[Core] Refactor _prepare_model_input_tensors - take 2

tests/worker/test_model_input.py

@@ -3,7 +3,7 @@
 
 import torch
 
-from vllm.attention import AttentionMetadata
+from vllm.attention import AttentionMetadata, AttentionMetadataBuilder
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.pooling_metadata import PoolingMetadata
@@ -26,6 +26,10 @@ def get_impl_cls():
     def get_metadata_cls() -> Type["AttentionMetadata"]:
         return AttentionMetadata
 
+    @staticmethod
+    def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
+        raise AttentionMetadataBuilder
+
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,

vllm/attention/__init__.py

@@ -1,12 +1,14 @@
 from vllm.attention.backends.abstract import (AttentionBackend,
-                                              AttentionMetadata)
+                                              AttentionMetadata,
+                                              AttentionMetadataBuilder)
 from vllm.attention.layer import Attention
 from vllm.attention.selector import get_attn_backend
 
 __all__ = [
     "Attention",
     "AttentionBackend",
     "AttentionMetadata",
+    "AttentionMetadataBuilder",
     "Attention",
     "get_attn_backend",
 ]

vllm/attention/backends/abstract.py

@@ -1,11 +1,15 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
 from enum import Enum, auto
-from typing import (Any, Dict, Generic, List, Optional, Set, Tuple, Type,
-                    TypeVar)
+from typing import (TYPE_CHECKING, Any, Dict, Generic, List, Optional, Set,
+                    Tuple, Type, TypeVar)
 
 import torch
 
+if TYPE_CHECKING:
+    from vllm.sequence import SequenceGroupMetadata
+    from vllm.worker.model_runner_base import ModelRunnerInputBuilderBase
+
 
 class AttentionType(Enum):
     DECODER = auto()  # Decoder attention between previous layer Q/K/V
@@ -35,6 +39,16 @@ def get_metadata_cls() -> Type["AttentionMetadata"]:
     def make_metadata(cls, *args, **kwargs) -> "AttentionMetadata":
         return cls.get_metadata_cls()(*args, **kwargs)
 
+    @staticmethod
+    @abstractmethod
+    def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
+        raise NotImplementedError
+
+    @classmethod
+    def make_metadata_builder(cls, *args,
+                              **kwargs) -> "AttentionMetadataBuilder":
+        return cls.get_builder_cls()(*args, **kwargs)
+
     @staticmethod
     @abstractmethod
     def get_kv_cache_shape(
@@ -110,6 +124,33 @@ def asdict_zerocopy(self,
 T = TypeVar("T", bound=AttentionMetadata)
 
 
+class AttentionMetadataBuilder(ABC, Generic[T]):
+    """Abstract class for attention metadata builders."""
+
+    @abstractmethod
+    def __init__(self, input_builder) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def add_seq_group(self, seq_group_metadata: "SequenceGroupMetadata",
+                      token_lens: List[int], seq_lens: List[int],
+                      curr_seq_lens: List[int], query_lens: List[int],
+                      context_lens: List[int],
+                      curr_sliding_window_blocks: List[int],
+                      prefix_cache_hit: bool, chunked_prefill_enabled: bool):
+        """Add a sequence group to the metadata and update
+        corresponding fields (in Python objects).
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def build(self, runner: "ModelRunnerInputBuilderBase", seq_lens: List[int],
+              query_lens: List[int], cuda_graph_pad_size: int,
+              batch_size: int) -> T:
+        """Build attention metadata with on-device tensors."""
+        raise NotImplementedError
+
+
 class AttentionImpl(ABC, Generic[T]):
 
     @abstractmethod

vllm/attention/backends/blocksparse_attn.py

@@ -5,6 +5,7 @@
 
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType)
+from vllm.attention.backends.utils import CommonMetadataBuilder
 from vllm.attention.ops.blocksparse_attention.interface import (
     LocalStridedBlockSparseAttn, get_head_sliding_step)
 from vllm.attention.ops.paged_attn import PagedAttention
@@ -93,6 +94,10 @@ def get_impl_cls() -> Type["BlocksparseFlashAttentionImpl"]:
     def get_metadata_cls() -> Type["AttentionMetadata"]:
         return BlocksparseFlashAttentionMetadata
 
