forked from EngineX-Hygon/enginex-hygon-vllm
init src 0.9.2
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935
vllm/v1/attention/backends/flash_attn.py
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935
vllm/v1/attention/backends/flash_attn.py
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# 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 TYPE_CHECKING, Any, ClassVar, Optional, Tuple
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import numpy as np
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import torch
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import vllm.envs as envs
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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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from vllm.platforms import current_platform
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if is_flash_attn_varlen_func_available():
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if not current_platform.is_rocm():
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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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else:
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from vllm.attention.utils.fa_utils import (vllm_flash_attn_varlen_func,
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reshape_and_cache_cuda)
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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 (
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AttentionMetadataBuilder, CommonAttentionMetadata, get_kv_cache_layout,
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make_local_attention_virtual_batches)
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from vllm.v1.kv_cache_interface import AttentionSpec
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from vllm.v1.worker.block_table import BlockTable
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if TYPE_CHECKING:
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from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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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_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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if not current_platform.is_rocm():
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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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else:
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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[tuple[int, ...], 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 (
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(num_blocks, num_kv_heads, block_size, head_size),
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(num_blocks, num_kv_heads, head_size, block_size),
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)
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@staticmethod
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def get_kv_cache_stride_order() -> tuple[tuple[int, ...], 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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key_stride_order = (0, 1, 2, 3)
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value_stride_order = (0, 1, 2, 3)
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elif cache_layout == "HND":
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key_stride_order = (0, 2, 1, 3)
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value_stride_order = (0, 2, 1, 3)
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else:
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raise ValueError(f"Unknown cache layout format {cache_layout}.")
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return key_stride_order, value_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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# for local attention
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@dataclass
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class LocalAttentionMetadata:
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local_query_start_loc: torch.Tensor
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local_seqused_k: torch.Tensor
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local_block_table: torch.Tensor
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local_max_query_len: int
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local_max_seq_len: int
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local_scheduler_metadata: Optional[torch.Tensor]
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local_attn_metadata: Optional[LocalAttentionMetadata] = None
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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 or current_platform.is_rocm()
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def __init__(self, runner: "GPUModelRunner", kv_cache_spec: AttentionSpec,
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block_table: BlockTable):
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model_config = runner.model_config
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compilation_config = runner.vllm_config.compilation_config
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self.runner = runner
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self.num_heads_q = model_config.get_num_attention_heads(
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runner.parallel_config)
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self.num_heads_kv = model_config.get_num_kv_heads(
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runner.parallel_config)
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self.headdim = model_config.get_head_size()
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self.block_size = kv_cache_spec.block_size
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self.kv_cache_spec = kv_cache_spec
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self.block_table = block_table
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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 = compilation_config.full_cuda_graph
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if self.use_full_cuda_graph:
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if not current_platform.is_rocm():
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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 = 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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self.runner.max_num_reqs + 1,
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dtype=torch.int32,
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device=self.runner.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(
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self, common_prefix_len: int,
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common_attn_metadata: CommonAttentionMetadata
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) -> FlashAttentionMetadata:
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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(self.runner.seq_lens_np[:num_reqs].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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block_table = self.block_table
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block_table_tensor = block_table.get_device_tensor()[:num_reqs]
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block_table.slot_mapping[:num_actual_tokens].copy_(
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block_table.slot_mapping_cpu[:num_actual_tokens],
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non_blocking=True)
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# Fill unused with -1. Needed for reshape_and_cache in full cuda graph
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# mode.
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block_table.slot_mapping[num_actual_tokens:].fill_(-1)
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slot_mapping = block_table.slot_mapping[:num_actual_tokens]
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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 self.aot_schedule:
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sliding_window_configs = _get_sliding_window_configs(
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self.runner.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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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 self.aot_schedule:
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sliding_window_configs = _get_sliding_window_configs(
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self.runner.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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def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
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max_seq_len, causal):
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if self.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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# for local attention
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local_attn_metadata = None
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if self.runner.attention_chunk_size is not None:
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seqlens_q_local_np, virt_q_cu_seqlens_np, virt_k_seqlens_np, \
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virt_block_table_tensor = make_local_attention_virtual_batches(
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self.runner.attention_chunk_size,
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self.runner.query_start_loc_np[:num_reqs + 1],
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self.runner.seq_lens_np[:num_reqs],
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block_table_tensor,
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self.block_size,
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)
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local_query_start_loc = torch.from_numpy(virt_q_cu_seqlens_np).to(
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self.runner.device, non_blocking=True)
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local_seqused_k = torch.from_numpy(virt_k_seqlens_np).to(
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self.runner.device, non_blocking=True)
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local_max_query_len = seqlens_q_local_np.max()
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local_max_seq_len = virt_k_seqlens_np.max()
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local_scheduler_metadata = schedule(
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batch_size=local_query_start_loc.shape[0] - 1,
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cu_query_lens=local_query_start_loc,
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max_query_len=local_max_query_len,
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seqlens=local_seqused_k,
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max_seq_len=local_max_seq_len,
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causal=True)
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local_attn_metadata = FlashAttentionMetadata.LocalAttentionMetadata(
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local_query_start_loc=local_query_start_loc,
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local_seqused_k=local_seqused_k,
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local_block_table=virt_block_table_tensor,
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local_max_query_len=local_max_query_len,
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local_max_seq_len=local_max_seq_len,
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local_scheduler_metadata=local_scheduler_metadata,
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)
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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.runner.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.runner.device)
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suffix_kv_lens = (self.runner.seq_lens_np[:num_reqs] -
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common_prefix_len)
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suffix_kv_lens = torch.from_numpy(suffix_kv_lens).to(
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self.runner.device)
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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=True)
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if not current_platform.is_rocm() and 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]
|
||||
|
||||
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):
|
||||
# NOTE(woosuk): Setting num_splits > 1 may increase the memory
|
||||
# usage, because the intermediate buffers of size [num_splits,
|
||||
# num_heads, num_tokens, head_size] are allocated. Therefore,
|
||||
# we only set num_splits when using cuda graphs.
|
||||
max_num_splits = self.max_num_splits
|
||||
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||||
attn_metadata = FlashAttentionMetadata(
|
||||
num_actual_tokens=num_actual_tokens,
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||||
max_query_len=max_query_len,
|
||||
query_start_loc=query_start_loc,
|
||||
max_seq_len=max_seq_len,
|
||||
seq_lens=seq_lens,
|
||||
block_table=block_table_tensor,
|
||||
slot_mapping=slot_mapping,
|
||||
use_cascade=use_cascade,
|
||||
common_prefix_len=common_prefix_len,
|
||||
scheduler_metadata=scheduler_metadata,
|
||||
cu_prefix_query_lens=cu_prefix_query_lens,
|
||||
prefix_kv_lens=prefix_kv_lens,
|
||||
suffix_kv_lens=suffix_kv_lens,
|
||||
local_attn_metadata=local_attn_metadata,
|
||||
prefix_scheduler_metadata=prefix_scheduler_metadata,
|
||||
max_num_splits=max_num_splits,
|
||||
)
|
||||
return attn_metadata
|
||||
|
||||
def can_run_in_cudagraph(
|
||||
self, common_attn_metadata: CommonAttentionMetadata) -> bool:
|
||||
# Full CUDA Graph always supported (FA2 support checked separately)
|
||||
return True
|
||||
|
||||
def use_cascade_attention(self, *args, **kwargs) -> bool:
|
||||
return use_cascade_attention(*args, **kwargs)
|
||||
|
||||
|
||||
class FlashAttentionImpl(AttentionImpl):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
num_heads: int,
|
||||
head_size: int,
|
||||
scale: float,
|
||||
num_kv_heads: int,
|
||||
alibi_slopes: Optional[list[float]],
|
||||
sliding_window: Optional[int],
|
||||
kv_cache_dtype: str,
|
||||
blocksparse_params: Optional[dict[str, Any]] = None,
|
||||
logits_soft_cap: Optional[float] = None,
|
||||
attn_type: AttentionType = AttentionType.DECODER,
|
||||
kv_sharing_target_layer_name: Optional[str] = None,
|
||||
use_irope: bool = False,
|
||||
) -> None:
|
||||
if blocksparse_params is not None:
|
||||
raise ValueError(
|
||||
"FlashAttention does not support block-sparse attention.")
|
||||
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)
|
||||
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)
|
||||
|
||||
if attn_type != AttentionType.DECODER:
|
||||
raise NotImplementedError("Encoder self-attention and "
|
||||
"encoder/decoder cross-attention "
|
||||
"are not implemented for "
|
||||
"FlashAttentionImpl")
|
||||
self.use_irope = use_irope
|
||||
self.vllm_flash_attn_version = get_flash_attn_version()
|
||||
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.")
