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| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| try: # v1 | |
| from flash_attn.flash_attn_interface import \ | |
| flash_attn_unpadded_qkvpacked_func | |
| except: # v2 | |
| from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func | |
| from flash_attn.bert_padding import pad_input, unpad_input | |
| 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 = flash_attn_unpadded_qkvpacked_func( | |
| qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, | |
| softmax_scale=self.softmax_scale, causal=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 = flash_attn_unpadded_qkvpacked_func( | |
| x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, | |
| softmax_scale=self.softmax_scale, causal=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 = flash_attn_unpadded_qkvpacked_func( | |
| qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, | |
| softmax_scale=self.softmax_scale, causal=causal | |
| ) | |
| return output, None |