+    @staticmethod
+    def get_builder_cls() -> Type["BlocksparseFlashAttentionMetadataBuilder"]:
+        return BlocksparseFlashAttentionMetadataBuilder
+
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,
@@ -244,6 +249,12 @@ def decode_metadata(self) -> Optional["BlocksparseFlashAttentionMetadata"]:
         return self._cached_decode_metadata
 
 
+class BlocksparseFlashAttentionMetadataBuilder(
+        CommonMetadataBuilder[BlocksparseFlashAttentionMetadata]):
+
+    _metadata_cls = BlocksparseFlashAttentionMetadata
+
+
 class BlocksparseFlashAttentionImpl(AttentionImpl):
     """
     If the input tensors contain prompt tokens, the layout is as follows:

vllm/attention/backends/flash_attn.py

@@ -1,13 +1,24 @@
 """Attention layer with FlashAttention."""
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, Tuple, Type
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
 
 import torch
 from vllm_flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache
 
 from vllm import _custom_ops as ops
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
-                                              AttentionMetadata, AttentionType)
+                                              AttentionMetadata,
+                                              AttentionMetadataBuilder,
+                                              AttentionType)
+from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
+                                           compute_slot_mapping_start_idx,
+                                           is_block_tables_empty)
+from vllm.sequence import SequenceGroupMetadata
+from vllm.utils import make_tensor_with_pad
+
+if TYPE_CHECKING:
+    from vllm.worker.model_runner import (GPUModelRunnerBase,
+                                          ModelInputForGPUBuilder)
 
 
 class FlashAttentionBackend(AttentionBackend):
@@ -28,6 +39,10 @@ def get_impl_cls() -> Type["FlashAttentionImpl"]:
     def get_metadata_cls() -> Type["AttentionMetadata"]:
         return FlashAttentionMetadata
 
+    @staticmethod
+    def get_builder_cls() -> Type["FlashAttentionMetadataBuilder"]:
+        return FlashAttentionMetadataBuilder
+
     @staticmethod
     def get_kv_cache_shape(
         num_blocks: int,
@@ -184,6 +199,170 @@ def decode_metadata(self) -> Optional["FlashAttentionMetadata"]:
         return self._cached_decode_metadata
 
