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463 lines
18 KiB
Python
463 lines
18 KiB
Python
"""Attention layer with xFormers 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 xformers import ops as xops
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from xformers.ops.fmha.attn_bias import (AttentionBias,
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BlockDiagonalCausalMask,
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LowerTriangularMaskWithTensorBias)
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionMetadata)
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from vllm.attention.ops.paged_attn import (PagedAttention,
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PagedAttentionMetadata)
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from vllm.logger import init_logger
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logger = init_logger(__name__)
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class XFormersBackend(AttentionBackend):
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@staticmethod
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def get_name() -> str:
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return "xformers"
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@staticmethod
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def get_impl_cls() -> Type["XFormersImpl"]:
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return XFormersImpl
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@staticmethod
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def make_metadata(*args, **kwargs) -> "XFormersMetadata":
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return XFormersMetadata(*args, **kwargs)
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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: Dict[int, int],
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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 XFormersMetadata(AttentionMetadata, PagedAttentionMetadata):
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"""Metadata for XFormersbackend.
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NOTE: Any python object stored here is not updated when it is
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cuda-graph replayed. If you have values that need to be changed
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dynamically, it should be stored in tensor. The tensor has to be
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updated from `CUDAGraphRunner.forward` API.
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"""
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# (batch_size,). The sequence length per sequence. Sequence length means
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# the computed tokens + new tokens None if it is a decoding.
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seq_lens: Optional[List[int]]
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# seq_lens stored as a tensor.
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seq_lens_tensor: Optional[torch.Tensor]
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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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# Maximum query length in the batch. None for decoding.
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max_query_len: Optional[int]
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# FIXME: It is for flash attn.
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# Maximum sequence length among prefill batch. 0 if there are decoding
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# requests only.
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max_prefill_seq_len: int
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# Maximum sequence length among decode batch. 0 if there are prefill
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# requests only.
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max_decode_seq_len: int
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# (batch_size + 1,). The cumulative subquery lengths of the sequences in
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# the batch, used to index into subquery. E.g., if the subquery length
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# is [4, 6], it is [0, 4, 10].
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query_start_loc: Optional[torch.Tensor]
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# FIXME: It is for flash attn.
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# (batch_size + 1,). The cumulative sequence lengths of the sequences in
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# the batch, used to index into sequence. E.g., if the sequence length is
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# [4, 6], it is [0, 4, 10].
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seq_start_loc: Optional[torch.Tensor]
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# (batch_size,) A tensor of context lengths (tokens that are computed
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# so far).
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context_lens_tensor: Optional[torch.Tensor]
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# Whether or not if cuda graph is enabled.
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# Cuda-graph is currently enabled for decoding only.
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# TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention.
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use_cuda_graph: bool
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_cached_prefill_metadata: Optional["XFormersMetadata"] = None
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_cached_decode_metadata: Optional["XFormersMetadata"] = None
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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[AttentionBias]] = None
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@property
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def prefill_metadata(self) -> Optional["XFormersMetadata"]:
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if self.num_prefills == 0:
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return None
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if self._cached_prefill_metadata is not None:
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return self._cached_prefill_metadata
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assert self.seq_lens is not None
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assert self.seq_lens_tensor is not None
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assert self.query_start_loc is not None
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assert self.context_lens_tensor is not None
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assert self.block_tables is not None
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self._cached_prefill_metadata = XFormersMetadata(
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num_prefills=self.num_prefills,
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num_prefill_tokens=self.num_prefill_tokens,
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num_decode_tokens=0,
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slot_mapping=self.slot_mapping[:self.num_prefill_tokens],
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seq_lens=self.seq_lens[:self.num_prefills],
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seq_lens_tensor=self.seq_lens_tensor[:self.num_prefills],
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max_query_len=self.max_query_len,
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max_prefill_seq_len=self.max_prefill_seq_len,
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max_decode_seq_len=0,
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query_start_loc=self.query_start_loc[:self.num_prefills + 1],
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seq_start_loc=None,
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context_lens_tensor=self.context_lens_tensor[:self.num_prefills],
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block_tables=self.block_tables[:self.num_prefills],
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use_cuda_graph=False,
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)
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return self._cached_prefill_metadata
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@property
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def decode_metadata(self) -> Optional["XFormersMetadata"]:
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if self.num_decode_tokens == 0:
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return None
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if self._cached_decode_metadata is not None:
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return self._cached_decode_metadata
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assert self.block_tables is not None
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assert self.seq_lens_tensor is not None
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self._cached_decode_metadata = XFormersMetadata(
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num_prefills=0,
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num_prefill_tokens=0,
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num_decode_tokens=self.num_decode_tokens,
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slot_mapping=self.slot_mapping[self.num_prefill_tokens:],
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seq_lens=None,
