765 lines
31 KiB
Python
Executable File
765 lines
31 KiB
Python
Executable File
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Attention layer with FlashAttention."""
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from dataclasses import dataclass
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from typing import ClassVar, Optional
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import numpy as np
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import torch
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from vllm import _custom_ops as ops
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata, AttentionType,
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is_quantized_kv_cache)
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from vllm.attention.layer import Attention
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from vllm.attention.ops.merge_attn_states import merge_attn_states
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from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8,
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get_flash_attn_version,
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is_flash_attn_varlen_func_available)
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if is_flash_attn_varlen_func_available():
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from vllm.attention.utils.fa_utils import (flash_attn_varlen_func,
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get_scheduler_metadata,
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reshape_and_cache_flash)
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from vllm.config import VllmConfig, get_layers_from_vllm_config
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from vllm.logger import init_logger
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from vllm.utils import cdiv
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from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
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CommonAttentionMetadata,
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get_kv_cache_layout)
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from vllm.v1.kv_cache_interface import AttentionSpec
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logger = init_logger(__name__)
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# NOTE(woosuk): This is an arbitrary number. Tune it if needed.
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_DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH = 16
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class FlashAttentionBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@classmethod
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def get_supported_dtypes(cls) -> list[torch.dtype]:
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return [torch.float16, torch.bfloat16]
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@classmethod
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def get_supported_head_sizes(cls) -> list[int]:
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return [32, 64, 96, 128, 160, 192, 224, 256]
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@classmethod
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def validate_head_size(cls, head_size: int) -> None:
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supported_head_sizes = cls.get_supported_head_sizes()
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if head_size not in supported_head_sizes:
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attn_type = cls.__name__.removesuffix("Backend")
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raise ValueError(
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f"Head size {head_size} is not supported by {attn_type}. "
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f"Supported head sizes are: {supported_head_sizes}. "
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"Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
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"FlexAttention backend which supports all head sizes.")
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@staticmethod
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def get_name() -> str:
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return "FLASH_ATTN_VLLM_V1"
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@staticmethod
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def get_impl_cls() -> type["FlashAttentionImpl"]:
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return FlashAttentionImpl
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@staticmethod
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def get_metadata_cls() -> type["AttentionMetadata"]:
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return FlashAttentionMetadata
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@staticmethod
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def get_builder_cls() -> type["FlashAttentionMetadataBuilder"]:
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return FlashAttentionMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> tuple[int, ...]:
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if block_size % 16 != 0:
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raise ValueError("Block size must be a multiple of 16.")
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return (2, num_blocks, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_kv_cache_stride_order() -> tuple[int, ...]:
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# `stride_order` indicates the permutation that gets
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# us from `get_kv_cache_shape` to the actual memory layout we want.
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cache_layout = get_kv_cache_layout()
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if cache_layout == "NHD":
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stride_order = (0, 1, 2, 3, 4)
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elif cache_layout == "HND":
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stride_order = (0, 1, 3, 2, 4)
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else:
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raise ValueError(f"Unknown cache layout format {cache_layout}.")
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return stride_order
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@dataclass
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class FlashAttentionMetadata:
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# NOTE(sang): Definition of context_len, query_len, and seq_len.
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# |---------- N-1 iteration --------|
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# |---------------- N iteration ---------------------|
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# |- tokenA -|......................|-- newTokens ---|
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# |---------- context_len ----------|
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# |-------------------- seq_len ---------------------|
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# |-- query_len ---|
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num_actual_tokens: int # Number of tokens excluding padding.
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max_query_len: int
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query_start_loc: torch.Tensor
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max_seq_len: int
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seq_lens: torch.Tensor
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block_table: torch.Tensor
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slot_mapping: torch.Tensor
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# For cascade attention.
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use_cascade: bool
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common_prefix_len: int
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cu_prefix_query_lens: Optional[torch.Tensor]
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prefix_kv_lens: Optional[torch.Tensor]
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suffix_kv_lens: Optional[torch.Tensor]
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# Optional aot scheduling
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scheduler_metadata: Optional[torch.Tensor] = None
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prefix_scheduler_metadata: Optional[torch.Tensor] = None
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max_num_splits: int = 0
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causal: bool = True
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def _get_sliding_window_configs(
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vllm_config: VllmConfig) -> set[Optional[tuple[int, int]]]:
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"""Get the set of all sliding window configs used in the model."""
