diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 60c0b479a4..6b9bdfef53 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,7 +3,7 @@ default_language_version: # The vendored upstream module stays byte-diffable against its source; keep # formatters and linters away so upstream-tracking diffs remain minimal. -exclude: '^miles/utils/chat_template_utils/templates/encoding_dsv32\.py$' +exclude: '^miles/utils/chat_template_utils/templates/encoding_dsv(32|4|41)\.py$' ci: autofix_prs: true diff --git a/docker/Dockerfile b/docker/Dockerfile index 59c6c0ce2a..a97d7c44f4 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -10,7 +10,7 @@ # release for the current arch picked from TARGETARCH — WHEELS_TAG_X86 on amd64, # WHEELS_TAG_ARM64 on arm64. -ARG SGLANG_IMAGE_TAG=v0.5.22 +ARG SGLANG_IMAGE_TAG=v0.5.23 FROM lmsysorg/sglang:${SGLANG_IMAGE_TAG} AS sglang # ======================================== Arguments ============================================= @@ -136,8 +136,21 @@ RUN if ls /tmp/wheels/fast_hadamard_transform-*.whl 2>/dev/null | grep -q .; the # Megatron #5992 requires on SM100; 1.26.0 has no compact wrapper and fails # TransformerConfig validation once that change is in miles-main. A floor, not a # pin: the base image may already ship a newer one. -RUN pip install /tmp/wheels/flash_mla-*.whl && \ - pip install --no-deps "nvidia-cudnn-frontend>=1.28" +RUN pip install --no-deps "nvidia-cudnn-frontend>=1.28" + +# Two FlashMLA builds, both packaged as `flash_mla`. SGLang pins sgl-flash-mla for decode +# APIs the nv_dev build lacks; Megatron's CSA forward and miles' DSA kernel need nv_dev +# (`indexer_topk`, SM90 and 576-dim sparse prefill). sglang-miles imports `sgl_flash_mla` +# first, so move the base image's package there and install nv_dev as `flash_mla`. +RUN FLASH_MLA_DIR=$(python3 -c 'import importlib.util; print(importlib.util.find_spec("flash_mla").submodule_search_locations[0])') && \ + SGL_FLASH_MLA_DIR="$(dirname "$FLASH_MLA_DIR")/sgl_flash_mla" && \ + mv "$FLASH_MLA_DIR" "$SGL_FLASH_MLA_DIR" && \ + rm -rf "$SGL_FLASH_MLA_DIR/__pycache__" && \ + sed -i 's/\bfrom flash_mla\b/from sgl_flash_mla/' "$SGL_FLASH_MLA_DIR"/*.py && \ + if grep -nE '^\s*(from|import) flash_mla\b' "$SGL_FLASH_MLA_DIR"/*.py; then \ + echo "sgl_flash_mla still imports flash_mla; extend the rename above" >&2; exit 1; \ + fi && \ + pip install /tmp/wheels/flash_mla-*.whl # Mamba kernels for nemotron_h hybrid (mamba+attention) models. RUN if ls /tmp/wheels/causal_conv1d-*.whl 2>/dev/null | grep -q . && \ diff --git a/docs/developer/versions.md b/docs/developer/versions.md index 6c7c444443..959e032121 100644 --- a/docs/developer/versions.md +++ b/docs/developer/versions.md @@ -37,7 +37,7 @@ The default build-args are the version surface: | Build-arg | Default | What it selects | |---|---|---| -| `SGLANG_IMAGE_TAG` | `v0.5.22` | The `lmsysorg/sglang` base image, which brings torch, CUDA, cuDNN and Mooncake | +| `SGLANG_IMAGE_TAG` | `v0.5.23` | The `lmsysorg/sglang` base image, which brings torch, CUDA, cuDNN and Mooncake | | `SGLANG_BRANCH` | `sglang-miles` | The branch fetched into the base image's SGLang checkout | | `SGLANG_COMMIT` | empty | Empty means the branch HEAD at build time; set it to freeze one commit | | `MEGATRON_REPO` / `MEGATRON_BRANCH` / `MEGATRON_COMMIT` | `radixark/Megatron-LM` / `miles-main` / empty | The Megatron-LM checkout; an empty commit follows branch HEAD, while a release build supplies the locked commit | diff --git a/miles/backends/megatron_utils/megatron_to_hf/processors/quantizer_fp8.py b/miles/backends/megatron_utils/megatron_to_hf/processors/quantizer_fp8.py index 696a53f052..12ff71e18a 100644 --- a/miles/backends/megatron_utils/megatron_to_hf/processors/quantizer_fp8.py +++ b/miles/backends/megatron_utils/megatron_to_hf/processors/quantizer_fp8.py @@ -179,13 +179,15 @@ def _quantize_param(args, name, weight, weight_block_size, is_skipped): def _get_scale_format(args, name, weight_block_size): + is_moe_expert = ".experts." in name + # grouped_moe mirrors SGLang's own MoE requant call: SM120 requantizes only grouped MoE scales. if not ( should_deepgemm_weight_requant_ue8m0 - and should_deepgemm_weight_requant_ue8m0(weight_block_size=weight_block_size) + and should_deepgemm_weight_requant_ue8m0(weight_block_size=weight_block_size, grouped_moe=is_moe_expert) ): return None # use default fp32 scale format - if ".experts." not in name: + if not is_moe_expert: # Non-MoE linear weights: ue8m0 when deepgemm is enabled return "ue8m0" diff --git a/miles/backends/sglang_utils/sglang_engine.py b/miles/backends/sglang_utils/sglang_engine.py index 7c7dc6b14e..9bd81f127e 100644 --- a/miles/backends/sglang_utils/sglang_engine.py +++ b/miles/backends/sglang_utils/sglang_engine.py @@ -145,6 +145,11 @@ def _compute_server_args( kwargs["enable_return_routed_experts"] = True if args.use_rollout_indexer_replay: kwargs["enable_return_indexer_topk"] = True + # SGLang v0.5.23 turns DeepSeek-V4.1 decoder SWA bounded replay on when this is unset; it is not + # numerically equivalent to full prefill, so rollout logprobs and replayed routing would drift + # from the trainer. Keep full prefill unless --sglang-enable-decoder-swa-bounded-replay is set. + if base_args.get("enable_decoder_swa_bounded_replay") is None: + kwargs["enable_decoder_swa_bounded_replay"] = False if args.use_output_store: # Every engine, eval included: an eval request never asks for the store. kwargs.update(sglang_output_store_server_args(args, local_hostname=host.strip("[]"))) diff --git a/miles/utils/chat_template_utils/deepseek.py b/miles/utils/chat_template_utils/deepseek.py index 4c413dea6d..849ab390da 100644 --- a/miles/utils/chat_template_utils/deepseek.py +++ b/miles/utils/chat_template_utils/deepseek.py @@ -1,11 +1,11 @@ """Shared bridge for the DeepSeek official-encoder families (V3.2, V4, V4.1). -None of the families ships a jinja chat_template: V4 and V4.1 render through -sglang's ``encoding_dsv4`` / ``encoding_dsv41``, while V3.2 uses miles' vendored -``templates.encoding_dsv32``. Both modules share one calling convention, -and miles' ``apply_chat_template`` routes any matching tokenizer here. Each -family is one ``DeepSeekFamily`` instance wrapping its encoder module; -everything else is shared. +None of the families ships a jinja chat_template: each renders through miles' +vendored official encoder (``templates.encoding_dsv32`` / ``encoding_dsv4`` / +``encoding_dsv41``). The modules share one calling convention, and miles' +``apply_chat_template`` routes any matching tokenizer here. Each family is one +``DeepSeekFamily`` instance wrapping its encoder module; everything else is +shared. """ from __future__ import annotations @@ -17,10 +17,9 @@ from collections.abc import Callable from typing import Any -from sglang.srt.entrypoints.openai import chat_encoding, encoding_dsv4, encoding_dsv41 from sglang.srt.entrypoints.openai.protocol import Tool -from miles.utils.chat_template_utils.templates import encoding_dsv32 +from miles.utils.chat_template_utils.templates import encoding_dsv4, encoding_dsv32, encoding_dsv41 _ASSISTANT_SP_TOKEN = "<｜Assistant｜>" @@ -51,9 +50,10 @@ def _inject_tools_into_system( """Put *tools* in the system message, where ``encode_messages`` reads them. The encoder serializes each tool dict verbatim into ````, so they - must round-trip through the same pydantic payload sglang's ``serving_chat`` - builds (``Tool.model_dump()`` fills defaults / orders fields; V4.1 keeps - only the fields the client sent) or the token ids drift from what sglang serves. + must round-trip through the payload the encoder is paired with or the token + ids drift: V3.2 uses ``Tool.model_dump()`` as sglang's dsv32 serving path + does, V4 keeps the v0.5.22 payload (``dsv4_tool_payload``), and V4.1 keeps + only the fields the client sent (``dsv41_tool_payload``). """ out = copy.deepcopy(messages) if not out or out[0].get("role") != "system": @@ -97,7 +97,7 @@ def render_messages( add_generation_prompt: bool = True, **kwargs: Any, ) -> str: - """Render *messages* into this family's prompt via sglang ``encode_messages``. + """Render *messages* into this family's prompt via its encoder's ``encode_messages``. Tool_call ``arguments`` must already be JSON strings; *tools*, if given, are injected into the system message (see ``_inject_tools_into_system``). The @@ -133,34 +133,105 @@ def _generation_prompt_suffix(self, tail_role: str | None, thinking_token: str) return None +def dsv4_tool_payload(tool: Tool) -> dict[str, Any]: + """The tool dict sglang v0.5.22's ``Tool.model_dump()`` gave the V4 encoder. + + v0.5.23 keeps the client's key order and drops an unset ``strict`` + (sgl-project/sglang#46133); V4 renders the dict verbatim, so rebuild the + declared field order with ``strict`` defaulting to False. + """ + payload = tool.model_dump() + function = payload["function"] + declared = { + "description": function.get("description"), + "name": function["name"], + "parameters": function.get("parameters"), + "strict": bool(function.get("strict", False)), + } + if "defer_loading" in function: + declared["defer_loading"] = function["defer_loading"] + payload["function"] = declared + return payload + + class DeepSeekV4Family(DeepSeekFamily): template = encoding_dsv4 + def _inject_tools(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]) -> list[dict[str, Any]]: + return _inject_tools_into_system(messages, tools, dsv4_tool_payload) + def _generation_prompt_suffix(self, tail_role: str | None, thinking_token: str) -> str | None: if tail_role in {"user", "developer", "tool"}: return _ASSISTANT_SP_TOKEN + thinking_token return None +def parse_dsv41_reasoning_effort(value: Any) -> str | int | None: + """Map an API ``reasoning_effort`` onto what the V4.1 encoder accepts. + + An int budget only reaches here through ``chat_template_kwargs``; None + means unsupported, and the caller applies its default. + """ + if isinstance(value, bool): + return None + if isinstance(value, int): + return value if 1 <= value <= 100 else None + if isinstance(value, float): + return max(1, round(value * 100)) if 0.0 <= value <= 0.99 else None + if value in encoding_dsv41.REASONING_EFFORT_MAPPINGS: + return value + return None + + +_OPENAI_FUNCTION_FIELD_ORDER = ("name", "description", "parameters") + + +def dsv41_tool_payload(tool: Tool) -> dict[str, Any]: + """The tool dict the V4.1 encoder renders verbatim into the prompt. + + Only fields the client sent, in the OpenAI field order; pydantic would + otherwise add defaults (strict=false) and reorder keys by declaration. + """ + payload = tool.model_dump(exclude_unset=True, exclude_none=True) + function = dict(payload.get("function") or {}) + ordered = {k: function.pop(k) for k in _OPENAI_FUNCTION_FIELD_ORDER if k in function} + ordered.update(function) + payload["function"] = ordered + return payload + + +def default_dsv41_reasoning_effort_from_env(raw: str | None) -> str | int: + """Parse ``SGLANG_DSV41_REASONING_EFFORT``; raises so a bad value fails at boot.""" + if raw is None or not raw.strip(): + return encoding_dsv41.DEFAULT_REASONING_EFFORT + value: Any = int(raw) if raw.strip().isdigit() else raw.strip() + effort = parse_dsv41_reasoning_effort(value) + if effort is None: + raise ValueError( + f"Invalid SGLANG_DSV41_REASONING_EFFORT={raw!r}; expected one of " + f"{list(encoding_dsv41.REASONING_EFFORT_MAPPINGS)} or an integer in [1, 100]" + ) + return effort + + class DeepSeekV41Family(DeepSeekV4Family): - """V4.1 mirrors sglang's ``serving_chat`` dsv41 branch: tools keep only the - fields the client sent, ``reasoning_effort`` always reaches the encoder (the - server default comes from ``SGLANG_DSV41_REASONING_EFFORT``), and a - mid-conversation system message also opens an assistant turn.""" + """V4.1 keeps the rendering of sglang's ``serving_chat`` dsv41 branch from + before v0.5.23 moved it to deepseek-recipe: tools keep only the fields the + client sent, ``reasoning_effort`` always reaches the encoder (the default + comes from ``SGLANG_DSV41_REASONING_EFFORT``), and a mid-conversation system + message also opens an assistant turn.""" template = encoding_dsv41 def _build_encode_config(self, kwargs: dict) -> dict: kwargs = super()._build_encode_config(kwargs) - default = chat_encoding.default_dsv41_reasoning_effort_from_env( - os.environ.get("SGLANG_DSV41_REASONING_EFFORT", "high") - ) - effort = chat_encoding.parse_dsv41_reasoning_effort(kwargs.get("reasoning_effort")) + default = default_dsv41_reasoning_effort_from_env(os.environ.get("SGLANG_DSV41_REASONING_EFFORT", "high")) + effort = parse_dsv41_reasoning_effort(kwargs.get("reasoning_effort")) kwargs["reasoning_effort"] = default if effort is None else effort return kwargs def _inject_tools(self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]) -> list[dict[str, Any]]: - return _inject_tools_into_system(messages, tools, chat_encoding.dsv41_tool_payload) + return _inject_tools_into_system(messages, tools, dsv41_tool_payload) def _generation_prompt_suffix(self, tail_role: str | None, thinking_token: str) -> str | None: if tail_role in {"user", "developer", "tool", "system"}: diff --git a/miles/utils/chat_template_utils/templates/__init__.py b/miles/utils/chat_template_utils/templates/__init__.py index 222cd793ba..34bf288e10 100644 --- a/miles/utils/chat_template_utils/templates/__init__.py +++ b/miles/utils/chat_template_utils/templates/__init__.py @@ -1 +1 @@ -"""Bundled fixed chat templates and the vendored official DeepSeek V3.2 encoder.""" +"""Bundled fixed chat templates and the vendored official DeepSeek V3.2 / V4 / V4.1 encoders.""" diff --git a/miles/utils/chat_template_utils/templates/encoding_dsv4.py b/miles/utils/chat_template_utils/templates/encoding_dsv4.py new file mode 100644 index 0000000000..22c25ec6ec --- /dev/null +++ b/miles/utils/chat_template_utils/templates/encoding_dsv4.py @@ -0,0 +1,885 @@ +# Vendored from sglang-miles 29b34bc6f7 (SGLang v0.5.22) srt/entrypoints/openai/encoding_dsv4.py; SGLang v0.5.23 dropped it for deepseek-recipe (sgl-project/sglang#45315). +# Adapted from the DeepSeek-V4 release reference implementation. +""" +DeepSeek-V4 Encoding + +A self-contained implementation for encoding/decoding DeepSeek-V4 chat messages +with tool calling, thinking mode, and quick instruction task support. +""" + +import copy +import json +import re +from typing import Any, Dict, List, Optional, Tuple, Union + +# ============================================================ +# Special Tokens +# ============================================================ + +bos_token: str = "<｜begin▁of▁sentence｜>" +eos_token: str = "<｜end▁of▁sentence｜>" +thinking_start_token: str = "" +thinking_end_token: str = "" +dsml_token: str = "｜DSML｜" + +USER_SP_TOKEN = "<｜User｜>" +ASSISTANT_SP_TOKEN = "<｜Assistant｜>" +LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>" + +# Task special tokens for internal classification tasks +DS_TASK_SP_TOKENS = { + "action": "<｜action｜>", + "query": "<｜query｜>", + "authority": "<｜authority｜>", + "domain": "<｜domain｜>", + "title": "<｜title｜>", + "read_url": "<｜read_url｜>", +} +VALID_TASKS = set(DS_TASK_SP_TOKENS.keys()) + +# ============================================================ +# Templates +# ============================================================ + +system_msg_template: str = "{content}" +user_msg_template: str = "{content}" +latest_reminder_msg_template: str = "{content}" +assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token +assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}" +thinking_template: str = "{reasoning_content}" + +response_format_template: str = "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}" +tool_call_template: str = ( + '<{dsml_token}invoke name="{name}">\n{arguments}\n' +) +tool_calls_template = ( + "<{dsml_token}{tc_block_name}>\n{tool_calls}\n" +) +tool_calls_block_name: str = "tool_calls" + +tool_output_template: str = "{content}" + +REASONING_EFFORT_PREVIEW_MAX = ( + "Reasoning Effort: Absolute maximum with no shortcuts permitted.