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