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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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