsPerWarp, kNamedBarrierEpilogue).arrive_and_wait(); } @@ -694,14 +694,14 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { for (; tile_scheduler.is_valid(); ++tile_scheduler) { auto blk_coord = tile_scheduler.get_block_coord(); auto problem_shape = params.problem_shape; - auto local_split_kv = params.split_kv; + auto local_split_kv = params.split_kv; if (params.mainloop.ptr_seq != nullptr) { get<1>(problem_shape) = params.mainloop.ptr_seq[get<2>(blk_coord)]; if (params.ptr_split_kv != nullptr) { local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; } } - if (local_split_kv <= get<3>(blk_coord)) + if (local_split_kv <= get<3>(blk_coord)) continue; mma(blk_coord, problem_shape, @@ -711,7 +711,7 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { pipeline_mma_s, pipeline_mma_s_producer_state, pipeline_p_mma, pipeline_p_mma_consumer_state, pipeline_mma_o, pipeline_mma_o_producer_state, - local_split_kv + local_split_kv ); } } @@ -726,15 +726,15 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { for (; tile_scheduler.is_valid(); ++tile_scheduler) { auto blk_coord = tile_scheduler.get_block_coord(); auto problem_shape = params.problem_shape; - auto split_kv = params.split_kv; - auto local_split_kv = split_kv; + auto split_kv = params.split_kv; + auto local_split_kv = split_kv; if (params.mainloop.ptr_seq != nullptr) { get<1>(problem_shape) = params.mainloop.ptr_seq[get<2>(blk_coord)]; - if (params.ptr_split_kv != nullptr) { + if (params.ptr_split_kv != nullptr) { local_split_kv = params.ptr_split_kv[get<2>(blk_coord)]; } } - if (local_split_kv <= get<3>(blk_coord)) + if (local_split_kv <= get<3>(blk_coord)) continue; compute( blk_coord, @@ -745,7 +745,7 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { pipeline_mma_s, pipeline_mma_s_consumer_state, pipeline_p_mma, pipeline_p_mma_producer_state, pipeline_mma_o, pipeline_mma_o_consumer_state, - local_split_kv + local_split_kv ); } @@ -1900,7 +1900,7 @@ struct Sm100FmhaMlaKernelTmaWarpspecialized { cutlass::arch::NamedBarrier( (kNumComputeWarps + kNumLoadWarps) * NumThreadsPerWarp, kNamedBarrierEpilogue - ).arrive(); + ).arrive_and_wait(); return; } From 559ff41a268d42689380b2ddffa7a38cf8bb19b4 Mon Sep 17 00:00:00 2001 From: Hosang <156028780+hyoon1@users.noreply.github.com> Date: Wed, 1 Oct 2025 17:39:49 -0400 Subject: [PATCH 142/166] [ROCm][Build] Add support for AMD Ryzen AI MAX / AI 300 Series (#25908) Signed-off-by: Hosang Yoon --- CMakeLists.txt | 2 +- csrc/rocm/attention.cu | 3 ++- docker/Dockerfile.rocm_base | 4 ++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f07ca9282563..5f2c43fea284 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -37,7 +37,7 @@ install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS) set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12" "3.13") # Supported AMD GPU architectures. -set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201") +set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151") # # Supported/expected torch versions for CUDA/ROCm. diff --git a/csrc/rocm/attention.cu b/csrc/rocm/attention.cu index 133a545045b1..df3208a120f1 100644 --- a/csrc/rocm/attention.cu +++ b/csrc/rocm/attention.cu @@ -40,7 +40,8 @@ using __hip_fp8_e5m2 = __hip_fp8_e5m2_fnuz; #define __HIP__FP8MFMA__ #endif -#if defined(__HIPCC__) && (defined(__gfx1100__) || defined(__gfx1101__)) +#if defined(__HIPCC__) && (defined(__gfx1100__) || defined(__gfx1101__) || \ + defined(__gfx1150__) || defined(__gfx1151__)) #define __HIP__GFX11__ #endif diff --git a/docker/Dockerfile.rocm_base b/docker/Dockerfile.rocm_base index 87fcb18b1c03..873c2fbcd4d3 100644 --- a/docker/Dockerfile.rocm_base +++ b/docker/Dockerfile.rocm_base @@ -15,7 +15,7 @@ FROM ${BASE_IMAGE} AS base ENV PATH=/opt/rocm/llvm/bin:/opt/rocm/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin ENV ROCM_PATH=/opt/rocm ENV LD_LIBRARY_PATH=/opt/rocm/lib:/usr/local/lib: -ARG PYTORCH_ROCM_ARCH=gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201 +ARG PYTORCH_ROCM_ARCH=gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151 ENV PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} ENV AITER_ROCM_ARCH=gfx942;gfx950 @@ -141,4 +141,4 @@ RUN echo "BASE_IMAGE: ${BASE_IMAGE}" > /app/versions.txt \ && echo "FA_BRANCH: ${FA_BRANCH}" >> /app/versions.txt \ && echo "FA_REPO: ${FA_REPO}" >> /app/versions.txt \ && echo "AITER_BRANCH: ${AITER_BRANCH}" >> /app/versions.txt \ - && echo "AITER_REPO: ${AITER_REPO}" >> /app/versions.txt \ No newline at end of file + && echo "AITER_REPO: ${AITER_REPO}" >> /app/versions.txt From 752d3d5be7bfb465a6c1e08f62ee97f5679435ea Mon Sep 17 00:00:00 2001 From: Wentao Ye <44945378+yewentao256@users.noreply.github.com> Date: Wed, 1 Oct 2025 18:16:26 -0400 Subject: [PATCH 143/166] [Bug] Fix Negative Cuda Memory Usage (#25683) Signed-off-by: yewentao256 --- vllm/v1/worker/gpu_model_runner.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py index 9941cacae8ab..efb4a8c0054f 100644 --- a/vllm/v1/worker/gpu_model_runner.py +++ b/vllm/v1/worker/gpu_model_runner.py @@ -3517,7 +3517,6 @@ def capture_model(self) -> int: compilation_counter.num_gpu_runner_capture_triggers += 1 start_time = time.perf_counter() - start_free_gpu_memory = torch.cuda.mem_get_info()[0] @contextmanager def freeze_gc(): @@ -3540,6 +3539,7 @@ def freeze_gc(): # can reuse the memory pool allocated for the large shapes. set_cudagraph_capturing_enabled(True) with freeze_gc(), graph_capture(device=self.device): + start_free_gpu_memory = torch.cuda.mem_get_info()[0] cudagraph_mode = self.compilation_config.cudagraph_mode assert cudagraph_mode is not None if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE: @@ -3568,6 +3568,9 @@ def freeze_gc(): cudagraph_runtime_mode=CUDAGraphMode.FULL, uniform_decode=True) + torch.cuda.synchronize() + end_free_gpu_memory = torch.cuda.mem_get_info()[0] + # Disable cudagraph capturing globally, so any unexpected cudagraph # capturing will be detected and raise an error after here. # Note: We don't put it into graph_capture context manager because @@ -3576,7 +3579,6 @@ def freeze_gc(): set_cudagraph_capturing_enabled(False) end_time = time.perf_counter() - end_free_gpu_memory = torch.cuda.mem_get_info()[0] elapsed_time = end_time - start_time cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory # This usually takes 5~20 seconds. From 4095627ae74de659a3b3a7a7f77b9bc9abc9f6dd Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Wed, 1 Oct 2025 19:28:00 -0400 Subject: [PATCH 144/166] [BugFix] ChunkedLocalAttention is currently not CG compatible (#26034) Signed-off-by: Lucas Wilkinson --- vllm/attention/layers/chunked_local_attention.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/vllm/attention/layers/chunked_local_attention.py b/vllm/attention/layers/chunked_local_attention.py index 3964eca7d36b..2d11b2238e78 100644 --- a/vllm/attention/layers/chunked_local_attention.py +++ b/vllm/attention/layers/chunked_local_attention.py @@ -1,7 +1,7 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import functools -from typing import List, Optional +from typing import ClassVar, List, Optional import torch @@ -12,8 +12,8 @@ from vllm.config import CacheConfig from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.v1.attention.backends.utils import ( - CommonAttentionMetadata, make_local_attention_virtual_batches, - subclass_attention_backend) + AttentionCGSupport, CommonAttentionMetadata, + make_local_attention_virtual_batches, subclass_attention_backend) from ..layer import Attention @@ -29,6 +29,8 @@ def create_chunked_local_attention_backend( underlying_builder = underlying_attn_backend.get_builder_cls() class ChunkedLocalAttentionBuilder(underlying_builder): # type: ignore + cudagraph_support: ClassVar[AttentionCGSupport] = \ + AttentionCGSupport.NEVER def build(self, common_prefix_len: int, From 815431cb762385dc3d23baf0cd1266ac282155fb Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Wed, 1 Oct 2025 16:39:29 -0700 Subject: [PATCH 145/166] Support RL online quantization with torchao (#23014) Signed-off-by: Jerry Zhang --- tests/quantization/test_torchao.py | 126 +++++++++- .../layers/quantization/torchao.py | 72 +++++- .../model_loader/default_loader.py | 31 ++- .../model_loader/online_quantization.py | 217 ++++++++++++++++++ vllm/model_executor/model_loader/utils.py | 9 +- .../model_loader/weight_utils.py | 26 +++ 6 files changed, 465 insertions(+), 16 deletions(-) create mode 100644 vllm/model_executor/model_loader/online_quantization.py diff --git a/tests/quantization/test_torchao.py b/tests/quantization/test_torchao.py index 8e68f6a2e019..37cf7ef8417b 100644 --- a/tests/quantization/test_torchao.py +++ b/tests/quantization/test_torchao.py @@ -20,7 +20,6 @@ def test_pre_quantized_model(vllm_runner): output = llm.generate_greedy(["The capital of France is"], max_tokens=32) assert output - print(output) @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") @@ -42,7 +41,6 @@ def test_opt_125m_int8wo_model_loading_with_params(vllm_runner, max_tokens=32) assert output - print(output) @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") @@ -57,7 +55,6 @@ def test_opt_125m_int4wo_model_per_module_quant(vllm_runner): max_tokens=32) assert output - print(output) @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") @@ -72,7 +69,6 @@ def test_qwenvl_int8wo_model_loading_with_params(vllm_runner): max_tokens=32) assert output - print(output) @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") @@ -92,7 +88,127 @@ def test_opt_125m_awq_int4wo_model_loading_with_params(vllm_runner): max_tokens=32) assert output - print(output) + + +@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") +def test_on_the_fly_quant_config_dict_json(vllm_runner): + """Testing on the fly quantization, load_weights integration point, + with config dict serialized to json string + """ + torch._dynamo.reset() + model_name = "facebook/opt-125m" + + import json + + from torchao.core.config import config_to_dict + from torchao.quantization import ( + Float8DynamicActivationFloat8WeightConfig, PerRow) + + torchao_quant_config = Float8DynamicActivationFloat8WeightConfig( + granularity=PerRow()) + hf_overrides = { + "quantization_config_dict_json": + json.dumps(config_to_dict(torchao_quant_config)) + } + with vllm_runner(model_name=model_name, + dtype="bfloat16", + pt_load_map_location="cuda:0", + quantization="torchao", + hf_overrides=hf_overrides) as llm: + output = llm.generate_greedy(["The capital of France is"], + max_tokens=32) + + assert output + + +@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") +def test_on_the_fly_quant_config_file(vllm_runner): + """Testing on the fly quantization, load_weights integration point, + with config file + """ + torch._dynamo.reset() + model_name = "facebook/opt-125m" + import json + from tempfile import NamedTemporaryFile + + from torchao.core.config import config_to_dict + from torchao.quantization import ( + Float8DynamicActivationFloat8WeightConfig, PerRow) + + config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) + + with NamedTemporaryFile(mode="w", delete=False) as f: + f.write(json.dumps(config_to_dict(config))) + # close the file to save it + f.close() + config_file_name = str(f.name) + + hf_overrides = {"quantization_config_file": config_file_name} + with vllm_runner(model_name=model_name, + dtype="bfloat16", + pt_load_map_location="cuda:0", + quantization="torchao", + hf_overrides=hf_overrides) as llm: + output = llm.generate_greedy(["The capital of France is"], + max_tokens=32) + + assert output + + +@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available") +def test_reload_weights(): + import json + + from torchao.core.config import config_to_dict + from torchao.quantization import ( + Float8DynamicActivationFloat8WeightConfig, PerRow) + + from vllm import LLM, SamplingParams + + torchao_quant_config = Float8DynamicActivationFloat8WeightConfig( + granularity=PerRow()) + + hf_overrides = { + "quantization_config_dict_json": + json.dumps(config_to_dict(torchao_quant_config)) + } + + llm = LLM( + model="Qwen/Qwen3-0.6B", + dtype="bfloat16", + load_format="dummy", + enforce_eager=True, + quantization="torchao", + hf_overrides=hf_overrides, + ) + # Update load format from `dummy` to `auto` + llm.collective_rpc("update_config", + args=({ + "load_config": { + "load_format": "auto" + } + }, )) + # Now reload real weights inplace + llm.collective_rpc("reload_weights") + prompts = [ + "Hello, my name is", + "The president of the United States is", + "The capital of France is", + "The future of AI is", + ] + # Create a sampling params object. + sampling_params = SamplingParams(temperature=0, top_p=0.95) + outputs = llm.generate(prompts, sampling_params) + # make sure it runs + for output in outputs: + generated_text = output.outputs[0].text + assert generated_text + # can also uncomment locally to make sure the generated + # output makes sense + # prompt = output.prompt + # print(f"Prompt: {prompt!r}") + # print(f"Output: {generated_text!r}") + # print("-" * 60) if __name__ == "__main__": diff --git a/vllm/model_executor/layers/quantization/torchao.py b/vllm/model_executor/layers/quantization/torchao.py index 2efb605f203f..7e38304ad6d9 100644 --- a/vllm/model_executor/layers/quantization/torchao.py +++ b/vllm/model_executor/layers/quantization/torchao.py @@ -1,5 +1,6 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project +import json from typing import Any, Optional import torch @@ -40,7 +41,8 @@ class TorchAOConfig(QuantizationConfig): def __init__(self, torchao_config, - skip_modules: Optional[list[str]] = None) -> None: + skip_modules: Optional[list[str]] = None, + is_checkpoint_torchao_serialized: bool = False) -> None: """ # TorchAO quantization relies on tensor subclasses. In order, # to enable proper caching this needs standalone compile @@ -58,9 +60,11 @@ def __init__(self, super().__init__() self.torchao_config = torchao_config self.skip_modules = skip_modules or [] + self.is_checkpoint_torchao_serialized = is_checkpoint_torchao_serialized def __repr__(self) -> str: - return f"TorchAOConfig({self.torchao_config})" + return f"TorchAOConfig({self.torchao_config=}, {self.skip_modules=}, " \ + f"{self.is_checkpoint_torchao_serialized=})" def get_name(self) -> QuantizationMethods: return "torchao" @@ -74,7 +78,10 @@ def get_min_capability(cls) -> int: @staticmethod def get_config_filenames() -> list[str]: - return ["config.json"] + """torchao doesn't require additional config files, we use + `config.json` from huggingface: `model_config.hf_config` + """ + return [] @classmethod def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig": @@ -87,6 +94,10 @@ def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig": "`pip install torchao>=0.10.0` to use torchao quantization." ) from err + quant_method = cls.get_from_keys_or(config, ["quant_method"], None) + is_checkpoint_torchao_serialized = (quant_method is not None + and "torchao" in quant_method) + hf_config = cls.get_from_keys_or(config, ["quant_type"], None) assert hf_config is not None, "quant_type must be specified" assert len(hf_config) == 1 and "default" in hf_config, ( @@ -110,7 +121,38 @@ def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig": if layer_cfg is None: skip_modules.append(layer) - return cls(ao_config, skip_modules) + return cls(ao_config, skip_modules, is_checkpoint_torchao_serialized) + + @classmethod + def from_config_file(cls, config_file: str) -> "TorchAOConfig": + """Initialize class from a config file. Example: + ``` + config = ( + Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) + ) + fn = "torchao_config.json" + + with open(fn, "w") as f: + f.write(json.dumps(config_to_dict(config))) + ``` + """ + with open(config_file) as f: + f.seek(0) + f_read = f.read() + config_dict = json.loads(f_read) + + hf_config = {"quant_type": {"default": config_dict}} + return cls.from_config(hf_config) + + @classmethod + def from_config_dict_json(cls, config_dict_json: str) -> "TorchAOConfig": + """Iniitalize class from a config_dict json string, got from + torchao_config_object = some AOBaseConfig object + json.dumps(config_to_dict(torchao_config_object)) + """ + config_dict = json.loads(config_dict_json) + hf_config = {"quant_type": {"default": config_dict}} + return cls.from_config(hf_config) def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> Optional["QuantizeMethodBase"]: @@ -128,7 +170,9 @@ def get_quant_method(self, layer: torch.nn.Module, c = module_fqn_to_config.get( module_fqn) or module_fqn_to_config.get("_default", None) if c is not None: - current_torchao_config = TorchAOConfig(c, self.skip_modules) + current_torchao_config = TorchAOConfig( + c, self.skip_modules, + self.is_checkpoint_torchao_serialized) return TorchAOLinearMethod(current_torchao_config) else: return UnquantizedLinearMethod() @@ -172,7 +216,7 @@ class TorchAOLinearMethod(LinearMethodBase): """Linear method for torchao. Args: - quant_config: The torchao quantization config, a string that encodes + quant_config: The torchao quantization config, a string that encodes the type of quantization and all relevant arguments. """ @@ -197,8 +241,9 @@ def create_weights( ), requires_grad=False, ) - weight = torchao_quantize_param_data(weight, - self.quant_config.torchao_config) + if self.quant_config.is_checkpoint_torchao_serialized: + weight = torchao_quantize_param_data( + weight, self.quant_config.torchao_config) set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0}) @@ -212,3 +257,14 @@ def apply( bias: Optional[torch.Tensor] = None, ) -> torch.Tensor: return F.linear(x, layer.weight, bias) + + def process_weights_after_loading(self, layer: torch.nn.Module) -> None: + if self.quant_config.is_checkpoint_torchao_serialized: + return + + # quantize the weight on the fly if the checkpoint is not already + # quantized by torchao + weight = torchao_quantize_param_data(layer.weight, + self.quant_config.torchao_config) + set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0}) + layer.register_parameter("weight", weight) diff --git a/vllm/model_executor/model_loader/default_loader.py b/vllm/model_executor/model_loader/default_loader.py index 4b7bcd37d4bc..8e2db9292ff8 100644 --- a/vllm/model_executor/model_loader/default_loader.py +++ b/vllm/model_executor/model_loader/default_loader.py @@ -261,8 +261,35 @@ def download_model(self, model_config: ModelConfig) -> None: def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None: weights_to_load = {name for name, _ in model.named_parameters()} - loaded_weights = model.load_weights( - self.get_all_weights(model_config, model)) + + # if we don't have `model.weight_metadata_and_attr_saved` defined and + # set to True, it means that this is either offline quantization case + # or the first run of online quantization + # see online_quantization.py for detailed notes + offline_quantization_or_first_run_of_online_quantization = not getattr( + model, "weight_metadata_and_attr_saved", False) + + if model_config.quantization is None: + # model is not quantized + loaded_weights = model.load_weights( + self.get_all_weights(model_config, model)) + elif offline_quantization_or_first_run_of_online_quantization: + # case 1: offline quantized checkpoint + # case 2: Step I1 first run of weight loading with + # online quantization + # see online_quantization.py for detailed notes + loaded_weights = model.load_weights( + self.get_all_weights(model_config, model)) + else: + # to avoid circular dependency + from vllm.model_executor.model_loader.online_quantization import ( + load_weights_and_online_quantize) + + # subsequent runs of weight loading with online + # quantization + loaded_weights = load_weights_and_online_quantize( + self, model, model_config) + self.counter_after_loading_weights = time.perf_counter() logger.info( "Loading weights took %.2f seconds", diff --git a/vllm/model_executor/model_loader/online_quantization.py b/vllm/model_executor/model_loader/online_quantization.py new file mode 100644 index 000000000000..beec2d20ad69 --- /dev/null +++ b/vllm/model_executor/model_loader/online_quantization.py @@ -0,0 +1,217 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project + +import types + +import torch +from torch import nn + +from vllm.config import ModelConfig +from vllm.logger import init_logger +from vllm.model_executor.model_loader.default_loader import DefaultModelLoader +from vllm.model_executor.model_loader.utils import ( + process_weights_after_loading) + +logger = init_logger(__name__) + +# Notes for Online Quantization +# In terms of state of checkpoints, quantization config and their +# correspondance to online quantization: +# | Use Case | Checkpoints | model_config.quantization | +# | no quant | high precision | None | +# | offline quant | quantized | fp8, torchao etc. | +# | online quant | high precision | torchao etc. | +# +# The process for loading non-quantized checkpoint +# 1. load non-quantized weights (load_weights) +# 2. do any additional post processing (process_weights_after_loading) +# +# The process for loading offline quantized checkpoint +# 1. load offline-quantized weights (load_weights) +# 2. do any additional post processing (process_weights_after_loading) + +# The process for unquantized model reloading +# (repeated run in RL training loop) +# first run +# UI1. load_weights: load bfloat16 weights +# UI2. process_weights_after_loading: any additional post processing +# subsequent run +# UC1: load_weights: load bfloat16 weights +# (shouldn't be any issues since we didn't change any attributes +# of the weights) +# UC2: process_weights_after_loading: any additional post processing + +# The process for weight reloading with online quantization +# (repeated run in RL training loop) +# first run +# I1. load_weights: load bfloat16 weights +# I2. process_weights_after_loading: +# record weight metadata and attributes for R1 and R2 +# quantize weights to fp8 +# subsequent run +# (beginning model weight is in fp8) +# load_weights: +# R1. restore bfloat16 model weight metadata +# R2. restore the model weight attributes +# R3. reload bfloat16 weights +# R4. quantize weights (by calling process_weights_after_loading), +# also set `process_weights_after_loading_already_called` to +# True to stop it from running again +# process_weights_after_loading (if called): +# this will be skipped since it's already ran in +# load_weights + + +def maybe_save_metadata_and_attributes_for_weight_reloading( + model: nn.Module, model_config: ModelConfig): + # following is to support on the fly quantization, currently only supported + # for torchao + if model_config.quantization != "torchao": + return + + if getattr(model, "process_weights_after_loading_already_called", False): + # In case `process_weights_after_loading` is called multiple times + # we'll skip it at later times + logger.warning( + "process_weights_after_loading already called for model %s", model) + return + + from vllm.model_executor.model_loader.weight_utils import get_quant_config + quant_config = get_quant_config(model_config, None) + + # If checkpoint is already torchao serialized, this means it's + # pre-quantized quantization case, we'll skip saving the metadata + # Otherwise, this is Step I2 of initialization steps of + # online quantization + # This step record the weights metadata and weight attributes so we can + # restore the bfloat16 model weights during the relad step (R1 and R2) + # see Notes in online_quantization.py for more details + if not (hasattr(quant_config, "is_checkpoint_torchao_serialized") and \ + not quant_config.is_checkpoint_torchao_serialized): + return + + # This is the I2 step of online quantiztion that saves + # metadata and attributes of weights so they can be used in R1 and + # R2 step, note that we only save these during initialization + + # Includes two things + # 1. save floating point metadata (shape, dtype, device) for init + # 2. save weight attributes, e.g. `output_dim`, `weight_loader` for init + + if getattr(model, "weight_metadata_and_attr_saved", False): + return + + # save the dtype, shape and device for model parameter, used for + # restoring the model high precision parameters before + # reloading the weights + assert not hasattr(model, "original_weights_rebuild_keys") + model.original_weights_rebuild_keys = {} + for name, p in model.named_parameters(): + model.original_weights_rebuild_keys[name] = { + "shape": p.shape, + "dtype": p.dtype, + "device": p.device, + } + + # record the weight attributes (loader functions etc.) + # so these can be recovered later when we reload the weights + # structure: {"weight_name": {"weight_attr_key": attr}} + assert not hasattr(model, "recorded_weight_attr") + model.recorded_weight_attr = {} + for name, param in model.named_parameters(): + model.recorded_weight_attr[name] = {} + for key in param.