\n" + "You MUST be very thorough in your thinking and comprehensively decompose the problem to resolve the root cause, rigorously stress-testing your logic against all potential paths, edge cases, and adversarial scenarios.\n" + "Explicitly write out your entire deliberation process, documenting every intermediate step, considered alternative, and rejected hypothesis to ensure absolutely no assumption is left unchecked.\n\n" +) + +REASONING_EFFORT_OFFICIAL_MAX = ( + "Reasoning Effort: Beyond maximum — exhaustive, relentless, and uncompromising.\n" + "You MUST reason with the utmost depth and rigor, leaving absolutely nothing to chance: exhaustively decompose the problem into its most fundamental components, trace every causal chain to its root, and resolve the underlying cause rather than any surface symptom.\n" + "Do not stop reasoning until you have independently verified the solution from multiple angles and are certain that no assumption remains unchecked and no error remains undiscovered.\n\n" +) + +REASONING_EFFORT_PROFILES = { + "preview": { + "high": "", + "max": REASONING_EFFORT_PREVIEW_MAX, + }, + "official": { + "low": "", + "high": REASONING_EFFORT_PREVIEW_MAX, + "max": REASONING_EFFORT_OFFICIAL_MAX, + }, +} + +TOOLS_TEMPLATE = """## Tools + +You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<{dsml_token}tool_calls>" block like the following: + +<{dsml_token}tool_calls> +<{dsml_token}invoke name="$TOOL_NAME"> +<{dsml_token}parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE +... + +<{dsml_token}invoke name="$TOOL_NAME2"> +... + + + +String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`. + +If thinking_mode is enabled (triggered by {thinking_start_token}), you MUST output your complete reasoning inside {thinking_start_token}...{thinking_end_token} BEFORE any tool calls or final response. + +Otherwise, output directly after {thinking_end_token} with tool calls or final response. + +### Available Tool Schemas + +{tool_schemas} + +You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls. +""" + +# ============================================================ +# Utility Functions +# ============================================================ + + +def to_json(value: Any) -> str: + """Serialize a value to JSON string.""" + try: + return json.dumps(value, ensure_ascii=False) + except: + return json.dumps(value, ensure_ascii=True) + + +def tools_from_openai_format(tools): + """Extract function definitions from OpenAI-format tool list.""" + return [tool["function"] for tool in tools] + + +def tool_calls_from_openai_format(tool_calls): + """Convert OpenAI-format tool calls to internal format.""" + return [ + { + "name": tool_call["function"]["name"], + "arguments": tool_call["function"]["arguments"], + } + for tool_call in tool_calls + ] + + +def tool_calls_to_openai_format(tool_calls): + """Convert internal tool calls to OpenAI format.""" + return [ + { + "type": "function", + "function": { + "name": tool_call["name"], + "arguments": tool_call["arguments"], + }, + } + for tool_call in tool_calls + ] + + +def encode_arguments_to_dsml(tool_call: Dict[str, str]) -> str: + """ + Encode tool call arguments into DSML parameter format. + + Args: + tool_call: Dict with "name" and "arguments" keys. + + Returns: + DSML-formatted parameter string. + """ + p_dsml_template = '<{dsml_token}parameter name="{key}" string="{is_str}">{value}' + P_dsml_strs = [] + + raw_arguments = tool_call["arguments"] + arguments = ( + json.loads(raw_arguments) if isinstance(raw_arguments, str) else raw_arguments + ) + if not isinstance(arguments, dict): + raise ValueError( + "Assistant tool call function.arguments must be a JSON object." + ) + + for k, v in arguments.items(): + p_dsml_str = p_dsml_template.format( + dsml_token=dsml_token, + key=k, + is_str="true" if isinstance(v, str) else "false", + value=v if isinstance(v, str) else to_json(v), + ) + P_dsml_strs.append(p_dsml_str) + + return "\n".join(P_dsml_strs) + + +def decode_dsml_to_arguments( + tool_name: str, tool_args: Dict[str, Tuple[str, str]] +) -> Dict[str, str]: + """ + Decode DSML parameters back to a tool call dict. + + Args: + tool_name: Name of the tool. + tool_args: Dict mapping param_name -> (value, is_string_flag). + + Returns: + Dict with "name" and "arguments" (JSON string) keys. + """ + + def _decode_value(key: str, value: str, string: str): + if string == "true": + value = to_json(value) + return f"{to_json(key)}: {value}" + + tool_args_json = ( + "{" + + ", ".join( + [_decode_value(k, v, string=is_str) for k, (v, is_str) in tool_args.items()] + ) + + "}" + ) + return dict(name=tool_name, arguments=tool_args_json) + + +def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str: + """ + Render tool schemas into the system prompt format. + + Args: + tools: List of tool schema dicts (each with name, description, parameters). + + Returns: + Formatted tools section string. + """ + tools_json = [to_json(t) for t in tools] + + return TOOLS_TEMPLATE.format( + tool_schemas="\n".join(tools_json), + dsml_token=dsml_token, + thinking_start_token=thinking_start_token, + thinking_end_token=thinking_end_token, + ) + + +def find_last_user_index(messages: List[Dict[str, Any]]) -> int: + """Find the index of the last user/developer message.""" + last_user_index = -1 + for idx in range(len(messages) - 1, -1, -1): + if messages[idx].get("role") in ["user", "developer"]: + last_user_index = idx + break + return last_user_index + + +def attach_task_to_last_user_message(messages: List[Dict[str, Any]], task: str) -> None: + """Set `task` on the most recent user/developer message; raise if none exists.""" + idx = find_last_user_index(messages) + if idx == -1: + raise ValueError( + "`task` requires at least one message with role='user' or 'developer'." + ) + messages[idx]["task"] = task + + +# ============================================================ +# Message Rendering +# ============================================================ + + +def render_message( + index: int, + messages: List[Dict[str, Any]], + thinking_mode: str, + drop_thinking: bool = True, + reasoning_effort: Optional[str] = None, + reasoning_effort_profile: str = "preview", +) -> str: + """ + Render a single message at the given index into its encoded string form. + + This is the core function that converts each message in the conversation + into the DeepSeek-V4 format. + + Args: + index: Index of the message to render. + messages: Full list of messages in the conversation. + thinking_mode: Either "chat" or "thinking". + drop_thinking: Whether to drop reasoning content from earlier turns. + reasoning_effort: Optional reasoning effort level. The preview profile accepts + "high" and "max"; the official profile accepts "low", "high", and "max". + reasoning_effort_profile: DeepSeek-V4 effort mapping ("preview" or "official"). + + Returns: + Encoded string for this message. + """ + assert 0 <= index < len(messages) + assert thinking_mode in [ + "chat", + "thinking", + ], f"Invalid thinking_mode `{thinking_mode}`" + + prompt = "" + msg = messages[index] + last_user_idx = find_last_user_index(messages) + + role = msg.get("role") + content = msg.get("content") + tools = msg.get("tools") + response_format = msg.get("response_format") + tool_calls = msg.get("tool_calls") + reasoning_content = msg.get("reasoning_content") + wo_eos = msg.get("wo_eos", False) + + if tools: + tools = tools_from_openai_format(tools) + if tool_calls: + tool_calls = tool_calls_from_openai_format(tool_calls) + + if reasoning_effort_profile not in REASONING_EFFORT_PROFILES: + raise ValueError( + f"Invalid reasoning effort profile: {reasoning_effort_profile!r}; " + f"expected one of {list(REASONING_EFFORT_PROFILES)}" + ) + effort_prompts = REASONING_EFFORT_PROFILES[reasoning_effort_profile] + if reasoning_effort is None: + reasoning_effort = "low" if reasoning_effort_profile == "official" else "high" + if reasoning_effort not in effort_prompts: + raise ValueError( + f"Invalid reasoning effort {reasoning_effort!r} for profile " + f"{reasoning_effort_profile!r}; expected one of {list(effort_prompts)}" + ) + if index == 0 and thinking_mode == "thinking": + prompt += effort_prompts[reasoning_effort] + + if role == "system": + prompt += system_msg_template.format(content=content or "") + if tools: + prompt += "\n\n" + render_tools(tools) + if response_format: + prompt += "\n\n" + response_format_template.format( + schema=to_json(response_format) + ) + + elif role == "developer": + assert content, f"Invalid message for role `{role}`: {msg}" + + content_developer = USER_SP_TOKEN + content_developer += content + + if tools: + content_developer += "\n\n" + render_tools(tools) + if response_format: + content_developer += "\n\n" + response_format_template.format( + schema=to_json(response_format) + ) + + prompt += user_msg_template.format(content=content_developer) + + elif role == "user": + prompt += USER_SP_TOKEN + + # Handle content blocks (tool results mixed with text) + content_blocks = msg.get("content_blocks") + if content_blocks: + parts = [] + for block in content_blocks: + block_type = block.get("type") + if block_type == "text": + parts.append(block.get("text", "")) + elif block_type == "tool_result": + tool_content = block.get("content", "") + if isinstance(tool_content, list): + text_parts = [] + for b in tool_content: + if b.get("type") == "text": + text_parts.append(b.get("text", "")) + else: + text_parts.append(f"[Unsupported {b.get('type')}]") + tool_content = "\n\n".join(text_parts) + parts.append(tool_output_template.format(content=tool_content)) + else: + parts.append(f"[Unsupported {block_type}]") + prompt += "\n\n".join(parts) + else: + prompt += content or "" + + elif role == "latest_reminder": + prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format( + content=content + ) + + elif role == "tool": + raise NotImplementedError( + "deepseek_v4 merges tool messages into user; please preprocess with merge_tool_messages()" + ) + + elif role == "assistant": + thinking_part = "" + tc_content = "" + + if tool_calls: + tc_list = [ + tool_call_template.format( + dsml_token=dsml_token, + name=tc.get("name"), + arguments=encode_arguments_to_dsml(tc), + ) + for tc in tool_calls + ] + tc_content += "\n\n" + tool_calls_template.format( + dsml_token=dsml_token, + tool_calls="\n".join(tc_list), + tc_block_name=tool_calls_block_name, + ) + + summary_content = content or "" + rc = reasoning_content or "" + + # Check if previous message has a task - if so, this is a task output (no thinking) + prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None + + if thinking_mode == "thinking" and not prev_has_task: + if not drop_thinking or index > last_user_idx: + thinking_part = ( + thinking_template.format(reasoning_content=rc) + thinking_end_token + ) + else: + thinking_part = "" + + if wo_eos: + prompt += assistant_msg_wo_eos_template.format( + reasoning=thinking_part, + content=summary_content, + tool_calls=tc_content, + ) + else: + prompt += assistant_msg_template.format( + reasoning=thinking_part, + content=summary_content, + tool_calls=tc_content, + ) + else: + raise NotImplementedError(f"Unknown role: {role}") + + # Append transition tokens based on what follows + if index + 1 < len(messages) and messages[index + 1].get("role") not in [ + "assistant", + "latest_reminder", + ]: + return prompt + + task = messages[index].get("task") + if task is not None: + # Task special token for internal classification tasks + assert task in VALID_TASKS, ( + f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}" + ) + task_sp_token = DS_TASK_SP_TOKENS[task] + + if task != "action": + # Non-action tasks: append task sp token directly after the message + prompt += task_sp_token + else: + # Action task: append Assistant + thinking token + action sp token + prompt += ASSISTANT_SP_TOKEN + prompt += ( + thinking_end_token + if thinking_mode != "thinking" + else thinking_start_token + ) + prompt += task_sp_token + + elif messages[index].get("role") in ["user", "developer"]: + # Normal generation: append Assistant + thinking token + prompt += ASSISTANT_SP_TOKEN + if not drop_thinking and thinking_mode == "thinking": + prompt += thinking_start_token + elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx: + prompt += thinking_start_token + else: + prompt += thinking_end_token + + return prompt + + +# ============================================================ +# Preprocessing +# ============================================================ + + +def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Merge tool messages