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238 lines
7.6 KiB
Python
238 lines
7.6 KiB
Python
"""Attention layer with FlashAttention."""
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Type
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import torch
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata, AttentionType)
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from vllm.forward_context import get_forward_context
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from vllm.vllm_flash_attn import flash_attn_varlen_func
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class FlashAttentionBackend(AttentionBackend):
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@staticmethod
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def get_supported_head_sizes() -> 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_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_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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@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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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_start_loc: torch.Tensor
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block_table: torch.Tensor
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slot_mapping: torch.Tensor
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class FlashAttentionImpl(AttentionImpl):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[List[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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blocksparse_params: Optional[Dict[str, Any]] = None,
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logits_soft_cap: Optional[float] = None,
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) -> None:
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if blocksparse_params is not None:
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raise ValueError(
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"FlashAttention does not support block-sparse attention.")
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self.num_heads = num_heads
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self.head_size = head_size
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self.scale = float(scale)
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self.num_kv_heads = num_kv_heads
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if alibi_slopes is not None:
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alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
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self.alibi_slopes = alibi_slopes
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if sliding_window is None:
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self.sliding_window = (-1, -1)
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else:
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self.sliding_window = (sliding_window - 1, 0)
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self.kv_cache_dtype = kv_cache_dtype
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if logits_soft_cap is None:
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# In flash-attn, setting logits_soft_cap as 0 means no soft cap.
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logits_soft_cap = 0
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self.logits_soft_cap = logits_soft_cap
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assert self.num_heads % self.num_kv_heads == 0
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self.num_queries_per_kv = self.num_heads // self.num_kv_heads
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support_head_sizes = FlashAttentionBackend.get_supported_head_sizes()
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if head_size not in support_head_sizes:
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raise ValueError(
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f"Head size {head_size} is not supported by FlashAttention. "
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f"Supported head sizes are: {support_head_sizes}.")
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: FlashAttentionMetadata,
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k_scale: float = 1.0,
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v_scale: float = 1.0,
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attn_type: AttentionType = AttentionType.DECODER,
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) -> torch.Tensor:
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"""Forward pass with FlashAttention.
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Args:
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query: shape = [num_tokens, num_heads * head_size]
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key: shape = [num_tokens, num_kv_heads * head_size]
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value: shape = [num_tokens, num_kv_heads * head_size]
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kv_cache = [2, num_blocks, block_size, num_kv_heads, head_size]
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attn_metadata: Metadata for attention.
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Returns:
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shape = [num_tokens, num_heads * head_size]
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"""
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if attn_type != AttentionType.DECODER:
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raise NotImplementedError("Encoder self-attention and "
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"encoder/decoder cross-attention "
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"are not implemented for "
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"FlashAttentionImpl")
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# NOTE(woosuk): FlashAttention does not support FP8 KV cache.
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assert k_scale == 1.0 and v_scale == 1.0, (
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"key/v_scale is not supported in FlashAttention.")
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output = torch.ops.vllm.unified_flash_attention(
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query,
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key,
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value,
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self.num_heads,
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self.head_size,
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self.num_kv_heads,
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kv_cache,
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self.kv_cache_dtype,
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k_scale,
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v_scale,
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self.scale,
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self.sliding_window,
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self.alibi_slopes,
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self.logits_soft_cap,
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)
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return output
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@torch.library.custom_op("vllm::unified_flash_attention",
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mutates_args=["kv_cache"])
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def unified_flash_attention(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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num_heads: int,
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head_size: int,
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num_kv_heads: int,
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kv_cache: torch.Tensor,
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kv_cache_dtype: str,
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k_scale: float,
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v_scale: float,
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softmax_scale: float,
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window_size: Optional[List[int]] = None,
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alibi_slopes: Optional[torch.Tensor] = None,
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logits_soft_cap: Optional[float] = None,
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) -> torch.Tensor:
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current_metadata = get_forward_context()
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if current_metadata is None:
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# Profiling run.
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return torch.empty_like(query)
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assert current_metadata is not None
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assert isinstance(current_metadata, FlashAttentionMetadata)
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attn_metadata: FlashAttentionMetadata = current_metadata
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num_tokens, hidden_size = query.shape
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# Reshape the query, key, and value tensors.
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query = query.view(-1, num_heads, head_size)
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key = key.view(-1, num_kv_heads, head_size)
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value = value.view(-1, num_kv_heads, head_size)
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# Reshape the input keys and values and store them in the cache.
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key_cache = kv_cache[0]
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value_cache = kv_cache[1]
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torch.ops._C_cache_ops.reshape_and_cache_flash(
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key,
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value,
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kv_cache[0],
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kv_cache[1],
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attn_metadata.slot_mapping,
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kv_cache_dtype,
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k_scale,
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v_scale,
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)
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output = flash_attn_varlen_func(
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q=query,
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k=key_cache,
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v=value_cache,
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cu_seqlens_q=attn_metadata.query_start_loc,
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max_seqlen_q=attn_metadata.max_query_len,
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cu_seqlens_k=attn_metadata.seq_start_loc,
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max_seqlen_k=attn_metadata.max_seq_len,
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softmax_scale=softmax_scale,
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causal=True,
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alibi_slopes=alibi_slopes,
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window_size=window_size,
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block_table=attn_metadata.block_table,
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softcap=logits_soft_cap,
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)
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return output.view(num_tokens, hidden_size)
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@unified_flash_attention.register_fake
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def _(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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num_heads: int,
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head_size: int,
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num_kv_heads: int,
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kv_cache: torch.Tensor,
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kv_cache_dtype: str,
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k_scale: float,
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v_scale: float,
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softmax_scale: float,
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window_size: Optional[List[int]] = None,
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alibi_slopes: Optional[torch.Tensor] = None,
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logits_soft_cap: Optional[float] = None,
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) -> torch.Tensor:
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return torch.empty_like(query)
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