__dict__: + if hasattr(param, key): + attr = getattr(param, key) + if not callable(attr): + model.recorded_weight_attr[name][key] = attr + elif hasattr(attr, "__self__") and param is attr.__self__: + # if attr is a bonded method for an instance, and + # attr.__self__ points to the instance (param) + # we'll record the underlying function object + model.recorded_weight_attr[name][key] = attr.__func__ + else: + model.recorded_weight_attr[name][key] = attr + # mark the metadata and attributes saved so we don't run it again + model.weight_metadata_and_attr_saved = True + + +def _bond_method_to_cls(func, obj): + if hasattr(func, "__self__") or not callable(func): + # If the function is already bound to an instance, return it as is + return func + else: + return types.MethodType(func, obj) + + +def load_weights_and_online_quantize(model_loader: DefaultModelLoader, + model: nn.Module, + model_config: ModelConfig) -> set[str]: + # online quantization, right now only enabled for + # torchao + # R1, R2, R3, R4 in the Notes + + # TODO: Add fp8 support + assert model_config.quantization == "torchao", "online " \ + "quantization is only enabled for torchao currently" + # TODO: use create_weights to restore the weights to original state + + # Step R1: First restore the quantized weights to original bfloat16 + # weights, with original metadata (shape, dtype, device) + # and attributes, so that bfloat16 weights can be loaded properly + existing_param_names = dict( + model.named_parameters(remove_duplicate=False)).keys() + named_modules = dict(model.named_modules(remove_duplicate=False)) + model_device = None + + # Step R2: recover the parameter to the state before first loading + for name, d in model.original_weights_rebuild_keys.items(): + _shape = d["shape"] + _dtype = d["dtype"] + _device = d["device"] + if model_device is not None: + assert model_device == _device, "Expecting all weights " \ + "to be in the same device for now, got both: " \ + f"{model_device} and {_device}" + else: + model_device = _device + + if name in existing_param_names: + module_name, weight_name = name.rsplit(".", 1) + module = named_modules[module_name] + setattr( + module, weight_name, + torch.nn.Parameter( + torch.empty(_shape, dtype=_dtype, device=_device))) + + # recorded_weight_attr is + # {"weight_name": {"weight_attr_key": attr}} + # e.g. + # { + # { + # "layer.0.weight": { + # "weight_loader": weight_loader_function_object, + # "input_dim": 0, ... + # }, + # "layer.1.weight": ..., + # } + # } + for full_weight_name, weight_attr_dict in \ + model.recorded_weight_attr.items(): + for attr_name, attr in weight_attr_dict.items(): + module_name, weight_name = full_weight_name.rsplit(".", 1) + module = named_modules[module_name] + weight = getattr(module, weight_name) + if not hasattr(weight, attr_name): + setattr(weight, attr_name, _bond_method_to_cls(attr, weight)) + + # Step I1: reload bfloat16 / high precision weights + loaded_weights = model.load_weights( + model_loader.get_all_weights(model_config, model)) + + # Step I2: online quantize the weights + # manually process weights after loading + model.process_weights_after_loading_already_called = False + process_weights_after_loading(model, model_config, model_device) + model.process_weights_after_loading_already_called = True + return loaded_weights diff --git a/vllm/model_executor/model_loader/utils.py b/vllm/model_executor/model_loader/utils.py index 03202e13c280..293edadcc240 100644 --- a/vllm/model_executor/model_loader/utils.py +++ b/vllm/model_executor/model_loader/utils.py @@ -95,6 +95,13 @@ def initialize_model( def process_weights_after_loading(model: nn.Module, model_config: ModelConfig, target_device: torch.device) -> None: + + # to avoid circular dependency + from vllm.model_executor.model_loader.online_quantization import ( + maybe_save_metadata_and_attributes_for_weight_reloading) + maybe_save_metadata_and_attributes_for_weight_reloading( + model, model_config) + for _, module in model.named_modules(): if isinstance(module, QKVCrossParallelLinear): # NOTE(Isotr0py): special case for cross QKV layer because @@ -243,7 +250,7 @@ def get_architecture_class_name(model_config: ModelConfig) -> str: class ParamMapping: """ A class to handle parameter mapping for model weight loading. - It creates a bidirectional mapping between packed parameters and their + It creates a bidirectional mapping between packed parameters and their constituent parts. """ packed_mapping: dict[str, list[str]] diff --git a/vllm/model_executor/model_loader/weight_utils.py b/vllm/model_executor/model_loader/weight_utils.py index f52d9dd2f534..bbed43b17543 100644 --- a/vllm/model_executor/model_loader/weight_utils.py +++ b/vllm/model_executor/model_loader/weight_utils.py @@ -246,8 +246,34 @@ def get_quant_config(model_config: ModelConfig, # compressed-tensors uses a compressions_config hf_quant_config = getattr(model_config.hf_config, "compression_config", None) + if hf_quant_config is not None: return quant_cls.from_config(hf_quant_config) + + # if hf_quant_config is None, we will try to get config from + # hf_overrides + hf_overrides = model_config.hf_overrides + quantization_config_file = hf_overrides.get("quantization_config_file", + None) + if quantization_config_file is not None: + if hasattr(quant_cls, "from_config_file"): + return quant_cls.from_config_file(quantization_config_file) + else: + raise NotImplementedError( + "from_config_file is specified in hf_override config, " + "but quant_cls.from_config_file is not implemented in " + f"{quant_cls}") + quantization_config_json = hf_overrides.get( + "quantization_config_dict_json", None) + if quantization_config_json is not None: + if hasattr(quant_cls, "from_config_dict_json"): + return quant_cls.from_config_dict_json(quantization_config_json) + else: + raise NotImplementedError( + "from_config_dict_json is specified in hf_override config, " + "but quant_cls.from_config_dict_json is not implemented in " + f"{quant_cls}") + # Inflight BNB quantization if model_config.quantization == "bitsandbytes": return quant_cls.from_config({}) From 1609496f70451b904a68b563c9e700d0df58ef41 Mon Sep 17 00:00:00 2001 From: Gregory Shtrasberg <156009573+gshtras@users.noreply.github.com> Date: Wed, 1 Oct 2025 22:23:14 -0400 Subject: [PATCH 146/166] [ROCm][Bugfix] Add missing parameter to ROCm backend (#26029) Signed-off-by: Gregory Shtrasberg --- vllm/v1/attention/backends/rocm_attn.py | 1 + 1 file changed, 1 insertion(+) diff --git a/vllm/v1/attention/backends/rocm_attn.py b/vllm/v1/attention/backends/rocm_attn.py index e973be16d779..37fc85bf351a 100644 --- a/vllm/v1/attention/backends/rocm_attn.py +++ b/vllm/v1/attention/backends/rocm_attn.py @@ -175,6 +175,7 @@ def get_kv_cache_shape( block_size: int, num_kv_heads: int, head_size: int, + cache_dtype_str: str = "auto", ) -> tuple[int, ...]: if block_size % 16 != 0: raise ValueError("Block size must be a multiple of 16.") From 20e0d34e2a607713d1ccb7c45c37d5fd7eab9867 Mon Sep 17 00:00:00 2001 From: Nick Hill Date: Wed, 1 Oct 2025 19:31:39 -0700 Subject: [PATCH 147/166] [Misc] Make handling of SamplingParams clearer in n>1 case (#26032) Signed-off-by: Nick Hill --- vllm/v1/engine/async_llm.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/vllm/v1/engine/async_llm.py b/vllm/v1/engine/async_llm.py index 574f8b19c379..36d0d50bf23d 100644 --- a/vllm/v1/engine/async_llm.py +++ b/vllm/v1/engine/async_llm.py @@ -289,13 +289,19 @@ async def add_request( await self._add_request(request, prompt_str, None, 0, queue) return queue + # Get the updated SamplingParams from the request, which + # were cloned/updated in processor.process_inputs above. + parent_params = request.sampling_params + assert parent_params is not None + # Fan out child requests (for n>1). - parent_request = ParentRequest(request_id, request.sampling_params) - for idx in range(params.n): - request_id, params = parent_request.get_child_info(idx) - child_request = request if idx == params.n - 1 else copy(request) + parent_request = ParentRequest(request_id, parent_params) + for idx in range(parent_params.n): + request_id, child_params = parent_request.get_child_info(idx) + child_request = request if idx == parent_params.n - 1 else copy( + request) child_request.request_id = request_id - child_request.sampling_params = params + child_request.sampling_params = child_params await self._add_request(child_request, prompt_str, parent_request, idx, queue) return queue From dac1ec39410d0595d9e39ef70b3dddcd91280737 Mon Sep 17 00:00:00 2001 From: pwschuurman Date: Wed, 1 Oct 2025 20:59:13 -0700 Subject: [PATCH 148/166] Run:ai model streamer add GCS package support (#24909) Signed-off-by: Peter Schuurman --- .../models/extensions/runai_model_streamer.md | 7 +++++ requirements/nightly_torch_test.txt | 2 +- requirements/rocm.txt | 4 +-- requirements/test.in | 2 +- requirements/test.txt | 21 ++++++++++++++- .../runai_model_streamer/test_runai_utils.py | 27 ++++++++++++++++--- 6 files changed, 54 insertions(+), 9 deletions(-) diff --git a/docs/models/extensions/runai_model_streamer.md b/docs/models/extensions/runai_model_streamer.md index 992dddf385d0..8a97a49825a4 100644 --- a/docs/models/extensions/runai_model_streamer.md +++ b/docs/models/extensions/runai_model_streamer.md @@ -24,6 +24,13 @@ vllm serve s3://core-llm/Llama-3-8b \ --load-format runai_streamer ``` +To run model from Google Cloud Storage run: + +```bash +vllm serve gs://core-llm/Llama-3-8b \ + --load-format runai_streamer +``` + To run model from a S3 compatible object store run: ```bash diff --git a/requirements/nightly_torch_test.txt b/requirements/nightly_torch_test.txt index 67c66a0c03e6..33f1bc04ea90 100644 --- a/requirements/nightly_torch_test.txt +++ b/requirements/nightly_torch_test.txt @@ -43,6 +43,6 @@ tritonclient==2.51.0 numba == 0.60.0; python_version == '3.9' # v0.61 doesn't support Python 3.9. Required for N-gram speculative decoding numba == 0.61.2; python_version > '3.9' numpy -runai-model-streamer[s3]==0.14.0 +runai-model-streamer[s3,gcs]==0.14.0 fastsafetensors>=0.1.10 pydantic>=2.10 # 2.9 leads to error on python 3.10 diff --git a/requirements/rocm.txt b/requirements/rocm.txt index c4aabe2a7314..9077085f2621 100644 --- a/requirements/rocm.txt +++ b/requirements/rocm.txt @@ -13,6 +13,6 @@ tensorizer==2.10.1 packaging>=24.2 setuptools>=77.0.3,<80.0.0 setuptools-scm>=8 -runai-model-streamer[s3]==0.14.0 +runai-model-streamer[s3,gcs]==0.14.0 conch-triton-kernels==1.2.1 -timm>=1.0.17 \ No newline at end of file +timm>=1.0.17 diff --git a/requirements/test.in b/requirements/test.in index e0a9311af29d..ef21d6db5b4f 100644 --- a/requirements/test.in +++ b/requirements/test.in @@ -51,7 +51,7 @@ tritonclient==2.51.0 numba == 0.60.0; python_version == '3.9' # v0.61 doesn't support Python 3.9. Required for N-gram speculative decoding numba == 0.61.2; python_version > '3.9' numpy -runai-model-streamer[s3]==0.14.0 +runai-model-streamer[s3,gcs]==0.14.0 fastsafetensors>=0.1.10 pydantic>=2.10 # 2.9 leads to error on python 3.10 decord==0.6.0 diff --git a/requirements/test.txt b/requirements/test.txt index 07457e14ccbd..9cab85ce0ef6 100644 --- a/requirements/test.txt +++ b/requirements/test.txt @@ -251,11 +251,27 @@ gitdb==4.0.12 gitpython==3.1.44 # via mlflow-skinny google-api-core==2.24.2 - # via opencensus + # via + # google-cloud-core + # google-cloud-storage + # opencensus google-auth==2.40.2 # via # databricks-sdk # google-api-core + # google-cloud-core + # google-cloud-storage + # runai-model-streamer-gcs +google-cloud-core==2.4.3 + # via google-cloud-storage +google-cloud-storage==3.4.0 + # via runai-model-streamer-gcs +google-crc32c==1.7.1 + # via + # google-cloud-storage + # google-resumable-media +google-resumable-media==2.7.2 + # via google-cloud-storage googleapis-common-protos==1.70.0 # via google-api-core graphene==3.4.3 @@ -890,6 +906,7 @@ requests==2.32.3 # docker # evaluate # google-api-core + # google-cloud-storage # huggingface-hub # lightly # lm-eval @@ -929,6 +946,8 @@ rtree==1.4.0 # via torchgeo runai-model-streamer==0.14.0 # via -r requirements/test.in +runai-model-streamer-gcs==0.14.0 + # via runai-model-streamer runai-model-streamer-s3==0.14.0 # via runai-model-streamer s3transfer==0.10.3 diff --git a/tests/model_executor/model_loader/runai_model_streamer/test_runai_utils.py b/tests/model_executor/model_loader/runai_model_streamer/test_runai_utils.py index bde77ff66506..e11e4c7289bc 100644 --- a/tests/model_executor/model_loader/runai_model_streamer/test_runai_utils.py +++ b/tests/model_executor/model_loader/runai_model_streamer/test_runai_utils.py @@ -2,6 +2,7 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import glob +import hashlib import os import tempfile @@ -9,7 +10,8 @@ from vllm.model_executor.model_loader.weight_utils import ( download_weights_from_hf) -from vllm.transformers_utils.runai_utils import (is_runai_obj_uri, +from vllm.transformers_utils.runai_utils import (ObjectStorageModel, + is_runai_obj_uri, list_safetensors) @@ -34,6 +36,23 @@ def test_runai_list_safetensors_local(): assert len(safetensors) == len(files) -if __name__ == "__main__": - test_is_runai_obj_uri() - test_runai_list_safetensors_local() +def test_runai_pull_files_gcs(monkeypatch): + monkeypatch.setenv("RUNAI_STREAMER_GCS_USE_ANONYMOUS_CREDENTIALS", "true") + # Bypass default project lookup by setting GOOGLE_CLOUD_PROJECT + monkeypatch.setenv("GOOGLE_CLOUD_PROJECT", "fake-project") + filename = "LT08_L1GT_074061_20130309_20170505_01_T2_MTL.txt" + gcs_bucket = "gs://gcp-public-data-landsat/LT08/01/074/061/LT08_L1GT_074061_20130309_20170505_01_T2/" + gcs_url = f"{gcs_bucket}/{filename}" + model = ObjectStorageModel(gcs_url) + model.pull_files(gcs_bucket, allow_pattern=[f"*{filename}"]) + # To re-generate / change URLs: + # gsutil ls -L gs:// | grep "Hash (md5)" | tr -d ' ' \ + # | cut -d":" -f2 | base64 -d | xxd -p + expected_checksum = "f60dea775da1392434275b311b31a431" + hasher = hashlib.new("md5") + with open(os.path.join(model.dir, filename), 'rb') as f: + # Read the file in chunks to handle large files efficiently + for chunk in iter(lambda: f.read(4096), b''): + hasher.update(chunk) + actual_checksum = hasher.hexdigest() + assert actual_checksum == expected_checksum From 38bb882af6af03c2b2ddf3003f47015b866116a1 Mon Sep 17 00:00:00 2001 From: Huy Do Date: Thu, 2 Oct 2025 05:48:04 -0700 Subject: [PATCH 149/166] Update base image to 22.04 (jammy) (#26065) Signed-off-by: Huy Do --- docker/Dockerfile | 4 ++-- docker/Dockerfile.nightly_torch | 2 +- .../dockerfile-stages-dependency.png | Bin 121821 -> 130446 bytes 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index a6b24f69f64e..ccc1b024316a 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -13,8 +13,8 @@ ARG PYTHON_VERSION=3.12 # private registries that use a different repository naming conventions. # # Example: -# docker build --build-arg BUILD_BASE_IMAGE=registry.acme.org/mirror/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 -ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 +# docker build --build-arg BUILD_BASE_IMAGE=registry.acme.org/mirror/nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 +ARG BUILD_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 # TODO: Restore to base image after FlashInfer AOT wheel fixed ARG FINAL_BASE_IMAGE=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 diff --git a/docker/Dockerfile.nightly_torch b/docker/Dockerfile.nightly_torch index ae12ed0f7cab..6a9c3fa7dbed 100644 --- a/docker/Dockerfile.nightly_torch +++ b/docker/Dockerfile.nightly_torch @@ -6,7 +6,7 @@ ARG CUDA_VERSION=12.8.0 # #################### BASE BUILD IMAGE #################### # prepare basic build environment -FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base +FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS base ARG CUDA_VERSION=12.8.0 ARG PYTHON_VERSION=3.12 ARG TARGETPLATFORM diff --git a/docs/assets/contributing/dockerfile-stages-dependency.png b/docs/assets/contributing/dockerfile-stages-dependency.png index 0838bfa37fe62d60fba9adcbd18c81de0809f253..db3452650c3679464bdd3788f69a452e04365457 100644 GIT binary patch literal 130446 zcmb5X2Ut{B+XXtts0oTDQ6v^XNj z(vBQZqfnNOP$)mX{b@0N($lc-KK^U*>7$1aP-e;hgyqF}Qz#oKv;%uJE(U#Ra=xco z8@}Uf%DP|99umFmAIlxFf#Z+kD-W;A6OlYvqbM42;je{WqXK_R{^EW9K>5L6{ua2Q zSn9)_ciKjA{Vz8ZFAe<|`+MiFzHz?c4~J%6l{qucD}*;FjSgtnYPu$+e{XK-jxf^> zE~-z6&=Bw8*e;M(Ua=ROf>S{;3`m^B-9sW;W)Gz6u8WDdjO{a|~U-9X-GNAyFh($Lwd&esnm zQo4>%I1k-gy*+KT*|qbpOA0`-abf;xOpS~_Ie(%@&$TbZ zxi6zW-$n_8u6lI%u0>9FG;XY?y^Y;(!i#j3S@%fX%YNLcg!uUQ+iS%H{R};v<;L5*xeUx(^4%v!di^df zTw>yQH1H4t_r0{!-qFM8r{|EchGyQiYnqS@68shKT 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z&rrG^I(SgFk)b@D131&v8&AI9g0&$6pa=nt;fBr!-k)T+wNgpYHJeUiHZF8>G!j?1 zhvLg6HhYGk#=A_eObMzyN{?6f%!p zjEdjj<|kCiPSHO+9PB(5LoabWtDC7cY(L=4#1rp0gnN~@T&0FjW#Gm&0{oG!XIcQ> zqk*qz$n2KWJjawlG*0wEM;zz-Q(WhBB>8jdMZ1`{6t+582s!0Ca#hm@y62Vj$OX4%rEj7t2zvgKm5 zSgg((zS}Uv#;y5c(@WT%#WjzP7MPIlJO?U35aOeu-2u44Tj1#SeS7vS4-TfoJq*~S zH%|9^JbPk|6Ot!Oh?cJz6#0;@%r9D-H%1iaHuxl*!J?w67cwk+;{cp+R~H@jTy~@< zL_TrT?EnsW)_L>gWQXp@B~KS6s7-BK1YtQH z*d3hQA=?Aa2`h_^(9a{88jffZdClh46Lb{&NhVkHRe*E=y z{-#d_je+l}udK)oU%zf057zEIdt5v`j=#CBU^<`i4_sXX{>Y6z8+UIFMzVef>;*77; z)8>zByrKS^-50+ Date: Thu, 2 Oct 2025 16:14:28 +0200 Subject: [PATCH 150/166] Change size of single CUDA graph for CI to 4 (#26089) Signed-off-by: Thomas Parnell --- tests/conftest.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/conftest.py b/tests/conftest.py index e3e412f90af5..66106d1bf779 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -732,7 +732,7 @@ def __init__( set_default_torch_num_threads(default_torch_num_threads)) if not kwargs.get("compilation_config", None): - kwargs["compilation_config"] = {"cudagraph_capture_sizes": [8]} + kwargs["compilation_config"] = {"cudagraph_capture_sizes": [4]} with init_ctx: self.llm = LLM( From 6c56006a0f1ae58b1a1966fa794dde09b5767ce4 Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Thu, 2 Oct 2025 11:06:14 -0400 Subject: [PATCH 151/166] [FA/Chore] Bump vllm-flash-attention (#25537) Signed-off-by: Lucas Wilkinson --- cmake/external_projects/vllm_flash_attn.