into the preceding user message using content_blocks format. + + DeepSeek-V4 does not have a standalone "tool" role; instead, tool results + are encoded as blocks within user messages. + + This function converts a standard OpenAI-format conversation (with separate + "tool" role messages) into V4 format where tool results are merged into + user messages. + + Args: + messages: List of message dicts in OpenAI format. + + Returns: + Processed message list with tool messages merged into user messages. + """ + merged: List[Dict[str, Any]] = [] + + for msg in messages: + msg = copy.deepcopy(msg) + role = msg.get("role") + + if role == "tool": + # Convert tool message to a user message with tool_result block + tool_block = { + "type": "tool_result", + "tool_use_id": msg.get("tool_call_id", ""), + "content": msg.get("content", ""), + } + # Merge into previous message if it's already a user (merged tool) + if ( + merged + and merged[-1].get("role") == "user" + and "content_blocks" in merged[-1] + ): + merged[-1]["content_blocks"].append(tool_block) + else: + merged.append( + { + "role": "user", + "content_blocks": [tool_block], + } + ) + elif role == "user": + text_block = {"type": "text", "text": msg.get("content", "")} + if ( + merged + and merged[-1].get("role") == "user" + and "content_blocks" in merged[-1] + and merged[-1].get("task") is None + ): + merged[-1]["content_blocks"].append(text_block) + else: + new_msg = { + "role": "user", + "content": msg.get("content", ""), + "content_blocks": [text_block], + } + # Preserve extra fields (task, wo_eos, mask, etc.) + for key in ("task", "wo_eos", "mask"): + if key in msg: + new_msg[key] = msg[key] + merged.append(new_msg) + else: + merged.append(msg) + + return merged + + +def sort_tool_results_by_call_order( + messages: List[Dict[str, Any]], +) -> List[Dict[str, Any]]: + """ + Sort tool_result blocks within user messages by the order of tool_calls + in the preceding assistant message. + + Args: + messages: Preprocessed message list (after merge_tool_messages). + + Returns: + Message list with sorted tool result blocks. + """ + last_tool_call_order: Dict[str, int] = {} + + for msg in messages: + role = msg.get("role") + if role == "assistant" and msg.get("tool_calls"): + last_tool_call_order = {} + for idx, tc in enumerate(msg["tool_calls"]): + tc_id = tc.get("id") or tc.get("function", {}).get("id", "") + if tc_id: + last_tool_call_order[tc_id] = idx + + elif role == "user" and msg.get("content_blocks"): + tool_blocks = [ + b for b in msg["content_blocks"] if b.get("type") == "tool_result" + ] + if len(tool_blocks) > 1 and last_tool_call_order: + sorted_blocks = sorted( + tool_blocks, + key=lambda b: last_tool_call_order.get(b.get("tool_use_id", ""), 0), + ) + sorted_idx = 0 + new_blocks = [] + for block in msg["content_blocks"]: + if block.get("type") == "tool_result": + new_blocks.append(sorted_blocks[sorted_idx]) + sorted_idx += 1 + else: + new_blocks.append(block) + msg["content_blocks"] = new_blocks + + return messages + + +# ============================================================ +# Main Encoding Function +# ============================================================ + + +def encode_messages( + messages: List[Dict[str, Any]], + thinking_mode: str, + context: Optional[List[Dict[str, Any]]] = None, + drop_thinking: bool = True, + add_default_bos_token: bool = True, + reasoning_effort: Optional[str] = None, + reasoning_effort_profile: str = "preview", +) -> str: + """ + Encode a list of messages into the DeepSeek-V4 prompt format. + + This is the main entry point for encoding conversations. It handles: + - BOS token insertion + - Thinking mode with optional reasoning content dropping + - Tool message merging into user messages + - Multi-turn conversation context + + Args: + messages: List of message dicts to encode. + thinking_mode: Either "chat" or "thinking". + context: Optional preceding context messages (already encoded prefix). + drop_thinking: If True, drop reasoning_content from earlier assistant turns + (only keep reasoning for messages after the last user message). + add_default_bos_token: Whether to prepend BOS token at conversation start. + reasoning_effort: Optional reasoning effort level. The preview profile accepts + "high" and "max"; the official profile accepts "low", "high", and "max". + reasoning_effort_profile: DeepSeek-V4 effort mapping ("preview" or "official"). + + Returns: + The encoded prompt string. + """ + context = context if context else [] + + # Preprocess: merge tool messages and sort tool results + messages = merge_tool_messages(messages) + messages = sort_tool_results_by_call_order(context + messages)[len(context) :] + if context: + context = merge_tool_messages(context) + context = sort_tool_results_by_call_order(context) + + full_messages = context + messages + + prompt = bos_token if add_default_bos_token and len(context) == 0 else "" + + # Resolve drop_thinking: if any message has tools defined, don't drop thinking + effective_drop_thinking = drop_thinking + if any(m.get("tools") for m in full_messages): + effective_drop_thinking = False + + if thinking_mode == "thinking" and effective_drop_thinking: + full_messages = _drop_thinking_messages(full_messages) + # After dropping, recalculate how many messages to render + # (context may have shrunk too) + num_to_render = len(full_messages) - len(_drop_thinking_messages(context)) + context_len = len(full_messages) - num_to_render + else: + num_to_render = len(messages) + context_len = len(context) + + for idx in range(num_to_render): + prompt += render_message( + idx + context_len, + full_messages, + thinking_mode=thinking_mode, + drop_thinking=effective_drop_thinking, + reasoning_effort=reasoning_effort, + reasoning_effort_profile=reasoning_effort_profile, + ) + + return prompt + + +def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Drop reasoning_content and non-essential messages before the last user message. + + Behavior: + - Messages with role in ["user", "system", "tool", "latest_reminder"] are always kept. + - Messages at or after the last user index are always kept. + - Assistant messages before the last user get reasoning_content removed. + - Developer messages before the last user are dropped entirely. + """ + last_user_idx = find_last_user_index(messages) + result = [] + keep_roles = {"user", "system", "tool", "latest_reminder", "direct_search_results"} + + for idx, msg in enumerate(messages): + role = msg.get("role") + if role in keep_roles or idx >= last_user_idx: + result.append(msg) + elif role == "assistant": + msg = copy.copy(msg) + msg.pop("reasoning_content", None) + result.append(msg) + # developer and other roles before last_user_idx are dropped + + return result + + +# ============================================================ +# Parsing (Decoding model output) +# ============================================================ + + +def _read_until_stop( + index: int, text: str, stop: List[str] +) -> Tuple[int, str, Optional[str]]: + """ + Read text from index until one of the stop strings is found. + + Returns: + Tuple of (new_index, content_before_stop, matched_stop_string_or_None). + """ + min_pos = len(text) + matched_stop = None + + for s in stop: + pos = text.find(s, index) + if pos != -1 and pos < min_pos: + min_pos = pos + matched_stop = s + + if matched_stop: + content = text[index:min_pos] + return min_pos + len(matched_stop), content, matched_stop + else: + content = text[index:] + return len(text), content, None + + +def parse_tool_calls( + index: int, text: str +) -> Tuple[int, Optional[str], List[Dict[str, str]]]: + """ + Parse DSML tool calls from text starting at the given index. + + Args: + index: Starting position in text. + text: The full text to parse. + + Returns: + Tuple of (new_index, last_stop_token, list_of_tool_call_dicts). + Each tool call dict has "name" and "arguments" keys. + """ + tool_calls: List[Dict[str, Any]] = [] + stop_token = None + tool_calls_end_token = f"" + + while index < len(text): + index, _, stop_token = _read_until_stop( + index, text, [f"<{dsml_token}invoke", tool_calls_end_token] + ) + if _ != ">\n": + raise ValueError(f"Tool call format error: expected '>\\n' but got '{_}'") + + if stop_token == tool_calls_end_token: + break + + if stop_token is None: + raise ValueError("Missing special token in tool calls") + + index, tool_name_content, stop_token = _read_until_stop( + index, text, [f"<{dsml_token}parameter", f"\n$', tool_name_content, flags=re.DOTALL + ) + if len(p_tool_name) != 1: + raise ValueError(f"Tool name format error: '{tool_name_content}'") + tool_name = p_tool_name[0] + + tool_args: Dict[str, Tuple[str, str]] = {} + while stop_token == f"<{dsml_token}parameter": + index, param_content, stop_token = _read_until_stop( + index, text, [f"/{dsml_token}parameter"] + ) + + param_kv = re.findall( + r'^ name="(.*?)" string="(true|false)">(.*?)<$', + param_content, + flags=re.DOTALL, + ) + if len(param_kv) != 1: + raise ValueError(f"Parameter format error: '{param_content}'") + param_name, string, param_value = param_kv[0] + + if param_name in tool_args: + raise ValueError(f"Duplicate parameter name: '{param_name}'") + tool_args[param_name] = (param_value, string) + + index, content, stop_token = _read_until_stop( + index, text, [f"<{dsml_token}parameter", f"\n": + raise ValueError( + f"Parameter format error: expected '>\\n' but got '{content}'" + ) + + tool_call = decode_dsml_to_arguments(tool_name=tool_name, tool_args=tool_args) + tool_calls.append(tool_call) + + return index, stop_token, tool_calls + + +def parse_message_from_completion_text(text: str, thinking_mode: str) -> Dict[str, Any]: + """ + Parse a model completion text into a structured assistant message. + + This function takes the raw text output from the model (a single assistant turn) + and extracts: + - reasoning_content (thinking block) + - content (summary/response) + - tool_calls (if any) + + NOTE: This function is designed to parse only correctly formatted strings and + will raise ValueError for malformed output. + + Args: + text: The raw completion text (including EOS token). + thinking_mode: Either "chat" or "thinking". + + Returns: + Dict with keys: "role", "content", "reasoning_content", "tool_calls". + tool_calls are in OpenAI format. + """ + summary_content, reasoning_content, tool_calls = "", "", [] + index, stop_token = 0, None + tool_calls_start_token = f"\n\n<{dsml_token}{tool_calls_block_name}" + + is_thinking = thinking_mode == "thinking" + is_tool_calling = False + + if is_thinking: + index, content_delta, stop_token = _read_until_stop( + index, text, [thinking_end_token, tool_calls_start_token] + ) + reasoning_content = content_delta + assert stop_token == thinking_end_token, ( + "Invalid thinking format: missing " + ) + + index, content_delta, stop_token = _read_until_stop( + index, text, [eos_token, tool_calls_start_token] + ) + summary_content = content_delta + if stop_token == tool_calls_start_token: + is_tool_calling = True + else: + assert stop_token == eos_token, "Invalid format: missing EOS token" + + if is_tool_calling: + index, stop_token, tool_calls = parse_tool_calls(index, text) + + index, tool_ends_text, stop_token = _read_until_stop(index, text, [eos_token]) + assert not tool_ends_text, "Unexpected content after tool calls" + + assert len(text) == index and stop_token in [ + eos_token, + None, + ], "Unexpected content at end" + + for sp_token in [ + bos_token, + eos_token, + thinking_start_token, + thinking_end_token, + dsml_token, + ]: + assert sp_token not in summary_content and sp_token not in reasoning_content, ( + f"Unexpected special token '{sp_token}' in content" + ) + + return { + "role": "assistant", + "content": summary_content, + "reasoning_content": reasoning_content, + "tool_calls": tool_calls_to_openai_format(tool_calls), + } diff --git a/miles/utils/chat_template_utils/templates/encoding_dsv41.py b/miles/utils/chat_template_utils/templates/encoding_dsv41.py new file mode 100644 index 0000000000..0912681803 --- /dev/null +++ b/miles/utils/chat_template_utils/templates/encoding_dsv41.py @@ -0,0 +1,706 @@ +# Vendored from sglang-miles 29b34bc6f7 (SGLang v0.5.22) srt/entrypoints/openai/encoding_dsv41.py; SGLang v0.5.23 dropped it for deepseek-recipe (sgl-project/sglang#45315). +# Adapted from the DeepSeek-V4.1 release reference implementation. +"""Encode DeepSeek-V4.1 chat messages. + +Mid-conversation system messages trigger the assistant generation header. +""" + +import copy +import json +from typing import Any, Dict, List, Optional, Tuple, Union + +# ============================================================ +# Special Tokens +# ============================================================ + +bos_token: str = "<｜begin▁of▁sentence｜>" +eos_token: str = "<｜end▁of▁sentence｜>" +thinking_start_token: str = "" +thinking_end_token: str = "" +dsml_token: str = "｜DSML｜" + +USER_SP_TOKEN = "<｜User｜>" +ASSISTANT_SP_TOKEN = "<｜Assistant｜>" +SYSTEM_SP_TOKEN = "<｜System｜>" +LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>" + +IMAGE_PLACEHOLDER = "<｜deepseek_image｜>" + +# Task special tokens for internal classification tasks +DS_TASK_SP_TOKENS = { + "action": "<｜action｜>", + "query": "<｜query｜>", + "authority": "<｜authority｜>", + "domain": "<｜domain｜>", + "title": "<｜title｜>", + "read_url": "<｜read_url｜>", +} +VALID_TASKS = set(DS_TASK_SP_TOKENS.keys()) + +# ============================================================ +# Templates +# ============================================================ + +system_msg_template: str = "{content}" +user_msg_template: str = "{content}" +latest_reminder_msg_template: str = "{content}" +assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token +assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}" +thinking_template: str = "{reasoning_content}" + +response_format_template: str = "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}" + +tool_calls_block_name: str = " calls" +tool_call_tag_name: str = " invoke" +tool_parameter_tag_name: str = " parameter" + +tool_call_template: str = '<{dsml_token}{tool_call_tag_name} name="{name}">\n{arguments}\n' +tool_calls_template = ( + "<{dsml_token}{tc_block_name}>\n{tool_calls}\n" +) + +tool_output_template: str = "{content}" + +REASONING_EFFORT_TEMPLATE = ( + "Reasoning Effort: {budget} " + "(range 1-100, the higher the value, the more thorough the reasoning)\n\n" +) + +REASONING_EFFORT_MAPPINGS: Dict[str, int] = { + "low": 50, + "high": 75, + "xhigh": 75, + "max": 100, +} +DEFAULT_REASONING_EFFORT = "high" + +TOOLS_TEMPLATE = """## Tools + +You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<{dsml_token}{tc_block_name}>" block like the following: + +<{dsml_token}{tc_block_name}> +<{dsml_token}{tool_call_tag_name} name="$TOOL_NAME"> +<{dsml_token}{tool_parameter_tag_name} name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE +... + +<{dsml_token}{tool_call_tag_name} name="$TOOL_NAME2"> +... + + + +String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`. + +If thinking_mode is enabled (triggered by {thinking_start_token}), you MUST output your complete reasoning inside {thinking_start_token}...