996 lines
38 KiB
Python
Executable File
996 lines
38 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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import numpy as np
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import torch
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from vllm import envs
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from vllm.attention.backends.abstract import (
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AttentionBackend,
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AttentionImpl,
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AttentionMetadata,
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AttentionType,
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MultipleOf,
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is_quantized_kv_cache,
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)
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from vllm.attention.layer import Attention
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from vllm.attention.ops.common import cp_lse_ag_out_rs
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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 (
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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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)
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if is_flash_attn_varlen_func_available():
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from vllm.attention.utils.fa_utils import (
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flash_attn_varlen_func,
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get_scheduler_metadata,
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reshape_and_cache_flash,
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)
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from vllm.config import VllmConfig, get_layers_from_vllm_config
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from vllm.distributed.parallel_state import get_dcp_group
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from vllm.logger import init_logger
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from vllm.model_executor.layers.batch_invariant import (
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vllm_is_batch_invariant,
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)
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from vllm.utils import cdiv
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from vllm.v1.attention.backends.utils import (
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AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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get_kv_cache_layout,
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)
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from vllm.v1.kv_cache_interface import AttentionSpec
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logger = init_logger(__name__)
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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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@staticmethod
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def get_supported_kernel_block_size() -> list[int | MultipleOf]:
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return [MultipleOf(16)]
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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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)
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@staticmethod
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def get_name() -> str:
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return "FLASH_ATTN"
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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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cache_dtype_str: str = "auto",
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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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@staticmethod
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def get_fp8_dtype_for_flashattn(kv_cache_dtype: str) -> torch.dtype:
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if kv_cache_dtype in ("fp8", "fp8_e4m3"):
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return torch.float8_e4m3fn
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else:
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raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
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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: torch.Tensor | None
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prefix_kv_lens: torch.Tensor | None
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suffix_kv_lens: torch.Tensor | None
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# For GQA DCP
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max_dcp_context_kv_len: int | None = None
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dcp_context_kv_lens: torch.Tensor | None = None
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# Optional aot scheduling
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scheduler_metadata: torch.Tensor | None = None
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prefix_scheduler_metadata: torch.Tensor | None = 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,
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) -> set[tuple[int, int] | None]:
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"""Get the set of all sliding window configs used in the model."""
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sliding_window_configs: set[tuple[int, int] | None] = 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(AttentionMetadataBuilder[FlashAttentionMetadata]):
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# FA3:
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# Supports full cudagraphs for all cases.
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#
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# FA2:
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# For FA2, a graph is captured with max_query_len=1, (which is what we
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# capture by default for num_tokens <= max_num_seqs when there is no
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# spec-decode) then these graphs will not work for mixed prefill-decode
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# (unlike FA3). This is due to special max_query_len=1 packed-GQA handling
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# in FA2.
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# In summary if we are running with spec decodes the graphs would
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# work for mixed prefill-decode and uniform-decode. But for non-spec decodes
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# the graphs would not work for mixed prefill-decode; sorta the inverse
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# of UNIFORM_SINGLE_TOKEN_DECODE.
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# There's probably a better way to describe this using `AttentionCGSupport`
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# but for now just set it to `UNIFORM_BATCH` to get use to drop down
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# to FULL_AND_PIECEWISE.
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# TODO(luka, lucas): audit FA2 as part of:
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# https://github.com/vllm-project/vllm/issues/22945
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cudagraph_support = (
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AttentionCGSupport.ALWAYS
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if get_flash_attn_version() == 3
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else AttentionCGSupport.UNIFORM_BATCH
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)
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def __init__(
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self,
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kv_cache_spec: AttentionSpec,
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layer_names: list[str],
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vllm_config: VllmConfig,
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device: torch.device,
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):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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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.num_heads_q = self.model_config.get_num_attention_heads(
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self.parallel_config
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)
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self.num_heads_kv = self.model_config.get_num_kv_heads(self.parallel_config)
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self.kv_cache_dtype = kv_cache_spec.dtype
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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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try:
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from vllm.distributed.parallel_state import get_dcp_group
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self.dcp_world_size = get_dcp_group().world_size
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self.dcp_rank = get_dcp_group().rank_in_group
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except AssertionError:
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# DCP might not be initialized in testing
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self.dcp_world_size = 1
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self.dcp_rank = 0
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self.use_full_cuda_graph = (
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self.compilation_config.cudagraph_mode.has_full_cudagraphs()
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)
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self.max_cudagraph_size = self.compilation_config.max_capture_size
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if self.use_full_cuda_graph and self.aot_schedule:
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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 full cuda graph."