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sliding_window_configs: set[Optional[tuple[int, int]]] = set()
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layers = get_layers_from_vllm_config(vllm_config, Attention)
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for layer in layers.values():
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assert isinstance(layer.impl, FlashAttentionImpl)
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sliding_window_configs.add(layer.impl.sliding_window)
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return sliding_window_configs
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class FlashAttentionMetadataBuilder(
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AttentionMetadataBuilder[FlashAttentionMetadata]):
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full_cudagraph_supported: ClassVar[bool] = get_flash_attn_version() == 3
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def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
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vllm_config: VllmConfig, device: torch.device):
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self.vllm_config = vllm_config
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self.model_config = vllm_config.model_config
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self.parallel_config = vllm_config.parallel_config
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self.cache_config = vllm_config.cache_config
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self.compilation_config = vllm_config.compilation_config
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self.device = device
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self.num_heads_q = self.model_config.get_num_attention_heads(
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self.parallel_config)
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self.num_heads_kv = self.model_config.get_num_kv_heads(
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self.parallel_config)
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self.headdim = self.model_config.get_head_size()
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self.block_size = kv_cache_spec.block_size
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self.max_num_splits = 0 # No upper bound on the number of splits.
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self.aot_schedule = (get_flash_attn_version() == 3)
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self.use_full_cuda_graph = self.compilation_config.full_cuda_graph
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if self.use_full_cuda_graph:
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if not self.aot_schedule:
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raise ValueError(
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"AoT scheduling is required for full cuda graph.")
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capture_sizes = self.compilation_config.cudagraph_capture_sizes
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if not capture_sizes:
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raise ValueError(
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"cudagraph_capture_sizes should not be None when "
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"full_cuda_graph is True.")
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self.max_cudagraph_size = max(capture_sizes)
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if self.max_cudagraph_size > 992:
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# This condition derives from FA3's internal heuristic.
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# TODO(woosuk): Support larger cudagraph sizes.
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raise ValueError(
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"Capture size larger than 992 is not supported for "
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"full cuda graph.")
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self.scheduler_metadata = torch.zeros(
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vllm_config.scheduler_config.max_num_seqs + 1,
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dtype=torch.int32,
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device=self.device,
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)
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# When using cuda graph, we need to set the upper bound of the
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# number of splits so that large enough intermediate buffers are
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# pre-allocated during capture.
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self.max_num_splits = _DEFAULT_MAX_NUM_SPLITS_FOR_CUDA_GRAPH
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# Sliding window size to be used with the AOT scheduler will be
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# populated on first build() call.
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self.aot_sliding_window: Optional[tuple[int, int]] = None
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def build(self,
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common_prefix_len: int,
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common_attn_metadata: CommonAttentionMetadata,
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fast_build: bool = False) -> FlashAttentionMetadata:
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"""
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fast_build disables AOT scheduling, used when there will be few
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iterations i.e. spec-decode
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"""
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num_reqs = common_attn_metadata.num_reqs
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num_actual_tokens = common_attn_metadata.num_actual_tokens
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max_query_len = common_attn_metadata.max_query_len
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max_seq_len = int(common_attn_metadata.seq_lens_cpu.max())
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query_start_loc = common_attn_metadata.query_start_loc
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seq_lens = common_attn_metadata.seq_lens
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seq_lens_cpu = common_attn_metadata.seq_lens_cpu
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block_table_tensor = common_attn_metadata.block_table_tensor
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slot_mapping = common_attn_metadata.slot_mapping
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causal = common_attn_metadata.causal
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# the overhead of the aot schedule is not worth it for spec-decode
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aot_schedule = self.aot_schedule and not fast_build
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if self.aot_sliding_window is None:
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self.aot_sliding_window = (-1, -1)
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# For the AOT scheduler we need the sliding window value to be
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# constant for all layers to. We have to populate this on the first
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# build() call so the layers are constructed (cannot populate)
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# in __init__.