{thinking_end_token} BEFORE any tool calls or final response. + +Otherwise, output directly after {thinking_end_token} with tool calls or final response. + +### Available Tool Schemas + +{tool_schemas} + +You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls. +""" + +# ============================================================ +# Utility Functions +# ============================================================ + + +def to_json(value: Any) -> str: + try: + return json.dumps(value, ensure_ascii=False) + except: + return json.dumps(value, ensure_ascii=True) + + +def tools_from_openai_format(tools): + return [tool["function"] for tool in tools] + + +def tool_calls_from_openai_format(tool_calls): + return [ + { + "name": tool_call["function"]["name"], + "arguments": tool_call["function"]["arguments"], + } + for tool_call in tool_calls + ] + + +def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str: + p_dsml_template = ( + '<{dsml_token}{tool_parameter_tag_name} name="{key}" string="{is_str}">' + "{value}" + ) + P_dsml_strs = [] + + raw_arguments = tool_call["arguments"] + arguments = ( + json.loads(raw_arguments) if isinstance(raw_arguments, str) else raw_arguments + ) + if not isinstance(arguments, dict): + raise ValueError( + "Assistant tool call function.arguments must be a JSON object." + ) + + for k, v in arguments.items(): + P_dsml_strs.append( + p_dsml_template.format( + dsml_token=dsml_token, + tool_parameter_tag_name=tool_parameter_tag_name, + key=k, + is_str="true" if isinstance(v, str) else "false", + value=v if isinstance(v, str) else to_json(v), + ) + ) + + return "\n".join(P_dsml_strs) + + +def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str: + tools_json = [to_json(t) for t in tools] + + return TOOLS_TEMPLATE.format( + tool_schemas="\n".join(tools_json), + dsml_token=dsml_token, + tc_block_name=tool_calls_block_name, + tool_call_tag_name=tool_call_tag_name, + tool_parameter_tag_name=tool_parameter_tag_name, + thinking_start_token=thinking_start_token, + thinking_end_token=thinking_end_token, + ) + + +def render_reasoning_effort( + index: int, + thinking_mode: str, + effort: Union[str, int, None], +) -> str: + """Render the numeric reasoning effort prefix (thinking mode, index 0 only).""" + if effort is None: + effort = DEFAULT_REASONING_EFFORT + if not ( + (type(effort) is int and 1 <= effort <= 100) + or effort in REASONING_EFFORT_MAPPINGS + ): + raise ValueError( + f"Invalid reasoning effort for deepseek_v41: {effort!r}, should be " + f"int within [1,100] or {list(REASONING_EFFORT_MAPPINGS)}" + ) + if type(effort) is str: + effort = REASONING_EFFORT_MAPPINGS[effort] + if index == 0 and thinking_mode == "thinking": + return REASONING_EFFORT_TEMPLATE.format(budget=effort) + return "" + + +def find_last_user_index(messages: List[Dict[str, Any]]) -> int: + """Mid-conversation system messages also count as user messages here; + they trigger the assistant generation header. + """ + last_user_index = -1 + for idx in range(len(messages) - 1, -1, -1): + role = messages[idx].get("role") + if role in ["user", "developer"] or (role == "system" and idx > 0): + last_user_index = idx + break + return last_user_index + + +def attach_task_to_last_user_message(messages: List[Dict[str, Any]], task: str) -> None: + """Set `task` on the most recent user/developer message; raise if none exists.""" + idx = find_last_user_index(messages) + if idx == -1: + raise ValueError( + "`task` requires at least one message with role='user' or 'developer'." + ) + messages[idx]["task"] = task + + +# ============================================================ +# Message Rendering +# ============================================================ + + +def render_message( + index: int, + messages: List[Dict[str, Any]], + thinking_mode: str, + drop_thinking: bool = True, + reasoning_effort: Union[str, int, None] = None, +) -> str: + assert 0 <= index < len(messages) + assert thinking_mode in [ + "chat", + "thinking", + ], f"Invalid thinking_mode `{thinking_mode}`" + + msg = messages[index] + last_user_idx = find_last_user_index(messages) + + role = msg.get("role") + content = msg.get("content") + tools = msg.get("tools") + response_format = msg.get("response_format") + tool_calls = msg.get("tool_calls") + reasoning_content = msg.get("reasoning_content") + wo_eos = msg.get("wo_eos", False) + + if tools: + tools = tools_from_openai_format(tools) + if tool_calls: + tool_calls = tool_calls_from_openai_format(tool_calls) + + reasoning_effort_prompt = render_reasoning_effort( + index, thinking_mode, reasoning_effort + ) + # Index 0 also emits the system token for the effort prompt, even before a user. + prompt = ( + SYSTEM_SP_TOKEN + if index == 0 and (reasoning_effort_prompt or role == "system") + else "" + ) + prompt += reasoning_effort_prompt + + if role == "system": + if index > 0: + prompt += SYSTEM_SP_TOKEN + prompt += system_msg_template.format(content=content or "") + if tools: + prompt += "\n\n" + render_tools(tools) + if response_format: + prompt += "\n\n" + response_format_template.format( + schema=to_json(response_format) + ) + + elif role == "developer": + assert content, f"Invalid message for role `{role}`: {msg}" + + content_developer = USER_SP_TOKEN + content_developer += content + + if tools: + content_developer += "\n\n" + render_tools(tools) + if response_format: + content_developer += "\n\n" + response_format_template.format( + schema=to_json(response_format) + ) + + prompt += user_msg_template.format(content=content_developer) + + elif role == "user": + prompt += USER_SP_TOKEN + + # Handle content blocks (tool results mixed with text) + content_blocks = msg.get("content_blocks") + if content_blocks: + parts = [] + for block in content_blocks: + block_type = block.get("type") + if block_type == "text": + parts.append(block.get("text", "")) + elif block_type == "tool_result": + tool_content = block.get("content", "") + if isinstance(tool_content, list): + text_parts = [] + for b in tool_content: + if b.get("type") == "text": + text_parts.append(b.get("text", "")) + else: + text_parts.append(f"[Unsupported {b.get('type')}]") + tool_content = "\n\n".join(text_parts) + parts.append(tool_output_template.format(content=tool_content)) + else: + parts.append(f"[Unsupported {block_type}]") + prompt += "\n\n".join(parts) + else: + prompt += content or "" + + elif role == "latest_reminder": + prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format( + content=content + ) + + elif role == "tool": + raise NotImplementedError( + "deepseek_v41 merges tool messages into user; please preprocess with merge_tool_messages()" + ) + + elif role == "assistant": + thinking_part = "" + tc_content = "" + + if tool_calls: + tc_list = [ + tool_call_template.format( + dsml_token=dsml_token, + tool_call_tag_name=tool_call_tag_name, + name=tc.get("name"), + arguments=encode_arguments_to_dsml(tc), + ) + for tc in tool_calls + ] + tc_content += "\n\n" + tool_calls_template.format( + dsml_token=dsml_token, + tool_calls="\n".join(tc_list), + tc_block_name=tool_calls_block_name, + ) + + summary_content = content or "" + rc = reasoning_content or "" + + # Check if previous message has a task - if so, this is a task output (no thinking) + prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None + + if thinking_mode == "thinking" and not prev_has_task: + if not drop_thinking or index > last_user_idx: + thinking_part = ( + thinking_template.format(reasoning_content=rc) + thinking_end_token + ) + else: + thinking_part = "" + + if wo_eos: + prompt += assistant_msg_wo_eos_template.format( + reasoning=thinking_part, + content=summary_content, + tool_calls=tc_content, + ) + else: + prompt += assistant_msg_template.format( + reasoning=thinking_part, + content=summary_content, + tool_calls=tc_content, + ) + else: + raise NotImplementedError(f"Unknown role: {role}") + + # Append transition tokens based on what follows + if index + 1 < len(messages) and messages[index + 1].get("role") not in [ + "assistant", + "latest_reminder", + ]: + return prompt + + task = messages[index].get("task") + if task is not None: + # Task special token for internal classification tasks + assert task in VALID_TASKS, ( + f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}" + ) + task_sp_token = DS_TASK_SP_TOKENS[task] + + if task != "action": + # Non-action tasks: append task sp token directly after the message + prompt += task_sp_token + else: + # Action task: append Assistant + thinking token + action sp token + prompt += ASSISTANT_SP_TOKEN + prompt += ( + thinking_end_token + if thinking_mode != "thinking" + else thinking_start_token + ) + prompt += task_sp_token + + elif messages[index].get("role") in ["user", "developer"] or ( + messages[index].get("role") == "system" and index > 0 + ): + # Normal generation: append Assistant + thinking token + prompt += ASSISTANT_SP_TOKEN + if not drop_thinking and thinking_mode == "thinking": + prompt += thinking_start_token + elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx: + prompt += thinking_start_token + else: + prompt += thinking_end_token + + return prompt + + +# ============================================================ +# Preprocessing +# ============================================================ + + +def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Tool results are encoded within user messages; + DeepSeek-V4.1 has no standalone tool role. + """ + merged: List[Dict[str, Any]] = [] + + for msg in messages: + msg = copy.deepcopy(msg) + role = msg.get("role") + + if role == "tool": + # Convert tool message to a user message with tool_result block + tool_block = { + "type": "tool_result", + "tool_use_id": msg.get("tool_call_id", ""), + "content": msg.get("content", ""), + } + # Merge into previous message if it's already a user (merged tool) + if ( + merged + and merged[-1].get("role") == "user" + and "content_blocks" in merged[-1] + ): + merged[-1]["content_blocks"].append(tool_block) + else: + merged.append( + { + "role": "user", + "content_blocks": [tool_block], + } + ) + elif role == "user": + content_blocks = msg.get("content_blocks") + if content_blocks is None: + content_blocks = [{"type": "text", "text": msg.get("content", "")}] + if ( + merged + and merged[-1].get("role") == "user" + and "content_blocks" in merged[-1] + and merged[-1].get("task") is None + ): + merged[-1]["content_blocks"].extend(content_blocks) + else: + # Keeps structured content and every message-level field. + new_msg = msg + new_msg["content_blocks"] = content_blocks + merged.append(new_msg) + else: + merged.append(msg) + + return merged + + +def sort_tool_results_by_call_order( + messages: List[Dict[str, Any]], +) -> List[Dict[str, Any]]: + last_tool_call_order: Dict[str, int] = {} + + for msg in messages: + role = msg.get("role") + if role == "assistant" and msg.get("tool_calls"): + last_tool_call_order = {} + for idx, tc in enumerate(msg["tool_calls"]): + tc_id = tc.get("id") or tc.get("function", {}).get("id", "") + if tc_id: + last_tool_call_order[tc_id] = idx + + elif role == "user" and msg.get("content_blocks"): + tool_blocks = [ + b for b in msg["content_blocks"] if b.get("type") == "tool_result" + ] + if len(tool_blocks) > 1 and last_tool_call_order: + sorted_blocks = sorted( + tool_blocks, + key=lambda b: last_tool_call_order.get(b.get("tool_use_id", ""), 0), + ) + sorted_idx = 0 + new_blocks = [] + for block in msg["content_blocks"]: + if block.get("type") == "tool_result": + new_blocks.append(sorted_blocks[sorted_idx]) + sorted_idx += 1 + else: + new_blocks.append(block) + msg["content_blocks"] = new_blocks + + return messages + + +# ============================================================ +# Vision Message Preprocessing +# ============================================================ + + +def _is_image_block(block: Dict[str, Any]) -> bool: + return isinstance(block, dict) and block.get("type") == "image_url" + + +def _extract_image(block: Dict[str, Any]) -> Dict[str, Any]: + image_url = block.get("image_url") + url = image_url if isinstance(image_url, str) else (image_url or {}).get("url", "") + if not url: + raise ValueError("Image block does not contain a valid source") + return {"url": url} + + +def _process_image_blocks( + blocks: List[Any], image_placeholder: str = IMAGE_PLACEHOLDER +) -> Tuple[List[Any], List[Dict[str, Any]]]: + new_blocks: List[Any] = [] + images: List[Dict[str, Any]] = [] + for block in blocks: + if not isinstance(block, dict): + new_blocks.append(block) + continue + if _is_image_block(block): + new_blocks.append({"type": "text", "text": image_placeholder}) + images.append(_extract_image(block)) + elif block.get("type") == "tool_result" and isinstance( + block.get("content"), list + ): + block = copy.copy(block) + block["content"], nested_images = _process_image_blocks( + block["content"], image_placeholder + ) + new_blocks.append(block) + images.extend(nested_images) + elif block.get("type") == "text": + text = block.get("text") or "" + if IMAGE_PLACEHOLDER in text: + raise ValueError( + f"Text block contains image placeholder '{IMAGE_PLACEHOLDER}': " + f"'{text[:100]}'. Images should be separate content blocks." + ) + new_blocks.append(block) + else: + new_blocks.append(block) + return new_blocks, images + + +def _validate_no_image_sp_tokens(msg: Dict[str, Any]) -> None: + content = msg.get("content") + if isinstance(content, str) and IMAGE_PLACEHOLDER in content: + raise ValueError( + f"Message content