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/external_projects/vllm_flash_attn.cmake b/cmake/external_projects/vllm_flash_attn.cmake index 3d32121f13ac..e6686275cabb 100644 --- a/cmake/external_projects/vllm_flash_attn.cmake +++ b/cmake/external_projects/vllm_flash_attn.cmake @@ -38,7 +38,7 @@ else() FetchContent_Declare( vllm-flash-attn GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git - GIT_TAG ee4d25bd84e0cbc7e0b9b9685085fd5db2dcb62a + GIT_TAG 4695e6bed5366c41e28c06cd86170166e4f43d00 GIT_PROGRESS TRUE # Don't share the vllm-flash-attn build between build types BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn From 3c64522cc09ce3cc26108c91f29beaf1e2b663fa Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Thu, 2 Oct 2025 23:17:31 +0800 Subject: [PATCH 152/166] [Model] Use `merge_by_field_config` for MM models (A-C) (#26073) Signed-off-by: DarkLight1337 --- vllm/model_executor/models/aria.py | 19 +++++++++++++------ vllm/model_executor/models/aya_vision.py | 7 ++++--- vllm/model_executor/models/blip2.py | 13 ++++--------- vllm/model_executor/models/chameleon.py | 7 ++++--- vllm/model_executor/models/cohere2_vision.py | 7 ++++--- 5 files changed, 29 insertions(+), 24 deletions(-) diff --git a/vllm/model_executor/models/aria.py b/vllm/model_executor/models/aria.py index 82f35d889605..e4a11a3d4a51 100644 --- a/vllm/model_executor/models/aria.py +++ b/vllm/model_executor/models/aria.py @@ -1,7 +1,7 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from collections.abc import Iterable, Mapping, Sequence -from typing import Annotated, Optional, Union +from typing import Annotated, Literal, Optional, Union import torch import torch.nn as nn @@ -38,8 +38,8 @@ # yapf: enable from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsQuant from .llama import LlamaDecoderLayer, LlamaMLP, LlamaModel -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, - is_pp_missing_parameter, maybe_prefix) +from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter, + maybe_prefix) class AriaImagePixelInputs(TensorSchema): @@ -52,6 +52,8 @@ class AriaImagePixelInputs(TensorSchema): - w: Width of each image """ + type: Literal["pixel_values"] + pixel_values: Annotated[ torch.Tensor, TensorShape("bn", 3, "h", "w"), @@ -485,6 +487,8 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal): This model combines a vision tower, a multi-modal projector, and a language model to perform tasks that involve both image and text inputs. """ + merge_by_field_config = True + hf_to_vllm_mapper = WeightsMapper( orig_to_new_prefix={ # mapping for new names in checkpoint saved after transformers v4.52 @@ -551,12 +555,15 @@ def _parse_and_validate_image_input( return None return AriaImagePixelInputs( - pixel_values=flatten_bn(pixel_values, concat=True), - pixel_mask=flatten_bn(pixel_mask, concat=True), + type="pixel_values", + pixel_values=pixel_values, + pixel_mask=pixel_mask, ) def _create_patch_attention_mask( - self, pixel_mask: Optional[torch.Tensor]) -> torch.Tensor: + self, + pixel_mask: Optional[torch.Tensor], + ) -> Optional[torch.Tensor]: if pixel_mask is None: return None diff --git a/vllm/model_executor/models/aya_vision.py b/vllm/model_executor/models/aya_vision.py index 81bab5b34bc6..04100fb8c412 100644 --- a/vllm/model_executor/models/aya_vision.py +++ b/vllm/model_executor/models/aya_vision.py @@ -31,7 +31,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, +from .utils import (AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, maybe_prefix) @@ -295,6 +295,7 @@ def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int: dummy_inputs=AyaVisionDummyInputsBuilder) class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP): + merge_by_field_config = True hf_to_vllm_mapper = WeightsMapper( orig_to_new_prefix={ @@ -379,8 +380,8 @@ def _parse_and_validate_image_input( return AyaVisionImagePixelInputs( type="pixel_values", - pixel_values=flatten_bn(pixel_values, concat=True), - num_patches=flatten_bn(num_patches, concat=True), + pixel_values=pixel_values, + num_patches=num_patches, resolve_bindings={ "h": self.config.vision_config.image_size, "w": self.config.vision_config.image_size, diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py index 334743a7358c..940d39c7a67c 100644 --- a/vllm/model_executor/models/blip2.py +++ b/vllm/model_executor/models/blip2.py @@ -26,12 +26,7 @@ from .blip import BlipVisionModel from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP, SupportsQuant) -from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model, - maybe_prefix) - -# We use this internally as placeholders since there is no image token -# defined on the HuggingFace repo -_IMAGE_TOKEN_ID = 50265 +from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix class Blip2ImagePixelInputs(TensorSchema): @@ -514,6 +509,7 @@ def _get_prompt_updates( dummy_inputs=Blip2DummyInputsBuilder) class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant): + merge_by_field_config = True @classmethod def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]: @@ -570,8 +566,7 @@ def _parse_and_validate_image_input( if pixel_values is not None: expected_h = expected_w = self.config.vision_config.image_size return Blip2ImagePixelInputs(type="pixel_values", - data=flatten_bn(pixel_values, - concat=True), + data=pixel_values, resolve_bindings={ "h": expected_h, "w": expected_w @@ -580,7 +575,7 @@ def _parse_and_validate_image_input( if image_embeds is not None: return Blip2ImageEmbeddingInputs( type="image_embeds", - data=flatten_bn(image_embeds, concat=True), + data=image_embeds, ) raise AssertionError("This line should be unreachable.") diff --git a/vllm/model_executor/models/chameleon.py b/vllm/model_executor/models/chameleon.py index 86dbf63fa5df..4999f7904b14 100644 --- a/vllm/model_executor/models/chameleon.py +++ b/vllm/model_executor/models/chameleon.py @@ -42,7 +42,7 @@ from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP, SupportsQuant) -from .utils import (flatten_bn, is_pp_missing_parameter, +from .utils import (is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) @@ -935,6 +935,8 @@ def forward( dummy_inputs=ChameleonDummyInputsBuilder) class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP, SupportsQuant): + merge_by_field_config = True + packed_modules_mapping = { "qkv_proj": ["q_proj", "k_proj", "v_proj"], "gate_up_proj": ["gate_proj", "up_proj"] @@ -981,8 +983,7 @@ def _parse_and_validate_image_input( expected_h = expected_w = vq_config.resolution return ChameleonImagePixelInputs(type="pixel_values", - data=flatten_bn(pixel_values, - concat=True), + data=pixel_values, resolve_bindings={ "h": expected_h, "w": expected_w diff --git a/vllm/model_executor/models/cohere2_vision.py b/vllm/model_executor/models/cohere2_vision.py index 7162571c08d9..78a7f0a52c72 100644 --- a/vllm/model_executor/models/cohere2_vision.py +++ b/vllm/model_executor/models/cohere2_vision.py @@ -36,7 +36,7 @@ from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP from .siglip import SiglipVisionModel -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, +from .utils import (AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, maybe_prefix) @@ -317,6 +317,7 @@ def get_replacement(item_idx: int): dummy_inputs=Cohere2VisionDummyInputsBuilder) class Cohere2VisionForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP): + merge_by_field_config = True hf_to_vllm_mapper = WeightsMapper( orig_to_new_prefix={ @@ -399,8 +400,8 @@ def _parse_and_validate_image_input( return Cohere2VisionImagePixelInputs( type="pixel_values", - pixel_values=flatten_bn(pixel_values, concat=True), - num_patches=flatten_bn(num_patches, concat=True), + pixel_values=pixel_values, + num_patches=num_patches, resolve_bindings={ "h": self.config.vision_config.image_size, "w": self.config.vision_config.image_size, From ae28032c2dd754154f2928f4ca30aef6015dad41 Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Thu, 2 Oct 2025 23:17:35 +0800 Subject: [PATCH 153/166] [Model] Use `merge_by_field_config` for MM models (D-F) (#26076) Signed-off-by: DarkLight1337 --- vllm/model_executor/models/deepseek_vl2.py | 58 +++++++---------- vllm/model_executor/models/dots_ocr.py | 75 +++++++-------------- vllm/model_executor/models/ernie45_vl.py | 76 +++++++--------------- vllm/model_executor/models/fuyu.py | 67 ++++++++----------- 4 files changed, 99 insertions(+), 177 deletions(-) diff --git a/vllm/model_executor/models/deepseek_vl2.py b/vllm/model_executor/models/deepseek_vl2.py index 0f87fb34bf32..3e3b4e59f833 100644 --- a/vllm/model_executor/models/deepseek_vl2.py +++ b/vllm/model_executor/models/deepseek_vl2.py @@ -20,8 +20,7 @@ from vllm.model_executor.models.transformers import replace_linear_class from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, - MultiModalKwargsItems, MultiModalUUIDDict, - NestedTensors) + MultiModalKwargsItems, MultiModalUUIDDict) from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems, ImageSize, MultiModalDataItems) from vllm.multimodal.processing import (BaseMultiModalProcessor, @@ -40,7 +39,7 @@ from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP -from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, +from .utils import (AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, maybe_prefix) # The image token id may be various @@ -50,15 +49,15 @@ class DeepseekVL2ImagePixelInputs(TensorSchema): """ Dimensions: - - bn: Batch size * number of images + - bnp: Batch size * number of images * number of patches - p: Number of patches - c: Number of channels (3) - h: Height of each image - w: Width of each image """ type: Literal["pixel_values"] - data: Annotated[Union[torch.Tensor, list[torch.Tensor]], - TensorShape("bn", "p", 3, "h", "w", dynamic_dims={"p"})] + data: Annotated[torch.Tensor, + TensorShape("bnp", 3, "h", "w", dynamic_dims={"bnp"})] images_spatial_crop: Annotated[torch.Tensor, TensorShape("bn", 2)] @@ -228,12 +227,8 @@ def _call_hf_processor( tok_kwargs=tok_kwargs, ) - pixel_values = processed_outputs["pixel_values"] - # split pixel values into patches corresponding to each image - images_spatial_crop = processed_outputs["images_spatial_crop"] - patches_per_image = [x.prod().item() + 1 for x in images_spatial_crop] - pixel_values = pixel_values.split(patches_per_image) - processed_outputs["pixel_values"] = pixel_values + processed_outputs["num_patches"] = ( + processed_outputs["images_spatial_crop"].prod(-1) + 1) return processed_outputs @@ -242,8 +237,11 @@ def _get_mm_fields_config( hf_inputs: BatchFeature, hf_processor_mm_kwargs: Mapping[str, object], ) -> Mapping[str, MultiModalFieldConfig]: + num_patches = hf_inputs.get("num_patches", torch.empty(0)) + return dict( - pixel_values=MultiModalFieldConfig.batched("image"), + pixel_values=MultiModalFieldConfig.flat_from_sizes( + "image", num_patches), images_spatial_crop=MultiModalFieldConfig.batched("image"), image_embeds=MultiModalFieldConfig.batched("image"), ) @@ -318,6 +316,7 @@ def _cached_apply_hf_processor( info=DeepseekVL2ProcessingInfo, dummy_inputs=DeepseekVL2DummyInputsBuilder) class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP): + merge_by_field_config = True hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={ "language.": "language_model.", @@ -460,37 +459,30 @@ def _parse_and_validate_image_input( if pixel_values is not None: expected_h = expected_w = self.vision_config.image_size - return DeepseekVL2ImagePixelInputs(type="pixel_values", - data=flatten_bn(pixel_values), - images_spatial_crop=flatten_bn( - images_spatial_crop, - concat=True), - resolve_bindings={ - "h": expected_h, - "w": expected_w, - }) + return DeepseekVL2ImagePixelInputs( + type="pixel_values", + data=pixel_values, + images_spatial_crop=images_spatial_crop, + resolve_bindings={ + "h": expected_h, + "w": expected_w, + }) if image_embeds is not None: return DeepseekVL2VImageEmbeddingInputs( type="image_embeds", - data=flatten_bn(image_embeds), + data=image_embeds, ) raise AssertionError("This line should be unreachable.") def _pixel_values_to_embedding( self, - pixel_values: NestedTensors, + pixel_values: torch.Tensor, images_spatial_crop: torch.Tensor, - ) -> NestedTensors: - # Pixel_values: n_image * batch_size * [patch_per_img, 3, height, width] - total_tiles = [x for x in pixel_values] - - # [batch_all_tiles, 3, height, width] - total_tiles = torch.cat(total_tiles, dim=0) - + ) -> list[torch.Tensor]: # [batch_all_tiles, vit_seq_len, c] - images_feature = self.vision.forward_features(total_tiles) + images_feature = self.vision.forward_features(pixel_values) # [batch_all_tiles, hw, D] images_embeds = self.projector(images_feature) @@ -573,7 +565,7 @@ def _pixel_values_to_embedding( return vision_embeddings def _process_image_input( - self, image_input: DeepseekVL2ImageInputs) -> torch.Tensor: + self, image_input: DeepseekVL2ImageInputs) -> list[torch.Tensor]: if image_input["type"] == "image_embeds": image_data = image_input["data"] if is_list_of(image_data, torch.Tensor): diff --git a/vllm/model_executor/models/dots_ocr.py b/vllm/model_executor/models/dots_ocr.py index 4845f19bcbc4..e68777aab6bf 100644 --- a/vllm/model_executor/models/dots_ocr.py +++ b/vllm/model_executor/models/dots_ocr.py @@ -1,7 +1,7 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from collections.abc import Iterable, Mapping -from typing import Literal, Optional, TypedDict, Union +from typing import Annotated, Literal, Optional, Union import torch import torch.nn as nn @@ -42,34 +42,38 @@ from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.dotsocr import (DotsOCRConfig, DotsVisionConfig) +from vllm.utils.tensor_schema import TensorSchema, TensorShape from .vision import run_dp_sharded_mrope_vision_model IMAGE_TOKEN = "<|imgpad|>" -class DotsOCRImagePixelInputs(TypedDict): - type: Literal["pixel_values", "image_grid_thw"] +class DotsOCRImagePixelInputs(TensorSchema): + """ + Dimensions: + - np: The total number of patches over each image over each prompt in + the batch + - ni: Number of images + - cps: Number of channels * patch_size * patch_size + """ + type: Literal["pixel_values"] - pixel_values: torch.Tensor - image_grid_thw: torch.Tensor + pixel_values: Annotated[torch.Tensor, TensorShape("np", "cps")] + image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)] -class DotsOCRImageEmbeddingInputs(TypedDict): - type: Literal["image_embeds", "image_grid_thw"] - image_embeds: torch.Tensor - """Supported types: - - List[`torch.Tensor`]: A list of tensors holding all images' features. - Each tensor holds an image's features. - - `torch.Tensor`: A tensor holding all images' features - (concatenation of all images' feature tensors). - Tensor shape: `(num_image_features, hidden_size)` - - `num_image_features` varies based on - the number and resolution of the images. - - `hidden_size` must match the hidden size of language model backbone. +class DotsOCRImageEmbeddingInputs(TensorSchema): """ + Dimensions: + - nf: Number of image features + - hs: Hidden size + - ni: Number of images + """ + type: Literal["image_embeds"] - image_grid_thw: torch.Tensor + image_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")] + image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)] DotsOCRImageInputs = Union[DotsOCRImagePixelInputs, @@ -654,6 +658,8 @@ def forward(self, hidden_states: torch.Tensor, ) class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA): + merge_by_field_config = True + hf_to_vllm_mapper = WeightsMapper( orig_to_new_substr={ ".attn.qkv_proj.": ".attn.qkv.", @@ -709,22 +715,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): architectures=["Qwen2ForCausalLM"], ) - def _validate_and_reshape_mm_tensor(self, mm_input: object, - name: str) -> torch.Tensor: - if not isinstance(mm_input, (torch.Tensor, list)): - raise ValueError(f"Incorrect type of {name}. " - f"Got type: {type(mm_input)}") - if isinstance(mm_input, torch.Tensor): - if mm_input.ndim == 2: - return mm_input - if mm_input.ndim != 3: - raise ValueError(f"{name} should be 2D or batched 3D tensor. " - f"Got ndim: {mm_input.ndim} " - f"(shape={mm_input.shape})") - return torch.concat(list(mm_input)) - else: - return torch.concat(mm_input) - def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[DotsOCRImageInputs]: pixel_values = kwargs.pop("pixel_values", None) @@ -735,28 +725,11 @@ def _parse_and_validate_image_input( return None if pixel_values is not None: - pixel_values = self._validate_and_reshape_mm_tensor( - pixel_values, "image pixel values") - image_grid_thw = self._validate_and_reshape_mm_tensor( - image_grid_thw, "image grid_thw") - - if not isinstance(pixel_values, (torch.Tensor, list)): - raise ValueError("Incorrect type of image pixel values. " - f"Got type: {type(pixel_values)}") - return DotsOCRImagePixelInputs(type="pixel_values", pixel_values=pixel_values, image_grid_thw=image_grid_thw) if image_embeds is not None: - image_embeds = self._validate_and_reshape_mm_tensor( - image_embeds, "image embeds") - image_grid_thw = self._validate_and_reshape_mm_tensor( - image_grid_thw, "image grid_thw") - - if not isinstance(image_embeds, torch.Tensor): - raise ValueError("Incorrect type of image embeddings. " - f"Got type: {type(image_embeds)}") return DotsOCRImageEmbeddingInputs(type="image_embeds", image_embeds=image_embeds, image_grid_thw=image_grid_thw) diff --git a/vllm/model_executor/models/ernie45_vl.py b/vllm/model_executor/models/ernie45_vl.py index a73ec4f88ffe..c62658fa4c21 100644 --- a/vllm/model_executor/models/ernie45_vl.py +++ b/vllm/model_executor/models/ernie45_vl.py @@ -25,7 +25,7 @@ import math from collections.abc import Iterable, Mapping, Sequence from functools import partial -from typing import Any, Callable, Literal, Optional, TypedDict, Union +from typing import Annotated, Any, Callable, Literal, Optional, Union import numpy as np import torch @@ -56,6 +56,7 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.platforms import _Backend, current_platform from vllm.sequence import IntermediateTensors +from vllm.utils.tensor_schema import TensorSchema, TensorShape from .ernie45_vl_moe import Ernie4_5_VLMoeForCausalLM from .interfaces import (MultiModalEmbeddings, SupportsLoRA, @@ -579,38 +580,38 @@ def load_weights(self, weights) -> set[str]: # === Vision Inputs === # -class Ernie4_5_VLImagePixelInputs(TypedDict): - type: Literal["pixel_values"] - pixel_values: torch.Tensor - """Shape: - `(num_patches, num_channels * patch_size * patch_size)` +class Ernie4_5_VLImagePixelInputs(TensorSchema): """ - - grid_thw: torch.Tensor - """Shape: `(num_images, 3)` - This should be in `(grid_t, grid_h, grid_w)` format. + Dimensions: + - np: The total number of patches over each image over each prompt in + the batch + - ni: Number of images + - cps: Number of channels * patch_size * patch_size """ + type: Literal["pixel_values"] + + pixel_values: Annotated[torch.Tensor, TensorShape("np", "cps")] + image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)] Ernie4_5_VLImageInputs = Ernie4_5_VLImagePixelInputs -class Ernie4_5_VLVideoPixelInputs(TypedDict): - type: Literal["pixel_values_videos"] - pixel_values_videos: torch.Tensor - """Shape: - `(num_patches, - num_channels * temporal_patch_size * patch_size * patch_size)` +class Ernie4_5_VLVideoPixelInputs(TensorSchema): """ - - video_grid_thw: torch.Tensor - """Shape: `(num_videos, 3)` - - This should be in `(grid_t, grid_h, grid_w)` format. + Dimensions: + - np: The total number of patches over each image over each prompt in + the batch + - ni: Number of images + - cps: Number of channels * temporal_patch_size * patch_size * + patch_size """ + type: Literal["pixel_values_videos"] + pixel_values_videos: Annotated[torch.Tensor, TensorShape("np", "cps")] + video_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)] -Ernie4_5_VLVideoInputs = Ernie4_5_VLImagePixelInputs +Ernie4_5_VLVideoInputs = Ernie4_5_VLVideoPixelInputs # === Vision Processor === # @@ -1213,6 +1214,7 @@ def get_dummy_mm_data( dummy_inputs=Ernie4_5_VLDummyInputsBuilder) class Ernie4_5_VLMoeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP): + merge_by_field_config = True packed_modules_mapping = { "qkv_proj": [ @@ -1325,22 +1327,6 @@ def _set_visual_token_mask(self, input_ids: torch.Tensor) -> None: def get_language_model(self) -> torch.nn.Module: return self.language_model - def _validate_and_reshape_mm_tensor(self, mm_input: object, - name: str) -> torch.Tensor: - if not isinstance(mm_input, (torch.Tensor, list)): - raise ValueError(f"Incorrect type of {name}. " - f"Got type: {type(mm_input)}") - if isinstance(mm_input, torch.Tensor): - if mm_input.ndim == 2: - return mm_input - if mm_input.ndim != 3: - raise ValueError(f"{name} should be 2D or batched 3D tensor. " - f"Got ndim: {mm_input.ndim} " - f"(shape={mm_input.shape})") - return mm_input.reshape(-1, mm_input.shape[-1]) - else: - return torch.concat(mm_input) - def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[Ernie4_5_VLImageInputs]: pixel_values = kwargs.pop("pixel_values", None) @@ -1350,15 +1336,6 @@ def _parse_and_validate_image_input( return None if pixel_values is not None: - pixel_values = self._validate_and_reshape_mm_tensor( - pixel_values, "image pixel values") - image_grid_thw = self._validate_and_reshape_mm_tensor( - image_grid_thw, "image grid_thw") - - if not isinstance(pixel_values, (torch.Tensor, list)): - raise ValueError("Incorrect type of image pixel values. " - f"Got type: {type(pixel_values)}") - return Ernie4_5_VLImagePixelInputs(type="pixel_values", pixel_values=pixel_values, image_grid_thw=image_grid_thw) @@ -1372,11 +1349,6 @@ def _parse_and_validate_video_input( return None if pixel_values_videos is not None: - pixel_values_videos = self._validate_and_reshape_mm_tensor( - pixel_values_videos, "video pixel values") - video_grid_thw = self._validate_and_reshape_mm_tensor( - video_grid_thw, "video grid_thw") - return Ernie4_5_VLVideoPixelInputs( type="pixel_values_videos", pixel_values_videos=pixel_values_videos, diff --git a/vllm/model_executor/models/fuyu.py b/vllm/model_executor/models/fuyu.py index 9e491c0b50d2..2ab2cf9b17b3 100644 --- a/vllm/model_executor/models/fuyu.py +++ b/vllm/model_executor/models/fuyu.py @@ -59,17 +59,14 @@ class FuyuImagePatchInputs(TensorSchema): type: Literal["image_patches"] = "image_patches" - flat_data: Annotated[ - torch.Tensor, - TensorShape("bnp", "fn"), - ] + image_patches_flat: Annotated[torch.Tensor, TensorShape("bnp", "fn")] patches_per_image: Annotated[list[int], TensorShape("bn")] """ The number of total patches for each image in the batch. This is used to split the embeddings which has the first two dimensions - flattened just like `flat_data`. + flattened just like `image_patches_flat`. """ @@ -174,28 +171,10 @@ def _call_hf_processor( tok_kwargs=tok_kwargs, ) - image_patches = processed_outputs.get("image_patches") - if image_patches is not None: - images = mm_data["images"] - assert isinstance(images, list) - - # Original output: (1, num_images, Pn, Px * Py * C) - # New output: (num_images, Pn, Px * Py * C) - # image_patches is a list with shape: - # (1, num_images, Pn, Px * Py * C) - # before Transformers 4.53 - if isinstance(image_patches, list): - assert len(image_patches) == 1 - assert (isinstance(image_patches[0], torch.Tensor) - and len(image_patches[0]) == len(images)) - processed_outputs["image_patches"] = image_patches[0] - # image_patches is a tensor with shape: - # (num_images, Pn, Px * Py * C) - # after Transformers 4.53 - elif isinstance(image_patches, torch.Tensor): - assert len(image_patches) == len(images) - else: - raise AssertionError("This line should be unreachable.") + image_patches = processed_outputs["image_patches"] + processed_outputs["image_patches"] = flatten_bn(image_patches) + processed_outputs["patches_per_image"] = torch.tensor( + [len(p) for p in image_patches]) return processed_outputs @@ -218,7 +197,13 @@ def _get_mm_fields_config( hf_inputs: BatchFeature, hf_processor_mm_kwargs: Mapping[str, object], ) -> Mapping[str, MultiModalFieldConfig]: - return dict(image_patches=MultiModalFieldConfig.batched("image")) + patches_per_image = hf_inputs.get("patches_per_image", torch.empty(0)) + + return dict( + image_patches=MultiModalFieldConfig.flat_from_sizes( + "image", patches_per_image), + patches_per_image=MultiModalFieldConfig.batched("image"), + ) def _get_prompt_updates( self, @@ -263,6 +248,7 @@ def get_replacement_fuyu(item_idx: int): info=FuyuProcessingInfo, dummy_inputs=FuyuDummyInputsBuilder) class FuyuForCausalLM(nn.Module, SupportsMultiModal, SupportsPP): + merge_by_field_config = True hf_to_vllm_mapper = WeightsMapper( orig_to_new_prefix={ @@ -306,29 +292,28 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[FuyuImagePatchInputs]: image_patches = kwargs.pop("image_patches", None) - if image_patches is not None: - image_patches_flat = flatten_bn(image_patches) - flat_data = flatten_bn(image_patches_flat, concat=True) - - return FuyuImagePatchInputs( - type="image_patches", - flat_data=flat_data, - patches_per_image=[x.size(0) for x in image_patches_flat], - resolve_bindings={"fn": self.image_feature_size}, - ) + patches_per_image = kwargs.pop("patches_per_image", None) - return None + if image_patches is None: + return None + + return FuyuImagePatchInputs( + type="image_patches", + image_patches_flat=image_patches, + patches_per_image=patches_per_image, + resolve_bindings={"fn": self.image_feature_size}, + ) def _process_image_input( self, image_input: FuyuImagePatchInputs) -> MultiModalEmbeddings: - image_patches_flat = image_input["flat_data"] + image_patches_flat = image_input["image_patches_flat"] patches_per_image = image_input["patches_per_image"] assert self.vision_embed_tokens is not None vision_embeddings_flat, _ = self.vision_embed_tokens( image_patches_flat) - return vision_embeddings_flat.split(patches_per_image, dim=0) + return vision_embeddings_flat.split(patches_per_image.tolist(), dim=0) def get_language_model(self) -> torch.nn.Module: return self.language_model From 2614dde3f8dba22d2a030a235cdae693f071f620 Mon Sep 17 00:00:00 2001 From: leo-pony Date: Thu, 2 Oct 2025 23:19:22 +0800 Subject: [PATCH 154/166] [Platform][CI] Added OOT platform interface e2e test that running on Ascend NPU (#25470) Signed-off-by: leo-pony --- .../scripts/hardware_ci/run-npu-test.sh | 191 ++++++++++++++++++ 1 file changed, 191 insertions(+) create mode 100644 .buildkite/scripts/hardware_ci/run-npu-test.sh diff --git a/.buildkite/scripts/hardware_ci/run-npu-test.sh b/.buildkite/scripts/hardware_ci/run-npu-test.sh new file mode 100644 index 000000000000..29c8f5ed5a91 --- /dev/null +++ b/.buildkite/scripts/hardware_ci/run-npu-test.sh @@ -0,0 +1,191 @@ +#!/bin/bash + +# This script build the Ascend NPU docker image and run the offline inference inside the container. +# It serves a sanity check for compilation and basic model usage. +set -ex + +# Base ubuntu image with basic ascend development libraries and python installed +VLLM_ASCEND_REPO="https://github.com/vllm-project/vllm-ascend.git" +CONFIG_FILE_REMOTE_PATH="tests/e2e/vllm_interface/vllm_test.cfg" +TEST_RUN_CONFIG_FILE="vllm_test.cfg" +VLLM_ASCEND_TMP_DIR= +# Get the test run configuration file from the vllm-ascend repository +fetch_vllm_test_cfg() { + VLLM_ASCEND_TMP_DIR=$(mktemp -d) + # Ensure that the temporary directory is cleaned up when an exception occurs during configuration file retrieval + cleanup() { + rm -rf "${VLLM_ASCEND_TMP_DIR}" + } + trap cleanup EXIT + + GIT_TRACE=1 git clone -v --depth 1 "${VLLM_ASCEND_REPO}" "${VLLM_ASCEND_TMP_DIR}" + if [ ! -f "${VLLM_ASCEND_TMP_DIR}/${CONFIG_FILE_REMOTE_PATH}" ]; then + echo "Error: file '${CONFIG_FILE_REMOTE_PATH}' does not exist in the warehouse" >&2 + exit 1 + fi + + # If the file already exists locally, just overwrite it + cp "${VLLM_ASCEND_TMP_DIR}/${CONFIG_FILE_REMOTE_PATH}" "${TEST_RUN_CONFIG_FILE}" + echo "Copied ${CONFIG_FILE_REMOTE_PATH} to ${TEST_RUN_CONFIG_FILE}" + + # Since the trap will be overwritten later, and when it is executed here, the task of cleaning up resources + # when the trap is abnormal has been completed, so the temporary resources are manually deleted here. + rm -rf "${VLLM_ASCEND_TMP_DIR}" + trap - EXIT +} + +# Downloads test run configuration file from a remote URL. +# Loads the configuration into the current script environment. +get_config() { + if [ ! -f "${TEST_RUN_CONFIG_FILE}" ]; then + echo "Error: file '${TEST_RUN_CONFIG_FILE}' does not exist in the warehouse" >&2 + exit 1 + fi + source "${TEST_RUN_CONFIG_FILE}" + echo "Base docker image name that get from configuration: ${BASE_IMAGE_NAME}" + return 0 +} + +# get test running configuration. +fetch_vllm_test_cfg +get_config +# Check if the function call was successful. If not, exit the script. +if [ $? -ne 0 ]; then + exit 1 +fi + +image_name="npu/vllm-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}" +container_name="npu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)" + +# BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards +agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}') +echo "agent_idx: ${agent_idx}" +builder_name="cachebuilder${agent_idx}" +builder_cache_dir="/mnt/docker-cache${agent_idx}" +mkdir -p ${builder_cache_dir} + +# Try building the docker image +cat <=6.0 modelscope + +WORKDIR /workspace/vllm + +# Install vLLM dependencies in advance. Effect: As long as common.txt remains unchanged, the docker cache layer will be valid. +COPY requirements/common.txt /workspace/vllm/requirements/common.txt +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install -r requirements/common.txt + +COPY . . + +# Install vLLM +RUN --mount=type=cache,target=/root/.cache/pip \ + VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /workspace/vllm/ --extra-index https://download.pytorch.org/whl/cpu/ && \ + python3 -m pip uninstall -y triton + +# Install vllm-ascend +WORKDIR /workspace +ARG VLLM_ASCEND_REPO=https://github.com/vllm-project/vllm-ascend.git +ARG VLLM_ASCEND_TAG=main +RUN git config --global url."https://gh-proxy.test.osinfra.cn/https://github.com/".insteadOf "https://github.com/" && \ + git clone --depth 1 \$VLLM_ASCEND_REPO --branch \$VLLM_ASCEND_TAG /workspace/vllm-ascend + +# Install vllm dependencies in advance. Effect: As long as common.txt remains unchanged, the docker cache layer will be valid. +RUN --mount=type=cache,target=/root/.cache/pip \ + pip install -r /workspace/vllm-ascend/requirements.txt + +RUN --mount=type=cache,target=/root/.cache/pip \ + export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \ + source /usr/local/Ascend/ascend-toolkit/set_env.sh && \ + source /usr/local/Ascend/nnal/atb/set_env.sh && \ + export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \ + python3 -m pip install -v -e /workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/ + +ENV VLLM_WORKER_MULTIPROC_METHOD=spawn +ENV VLLM_USE_MODELSCOPE=True + +WORKDIR /workspace/vllm-ascend + +CMD ["/bin/bash"] + +EOF + +# Setup cleanup +remove_docker_container() { + docker rm -f "${container_name}" || true; + docker image rm -f "${image_name}" || true; + docker system prune -f || true; +} +trap remove_docker_container EXIT + +# Generate corresponding --device args based on BUILDKITE_AGENT_NAME +# Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1. +# e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards. +# returns --device /dev/davinci0 --device /dev/davinci1 +parse_and_gen_devices() { + local input="$1" + local index cards_num + if [[ "$input" =~ ([0-9]+)-([0-9]+)cards$ ]]; then + index="${BASH_REMATCH[1]}" + cards_num="${BASH_REMATCH[2]}" + else + echo "parse error" >&2 + return 1 + fi + + local devices="" + local i=0 + while (( i < cards_num )); do + local dev_idx=$(((index - 1)*cards_num + i )) + devices="$devices --device /dev/davinci${dev_idx}" + ((i++)) + done + + # trim leading space + devices="${devices#"${devices%%[![:space:]]*}"}" + # Output devices: assigned to the caller variable + printf '%s' "$devices" +} + +devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1 + +# Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware. +# This test checks whether the OOT platform interface is functioning properly in conjunction with +# the hardware plugin vllm-ascend. +model_cache_dir=/mnt/modelscope${agent_idx} +mkdir -p ${model_cache_dir} +docker run \ + ${devices} \ + --device /dev/davinci_manager \ + --device /dev/devmm_svm \ + --device /dev/hisi_hdc \ + -v /usr/local/dcmi:/usr/local/dcmi \ + -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ + -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ + -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ + -v /etc/ascend_install.info:/etc/ascend_install.info \ + -v ${model_cache_dir}:/root/.cache/modelscope \ + --entrypoint="" \ + --name "${container_name}" \ + "${image_name}" \ + bash -c ' + set -e + pytest -v -s tests/e2e/vllm_interface/ +' From c48129c013809b3510250a40d11faf19a6939e1d Mon Sep 17 00:00:00 2001 From: vllmellm Date: Thu, 2 Oct 2025 23:26:08 +0800 Subject: [PATCH 155/166] [Qwen][ROCm] Flash Attention Rotary Embeddings (#24642) Signed-off-by: vllmellm --- .../layers/rotary_embedding/common.py | 23 +++++++++++++++++++ vllm/model_executor/models/qwen2_vl.py | 10 ++++---- 2 files changed, 28 insertions(+), 5 deletions(-) diff --git a/vllm/model_executor/layers/rotary_embedding/common.py b/vllm/model_executor/layers/rotary_embedding/common.py index 861965106774..4f02c996bda1 100644 --- a/vllm/model_executor/layers/rotary_embedding/common.py +++ b/vllm/model_executor/layers/rotary_embedding/common.py @@ -2,15 +2,21 @@ # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import math +from functools import cache +from importlib.util import find_spec +from typing import Callable import torch +from vllm.logger import init_logger from vllm.platforms import current_platform from vllm.utils import direct_register_custom_op if current_platform.is_cuda(): from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb +logger = init_logger(__name__) + # common functions def rotate_neox(x: torch.Tensor) -> torch.Tensor: @@ -65,6 +71,23 @@ def apply_rotary_emb_dispatch(x: torch.Tensor, cos: torch.Tensor, return apply_rotary_emb_torch(x, cos, sin, is_neox_style) +@cache +def dispatch_rotary_emb_function() -> Callable[..., torch.Tensor]: + if current_platform.is_cuda(): + return apply_rotary_emb + + if current_platform.is_rocm(): + if find_spec("flash_attn") is not None: + from flash_attn.ops.triton.rotary import apply_rotary + return apply_rotary + else: + logger.warning( + "flash_attn is not installed. Falling back to PyTorch " + "implementation for rotary embeddings.") + + return apply_rotary_emb_torch + + # yarn functions # Inverse dim formula to find dim based on number of rotations def yarn_find_correction_dim(num_rotations: int, diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py index f83a411459cc..38435a69444e 100644 --- a/vllm/model_executor/models/qwen2_vl.py +++ b/vllm/model_executor/models/qwen2_vl.py @@ -50,6 +50,8 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.layers.rotary_embedding.common import ( + dispatch_rotary_emb_function) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.module_mapping import MultiModelKeys from vllm.multimodal import MULTIMODAL_REGISTRY @@ -63,7 +65,7 @@ BaseProcessingInfo, PromptReplacement, PromptUpdate) from vllm.multimodal.profiling import BaseDummyInputsBuilder -from vllm.platforms import _Backend, current_platform +from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors from vllm.transformers_utils.tokenizer import AnyTokenizer from vllm.utils.tensor_schema import TensorSchema, TensorShape @@ -272,13 +274,11 @@ def apply_rotary_emb_torch(x: torch.Tensor, def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: + rotary_emb_function = dispatch_rotary_emb_function() t_ = t.float() cos = freqs.cos() sin = freqs.sin() - apply_rotary_emb = apply_rotary_emb_torch - if current_platform.is_cuda(): - from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb - output = apply_rotary_emb(t_, cos, sin).type_as(t) + output = rotary_emb_function(t_, cos, sin).type_as(t) return output From e7290aa9361383267646e213b77552fd3fccfebd Mon Sep 17 00:00:00 2001 From: Michael Goin Date: Thu, 2 Oct 2025 12:07:19 -0400 Subject: [PATCH 156/166] [CI] Add Blackwell DeepSeek FP8 FlashInfer MoE tests (#26040) Signed-off-by: mgoin --- tests/quantization/test_blackwell_moe.py | 40 +++++++++++++++++------- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/tests/quantization/test_blackwell_moe.py b/tests/quantization/test_blackwell_moe.py index c021126720af..906693a1f401 100644 --- a/tests/quantization/test_blackwell_moe.py +++ b/tests/quantization/test_blackwell_moe.py @@ -3,6 +3,7 @@ import json import os +from typing import Optional import pytest @@ -20,9 +21,10 @@ dummy_hf_overrides = {"num_layers": 4, "num_hidden_layers": 4} -def can_initialize(model: str, extra_args: list[str]): +def can_initialize(model: str, extra_args: Optional[list[str]] = None): # Server arguments + extra_args = extra_args if extra_args is not None else [] server_args = [ "--max-model-len", "2048", @@ -65,7 +67,7 @@ def test_llama4_fp8_tensor_moe_flashinfer_cutlass( monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP8", "1") monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") - can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP8", []) + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP8") @pytest.mark.skip(reason="Works, but takes too long to run") @@ -73,21 +75,21 @@ def test_llama4_fp8_tensor_moe_flashinfer_trtllm( monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP8", "1") monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") - can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP8", []) + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP8") @pytest.mark.skip(reason="Works, but takes too long to run") def test_llama4_nvfp4_moe_flashinfer_cutlass(monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") - can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP4", []) + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP4") @pytest.mark.skip(reason="RuntimeError: No kernel found for the given options") def test_llama4_nvfp4_moe_flashinfer_trtllm(monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") - can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP4", []) + can_initialize("nvidia/Llama-4-Scout-17B-16E-Instruct-FP4") ## DeepSeekV3 ## @@ -95,21 +97,37 @@ def test_llama4_nvfp4_moe_flashinfer_trtllm(monkeypatch: pytest.MonkeyPatch): def test_deepseek_fp8_block_moe_deep_gemm(monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_DEEP_GEMM", "1") - can_initialize("deepseek-ai/DeepSeek-V3.1", []) + can_initialize("deepseek-ai/DeepSeek-V3.1") + + +@pytest.mark.skip(reason=("Known issue: lack of kernel support. " + "Expected failure: assert self.block_quant is None")) +def test_deepseek_fp8_block_moe_flashinfer_cutlass( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP8", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") + can_initialize("deepseek-ai/DeepSeek-V3.1") + + +def test_deepseek_fp8_block_moe_flashinfer_trtllm( + monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP8", "1") + monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") + can_initialize("deepseek-ai/DeepSeek-V3.1") def test_deepseek_nvfp4_moe_flashinfer_cutlass( monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "throughput") - can_initialize("nvidia/DeepSeek-R1-0528-FP4-v2", []) + can_initialize("nvidia/DeepSeek-R1-0528-FP4-v2") @pytest.mark.skip(reason="RuntimeError: No kernel found for the given options") def test_deepseek_nvfp4_moe_flashinfer_trtllm(monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_FP4", "1") monkeypatch.setenv("VLLM_FLASHINFER_MOE_BACKEND", "latency") - can_initialize("nvidia/DeepSeek-R1-0528-FP4-v2", []) + can_initialize("nvidia/DeepSeek-R1-0528-FP4-v2") ## GPT-OSS ## @@ -117,16 +135,16 @@ def test_deepseek_nvfp4_moe_flashinfer_trtllm(monkeypatch: pytest.MonkeyPatch): def test_gptoss_mxfp4bf16_moe_flashinfer(monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "1") - can_initialize("openai/gpt-oss-20b", []) + can_initialize("openai/gpt-oss-20b") def test_gptoss_mxfp4mxfp8_moe_flashinfer_cutlass( monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "1") - can_initialize("openai/gpt-oss-20b", []) + can_initialize("openai/gpt-oss-20b") def test_gptoss_mxfp4mxfp8_moe_flashinfer_trtllm( monkeypatch: pytest.MonkeyPatch): monkeypatch.setenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "1") - can_initialize("openai/gpt-oss-20b", []) + can_initialize("openai/gpt-oss-20b") From 2e83ce3a671b2a81fe63b61af7c1081dbfb133ec Mon Sep 17 00:00:00 2001 From: Cyrus Leung Date: Fri, 3 Oct 2025 01:04:57 +0800 Subject: [PATCH 157/166] [CI/Build] Replace `vllm.entrypoints.openai.api_server` entrypoint with `vllm serve` command (#25967) Signed-off-by: DarkLight1337 --- .../scripts/launch-server.sh | 8 +--- .../scripts/run-performance-benchmarks.sh | 3 +- .buildkite/scripts/run-benchmarks.sh | 2 +- .../disagg_overhead_benchmark.sh | 8 +--- .../disagg_performance_benchmark.sh | 16 ++----- docker/Dockerfile | 2 +- docker/Dockerfile.cpu | 2 +- docker/Dockerfile.ppc64le | 2 +- docker/Dockerfile.s390x | 2 +- docker/Dockerfile.xpu | 2 +- docs/contributing/benchmarks.md | 6 +-- docs/contributing/profiling.md | 3 +- docs/deployment/frameworks/autogen.md | 3 +- docs/deployment/frameworks/open-webui.md | 2 +- docs/deployment/frameworks/skypilot.md | 12 ++--- docs/design/arch_overview.md | 5 +++ docs/features/sleep_mode.md | 3 +- docs/features/spec_decode.md | 3 +- .../installation/gpu/xpu.inc.md | 3 +- .../online_serving/sagemaker-entrypoint.sh | 2 +- tests/utils_/test_utils.py | 44 ++++++++++++++++--- vllm/utils/__init__.py | 34 +++++++++++--- 22 files changed, 101 insertions(+), 66 deletions(-) diff --git a/.buildkite/nightly-benchmarks/scripts/launch-server.sh b/.buildkite/nightly-benchmarks/scripts/launch-server.sh index fb5063db8694..ebacdcbd6821 100644 --- a/.buildkite/nightly-benchmarks/scripts/launch-server.sh +++ b/.buildkite/nightly-benchmarks/scripts/launch-server.sh @@ -181,18 +181,14 @@ launch_vllm_server() { if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience." model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model') - server_command="python3 \ - -m vllm.entrypoints.openai.api_server \ + server_command="vllm serve $model \ -tp $tp \ - --model $model \ --port $port \ $server_args" else echo "Key 'fp8' does not exist in common params." - server_command="python3 \ - -m vllm.entrypoints.openai.api_server \ + server_command="vllm serve $model \ -tp $tp \ - --model $model \ --port $port \ $server_args" fi diff --git a/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh b/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh index b1b7d2d77a44..34effbb6073d 100644 --- a/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh +++ b/.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh @@ -365,8 +365,7 @@ run_serving_tests() { continue fi - server_command="$server_envs python3 \ - -m vllm.entrypoints.openai.api_server \ + server_command="$server_envs vllm serve \ $server_args" # run the server diff --git a/.buildkite/scripts/run-benchmarks.sh b/.buildkite/scripts/run-benchmarks.sh index 72812218cb66..51536b36b808 100644 --- a/.buildkite/scripts/run-benchmarks.sh +++ b/.buildkite/scripts/run-benchmarks.sh @@ -18,7 +18,7 @@ vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_ bench_throughput_exit_code=$? # run server-based benchmarks and upload the result to buildkite -python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf & +vllm serve meta-llama/Llama-2-7b-chat-hf & server_pid=$! wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json diff --git a/benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh b/benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh index 2c72941cf7e5..d683835db96a 100644 --- a/benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh +++ b/benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh @@ -55,9 +55,7 @@ benchmark() { output_len=$2 - CUDA_VISIBLE_DEVICES=0 python3 \ - -m vllm.entrypoints.openai.api_server \ - --model $model \ + CUDA_VISIBLE_DEVICES=0 vllm serve $model \ --port 8100 \ --max-model-len 10000 \ --gpu-memory-utilization 0.6 \ @@ -65,9 +63,7 @@ benchmark() { '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' & - CUDA_VISIBLE_DEVICES=1 python3 \ - -m vllm.entrypoints.openai.api_server \ - --model $model \ + CUDA_VISIBLE_DEVICES=1 vllm serve $model \ --port 8200 \ --max-model-len 10000 \ --gpu-memory-utilization 0.6 \ diff --git a/benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh b/benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh index 0bbf7cd2b1c8..35c86cc84522 100644 --- a/benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh +++ b/benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh @@ -38,16 +38,12 @@ wait_for_server() { launch_chunked_prefill() { model="meta-llama/Meta-Llama-3.1-8B-Instruct" # disagg prefill - CUDA_VISIBLE_DEVICES=0 python3 \ - -m vllm.entrypoints.openai.api_server \ - --model $model \ + CUDA_VISIBLE_DEVICES=0 vllm serve $model \ --port 8100 \ --max-model-len 10000 \ --enable-chunked-prefill \ --gpu-memory-utilization 0.6 & - CUDA_VISIBLE_DEVICES=1 python3 \ - -m vllm.entrypoints.openai.api_server \ - --model $model \ + CUDA_VISIBLE_DEVICES=1 vllm serve $model \ --port 8200 \ --max-model-len 10000 \ --enable-chunked-prefill \ @@ -62,18 +58,14 @@ launch_chunked_prefill() { launch_disagg_prefill() { model="meta-llama/Meta-Llama-3.1-8B-Instruct" # disagg prefill - CUDA_VISIBLE_DEVICES=0 python3 \ - -m vllm.entrypoints.openai.api_server \ - --model $model \ + CUDA_VISIBLE_DEVICES=0 vllm serve $model \ --port 8100 \ --max-model-len 10000 \ --gpu-memory-utilization 0.6 \ --kv-transfer-config \ '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' & - CUDA_VISIBLE_DEVICES=1 python3 \ - -m vllm.entrypoints.openai.api_server \ - --model $model \ + CUDA_VISIBLE_DEVICES=1 vllm serve $model \ --port 8200 \ --max-model-len 10000 \ --gpu-memory-utilization 0.6 \ diff --git a/docker/Dockerfile b/docker/Dockerfile index ccc1b024316a..56bbc3d5f1a3 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -565,5 +565,5 @@ ENTRYPOINT ["./sagemaker-entrypoint.sh"] FROM vllm-openai-base AS vllm-openai -ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"] +ENTRYPOINT ["vllm", "serve"] #################### OPENAI API SERVER #################### diff --git a/docker/Dockerfile.cpu b/docker/Dockerfile.cpu index 388596efd21c..2ed02ff9e3ac 100644 --- a/docker/Dockerfile.cpu +++ b/docker/Dockerfile.cpu @@ -177,4 +177,4 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \ uv pip install dist/*.whl -ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"] +ENTRYPOINT ["vllm", "serve"] diff --git a/docker/Dockerfile.ppc64le b/docker/Dockerfile.ppc64le index aaff240388f2..5eaef4ea980d 100644 --- a/docker/Dockerfile.ppc64le +++ b/docker/Dockerfile.ppc64le @@ -314,4 +314,4 @@ WORKDIR /workspace/ RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks -ENTRYPOINT ["python", "-m", "vllm.entrypoints.openai.api_server"] +ENTRYPOINT ["vllm", "serve"] diff --git a/docker/Dockerfile.s390x b/docker/Dockerfile.s390x index 9942b7626f81..7fd7598b8bd9 100644 --- a/docker/Dockerfile.s390x +++ b/docker/Dockerfile.s390x @@ -309,4 +309,4 @@ USER 2000 WORKDIR /home/vllm # Set the default entrypoint -ENTRYPOINT ["python", "-m", "vllm.entrypoints.openai.api_server"] +ENTRYPOINT ["vllm", "serve"] diff --git a/docker/Dockerfile.xpu b/docker/Dockerfile.xpu index ef422352509a..ffc3abd38965 100644 --- a/docker/Dockerfile.xpu +++ b/docker/Dockerfile.xpu @@ -69,4 +69,4 @@ RUN --mount=type=cache,target=/root/.cache/pip \ # install development dependencies (for testing) RUN python3 -m pip install -e tests/vllm_test_utils -ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"] +ENTRYPOINT ["vllm", "serve"] diff --git a/docs/contributing/benchmarks.md b/docs/contributing/benchmarks.md index cf14770c01a6..d53a680c9182 100644 --- a/docs/contributing/benchmarks.md +++ b/docs/contributing/benchmarks.md @@ -661,8 +661,7 @@ Benchmark the performance of multi-modal requests in vLLM. Start vLLM: ```bash -python -m vllm.entrypoints.openai.api_server \ - --model Qwen/Qwen2.5-VL-7B-Instruct \ +vllm serve Qwen/Qwen2.5-VL-7B-Instruct \ --dtype bfloat16 \ --limit-mm-per-prompt '{"image": 1}' \ --allowed-local-media-path /path/to/sharegpt4v/images @@ -688,8 +687,7 @@ vllm bench serve \ Start vLLM: ```bash -python -m vllm.entrypoints.openai.api_server \ - --model Qwen/Qwen2.5-VL-7B-Instruct \ +vllm serve Qwen/Qwen2.5-VL-7B-Instruct \ --dtype bfloat16 \ --limit-mm-per-prompt '{"video": 1}' \ --allowed-local-media-path /path/to/sharegpt4video/videos diff --git a/docs/contributing/profiling.md b/docs/contributing/profiling.md index b62560a58748..f6a73e99546e 100644 --- a/docs/contributing/profiling.md +++ b/docs/contributing/profiling.md @@ -39,8 +39,7 @@ Refer to for an example ```bash VLLM_TORCH_PROFILER_DIR=./vllm_profile \ - python -m vllm.entrypoints.openai.api_server \ - --model meta-llama/Meta-Llama-3-70B + vllm serve meta-llama/Meta-Llama-3-70B ``` vllm bench command: diff --git a/docs/deployment/frameworks/autogen.md b/docs/deployment/frameworks/autogen.md index 7517ee771c09..5790087ed5c2 100644 --- a/docs/deployment/frameworks/autogen.md +++ b/docs/deployment/frameworks/autogen.md @@ -19,8 +19,7 @@ pip install -U "autogen-agentchat" "autogen-ext[openai]" 1. Start the vLLM server with the supported chat completion model, e.g. ```bash - python -m vllm.entrypoints.openai.api_server \ - --model mistralai/Mistral-7B-Instruct-v0.2 + vllm serve mistralai/Mistral-7B-Instruct-v0.2 ``` 1. Call it with AutoGen: diff --git a/docs/deployment/frameworks/open-webui.md b/docs/deployment/frameworks/open-webui.md index eaa51bb61328..505c129613de 100644 --- a/docs/deployment/frameworks/open-webui.md +++ b/docs/deployment/frameworks/open-webui.md @@ -20,7 +20,7 @@ To get started with Open WebUI using vLLM, follow these steps: For example: ```console - python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000 + vllm serve --host 0.0.0.0 --port 8000 ``` 3. Start the Open WebUI Docker container: diff --git a/docs/deployment/frameworks/skypilot.md b/docs/deployment/frameworks/skypilot.md index 06e2fed38f05..f4a984a6433e 100644 --- a/docs/deployment/frameworks/skypilot.md +++ b/docs/deployment/frameworks/skypilot.md @@ -32,6 +32,7 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil ports: 8081 # Expose to internet traffic. envs: + PYTHONUNBUFFERED: 1 MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct HF_TOKEN: # Change to your own huggingface token, or use --env to pass. @@ -47,9 +48,8 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil run: | conda activate vllm echo 'Starting vllm api server...' - python -u -m vllm.entrypoints.openai.api_server \ + vllm serve $MODEL_NAME \ --port 8081 \ - --model $MODEL_NAME \ --trust-remote-code \ --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \ 2>&1 | tee api_server.log & @@ -131,6 +131,7 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut ports: 8081 # Expose to internet traffic. envs: + PYTHONUNBUFFERED: 1 MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct HF_TOKEN: # Change to your own huggingface token, or use --env to pass. @@ -146,9 +147,8 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut run: | conda activate vllm echo 'Starting vllm api server...' - python -u -m vllm.entrypoints.openai.api_server \ + vllm serve $MODEL_NAME \ --port 8081 \ - --model $MODEL_NAME \ --trust-remote-code \ --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \ 2>&1 | tee api_server.log @@ -243,6 +243,7 @@ This will scale the service up to when the QPS exceeds 2 for each replica. ports: 8081 # Expose to internet traffic. envs: + PYTHONUNBUFFERED: 1 MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct HF_TOKEN: # Change to your own huggingface token, or use --env to pass. @@ -258,9 +259,8 @@ This will scale the service up to when the QPS exceeds 2 for each replica. run: | conda activate vllm echo 'Starting vllm api server...' - python -u -m vllm.entrypoints.openai.api_server \ + vllm serve $MODEL_NAME \ --port 8081 \ - --model $MODEL_NAME \ --trust-remote-code \ --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \ 2>&1 | tee api_server.log diff --git a/docs/design/arch_overview.md b/docs/design/arch_overview.md index 6b7086776025..f1300a73c26c 100644 --- a/docs/design/arch_overview.md +++ b/docs/design/arch_overview.md @@ -69,6 +69,11 @@ Sometimes you may see the API server entrypoint used directly instead of via the python -m vllm.entrypoints.openai.api_server --model ``` +!!! warning + + `python -m vllm.entrypoints.openai.api_server` is deprecated + and may become unsupported in a future release. + That code can be found in . More details on the API server can be found in the [OpenAI-Compatible Server](../serving/openai_compatible_server.md) document. diff --git a/docs/features/sleep_mode.md b/docs/features/sleep_mode.md index 5749b02d26f4..e7dd9fee12d3 100644 --- a/docs/features/sleep_mode.md +++ b/docs/features/sleep_mode.md @@ -64,8 +64,7 @@ To enable sleep mode in a vLLM server you need to initialize it with the flag `V When using the flag `VLLM_SERVER_DEV_MODE=1` you enable development endpoints, and these endpoints should not be exposed to users. ```bash -VLLM_SERVER_DEV_MODE=1 python -m vllm.entrypoints.openai.api_server \ - --model Qwen/Qwen3-0.6B \ +VLLM_SERVER_DEV_MODE=1 vllm serve Qwen/Qwen3-0.6B \ --enable-sleep-mode \ --port 8000 ``` diff --git a/docs/features/spec_decode.md b/docs/features/spec_decode.md index 597a8e864427..25c308a6ff20 100644 --- a/docs/features/spec_decode.md +++ b/docs/features/spec_decode.md @@ -48,10 +48,9 @@ The following code configures vLLM in an offline mode to use speculative decodin To perform the same with an online mode launch the server: ```bash -python -m vllm.entrypoints.openai.api_server \ +vllm serve facebook/opt-6.7b \ --host 0.0.0.0 \ --port 8000 \ - --model facebook/opt-6.7b \ --seed 42 \ -tp 1 \ --gpu_memory_utilization 0.8 \ diff --git a/docs/getting_started/installation/gpu/xpu.inc.md b/docs/getting_started/installation/gpu/xpu.inc.md index ed1dc0418cf7..2e73ac182569 100644 --- a/docs/getting_started/installation/gpu/xpu.inc.md +++ b/docs/getting_started/installation/gpu/xpu.inc.md @@ -67,8 +67,7 @@ docker run -it \ XPU platform supports **tensor parallel** inference/serving and also supports **pipeline parallel** as a beta feature for online serving. For **pipeline parallel**, we support it on single node with mp as the backend. For example, a reference execution like following: ```bash -python -m vllm.entrypoints.openai.api_server \ - --model=facebook/opt-13b \ +vllm serve facebook/opt-13b \ --dtype=bfloat16 \ --max_model_len=1024 \ --distributed-executor-backend=mp \ diff --git a/examples/online_serving/sagemaker-entrypoint.sh b/examples/online_serving/sagemaker-entrypoint.sh index 75a99ffc1f15..1a6b6780ef2a 100644 --- a/examples/online_serving/sagemaker-entrypoint.sh +++ b/examples/online_serving/sagemaker-entrypoint.sh @@ -21,4 +21,4 @@ while IFS='=' read -r key value; do done < <(env | grep "^${PREFIX}") # Pass the collected arguments to the main entrypoint -exec python3 -m vllm.entrypoints.openai.api_server "${ARGS[@]}" \ No newline at end of file +exec vllm serve "${ARGS[@]}" \ No newline at end of file diff --git a/tests/utils_/test_utils.py b/tests/utils_/test_utils.py index 658ae7e7451a..bdd92cc8e35e 100644 --- a/tests/utils_/test_utils.py +++ b/tests/utils_/test_utils.py @@ -786,13 +786,43 @@ def test_model_specification(parser_with_config, cli_config_file, parser_with_config.parse_args(['serve', '--config', cli_config_file]) # Test using --model option raises error - with pytest.raises( - ValueError, - match= - ("With `vllm serve`, you should provide the model as a positional " - "argument or in a config file instead of via the `--model` option."), - ): - parser_with_config.parse_args(['serve', '--model', 'my-model']) + # with pytest.raises( + # ValueError, + # match= + # ("With `vllm serve`, you should provide the model as a positional " + # "argument or in a config file instead of via the `--model` option."), + # ): + # parser_with_config.parse_args(['serve', '--model', 'my-model']) + + # Test using --model option back-compatibility + # (when back-compatibility ends, the above test should be uncommented + # and the below test should be removed) + args = parser_with_config.parse_args([ + 'serve', + '--tensor-parallel-size', + '2', + '--model', + 'my-model', + '--trust-remote-code', + '--port', + '8001', + ]) + assert args.model is None + assert args.tensor_parallel_size == 2 + assert args.trust_remote_code is True + assert args.port == 8001 + + args = parser_with_config.parse_args([ + 'serve', + '--tensor-parallel-size=2', + '--model=my-model', + '--trust-remote-code', + '--port=8001', + ]) + assert args.model is None + assert args.tensor_parallel_size == 2 + assert args.trust_remote_code is True + assert args.port == 8001 # Test other config values are preserved args = parser_with_config.parse_args([ diff --git a/vllm/utils/__init__.py b/vllm/utils/__init__.py index 11d6686009b2..8c69870b2bc3 100644 --- a/vllm/utils/__init__.py +++ b/vllm/utils/__init__.py @@ -1855,13 +1855,37 @@ def parse_args( # type: ignore[override] # Check for --model in command line arguments first if args and args[0] == "serve": - model_in_cli_args = any(arg == '--model' for arg in args) - - if model_in_cli_args: - raise ValueError( + try: + model_idx = next( + i for i, arg in enumerate(args) + if arg == "--model" or arg.startswith("--model=")) + logger.warning( "With `vllm serve`, you should provide the model as a " "positional argument or in a config file instead of via " - "the `--model` option.") + "the `--model` option. " + "The `--model` option will be removed in v0.13.") + + if args[model_idx] == "--model": + model_tag = args[model_idx + 1] + rest_start_idx = model_idx + 2 + else: + model_tag = args[model_idx].removeprefix("--model=") + rest_start_idx = model_idx + 1 + + # Move to the front, e,g: + # [Before] + # vllm serve -tp 2 --model --enforce-eager --port 8001 + # [After] + # vllm serve -tp 2 --enforce-eager --port 8001 + args = [ + "serve", + model_tag, + *args[1:model_idx], + *args[rest_start_idx:], + ] + print("args", args) + except StopIteration: + pass if '--config' in args: args = self._pull_args_from_config(args) From 2047f0c4fa282c64ccd90b7dcf5929d5d087a078 Mon Sep 17 00:00:00 2001 From: Lucas Wilkinson Date: Thu, 2 Oct 2025 13:18:13 -0400 Subject: [PATCH 158/166] [BugFix] Fix FI accuracy issue when used for MLA prefill (#26063) Signed-off-by: Lucas Wilkinson Signed-off-by: Lucas Wilkinson Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: mgoin --- vllm/v1/attention/backends/mla/common.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/vllm/v1/attention/backends/mla/common.py b/vllm/v1/attention/backends/mla/common.py index 561793b6a377..963f1c5abf2a 100755 --- a/vllm/v1/attention/backends/mla/common.py +++ b/vllm/v1/attention/backends/mla/common.py @@ -1211,13 +1211,18 @@ def _run_prefill_new_tokens_fi(self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse): assert isinstance(prefill, FlashInferPrefillMetadata) assert prefill.prefill_main is not None - return prefill.prefill_main.run( + ret = prefill.prefill_main.run( q=q, k=k, v=v, return_lse=return_softmax_lse, ) + if isinstance(ret, tuple): + # Convert from (q_len, num_heads) to (num_heads, q_len) + return ret[0], ret[1].transpose(0, 1).contiguous() + return ret + def _run_prefill_new_tokens_cudnn(self, prefill: MLACommonPrefillMetadata, q, k, v, return_softmax_lse): assert isinstance(prefill, CudnnPrefillMetadata) @@ -1260,12 +1265,14 @@ def _run_prefill_context_chunk_fa(self, prefill: MLACommonPrefillMetadata, def _run_prefill_context_chunk_fi(self, prefill: MLACommonPrefillMetadata, chunk_idx: int, q, k, v): assert isinstance(prefill, FlashInferPrefillMetadata) - return prefill.prefill_chunks[chunk_idx].run( + attn_out, lse = prefill.prefill_chunks[chunk_idx].run( q=q, k=k, v=v, return_lse=True, ) + # Convert from (q_len, num_heads) to (num_heads, q_len) + return attn_out, lse.transpose(0, 1).contiguous() def _run_prefill_context_chunk_cudnn(self, prefill: MLACommonPrefillMetadata, From 4937686b0e78b205758450b294457382bbe1cb98 Mon Sep 17 00:00:00 2001 From: Chenheli Hua Date: Thu, 2 Oct 2025 10:27:10 -0700 Subject: [PATCH 159/166] [Small] Prevent bypassing media domain restriction via HTTP redirects (#26035) Signed-off-by: Chenheli Hua --- docs/features/multimodal_inputs.md | 3 +++ docs/usage/security.md | 3 +++ vllm/connections.py | 24 ++++++++++++++++++------ vllm/envs.py | 6 ++++++ vllm/multimodal/utils.py | 12 ++++++++++-- 5 files changed, 40 insertions(+), 8 deletions(-) diff --git a/docs/features/multimodal_inputs.md b/docs/features/multimodal_inputs.md index b61a54d3ca7f..dcc5ea3b9096 100644 --- a/docs/features/multimodal_inputs.md +++ b/docs/features/multimodal_inputs.md @@ -8,6 +8,9 @@ This page teaches you how to pass multi-modal inputs to [multi-modal models][sup !!! tip When serving multi-modal models, consider setting `--allowed-media-domains` to restrict domain that vLLM can access to prevent it from accessing arbitrary endpoints that can potentially be vulnerable to Server-Side Request Forgery (SSRF) attacks. You can provide a list of domains for this arg. For example: `--allowed-media-domains upload.wikimedia.org github.com www.bogotobogo.com` + + Also, consider setting `VLLM_MEDIA_URL_ALLOW_REDIRECTS=0` to prevent HTTP redirects from being followed to bypass domain restrictions. + This restriction is especially important if you run vLLM in a containerized environment where the vLLM pods may have unrestricted access to internal networks. ## Offline Inference diff --git a/docs/usage/security.md b/docs/usage/security.md index 5d85e889c80c..9d10b66a5a97 100644 --- a/docs/usage/security.md +++ b/docs/usage/security.md @@ -66,6 +66,9 @@ Restrict domains that vLLM can access for media URLs by setting `--allowed-media-domains` to prevent Server-Side Request Forgery (SSRF) attacks. (e.g. `--allowed-media-domains upload.wikimedia.org github.com www.bogotobogo.com`) +Also, consider setting `VLLM_MEDIA_URL_ALLOW_REDIRECTS=0` to prevent HTTP +redirects from being followed to bypass domain restrictions. + ## Security and Firewalls: Protecting Exposed vLLM Systems While vLLM is designed to allow unsafe network services to be isolated to diff --git a/vllm/connections.py b/vllm/connections.py index 103505eb3d81..1f341719ae30 100644 --- a/vllm/connections.py +++ b/vllm/connections.py @@ -54,6 +54,7 @@ def get_response( stream: bool = False, timeout: Optional[float] = None, extra_headers: Optional[Mapping[str, str]] = None, + allow_redirects: bool = True, ): self._validate_http_url(url) @@ -63,7 +64,8 @@ def get_response( return client.get(url, headers=self._headers(**extra_headers), stream=stream, - timeout=timeout) + timeout=timeout, + allow_redirects=allow_redirects) async def get_async_response( self, @@ -71,6 +73,7 @@ async def get_async_response( *, timeout: Optional[float] = None, extra_headers: Optional[Mapping[str, str]] = None, + allow_redirects: bool = True, ): self._validate_http_url(url) @@ -79,10 +82,17 @@ async def get_async_response( return client.get(url, headers=self._headers(**extra_headers), - timeout=timeout) - - def get_bytes(self, url: str, *, timeout: Optional[float] = None) -> bytes: - with self.get_response(url, timeout=timeout) as r: + timeout=timeout, + allow_redirects=allow_redirects) + + def get_bytes(self, + url: str, + *, + timeout: Optional[float] = None, + allow_redirects: bool = True) -> bytes: + with self.get_response(url, + timeout=timeout, + allow_redirects=allow_redirects) as r: r.raise_for_status() return r.content @@ -92,8 +102,10 @@ async def async_get_bytes( url: str, *, timeout: Optional[float] = None, + allow_redirects: bool = True, ) -> bytes: - async with await self.get_async_response(url, timeout=timeout) as r: + async with await self.get_async_response( + url, timeout=timeout, allow_redirects=allow_redirects) as r: r.raise_for_status() return await r.read() diff --git a/vllm/envs.py b/vllm/envs.py index 03a22e4b2c7e..3d7d3c576dab 100755 --- a/vllm/envs.py +++ b/vllm/envs.py @@ -68,6 +68,7 @@ VLLM_IMAGE_FETCH_TIMEOUT: int = 5 VLLM_VIDEO_FETCH_TIMEOUT: int = 30 VLLM_AUDIO_FETCH_TIMEOUT: int = 10 + VLLM_MEDIA_URL_ALLOW_REDIRECTS: bool = True VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8 VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25 VLLM_VIDEO_LOADER_BACKEND: str = "opencv" @@ -733,6 +734,11 @@ def get_vllm_port() -> Optional[int]: "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")), + # Whether to allow HTTP redirects when fetching from media URLs. + # Default to True + "VLLM_MEDIA_URL_ALLOW_REDIRECTS": + lambda: bool(int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))), + # Max number of workers for the thread pool handling # media bytes loading. Set to 1 to disable parallel processing. # Default is 8 diff --git a/vllm/multimodal/utils.py b/vllm/multimodal/utils.py index 1f1eea6bfee7..bab12fd1681a 100644 --- a/vllm/multimodal/utils.py +++ b/vllm/multimodal/utils.py @@ -140,7 +140,11 @@ def load_from_url( self._assert_url_in_allowed_media_domains(url_spec) connection = self.connection - data = connection.get_bytes(url, timeout=fetch_timeout) + data = connection.get_bytes( + url, + timeout=fetch_timeout, + allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS, + ) return media_io.load_bytes(data) @@ -167,7 +171,11 @@ async def load_from_url_async( self._assert_url_in_allowed_media_domains(url_spec) connection = self.connection - data = await connection.async_get_bytes(url, timeout=fetch_timeout) + data = await connection.async_get_bytes( + url, + timeout=fetch_timeout, + allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS, + ) future = loop.run_in_executor(global_thread_pool, media_io.load_bytes, data) return await future From b7e4aac0a0d9fe50ad0b98cd06a6d342f9e308af Mon Sep 17 00:00:00 2001 From: Chen Zhang Date: Thu, 2 Oct 2025 10:29:12 -0700 Subject: [PATCH 160/166] [Deepseek v3.2] Support indexer prefill chunking (#25999) Signed-off-by: Chen Zhang --- .../v1/attention/test_sparse_mla_backends.py | 22 +++ vllm/model_executor/models/deepseek_v2.py | 75 +++++----- vllm/v1/attention/backends/mla/indexer.py | 131 ++++++++++++------ 3 files changed, 149 insertions(+), 79 deletions(-) diff --git a/tests/v1/attention/test_sparse_mla_backends.py b/tests/v1/attention/test_sparse_mla_backends.py index 74eea6f716fe..ddad9342fad0 100644 --- a/tests/v1/attention/test_sparse_mla_backends.py +++ b/tests/v1/attention/test_sparse_mla_backends.py @@ -22,6 +22,7 @@ from vllm.v1.attention.backends.mla.flashmla_sparse import ( FlashMLASparseBackend, FlashMLASparseDecodeAndContextMetadata, FlashMLASparseImpl, FlashMLASparseMetadata) +from vllm.v1.attention.backends.mla.indexer import split_prefill_chunks SPARSE_BACKEND_BATCH_SPECS = { name: BATCH_SPECS[name] @@ -424,3 +425,24 @@ def