803 lines
33 KiB
Python
803 lines
33 KiB
Python
# 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 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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BlockDiagonalMask,
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LowerTriangularMaskWithTensorBias)
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionLayer,
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AttentionMetadata, AttentionType)
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from vllm.attention.backends.utils import (
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CommonAttentionState, CommonMetadataBuilder,
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get_num_prefill_decode_query_kv_tokens, get_seq_len_block_table_args,
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is_all_cross_attn_metadata_set, is_all_encoder_attn_metadata_set)
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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 get_metadata_cls() -> Type["AttentionMetadata"]:
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return XFormersMetadata
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@staticmethod
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def get_builder_cls() -> Type["XFormersMetadataBuilder"]:
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return XFormersMetadataBuilder
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@staticmethod
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def get_state_cls() -> Type["CommonAttentionState"]:
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return CommonAttentionState
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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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# |---------- 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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# seq_lens stored as a tensor.
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seq_lens_tensor: Optional[torch.Tensor]
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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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# 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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# (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]] = None
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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] = None
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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] = None
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# Maximum query length in the batch. None for decoding.
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max_query_len: Optional[int] = None
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# Max number of query tokens among request in the batch.
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max_decode_query_len: Optional[int] = None
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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] = None
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# Self-attention prefill/decode metadata cache
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_cached_prefill_metadata: Optional["XFormersMetadata"] = None
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_cached_decode_metadata: Optional["XFormersMetadata"] = None
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# Begin encoder attn & enc/dec cross-attn fields...
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# Encoder sequence lengths representation
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encoder_seq_lens: Optional[List[int]] = None
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encoder_seq_lens_tensor: Optional[torch.Tensor] = None
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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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encoder_seq_start_loc: Optional[torch.Tensor] = None
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# Maximum sequence length among encoder sequences
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max_encoder_seq_len: Optional[int] = None
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# Number of tokens input to encoder
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num_encoder_tokens: Optional[int] = None
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# Cross-attention memory-mapping data structures: slot mapping
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# and block tables
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cross_slot_mapping: Optional[torch.Tensor] = None
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cross_block_tables: Optional[torch.Tensor] = 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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self.encoder_attn_bias: Optional[List[AttentionBias]] = None
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self.cross_attn_bias: Optional[List[AttentionBias]] = None
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@property
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def is_all_encoder_attn_metadata_set(self):
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'''
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All attention metadata required for encoder attention is set.
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'''
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return is_all_encoder_attn_metadata_set(self)
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@property
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def is_all_cross_attn_metadata_set(self):
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'''
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All attention metadata required for enc/dec cross-attention is set.
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Superset of encoder attention required metadata.
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'''
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return is_all_cross_attn_metadata_set(self)
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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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# Recover cached prefill-phase attention
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# metadata structure
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return self._cached_prefill_metadata
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assert ((self.seq_lens is not None)
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or (self.encoder_seq_lens is not None))
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assert ((self.seq_lens_tensor is not None)
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or (self.encoder_seq_lens_tensor is not None))
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# Compute some attn_metadata fields which default to None
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query_start_loc = (None if self.query_start_loc is None else
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self.query_start_loc[:self.num_prefills + 1])
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seq_start_loc = (None if self.seq_start_loc is None else
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self.seq_start_loc[:self.num_prefills + 1])
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slot_mapping = (None if self.slot_mapping is None else
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self.slot_mapping[:self.num_prefill_tokens])
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seq_lens = (None if self.seq_lens is None else
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self.seq_lens[:self.num_prefills])
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seq_lens_tensor = (None if self.seq_lens_tensor is None else
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self.seq_lens_tensor[:self.num_prefills])
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context_lens_tensor = (None if self.context_lens_tensor is None else
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self.context_lens_tensor[:self.num_prefills])
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block_tables = (None if self.block_tables is None else
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self.block_tables[:self.num_prefills])
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# Construct & cache prefill-phase attention metadata structure
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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=slot_mapping,
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multi_modal_placeholder_index_maps=self.
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multi_modal_placeholder_index_maps,
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enable_kv_scales_calculation=self.enable_kv_scales_calculation,
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seq_lens=seq_lens,
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seq_lens_tensor=seq_lens_tensor,
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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=query_start_loc,
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seq_start_loc=seq_start_loc,
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context_lens_tensor=context_lens_tensor,
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block_tables=block_tables,
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use_cuda_graph=False,
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# Begin encoder & cross attn fields below...
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encoder_seq_lens=self.encoder_seq_lens,
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encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
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max_encoder_seq_len=self.max_encoder_seq_len,
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cross_slot_mapping=self.cross_slot_mapping,
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cross_block_tables=self.cross_block_tables)
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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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# Recover cached decode-phase attention
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# metadata structure
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return self._cached_decode_metadata
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assert ((self.seq_lens_tensor is not None)
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or (self.encoder_seq_lens_tensor is not None))
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# Compute some attn_metadata fields which default to None
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slot_mapping = (None if self.slot_mapping is None else
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self.slot_mapping[self.num_prefill_tokens:])
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seq_lens_tensor = (None if self.seq_lens_tensor is None else
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self.seq_lens_tensor[self.num_prefills:])
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block_tables = (None if self.block_tables is None else
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self.block_tables[self.num_prefills:])
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# Construct & cache decode-phase attention metadata structure
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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=slot_mapping,
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multi_modal_placeholder_index_maps=None,
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enable_kv_scales_calculation=True,
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seq_lens_tensor=seq_lens_tensor,
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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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block_tables=block_tables,
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use_cuda_graph=self.use_cuda_graph,
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# Begin encoder & cross attn fields below...
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encoder_seq_lens=self.encoder_seq_lens,
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encoder_seq_lens_tensor=self.encoder_seq_lens_tensor,
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max_encoder_seq_len=self.max_encoder_seq_len,
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cross_slot_mapping=self.cross_slot_mapping,
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cross_block_tables=self.cross_block_tables)
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# Batch may be composed of prefill|decodes, adjust query start indices
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# to refer to the start of decodes when the two are split apart.
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# E.g. in tokens:[3 prefills|6 decodes], query_start_loc=[3,9] => [0,6].
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if self._cached_decode_metadata.query_start_loc is not None:
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qs = self._cached_decode_metadata.query_start_loc
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self._cached_decode_metadata.query_start_loc = qs - qs[0]
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return self._cached_decode_metadata
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def _get_attn_bias(
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attn_metadata: XFormersMetadata,
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attn_type: str,
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) -> Optional[AttentionBias]:
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'''
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Extract appropriate attention bias from attention metadata
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according to attention type.
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Arguments:
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* attn_metadata: Attention metadata structure associated with attention
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* attn_type: encoder attention, decoder self-attention,
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encoder/decoder cross-attention
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Returns:
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* Appropriate attention bias value given the attention type
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'''
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if (attn_type == AttentionType.DECODER
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or attn_type == AttentionType.ENCODER_ONLY):
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return attn_metadata.attn_bias
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elif attn_type == AttentionType.ENCODER:
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return attn_metadata.encoder_attn_bias
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elif attn_type == AttentionType.ENCODER_DECODER:
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return attn_metadata.cross_attn_bias
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else:
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raise AttributeError(f"Invalid attention type {str(attn_type)}")
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def _set_attn_bias(
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attn_metadata: XFormersMetadata,
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attn_bias: List[Optional[AttentionBias]],
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attn_type: str,
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) -> None:
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'''
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Update appropriate attention bias field of attention metadata,
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according to attention type.
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Arguments:
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* attn_metadata: Attention metadata structure associated with attention
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* attn_bias: The desired attention bias value
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* attn_type: encoder attention, decoder self-attention,
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encoder/decoder cross-attention
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'''
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if (attn_type == AttentionType.DECODER
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or attn_type == AttentionType.ENCODER_ONLY):
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attn_metadata.attn_bias = attn_bias
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elif attn_type == AttentionType.ENCODER:
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attn_metadata.encoder_attn_bias = attn_bias
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elif attn_type == AttentionType.ENCODER_DECODER:
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attn_metadata.cross_attn_bias = attn_bias
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else:
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raise AttributeError(f"Invalid attention type {str(attn_type)}")
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class XFormersMetadataBuilder(CommonMetadataBuilder[XFormersMetadata]):
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_metadata_cls = XFormersMetadata
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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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logits_soft_cap: Optional[float] = None,
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attn_type: str = AttentionType.DECODER,
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kv_sharing_target_layer_name: Optional[str] = None,
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use_irope: bool = False,
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) -> None:
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if kv_sharing_target_layer_name is not None:
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raise NotImplementedError("KV sharing is not supported in V0.")
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if blocksparse_params is not None:
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raise ValueError(
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"XFormers does not support block-sparse attention.")
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if logits_soft_cap is not None:
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logger.warning_once("XFormers does not support logits soft cap. "
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"Outputs may be slightly off.")
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if use_irope:
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logger.warning_once(
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"Using irope in XFormers is not supported yet, it will fall"
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" back to global attention for long context.")
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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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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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self.attn_type = attn_type
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def forward(
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self,
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layer: AttentionLayer,
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query: torch.Tensor,
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key: Optional[torch.Tensor],
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value: Optional[torch.Tensor],
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kv_cache: torch.Tensor,
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attn_metadata: "XFormersMetadata",
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output: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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"""Forward pass with xFormers and PagedAttention.
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For decoder-only models: query, key and value must be non-None.
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For encoder/decoder models:
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* XFormersImpl.forward() may be invoked for both self- and cross-
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attention layers.
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* For self-attention: query, key and value must be non-None.
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* For cross-attention:
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* Query must be non-None
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* During prefill, key and value must be non-None; key and value
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get cached for use during decode.
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* During decode, key and value may be None, since:
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(1) key and value tensors were cached during prefill, and
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(2) cross-attention key and value tensors do not grow during
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decode
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A note on how the attn_type (attention type enum) argument impacts
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attention forward() behavior:
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* DECODER: normal decoder-only behavior;
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use decoder self-attention block table
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* ENCODER: no KV caching; pass encoder sequence
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attributes (encoder_seq_lens/encoder_seq_lens_tensor/
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max_encoder_seq_len) to kernel, in lieu of decoder
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sequence attributes (seq_lens/seq_lens_tensor/max_seq_len).
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Used for encoder branch of encoder-decoder models.
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* ENCODER_ONLY: no kv_caching, uses the normal attention
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attributes (seq_lens/seq_lens_tensor/max_seq_len).
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* ENCODER_DECODER: cross-attention behavior;
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use cross-attention block table for caching KVs derived
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from encoder hidden states; since KV sequence lengths
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will match encoder sequence lengths, pass encoder sequence
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attributes to kernel (encoder_seq_lens/encoder_seq_lens_tensor/
|
|
max_encoder_seq_len)
|
|
|
|
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]
|
|
NOTE: kv_cache will be an empty tensor with shape [0]
|
|
for profiling run.
|
|
attn_metadata: Metadata for attention.
|
|
attn_type: Select attention type, between encoder attention,
|
|
decoder self-attention, or encoder/decoder cross-
|
|
attention. Defaults to decoder self-attention,
|
|
which is the vLLM default generally
|
|
Returns:
|
|
shape = [num_tokens, num_heads * head_size]
|
|
"""
|
|
attn_type = self.attn_type
|
|
# Check that appropriate attention metadata attributes are
|
|
# selected for the desired attention type
|
|
if (attn_type == AttentionType.ENCODER
|
|
and (not attn_metadata.is_all_encoder_attn_metadata_set)):
|
|
raise AttributeError("Encoder attention requires setting "
|
|
"encoder metadata attributes.")
|
|
|
|
elif (attn_type == AttentionType.ENCODER_DECODER
|
|
and (not attn_metadata.is_all_cross_attn_metadata_set)):
|
|
raise AttributeError("Encoder/decoder cross-attention "
|
|
"requires setting cross-attention "
|
|
"metadata attributes.")
|
|
|
|
query = query.view(-1, self.num_heads, self.head_size)
|
|
if key is not None:
|
|
assert value is not None
|
|
key = key.view(-1, self.num_kv_heads, self.head_size)
|
|
value = value.view(-1, self.num_kv_heads, self.head_size)
|
|
else:
|
|
assert value is None
|
|
|
|
# Self-attention vs. cross-attention will impact
|
|
# which KV cache memory-mapping & which
|
|
# seqlen datastructures we utilize
|
|
|
|
if (attn_type != AttentionType.ENCODER and kv_cache.numel() > 0):
|
|
# KV-cache during decoder-self- or
|
|
# encoder-decoder-cross-attention, but not
|
|
# during encoder attention.
|
|
#
|
|
# Even if there are no new key/value pairs to cache,
|
|
# we still need to break out key_cache and value_cache
|
|
# i.e. for later use by paged attention
|
|
key_cache, value_cache = PagedAttention.split_kv_cache(
|
|
kv_cache, self.num_kv_heads, self.head_size)
|
|
|
|
if (key is not None) and (value is not None):
|
|
|
|
if attn_type == AttentionType.ENCODER_DECODER:
|
|
# Update cross-attention KV cache (prefill-only)
|
|
# During cross-attention decode, key & value will be None,
|
|
# preventing this IF-statement branch from running
|
|
updated_slot_mapping = attn_metadata.cross_slot_mapping
|
|
else:
|
|
# Update self-attention KV cache (prefill/decode)
|
|
updated_slot_mapping = attn_metadata.slot_mapping
|
|
|
|
# Reshape the input keys and values and store them in the cache.
|
|
# If kv_cache is not provided, the new key and value tensors are
|
|
# not cached. This happens during the initial memory
|
|
# profiling run.
|
|
PagedAttention.write_to_paged_cache(
|
|
key, value, key_cache, value_cache, updated_slot_mapping,
|
|
self.kv_cache_dtype, layer._k_scale, layer._v_scale)
|
|
(num_prefill_query_tokens, num_prefill_kv_tokens,
|
|
num_decode_query_tokens) = \
|
|
get_num_prefill_decode_query_kv_tokens(attn_metadata, attn_type)
|
|
|
|
output = torch.empty_like(query)
|
|
# Query for decode. KV is not needed because it is already cached.
|
|
decode_query = query[num_prefill_query_tokens:]
|
|
# QKV for prefill.
|
|
query = query[:num_prefill_query_tokens]
|
|
if key is not None and value is not None:
|
|
key = key[:num_prefill_kv_tokens]
|
|
value = value[:num_prefill_kv_tokens]
|
|
|
|
assert query.shape[0] == num_prefill_query_tokens
|
|
assert decode_query.shape[0] == num_decode_query_tokens
|
|
|
|
if prefill_meta := attn_metadata.prefill_metadata:
|
|
# Prompt run.
|
|
if kv_cache.numel() == 0 or prefill_meta.block_tables.numel() == 0:
|
|
# normal attention.
|
|
# block tables are empty if the prompt does not have a cached
|
|
# prefix.
|
|
out = self._run_memory_efficient_xformers_forward(
|
|
query, key, value, prefill_meta, attn_type=attn_type)
|
|
assert out.shape == output[:num_prefill_query_tokens].shape
|
|
output[:num_prefill_query_tokens] = out
|
|
else:
|
|
assert attn_type != AttentionType.ENCODER_ONLY, (
|
|
"Encoder-only models should not have prefix attention.")
|
|
|
|
assert prefill_meta.query_start_loc is not None
|
|
assert prefill_meta.max_query_len is not None
|
|
|
|
# prefix-enabled attention
|
|
# TODO(Hai) this triton kernel has regression issue (broke) to
|
|
# deal with different data types between KV and FP8 KV cache,
|
|
# to be addressed separately.
|
|
out = PagedAttention.forward_prefix(
|
|
query,
|
|
key,
|
|
value,
|
|
self.kv_cache_dtype,
|
|
key_cache,
|
|
value_cache,
|
|
prefill_meta.block_tables,
|
|
prefill_meta.query_start_loc,
|
|
prefill_meta.seq_lens_tensor,
|
|
prefill_meta.max_query_len,
|
|
self.alibi_slopes,
|
|
self.sliding_window,
|
|
layer._k_scale,
|
|
layer._v_scale,
|
|
)
|
|
assert output[:num_prefill_query_tokens].shape == out.shape
|
|
output[:num_prefill_query_tokens] = out
|
|
|
|
if decode_meta := attn_metadata.decode_metadata:
|
|
assert attn_type != AttentionType.ENCODER_ONLY, (
|
|
"Encoder-only models should not have decode metadata.")
|
|
|
|
(
|
|
seq_lens_arg,
|
|
max_seq_len_arg,
|
|
block_tables_arg,
|
|
) = get_seq_len_block_table_args(decode_meta, False, attn_type)
|
|
|
|
output[num_prefill_query_tokens:] = PagedAttention.forward_decode(
|
|
decode_query,
|
|
key_cache,
|
|
value_cache,
|
|
block_tables_arg,
|
|
seq_lens_arg,
|
|
max_seq_len_arg,
|
|
self.kv_cache_dtype,
|
|
self.num_kv_heads,
|
|
self.scale,
|
|
self.alibi_slopes,
|
|
layer._k_scale,
|
|
layer._v_scale,
|
|
)
|
|
|
|
# Reshape the output tensor.
|
|
return output.view(-1, self.num_heads * self.head_size)
|
|
|
|
def _run_memory_efficient_xformers_forward(
|
|
self,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attn_metadata: XFormersMetadata,
|
|
attn_type: str = AttentionType.DECODER,
|
|
) -> torch.Tensor:
|
|
"""Attention for 1D query of multiple prompts. Multiple prompt
|
|
tokens are flattened in to `query` input.
|
|
|
|
See https://facebookresearch.github.io/xformers/components/ops.html
|
|
for API spec.
|
|
|
|
Args:
|
|
output: shape = [num_prefill_tokens, num_heads, head_size]
|
|
query: shape = [num_prefill_tokens, num_heads, head_size]
|
|
key: shape = [num_prefill_tokens, num_kv_heads, head_size]
|
|
value: shape = [num_prefill_tokens, num_kv_heads, head_size]
|
|
attn_metadata: Metadata for attention.
|
|
attn_type: Select attention type, between encoder attention,
|
|
decoder self-attention, or encoder/decoder cross-
|
|
attention. Defaults to decoder self-attention,
|
|
which is the vLLM default generally
|
|
"""
|
|
|
|
original_query = query
|
|
if self.num_kv_heads != self.num_heads:
|
|
# GQA/MQA requires the shape [B, M, G, H, K].
|
|
# Note that the output also has the same shape (which is different
|
|
# from a spec from the doc).
|
|
query = query.view(query.shape[0], self.num_kv_heads,
|
|
self.num_queries_per_kv, query.shape[-1])
|
|
key = key[:, :,
|
|
None, :].expand(key.shape[0], self.num_kv_heads,
|
|
self.num_queries_per_kv, key.shape[-1])
|
|
value = value[:, :,
|
|
None, :].expand(value.shape[0], self.num_kv_heads,
|
|
self.num_queries_per_kv,
|
|
value.shape[-1])
|
|
|
|
# Set attention bias if not provided. This typically happens at
|
|
# the very attention layer of every iteration.
|
|
# FIXME(woosuk): This is a hack.
|
|
attn_bias = _get_attn_bias(attn_metadata, attn_type)
|
|
if attn_bias is None:
|
|
if self.alibi_slopes is None:
|
|
|
|
# Cross attention block of decoder branch of encoder-decoder
|
|
# model uses seq_lens for dec / encoder_seq_lens for enc
|
|
if (attn_type == AttentionType.ENCODER_DECODER):
|
|
assert attn_metadata.seq_lens is not None
|
|
assert attn_metadata.encoder_seq_lens is not None
|
|
|
|
# Cross-attention mask is non-causal
|
|
attn_bias = BlockDiagonalMask.from_seqlens(
|
|
attn_metadata.seq_lens,
|
|
attn_metadata.encoder_seq_lens,
|
|
device=query.device)
|
|
|
|
# Encoder branch of encoder-decoder model uses
|
|
# attn_metadata.encoder_seq_lens
|
|
elif attn_type == AttentionType.ENCODER:
|
|
|
|
assert attn_metadata.encoder_seq_lens is not None
|
|
|
|
# Encoder self-attention mask is non-causal
|
|
attn_bias = BlockDiagonalMask.from_seqlens(
|
|
attn_metadata.encoder_seq_lens, device=query.device)