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)
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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 = envs.VLLM_FLASH_ATTN_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: tuple[int, int] | None = None
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def build(
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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,
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) -> 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 = common_attn_metadata.max_seq_len
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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(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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max_num_splits = 0 # 0 means use FA3's heuristics, not CG compatible
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if self.use_full_cuda_graph 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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if vllm_is_batch_invariant():
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max_num_splits = 1
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def schedule(
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batch_size, cu_query_lens, max_query_len, seqlens, max_seq_len, causal
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):
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cache_dtype = self.cache_config.cache_dtype
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if cache_dtype.startswith("fp8"):
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qkv_dtype = FlashAttentionBackend.get_fp8_dtype_for_flashattn(
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cache_dtype
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)
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else:
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qkv_dtype = self.kv_cache_dtype
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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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num_heads_q=self.num_heads_q * self.dcp_world_size,
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num_heads_kv=self.num_heads_kv,
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headdim=self.headdim,
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cache_seqlens=seqlens,
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qkv_dtype=qkv_dtype,
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cu_seqlens_q=cu_query_lens,
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page_size=self.block_size,
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causal=causal,
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window_size=self.aot_sliding_window,
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num_splits=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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max_dcp_context_kv_len = 0
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dcp_context_kv_lens = None
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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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if self.dcp_world_size > 1:
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query_kv_lens_cpu = (
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common_attn_metadata.query_start_loc_cpu[1:]
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- common_attn_metadata.query_start_loc_cpu[:-1]
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)
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dcp_context_kv_lens_cpu = seq_lens_cpu - query_kv_lens_cpu
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dcp_context_kv_lens_cpu = dcp_context_kv_lens_cpu // self.dcp_world_size + (
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self.dcp_rank <= (dcp_context_kv_lens_cpu - 1) % self.dcp_world_size
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)
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dcp_context_kv_lens = dcp_context_kv_lens_cpu.to(self.device)
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max_dcp_context_kv_len = dcp_context_kv_lens.max().item()
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scheduler_metadata = schedule(
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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=dcp_context_kv_lens,
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max_seq_len=max_dcp_context_kv_len,
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causal=False,
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)
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elif use_cascade:
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cu_prefix_query_lens = torch.tensor(
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[0, num_actual_tokens], dtype=torch.int32, device=self.device
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)
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prefix_kv_lens = torch.tensor(
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[common_prefix_len], dtype=torch.int32, device=self.device
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)
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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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)
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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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)
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scheduler_metadata = schedule(
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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 - common_prefix_len,
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causal=True,
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)
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else:
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scheduler_metadata = schedule(
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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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)
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# For FA3 + full cudagraph
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if self.use_full_cuda_graph and 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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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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max_dcp_context_kv_len=max_dcp_context_kv_len,
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dcp_context_kv_lens=dcp_context_kv_lens,
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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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)
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return attn_metadata
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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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can_return_lse_for_decode: bool = True
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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: list[float] | None,
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sliding_window: int | None,
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kv_cache_dtype: str,
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logits_soft_cap: float | None = None,
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attn_type: AttentionType = AttentionType.DECODER,
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kv_sharing_target_layer_name: str | None = None,
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sinks: torch.Tensor | None = None,
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) -> None:
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self.num_heads = num_heads
|
|
self.head_size = head_size
|
|
self.scale = float(scale)
|
|
self.num_kv_heads = num_kv_heads
|
|
if alibi_slopes is not None:
|
|
alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
|
|
self.alibi_slopes = alibi_slopes
|
|
if sliding_window is None:
|
|
self.sliding_window = (-1, -1)
|
|
elif attn_type == AttentionType.ENCODER_ONLY:
|
|
self.sliding_window = (sliding_window - 1, sliding_window - 1)
|
|
else:
|
|
self.sliding_window = (sliding_window - 1, 0)
|
|
self.kv_cache_dtype = kv_cache_dtype
|
|
if logits_soft_cap is None:
|
|
# In flash-attn, setting logits_soft_cap as 0 means no soft cap.
|
|
logits_soft_cap = 0
|
|
self.logits_soft_cap = logits_soft_cap
|
|
self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
|
|
|
|
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
|
|
|
FlashAttentionBackend.validate_head_size(head_size)
|
|
|
|
self.attn_type = attn_type
|
|
self.vllm_flash_attn_version = get_flash_attn_version()
|
|
# Cache the batch invariant result for use in forward passes
|
|
self.batch_invariant_enabled = vllm_is_batch_invariant()
|
|
|
|
if is_quantized_kv_cache(self.kv_cache_dtype) and not flash_attn_supports_fp8():
|
|
raise NotImplementedError(
|
|
"FlashAttention does not support fp8 kv-cache on this device."