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if aot_schedule:
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sliding_window_configs = _get_sliding_window_configs(
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self.vllm_config)
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if len(sliding_window_configs) == 1:
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sliding_window_config = sliding_window_configs.pop()
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if sliding_window_config is not None:
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self.aot_sliding_window = sliding_window_config
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elif len(sliding_window_configs) > 1:
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self.aot_schedule = False
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aot_schedule = False
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def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
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max_seq_len, causal):
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if aot_schedule:
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return get_scheduler_metadata(
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batch_size=batch_size,
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max_seqlen_q=max_query_len,
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max_seqlen_k=max_seq_len,
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cache_seqlens=seqlens,
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num_heads_q=self.num_heads_q,
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num_heads_kv=self.num_heads_kv,
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headdim=self.headdim,
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page_size=self.block_size,
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cu_seqlens_q=cu_query_lens,
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causal=causal,
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window_size=self.aot_sliding_window,
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num_splits=self.max_num_splits,
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)
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return None
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use_cascade = common_prefix_len > 0
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if use_cascade:
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cu_prefix_query_lens = torch.tensor([0, num_actual_tokens],
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dtype=torch.int32,
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device=self.device)
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prefix_kv_lens = torch.tensor([common_prefix_len],
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dtype=torch.int32,
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device=self.device)
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suffix_kv_lens = (seq_lens_cpu[:num_reqs] - common_prefix_len).to(
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self.device, non_blocking=True)
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prefix_scheduler_metadata = schedule(
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batch_size=1,
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cu_query_lens=cu_prefix_query_lens,
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max_query_len=num_actual_tokens,
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seqlens=prefix_kv_lens,
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max_seq_len=common_prefix_len,
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causal=False)
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scheduler_metadata = schedule(batch_size=num_reqs,
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cu_query_lens=query_start_loc,
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max_query_len=max_query_len,
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seqlens=suffix_kv_lens,
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max_seq_len=max_seq_len -
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common_prefix_len,
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causal=True)
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else:
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cu_prefix_query_lens = None
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prefix_kv_lens = None
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suffix_kv_lens = None
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prefix_scheduler_metadata = None
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scheduler_metadata = schedule(batch_size=num_reqs,
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cu_query_lens=query_start_loc,
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max_query_len=max_query_len,
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seqlens=seq_lens,
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max_seq_len=max_seq_len,
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causal=causal)
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if self.use_full_cuda_graph:
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assert scheduler_metadata is not None
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n = scheduler_metadata.shape[0]
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self.scheduler_metadata[:n] = scheduler_metadata
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# NOTE(woosuk): We should zero out the rest of the scheduler
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# metadata to guarantee the correctness. Otherwise, some thread
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# blocks may use the invalid scheduler metadata and overwrite the
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# output buffer.
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self.scheduler_metadata[n:] = 0
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scheduler_metadata = self.scheduler_metadata[:n]
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max_num_splits = 0
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if (self.use_full_cuda_graph
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and num_actual_tokens <= self.max_cudagraph_size):
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# NOTE(woosuk): Setting num_splits > 1 may increase the memory
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# usage, because the intermediate buffers of size [num_splits,
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# num_heads, num_tokens, head_size] are allocated. Therefore,
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# we only set num_splits when using cuda graphs.
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max_num_splits = self.max_num_splits
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attn_metadata = FlashAttentionMetadata(
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num_actual_tokens=num_actual_tokens,
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max_query_len=max_query_len,
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query_start_loc=query_start_loc,
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max_seq_len=max_seq_len,
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seq_lens=seq_lens,
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block_table=block_table_tensor,
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slot_mapping=slot_mapping,
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use_cascade=use_cascade,
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common_prefix_len=common_prefix_len,
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scheduler_metadata=scheduler_metadata,
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cu_prefix_query_lens=cu_prefix_query_lens,
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prefix_kv_lens=prefix_kv_lens,
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suffix_kv_lens=suffix_kv_lens,
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prefix_scheduler_metadata=prefix_scheduler_metadata,
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max_num_splits=max_num_splits,
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causal=causal)
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return attn_metadata
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def can_run_in_cudagraph(
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self, common_attn_metadata: CommonAttentionMetadata) -> bool:
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# Full CUDA Graph always supported (FA2 support checked separately)
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return True
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def use_cascade_attention(self, *args, **kwargs) -> bool:
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return use_cascade_attention(*args, **kwargs)
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class FlashAttentionImpl(AttentionImpl):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[list[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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logits_soft_cap: Optional[float] = None,
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attn_type: AttentionType = AttentionType.DECODER,
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kv_sharing_target_layer_name: Optional[str] = None,
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) -> None:
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_kv_heads
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if alibi_slopes is not None:
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alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
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self.alibi_slopes = alibi_slopes
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if sliding_window is None:
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self.sliding_window = (-1, -1)
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else:
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self.sliding_window = (sliding_window - 1, 0)
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self.kv_cache_dtype = kv_cache_dtype
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if logits_soft_cap is None:
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# In flash-attn, setting logits_soft_cap as 0 means no soft cap.