|
|
|
|
# Self-attention block of encoder-only model just
|
|
# uses the seq_lens directly.
|
|
elif attn_type == AttentionType.ENCODER_ONLY:
|
|
assert attn_metadata.seq_lens is not None
|
|
|
|
# Encoder self-attention mask is non-causal
|
|
attn_bias = BlockDiagonalMask.from_seqlens(
|
|
attn_metadata.seq_lens, device=query.device)
|
|
|
|
# Self-attention block of decoder branch just
|
|
# uses the seq_lens directly
|
|
elif attn_type == AttentionType.DECODER:
|
|
assert attn_metadata.seq_lens is not None
|
|
|
|
# Decoder self-attention mask is causal
|
|
attn_bias = BlockDiagonalCausalMask.from_seqlens(
|
|
attn_metadata.seq_lens, device=query.device)
|
|
else:
|
|
raise ValueError("Unknown AttentionType: %s", attn_type)
|
|
|
|
if self.sliding_window is not None:
|
|
attn_bias = attn_bias.make_local_attention(
|
|
self.sliding_window)
|
|
attn_bias = [attn_bias]
|
|
else:
|
|
assert attn_type == AttentionType.DECODER
|
|
assert attn_metadata.seq_lens is not None
|
|
attn_bias = _make_alibi_bias(self.alibi_slopes,
|
|
self.num_kv_heads, query.dtype,
|
|
attn_metadata.seq_lens)
|
|
|
|
_set_attn_bias(attn_metadata, attn_bias, attn_type)
|
|
|
|
# No alibi slopes.
|
|
# TODO(woosuk): Too many view operations. Let's try to reduce
|
|
# them in the future for code readability.
|
|
if self.alibi_slopes is None:
|
|
# Add the batch dimension.
|
|
query = query.unsqueeze(0)
|
|
key = key.unsqueeze(0)
|
|
value = value.unsqueeze(0)
|
|
out = xops.memory_efficient_attention_forward(
|
|
query,
|
|
key,
|
|
value,
|
|
attn_bias=attn_bias[0],
|
|
p=0.0,
|
|
scale=self.scale)
|
|
return out.view_as(original_query)
|
|
|
|
# Attention with alibi slopes.
|
|
# FIXME(woosuk): Because xformers does not support dynamic sequence
|
|
# lengths with custom attention bias, we process each prompt one by
|
|
# one. This is inefficient, especially when we have many short prompts.
|
|
assert attn_metadata.seq_lens is not None
|
|
output = torch.empty_like(original_query)
|
|
start = 0
|
|
for i, seq_len in enumerate(attn_metadata.seq_lens):
|
|
end = start + seq_len
|
|
out = xops.memory_efficient_attention_forward(
|
|
query[None, start:end],
|
|
key[None, start:end],
|
|
value[None, start:end],
|
|
attn_bias=attn_bias[i],
|
|
p=0.0,
|
|
scale=self.scale)
|
|
# TODO(woosuk): Unnecessary copy. Optimize.
|
|
output[start:end].copy_(out.view_as(original_query[start:end]))
|
|
start += seq_len
|
|
return output
|
|
|
|
|
|
def _make_alibi_bias(
|
|
alibi_slopes: torch.Tensor,
|
|
num_kv_heads: int,
|
|
dtype: torch.dtype,
|
|
seq_lens: List[int],
|
|
) -> List[AttentionBias]:
|
|
attn_biases: List[AttentionBias] = []
|
|
for seq_len in seq_lens:
|
|
bias = torch.arange(seq_len, dtype=dtype)
|
|
# NOTE(zhuohan): HF uses
|
|
# `bias = bias[None, :].repeat(seq_len, 1)`
|
|
# here. We find that both biases give the same results, but
|
|
# the bias below more accurately follows the original ALiBi
|
|
# paper.
|
|
# Calculate a matrix where each element represents ith element- jth
|
|
# element.
|
|
bias = bias[None, :] - bias[:, None]
|
|
|
|
padded_len = (seq_len + 7) // 8 * 8
|
|
num_heads = alibi_slopes.shape[0]
|
|
bias = torch.empty(
|
|
1, # batch size
|
|
num_heads,
|
|
seq_len,
|
|
padded_len,
|
|
device=alibi_slopes.device,
|
|
dtype=dtype,
|
|
)[:, :, :, :seq_len].copy_(bias)
|
|
bias.mul_(alibi_slopes[:, None, None])
|
|
attn_biases.append(LowerTriangularMaskWithTensorBias(bias))
|
|
|
|
return attn_biases
|