test_sparse_backend_decode_correctness(dist_init, batch_name, sdpa_reference, rtol=0.5, atol=0.5) + + +@pytest.mark.parametrize( + "seq_lens,max_buf,start,expected", + [ + # Basic split: totals per chunk ≤ max_buf + (torch.tensor([2, 3, 4, 2]), 5, 0, [(0, 2), (2, 3), (3, 4)]), + # Non-zero start index + (torch.tensor([2, 3, 4, 2]), 5, 1, [(1, 2), (2, 3), (3, 4)]), + # Exact fits should split between items when adding the next would + # overflow + (torch.tensor([5, 5, 5]), 5, 0, [(0, 1), (1, 2), (2, 3)]), + # All requests fit in a single chunk + (torch.tensor([1, 1, 1]), 10, 0, [(0, 3)]), + # Large buffer with non-zero start + (torch.tensor([4, 4, 4]), 100, 1, [(1, 3)]), + ], +) +def test_split_prefill_chunks(seq_lens, max_buf, start, expected): + out = split_prefill_chunks(seq_lens, max_buf, start) + assert out == expected diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py index 03c43654d68f..b7f96d0d1552 100644 --- a/vllm/model_executor/models/deepseek_v2.py +++ b/vllm/model_executor/models/deepseek_v2.py @@ -583,44 +583,43 @@ def sparse_attn_indexer( topk_indices_buffer[:hidden_states.shape[0]] = -1 if has_prefill: prefill_metadata = attn_metadata.prefill - num_prefills = attn_metadata.num_prefills - k_fp8 = torch.empty([prefill_metadata.total_seq_lens, head_dim], - device=k.device, - dtype=torch.float8_e4m3fn) - k_scale = torch.empty([prefill_metadata.total_seq_lens, 1], - device=k.device, - dtype=torch.float32) - cp_gather_indexer_k_quant_cache( - kv_cache, - k_fp8, - k_scale, - prefill_metadata.block_table, - prefill_metadata.cu_seq_lens, - num_prefills, - ) - cu_seqlen_ks = prefill_metadata.cu_seqlen_ks - cu_seqlen_ke = prefill_metadata.cu_seqlen_ke - num_tokens = attn_metadata.num_actual_tokens - logits = fp8_mqa_logits( - q_fp8[num_decode_tokens:num_tokens], - (k_fp8, k_scale), - weights[num_decode_tokens:num_tokens], - cu_seqlen_ks, - cu_seqlen_ke, - ) - topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]), - dim=-1)[1] - topk_indices -= cu_seqlen_ks[:, None] - mask_lo = topk_indices >= 0 - mask_hi = topk_indices - (cu_seqlen_ke - cu_seqlen_ks)[:, None] < 0 - mask = torch.full_like(topk_indices, - False, - dtype=torch.bool, - device=topk_indices.device) - mask = mask_lo & mask_hi - topk_indices = topk_indices.masked_fill(~mask, -1) - topk_indices_buffer[num_decode_tokens:num_tokens, :topk_indices. - shape[-1]] = topk_indices.to(dtype=torch.int32) + for chunk in prefill_metadata.chunks: + k_fp8 = torch.empty([chunk.total_seq_lens, head_dim], + device=k.device, + dtype=torch.float8_e4m3fn) + k_scale = torch.empty([chunk.total_seq_lens, 1], + device=k.device, + dtype=torch.float32) + cp_gather_indexer_k_quant_cache( + kv_cache, + k_fp8, + k_scale, + chunk.block_table, + chunk.cu_seq_lens, + chunk.num_reqs, + ) + logits = fp8_mqa_logits( + q_fp8[chunk.token_start:chunk.token_end], + (k_fp8, k_scale), + weights[chunk.token_start:chunk.token_end], + chunk.cu_seqlen_ks, + chunk.cu_seqlen_ke, + ) + topk_indices = logits.topk(min(topk_tokens, logits.shape[-1]), + dim=-1)[1] + topk_indices -= chunk.cu_seqlen_ks[:, None] + mask_lo = topk_indices >= 0 + mask_hi = topk_indices - (chunk.cu_seqlen_ke - + chunk.cu_seqlen_ks)[:, None] < 0 + mask = torch.full_like(topk_indices, + False, + dtype=torch.bool, + device=topk_indices.device) + mask = mask_lo & mask_hi + topk_indices = topk_indices.masked_fill(~mask, -1) + topk_indices_buffer[ + chunk.token_start:chunk.token_end, :topk_indices. + shape[-1]] = topk_indices.to(dtype=torch.int32) if has_decode: decode_metadata = attn_metadata.decode diff --git a/vllm/v1/attention/backends/mla/indexer.py b/vllm/v1/attention/backends/mla/indexer.py index e87b51b15191..94b963f34e4a 100644 --- a/vllm/v1/attention/backends/mla/indexer.py +++ b/vllm/v1/attention/backends/mla/indexer.py @@ -49,14 +49,20 @@ def get_kv_cache_stride_order() -> tuple[int, ...]: @dataclass -class DeepseekV32IndexerPrefillMetadata: +class DeepseekV32IndexerPrefillChunkMetadata: block_table: torch.Tensor - query_start_loc: torch.Tensor - max_query_len: int cu_seqlen_ks: torch.Tensor cu_seqlen_ke: torch.Tensor cu_seq_lens: torch.Tensor total_seq_lens: int + token_start: int + token_end: int + num_reqs: int + + +@dataclass +class DeepseekV32IndexerPrefillMetadata: + chunks: list[DeepseekV32IndexerPrefillChunkMetadata] @dataclass @@ -98,8 +104,8 @@ class DeepseekV32IndexerMetadata: # TODO (zyongye) optimize this, this is now vibe coded def kv_spans_from_batches( - start_seq_loc: torch.Tensor, - seq_len_per_batch: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + start_seq_loc: torch.Tensor, seq_len_per_batch: torch.Tensor, + device: torch.device) -> tuple[torch.Tensor, torch.Tensor]: """ Args: start_seq_loc: 1D long tensor [B+1], cumulative counts of @@ -122,7 +128,7 @@ def kv_spans_from_batches( are the **last** `counts[i]` positions of that sequence. """ q = start_seq_loc.to(dtype=torch.long) - L = seq_len_per_batch.to(dtype=torch.long, device=q.device) + L = seq_len_per_batch.to(dtype=torch.long) assert q.dim() == 1 and L.dim() == 1 assert q.numel() == L.numel() + 1, "start_seq_loc must have length B+1" @@ -130,7 +136,6 @@ def kv_spans_from_batches( counts = q[1:] - q[:-1] # [B] N = int(q[-1].item()) # total selected tokens B = L.numel() - device = L.device if N == 0: return (torch.empty(0, dtype=torch.long, device=device), @@ -140,8 +145,7 @@ def kv_spans_from_batches( kv_starts_per_batch = torch.cumsum(L, dim=0) - L # [B] # For each selected token, which batch does it belong to? - batch_id = torch.repeat_interleave(torch.arange(B, device=device), - counts) # [N] + batch_id = torch.repeat_interleave(torch.arange(B), counts) # [N] # Map batch KV start to each token start_tensor = kv_starts_per_batch[batch_id] # [N] @@ -151,22 +155,51 @@ def kv_spans_from_batches( L_expand = torch.repeat_interleave(L, counts) # [N] m_expand = torch.repeat_interleave(counts, counts) # [N] # position within the selected block: 1..counts[b] - pos_within = (torch.arange(N, device=device, dtype=torch.long) - + pos_within = (torch.arange(N, dtype=torch.long) - torch.repeat_interleave(q[:-1], counts) + 1) local_pos = L_expand - m_expand + pos_within # [N], 1-based end_location = start_tensor + local_pos # exclusive end - return start_tensor.int(), end_location.int() + return start_tensor.int().to(device), end_location.int().to(device) def get_max_prefill_buffer_size(vllm_config: VllmConfig): max_model_len = vllm_config.model_config.max_model_len - # max_num_batched_tokens = \ - # vllm_config.scheduler_config.max_num_batched_tokens - max_num_seq = vllm_config.scheduler_config.max_num_seqs - # NOTE(Chen): an estimated max size of flattened_kv. Need to double check. - return max_model_len * max_num_seq + # NOTE(Chen): 2 is a magic number for controlling the prefill buffer size. + # May be tuned later. + return max_model_len * 2 + + +def split_prefill_chunks(seq_lens_cpu: torch.Tensor, + max_prefill_buffer_size: int, + reqs_start: int) -> list[tuple[int, int]]: + """ + Split the prefill chunks into a list of tuples of (reqs_start, reqs_end) + such that the total sequence length of each chunk is less than the + maximum prefill buffer size. + + Args: + seq_lens_cpu: The sequence lengths of the prefill requests. + max_prefill_buffer_size: The maximum prefill buffer size. + reqs_start: The start index of the prefill requests. + + Returns: + A list of tuples of (reqs_start, reqs_end). + """ + chunk_seq_ids = [] + total_seq_lens = 0 + for i in range(reqs_start, len(seq_lens_cpu)): + cur_seq_len = seq_lens_cpu[i].item() + assert cur_seq_len <= max_prefill_buffer_size + total_seq_lens += cur_seq_len + if total_seq_lens > max_prefill_buffer_size: + chunk_seq_ids.append((reqs_start, i)) + reqs_start = i + total_seq_lens = cur_seq_len + if total_seq_lens > 0: + chunk_seq_ids.append((reqs_start, len(seq_lens_cpu))) + return chunk_seq_ids class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder): @@ -201,6 +234,33 @@ def __init__(self, *args, **kwargs): dtype=torch.int32, device=self.device) + def build_one_prefill_chunk(self, reqs_start, reqs_end, + query_start_loc_cpu, seq_lens_cpu, + block_table): + prefill_query_start_loc = query_start_loc_cpu[ + reqs_start:reqs_end + 1] - query_start_loc_cpu[reqs_start] + cu_seqlen_ks, cu_seqlen_ke = kv_spans_from_batches( + prefill_query_start_loc, seq_lens_cpu[reqs_start:reqs_end], + self.device) + token_start = query_start_loc_cpu[reqs_start].item() + token_end = query_start_loc_cpu[reqs_end].item() + total_seq_lens = seq_lens_cpu[reqs_start:reqs_end].sum() + assert total_seq_lens <= self.max_prefill_buffer_size + cu_seq_lens = torch.cat([ + torch.zeros(1, dtype=torch.int32), + seq_lens_cpu[reqs_start:reqs_end].cumsum(dim=0) + ]).to(torch.int32).to(self.device) + return DeepseekV32IndexerPrefillChunkMetadata( + cu_seqlen_ks=cu_seqlen_ks, + cu_seqlen_ke=cu_seqlen_ke, + cu_seq_lens=cu_seq_lens, + total_seq_lens=total_seq_lens, + block_table=block_table[reqs_start:reqs_end], + token_start=token_start, + token_end=token_end, + num_reqs=reqs_end - reqs_start, + ) + def build(self, common_prefix_len: int, common_attn_metadata: CommonAttentionMetadata, @@ -209,11 +269,7 @@ def build(self, num_reqs = common_attn_metadata.num_reqs num_tokens = common_attn_metadata.num_actual_tokens - device = self.device - block_table_tensor = common_attn_metadata.block_table_tensor - - query_start_loc = common_attn_metadata.query_start_loc - + query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \ split_decodes_and_prefills( common_attn_metadata, @@ -224,27 +280,20 @@ def build(self, prefill_metadata = None if num_prefills > 0: - reqs_start = num_decodes - prefill_query_start_loc = query_start_loc[ - reqs_start:] - query_start_loc[reqs_start] - cu_seqlen_ks, cu_seqlen_ke = kv_spans_from_batches( - prefill_query_start_loc, - common_attn_metadata.seq_lens[reqs_start:]) - total_seq_lens = common_attn_metadata.seq_lens[reqs_start:].sum() - assert total_seq_lens < self.max_prefill_buffer_size - cu_seq_lens = torch.cat([ - torch.zeros(1, dtype=torch.int32, device=device), - common_attn_metadata.seq_lens[reqs_start:].cumsum(dim=0) - ]).to(torch.int32).cuda() - prefill_metadata = DeepseekV32IndexerPrefillMetadata( - block_table=block_table_tensor[reqs_start:, ...], - query_start_loc=prefill_query_start_loc, - max_query_len=common_attn_metadata.max_query_len, - cu_seqlen_ks=cu_seqlen_ks, - cu_seqlen_ke=cu_seqlen_ke, - cu_seq_lens=cu_seq_lens, - total_seq_lens=total_seq_lens, + chunk_seq_ids = split_prefill_chunks( + common_attn_metadata.seq_lens_cpu, + self.max_prefill_buffer_size, + num_decodes, ) + chunks = [ + self.build_one_prefill_chunk( + reqs_start, reqs_end, query_start_loc_cpu, + common_attn_metadata.seq_lens_cpu, + common_attn_metadata.block_table_tensor) + for reqs_start, reqs_end in chunk_seq_ids + ] + prefill_metadata = DeepseekV32IndexerPrefillMetadata( + chunks=chunks, ) decode_metadata = None if num_decodes > 0: From 6e67ce2d23b37999ae4cbf76371b5a3aecc5630b Mon Sep 17 00:00:00 2001 From: Ekagra Ranjan <3116519+ekagra-ranjan@users.noreply.github.com> Date: Thu, 2 Oct 2025 14:29:35 -0400 Subject: [PATCH 161/166] EAGLE 3: Fix preamble so that measured speedup over Eagle 1 becomes 32% instead of 5% on MTBench (#25916) Signed-off-by: Ekagra Ranjan <3116519+ekagra-ranjan@users.noreply.github.com> --- vllm/benchmarks/datasets.py | 72 ++++++++++++++++++++----------------- 1 file changed, 39 insertions(+), 33 deletions(-) diff --git a/vllm/benchmarks/datasets.py b/vllm/benchmarks/datasets.py index 807e543dc0cd..62e5194504d7 100644 --- a/vllm/benchmarks/datasets.py +++ b/vllm/benchmarks/datasets.py @@ -1151,6 +1151,12 @@ def add_dataset_parser(parser: FlexibleArgumentParser): help="Do not oversample if the dataset has " \ "fewer samples than num-prompts.", ) + parser.add_argument( + "--skip-chat-template", + action="store_true", + help= + "Skip applying chat template to prompt for datasets that support it.", + ) # group for dataset specific arguments custom_group = parser.add_argument_group("custom dataset options") @@ -1161,12 +1167,6 @@ def add_dataset_parser(parser: FlexibleArgumentParser): help= "Number of output tokens per request, used only for custom dataset.", ) - custom_group.add_argument( - "--custom-skip-chat-template", - action="store_true", - help= - "Skip applying chat template to prompt, used only for custom dataset.", - ) spec_bench_group = parser.add_argument_group("spec bench dataset options") spec_bench_group.add_argument( @@ -1435,7 +1435,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]: num_requests=args.num_prompts, tokenizer=tokenizer, output_len=args.custom_output_len, - skip_chat_template=args.custom_skip_chat_template, + skip_chat_template=args.skip_chat_template, request_id_prefix=args.request_id_prefix, no_oversample=args.no_oversample, ) @@ -1576,6 +1576,7 @@ def get_samples(args, tokenizer) -> list[SampleRequest]: output_len=args.hf_output_len, request_id_prefix=args.request_id_prefix, no_oversample=args.no_oversample, + skip_chat_template=args.skip_chat_template, **hf_kwargs ) @@ -1815,7 +1816,6 @@ def load_data(self) -> None: def sample(self, **kwargs) -> list: # leverage CustomDataset sample - kwargs["skip_chat_template"] = False return super().sample(**kwargs) @@ -2221,6 +2221,7 @@ def sample(self, num_requests: int, output_len: Optional[int] = None, enable_multimodal_chat: bool = False, + skip_chat_template: bool = False, request_id_prefix: str = "", no_oversample: bool = False, **kwargs) -> list: @@ -2236,14 +2237,15 @@ def sample(self, ) # apply template - prompt = tokenizer.apply_chat_template( - [{ - "role": "user", - "content": prompt - }], - add_generation_prompt=True, - tokenize=False, - ) + if not skip_chat_template: + prompt = tokenizer.apply_chat_template( + [{ + "role": "user", + "content": prompt + }], + add_generation_prompt=True, + tokenize=False, + ) prompt_len = len(tokenizer(prompt).input_ids) sampled_requests.append( @@ -2284,6 +2286,7 @@ def sample( num_requests: int, output_len: Optional[int] = None, enable_multimodal_chat: bool = False, + skip_chat_template: bool = False, request_id_prefix: str = "", no_oversample: bool = False, **kwargs, @@ -2298,14 +2301,15 @@ def sample( prompt = item["turns"][0] # apply template - prompt = tokenizer.apply_chat_template( - [{ - "role": "user", - "content": prompt - }], - add_generation_prompt=True, - tokenize=False, - ) + if not skip_chat_template: + prompt = tokenizer.apply_chat_template( + [{ + "role": "user", + "content": prompt + }], + add_generation_prompt=True, + tokenize=False, + ) prompt_len = len(tokenizer(prompt).input_ids) sampled_requests.append( @@ -2349,6 +2353,7 @@ def sample( tokenizer: PreTrainedTokenizerBase, num_requests: int, output_len: Optional[int] = None, + skip_chat_template: bool = False, request_id_prefix: str = "", no_oversample: bool = False, min_distance: float = 0.0, @@ -2372,7 +2377,7 @@ def sample( # template copied from # https://github.com/ise-uiuc/blazedit/blob/7765137e656fd62de877422d2e4cf8de51228054/dataset/create_refined_dataset.py#L94-L105 # noqa: E501 - instruction = f"""Given a code file, please apply the change requests and generate the new file. + prompt = f"""Given a code file, please apply the change requests and generate the new file. Original file: ```python @@ -2385,14 +2390,15 @@ def sample( Please generate the new code file in the "New file" section below.""" # noqa: E501 # apply template - prompt = tokenizer.apply_chat_template( - [{ - "role": "user", - "content": instruction - }], - add_generation_prompt=True, - tokenize=False, - ) + if not skip_chat_template: + prompt = tokenizer.apply_chat_template( + [{ + "role": "user", + "content": prompt + }], + add_generation_prompt=True, + tokenize=False, + ) prompt_len = len(tokenizer(prompt).input_ids) From bccb2c0e8378a17fe9d089268187a6278acd0be7 Mon Sep 17 00:00:00 2001 From: Chen Zhang Date: Thu, 2 Oct 2025 11:48:31 -0700 Subject: [PATCH 162/166] [Mamba][KVCacheManager] Simplify kv cache manage logic for mamba + MTP (#25119) Signed-off-by: Chen Zhang --- vllm/v1/core/single_type_kv_cache_manager.py | 29 +++----------------- 1 file changed, 4 insertions(+), 25 deletions(-) diff --git a/vllm/v1/core/single_type_kv_cache_manager.py b/vllm/v1/core/single_type_kv_cache_manager.py index 4ecd9c8157e2..27ea1c4db2a5 100644 --- a/vllm/v1/core/single_type_kv_cache_manager.py +++ b/vllm/v1/core/single_type_kv_cache_manager.py @@ -565,35 +565,14 @@ def get_num_common_prefix_blocks(self, request_id: str, def get_num_blocks_to_allocate( self, request_id: str, num_tokens: int, new_computed_blocks: list[KVCacheBlock]) -> int: - """ - Get the number of blocks needed to be allocated for the request. - - Args: - request_id: The request ID. - num_tokens: The total number of tokens that need a slot (including - tokens that are already allocated). - new_computed_blocks: The new computed blocks just hitting the - prefix caching. - - Returns: - The number of blocks - """ - + # Allocate extra `num_speculative_blocks` blocks for + # speculative decoding (MTP/EAGLE) with linear attention. assert isinstance(self.kv_cache_spec, MambaSpec) if self.kv_cache_spec.num_speculative_blocks > 0: num_tokens += (self.kv_cache_spec.block_size * self.kv_cache_spec.num_speculative_blocks) - num_required_blocks = cdiv(num_tokens, self.block_size) - num_new_blocks = (num_required_blocks - len(new_computed_blocks) - - len(self.req_to_blocks[request_id])) - # If a computed block of a request is an eviction candidate (in the - # free queue and ref_cnt == 0), it will be changed from a free block - # to a computed block when the request is allocated, so we also count - # it as needed to be allocated. - num_evictable_computed_blocks = sum( - blk.ref_cnt == 0 and not blk.is_null - for blk in new_computed_blocks) - return num_new_blocks + num_evictable_computed_blocks + return super().get_num_blocks_to_allocate(request_id, num_tokens, + new_computed_blocks) def allocate_new_blocks(self, request_id: str, num_tokens: int) -> list[KVCacheBlock]: From f36b3bec7e411ddf71a127945b4a3e54ab63c1ef Mon Sep 17 00:00:00 2001 From: Pradeep Dasigi Date: Thu, 2 Oct 2025 14:35:54 -0700 Subject: [PATCH 163/166] [Olmo 3] pre-commit fixes Signed-off-by: Pradeep Dasigi --- .../tool_parsers/test_olmo3_tool_parser.py | 128 +++++++++++------- .../openai/tool_parsers/olmo3_tool_parser.py | 43 +++--- 2 files changed, 100 insertions(+), 71 deletions(-) diff --git a/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py b/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py index 56394ace5488..15913c875d10 100644 --- a/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py +++ b/tests/entrypoints/openai/tool_parsers/test_olmo3_tool_parser.py @@ -79,59 +79,83 @@ def test_no_tool_call(streaming: bool): TEST_CASES = [ pytest.param(True, - f"{SIMPLE_FUNCTION_OUTPUT}", [SIMPLE_FUNCTION_CALL], + f"{SIMPLE_FUNCTION_OUTPUT}", + [SIMPLE_FUNCTION_CALL], id="simple_streaming"), pytest.param(False, - f"{SIMPLE_FUNCTION_OUTPUT}", [SIMPLE_FUNCTION_CALL], + f"{SIMPLE_FUNCTION_OUTPUT}", + [SIMPLE_FUNCTION_CALL], id="simple_nonstreaming"), - pytest.param(True, - f"{MORE_TYPES_FUNCTION_OUTPUT}", [MORE_TYPES_FUNCTION_CALL], - id="more_types_streaming"), - pytest.param(False, - f"{MORE_TYPES_FUNCTION_OUTPUT}", [MORE_TYPES_FUNCTION_CALL], - id="more_types_nonstreaming"), - pytest.param(True, - f"{MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS}", [MORE_TYPES_FUNCTION_CALL], - id="more_types_streaming_json_literals"), - pytest.param(False, - f"{MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS}", [MORE_TYPES_FUNCTION_CALL], - id="more_types_nonstreaming_json_literals"), - pytest.param(True, - f"{PARAMETERLESS_FUNCTION_OUTPUT}", - [PARAMETERLESS_FUNCTION_CALL], - id="parameterless_streaming"), - pytest.param(False, - f"{PARAMETERLESS_FUNCTION_OUTPUT}", - [PARAMETERLESS_FUNCTION_CALL], - id="parameterless_nonstreaming"), - pytest.param(True, - f"{EMPTY_DICT_FUNCTION_OUTPUT}", [EMPTY_DICT_FUNCTION_CALL], - id="empty_dict_streaming"), - pytest.param(False, - f"{EMPTY_DICT_FUNCTION_OUTPUT}", [EMPTY_DICT_FUNCTION_CALL], - id="empty_dict_nonstreaming"), - pytest.param(True, - f"{EMPTY_LIST_FUNCTION_OUTPUT}", [EMPTY_LIST_FUNCTION_CALL], - id="empty_list_streaming"), - pytest.param(False, - f"{EMPTY_LIST_FUNCTION_OUTPUT}", [EMPTY_LIST_FUNCTION_CALL], - id="empty_list_nonstreaming"), - pytest.param(True, - f"{ESCAPED_STRING_FUNCTION_OUTPUT}", - [ESCAPED_STRING_FUNCTION_CALL], - id="escaped_string_streaming"), - pytest.param(False, - f"{ESCAPED_STRING_FUNCTION_OUTPUT}", - [ESCAPED_STRING_FUNCTION_CALL], - id="escaped_string_nonstreaming"), - pytest.param(True, - f"{SIMPLE_FUNCTION_OUTPUT}\n{MORE_TYPES_FUNCTION_OUTPUT}", - [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL], - id="parallel_calls_streaming"), - pytest.param(False, - f"{SIMPLE_FUNCTION_OUTPUT}\n{MORE_TYPES_FUNCTION_OUTPUT}", - [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL], - id="parallel_calls_nonstreaming"), + pytest.param( + True, + f"{MORE_TYPES_FUNCTION_OUTPUT}", + [MORE_TYPES_FUNCTION_CALL], + id="more_types_streaming"), + pytest.param( + False, + f"{MORE_TYPES_FUNCTION_OUTPUT}", + [MORE_TYPES_FUNCTION_CALL], + id="more_types_nonstreaming"), + pytest.param( + True, + f"{MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS}", + [MORE_TYPES_FUNCTION_CALL], + id="more_types_streaming_json_literals"), + pytest.param( + False, + f"{MORE_TYPES_FUNCTION_OUTPUT_JSON_LITERALS}", + [MORE_TYPES_FUNCTION_CALL], + id="more_types_nonstreaming_json_literals"), + pytest.param( + True, + f"{PARAMETERLESS_FUNCTION_OUTPUT}", + [PARAMETERLESS_FUNCTION_CALL], + id="parameterless_streaming"), + pytest.param( + False, + f"{PARAMETERLESS_FUNCTION_OUTPUT}", + [PARAMETERLESS_FUNCTION_CALL], + id="parameterless_nonstreaming"), + pytest.param( + True, + f"{EMPTY_DICT_FUNCTION_OUTPUT}", + [EMPTY_DICT_FUNCTION_CALL], + id="empty_dict_streaming"), + pytest.param( + False, + f"{EMPTY_DICT_FUNCTION_OUTPUT}", + [EMPTY_DICT_FUNCTION_CALL], + id="empty_dict_nonstreaming"), + pytest.param( + True, + f"{EMPTY_LIST_FUNCTION_OUTPUT}", + [EMPTY_LIST_FUNCTION_CALL], + id="empty_list_streaming"), + pytest.param( + False, + f"{EMPTY_LIST_FUNCTION_OUTPUT}", + [EMPTY_LIST_FUNCTION_CALL], + id="empty_list_nonstreaming"), + pytest.param( + True, + f"{ESCAPED_STRING_FUNCTION_OUTPUT}", + [ESCAPED_STRING_FUNCTION_CALL], + id="escaped_string_streaming"), + pytest.param( + False, + f"{ESCAPED_STRING_FUNCTION_OUTPUT}", + [ESCAPED_STRING_FUNCTION_CALL], + id="escaped_string_nonstreaming"), + pytest.param( + True, + f"{SIMPLE_FUNCTION_OUTPUT}\n{MORE_TYPES_FUNCTION_OUTPUT}", + [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL], + id="parallel_calls_streaming"), + pytest.param( + False, + f"{SIMPLE_FUNCTION_OUTPUT}\n{MORE_TYPES_FUNCTION_OUTPUT}", + [SIMPLE_FUNCTION_CALL, MORE_TYPES_FUNCTION_CALL], + id="parallel_calls_nonstreaming"), ] @@ -179,8 +203,8 @@ def test_streaming_tool_call_with_large_steps(): def test_regex_timeout_handling(streaming: bool): """test regex timeout is handled gracefully""" mock_tokenizer = MagicMock() - tool_parser: ToolParser = ToolParserManager.get_tool_parser( - "olmo3")(mock_tokenizer) + tool_parser: ToolParser = ToolParserManager.get_tool_parser("olmo3")( + mock_tokenizer) fake_problematic_input = "hello world[A(A=" + "\t)A(A=,\t" * 2 diff --git a/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py index 41ee7ef5cf7a..432d891dff4b 100644 --- a/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py +++ b/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py @@ -1,3 +1,5 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright contributors to the vLLM project import ast import json from collections.abc import Sequence @@ -14,7 +16,6 @@ FunctionCall, ToolCall) from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import ( ToolParser, ToolParserManager) -from vllm.entrypoints.openai.tool_parsers.pythonic_tool_parser import PythonicToolParser from vllm.logger import init_logger logger = init_logger(__name__) @@ -27,11 +28,12 @@ class _UnexpectedAstError(Exception): @ToolParserManager.register_module("olmo3") class Olmo3PythonicToolParser(ToolParser): """ - Tool call parser for Olmo 3 models that produce tool calls as newline-separated pythonic strings. + Tool call parser for Olmo 3 models that produce tool calls as + newline-separated pythonic strings. Used when --enable-auto-tool-choice --tool-call-parser pythonic are all set - Code copied from vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py and updated to - - support newline separated pythonic tool calls. - - handle the argument values being null/true/false instead of Pythonic literals. + Code copied from pythonic_tool_parser.py and updated to handle + - newline separated pythonic tool calls. + - argument values being null/true/false instead of Pythonic literals. """ # TODO(mdepinet): Possible future improvements: # 1. Support text + tools separated by either <|python_tag|> or \n\n @@ -64,13 +66,12 @@ def extract_tool_calls( """ original_model_output = model_output # Remove xml tags. - model_output = ( - model_output.replace("", "").replace("", "").strip() - ) + model_output = (model_output.replace("", "").replace( + "", "").strip()) # Make the newline separated function calls into a list. - model_output = ", ".join( - [line.strip() for line in model_output.splitlines() if line.strip()] - ) + model_output = ", ".join([ + line.strip() for line in model_output.splitlines() if line.strip() + ]) model_output = f"[{model_output}]" is_tool_call_pattern = False @@ -121,7 +122,8 @@ def extract_tool_calls_streaming( delta_token_ids: Sequence[int], request: ChatCompletionRequest, ) -> Union[DeltaMessage, None]: - # All function calls start with the tag. But since this is a streaming parser, we may have seen only part of the tag. + # All function calls start with the tag. + # But since this is streaming, we may have seen only part of the tag. if not current_text.startswith("<"): return DeltaMessage(content=delta_text) @@ -139,16 +141,18 @@ def extract_tool_calls_streaming( valid_text, added_text = valid_and_added_text # Make the newline separated function calls into a list. - valid_text = ", ".join( - [line.strip() for line in valid_text.splitlines() if line.strip()] - ) + valid_text = ", ".join([ + line.strip() for line in valid_text.splitlines() + if line.strip() + ]) valid_text = f"[{valid_text}]" module = ast.parse(valid_text) parsed = getattr(module.body[0], "value", None) if not isinstance(parsed, ast.List) or not all( isinstance(e, ast.Call) for e in parsed.elts): raise _UnexpectedAstError( - "Tool output must be a sequence of newline-separated function calls") + "Tool output must be a sequence of newline-separated calls" + ) tool_calls = [ _handle_single_tool(e) # type: ignore for e in parsed.elts @@ -158,7 +162,7 @@ def extract_tool_calls_streaming( for index, new_call in enumerate(tool_calls): if index < self.current_tool_index: continue - + self.current_tool_index = index if len(self.streamed_args_for_tool) == index: self.streamed_args_for_tool.append("") @@ -208,7 +212,7 @@ def extract_tool_calls_streaming( "Skipping chunk as a result of tool streaming extraction " "error") return None - + def _get_parameter_value(val: ast.expr) -> Any: if isinstance(val, ast.Constant): @@ -223,7 +227,8 @@ def _get_parameter_value(val: ast.expr) -> Any: } elif isinstance(val, ast.List): return [_get_parameter_value(v) for v in val.elts] - # The model may return function calls where the values are null/true/false because the system prompt has API description in json. + # The model may return function calls where the values are null/true/false + # because the system prompt has API description in json. elif isinstance(val, ast.Name) and val.id in ["null", "true", "false"]: if val.id == "null": return None From ad3fb42c0da97e5deee129e0f8c3af409ad81f04 Mon Sep 17 00:00:00 2001 From: Pradeep Dasigi Date: Thu, 2 Oct 2025 14:37:53 -0700 Subject: [PATCH 164/166] [Olmo 3] safer xml tag removal in tool parser Signed-off-by: Pradeep Dasigi --- .../entrypoints/openai/tool_parsers/olmo3_tool_parser.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py b/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py index 432d891dff4b..d943fc66a880 100644 --- a/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py +++ b/vllm/entrypoints/openai/tool_parsers/olmo3_tool_parser.py @@ -66,8 +66,10 @@ def extract_tool_calls( """ original_model_output = model_output # Remove xml tags. - model_output = (model_output.replace("", "").replace( - "", "").strip()) + match = re.search(r"(.*?)", + model_output, re.DOTALL) + if match: + model_output = match.group(1).strip() # Make the newline separated function calls into a list. model_output = ", ".join([ line.strip() for line in model_output.splitlines() if line.strip() @@ -132,8 +134,7 @@ def extract_tool_calls_streaming( if current_text.startswith(""): current_text = current_text[len(""):] if current_text.endswith(""): - current_text = current_text[:current_text. - rfind("")] + current_text = current_text[:-len("")] valid_and_added_text = _make_valid_python(current_text) if valid_and_added_text is None: From 2d7cbf42a19124102ea2dba797299e06144563e3 Mon Sep 17 00:00:00 2001 From: ElizaWszola Date: Thu, 2 Oct 2025 21:35:13 +0200 Subject: [PATCH 165/166] [Perf] Fix and reapply move apply w8a8 block fp8 linear to class (#25696) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: ElizaWszola Signed-off-by: ElizaWszola Signed-off-by: Luka Govedič Signed-off-by: Luka Govedič Co-authored-by: Luka Govedič Co-authored-by: Michael Goin Co-authored-by: Luka Govedič --- .../cutlass_benchmarks/w8a8_benchmarks.py | 4 +- .../benchmark_fp8_block_dense_gemm.py | 4 +- tests/kernels/quantization/test_block_fp8.py | 5 +- .../quantization/test_fp8_quant_group.py | 30 +- .../model_executor/test_enabled_custom_ops.py | 30 -- tests/quantization/test_compressed_tensors.py | 35 ++ vllm/config/vllm.py | 17 + .../compressed_tensors/compressed_tensors.py | 8 + .../schemes/compressed_tensors_w8a8_fp8.py | 37 +- .../model_executor/layers/quantization/fp8.py | 40 +- .../layers/quantization/input_quant_fp8.py | 24 +- .../layers/quantization/utils/fp8_utils.py | 357 ++++++++++++------ vllm/utils/deep_gemm.py | 21 +- 13 files changed, 412 insertions(+), 200 deletions(-) diff --git a/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py b/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py index a5a5b52f6039..02f8c593392c 100644 --- a/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py +++ b/benchmarks/cutlass_benchmarks/w8a8_benchmarks.py @@ -17,7 +17,7 @@ from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - w8a8_block_fp8_matmul, + w8a8_triton_block_scaled_mm, ) from vllm.utils import FlexibleArgumentParser, cdiv @@ -158,7 +158,7 @@ def bench_fp8( "cutlass_fp8_fp8_fp16_scaled_mm_bias": lambda: ops.cutlass_scaled_mm( a, b, scale_a, scale_b, torch.float16, bias.to(dtype=torch.float16) ), - "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_block_fp8_matmul( + "triton_fp8_fp8_fp16_scaled_mm_blockwise": lambda: w8a8_triton_block_scaled_mm( a_cont, b.t(), block_scale_a, block_scale_b.t(), (128, 128) ), "cutlass_fp8_fp8_fp16_scaled_mm_blockwise": lambda: ops.cutlass_scaled_mm( diff --git a/benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py b/benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py index db2398fc40a4..2010b8038563 100644 --- a/benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py +++ b/benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py @@ -9,7 +9,7 @@ from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.utils.fp8_utils import ( per_token_group_quant_fp8, - w8a8_block_fp8_matmul, + w8a8_triton_block_scaled_mm, ) from vllm.triton_utils import triton from vllm.utils.deep_gemm import ( @@ -63,7 +63,7 @@ def deepgemm_gemm(): # === vLLM Triton Implementation === def vllm_triton_gemm(): - return w8a8_block_fp8_matmul(A_vllm, + return w8a8_triton_block_scaled_mm(A_vllm, B_vllm, A_scale_vllm, B_scale_vllm, diff --git a/tests/kernels/quantization/test_block_fp8.py b/tests/kernels/quantization/test_block_fp8.py index 211d1ecfe6e4..e02df540ce9d 100644 --- a/tests/kernels/quantization/test_block_fp8.py +++ b/tests/kernels/quantization/test_block_fp8.py @@ -11,7 +11,7 @@ native_w8a8_block_matmul) from vllm.config import VllmConfig from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - cutlass_scaled_mm, per_token_group_quant_fp8, w8a8_block_fp8_matmul) + cutlass_scaled_mm, per_token_group_quant_fp8, w8a8_triton_block_scaled_mm) from vllm.platforms import current_platform from vllm.utils import has_deep_gemm from vllm.utils.deep_gemm import (fp8_gemm_nt, @@ -91,7 +91,8 @@ def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed): ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype) - out = w8a8_block_fp8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype) + out = w8a8_triton_block_scaled_mm(A_fp8, B_fp8, As, Bs, block_size, + out_dtype) rel_diff = (torch.mean( torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) / diff --git a/tests/kernels/quantization/test_fp8_quant_group.py b/tests/kernels/quantization/test_fp8_quant_group.py index 720eee62760d..8f2bc6e3cee5 100644 --- a/tests/kernels/quantization/test_fp8_quant_group.py +++ b/tests/kernels/quantization/test_fp8_quant_group.py @@ -20,9 +20,11 @@ (8, 513, 64), # Non-divisible (native only) ]) @pytest.mark.parametrize("seed", [42]) +@pytest.mark.parametrize("use_ue8m0", [True, False]) @torch.inference_mode() def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int, - group_size: int, seed: int) -> None: + group_size: int, seed: int, + use_ue8m0: bool) -> None: """Test QuantFP8 group quantization with various configurations. Tests both CUDA and native implementations, column-major scales, @@ -38,7 +40,8 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int, group_shape = GroupShape(1, group_size) quant_op = QuantFP8(static=False, group_shape=group_shape, - column_major_scales=False) + column_major_scales=False, + use_ue8m0=use_ue8m0) # 1. Test native implementation (always available) x_quant_native, scales_native = quant_op.forward_native(x.clone()) @@ -48,9 +51,15 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int, # 2. Test column-major scales configuration quant_op_col = QuantFP8(static=False, group_shape=group_shape, - column_major_scales=True) + column_major_scales=True, + use_ue8m0=use_ue8m0) _, scales_col = quant_op_col.forward_native(x.clone()) - assert scales_col.shape == (expected_num_groups, batch_size) + assert scales_col.shape == (batch_size, expected_num_groups) + assert scales_col.stride(0) == 1 + assert scales_col.stride(1) == batch_size + + # Test column-major scales consistency + assert torch.allclose(scales_col, scales_native, rtol=1e-9, atol=1e-8) # 3. Test CUDA implementation (only for divisible dimensions) if is_divisible: @@ -68,21 +77,23 @@ def test_quantfp8_group_functionality(batch_size: int, hidden_dim: int, @pytest.mark.parametrize("seed", [42]) +@pytest.mark.parametrize("use_ue8m0", [True, False]) @torch.inference_mode() -def test_quantfp8_group_multidimensional(seed: int) -> None: +def test_quantfp8_group_multidimensional(seed: int, use_ue8m0: bool) -> None: current_platform.seed_everything(seed) group_size = 64 # Test with 3D input - batch1, batch2, hidden_dim = 4, 8, 512 + batch1, batch2, hidden_dim = 4, 8, 1024 x_3d = torch.randn( (batch1, batch2, hidden_dim), dtype=torch.bfloat16, device="cuda") * 8 group_shape = GroupShape(1, group_size) quant_op = QuantFP8(static=False, group_shape=group_shape, - column_major_scales=False) + column_major_scales=False, + use_ue8m0=use_ue8m0) x_quant, scales = quant_op.forward_native(x_3d.clone()) assert x_quant.shape == x_3d.shape @@ -91,9 +102,10 @@ def test_quantfp8_group_multidimensional(seed: int) -> None: # Test column_major_scales with multi-dim quant_op_col = QuantFP8(static=False, group_shape=group_shape, - column_major_scales=True) + column_major_scales=True, + use_ue8m0=use_ue8m0) _, scales_col = quant_op_col.forward_native(x_3d.clone()) - assert scales_col.shape == (batch1, hidden_dim // group_size, batch2) + assert scales_col.shape == (batch1, batch2, hidden_dim // group_size) # Test with 4D input batch1, batch2, batch3, hidden_dim = 2, 3, 4, 256 diff --git a/tests/model_executor/test_enabled_custom_ops.py b/tests/model_executor/test_enabled_custom_ops.py index 92ce10a9efc0..200b6ecd5852 100644 --- a/tests/model_executor/test_enabled_custom_ops.py +++ b/tests/model_executor/test_enabled_custom_ops.py @@ -17,8 +17,6 @@ from vllm.model_executor.layers.layernorm import (RMSNorm, dispatch_rocm_rmsnorm_func, fused_add_rms_norm, rms_norm) -from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - cutlass_scaled_mm, dispatch_w8a8_blockscale_func, w8a8_block_fp8_matmul) from vllm.platforms import current_platform RMS_NORM_SUPPORTED_DTYPES = [torch.float16, torch.bfloat16] @@ -111,34 +109,6 @@ def test_enabled_ops_invalid(env: str): RMSNorm(1024).enabled() -@pytest.mark.skipif( - not current_platform.is_rocm() or not current_platform.is_fp8_fnuz(), - reason="AITER is a feature exclusive for ROCm and FP8_FNUZ") -@pytest.mark.parametrize("use_cutlass", [True, False]) -@pytest.mark.parametrize("use_rocm_aiter", ["0", "1"]) -@pytest.mark.parametrize("use_rocm_aiter_gemm_w8a8_blockscale", ["0", "1"]) -def test_w8a8_blockscale_dispatch(use_cutlass: bool, use_rocm_aiter: str, - use_rocm_aiter_gemm_w8a8_blockscale: str, - monkeypatch): - - monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter) - monkeypatch.setenv("VLLM_ROCM_USE_AITER_LINEAR", - use_rocm_aiter_gemm_w8a8_blockscale) - - use_aiter_and_is_supported = (bool(int(use_rocm_aiter)) and bool( - int(use_rocm_aiter_gemm_w8a8_blockscale))) - block_scale_func = dispatch_w8a8_blockscale_func( - use_cutlass, use_aiter_and_is_supported=use_aiter_and_is_supported) - if use_cutlass: - assert block_scale_func == cutlass_scaled_mm - elif current_platform.is_rocm() and int(use_rocm_aiter) and int( - use_rocm_aiter_gemm_w8a8_blockscale): - assert block_scale_func == ( - torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale) - else: - assert block_scale_func == w8a8_block_fp8_matmul - - @pytest.mark.parametrize("use_rocm_aiter", ["0", "1"]) def test_topk_dispatch(use_rocm_aiter: str, monkeypatch): monkeypatch.setenv("VLLM_ROCM_USE_AITER", use_rocm_aiter) diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index c0ab3fbb1062..af8c7ec3b482 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -18,6 +18,9 @@ CompressedTensorsW4A16Fp4, CompressedTensorsW4A16Sparse24, CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8, CompressedTensorsW8A16Fp8, CompressedTensorsWNA16) +from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8 +from vllm.model_executor.layers.quantization.utils.fp8_utils import ( + W8A8BlockFp8LinearOp) from vllm.model_executor.layers.quantization.utils.quant_utils import ( cutlass_fp4_supported) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( @@ -742,3 +745,35 @@ def test_compressed_tensors_transforms_perplexity(vllm_runner, model, prompt, perplexity = llm.generate_prompt_perplexity([prompt])[0] print(perplexity) assert perplexity <= exp_perplexity + + +def test_compressed_tensors_fp8_block_enabled(vllm_runner): + model_path = "RedHatAI/Qwen3-0.6B-FP8-BLOCK" + with vllm_runner(model_path) as llm: + + fp8_dtype = current_platform.fp8_dtype() + + def check_model(model): + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + assert isinstance(qkv_proj.quant_method, + CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8) + assert isinstance(qkv_proj.scheme.w8a8_block_fp8_linear, + W8A8BlockFp8LinearOp) + + assert qkv_proj.weight.dtype is fp8_dtype + assert qkv_proj.weight_scale.dtype is torch.float32 + assert len(qkv_proj.weight.shape) == 2 + assert len(qkv_proj.weight_scale.shape) == 2 + + input_quant_op = \ + qkv_proj.scheme.w8a8_block_fp8_linear.input_quant_op + assert isinstance(input_quant_op, QuantFP8) + assert input_quant_op._forward_method == input_quant_op.forward_cuda + + llm.apply_model(check_model) + + output = llm.generate_greedy("Hello my name is", max_tokens=20) + assert output diff --git a/vllm/config/vllm.py b/vllm/config/vllm.py index 107df502e08e..ac40b0fd4783 100644 --- a/vllm/config/vllm.py +++ b/vllm/config/vllm.py @@ -516,6 +516,23 @@ def __post_init__(self): " by VLLM_DEBUG_DUMP_PATH to %s", env_path) self.compilation_config.debug_dump_path = env_path + def has_blocked_weights(): + if self.quant_config is not None: + if hasattr(self.quant_config, "weight_block_size"): + return self.quant_config.weight_block_size is not None + elif hasattr(self.quant_config, "has_blocked_weights"): + return self.quant_config.has_blocked_weights() + return False + + # Enable quant_fp8 CUDA ops (TODO disable in follow up) + # On H100 the CUDA kernel is faster than + # native implementation + # https://github.com/vllm-project/vllm/issues/25094 + if has_blocked_weights(): + custom_ops = self.compilation_config.custom_ops + if "none" not in custom_ops and "-quant_fp8" not in custom_ops: + custom_ops.append("+quant_fp8") + def update_sizes_for_sequence_parallelism(self, possible_sizes: list) -> list: # remove the sizes that not multiple of tp_size when diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index d6550dd16892..3f771ea2abd1 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -644,6 +644,14 @@ def get_cache_scale(self, name: str) -> Optional[str]: # If no matches, return None return None + def has_blocked_weights(self) -> bool: + for scheme in self.target_scheme_map.values(): + weight_quant = scheme.get("weights") + if (weight_quant is not None + and weight_quant.strategy == QuantizationStrategy.BLOCK): + return True + return False + @staticmethod def supports_cutlass_24( weight_quant: Optional[QuantizationArgs], diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index 5ad1b15b7ed5..4755c17c5967 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -11,7 +11,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - apply_fp8_block_linear, check_aiter_fp8_linear_support, + W8A8BlockFp8LinearOp, check_aiter_fp8_linear_support, create_fp8_input_scale, create_fp8_scale_parameter, create_fp8_weight_parameter, maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy, process_fp8_weight_channel_strategy, @@ -41,16 +41,30 @@ def __init__(self, weight_quant: QuantizationArgs, self.strategy = weight_quant.strategy self.out_dtype = torch.get_default_dtype() self.is_static_input_scheme = is_static_input_scheme - self.act_q_group_shape = GroupShape.PER_TENSOR \ - if is_static_input_scheme else GroupShape.PER_TOKEN - self.fp8_linear = Fp8LinearOp( - act_quant_static=self.is_static_input_scheme, - act_quant_group_shape=self.act_q_group_shape) self.weight_block_size = self.weight_quant.block_structure + if self.weight_block_size is not None: + self.act_q_group_shape = GroupShape(1, self.weight_block_size[0]) + else: + self.act_q_group_shape = GroupShape.PER_TENSOR \ + if is_static_input_scheme else GroupShape.PER_TOKEN + self.cutlass_block_fp8_supported = cutlass_block_fp8_supported() self.use_aiter_and_is_supported = check_aiter_fp8_linear_support() + if self.weight_block_size is not None: + assert not self.is_static_input_scheme + self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp( + weight_group_shape=GroupShape(*self.weight_block_size), + act_quant_group_shape=self.act_q_group_shape, + cutlass_block_fp8_supported=self.cutlass_block_fp8_supported, + use_aiter_and_is_supported=self.use_aiter_and_is_supported, + ) + else: + self.fp8_linear = Fp8LinearOp( + act_quant_static=self.is_static_input_scheme, + act_quant_group_shape=self.act_q_group_shape) + @classmethod def get_min_capability(cls) -> int: # lovelace and up @@ -142,13 +156,14 @@ def apply_weights(self, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - if layer.weight_block_size is not None: - return apply_fp8_block_linear( - layer, + if self.weight_block_size is not None: + return self.w8a8_block_fp8_linear.apply( input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, bias=bias, - cutlass_block_fp8_supported=self.cutlass_block_fp8_supported, - use_aiter_and_is_supported=self.use_aiter_and_is_supported) + ) return