contains image special token '{IMAGE_PLACEHOLDER}'. " + "Images should be provided as image content blocks." + ) + reasoning_content = msg.get("reasoning_content") + if isinstance(reasoning_content, str) and IMAGE_PLACEHOLDER in reasoning_content: + raise ValueError( + f"reasoning_content contains image special token '{IMAGE_PLACEHOLDER}'" + ) + + +def process_image_messages( + messages: List[Dict[str, Any]], +) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]: + processed: List[Dict[str, Any]] = [] + images: List[Dict[str, Any]] = [] + for msg in messages: + msg = copy.deepcopy(msg) + _validate_no_image_sp_tokens(msg) + + if isinstance(msg.get("content"), list) and "content_blocks" not in msg: + msg["content_blocks"] = msg.pop("content") + + if msg.get("content_blocks"): + msg["content_blocks"], message_images = _process_image_blocks( + msg["content_blocks"] + ) + images.extend(message_images) + if not isinstance(msg.get("content"), str): + texts = [ + block.get("text", "") + for block in msg["content_blocks"] + if isinstance(block, dict) and block.get("type") == "text" + ] + msg["content"] = "\n\n".join(texts) + + processed.append(msg) + return processed, images + + +# ============================================================ +# Main Encoding Function +# ============================================================ + + +def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + last_user_idx = find_last_user_index(messages) + result = [] + keep_roles = {"user", "system", "tool", "latest_reminder", "direct_search_results"} + + for idx, msg in enumerate(messages): + role = msg.get("role") + if role in keep_roles or idx >= last_user_idx: + result.append(msg) + elif role == "assistant": + msg = copy.copy(msg) + msg.pop("reasoning_content", None) + result.append(msg) + # developer and other roles before last_user_idx are dropped + + return result + + +def _encode_messages_text( + messages: List[Dict[str, Any]], + thinking_mode: str, + context: Optional[List[Dict[str, Any]]] = None, + drop_thinking: bool = True, + add_default_bos_token: bool = True, + reasoning_effort: Union[str, int, None] = None, +) -> str: + """Encode preprocessed (text-only) messages into the V4.1 prompt format.""" + context = context if context else [] + + # Preprocess: merge tool messages and sort tool results + messages = merge_tool_messages(messages) + messages = sort_tool_results_by_call_order(context + messages)[len(context) :] + if context: + context = merge_tool_messages(context) + context = sort_tool_results_by_call_order(context) + + full_messages = context + messages + + prompt = bos_token if add_default_bos_token and len(context) == 0 else "" + + # Resolve drop_thinking: if any message has tools defined, don't drop thinking + effective_drop_thinking = drop_thinking + if any(m.get("tools") for m in full_messages): + effective_drop_thinking = False + + if thinking_mode == "thinking" and effective_drop_thinking: + full_messages = _drop_thinking_messages(full_messages) + num_to_render = len(full_messages) - len(_drop_thinking_messages(context)) + context_len = len(full_messages) - num_to_render + else: + num_to_render = len(messages) + context_len = len(context) + + for idx in range(num_to_render): + prompt += render_message( + idx + context_len, + full_messages, + thinking_mode=thinking_mode, + drop_thinking=effective_drop_thinking, + reasoning_effort=reasoning_effort, + ) + + return prompt + + +def encode_messages( + messages: List[Dict[str, Any]], + thinking_mode: str, + context: Optional[List[Dict[str, Any]]] = None, + drop_thinking: bool = True, + add_default_bos_token: bool = True, + reasoning_effort: Union[str, int, None] = None, + return_multi_modal_data: bool = False, +) -> Any: + """Encode a list of messages into the DeepSeek-V4.1 prompt format. + + Returns the prompt string, or ``(prompt, {"images": [...]})`` when + ``return_multi_modal_data`` is set; the image records are in prompt order. + """ + context = context or [] + processed_context, _ = process_image_messages(context) if context else ([], []) + processed_messages, images = process_image_messages(messages) + prompt = _encode_messages_text( + processed_messages, + thinking_mode=thinking_mode, + context=processed_context if processed_context else None, + drop_thinking=drop_thinking, + add_default_bos_token=add_default_bos_token, + reasoning_effort=reasoning_effort, + ) + if return_multi_modal_data: + return prompt, {"images": images} + return prompt diff --git a/miles/utils/chat_template_utils/tito_tokenizer.py b/miles/utils/chat_template_utils/tito_tokenizer.py index 044320461a..0b5104267c 100644 --- a/miles/utils/chat_template_utils/tito_tokenizer.py +++ b/miles/utils/chat_template_utils/tito_tokenizer.py @@ -867,12 +867,12 @@ def resolve_thinking( class DeepSeekV4TITOTokenizer(TITOTokenizer): - """DeepSeek V4 — official encoder via sglang's ``encoding_dsv4``. + """DeepSeek V4 — official encoder via miles' vendored ``encoding_dsv4``. Like V3.2, V4 ships no jinja chat_template; miles' ``apply_chat_template`` routes any V4 tokenizer to the ``chat_template_utils.deepseek`` bridge, and TITO incremental tokenization rides that same bridge to stay byte-aligned - with what the runtime serves. + with what the session server sends as TITO input ids. """ reasoning_parser = "deepseek-v4" @@ -970,7 +970,7 @@ def tokenize_additional_messages( class DeepSeekV41TITOTokenizer(DeepSeekV4TITOTokenizer): - """DeepSeek V4.1 — official encoder via sglang's ``encoding_dsv41``; the + """DeepSeek V4.1 — official encoder via miles' vendored ``encoding_dsv41``; the V4 incremental-render machinery applies unchanged.""" reasoning_parser = "deepseek-v41" diff --git a/miles_plugins/models/deepseek_v4_1/ops/kvnorm.py b/miles_plugins/models/deepseek_v4_1/ops/kvnorm.py index 56455e7fd5..46dd021795 100644 --- a/miles_plugins/models/deepseek_v4_1/ops/kvnorm.py +++ b/miles_plugins/models/deepseek_v4_1/ops/kvnorm.py @@ -19,8 +19,8 @@ def kv_norm_rope( @torch.no_grad() def kv_cache_roundtrip(kv: torch.Tensor, weight: torch.Tensor, eps: float, freqs_cis: torch.Tensor) -> torch.Tensor: - from sglang.kernels.ops.attention.dsv4.dequant_k_cache import dequantize_k_cache_paged - from sglang.kernels.ops.attention.dsv4.elementwise import fused_k_norm_rope_flashmla + from sglang.kernels.ops.attention.dsv4.dequant_k_cache import dequantize_k_cache_paged_dsv4 + from sglang.kernels.ops.attention.dsv4.norm_rope import fused_k_norm_rope_flashmla bsz, seqlen, dim = kv.shape flat = kv.reshape(bsz * seqlen, dim).contiguous() @@ -39,7 +39,7 @@ def kv_cache_roundtrip(kv: torch.Tensor, weight: torch.Tensor, eps: float, freqs kvcache=cache, page_size=_PAGE_SIZE, ) - return dequantize_k_cache_paged(cache, loc, _PAGE_SIZE).reshape(bsz, seqlen, dim).to(kv.dtype) + return dequantize_k_cache_paged_dsv4(cache, loc, _PAGE_SIZE).reshape(bsz, seqlen, dim).to(kv.dtype) def kv_norm_rope_fp8( @@ -54,7 +54,7 @@ def kv_norm_rope_fp8( @torch.no_grad() def compressed_kv_cache_roundtrip(latent: torch.Tensor) -> torch.Tensor: from sglang.kernels.ops.attention.dsv4.attn import fused_store_cache - from sglang.kernels.ops.attention.dsv4.dequant_k_cache import dequantize_k_cache_paged + from sglang.kernels.ops.attention.dsv4.dequant_k_cache import dequantize_k_cache_paged_dsv4 bsz, seqlen, dim = latent.shape flat = latent.reshape(bsz * seqlen, dim).contiguous() @@ -63,7 +63,7 @@ def compressed_kv_cache_roundtrip(latent: torch.Tensor) -> torch.Tensor: page_bytes = -(-(_BYTES_PER_TOKEN * _PAGE_SIZE) // _ROW_BYTES) * _ROW_BYTES cache = torch.zeros(pages, page_bytes, dtype=torch.uint8, device=latent.device) fused_store_cache(input=flat, cache=cache, indices=loc, page_size=_PAGE_SIZE, type="flashmla") - return dequantize_k_cache_paged(cache, loc, _PAGE_SIZE).reshape(bsz, seqlen, dim).to(latent.dtype) + return dequantize_k_cache_paged_dsv4(cache, loc, _PAGE_SIZE).reshape(bsz, seqlen, dim).to(latent.dtype) def compressed_kv_stored(latent: torch.Tensor, rope_dim: int) -> torch.Tensor: diff --git a/requirements.txt b/requirements.txt index e5fa4b51bf..43b484d8a3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -30,7 +30,7 @@ sglang-router>=0.2.3 tensorboard torchft-nightly==2026.4.3; platform_system == "Linux" and platform_machine == "x86_64" tqdm -transformers==5.19.0 # Required by HF-native weight conversion; matches SGLang v0.5.22's pin. +transformers==5.19.0 # Required by HF-native weight conversion; matches SGLang v0.5.23's pin. typing_extensions>=4.5 wandb xxhash==3.7.1 # disk-delta weight sync (checksum) diff --git a/tests/fast/backends/megatron_utils/megatron_to_hf/processors/test_quantizer_fp8_config.py b/tests/fast/backends/megatron_utils/megatron_to_hf/processors/test_quantizer_fp8_config.py index 2df63690f9..6753b6cbfe 100644 --- a/tests/fast/backends/megatron_utils/megatron_to_hf/processors/test_quantizer_fp8_config.py +++ b/tests/fast/backends/megatron_utils/megatron_to_hf/processors/test_quantizer_fp8_config.py @@ -28,6 +28,23 @@ def test_expert_scale_format_uses_resolved_runner_and_communication_backend( assert result == expected + def test_grouped_moe_scales_follow_sglang_when_only_grouped_moe_requantizes( + self, monkeypatch: pytest.MonkeyPatch + ) -> None: + """On SM120 SGLang requantizes grouped MoE scales to UE8M0 but keeps dense fp32; + expert weights must be sent in UE8M0 there too, or the update writes the wrong scale format.""" + monkeypatch.setattr( + quantizer_fp8, + "should_deepgemm_weight_requant_ue8m0", + lambda *, weight_block_size, grouped_moe=False: grouped_moe, + ) + args = SimpleNamespace(sglang=make_sglang_config(moe_runner_backend="deep_gemm", moe_a2a_backend="none")) + + expert = quantizer_fp8._get_scale_format(args, "model.layers.0.mlp.experts.0.weight", [128, 128]) + dense = quantizer_fp8._get_scale_format(args, "model.layers.0.self_attn.o_proj.weight", [128, 128]) + + assert (expert, dense) == ("ue8m0", None) + def test_server_group_override_controls_scale_format_instead_of_the_base_value( self, monkeypatch: pytest.MonkeyPatch ) -> None: diff --git a/tests/fast/backends/sglang_utils/test_compute_server_args.py b/tests/fast/backends/sglang_utils/test_compute_server_args.py index b54f6eed8e..01730e54d3 100644 --- a/tests/fast/backends/sglang_utils/test_compute_server_args.py +++ b/tests/fast/backends/sglang_utils/test_compute_server_args.py @@ -157,6 +157,30 @@ def test_no_overrides_keeps_args_derived_values(self): assert server_args["mem_fraction_static"] == 0.7 +class TestDecoderSwaBoundedReplay: + """SGLang resolves an unset value to on for DeepSeek-V4.1, which breaks rollout/trainer logprob parity.""" + + def test_an_unset_value_keeps_full_prefill(self): + args = make_args() + args.sglang.base_args["enable_decoder_swa_bounded_replay"] = None + + assert compute(args)["enable_decoder_swa_bounded_replay"] is False + + def test_an_explicit_user_choice_is_kept(self): + args = make_args() + args.sglang.base_args["enable_decoder_swa_bounded_replay"] = True + + assert compute(args)["enable_decoder_swa_bounded_replay"] is True + + def test_a_group_override_still_wins(self): + args = make_args() + args.sglang.base_args["enable_decoder_swa_bounded_replay"] = None + + server_args = compute(args, sglang_overrides={"enable_decoder_swa_bounded_replay": True}) + + assert server_args["enable_decoder_swa_bounded_replay"] is True + + class TestOutputStore: def test_every_engine_writes_to_the_run_store_with_its_own_host(self, monkeypatch): """The engine's address goes into its store config, without the IPv6 brackets.""" diff --git a/tests/fast/backends/sglang_utils/test_server_args_utils.py b/tests/fast/backends/sglang_utils/test_server_args_utils.py index 1693b05dfc..e0dffa187c 100644 --- a/tests/fast/backends/sglang_utils/test_server_args_utils.py +++ b/tests/fast/backends/sglang_utils/test_server_args_utils.py @@ -51,6 +51,10 @@ def _assert_sweep_restores_env(): "enable_cp_tp_group_sharing": ( "Resolved from the model architecture and declared Arg(no_cli=True); sglang registers no CLI option for it." ), + "aux_hidden_state_capture": ( + "Resolved from --enable-return-aux-hidden-states and --speculative-algorithm and declared Arg(no_cli=True); " + "sglang registers no CLI option for it." + ), "grpc_worker_threads": ( "Env-only (SGLANG_GRPC_WORKER_THREADS) and declared Arg(no_cli=True); sglang registers no CLI option for it." ), diff --git a/tests/fast/router/test_session_anthropic.py b/tests/fast/router/test_session_anthropic.py index daaf8775b0..11c7a29c6b 100644 --- a/tests/fast/router/test_session_anthropic.py +++ b/tests/fast/router/test_session_anthropic.py @@ -407,7 +407,8 @@ def test_parity_with_equivalent_openai_request_with_tools(self, anthropic_env): """Tool-bearing parity pins the canonical tools spelling: the chat template renders tools in dict key order, so byte-identical TITO input_ids require the OpenAI client to write the codec's field order - (function.description before function.name, no extra defaults).""" + (sglang keeps the client's key order; the Anthropic codec writes name, + description, parameters, strict).""" anthropic_session = _create_session(anthropic_env.url) openai_session = _create_session(anthropic_env.url) @@ -426,9 +427,10 @@ def test_parity_with_equivalent_openai_request_with_tools(self, anthropic_env): { "type": "function", "function": { - "description": "weather", "name": "get_weather", + "description": "weather", "parameters": {"type": "object", "properties": {}}, + "strict": False, }, } ], diff --git a/tests/fast/utils/chat_template_utils/test_deepseek_v4.py b/tests/fast/utils/chat_template_utils/test_deepseek_v4.py index 40b59a423e..b271dff453 100644 --- a/tests/fast/utils/chat_template_utils/test_deepseek_v4.py +++ b/tests/fast/utils/chat_template_utils/test_deepseek_v4.py @@ -1,7 +1,7 @@ """Tests for the DeepSeek V4 chat-template bridge and its dispatch through ``apply_chat_template``. -Mirrors ``test_deepseek_v32.py``: the bridge renders via sglang's +Mirrors ``test_deepseek_v32.py``: the bridge renders via the vendored ``encoding_dsv4.encode_messages`` (a pure string operation, no tokenizer needed), so most cases use plain message lists. Detection and the ``tokenize=True`` dispatch use a tiny tokenizer stub backed by a temporary @@ -22,6 +22,7 @@ from pydantic import ValidationError from miles.utils.chat_template_utils import apply_chat_template, deepseek +from miles.utils.chat_template_utils.templates import encoding_dsv4 _MSGS_BASIC = [{"role": "user", "content": "Hello"}] @@ -59,8 +60,6 @@ def _tok_with_model_type(tmp_path, model_type: str, **extra) -> _FakeTokenizer: def _reference_encode(messages, *, thinking: bool = False, drop_thinking: bool = True, **extra) -> str: """The canonical V4 rendering: a direct ``encode_messages`` call. Locks ``render_messages`` to this thin-bridge contract (no preprocessing of its own).""" - from sglang.srt.entrypoints.openai import encoding_dsv4 - return encoding_dsv4.encode_messages( messages, thinking_mode="thinking" if thinking else "chat", drop_thinking=drop_thinking, **extra ) @@ -231,11 +230,10 @@ def test_render_with_tools_injects_tool_schemas(): def test_render_with_tools_matches_manual_system_injection(): # Passing tools= must equal pre-canonicalizing them into the system message and - # passing tools=None -- i.e. the injection is exactly the Tool-pydantic model_dump - # that sglang's serving path applies. + # passing tools=None -- i.e. the injection is exactly the V4 tool payload. from sglang.srt.entrypoints.openai.protocol import Tool - canonical = [Tool.model_validate(t).model_dump() for t in _TOOLS] + canonical = [deepseek.dsv4_tool_payload(Tool.model_validate(t)) for t in _TOOLS] msgs = [{"role": "user", "content": "weather?"