mirror of https://github.com/hpcaitech/ColossalAI
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import warnings
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HAS_MEM_EFF_ATTN = False
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try:
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from xformers.ops.fmha import memory_efficient_attention
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HAS_MEM_EFF_ATTN = True
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except ImportError:
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warnings.warn('please install xformers from https://github.com/facebookresearch/xformers')
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HAS_MEM_EFF_ATTN = False
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if HAS_MEM_EFF_ATTN:
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"""
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A general attention module using the flash attention kernels from xformers:
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https://github.com/facebookresearch/xformers/tree/main/xformers/ops/fmha
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"""
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from typing import Optional
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import torch
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from xformers.ops.fmha import MemoryEfficientAttentionCutlassOp
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from xformers.ops.fmha.attn_bias import (
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BlockDiagonalCausalMask,
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BlockDiagonalMask,
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LowerTriangularMask,
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LowerTriangularMaskWithTensorBias,
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)
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from .utils import SeqLenInfo
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allow_alibi = True
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for op in MemoryEfficientAttentionCutlassOp:
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allow_alibi = allow_alibi & (LowerTriangularMaskWithTensorBias in op.SUPPORTED_ATTN_BIAS_TYPES)
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def mem_eff_attention(q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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seq_len_info_q: SeqLenInfo,
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seq_len_info_kv: SeqLenInfo,
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bias: Optional[torch.Tensor] = None,
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dropout_p: float = 0.,
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scale: float = None,
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causal: bool = False,
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padded: bool = False):
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attn_bias = None
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if padded: # bert style
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if not causal:
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attn_bias = BlockDiagonalMask.from_seqlens(seq_len_info_q.seqlens, seq_len_info_kv.seqlens)
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else:
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attn_bias = BlockDiagonalCausalMask.from_seqlens(seq_len_info_q.seqlens, seq_len_info_kv.seqlens)
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elif causal: # gpt style
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attn_bias = LowerTriangularMask()
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if bias is not None: # alibi / relative position embedding
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assert allow_alibi, "flash attention with bias is not supported in this system."
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assert causal, \
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"attention with bias is only supported for causal attention so far."
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attn_bias = attn_bias.add_bias(bias)
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if padded:
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q = q.unsqueeze(0)
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k = k.unsqueeze(0)
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v = v.unsqueeze(0)
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out = memory_efficient_attention(q, k, v, attn_bias=attn_bias, p=dropout_p, scale=scale)
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# shape: (b*s, n, d)
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if padded:
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out = out.squeeze(0)
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return out
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