|
|
)
|
|
|
|
self.sinks = sinks
|
|
if self.sinks is not None:
|
|
assert self.vllm_flash_attn_version == 3, (
|
|
"Sinks are only supported in FlashAttention 3"
|
|
)
|
|
assert self.sinks.shape[0] == num_heads, (
|
|
"Sinks must have the same number of heads as the number of "
|
|
"heads in the layer"
|
|
)
|
|
|
|
def supports_quant_query_input(self) -> bool:
|
|
return True
|
|
|
|
def forward(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: FlashAttentionMetadata,
|
|
output: torch.Tensor | None = None,
|
|
output_scale: torch.Tensor | None = None,
|
|
output_block_scale: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
"""Forward pass with FlashAttention.
|
|
|
|
Args:
|
|
query: shape = [num_tokens, num_heads, head_size]
|
|
key: shape = [num_tokens, num_kv_heads, head_size]
|
|
value: shape = [num_tokens, num_kv_heads, head_size]
|
|
kv_cache: shape =
|
|
[2, num_blocks, block_size, num_kv_heads, head_size]
|
|
attn_metadata: Metadata for attention.
|
|
Returns:
|
|
shape = [num_tokens, num_heads * head_size]
|
|
NOTE: FP8 quantization, flash-attn expect the size of
|
|
{q,k,v}_descale to be (num_sequences, num_kv_heads).
|
|
We use torch's .expand() to avoid duplicating values
|
|
"""
|
|
assert output is not None, "Output tensor must be provided."
|
|
|
|
if output_scale is not None or output_block_scale is not None:
|
|
raise NotImplementedError(
|
|
"fused output quantization is not yet supported for FlashAttentionImpl"
|
|
)
|
|
|
|
if attn_metadata is None:
|
|
# Profiling run.
|
|
return output.fill_(0)
|
|
|
|
attn_type = self.attn_type
|
|
|
|
# IMPORTANT!
|
|
# NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
|
|
# eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
|
|
# in this method. For example, `view` and `slice` (or `[:n]`) operations
|
|
# are surprisingly slow even in the case they do not invoke any GPU ops.
|
|
# Minimize the PyTorch ops in this method as much as possible.
|
|
# Whenever making a change in this method, please benchmark the
|
|
# performance to make sure it does not introduce any overhead.
|
|
|
|
num_actual_tokens = attn_metadata.num_actual_tokens
|
|
|
|
# Handle encoder attention differently - no KV cache needed
|
|
if attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
|
|
# For encoder attention,
|
|
# we use direct Q, K, V tensors without caching
|
|
return self._forward_encoder_attention(
|
|
query[:num_actual_tokens],
|
|
key[:num_actual_tokens],
|
|
value[:num_actual_tokens],
|
|
output[:num_actual_tokens],
|
|
attn_metadata,
|
|
layer,
|
|
)
|
|
|
|
# For decoder and cross-attention, use KV cache as before
|
|
key_cache, value_cache = kv_cache.unbind(0)
|
|
|
|
# key and value may be None in the case of cross attention. They are
|
|
# calculated once based on the output from the encoder and then cached
|
|
# in KV cache.
|
|
if (
|
|
self.kv_sharing_target_layer_name is None
|
|
and key is not None
|
|
and value is not None
|
|
):
|
|
# 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"):
|
|
# queries are quantized in the attention layer
|
|
dtype = FlashAttentionBackend.get_fp8_dtype_for_flashattn(
|
|
self.kv_cache_dtype
|
|
)
|
|
key_cache = key_cache.view(dtype)
|
|
value_cache = value_cache.view(dtype)
|
|
|
|
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, self.num_kv_heads)
|
|
|
|
if self.dcp_world_size > 1:
|
|
self._forward_with_dcp(
|
|
query[:num_actual_tokens],
|
|
key[:num_actual_tokens],
|
|
value[:num_actual_tokens],
|
|
key_cache,
|
|
value_cache,
|
|
output[:num_actual_tokens],
|
|
attn_metadata,
|
|
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
|
|
else:
|
|
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,
|
|
s_aux=self.sinks,
|
|
)
|
|
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,
|
|
s_aux=self.sinks,
|
|
)
|
|
return output
|
|
|
|
def _forward_with_dcp(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
output: torch.Tensor,
|
|
attn_metadata: FlashAttentionMetadata,
|
|
q_descale: torch.Tensor | None = None,
|
|
k_descale: torch.Tensor | None = None,
|
|
v_descale: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
cu_seqlens_q = attn_metadata.query_start_loc
|
|
max_seqlen_q = attn_metadata.max_query_len
|
|
block_table = attn_metadata.block_table
|
|
|
|
query = query.contiguous()
|
|
query_across_dcp = get_dcp_group().all_gather(query, dim=1)
|
|
context_attn_out, context_lse = flash_attn_varlen_func(
|
|
q=query_across_dcp,