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logits_soft_cap = 0
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self.logits_soft_cap = logits_soft_cap
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self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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FlashAttentionBackend.validate_head_size(head_size)
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if attn_type not in [
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AttentionType.DECODER, AttentionType.ENCODER_ONLY
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]:
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raise NotImplementedError("Encoder/decoder cross-attention "
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"is not implemented for "
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"FlashAttentionImpl")
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self.attn_type = attn_type
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self.vllm_flash_attn_version = get_flash_attn_version()
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if is_quantized_kv_cache(self.kv_cache_dtype) \
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and not flash_attn_supports_fp8():
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raise NotImplementedError(
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"FlashAttention does not support fp8 kv-cache on this device.")
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def forward(
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self,
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layer: torch.nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: FlashAttentionMetadata,
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output: Optional[torch.Tensor] = None,
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output_scale: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Forward pass with FlashAttention.
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Args:
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query: shape = [num_tokens, num_heads, head_size]
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key: shape = [num_tokens, num_kv_heads, head_size]
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value: shape = [num_tokens, num_kv_heads, head_size]
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kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size]
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attn_metadata: Metadata for attention.
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Returns:
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shape = [num_tokens, num_heads * head_size]
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NOTE: FP8 quantization, flash-attn expect the size of
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{q,k,v}_descale to be (num_sequences, num_kv_heads).
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We use torch's .expand() to avoid duplicating values
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"""
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assert output is not None, "Output tensor must be provided."
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if output_scale is not None:
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raise NotImplementedError(
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"fused output quantization is not yet supported"
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" for FlashAttentionImpl")
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if attn_metadata is None:
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# Profiling run.
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return output
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attn_type = self.attn_type
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# IMPORTANT!
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# NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
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# eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
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# in this method. For example, `view` and `slice` (or `[:n]`) operations
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# are surprisingly slow even in the case they do not invoke any GPU ops.
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# Minimize the PyTorch ops in this method as much as possible.
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# Whenever making a change in this method, please benchmark the
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# performance to make sure it does not introduce any overhead.
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num_actual_tokens = attn_metadata.num_actual_tokens
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# Handle encoder attention differently - no KV cache needed
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if attn_type in (AttentionType.ENCODER_ONLY, ):
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# For encoder attention,
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# we use direct Q, K, V tensors without caching
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return self._forward_encoder_attention(query[:num_actual_tokens],
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key[:num_actual_tokens],
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value[:num_actual_tokens],
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output[:num_actual_tokens],
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attn_metadata, layer)
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# For decoder and cross-attention, use KV cache as before
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key_cache, value_cache = kv_cache.unbind(0)
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if self.kv_sharing_target_layer_name is None:
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# Reshape the input keys and values and store them in the cache.
|
|
# Skip this if sharing KV cache with an earlier attention layer.
|
|
# NOTE(woosuk): Here, key and value are padded while slot_mapping is
|
|
# not padded. However, we don't need to do key[:num_actual_tokens]
|
|
# and value[:num_actual_tokens] because the reshape_and_cache_flash
|
|
# op uses the slot_mapping's shape to determine the number of
|
|
# actual tokens.
|
|
reshape_and_cache_flash(
|
|
key,
|
|
value,
|
|
key_cache,
|
|
value_cache,
|
|
attn_metadata.slot_mapping,
|
|
self.kv_cache_dtype,
|
|
layer._k_scale,
|
|
layer._v_scale,
|
|
)
|
|
|
|
if self.kv_cache_dtype.startswith("fp8"):
|
|
key_cache = key_cache.view(torch.float8_e4m3fn)
|
|
value_cache = value_cache.view(torch.float8_e4m3fn)
|
|
num_tokens, num_heads, head_size = query.shape
|
|
query, _ = ops.scaled_fp8_quant(
|
|
query.reshape(
|
|
(num_tokens, num_heads * head_size)).contiguous(),
|
|
layer._q_scale)
|
|
query = query.reshape((num_tokens, num_heads, head_size))
|
|
|
|
if not attn_metadata.use_cascade:
|
|
cu_seqlens_q = attn_metadata.query_start_loc
|
|
seqused_k = attn_metadata.seq_lens
|
|
max_seqlen_q = attn_metadata.max_query_len
|
|
max_seqlen_k = attn_metadata.max_seq_len
|
|
block_table = attn_metadata.block_table
|
|
scheduler_metadata = attn_metadata.scheduler_metadata
|
|
|
|
descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
|
|
|
|
flash_attn_varlen_func(
|
|
q=query[:num_actual_tokens],
|
|
k=key_cache,
|
|
v=value_cache,
|
|
out=output[:num_actual_tokens],
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
max_seqlen_q=max_seqlen_q,
|
|
seqused_k=seqused_k,
|
|
max_seqlen_k=max_seqlen_k,
|
|
softmax_scale=self.scale,
|
|
causal=attn_metadata.causal,
|
|
alibi_slopes=self.alibi_slopes,
|
|
window_size=self.sliding_window,
|
|
block_table=block_table,
|
|
softcap=self.logits_soft_cap,
|
|
scheduler_metadata=scheduler_metadata,
|
|
fa_version=self.vllm_flash_attn_version,
|
|
q_descale=layer._q_scale.expand(descale_shape),
|
|
k_descale=layer._k_scale.expand(descale_shape),
|
|
v_descale=layer._v_scale.expand(descale_shape),
|
|
num_splits=attn_metadata.max_num_splits,
|
|
)
|
|
return output
|
|
|
|
# Cascade attention (rare case).
|
|
cascade_attention(
|
|
output[:num_actual_tokens],
|
|
query[:num_actual_tokens],
|
|
key_cache,
|
|
value_cache,
|
|
cu_query_lens=attn_metadata.query_start_loc,
|
|
max_query_len=attn_metadata.max_query_len,
|
|
cu_prefix_query_lens=attn_metadata.cu_prefix_query_lens,
|
|
prefix_kv_lens=attn_metadata.prefix_kv_lens,
|
|
suffix_kv_lens=attn_metadata.suffix_kv_lens,
|
|
max_kv_len=attn_metadata.max_seq_len,
|
|
softmax_scale=self.scale,
|
|
alibi_slopes=self.alibi_slopes,
|
|
sliding_window=self.sliding_window,
|
|
logits_soft_cap=self.logits_soft_cap,
|
|
block_table=attn_metadata.block_table,
|
|
common_prefix_len=attn_metadata.common_prefix_len,
|
|
fa_version=self.vllm_flash_attn_version,
|
|
prefix_scheduler_metadata=attn_metadata.prefix_scheduler_metadata,
|
|
suffix_scheduler_metadata=attn_metadata.scheduler_metadata,
|
|
q_descale=layer._q_scale,
|
|
k_descale=layer._k_scale,
|
|
v_descale=layer._v_scale,
|
|
)
|
|
return output
|
|
|
|
def _forward_encoder_attention(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
output: torch.Tensor,
|
|
attn_metadata: FlashAttentionMetadata,
|
|
layer: torch.nn.Module,
|
|
) -> torch.Tensor:
|
|
"""Forward pass for encoder attention without KV cache.
|
|
|
|
Args:
|
|
query: shape = [num_encoder_tokens, num_heads, head_size]
|
|
key: shape = [num_encoder_tokens, num_kv_heads, head_size]
|
|
value: shape = [num_encoder_tokens, num_kv_heads, head_size]
|
|
output: shape = [num_encoder_tokens, num_heads, head_size]
|
|
attn_metadata: Encoder attention metadata
|
|
layer: The attention layer
|
|
"""
|
|
# For encoder attention, process FP8 quantization if needed
|
|
if self.kv_cache_dtype.startswith("fp8"):
|
|
raise NotImplementedError(
|
|
"quantization is not supported for encoder attention")
|
|
|
|
# Use encoder-specific metadata for sequence information
|
|
cu_seqlens_q = attn_metadata.query_start_loc
|
|
cu_seqlens_k = attn_metadata.query_start_loc
|
|
max_seqlen_q = attn_metadata.max_query_len
|
|
max_seqlen_k = attn_metadata.max_query_len
|
|
|
|
descale_shape = (
|
|
cu_seqlens_q.shape[0] - 1, # type: ignore[union-attr]
|
|
self.num_kv_heads)
|
|
|
|
# Call flash attention directly on Q, K, V tensors
|
|
flash_attn_varlen_func(
|
|
q=query,
|
|
k=key,
|
|
v=value,
|
|
out=output,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
cu_seqlens_k=cu_seqlens_k,
|
|
max_seqlen_q=max_seqlen_q,
|
|
max_seqlen_k=max_seqlen_k,
|
|
softmax_scale=self.scale,
|
|
causal=False, # Encoder attention is bidirectional
|
|
alibi_slopes=self.alibi_slopes,
|
|
window_size=self.sliding_window,
|
|
softcap=self.logits_soft_cap,
|
|
fa_version=self.vllm_flash_attn_version,
|
|
q_descale=layer._q_scale.expand(descale_shape),
|
|
k_descale=layer._k_scale.expand(descale_shape),
|
|
v_descale=layer._v_scale.expand(descale_shape),
|
|
)
|
|
|
|
return output
|
|
|
|
|
|
def use_cascade_attention(
|
|
common_prefix_len: int,
|
|
query_lens: np.ndarray,
|
|
num_query_heads: int,
|
|
num_kv_heads: int,
|
|
use_alibi: bool,
|
|
use_sliding_window: bool,
|
|
use_local_attention: bool,
|
|
num_sms: int,
|
|
) -> bool:
|
|
"""Decide whether to use cascade attention.
|
|
|
|
This function 1) checks whether cascade attention is supported with the
|
|
given configuration, and 2) heuristically decides whether using cascade
|
|
attention can improve performance.
|
|
"""
|
|
# Too short common prefix. Probably not worth using cascade attention.
|
|
# We use an arbitrary threshold of 256 tokens. TODO: Tune this threshold.
|
|
# NOTE(woosuk): This is the common case. We should return False as soon as
|
|
# possible to avoid any unnecessary computation.
|
|
if common_prefix_len < 256:
|
|
return False
|
|
# Cascade attention is currently not supported with these variants.
|
|
if use_alibi or use_sliding_window or use_local_attention:
|
|
return False
|
|
# Too few queries. Probably not worth using cascade attention.
|
|
# We use an arbitrary threshold of 8 queries. TODO: Tune this threshold.
|
|
num_reqs = len(query_lens)
|
|
if num_reqs < 8:
|
|
return False
|
|
|
|
# Heuristics to decide whether using cascade attention is beneficial.
|
|
# 1. When FlashDecoding is not used for normal attention, cascade attention
|
|
# is likely to be faster since it saves memory bandwidth.
|
|
num_queries_per_kv = num_query_heads // num_kv_heads
|
|
# The criteria for using FlashDecoding can be found in the following link:
|
|
# https://github.com/vllm-project/flash-attention/blob/96266b1111111f3d11aabefaf3bacbab6a89d03c/csrc/flash_attn/flash_api.cpp#L535
|
|
use_flash_decoding = (num_queries_per_kv > 1 and not use_sliding_window
|
|
and not use_alibi and np.all(query_lens == 1))
|
|
if not use_flash_decoding:
|
|
# Use cascade attention.
|
|
return True
|
|
|
|
# 2. When FlashDecoding is used for normal attention, it is not clear
|
|
# whether cascade attention is beneficial, because FlashDecoding can
|
|
# launch more CTAs than cascade attention.
|
|
# We use a simple performance model to compare the two methods.
|
|
# NOTE(woosuk): The performance model is very rough and may not be
|
|
# accurate.
|
|
num_tokens = num_reqs
|
|
# NOTE(woosuk): These are default tile sizes. flash-attn might use
|
|
# different tile sizes (e.g., 64 or 256) depending on the configuration.
|
|
q_tile_size = 128
|
|
kv_tile_size = 128
|
|
num_prefix_tiles = cdiv(common_prefix_len, kv_tile_size)
|
|
|
|
cascade_ctas = num_query_heads * cdiv(num_tokens, q_tile_size)
|
|
cascade_waves = cdiv(cascade_ctas, num_sms)
|
|
cascade_time = cascade_waves * num_prefix_tiles
|
|
|
|
flash_decoding_ctas = (num_reqs * num_kv_heads *
|
|
cdiv(num_queries_per_kv, q_tile_size))
|
|
flash_decoding_ctas *= num_prefix_tiles
|
|
flash_decoding_time = cdiv(flash_decoding_ctas, num_sms)
|
|
|
|
# Use cascade attention if it is faster than FlashDecoding.
|
|
return cascade_time < flash_decoding_time
|
|
|
|
|
|
def cascade_attention(
|
|
output: torch.Tensor,
|
|
query: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
cu_query_lens: torch.Tensor,
|
|
max_query_len: int,
|
|
cu_prefix_query_lens: torch.Tensor,
|
|
prefix_kv_lens: torch.Tensor,
|
|
suffix_kv_lens: torch.Tensor,
|
|
max_kv_len: int,
|
|
softmax_scale: float,
|
|
alibi_slopes: Optional[torch.Tensor],
|
|
sliding_window: tuple[int, int],
|
|
logits_soft_cap: float,
|
|
block_table: torch.Tensor,
|
|
common_prefix_len: int,
|
|
fa_version: int,
|
|
prefix_scheduler_metadata: Optional[torch.Tensor] = None,
|
|
suffix_scheduler_metadata: Optional[torch.Tensor] = None,
|
|
q_descale: Optional[torch.Tensor] = None,
|
|
k_descale: Optional[torch.Tensor] = None,
|
|
v_descale: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
assert alibi_slopes is None, ("Cascade attention does not support ALiBi.")
|
|
# TODO: Support sliding window.
|
|
assert sliding_window == (-1, -1), (
|
|
"Cascade attention does not support sliding window.")
|
|
|
|
num_tokens = query.shape[0]
|
|
block_size = key_cache.shape[-3]
|
|
assert common_prefix_len % block_size == 0
|
|
num_common_kv_blocks = common_prefix_len // block_size
|
|
assert num_common_kv_blocks > 0
|
|
descale_shape = (cu_prefix_query_lens.shape[0] - 1, key_cache.shape[-2])
|
|
|
|
# Process shared prefix.
|
|
prefix_output, prefix_lse = flash_attn_varlen_func(
|
|
q=query,
|
|
k=key_cache,
|
|
v=value_cache,
|
|
cu_seqlens_q=cu_prefix_query_lens,
|
|
seqused_k=prefix_kv_lens,
|
|
max_seqlen_q=num_tokens,
|
|
max_seqlen_k=common_prefix_len,
|
|
softmax_scale=softmax_scale,
|
|
causal=False,
|
|
window_size=sliding_window,
|
|
block_table=block_table[:1],
|
|
softcap=logits_soft_cap,
|
|
return_softmax_lse=True,
|
|
scheduler_metadata=prefix_scheduler_metadata,
|
|
fa_version=fa_version,
|
|
q_descale=q_descale.expand(descale_shape)
|
|
if q_descale is not None else None,
|
|
k_descale=k_descale.expand(descale_shape)
|
|
if k_descale is not None else None,
|
|
v_descale=v_descale.expand(descale_shape)
|
|
if v_descale is not None else None,
|
|
)
|
|
|
|
descale_shape = (cu_query_lens.shape[0] - 1, key_cache.shape[-2])
|
|
|
|
# Process suffix per query.
|
|
suffix_output, suffix_lse = flash_attn_varlen_func(
|
|
q=query,
|
|
k=key_cache,
|
|
v=value_cache,
|
|
cu_seqlens_q=cu_query_lens,
|
|
seqused_k=suffix_kv_lens,
|
|
max_seqlen_q=max_query_len,
|
|
max_seqlen_k=max_kv_len - common_prefix_len,
|
|
softmax_scale=softmax_scale,
|
|
causal=True,
|
|
window_size=sliding_window,
|
|
block_table=block_table[:, num_common_kv_blocks:],
|
|
softcap=logits_soft_cap,
|
|
return_softmax_lse=True,
|
|
scheduler_metadata=suffix_scheduler_metadata,
|
|
fa_version=fa_version,
|
|
q_descale=q_descale.expand(descale_shape)
|
|
if q_descale is not None else None,
|
|
k_descale=k_descale.expand(descale_shape)
|
|
if k_descale is not None else None,
|
|
v_descale=v_descale.expand(descale_shape)
|
|
if v_descale is not None else None,
|
|
)
|
|
|
|
# Merge prefix and suffix outputs, and store the result in output.
|
|
merge_attn_states(output, prefix_output, prefix_lse, suffix_output,
|
|
suffix_lse)
|