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seq_lens_tensor=self.seq_lens_tensor[self.num_prefills:],
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max_query_len=None,
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max_prefill_seq_len=0,
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max_decode_seq_len=self.max_decode_seq_len,
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query_start_loc=None,
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seq_start_loc=None,
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context_lens_tensor=None,
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block_tables=self.block_tables[self.num_prefills:],
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use_cuda_graph=self.use_cuda_graph,
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)
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return self._cached_decode_metadata
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class XFormersImpl(AttentionImpl[XFormersMetadata]):
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"""
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If the input tensors contain prompt tokens, the layout is as follows:
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|<--------------- num_prefill_tokens ----------------->|
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|<--prefill_0-->|<--prefill_1-->|...|<--prefill_N-1--->|
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Otherwise, the layout is as follows:
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|<----------------- num_decode_tokens ------------------>|
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|<--decode_0-->|..........|<--decode_M-1-->|<--padding-->|
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Generation tokens can contain padding when cuda-graph is used.
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Currently, prompt tokens don't contain any padding.
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The prompts might have different lengths, while the generation tokens
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always have length 1.
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If chunked prefill is enabled, prefill tokens and decode tokens can be
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batched together in a flattened 1D query.
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|<----- num_prefill_tokens ---->|<------- num_decode_tokens --------->|
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|<-prefill_0->|...|<-prefill_N-1->|<--decode_0-->|...|<--decode_M-1-->|
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Currently, cuda graph is disabled for chunked prefill, meaning there's no
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padding between prefill and decode tokens.
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"""
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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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) -> None:
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assert blocksparse_params is None, ValueError(
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"XFormer 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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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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suppored_head_sizes = PagedAttention.get_supported_head_sizes()
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if head_size not in suppored_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: {suppored_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: Optional[torch.Tensor],
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attn_metadata: "XFormersMetadata",
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kv_scale: float = 1.0,
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) -> torch.Tensor:
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"""Forward pass with xFormers 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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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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# Reshape the input keys and values and store them in the cache.
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# If kv_cache is not provided, the new key and value tensors are
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# not cached. This happens during the initial memory profiling run.
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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, kv_scale)
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num_prefill_tokens = attn_metadata.num_prefill_tokens
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num_decode_tokens = attn_metadata.num_decode_tokens
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assert key.shape[0] == num_prefill_tokens + num_decode_tokens
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assert value.shape[0] == num_prefill_tokens + num_decode_tokens
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output = torch.empty_like(query)
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# Query for decode. KV is not needed because it is already cached.
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decode_query = query[num_prefill_tokens:]
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# QKV for prefill.
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query = query[:num_prefill_tokens]
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key = key[:num_prefill_tokens]
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value = value[:num_prefill_tokens]
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assert query.shape[0] == num_prefill_tokens
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assert decode_query.shape[0] == num_decode_tokens
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if prefill_meta := attn_metadata.prefill_metadata:
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# Prompt run.
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if kv_cache is None or prefill_meta.block_tables.numel() == 0:
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# normal attention.
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# block tables are empty if the prompt does not have a cached
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# prefix.
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out = self._run_memory_efficient_xformers_forward(
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query, key, value, prefill_meta)
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assert out.shape == output[:num_prefill_tokens].shape
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output[:num_prefill_tokens] = out
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else:
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# prefix-enabled attention
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# TODO(Hai) this triton kernel has regression issue (broke) to
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# deal with different data types between KV and FP8 KV cache,
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# to be addressed separately.
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out = PagedAttention.forward_prefix(
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query,
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key,
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value,
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key_cache,
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value_cache,
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prefill_meta.block_tables,
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prefill_meta.query_start_loc,
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prefill_meta.seq_lens_tensor,
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prefill_meta.context_lens_tensor,
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prefill_meta.max_query_len,
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self.alibi_slopes,
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self.sliding_window,
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)
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assert output[:num_prefill_tokens].shape == out.shape
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output[:num_prefill_tokens] = out
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if decode_meta := attn_metadata.decode_metadata:
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output[num_prefill_tokens:] = PagedAttention.forward_decode(
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decode_query,
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key_cache,
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value_cache,
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decode_meta.block_tables,
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decode_meta.seq_lens_tensor,
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decode_meta.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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kv_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 _run_memory_efficient_xformers_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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attn_metadata: XFormersMetadata,
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) -> torch.Tensor:
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"""Attention for 1D query of multiple prompts. Multiple prompt
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tokens are flattened in to `query` input.