 
+class FlashAttentionMetadataBuilder(
+        AttentionMetadataBuilder[FlashAttentionMetadata]):
+
+    def __init__(self, input_builder: "ModelInputForGPUBuilder"):
+        self.slot_mapping: List[int] = []
+        self.prefill_seq_lens: List[int] = []
+        self.context_lens: List[int] = []
+        self.block_tables: List[List[int]] = []
+        self.curr_seq_lens: List[int] = []
+        self.num_prefills = 0
+        self.num_prefill_tokens = 0
+        self.num_decode_tokens = 0
+
+        self.sliding_window = input_builder.sliding_window
+        self.block_size = input_builder.block_size
+        self.use_v2_block_manager = (
+            input_builder.scheduler_config.use_v2_block_manager)
+
+    def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata,
+                      token_lens: List[int], seq_lens: List[int],
+                      curr_seq_lens: List[int], query_lens: List[int],
+                      context_lens: List[int],
+                      curr_sliding_window_blocks: List[int],
+                      prefix_cache_hit: bool, chunked_prefill_enabled: bool):
+        """Add a sequence group to the metadata. Specifically update/append
+        1. context length.
+        2. block table.
+        3. slot mapping.
+        """
+        is_prompt = seq_group_metadata.is_prompt
+        block_tables = seq_group_metadata.block_tables
+
+        for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
+             curr_sliding_window_block) in zip(
+                 seq_group_metadata.seq_data.keys(), token_lens, seq_lens,
+                 curr_seq_lens, query_lens, context_lens,
+                 curr_sliding_window_blocks):
+            self.context_lens.append(context_len)
+
+            if is_prompt:
+                self.num_prefills += 1
+                self.num_prefill_tokens += token_len
+                self.prefill_seq_lens.append(seq_len)
+            else:
+                assert query_len == 1, (
+                    "seq_len: {}, context_len: {}, query_len: {}".format(
+                        seq_len, context_len, query_len))
+                self.num_decode_tokens += query_len
+                self.curr_seq_lens.append(curr_seq_len)
+
+            # Compute block table.
+            # TODO(sang): Combine chunked prefill and prefix caching by
+            # only allowing multiple of block_size chunk size.
+            # NOTE: This only works for oooooooxxx style attention.
+            block_table = []
+            if prefix_cache_hit:
+                # NOTE(woosuk): For flash-attn, the block table should
+                # include the entries for the incoming prefill tokens.
+                block_table = block_tables[seq_id]
+            elif ((chunked_prefill_enabled or not is_prompt)
+                  and block_tables is not None):
+                block_table = block_tables[seq_id][-curr_sliding_window_block:]
+            self.block_tables.append(block_table)
+
+            # Compute slot mapping.
+            is_profile_run = is_block_tables_empty(block_tables)
+            start_idx = compute_slot_mapping_start_idx(
+                is_prompt, query_len, context_len, self.sliding_window,
+                self.use_v2_block_manager)
+            compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
+                                 seq_len, context_len, start_idx,
+                                 self.block_size,
+                                 seq_group_metadata.block_tables)
+
+    def build(self, runner: "GPUModelRunnerBase", seq_lens, query_lens,
+              cuda_graph_pad_size: int, batch_size: int):
+        """Build attention metadata with on-device tensors."""
+        device = runner.device
+        use_captured_graph = cuda_graph_pad_size != -1
+
+        logits_soft_cap = getattr(runner.model_config.hf_config,
+                                  "attn_logit_softcapping", None)
+        if logits_soft_cap is not None:
+            raise ValueError(
+                "Please use Flashinfer backend for models with logits_soft_cap"
+                " (i.e., Gemma-2). Otherwise, the output might be wrong."
+                " Set Flashinfer backend by "
+                "export VLLM_ATTENTION_BACKEND=FLASHINFER.")
+
+        max_query_len = max(query_lens)
+        max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
+        max_decode_seq_len = max(self.curr_seq_lens, default=0)
+        num_decode_tokens = self.num_decode_tokens
+
+        if use_captured_graph:
+            self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
+            self.block_tables.extend([] * cuda_graph_pad_size)
+            num_decode_tokens = batch_size + cuda_graph_pad_size
+
+            # The shape of graph_block_tables is
+            # [max batch size, max context len // block size].
+            input_block_tables = runner.graph_block_tables[:batch_size]
+            for i, block_table in enumerate(self.block_tables):
+                if block_table:
+                    input_block_tables[i, :len(block_table)] = block_table
+            block_tables = torch.tensor(input_block_tables, device=device)
+        else:
+            max_block_table_len = max(
+                len(block_table) for block_table in self.block_tables)
+            block_tables = make_tensor_with_pad(
+                self.block_tables,
+                max_len=max_block_table_len,
+                pad=0,
+                dtype=torch.int,
+                device=device,
+            )
+        assert max_query_len > 0, ("query_lens: {}".format(query_lens))
+
+        context_lens_tensor = torch.tensor(self.context_lens,
+                                           dtype=torch.int,
+                                           device=device)
+        seq_lens_tensor = torch.tensor(seq_lens,
+                                       dtype=torch.int,
+                                       device=device)
+        query_lens_tensor = torch.tensor(query_lens,
+                                         dtype=torch.long,
+                                         device=device)
+        query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
+                                      dtype=torch.int32,
+                                      device=device)
+        seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
+                                    dtype=torch.int32,
+                                    device=device)
+        torch.cumsum(seq_lens_tensor,
+                     dim=0,
+                     dtype=seq_start_loc.dtype,
+                     out=seq_start_loc[1:])
+        torch.cumsum(query_lens_tensor,
+                     dim=0,
+                     dtype=query_start_loc.dtype,
+                     out=query_start_loc[1:])
+
+        slot_mapping_tensor = torch.tensor(self.slot_mapping,
+                                           dtype=torch.long,
+                                           device=device)
+
+        return FlashAttentionMetadata(
+            num_prefills=self.num_prefills,
+            slot_mapping=slot_mapping_tensor,
+            num_prefill_tokens=self.num_prefill_tokens,
+            num_decode_tokens=num_decode_tokens,
+            seq_lens=seq_lens,
+            seq_lens_tensor=seq_lens_tensor,
+            max_query_len=max_query_len,
+            max_prefill_seq_len=max_prefill_seq_len,
+            max_decode_seq_len=max_decode_seq_len,
+            query_start_loc=query_start_loc,
+            seq_start_loc=seq_start_loc,
+            context_lens_tensor=context_lens_tensor,
+            block_tables=block_tables,
+            use_cuda_graph=use_captured_graph,
+        )
+
+
 class FlashAttentionImpl(AttentionImpl):
     """
     If the input tensors contain prompt tokens, the layout is as follows: