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import torch
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import torch.nn as nn
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from einops import rearrange
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try:
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from flash_attn.flash_attn_interface import \
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flash_attn_unpadded_qkvpacked_func
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except:
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from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
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from flash_attn.bert_padding import pad_input, unpad_input
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class FlashAttention(nn.Module):
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"""Implement the scaled dot product attention with softmax.
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Arguments
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---------
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softmax_scale: The temperature to use for the softmax attention.
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(default: 1/sqrt(d_keys) where d_keys is computed at
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runtime)
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attention_dropout: The dropout rate to apply to the attention
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(default: 0.0)
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"""
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def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
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super().__init__()
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self.softmax_scale = softmax_scale
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self.dropout_p = attention_dropout
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def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
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max_s=None, need_weights=False):
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"""Implements the multihead softmax attention.
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Arguments
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---------
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qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
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if unpadded: (nnz, 3, h, d)
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key_padding_mask: a bool tensor of shape (B, S)
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"""
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assert not need_weights
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assert qkv.dtype in [torch.float16, torch.bfloat16]
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assert qkv.is_cuda
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if cu_seqlens is None:
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batch_size = qkv.shape[0]
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seqlen = qkv.shape[1]
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if key_padding_mask is None:
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qkv = rearrange(qkv, 'b s ... -> (b s) ...')
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max_s = seqlen
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cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
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device=qkv.device)
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output = flash_attn_unpadded_qkvpacked_func(
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qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
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softmax_scale=self.softmax_scale, causal=causal
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)
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output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
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else:
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nheads = qkv.shape[-2]
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x = rearrange(qkv, 'b s three h d -> b s (three h d)')
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x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
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x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
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output_unpad = flash_attn_unpadded_qkvpacked_func(
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x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
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softmax_scale=self.softmax_scale, causal=causal
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)
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output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
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indices, batch_size, seqlen),
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'b s (h d) -> b s h d', h=nheads)
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else:
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assert max_s is not None
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output = flash_attn_unpadded_qkvpacked_func(
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qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
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softmax_scale=self.softmax_scale, causal=causal
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)
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return output, None
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