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# Copyright 2024 MIT Han Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import torch
from flash_attn import flash_attn_func
from torch import nn
from torch.nn import functional as F
class FlashAttention(nn.Module):
def __init__(self, dim: int, num_heads: int):
super().__init__()
self.dim = dim
assert dim % num_heads == 0
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=False)
self.proj_out = torch.nn.Linear(dim, dim)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).view(B, N, 3, C) # B, N, 3, C
q, k, v = qkv.unbind(2) # B, N, C
k = k.reshape(B, N, self.num_heads, self.head_dim)
v = v.reshape(B, N, self.num_heads, self.head_dim)
q = q.reshape(B, N, self.num_heads, self.head_dim)
out = flash_attn_func(q, k, v) # B, N, H, c
out = self.proj_out(out.view(B, N, C)) # B, N, C
return out
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