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302 lines
11 KiB
Python
302 lines
11 KiB
Python
""" Attention layer with torch scaled_dot_product_attention
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and PagedAttention."""
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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 torch.nn.functional import scaled_dot_product_attention
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata, AttentionType)
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from vllm.attention.ops.paged_attn import PagedAttentionMetadata
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from vllm.utils import is_cpu
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if is_cpu():
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try:
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from vllm.attention.ops.ipex_attn import PagedAttention
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except ImportError:
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from vllm.attention.ops.paged_attn import PagedAttention
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else:
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from vllm.attention.ops.paged_attn import PagedAttention
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class TorchSDPABackend(AttentionBackend):
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@staticmethod
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def get_name() -> str:
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return "torch-sdpa"
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@staticmethod
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def get_impl_cls() -> Type["TorchSDPABackendImpl"]:
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return TorchSDPABackendImpl
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@staticmethod
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def get_metadata_cls() -> Type["AttentionMetadata"]:
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return TorchSDPAMetadata
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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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return PagedAttention.get_kv_cache_shape(num_blocks, block_size,
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num_kv_heads, head_size)
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@staticmethod
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def swap_blocks(
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src_kv_cache: torch.Tensor,
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dst_kv_cache: torch.Tensor,
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src_to_dst: torch.Tensor,
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) -> None:
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PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
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@staticmethod
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def copy_blocks(
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kv_caches: List[torch.Tensor],
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src_to_dists: torch.Tensor,
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) -> None:
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PagedAttention.copy_blocks(kv_caches, src_to_dists)
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@dataclass
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class TorchSDPAMetadata(AttentionMetadata, PagedAttentionMetadata):
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"""Metadata for TorchSDPABackend.
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"""
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# Currently, input sequences can only contain all prompts
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# or all decoding. True if all sequences are prompts.
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is_prompt: bool
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slot_mapping: torch.Tensor
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seq_lens: Optional[List[int]]
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def __post_init__(self):
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# Set during the execution of the first attention op.
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# It is a list because it is needed to set per prompt
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# when alibi slopes is used. It is because of the limitation
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# from xformer API.
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# will not appear in the __repr__ and __init__
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self.attn_bias: Optional[List[torch.Tensor]] = None
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@property
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def prefill_metadata(self) -> Optional["TorchSDPAMetadata"]:
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# Currently chunked prefill is not supported
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if self.num_decode_tokens == 0:
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assert self.num_prefills > 0
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return self
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return None
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@property
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def decode_metadata(self) -> Optional["TorchSDPAMetadata"]:
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# Currently chunked prefill is not supported
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if self.num_prefills > 0:
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assert self.num_decode_tokens == 0
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return None
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return self
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class TorchSDPABackendImpl(AttentionImpl[TorchSDPAMetadata]):
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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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"Torch SPDA does not support block-sparse attention.")
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if logits_soft_cap is not None:
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raise ValueError("Torch SPDA does not support logits soft cap.")
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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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self.sliding_window = sliding_window
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self.kv_cache_dtype = kv_cache_dtype
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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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self.need_mask = (self.alibi_slopes is not None
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or self.sliding_window is not None)
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supported_head_sizes = PagedAttention.get_supported_head_sizes()
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if head_size not in supported_head_sizes:
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raise ValueError(
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f"Head size {head_size} is not supported by PagedAttention. "
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f"Supported head sizes are: {supported_head_sizes}.")
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if kv_cache_dtype != "auto":
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raise NotImplementedError(
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"Torch SDPA backend does not support FP8 KV cache. "
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"Please use xFormers backend instead.")
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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: Optional[torch.Tensor],
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attn_metadata: TorchSDPAMetadata, # type: ignore
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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 torch SDPA and PagedAttention.
