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64 lines
1.9 KiB
64 lines
1.9 KiB
3 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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"""Layer normalization module."""
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import paddle
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class LayerNorm(paddle.nn.LayerNorm):
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"""Layer normalization module.
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Parameters
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----------
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nout : int
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Output dim size.
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dim : int
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Dimension to be normalized.
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"""
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def __init__(self, nout, dim=-1):
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"""Construct an LayerNorm object."""
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super(LayerNorm, self).__init__(nout)
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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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Parameters
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----------
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x : paddle.Tensor
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Input tensor.
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Returns
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----------
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paddle.Tensor
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Normalized 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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else:
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len_dim = len(x.shape)
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if self.dim < 0:
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self.dim = len_dim + self.dim
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assert self.dim >= 0
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orig_perm = list(range(len_dim))
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new_perm = orig_perm[:]
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new_perm[self.dim], new_perm[len_dim -
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1] = new_perm[len_dim -
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1], new_perm[self.dim]
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return paddle.transpose(
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super(LayerNorm, self).forward(paddle.transpose(x, new_perm)),
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new_perm)
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