}] expected = deepseek.V4.render_messages( [{"role": "system", "content": "", "tools": canonical}, *msgs], thinking_mode="chat" @@ -243,6 +241,22 @@ def test_render_with_tools_matches_manual_system_injection(): assert deepseek.V4.render_messages(msgs, tools=_TOOLS, thinking_mode="chat") == expected +def test_tool_payload_keeps_the_v0522_shape_whatever_the_client_order(): + """V4 renders each tool dict verbatim. sglang v0.5.23's Tool.model_dump keeps the client's key + order and drops an unset strict, which would change V4 prompts; the payload keeps v0.5.22's.""" + from sglang.srt.entrypoints.openai.protocol import Tool + + client_order = { + "type": "function", + "function": {"name": "f", "parameters": {"type": "object"}, "description": "d"}, + } + + payload = deepseek.dsv4_tool_payload(Tool.model_validate(client_order)) + + assert payload["function"] == {"description": "d", "name": "f", "parameters": {"type": "object"}, "strict": False} + assert list(payload["function"]) == ["description", "name", "parameters", "strict"] + + def test_render_with_tools_reuses_existing_system_message(): msgs = [{"role": "system", "content": "You are helpful."}, {"role": "user", "content": "hi"}] out = deepseek.V4.render_messages(msgs, tools=_TOOLS, thinking_mode="chat") diff --git a/tests/fast/utils/chat_template_utils/test_deepseek_v4_1.py b/tests/fast/utils/chat_template_utils/test_deepseek_v4_1.py index 70b45fd41d..5e73000fc8 100644 --- a/tests/fast/utils/chat_template_utils/test_deepseek_v4_1.py +++ b/tests/fast/utils/chat_template_utils/test_deepseek_v4_1.py @@ -1,6 +1,6 @@ """The DeepSeek V4.1 bridge must render exactly what sglang's ``serving_chat`` -dsv41 branch renders: the ``encoding_dsv41`` encoder, the server's default -``reasoning_effort``, client-only tool payloads, and no empty system message +dsv41 branch rendered before v0.5.23: the ``encoding_dsv41`` encoder, the +server's default ``reasoning_effort``, client-only tool payloads, and no empty system message unless tools need a host. """ @@ -13,10 +13,10 @@ import json import pytest -from sglang.srt.entrypoints.openai import chat_encoding, encoding_dsv4, encoding_dsv41 from sglang.srt.entrypoints.openai.protocol import Tool from miles.utils.chat_template_utils import apply_chat_template, deepseek +from miles.utils.chat_template_utils.templates import encoding_dsv4, encoding_dsv41 from miles.utils.chat_template_utils.tito_tokenizer import ( DeepSeekV4TITOTokenizer, DeepSeekV41TITOTokenizer, @@ -74,7 +74,7 @@ def _tok_with_model_type(tmp_path, model_type: str) -> _FakeTokenizer: def _server_default_effort(monkeypatch=None): - return chat_encoding.default_dsv41_reasoning_effort_from_env("high") + return deepseek.default_dsv41_reasoning_effort_from_env("high") def test_detect_by_config(tmp_path): @@ -127,7 +127,7 @@ def test_reasoning_effort_env_default_and_request_override(monkeypatch): def test_tools_use_client_only_payload(monkeypatch): monkeypatch.delenv("SGLANG_DSV41_REASONING_EFFORT", raising=False) - payload = chat_encoding.dsv41_tool_payload(Tool.model_validate(_TOOLS[0])) + payload = deepseek.dsv41_tool_payload(Tool.model_validate(_TOOLS[0])) assert payload != Tool.model_validate(_TOOLS[0]).model_dump() expected = encoding_dsv41.encode_messages( [{"role": "system", "content": "", "tools": [payload]}, *_MSGS["no_system"]], diff --git a/tests/fast/utils/chat_template_utils/test_template.py b/tests/fast/utils/chat_template_utils/test_template.py index 1040bdc3d2..a843a400b3 100644 --- a/tests/fast/utils/chat_template_utils/test_template.py +++ b/tests/fast/utils/chat_template_utils/test_template.py @@ -18,15 +18,14 @@ from __future__ import annotations import copy -import json from collections import OrderedDict from pathlib import Path from unittest.mock import MagicMock import pytest -from sglang.srt.entrypoints.openai.chat_encoding import resolve_dsv4_reasoning_effort_profile from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest from sglang.srt.entrypoints.openai.serving_chat import OpenAIServingChat +from sglang.srt.parser.reasoning_parser import ReasoningParser from transformers import AutoTokenizer from miles.utils.chat_template_utils import ( @@ -63,13 +62,22 @@ def _make_serving(tokenizer) -> OpenAIServingChat: serving.template_manager = MagicMock() serving.template_manager.chat_template_name = None serving.template_manager.jinja_template_content_format = "string" + # sglang v0.5.23 renders through a Python chat template when one is loaded; a + # MagicMock attribute would select that path, while HF Jinja templates have None. + serving.template_manager.python_chat_template = None serving.use_dpsk_v32_encoding = False serving.chat_encoding_spec = None serving.is_gpt_oss = False serving.is_gemma4 = False + serving.is_minimax_m3 = False + # sglang v0.5.23 routes only adapter-backed specs (dsv4, dsv41, inkling) + # through ``_encoding_adapter``; the Jinja and dsv32 paths tested here have none. + serving._encoding_adapter = None serving.tool_call_parser = None serving.reasoning_parser = None - serving._dsv4_reasoning_effort_profile = None + # sglang v0.5.23 reads the reasoning detector __init__ builds from reasoning_parser + # while rendering; None mirrors reasoning_parser=None. + serving._reasoning_detector = None # sglang v0.5.16 added server-level default chat-template kwargs, merged into # the request's chat_template_kwargs at the top of _process_messages. # __init__ always sets it (to `... or {}`); mirror the empty default so the @@ -81,6 +89,7 @@ def _make_serving(tokenizer) -> OpenAIServingChat: # takes the real production path instead of hitting AttributeError. serving._tokenizer_auto_adds_specials = len(tokenizer.encode("")) > 0 serving._prompt_text_round_trip_is_lossy = serving._probe_prompt_text_round_trip() + serving._template_reads_reasoning_content = serving._chat_template_reads_reasoning_content() serving._chat_template_cache = OrderedDict() return serving @@ -128,44 +137,23 @@ def sglang_dsv32_prompt_ids( serving = _make_serving(tokenizer) serving.chat_encoding_spec = "dsv32" serving.reasoning_parser = "deepseek-v3" + serving._reasoning_detector = ReasoningParser( + model_type=serving.reasoning_parser, stream_reasoning=True, tokenizer=tokenizer + ).detector serving.tool_call_parser = "deepseekv32" result = serving._process_messages(request, is_multimodal=False) return result.prompt_ids -def sglang_dsv4_prompt_ids( - tokenizer, - messages: list[dict], - tools: list[dict] | None = None, - **kwargs, -) -> list[int]: - """Get DeepSeek-V4 prompt_ids through SGLang's DSv4 encoder path.""" - request_data: dict = {"messages": copy.deepcopy(messages), "model": "test"} - if tools: - request_data["tools"] = copy.deepcopy(tools) - if kwargs: - request_data["chat_template_kwargs"] = kwargs - request = ChatCompletionRequest(**request_data) - - serving = _make_serving(tokenizer) - serving.chat_encoding_spec = "dsv4" - serving.reasoning_parser = "deepseek-v4" - serving.tool_call_parser = "deepseekv4" - serving._dsv4_reasoning_effort_profile = resolve_dsv4_reasoning_effort_profile(model_path=_DEEPSEEK_V4_MODEL) - result = serving._process_messages(request, is_multimodal=False) - return result.prompt_ids - - # --------------------------------------------------------------------------- # Tokenizer cache & fixed-template loader # --------------------------------------------------------------------------- _TOK_CACHE: dict[str, AutoTokenizer] = {} -# DeepSeek V4/V3.2 HF repos are huge and not pulled in CI; tests use the +# DeepSeek V3.2 HF repos are huge and not pulled in CI; tests use the # cluster-mounted copy when present and skip otherwise (Stage 2 only needs the # tokenizer). -_DEEPSEEK_V4_MODEL = "/cluster-storage/models/deepseek-ai/DeepSeek-V4-Flash" _DEEPSEEK_V32_MODEL = "/cluster-storage/models/deepseek-ai/DeepSeek-V3.2" @@ -181,12 +169,6 @@ def _get_deepseek_v32_tokenizer() -> AutoTokenizer: return _get_tokenizer(_DEEPSEEK_V32_MODEL) -def _get_deepseek_v4_tokenizer() -> AutoTokenizer: - if not Path(_DEEPSEEK_V4_MODEL).exists(): - pytest.skip(f"DeepSeek V4 tokenizer not found: {_DEEPSEEK_V4_MODEL}") - return _get_tokenizer(_DEEPSEEK_V4_MODEL) - - def _load_fixed_or_none(tito_model: TITOTokenizerType | None) -> str | None: """Return the bundled fixed chat-template content for *tito_model*, or ``None``.""" if tito_model is None: @@ -505,91 +487,6 @@ def test_prompt_ids_match_sglang_dsv32_with_tool_history(self, thinking): assert actual == expected -# --------------------------------------------------------------------------- -# DeepSeek V4 TITO alignment -# --------------------------------------------------------------------------- - - -class TestDeepSeekV4TITOAlignWithSGLang: - """DeepSeek V4 TITO prompt tokenization must match SGLang's DSv4 encoder.""" - - @pytest.mark.parametrize("thinking", [False, True], ids=["chat", "thinking"]) - def test_prompt_ids_match_sglang_dsv4_with_tools(self, thinking): - tokenizer = _get_deepseek_v4_tokenizer() - messages = [{"role": "user", "content": "What is 1+1?"}] - tools = [ - { - "type": "function", - "function": { - "name": "get_year", - "description": "Get current year", - "parameters": {"type": "object", "properties": {}, "required": []}, - }, - } - ] - - expected = sglang_dsv4_prompt_ids(tokenizer, messages, tools, thinking=thinking) - tito = get_tito_tokenizer( - tokenizer, - tokenizer_type=TITOTokenizerType.DEEPSEEKV4, - chat_template_kwargs={"enable_thinking": thinking}, - ) - - actual = tito.apply_chat_template( - messages, add_generation_prompt=True, tokenize=True, template_args=tito.default_template_args(tools) - ) - assert actual == expected - - @pytest.mark.parametrize("thinking", [False, True], ids=["chat", "thinking"]) - def test_prompt_ids_match_sglang_dsv4_with_tool_history(self, thinking): - tokenizer = _get_deepseek_v4_tokenizer() - tools = [ - { - "type": "function", - "function": { - "name": "get_weather", - "description": "Get weather", - "parameters": { - "type": "object", - "properties": {"city": {"type": "string"}}, - "required": ["city"], - }, - }, - } - ] - messages = [ - {"role": "system", "content": "You are helpful."}, - {"role": "user", "content": "What's the weather in Beijing?"}, - { - "role": "assistant", - "content": None, - "tool_calls": [ - { - "id": "call_1", - "type": "function", - "function": { - "name": "get_weather", - "arguments": json.dumps({"city": "Beijing"}, ensure_ascii=False), - }, - } - ], - }, - {"role": "tool", "content": '{"temperature": 25}', "tool_call_id": "call_1"}, - ] - - expected = sglang_dsv4_prompt_ids(tokenizer, messages, tools, thinking=thinking) - tito = get_tito_tokenizer( - tokenizer, - tokenizer_type=TITOTokenizerType.DEEPSEEKV4, - chat_template_kwargs={"enable_thinking": thinking}, - ) - - actual = tito.apply_chat_template( - messages, add_generation_prompt=True, tokenize=True, template_args=tito.default_template_args(tools) - ) - assert actual == expected - - class TestMessageMatches: """strict_message_matches compares template-relevant content, not wire idiosyncrasies.""" diff --git a/tests/snapshots/charts/miles-run/typical-values.yaml b/tests/snapshots/charts/miles-run/typical-values.yaml index 25d248bd7a..5f22cf426a 100644 --- a/tests/snapshots/charts/miles-run/typical-values.yaml +++ b/tests/snapshots/charts/miles-run/typical-values.yaml @@ -66,6 +66,7 @@ run: - round_robin - --disaggregation-bootstrap-port - '11000' + - --no-enable-decoder-swa-bounded-replay - --engine-info-bootstrap-port - '12000' - --mem-fraction-static @@ -151,6 +152,7 @@ run: - --enable-metrics - --disaggregation-mode - decode + - --no-enable-decoder-swa-bounded-replay - --engine-info-bootstrap-port - '12000' - --mem-fraction-static @@ -224,7 +226,7 @@ run: - -m - miles.utils.workers.serving.serve - --config - - 