|
||||
|
||||
def forward(
|
||||
self,
|
||||
layer: torch.nn.Module,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
kv_cache: torch.Tensor,
|
||||
attn_metadata: FlashAttentionMetadata,
|
||||
output: Optional[torch.Tensor] = None,
|
||||
output_scale: Optional[torch.Tensor] = 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 = [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:
|
||||
raise NotImplementedError(
|
||||
"fused output quantization is not yet supported"
|
||||
" for FlashAttentionImpl")
|
||||
|
||||
if attn_metadata is None:
|
||||
# Profiling run.
|
||||
return output
|
||||
|
||||
# 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
|
||||
if not current_platform.is_rocm():
|
||||
key_cache, value_cache = kv_cache.unbind(0)
|
||||
else:
|
||||
key_cache, value_cache = kv_cache
|
||||
|
||||
if self.kv_sharing_target_layer_name is 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.
|
||||
if not current_platform.is_rocm():
|
||||
reshape_and_cache_flash(
|
||||
key,
|
||||
value,
|
||||
key_cache,
|
||||
value_cache,
|
||||
attn_metadata.slot_mapping,
|
||||
self.kv_cache_dtype,
|
||||
layer._k_scale,
|
||||
layer._v_scale,
|
||||
)
|
||||
else:
|
||||
reshape_and_cache_cuda(
|
||||
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))
|
||||
|
||||
# Compute attention and update output up to `num_actual_tokens`.
|
||||
use_local_attn = \
|
||||
(self.use_irope and attn_metadata.local_attn_metadata is not None)
|
||||
|
||||
if not attn_metadata.use_cascade or use_local_attn:
|
||||
if use_local_attn:
|
||||
assert attn_metadata.local_attn_metadata is not None
|
||||
local_metadata = attn_metadata.local_attn_metadata
|
||||
cu_seqlens_q = local_metadata.local_query_start_loc
|
||||
seqused_k = local_metadata.local_seqused_k
|
||||
max_seqlen_q = local_metadata.local_max_query_len
|
||||
max_seqlen_k = local_metadata.local_max_seq_len
|
||||
block_table = local_metadata.local_block_table
|
||||
scheduler_metadata = local_metadata.local_scheduler_metadata
|
||||
else:
|
||||
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])
|
||||
|
||||
if not current_platform.is_rocm():
|
||||
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=True,
|
||||
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,
|
||||
)
|
||||
else:
|
||||
if envs.VLLM_USE_PA_PRINT_PARAM:
|
||||
print("PA SIZE:")
|
||||
print(f"q.shape = {query[:num_actual_tokens].shape}, key_cache.shape = {key_cache.shape}, value_cache.shape = {value_cache.shape}")
|
||||
print(f"cu_seqlens_q.shape = {cu_seqlens_q.shape}, max_seqlen_q = {max_seqlen_q}, seqused_k.shape = {seqused_k.shape}, max_seqlen_k = {max_seqlen_k}")
|
||||
print(f"softmax_scale = {self.scale:.3f}, alibi_slopes = {self.alibi_slopes}, window_size = {self.sliding_window}, block_tables.shape = {block_table.shape}, softcap = {self.logits_soft_cap}, scheduler_metadata = {scheduler_metadata}")
|
||||
|
||||
vllm_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=True,
|
||||
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,
|
||||
is_prefix_cache=True,
|
||||
)
|
||||
return output
|
||||
|
||||
assert not use_local_attn, (
|
||||
"Cascade attention does not support local attention.")
|
||||
# Cascade attention (rare case).
|
||||
if not current_platform.is_rocm():
|
||||
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,
|
||||
)
|
||||
else:
|
||||
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=2, #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 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,
|
||||
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:
|
||||
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.
|
||||
if not current_platform.is_rocm():
|
||||
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,
|
||||
)
|
||||
else:
|
||||
prefix_output, prefix_lse, _ = vllm_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,
|
||||
is_prefix_cache=True,
|
||||
)
|
||||
|
||||
descale_shape = (cu_query_lens.shape[0] - 1, key_cache.shape[-2])
|
||||
|
||||
# Process suffix per query.
|
||||
if not current_platform.is_rocm():
|
||||
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,
|
||||
)
|
||||
else:
|
||||
suffix_output, suffix_lse, _ = vllm_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,
|
||||
is_prefix_cache=True,
|
||||
)
|
||||
|
||||
# Merge prefix and suffix outputs, and store the result in output.
|
||||
merge_attn_states(output, prefix_output, prefix_lse, suffix_output,
|
||||
suffix_lse)
|
||||
Reference in New Issue
Block a user