flash_attention.py

import torch
import torch.nn as nn
from einops import rearrange

try:
 from .triton_flash_atn import _attention
 from .triton_bert_pading import pad_input, unpad_input
except:
 print("FlashAttention is not installed.")

 
class FlashAttention(nn.Module):
 """Implement the scaled dot product attention with softmax.
 Arguments
 ---------
 softmax_scale: The temperature to use for the softmax attention.
 (default: 1/sqrt(d_keys) where d_keys is computed at
 runtime)
 attention_dropout: The dropout rate to apply to the attention
 (default: 0.0)
 """

 def __init__(
 self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None
 ):
 super().__init__()
 self.softmax_scale = softmax_scale
 self.dropout_p = attention_dropout

 def forward(
 self,
 qkv,
 key_padding_mask=None,
 causal=False,
 cu_seqlens=None,
 max_s=None,
 need_weights=False,
 ):
 """Implements the multihead softmax attention.
 Arguments
 ---------
 qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
 if unpadded: (nnz, 3, h, d)
 key_padding_mask: a bool tensor of shape (B, S)
 """
 assert not need_weights
 assert qkv.dtype in [torch.float16, torch.bfloat16]
 assert qkv.is_cuda

 if cu_seqlens is None:
 batch_size = qkv.shape[0]
 seqlen = qkv.shape[1]
 if key_padding_mask is None:
 qkv = rearrange(qkv, "b s ... -> (b s) ...")
 max_s = seqlen
 cu_seqlens = torch.arange(
 0,
 (batch_size + 1) * seqlen,
 step=seqlen,
 dtype=torch.int32,
 device=qkv.device,
 )
 output = _attention.apply(
 qkv,
 cu_seqlens,
 max_s,
 self.dropout_p if self.training else 0.0,
 self.softmax_scale,
 causal
 )
 output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
 else:
 nheads = qkv.shape[-2]
 x = rearrange(qkv, "b s three h d -> b s (three h d)")
 x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
 x_unpad = rearrange(
 x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
 )
 output_unpad = _attention.apply(
 x_unpad,
 cu_seqlens,
 max_s,
 self.dropout_p if self.training else 0.0,
 self.softmax_scale,
 causal
 )
 output = rearrange(
 pad_input(
 rearrange(output_unpad, "nnz h d -> nnz (h d)"),
 indices,
 batch_size,
 seqlen,
 ),
 "b s (h d) -> b s h d",
 h=nheads,
 )
 else:
 assert max_s is not None
 output = _attention.apply(
 qkv,
 cu_seqlens,
 max_s,
 self.dropout_p if self.training else 0.0,
 self.softmax_scale,
 causal
 )

 return output, None