self.fp8_linear.apply(input=x, weight=layer.weight, diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index 3ebb20de9996..9b7b3f18baa7 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -33,7 +33,7 @@ register_moe_scaling_factors, rotate_flashinfer_fp8_moe_weights, select_cutlass_fp8_gemm_impl, swap_w13_to_w31) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - apply_fp8_block_linear, check_aiter_fp8_linear_support, + W8A8BlockFp8LinearOp, check_aiter_fp8_linear_support, create_fp8_input_scale, create_fp8_scale_parameter, create_fp8_weight_parameter, expert_weight_is_col_major, maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy, @@ -242,15 +242,28 @@ def __init__(self, quant_config: Fp8Config): self.weight_block_size = self.quant_config.weight_block_size self.block_quant = self.weight_block_size is not None self.act_q_static = self.quant_config.activation_scheme == "static" - # Use per-token quantization for better perf if dynamic and cutlass - if not self.act_q_static and cutlass_fp8_supported(): - self.act_q_group_shape = GroupShape.PER_TOKEN + if self.weight_block_size: + self.act_q_group_shape = GroupShape(1, self.weight_block_size[0]) else: - self.act_q_group_shape = GroupShape.PER_TENSOR + # Use per-token quantization for better perf if dynamic and cutlass + if not self.act_q_static and cutlass_fp8_supported(): + self.act_q_group_shape = GroupShape.PER_TOKEN + else: + self.act_q_group_shape = GroupShape.PER_TENSOR - self.fp8_linear = Fp8LinearOp( - act_quant_static=self.act_q_static, - act_quant_group_shape=self.act_q_group_shape) + if self.block_quant: + assert not self.act_q_static + assert self.weight_block_size is not None + self.w8a8_block_fp8_linear = W8A8BlockFp8LinearOp( + weight_group_shape=GroupShape(*self.weight_block_size), + act_quant_group_shape=self.act_q_group_shape, + cutlass_block_fp8_supported=self.cutlass_block_fp8_supported, + use_aiter_and_is_supported=self.use_aiter_and_is_supported, + ) + else: + self.fp8_linear = Fp8LinearOp( + act_quant_static=self.act_q_static, + act_quant_group_shape=self.act_q_group_shape) def create_weights( self, @@ -399,12 +412,15 @@ def apply(self, bias=bias) if self.block_quant: - return apply_fp8_block_linear( - layer, + assert self.weight_block_size is not None + + return self.w8a8_block_fp8_linear.apply( input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, bias=bias, - cutlass_block_fp8_supported=self.cutlass_block_fp8_supported, - use_aiter_and_is_supported=self.use_aiter_and_is_supported) + ) return self.fp8_linear.apply(input=x, weight=layer.weight, diff --git a/vllm/model_executor/layers/quantization/input_quant_fp8.py b/vllm/model_executor/layers/quantization/input_quant_fp8.py index 31182f40b48f..ece3e5817116 100644 --- a/vllm/model_executor/layers/quantization/input_quant_fp8.py +++ b/vllm/model_executor/layers/quantization/input_quant_fp8.py @@ -27,11 +27,14 @@ class QuantFP8(CustomOp): This CustomOp supports both static and dynamic quantization. """ - def __init__(self, - static: bool, - group_shape: GroupShape, - num_token_padding: Optional[int] = None, - column_major_scales: bool = False): + def __init__( + self, + static: bool, + group_shape: GroupShape, + num_token_padding: Optional[int] = None, + column_major_scales: bool = False, + use_ue8m0: Optional[bool] = None, # for Torch compile + ): """ :param static: static or dynamic quantization :param group_shape: quantization group shape (PER_TOKEN, PER_TENSOR, @@ -46,6 +49,7 @@ def __init__(self, self.group_shape = group_shape self.num_token_padding = num_token_padding self.column_major_scales = column_major_scales + self.use_ue8m0 = use_ue8m0 self.is_group_quant = group_shape.is_per_group() if self.is_group_quant: @@ -70,7 +74,8 @@ def forward_cuda( x, group_size=self.group_size, column_major_scales=self.column_major_scales, - dtype=_FP8_DTYPE) + dtype=_FP8_DTYPE, + use_ue8m0=self.use_ue8m0) assert (scale is not None) == self.static assert scale_ub is None or (not self.static and self.group_shape @@ -137,7 +142,10 @@ def _quantize_group_native( x_grouped = x.view(-1, num_groups, self.group_size) absmax = x_grouped.abs().max(dim=-1, keepdim=True)[0].float() - scales = (absmax / _FP8_MAX).clamp(min=_FP8_MIN_SCALING_FACTOR) + scales_raw = absmax / _FP8_MAX + if self.use_ue8m0: + scales_raw = torch.exp2(torch.ceil(torch.log2(scales_raw))) + scales = (scales_raw).clamp(min=_FP8_MIN_SCALING_FACTOR) x_scaled = x_grouped / scales x_quant = x_scaled.clamp(_FP8_MIN, _FP8_MAX).to(_FP8_DTYPE) @@ -151,6 +159,6 @@ def _quantize_group_native( scales = scales.reshape(orig_shape[:-1] + (num_groups, )) if self.column_major_scales: - scales = scales.transpose(-2, -1).contiguous() + scales = scales.transpose(-2, -1).contiguous().transpose(-1, -2) return x_quant, scales diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py index 828111dc299e..13bb69190eae 100644 --- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py +++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py @@ -13,8 +13,9 @@ import vllm.envs as envs from vllm import _custom_ops as ops from vllm.logger import init_logger +from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8 from vllm.model_executor.layers.quantization.utils.quant_utils import ( - group_broadcast) + GroupShape, group_broadcast) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( CUTLASS_BLOCK_FP8_SUPPORTED) from vllm.model_executor.parameter import (BlockQuantScaleParameter, @@ -24,6 +25,7 @@ from vllm.triton_utils import tl, triton from vllm.utils import direct_register_custom_op from vllm.utils.deep_gemm import (fp8_gemm_nt, is_deep_gemm_e8m0_used, + is_deep_gemm_supported, should_use_deepgemm_for_fp8_linear) logger = init_logger(__name__) @@ -35,6 +37,8 @@ def is_fp8(x: Union[torch.dtype, torch.Tensor]) -> bool: return x == torch.float8_e4m3fn or x == torch.float8_e4m3fnuz +# We need to pass in the is_hopper flag as argument because the function +# current_platform.is_device_capability() is not supported by Torch compiler. def cutlass_scaled_mm( A: torch.Tensor, B: torch.Tensor, @@ -42,15 +46,17 @@ def cutlass_scaled_mm( Bs: torch.Tensor, block_size: list[int], output_dtype: torch.dtype = torch.float16, + is_hopper: Optional[bool] = None, ) -> torch.Tensor: + if is_hopper is None: + is_hopper = current_platform.is_device_capability(90) return ops.cutlass_scaled_mm( A, B.T, out_dtype=output_dtype, scale_a=As, # SM90 block FP8 requires row-major scale_b, which we do ahead of time - scale_b=Bs if block_size is not None - and current_platform.is_device_capability(90) else Bs.T) + scale_b=Bs if block_size is not None and is_hopper else Bs.T) def rocm_aiter_gemm_w8a8_blockscale_impl( @@ -96,115 +102,251 @@ def rocm_aiter_gemm_w8a8_blockscale_fake( aiter_per1x128_quant = get_hip_quant(rocm_aiter.QuantType.per_1x128) -def dispatch_w8a8_blockscale_func( - use_cutlass: bool, use_aiter_and_is_supported: bool -) -> Callable[[ - torch.Tensor, - torch.Tensor, - torch.Tensor, - torch.Tensor, - list[int], - torch.dtype, -], torch.Tensor]: - if use_cutlass: - return cutlass_scaled_mm - if (use_aiter_and_is_supported): - return torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale - return w8a8_block_fp8_matmul +# TODO we should be able to change the type of block_size to GroupShape +# after we resolve GroupShape compilation issue +# https://github.com/vllm-project/vllm/issues/25270 +def _w8a8_triton_block_scaled_mm_func( + qx: torch.Tensor, + weight: torch.Tensor, + x_scale: torch.Tensor, + weight_scale: torch.Tensor, + block_size: list[int], + output_dtype: torch.dtype, +) -> torch.Tensor: + return w8a8_triton_block_scaled_mm(qx, weight, x_scale, weight_scale, + block_size, output_dtype) -# TODO fix ROCm->Triton custom path: -# https://github.com/vllm-project/vllm/issues/14397 -def apply_w8a8_block_fp8_linear( - input: torch.Tensor, +def _w8a8_triton_block_scaled_mm_fake( + qx: torch.Tensor, weight: torch.Tensor, - block_size: list[int], + x_scale: torch.Tensor, weight_scale: torch.Tensor, - input_scale: Optional[torch.Tensor] = None, - bias: Optional[torch.Tensor] = None, - cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED, - use_aiter_and_is_supported: bool = False, + block_size: list[int], + output_dtype: torch.dtype, ) -> torch.Tensor: - assert input_scale is None - # View input as 2D matrix for fp8 methods - input_2d = input.view(-1, input.shape[-1]) - output_shape = [*input.shape[:-1], weight.shape[0]] - output_dtype = input.dtype + return torch.empty((qx.size(0), weight.size(0)), + dtype=output_dtype, + device=qx.device) - if should_use_deepgemm_for_fp8_linear(output_dtype, weight): - input_2d = input.view(-1, input.shape[-1]) - output_shape = [*input.shape[:-1], weight.shape[0]] +direct_register_custom_op( + "w8a8_triton_block_scaled_mm_func", + _w8a8_triton_block_scaled_mm_func, + fake_impl=_w8a8_triton_block_scaled_mm_fake, +) - q_input, x_scale = per_token_group_quant_fp8( - input_2d, - block_size[1], - column_major_scales=True, - ) - output = torch.empty((q_input.shape[0], weight.shape[0]), - dtype=torch.bfloat16, - device=q_input.device) - fp8_gemm_nt((q_input, x_scale), (weight, weight_scale), output) - if bias is not None: - output += bias - return output.to(dtype=output_dtype).view(*output_shape) - - w8a8_blockscale_func = dispatch_w8a8_blockscale_func( - cutlass_block_fp8_supported, use_aiter_and_is_supported) - if cutlass_block_fp8_supported: - num_pad = 0 - if current_platform.is_device_capability(90): - # pad first dimension to be divisible by 4 due to - # cutlass blockwise gemm limitation for hopper - num_pad = 4 - (input_2d.shape[0] % 4) - if num_pad > 0: - input_2d = torch.nn.functional.pad(input_2d, - (0, 0, 0, num_pad), - "constant", 0) - q_input, x_scale = per_token_group_quant_fp8(input_2d, - block_size[1], - column_major_scales=True) - output = w8a8_blockscale_func(q_input, weight, x_scale, weight_scale, - block_size, input.dtype) - if num_pad > 0: - output = output[:-num_pad] - else: - if use_aiter_and_is_supported: - q_input, x_scale = aiter_per1x128_quant( - input_2d.contiguous(), quant_dtype=rocm_aiter.dtypes.fp8) - else: - q_input, x_scale = per_token_group_quant_fp8( - input_2d, block_size[1], column_major_scales=False) - output = w8a8_blockscale_func(q_input, weight, x_scale, weight_scale, - block_size, input.dtype) +def _padded_cutlass( + qx: torch.Tensor, + weight: torch.Tensor, + x_scale: torch.Tensor, + weight_scale: torch.Tensor, + block_size: list[int], + output_dtype: torch.dtype, +) -> torch.Tensor: + pad_multiple = 4 + dim = qx.shape[0] + padded = dim if dim % pad_multiple == 0 else dim + pad_multiple - ( + dim % pad_multiple) - if bias is not None: - output = output + bias - return output.to(dtype=input.dtype).view(*output_shape) + padded_shape = [padded, *qx.shape[1:]] + padded_qx = torch.zeros(padded_shape, device=qx.device, dtype=qx.dtype) + padded_qx[0:qx.shape[0], ...].copy_(qx) + padded_x_scale_shape = [*x_scale.shape[1:], padded] + padded_x_scale = torch.ones(padded_x_scale_shape, + device=x_scale.device, + dtype=x_scale.dtype).permute(-1, -2) + padded_x_scale[0:x_scale.shape[0], ...].copy_(x_scale) -def apply_w8a8_block_fp8_linear_fake( - input: torch.Tensor, + output = cutlass_scaled_mm(padded_qx, weight, padded_x_scale, weight_scale, + block_size, output_dtype, True) + return output[0:qx.shape[0], ...] + + +def _padded_cutlass_fake( + qx: torch.Tensor, weight: torch.Tensor, - block_size: list[int], + x_scale: torch.Tensor, weight_scale: torch.Tensor, - input_scale: Optional[torch.Tensor] = None, - bias: Optional[torch.Tensor] = None, - cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED, - use_aiter_and_is_supported: bool = False, + block_size: list[int], + output_dtype: torch.dtype, ) -> torch.Tensor: - output_shape = [*input.shape[:-1], weight.shape[0]] - return torch.empty(output_shape, dtype=input.dtype, device=input.device) + return torch.empty((qx.size(0), weight.size(0)), + dtype=output_dtype, + device=qx.device) -if not current_platform.is_cpu(): - direct_register_custom_op( - op_name="apply_w8a8_block_fp8_linear", - op_func=apply_w8a8_block_fp8_linear, - mutates_args=[], - fake_impl=apply_w8a8_block_fp8_linear_fake, - ) +direct_register_custom_op( + "padded_cutlass", + _padded_cutlass, + fake_impl=_padded_cutlass_fake, +) + + +def _fp8_gemm_nt_op(q_input: torch.Tensor, input_scale: torch.Tensor, + weight: torch.Tensor, weight_scale: torch.Tensor, + output: torch.Tensor, use_deep_gemm_e8m0: bool) -> None: + fp8_gemm_nt((q_input, input_scale), (weight, weight_scale), + output, + is_deep_gemm_e8m0_used=use_deep_gemm_e8m0) + + +def _fp8_gemm_nt_op_fake(q_input: torch.Tensor, input_scale: torch.Tensor, + weight: torch.Tensor, weight_scale: torch.Tensor, + output: torch.Tensor, + use_deep_gemm_e8m0: bool) -> None: + return None + + +direct_register_custom_op( + "fp8_gemm_nt_op", + _fp8_gemm_nt_op, + mutates_args=["output"], + fake_impl=_fp8_gemm_nt_op_fake, +) + + +# TODO fix ROCm->Triton custom path: +# https://github.com/vllm-project/vllm/issues/14397 +class W8A8BlockFp8LinearOp: + """ + This class executes a Blocked FP8 linear layer using cutlass if supported + and torch.scaled_mm otherwise. + """ + + def __init__( + self, + weight_group_shape: GroupShape, + act_quant_group_shape: GroupShape, + cutlass_block_fp8_supported: bool = CUTLASS_BLOCK_FP8_SUPPORTED, + use_aiter_and_is_supported: bool = False, + ): + self.weight_group_shape = weight_group_shape + self.act_quant_group_shape = act_quant_group_shape + self.is_deep_gemm_supported = is_deep_gemm_supported() + self.is_hopper = current_platform.is_device_capability(90) + self.use_deep_gemm_e8m0 = is_deep_gemm_e8m0_used() + + # Get the correct blockscale mul and input quant operations. + # We can't use _dispatch_w8a8_blockscale_op to figure out if we want + # to use deepgemm because we don't know the shape of weights (and + # whether deepgemm supports it) at the init time. + self.w8a8_blockscale_op, self.input_quant_op = \ + self._dispatch_w8a8_blockscale_op( + cutlass_block_fp8_supported, use_aiter_and_is_supported) + self.deepgemm_input_quant_op = (QuantFP8( + False, + self.act_quant_group_shape, + column_major_scales=True, + use_ue8m0=self.use_deep_gemm_e8m0) if self.is_deep_gemm_supported + else None) + + def apply( + self, + input: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + input_scale: Optional[torch.Tensor] = None, + bias: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + assert input_scale is None + # View input as 2D matrix for fp8 methods + input_2d = input.view(-1, input.shape[-1]) + output_shape = [*input.shape[:-1], weight.shape[0]] + output_dtype = input.dtype + + if should_use_deepgemm_for_fp8_linear(output_dtype, weight, + self.is_deep_gemm_supported): + output = self._run_deepgemm(input_2d, weight, weight_scale) + else: + output = self.w8a8_blockscale_op(input_2d, weight, weight_scale) + + if bias is not None: + output = output + bias + return output.to(dtype=input.dtype).view(*output_shape) + + def _run_deepgemm( + self, + input_2d: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + ) -> torch.Tensor: + assert self.deepgemm_input_quant_op is not None + q_input, input_scale = self.deepgemm_input_quant_op(input_2d) + output = torch.empty((q_input.shape[0], weight.shape[0]), + dtype=torch.bfloat16, + device=q_input.device) + torch.ops.vllm.fp8_gemm_nt_op(q_input, input_scale, weight, + weight_scale, output, + self.use_deep_gemm_e8m0) + return output + + def _run_cutlass( + self, + input_2d: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + ) -> torch.Tensor: + assert self.input_quant_op is not None + q_input, input_scale = self.input_quant_op(input_2d) + if self.is_hopper: + return torch.ops.vllm.padded_cutlass(q_input, weight, input_scale, + weight_scale, + list(self.weight_group_shape), + input_2d.dtype) + else: + return cutlass_scaled_mm(q_input, weight, + input_scale, weight_scale, + list(self.weight_group_shape), + input_2d.dtype, False) + + def _run_aiter( + self, + input_2d: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + ) -> torch.Tensor: + assert self.act_quant_group_shape == GroupShape(1, 128) + q_input, input_scale = aiter_per1x128_quant( + input_2d.contiguous(), quant_dtype=rocm_aiter.dtypes.fp8) + return torch.ops.vllm.rocm_aiter_gemm_w8a8_blockscale( + q_input, weight, input_scale, weight_scale, + self.weight_group_shape, input_2d.dtype) + + def _run_triton( + self, + input_2d: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + ) -> torch.Tensor: + assert self.input_quant_op is not None + q_input, input_scale = self.input_quant_op(input_2d) + return torch.ops.vllm.w8a8_triton_block_scaled_mm_func( + q_input, weight, input_scale, weight_scale, + self.weight_group_shape, input_2d.dtype) + + def _dispatch_w8a8_blockscale_op( + self, + use_cutlass: bool, + use_aiter_and_is_supported: bool, + ) -> tuple[Callable[[ + torch.Tensor, + torch.Tensor, + torch.Tensor, + ], torch.Tensor], Optional[QuantFP8]]: + if use_cutlass: + return self._run_cutlass, (QuantFP8(False, + self.act_quant_group_shape, + column_major_scales=True, + use_ue8m0=False)) + if use_aiter_and_is_supported: + return self._run_aiter, None + return self._run_triton, (QuantFP8(False, + self.act_quant_group_shape, + column_major_scales=False, + use_ue8m0=False)) def input_to_float8( @@ -456,7 +598,7 @@ def per_token_group_quant_fp8( @triton.jit -def _w8a8_block_fp8_matmul( +def _w8a8_triton_block_scaled_mm( # Pointers to inputs and output A, B, @@ -581,7 +723,7 @@ def get_w8a8_block_fp8_configs(N: int, K: int, block_n: int, return None -def w8a8_block_fp8_matmul( +def w8a8_triton_block_scaled_mm( A: torch.Tensor, B: torch.Tensor, As: torch.Tensor, @@ -641,7 +783,7 @@ def grid(META): return (triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]), ) - _w8a8_block_fp8_matmul[grid]( + _w8a8_triton_block_scaled_mm[grid]( A, B, C, @@ -924,25 +1066,6 @@ def maybe_post_process_fp8_weight_block(layer: torch.nn.Module, layer.weight_scale.data.T.contiguous(), requires_grad=False) -def apply_fp8_block_linear(layer: torch.nn.Module, input: torch.Tensor, - bias: Optional[torch.Tensor], - cutlass_block_fp8_supported: bool, - use_aiter_and_is_supported: bool) -> torch.Tensor: - """Apply block-wise FP8 linear operation.""" - assert layer.weight_block_size is not None - - return torch.ops.vllm.apply_w8a8_block_fp8_linear( - input=input, - weight=layer.weight, - block_size=layer.weight_block_size, - weight_scale=layer.weight_scale, - input_scale=layer.input_scale, - bias=bias, - cutlass_block_fp8_supported=cutlass_block_fp8_supported, - use_aiter_and_is_supported=use_aiter_and_is_supported, - ) - - def expert_weight_is_col_major(x: torch.Tensor) -> bool: assert x.dim() == 3 b, m, n = x.shape diff --git a/vllm/utils/deep_gemm.py b/vllm/utils/deep_gemm.py index 4f05f0bc35cc..7e28d5e452af 100644 --- a/vllm/utils/deep_gemm.py +++ b/vllm/utils/deep_gemm.py @@ -9,7 +9,7 @@ import functools import importlib import os -from typing import Any, Callable, NoReturn +from typing import Any, Callable, NoReturn, Optional import torch @@ -136,9 +136,12 @@ def fp8_gemm_nt(*args, **kwargs): _lazy_init() if _fp8_gemm_nt_impl is None: return _missing(*args, **kwargs) - return _fp8_gemm_nt_impl(*args, - disable_ue8m0_cast=not is_deep_gemm_e8m0_used(), - **kwargs) + if "is_deep_gemm_e8m0_used" in kwargs: + use_ue8m0 = kwargs["is_deep_gemm_e8m0_used"] + del kwargs["is_deep_gemm_e8m0_used"] + else: + use_ue8m0 = is_deep_gemm_e8m0_used() + return _fp8_gemm_nt_impl(*args, disable_ue8m0_cast=not use_ue8m0, **kwargs) def m_grouped_fp8_gemm_nt_contiguous(*args, **kwargs): @@ -301,9 +304,13 @@ def calc_diff(x: torch.Tensor, y: torch.Tensor): return 1 - sim -def should_use_deepgemm_for_fp8_linear(output_dtype: torch.dtype, - weight: torch.Tensor): - return (is_deep_gemm_supported() and output_dtype == torch.bfloat16 +def should_use_deepgemm_for_fp8_linear( + output_dtype: torch.dtype, + weight: torch.Tensor, + supports_deep_gemm: Optional[bool] = None): + if supports_deep_gemm is None: + supports_deep_gemm = is_deep_gemm_supported() + return (supports_deep_gemm and output_dtype == torch.bfloat16 and weight.shape[0] % 128 == 0 and weight.shape[1] % 128 == 0) From 1f88f47bdb76e6371c58e141c6bfa07b165853cb Mon Sep 17 00:00:00 2001 From: Matthew Bonanni Date: Thu, 2 Oct 2025 17:29:49 -0400 Subject: [PATCH 166/166] Fix MTP with deepep_low_latency (#25904) Signed-off-by: Matthew Bonanni --- vllm/model_executor/layers/fused_moe/layer.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/vllm/model_executor/layers/fused_moe/layer.py b/vllm/model_executor/layers/fused_moe/layer.py index 8de1d14d46b3..9a7ca7b6d124 100644 --- a/vllm/model_executor/layers/fused_moe/layer.py +++ b/vllm/model_executor/layers/fused_moe/layer.py @@ -1899,6 +1899,15 @@ def process_chunk(chunk_start, chunk_end, skip_result_store=False): staged_hidden_states.copy_(hidden_states, non_blocking=True) staged_router_logits.copy_(router_logits, non_blocking=True) + # If there are shared experts but we are not using a modular kernel, + # the shared experts must be called here + if (not isinstance(self.quant_method.fused_experts, + FusedMoEModularKernel) + and self.shared_experts is not None): + shared_output = self.shared_experts(staged_hidden_states) + else: + shared_output = None + # Matrix multiply. final_hidden_states = self.quant_method.apply( layer=self, @@ -1922,8 +1931,13 @@ def process_chunk(chunk_start, chunk_end, skip_result_store=False): logical_replica_count=self.logical_replica_count, ) - assert self.shared_experts is None or isinstance( - final_hidden_states, tuple) + if shared_output is not None: + assert not isinstance(final_hidden_states, tuple) + assert self.shared_experts is not None + final_hidden_states = ( + shared_output, + final_hidden_states, + ) if self.zero_expert_num is not None and self.zero_expert_num > 0: assert isinstance(final_hidden_states, tuple)

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