31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
# Copyright (c) 2022, Tri Dao.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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try:
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from flash_attn.ops.fused_dense import FusedDenseGeluDense, ParallelFusedDenseGeluDense
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except ImportError:
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FusedDenseGeluDense, ParallelFusedDenseGeluDense = None, None
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class Mlp(nn.Module):
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def __init__(self, in_features, hidden_features=None, out_features=None, activation=F.gelu,
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return_residual=False, device=None, dtype=None):
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.return_residual = return_residual
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self.fc1 = nn.Linear(in_features, hidden_features, **factory_kwargs)
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self.activation = activation
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self.fc2 = nn.Linear(hidden_features, out_features, **factory_kwargs)
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def forward(self, x):
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y = self.fc1(x)
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y = self.activation(y)
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y = self.fc2(y)
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return y if not self.return_residual else (y, x)
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