|
|
k=key_cache,
|
|
v=value_cache,
|
|
out=None,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
max_seqlen_q=max_seqlen_q,
|
|
seqused_k=attn_metadata.dcp_context_kv_lens,
|
|
max_seqlen_k=attn_metadata.max_dcp_context_kv_len,
|
|
softmax_scale=self.scale,
|
|
causal=False,
|
|
alibi_slopes=self.alibi_slopes,
|
|
window_size=self.sliding_window,
|
|
block_table=block_table,
|
|
softcap=self.logits_soft_cap,
|
|
return_softmax_lse=True,
|
|
scheduler_metadata=attn_metadata.scheduler_metadata,
|
|
fa_version=self.vllm_flash_attn_version,
|
|
q_descale=q_descale,
|
|
k_descale=k_descale,
|
|
v_descale=v_descale,
|
|
)
|
|
# FA returns LSE in shape [ H, B ] but cp_lse_ag_out_rs wants [ B, H ]
|
|
context_attn_out_cor, context_lse_cor = cp_lse_ag_out_rs(
|
|
context_attn_out,
|
|
context_lse.transpose(0, 1),
|
|
get_dcp_group(),
|
|
return_lse=True,
|
|
)
|
|
context_lse_cor = context_lse_cor.transpose(0, 1).contiguous()
|
|
|
|
query_attn_out, query_lse = flash_attn_varlen_func(
|
|
q=query,
|
|
k=key,
|
|
v=value,
|
|
out=None,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
max_seqlen_q=max_seqlen_q,
|
|
cu_seqlens_k=cu_seqlens_q,
|
|
max_seqlen_k=max_seqlen_q,
|
|
softmax_scale=self.scale,
|
|
causal=attn_metadata.causal,
|
|
alibi_slopes=self.alibi_slopes,
|
|
window_size=self.sliding_window,
|
|
softcap=self.logits_soft_cap,
|
|
return_softmax_lse=True,
|
|
fa_version=self.vllm_flash_attn_version,
|
|
q_descale=q_descale,
|
|
k_descale=k_descale,
|
|
v_descale=v_descale,
|
|
)
|
|
assert context_attn_out_cor.shape == query_attn_out.shape
|
|
assert context_lse_cor.shape == query_lse.shape
|
|
merge_attn_states(
|
|
output,
|
|
context_attn_out_cor,
|
|
context_lse_cor,
|
|
query_attn_out,
|
|
query_lse,
|
|
)
|
|
|
|
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),
|
|
num_splits=1 if self.batch_invariant_enabled else 0,
|
|
)
|
|
|
|
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,
|
|
dcp_world_size: 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
|
|
# disable cascade attention for DCP
|
|
if dcp_world_size > 1:
|
|
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: torch.Tensor | None,
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sliding_window: tuple[int, int],
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logits_soft_cap: float,
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|
block_table: torch.Tensor,
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|
common_prefix_len: int,
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|
fa_version: int,
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|
prefix_scheduler_metadata: torch.Tensor | None = None,
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|
suffix_scheduler_metadata: torch.Tensor | None = None,
|
|
q_descale: torch.Tensor | None = None,
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|
k_descale: torch.Tensor | None = None,
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|
v_descale: torch.Tensor | None = None,
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|
s_aux: torch.Tensor | None = None,
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|
) -> torch.Tensor:
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assert alibi_slopes is None, "Cascade attention does not support ALiBi."
|
|
# TODO: Support sliding window.
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|
assert sliding_window == (-1, -1), (
|
|
"Cascade attention does not support sliding window."
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|
)
|
|
|
|
num_tokens = query.shape[0]
|
|
block_size = key_cache.shape[-3]
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|
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.
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|
prefix_output, prefix_lse = flash_attn_varlen_func(
|
|
q=query,
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|
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,
|
|
# s_aux is incorporated into prefix_lse inside the GPU kernel,
|
|
# enabling its effect during the final attention merge.
|
|
s_aux=s_aux,
|
|
num_splits=1 if vllm_is_batch_invariant() else 0,
|
|
)
|
|
|
|
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,
|
|
num_splits=1 if vllm_is_batch_invariant() else 0,
|
|
)
|
|
|
|
# Merge prefix and suffix outputs, and store the result in output.
|
|
merge_attn_states(output, prefix_output, prefix_lse, suffix_output, suffix_lse)
|