65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
# Copyright 2025 ByteDance and/or its affiliates.
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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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import torch
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from torch import nn
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class LayerNorm(torch.nn.LayerNorm):
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"""Layer normalization module.
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:param int nout: output dim size
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:param int dim: dimension to be normalized
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"""
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def __init__(self, nout, dim=-1, eps=1e-5):
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"""Construct an LayerNorm object."""
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super(LayerNorm, self).__init__(nout, eps=eps)
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self.dim = dim
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def forward(self, x):
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"""Apply layer normalization.
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:param torch.Tensor x: input tensor
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:return: layer normalized tensor
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:rtype torch.Tensor
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"""
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if self.dim == -1:
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return super(LayerNorm, self).forward(x)
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return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
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class Reshape(nn.Module):
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def __init__(self, *args):
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super(Reshape, self).__init__()
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self.shape = args
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def forward(self, x):
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return x.view(self.shape)
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class Permute(nn.Module):
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def __init__(self, *args):
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super(Permute, self).__init__()
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self.args = args
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def forward(self, x):
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return x.permute(self.args)
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def Embedding(num_embeddings, embedding_dim, padding_idx=None):
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m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
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nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
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if padding_idx is not None:
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nn.init.constant_(m.weight[padding_idx], 0)
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return m
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