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See https://facebookresearch.github.io/xformers/components/ops.html
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for API spec.
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Args:
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output: shape = [num_prefill_tokens, num_heads, head_size]
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query: shape = [num_prefill_tokens, num_heads, head_size]
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key: shape = [num_prefill_tokens, num_kv_heads, head_size]
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value: shape = [num_prefill_tokens, num_kv_heads, head_size]
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attn_metadata: Metadata for attention.
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"""
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assert attn_metadata.seq_lens is not None
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original_query = query
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if self.num_kv_heads != self.num_heads:
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# GQA/MQA requires the shape [B, M, G, H, K].
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# Note that the output also has the same shape (which is different
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# from a spec from the doc).
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query = query.view(query.shape[0], self.num_kv_heads,
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self.num_queries_per_kv, query.shape[-1])
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key = key[:, :,
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None, :].expand(key.shape[0], self.num_kv_heads,
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self.num_queries_per_kv, key.shape[-1])
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value = value[:, :,
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None, :].expand(value.shape[0], self.num_kv_heads,
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self.num_queries_per_kv,
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value.shape[-1])
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# Set attention bias if not provided. This typically happens at
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# the very attention layer of every iteration.
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# FIXME(woosuk): This is a hack.
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if attn_metadata.attn_bias is None:
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if self.alibi_slopes is None:
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attn_bias = BlockDiagonalCausalMask.from_seqlens(
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attn_metadata.seq_lens)
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if self.sliding_window is not None:
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attn_bias = attn_bias.make_local_attention(
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self.sliding_window)
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attn_metadata.attn_bias = [attn_bias]
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else:
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attn_metadata.attn_bias = _make_alibi_bias(
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self.alibi_slopes, self.num_kv_heads, query.dtype,
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attn_metadata.seq_lens)
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# No alibi slopes.
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# TODO(woosuk): Too many view operations. Let's try to reduce
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# them in the future for code readability.
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if self.alibi_slopes is None:
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# Add the batch dimension.
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query = query.unsqueeze(0)
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key = key.unsqueeze(0)
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value = value.unsqueeze(0)
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out = xops.memory_efficient_attention_forward(
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query,
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key,
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value,
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attn_bias=attn_metadata.attn_bias[0],
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p=0.0,
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scale=self.scale)
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return out.view_as(original_query)
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# Attention with alibi slopes.
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# FIXME(woosuk): Because xformers does not support dynamic sequence
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# lengths with custom attention bias, we process each prompt one by
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# one. This is inefficient, especially when we have many short prompts.
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output = torch.empty_like(original_query)
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start = 0
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for i, seq_len in enumerate(attn_metadata.seq_lens):
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end = start + seq_len
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out = xops.memory_efficient_attention_forward(
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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_bias=attn_metadata.attn_bias[i],
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p=0.0,
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scale=self.scale)
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# TODO(woosuk): Unnecessary copy. Optimize.
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output[start:end].copy_(out.view_as(original_query[start:end]))
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start += seq_len
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return output
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def _make_alibi_bias(
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alibi_slopes: torch.Tensor,
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num_kv_heads: int,
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dtype: torch.dtype,
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seq_lens: List[int],
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) -> List[AttentionBias]:
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attn_biases: List[AttentionBias] = []
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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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# Calculate a matrix where each element represents ith element- jth
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# element.
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bias = bias[None, :] - bias[:, None]
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padded_len = (seq_len + 7) // 8 * 8
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num_heads = alibi_slopes.shape[0]
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bias = torch.empty(
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1, # batch size
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num_heads,
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seq_len,
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padded_len,
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device=alibi_slopes.device,
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dtype=dtype,
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)[:, :, :, :seq_len].copy_(bias)
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bias.mul_(alibi_slopes[:, None, None])
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if num_heads != num_kv_heads:
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bias = bias.unflatten(1, (num_kv_heads, num_heads // num_kv_heads))
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attn_biases.append(LowerTriangularMaskWithTensorBias(bias))
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return attn_biases
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