Delete flash_attention.py

flash_attention.py

@@ -1,76 +0,0 @@
-# https://github.com/Dao-AILab/flash-attention/blob/v0.2.8/flash_attn/flash_attention.py
-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