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"{\"worker_type\":\"rollout\",\"args\":{\"sglang_server_concurrency\":512,\"sglang\":{\"models\":[{\"name\":\"default\",\"model_path\":null,\"server_groups\":[{\"worker_type\":\"prefill\",\"num_gpus_per_engine\":16,\"overrides\":{\"model_path\":\"/typical-model\"},\"needs_offload\":true},{\"worker_type\":\"decode\",\"num_gpus_per_engine\":8,\"overrides\":{\"model_path\":\"/typical-model\"},\"needs_offload\":true}],\"update_weights\":true}],\"base_args\":{\"tokenizer_path\":null,\"tokenizer_mode\":\"auto\",\"tokenizer_backend\":\"huggingface\",\"tokenizer_worker_num\":1,\"detokenizer_worker_num\":1,\"skip_tokenizer_init\":false,\"load_format\":\"auto\",\"model_loader_extra_config\":\"{}\",\"context_length\":null,\"is_embedding\":false,\"enable_multimodal\":null,\"revision\":null,\"model_impl\":\"auto\",\"model_config_parser\":\"auto\",\"json_model_override_args\":\"{}\",\"dtype\":\"auto\",\"quantization\":null,\"quantization_param_path\":null,\"kv_cache_dtype\":\"auto\",\"enable_fp32_lm_head\":false,\"modelopt_quant\":null,\"modelopt_checkpoint_restore_path\":null,\"modelopt_checkpoint_save_path\":null,\"modelopt_export_path\":null,\"quantize_and_serve\":false,\"rl_quant_profile\":null,\"enable_tf32_matmul\":false,\"mem_fraction_static\":0.7,\"max_running_requests\":null,\"max_queued_requests\":null,\"max_total_tokens\":null,\"chunked_prefill_size\":4096,\"prefill_decode_interval\":null,\"enable_dynamic_chunking\":false,\"max_prefill_tokens\":16384,\"prefill_max_requests\":null,\"schedule_policy\":\"fcfs\",\"enable_priority_scheduling\":false,\"disable_priority_preemption\":false,\"default_priority_value\":null,\"abort_on_priority_when_disabled\":false,\"schedule_low_priority_values_first\":false,\"priority_scheduling_preemption_threshold\":10,\"retraction_policy\":\"length\",\"schedule_conservativeness\":1.0,\"page_size\":null,\"c128_page_size\":16,\"swa_full_tokens_ratio\":null,\"disable_hybrid_swa_memory\":false,\"radix_eviction_policy\":\"lru\",\"radix_eviction_policy_config\":null,\"prefill_only_disable_kv_cache\":false,\"disable_radix_cache\":false,\"enable_page_major_kv_layout\":false,\"enable_unified_memory\":false,\"disable_chunked_prefix_cache\":false,\"disable_overlap_schedule\":false,\"num_continuous_decode_steps\":1,\"scheduler_recv_interval\":1,\"enable_mixed_chunk\":false,\"dist_timeout\":null,\"tp_size\":1,\"dcp_size\":1,\"pp_size\":1,\"pp_max_micro_batch_size\":null,\"pp_async_batch_depth\":0,\"dp_size\":1,\"attn_dp_size\":1,\"load_balance_method\":\"auto\",\"attn_cp_size\":1,\"moe_dp_size\":1,\"dwdp_size\":1,\"dcp_comm_backend\":null,\"dcp_replicate_q_proj\":null,\"enable_prefill_cp\":false,\"cp_strategy\":null,\"enable_dsa_cache_layer_split\":false,\"enable_cp_decode_attn_tp\":false,\"enable_kv_cache_sharding\":false,\"enable_dp_attention_local_control_broadcast\":false,\"enable_dp_lm_head\":false,\"enable_tp_lm_head_all_to_all\":null,\"enable_attn_tp_input_scattered\":false,\"enable_shared_experts_attn_tp\":false,\"shared_experts_tp_size\":null,\"enable_dense_mlp_attn_tp\":false,\"enable_layernorm_sp\":false,\"disable_attn_tp_gather\":false,\"enable_p2p_check\":false,\"device\":\"cuda\",\"mlx_enable_sampling\":false,\"watchdog_timeout\":300,\"soft_watchdog_timeout\":null,\"sleep_on_idle\":false,\"use_ray\":false,\"numa_node\":null,\"gc_threshold\":null,\"host\":\"127.0.0.1\",\"fastapi_root_path\":\"\",\"smg_grpc_mode\":false,\"grpc_mode\":false,\"grpc_port\":null,\"sidecar\":null,\"sidecar_args\":null,\"warmups\":null,\"enable_http2\":false,\"http2_max_concurrent_streams\":200,\"http2_initial_connection_window_size\":1048576,\"ssl_keyfile\":null,\"ssl_certfile\":null,\"ssl_ca_certs\":null,\"ssl_keyfile_password\":null,\"enable_ssl_refresh\":false,\"api_key\":null,\"admin_api_key\":null,\"served_model_name\":null,\"weight_version\":\"default\",\"chat_template\":null,\"trust_request_chat_template\":false,\"hf_chat_template_name\":null,\"completion_template\":null,\"file_storage_path\":null,\"enable_cache_report\":false,\"return_input_ids\":false,\"return_output_ids\":false,\"default_chat_template_kwargs\":null,\"strip_thinking_cache\":false,\"enable_strict_thinking\":false,\"tool_server\":null,\"sampling_defaults\":\"model\",\"asr_max_buffer_seconds\":60,\"asr_max_concurrent_sessions\":32,\"preferred_sampling_params\":null,\"allow_auto_truncate\":false,\"stream_interval\":1,\"batch_notify_size\":16,\"stream_response_default_include_usage\":false,\"incremental_streaming_output\":false,\"enable_streaming_session\":false,\"enable_session_radix_cache\":false,\"log_level\":\"info\",\"log_level_http\":null,\"log_requests\":false,\"log_requests_level\":2,\"log_requests_format\":\"text\",\"log_requests_target\":null,\"uvicorn_access_log_exclude_prefixes\":[],\"crash_dump_folder\":null,\"show_time_cost\":false,\"enable_metrics\":false,\"smg_http_sidecar_port\":null,\"enable_mfu_metrics\":false,\"enable_metrics_for_all_schedulers\":false,\"load_snapshot_publish_interval\":15,\"tokenizer_metrics_custo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:1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"session-server\":{\"name\":\"session-server\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":32,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"trainer-controller-actor\":{\"name\":\"trainer-controller-actor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.train.group.TrainerController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1}}}}" env: - name: MILES_WORKER_METADATA valueFrom: @@ -762,7 +762,7 @@ spec: - "-m" - "miles.utils.workers.serving.serve" - "--config" - - 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env: - name: MILES_WORKER_METADATA valueFrom: @@ -1124,7 +1124,7 @@ spec: - "-m" - "miles.utils.workers.serving.serve" - "--config" - - 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"use_mamba_mem_eff_path\":true,\"use_megatron_fsdp\":false,\"use_mp_args_from_checkpoint_args\":false,\"use_mup\":false,\"use_one_sent_docs\":false,\"use_persistent_ckpt_worker\":false,\"use_precision_aware_optimizer\":false,\"use_pytorch_profiler\":false,\"use_ring_exchange_p2p\":false,\"use_rope_scaling\":false,\"use_rotary_position_embeddings\":true,\"use_sharp\":false,\"use_te_activation_func\":false,\"use_tokenizer_model_from_checkpoint_args\":true,\"use_torch_fsdp2\":false,\"use_torch_optimizer_for_cpu_offload\":false,\"use_tp_pp_dp_mapping\":false,\"use_transformer_engine_op_fuser\":false,\"use_varlen_dataset\":false,\"v_head_dim\":128,\"valid_data_path\":null,\"validation_set_names\":null,\"variable_seq_lengths\":true,\"varlen_mock_dataset_config_json\":null,\"varlen_sbhd_validation\":false,\"verify_integrity\":false,\"virtual_pipeline_model_parallel_size\":null,\"vision_backbone_type\":\"vit\",\"vision_pretraining\":false,\"vision_pretraining_type\":\"classify\",\"vocab_extra_ids\":0,\"vocab_file\":null,\"vocab_size\":151936,\"wandb_entity\":null,\"wandb_exp_name\":null,\"wandb_project\":null,\"wandb_save_dir\":null,\"weight_decay\":0.1,\"weight_decay_incr_style\":\"constant\",\"wgrad_deferral_limit\":0,\"window_attn_skip_freq\":null,\"window_size\":null,\"world_size\":32,\"yaml_cfg\":null,\"yarn_beta_fast\":null,\"yarn_beta_slow\":null,\"yarn_correction_range_round_to_int\":null,\"yarn_original_max_position_embeddings\":null},\"trainer_role\":\"actor\",\"trainer_actor_index\":0},\"static_connections\":{\"static_conn_infos\":{\"rollout-executor\":{\"name\":\"rollout-executor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.rollout.rollout_executor.RolloutExecutor\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"inference-controller\":{\"name\":\"inference-controller\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.rollout.inference_controller.InferenceController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"inference-router-0\":{\"name\":\"inference-router-0\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false},{\"name\":\"prometheus\",\"static_port\":9000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"session-server\":{\"name\":\"session-server\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":32,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"trainer-controller-actor\":{\"name\":\"trainer-controller-actor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.train.group.TrainerController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1}}}}" env: - name: MILES_WORKER_METADATA valueFrom: @@ -1276,6 +1276,7 @@ spec: - "round_robin" - "--disaggregation-bootstrap-port" - "11000" + - "--no-enable-decoder-swa-bounded-replay" - "--engine-info-bootstrap-port" - "12000" - "--mem-fraction-static" @@ -1492,6 +1493,7 @@ spec: - "--enable-metrics" - "--disaggregation-mode" - "decode" + - "--no-enable-decoder-swa-bounded-replay" - "--engine-info-bootstrap-port" - "12000" - "--mem-fraction-static" @@ -1677,7 +1679,7 @@ spec: command: - "bash" - "-c" - - "mkdir -p /scratch/Qwen3-4B && rsync -a --info=progress2 /cluster-storage/models/Qwen3-4B/ /scratch/Qwen3-4B && exec -m miles.utils.workers.process_supervisor --num-subprocesses 8 -- -m miles.utils.workers.serving.serve --config '{\"worker_type\":\"trainer\",\"args\":{\"sglang_server_concurrency\":512,\"sglang\":{\"models\":[{\"name\":\"default\",\"model_path\":null,\"server_groups\":[{\"worker_type\":\"prefill\",\"num_gpus_per_engine\":16,\"overrides\":{\"model_path\":\"/typical-model\"},\"needs_offload\":true},{\"worker_type\":\"decode\",\"num_gpus_per_engine\":8,\"overrides\":{\"model_path\":\"/typical-model\"},\"needs_offload\":true}],\"update_weights\":true}],\"base_args\":{\"tokenizer_path\":null,\"tokenizer_mode\":\"auto\",\"tokenizer_backend\":\"huggingface\",\"tokenizer_worker_num\":1,\"detokenizer_worker_num\":1,\"skip_tokenizer_init\":false,\"load_format\":\"auto\",\"model_loader_extra_config\":\"{}\",\"context_length\":null,\"is_embedding\":false,\"enable_multimodal\":null,\"revision\":null,\"model_impl\":\"auto\",\"model_config_parser\":\"auto\",\"json_model_override_args\":\"{}\",\"dtype\":\"auto\",\"quantization\":null,\"quantization_param_path\":null,\"kv_cache_dtype\":\"auto\",\"enable_fp32_lm_head\":false,\"modelopt_quant\":null,\"modelopt_checkpoint_restore_path\":null,\"modelopt_checkpoint_save_path\":null,\"modelopt_export_path\":null,\"quantize_and_serve\":false,\"rl_quant_profile\":null,\"enable_tf32_matmul\":false,\"mem_fraction_static\":0.7,\"max_running_requests\":null,\"max_queued_requests\":null,\"max_total_tokens\":null,\"chunked_prefill_size\":4096,\"prefill_decode_interval\":null,\"enable_dynamic_chunking\":false,\"max_prefill_tokens\":16384,\"prefill_max_requests\":null,\"schedule_policy\":\"fcfs\",\"enable_priority_scheduling\":false,\"disable_priority_preemption\":false,\"default_priority_value\":null,\"abort_on_priority_when_disabled\":false,\"schedule_low_priority_values_first\":false,\"priority_scheduling_preemption_threshold\":10,\"retraction_policy\":\"length\",\"schedule_conservativeness\":1.0,\"page_size\":null,\"c128_page_size\":16,\"swa_full_tokens_ratio\":null,\"disable_hybrid_swa_memory\":false,\"radix_eviction_policy\":\"lru\",\"radix_eviction_policy_config\":null,\"prefill_only_disable_kv_cache\":false,\"disable_radix_cache\":false,\"enable_page_major_kv_layout\":false,\"enable_unified_memory\":false,\"disable_chunked_prefix_cache\":false,\"disable_overlap_schedule\":false,\"num_continuous_decode_steps\":1,\"scheduler_recv_interval\":1,\"enable_mixed_chunk\":false,\"dist_timeout\":null,\"tp_size\":1,\"dcp_size\":1,\"pp_size\":1,\"pp_max_micro_batch_size\":null,\"pp_async_batch_depth\":0,\"dp_size\":1,\"attn_dp_size\":1,\"load_balance_method\":\"auto\",\"attn_cp_size\":1,\"moe_dp_size\":1,\"dwdp_size\":1,\"dcp_comm_backend\":null,\"dcp_replicate_q_proj\":null,\"enable_prefill_cp\":false,\"cp_strategy\":null,\"enable_dsa_cache_layer_split\":false,\"enable_cp_decode_attn_tp\":false,\"enable_kv_cache_sharding\":false,\"enable_dp_attention_local_control_broadcast\":false,\"enable_dp_lm_head\":false,\"enable_tp_lm_head_all_to_all\":null,\"enable_attn_tp_input_scattered\":false,\"enable_shared_experts_attn_tp\":false,\"shared_experts_tp_size\":null,\"enable_dense_mlp_attn_tp\":false,\"enable_layernorm_sp\":false,\"disable_attn_tp_gather\":false,\"enable_p2p_check\":false,\"device\":\"cuda\",\"mlx_enable_sampling\":false,\"watchdog_timeout\":300,\"soft_watchdog_timeout\":null,\"sleep_on_idle\":false,\"use_ray\":false,\"numa_node\":null,\"gc_threshold\":null,\"host\":\"127.0.0.1\",\"fastapi_root_path\":\"\",\"smg_grpc_mode\":false,\"grpc_mode\":false,\"grpc_port\":null,\"sidecar\":null,\"sidecar_args\":null,\"warmups\":null,\"enable_http2\":false,\"http2_max_concurrent_streams\":200,\"http2_initial_connection_window_size\":1048576,\"ssl_keyfile\":null,\"ssl_certfile\":null,\"ssl_ca_certs\":null,\"ssl_keyfile_password\":null,\"enable_ssl_refresh\":false,\"api_key\":null,\"admin_api_key\":null,\"served_model_name\":null,\"weight_version\":\"default\",\"chat_template\":null,\"trust_request_chat_template\":false,\"hf_chat_template_name\":null,\"completion_template\":null,\"file_storage_path\":null,\"enable_cache_report\":false,\"return_input_ids\":false,\"return_output_ids\":false,\"default_chat_template_kwargs\":null,\"strip_thinking_cache\":false,\"enable_strict_thinking\":false,\"tool_server\":null,\"sampling_defaults\":\"model\",\"asr_max_buffer_seconds\":60,\"asr_max_concurrent_sessions\":32,\"preferred_sampling_params\":null,\"allow_auto_truncate\":false,\"stream_interval\":1,\"batch_notify_size\":16,\"stream_response_default_include_usage\":false,\"incremental_streaming_output\":false,\"enable_streaming_session\":false,\"enable_session_radix_cache\":false,\"log_level\":\"info\",\"log_level_http\":null,\"log_requests\":false,\"log_requests_level\":2,\"log_requests_format\":\"text\",\"log_requests_target\":null,\"uvicorn_access_log_exclude_prefixes\":[],\"crash_dump_folder\":null,\"show_time_cost\":false,\"enable_metrics\":false,\"smg_http_sidecar_port\":null,\"enable_mfu_metrics\":false,\"enable_metrics_for_all_schedulers\":false,\"load_snapshot_publish_interval\":15,\"tokenizer_metrics_custom_labels_header\":\"x-custom-labels\",\"tokenizer_metrics_allowed_custom_labels\":null,\"extra_metric_labels\":null,\"bucket_time_to_first_token\":null,\"bucket_inter_token_latency\":null,\"bucket_e2e_request_latency\":null,\"prompt_tokens_buckets\":null,\"generation_tokens_buckets\":null,\"gc_warning_threshold_secs\":0.0,\"decode_log_interval\":40,\"enable_request_time_stats_logging\":false,\"kv_events_config\":null,\"load_publish_endpoint\":null,\"enable_forward_pass_metrics\":false,\"forward_pass_metrics_worker_id\":\"\",\"forward_pass_metrics_ipc_name\":null,\"enable_trace\":false,\"trace_modules\":\"request\",\"otlp_traces_endpoint\":\"localhost:4317\",\"export_metrics_to_file\":false,\"export_metrics_to_file_dir\":null,\"constrained_json_whitespace_pattern\":null,\"constrained_json_disable_any_whitespace\":false,\"constrained_json_max_whitespace_cnt\":null,\"attention_backend\":null,\"decode_attention_backend\":null,\"enable_lean_attention\":null,\"prefill_attention_backend\":null,\"grammar_backend\":null,\"radix_cache_backend\":null,\"mm_attention_backend\":null,\"fp8_gemm_runner_backend\":\"auto\",\"fp4_gemm_runner_backend\":\"auto\",\"bf16_gemm_backend\":\"auto\",\"dsa_prefill_backend\":null,\"dsv4_prefill_backend\":\"auto\",\"dsa_decode_backend\":null,\"dsa_paged_mqa_logits_backend\":\"auto\",\"dsa_topk_backend\":\"sgl-kernel\",\"disable_flashinfer_autotune\":false,\"flashinfer_autotune_skip_ops\":null,\"mamba_backend\":\"triton\",\"cuda_graph_config\":null,\"cuda_graph_backend_decode\":null,\"cuda_graph_backend_prefill\":\"disabled\",\"cuda_graph_max_bs_decode\":null,\"cuda_graph_max_bs_prefill\":null,\"cuda_graph_bs_decode\":null,\"cuda_graph_bs_prefill\":null,\"cuda_graph_prefill_max_context\":null,\"disable_prefill_cuda_graph\":false,\"disable_decode_cuda_graph\":false,\"disable_cuda_graph_padding\":false,\"enable_profile_cuda_graph\":false,\"enable_cudagraph_gc\":false,\"debug_cuda_graph\":false,\"enable_layerwise_nvtx_marker\":false,\"enable_nccl_nvls\":false,\"enable_symm_mem\":false,\"triton_attention_reduce_in_fp32\":false,\"triton_attention_num_kv_splits\":8,\"triton_attention_split_tile_size\":null,\"flashinfer_mla_disable_ragged\":false,\"enable_fused_qk_norm_rope\":false,\"enable_precise_embedding_interpolation\":false,\"enable_fused_moe_sum_all_reduce\":false,\"enable_deepseek_v4_fp4_indexer\":false,\"disable_custom_all_reduce\":false,\"enable_mscclpp\":false,\"enable_torch_symm_mem\":false,\"enable_scattered_sconv\":false,\"pre_warm_nccl\":false,\"boundary_reduction\":\"auto\",\"enable_quant_communications\":false,\"enforce_disable_flashinfer_allreduce_fusion\":false,\"flashinfer_allreduce_fusion_backend\":null,\"enable_aiter_allreduce_fusion\":false,\"enable_torch_compile\":false,\"enable_torch_compile_debug_mode\":false,\"torch_compile_max_bs\":32,\"speculative_algorithm\":null,\"uno_lora_path\":null,\"speculative_draft_model_path\":null,\"speculative_draft_model_revision\":null,\"speculative_draft_load_format\":null,\"speculative_num_steps\":null,\"speculative_eagle_topk\":null,\"speculative_num_draft_tokens\":null,\"speculative_dflash_block_size\":null,\"speculative_dspark_block_size\":null,\"speculative_dspark_sps_table_path\":null,\"speculative_dspark_confidence_sts_path\":null,\"speculative_dspark_align_verify_tokens_to_graph_tier\":false,\"speculative_accept_threshold_single\":1.0,\"speculative_accept_threshold_acc\":1.0,\"speculative_use_rejection_sampling\":false,\"speculative_token_map\":null,\"speculative_attention_mode\":\"prefill\",\"speculative_draft_attention_backend\":null,\"speculative_dsa_topk_backend\":\"sgl-kernel\",\"speculative_draft_kv_cache_dtype\":null,\"speculative_draft_window_size\":null,\"speculative_moe_runner_backend\":null,\"speculative_moe_a2a_backend\":null,\"speculative_enable_w4a4_mxfp4_megamoe\":null,\"speculative_draft_model_quantization\":null,\"speculative_skip_dp_mlp_sync\":false,\"enable_multi_layer_eagle\":false,\"speculative_adaptive\":false,\"speculative_adaptive_config\":null,\"decoupled_spec_bind_endpoint\":null,\"decoupled_spec_connect_endpoints\":null,\"decoupled_spec_rank\":null,\"decoupled_spec_role\":\"null\",\"spec_trace_dir\":null,\"speculative_ngram_min_bfs_breadth\":1,\"speculative_ngram_max_bfs_breadth\":10,\"speculative_ngram_match_type\":\"BFS\",\"speculative_ngram_max_trie_depth\":18,\"speculative_ngram_capacity\":10000000,\"speculative_ngram_external_corpus_path\":null,\"speculative_ngram_external_sam_budget\":0,\"speculative_ngram_external_corpus_max_tokens\":10000000,\"ep_size\":1,\"moe_a2a_backend\":\"none\",\"enable_w4a4_mxfp4_megamoe\":false,\"deepep_v2_mode\":\"direct\",\"moe_runner_backend\":\"auto\",\"flashinfer_mxfp4_moe_precision\":\"default\",\"deepep_mode\":\"auto\",\"fuseep_mode\":2,\"deepep_dispatcher_output_dtype\":\"auto\",\"ep_num_redundant_experts\":0,\"ep_dispatch_algorithm\":null,\"init_expert_location\":\"trivial\",\"enable_eplb\":false,\"eplb_algorithm\":\"auto\",\"eplb_rebalance_num_iterations\":100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rue,\"use_sharp\":false,\"use_te_activation_func\":false,\"use_tokenizer_model_from_checkpoint_args\":true,\"use_torch_fsdp2\":false,\"use_torch_optimizer_for_cpu_offload\":false,\"use_tp_pp_dp_mapping\":false,\"use_transformer_engine_op_fuser\":false,\"use_varlen_dataset\":false,\"v_head_dim\":128,\"valid_data_path\":null,\"validation_set_names\":null,\"variable_seq_lengths\":true,\"varlen_mock_dataset_config_json\":null,\"varlen_sbhd_validation\":false,\"verify_integrity\":false,\"virtual_pipeline_model_parallel_size\":null,\"vision_backbone_type\":\"vit\",\"vision_pretraining\":false,\"vision_pretraining_type\":\"classify\",\"vocab_extra_ids\":0,\"vocab_file\":null,\"vocab_size\":151936,\"wandb_entity\":null,\"wandb_exp_name\":null,\"wandb_project\":null,\"wandb_save_dir\":null,\"weight_decay\":0.1,\"weight_decay_incr_style\":\"constant\",\"wgrad_deferral_limit\":0,\"window_attn_skip_freq\":null,\"window_size\":null,\"world_size\":32,\"yaml_cfg\":null,\"yarn_beta_fast\":null,\"yarn_beta_slow\":null,\"yarn_correction_range_round_to_int\":null,\"yarn_original_max_position_embeddings\":null},\"trainer_role\":\"actor\",\"trainer_actor_index\":0},\"static_connections\":{\"static_conn_infos\":{\"rollout-executor\":{\"name\":\"rollout-executor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.rollout.rollout_executor.RolloutExecutor\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"inference-controller\":{\"name\":\"inference-controller\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.rollout.inference_controller.InferenceController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"inference-router-0\":{\"name\":\"inference-router-0\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false},{\"name\":\"prometheus\",\"static_port\":9000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"session-server\":{\"name\":\"session-server\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":32,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"trainer-controller-actor\":{\"name\":\"trainer-controller-actor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.train.group.TrainerController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1}}}}'" + - "mkdir -p /scratch/Qwen3-4B && rsync -a --info=progress2 /cluster-storage/models/Qwen3-4B/ /scratch/Qwen3-4B && exec -m miles.utils.workers.process_supervisor --num-subprocesses 8 -- -m miles.utils.workers.serving.serve --config '{\"worker_type\":\"trainer\",\"args\":{\"sglang_server_concurrency\":512,\"sglang\":{\"models\":[{\"name\":\"default\",\"model_path\":null,\"server_groups\":[{\"worker_type\":\"prefill\",\"num_gpus_per_engine\":16,\"overrides\":{\"model_path\":\"/typical-model\"},\"needs_offload\":true},{\"worker_type\":\"decode\",\"num_gpus_per_engine\":8,\"overrides\":{\"model_path\":\"/typical-model\"},\"needs_offload\":true}],\"update_weights\":true}],\"base_args\":{\"tokenizer_path\":null,\"tokenizer_mode\":\"auto\",\"tokenizer_backend\":\"huggingface\",\"tokenizer_worker_num\":1,\"detokenizer_worker_num\":1,\"skip_tokenizer_init\":false,\"load_format\":\"auto\",\"model_loader_extra_config\":\"{}\",\"context_length\":null,\"is_embedding\":false,\"enable_multimodal\":null,\"revision\":null,\"model_impl\":\"auto\",\"model_config_parser\":\"auto\",\"json_model_override_args\":\"{}\",\"dtype\":\"auto\",\"quantization\":null,\"quantization_param_path\":null,\"kv_cache_dtype\":\"auto\",\"enable_fp32_lm_head\":false,\"modelopt_quant\":null,\"modelopt_checkpoint_restore_path\":null,\"modelopt_checkpoint_save_path\":null,\"modelopt_export_path\":null,\"quantize_and_serve\":false,\"rl_quant_profile\":null,\"enable_tf32_matmul\":null,\"mem_fraction_static\":0.7,\"max_running_requests\":null,\"max_queued_requests\":null,\"max_total_tokens\":null,\"chunked_prefill_size\":4096,\"prefill_decode_interval\":null,\"enable_dynamic_chunking\":false,\"max_prefill_tokens\":16384,\"prefill_max_requests\":null,\"schedule_policy\":\"fcfs\",\"enable_priority_scheduling\":false,\"disable_priority_preemption\":false,\"default_priority_value\":null,\"abort_on_priority_when_disabled\":false,\"schedule_low_priority_values_first\":false,\"priority_scheduling_preemption_threshold\":10,\"retraction_policy\":\"length\",\"schedule_conservativeness\":1.0,\"page_size\":null,\"c128_page_size\":16,\"swa_full_tokens_ratio\":null,\"disable_hybrid_swa_memory\":false,\"radix_eviction_policy\":\"lru\",\"radix_eviction_policy_config\":null,\"prefill_only_disable_kv_cache\":false,\"disable_radix_cache\":false,\"enable_page_major_kv_layout\":false,\"enable_unified_memory\":false,\"disable_chunked_prefix_cache\":false,\"disable_overlap_schedule\":false,\"num_continuous_decode_steps\":1,\"scheduler_recv_interval\":1,\"enable_mixed_chunk\":false,\"dist_timeout\":null,\"tp_size\":1,\"dcp_size\":1,\"pp_size\":1,\"pp_max_micro_batch_size\":null,\"pp_async_batch_depth\":0,\"dp_size\":1,\"attn_dp_size\":1,\"load_balance_method\":\"auto\",\"attn_cp_size\":1,\"moe_dp_size\":1,\"dwdp_size\":1,\"dcp_comm_backend\":null,\"dcp_replicate_q_proj\":null,\"enable_prefill_cp\":false,\"cp_strategy\":null,\"enable_dsa_cache_layer_split\":false,\"enable_cp_decode_attn_tp\":false,\"enable_kv_cache_sharding\":false,\"enable_dp_attention_local_control_broadcast\":false,\"enable_dp_lm_head\":false,\"enable_tp_lm_head_all_to_all\":null,\"enable_attn_tp_input_scattered\":false,\"enable_shared_experts_attn_tp\":false,\"shared_experts_tp_size\":null,\"enable_dense_mlp_attn_tp\":false,\"enable_layernorm_sp\":false,\"disable_attn_tp_gather\":false,\"enable_p2p_check\":false,\"device\":\"cuda\",\"mlx_enable_sampling\":false,\"watchdog_timeout\":300,\"soft_watchdog_timeout\":null,\"sleep_on_idle\":false,\"use_ray\":false,\"numa_node\":null,\"gc_threshold\":null,\"host\":\"127.0.0.1\",\"fastapi_root_path\":\"\",\"smg_grpc_mode\":false,\"grpc_mode\":false,\"grpc_port\":null,\"sidecar\":null,\"sidecar_args\":null,\"warmups\":null,\"enable_http2\":false,\"http2_max_concurrent_streams\":200,\"http2_initial_connection_window_size\":1048576,\"ssl_keyfile\":null,\"ssl_certfile\":null,\"ssl_ca_certs\":null,\"ssl_keyfile_password\":null,\"enable_ssl_refresh\":false,\"api_key\":null,\"admin_api_key\":null,\"served_model_name\":null,\"weight_version\":\"default\",\"chat_template\":null,\"trust_request_chat_template\":false,\"hf_chat_template_name\":null,\"completion_template\":null,\"file_storage_path\":null,\"enable_cache_report\":false,\"return_input_ids\":false,\"return_output_ids\":false,\"default_chat_template_kwargs\":null,\"strip_thinking_cache\":false,\"enable_strict_thinking\":false,\"tool_server\":null,\"sampling_defaults\":\"model\",\"asr_max_buffer_seconds\":60,\"asr_max_concurrent_sessions\":32,\"preferred_sampling_params\":null,\"allow_auto_truncate\":false,\"stream_interval\":1,\"batch_notify_size\":16,\"stream_response_default_include_usage\":false,\"incremental_streaming_output\":false,\"enable_streaming_session\":false,\"enable_session_radix_cache\":false,\"log_level\":\"info\",\"log_level_http\":null,\"log_requests\":false,\"log_requests_level\":2,\"log_requests_format\":\"text\",\"log_requests_target\":null,\"uvicorn_access_log_exclude_prefixes\":[],\"crash_dump_folder\":null,\"show_time_cost\":false,\"enable_metrics\":false,\"smg_http_sidecar_port\":null,\"enable_mfu_metrics\":false,\"enable_metrics_for_all_schedulers\":false,\"load_snapshot_publish_interval\":15,\"tokenizer_metrics_custom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mem_eff_path\":true,\"use_megatron_fsdp\":false,\"use_mp_args_from_checkpoint_args\":false,\"use_mup\":false,\"use_one_sent_docs\":false,\"use_persistent_ckpt_worker\":false,\"use_precision_aware_optimizer\":false,\"use_pytorch_profiler\":false,\"use_ring_exchange_p2p\":false,\"use_rope_scaling\":false,\"use_rotary_position_embeddings\":true,\"use_sharp\":false,\"use_te_activation_func\":false,\"use_tokenizer_model_from_checkpoint_args\":true,\"use_torch_fsdp2\":false,\"use_torch_optimizer_for_cpu_offload\":false,\"use_tp_pp_dp_mapping\":false,\"use_transformer_engine_op_fuser\":false,\"use_varlen_dataset\":false,\"v_head_dim\":128,\"valid_data_path\":null,\"validation_set_names\":null,\"variable_seq_lengths\":true,\"varlen_mock_dataset_config_json\":null,\"varlen_sbhd_validation\":false,\"verify_integrity\":false,\"virtual_pipeline_model_parallel_size\":null,\"vision_backbone_type\":\"vit\",\"vision_pretraining\":false,\"vision_pretraining_type\":\"classify\",\"vocab_extra_ids\":0,\"vocab_file\":null,\"vocab_size\":151936,\"wandb_entity\":null,\"wandb_exp_name\":null,\"wandb_project\":null,\"wandb_save_dir\":null,\"weight_decay\":0.1,\"weight_decay_incr_style\":\"constant\",\"wgrad_deferral_limit\":0,\"window_attn_skip_freq\":null,\"window_size\":null,\"world_size\":32,\"yaml_cfg\":null,\"yarn_beta_fast\":null,\"yarn_beta_slow\":null,\"yarn_correction_range_round_to_int\":null,\"yarn_original_max_position_embeddings\":null},\"trainer_role\":\"actor\",\"trainer_actor_index\":0},\"static_connections\":{\"static_conn_infos\":{\"rollout-executor\":{\"name\":\"rollout-executor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.rollout.rollout_executor.RolloutExecutor\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"inference-controller\":{\"name\":\"inference-controller\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.rollout.inference_controller.InferenceController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"inference-router-0\":{\"name\":\"inference-router-0\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false},{\"name\":\"prometheus\",\"static_port\":9000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"session-server\":{\"name\":\"session-server\",\"port_infos\":[{\"name\":\"primary\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":null,\"num_cells\":32,\"num_workers_per_cell\":1,\"pods_per_cell\":1},\"trainer-controller-actor\":{\"name\":\"trainer-controller-actor\",\"port_infos\":[{\"name\":\"rpc\",\"static_port\":8000,\"mode\":\"per_worker\",\"allow_dynamic\":true,\"num_consecutive\":1,\"offset_by_cell\":false}],\"worker_class\":\"miles.ray.train.group.TrainerController\",\"num_cells\":1,\"num_workers_per_cell\":1,\"pods_per_cell\":1}}}}'" env: - name: MILES_WORKER_METADATA valueFrom: diff --git a/tests/snapshots/helm_backend/kubernetes_launch.txt b/tests/snapshots/helm_backend/kubernetes_launch.txt index e2eac39c72..12ce34668c 100644 --- a/tests/snapshots/helm_backend/kubernetes_launch.txt +++ b/tests/snapshots/helm_backend/kubernetes_launch.txt @@ -58,7 +58,7 @@ run: - 'python' - '/repo/train.py' - '--orchestrator-config' - - 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