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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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assert k_scale == 1.0 and v_scale == 1.0
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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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"TorchSDPABackendImpl")
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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, self.num_heads, self.head_size)
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key = key.view(-1, self.num_kv_heads, self.head_size)
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value = value.view(-1, self.num_kv_heads, self.head_size)
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if kv_cache is not None:
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key_cache, value_cache = PagedAttention.split_kv_cache(
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kv_cache, self.num_kv_heads, self.head_size)
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PagedAttention.write_to_paged_cache(key, value, key_cache,
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value_cache,
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attn_metadata.slot_mapping,
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self.kv_cache_dtype, k_scale,
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v_scale)
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if attn_metadata.is_prompt:
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assert attn_metadata.seq_lens is not None
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if (kv_cache is None or attn_metadata.block_tables.numel() == 0):
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if self.num_kv_heads != self.num_heads:
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key = key.repeat_interleave(self.num_queries_per_kv, dim=1)
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value = value.repeat_interleave(self.num_queries_per_kv,
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dim=1)
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if attn_metadata.attn_bias is None:
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if self.alibi_slopes is not None:
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att_masks = _make_alibi_bias(
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self.alibi_slopes, query.dtype,
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attn_metadata.seq_lens) # type: ignore
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elif self.sliding_window is not None:
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att_masks = _make_sliding_window_bias(
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attn_metadata.seq_lens, self.sliding_window,
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query.dtype) # type: ignore
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else:
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att_masks = [None] * len(attn_metadata.seq_lens)
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attn_metadata.attn_bias = att_masks
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query = query.movedim(0, query.dim() - 2)
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key = key.movedim(0, key.dim() - 2)
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value = value.movedim(0, value.dim() - 2)
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start = 0
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output = torch.empty(
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(num_tokens, self.num_heads, self.head_size),
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dtype=query.dtype)
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for seq_len, mask in zip(attn_metadata.seq_lens,
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attn_metadata.attn_bias):
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end = start + seq_len
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sub_out = scaled_dot_product_attention(
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query[None, :, start:end, :],
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key[None, :, start:end, :],
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value[None, :, start:end, :],
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attn_mask=mask,
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dropout_p=0.0,
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is_causal=not self.need_mask,
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scale=self.scale).squeeze(0).movedim(
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query.dim() - 2, 0)
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output[start:end, :, :] = sub_out
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start = end
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else:
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# prefix-enabled attention
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raise RuntimeError(
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"Torch SDPA backend doesn't support prefix decoding.")
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else:
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# Decoding run.
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output = PagedAttention.forward_decode(
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query,
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key_cache,
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value_cache,
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attn_metadata.block_tables,
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attn_metadata.seq_lens_tensor,
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attn_metadata.max_decode_seq_len,
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self.kv_cache_dtype,
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self.num_kv_heads,
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self.scale,
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self.alibi_slopes,
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k_scale,
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v_scale,
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)
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# Reshape the output tensor.
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return output.view(-1, self.num_heads * self.head_size)
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def _make_alibi_bias(
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alibi_slopes: torch.Tensor,
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dtype: torch.dtype,
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seq_lens: List[int],
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) -> List[torch.Tensor]:
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attn_biases: List[torch.Tensor] = []
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for seq_len in seq_lens:
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bias = torch.arange(seq_len, dtype=dtype)
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# NOTE(zhuohan): HF uses
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# `bias = bias[None, :].repeat(seq_len, 1)`
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# here. We find that both biases give the same results, but
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# the bias below more accurately follows the original ALiBi
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# paper.
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bias = bias[None, :] - bias[:, None]
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num_heads = alibi_slopes.shape[0]
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bias = bias[None, :].repeat((num_heads, 1, 1))
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bias.mul_(alibi_slopes[:, None, None]).unsqueeze_(0)
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inf_mask = torch.empty(
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(1, seq_len, seq_len),
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dtype=bias.dtype).fill_(-torch.inf).triu_(diagonal=1)
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attn_biases.append((bias + inf_mask).to(dtype))
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return attn_biases
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def _make_sliding_window_bias(
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seq_lens: List[int],
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window_size: Optional[int],
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dtype: torch.dtype,
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) -> List[torch.Tensor]:
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attn_biases: List[torch.Tensor] = []
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for seq_len in seq_lens:
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tensor = torch.full(
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(1, seq_len, seq_len),
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dtype=dtype,
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fill_value=1,
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)
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shift = 0
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mask = torch.tril(tensor, diagonal=shift).to(dtype) # type: ignore
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if window_size is not None:
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mask = torch.triu(mask, diagonal=shift - window_size + 1)
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mask = torch.log(mask)
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attn_biases.append(mask.to(dtype))
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return attn_biases
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