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121 lines
3.4 KiB
121 lines
3.4 KiB
# Copyright (c) 2020 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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import paddle
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__all__ = [
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"id_mask",
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"feature_mask",
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"combine_mask",
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"future_mask",
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]
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def id_mask(input, padding_index=0, dtype="bool"):
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"""Generate mask with input ids.
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Those positions where the value equals ``padding_index`` correspond to 0 or
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``False``, otherwise, 1 or ``True``.
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Parameters
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----------
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input : Tensor [dtype: int]
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The input tensor. It represents the ids.
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padding_index : int, optional
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The id which represents padding, by default 0.
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dtype : str, optional
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Data type of the returned mask, by default "bool".
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Returns
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-------
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Tensor
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The generate mask. It has the same shape as ``input`` does.
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"""
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return paddle.cast(input != padding_index, dtype)
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def feature_mask(input, axis, dtype="bool"):
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"""Compute mask from input features.
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For a input features, represented as batched feature vectors, those vectors
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which all zeros are considerd padding vectors.
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Parameters
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----------
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input : Tensor [dtype: float]
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The input tensor which represents featues.
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axis : int
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The index of the feature dimension in ``input``. Other dimensions are
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considered ``spatial`` dimensions.
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dtype : str, optional
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Data type of the generated mask, by default "bool"
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Returns
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-------
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Tensor
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The geenrated mask with ``spatial`` shape as mentioned above.
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It has one less dimension than ``input`` does.
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"""
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feature_sum = paddle.sum(paddle.abs(input), axis)
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return paddle.cast(feature_sum != 0, dtype)
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def combine_mask(mask1, mask2):
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"""Combine two mask with multiplication or logical and.
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Parameters
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-----------
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mask1 : Tensor
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The first mask.
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mask2 : Tensor
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The second mask with broadcastable shape with ``mask1``.
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Returns
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--------
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Tensor
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Combined mask.
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Notes
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------
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It is mainly used to combine the padding mask and no future mask for
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transformer decoder.
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Padding mask is used to mask padding positions of the decoder inputs and
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no future mask is used to prevent the decoder to see future information.
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"""
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if mask1.dtype == paddle.fluid.core.VarDesc.VarType.BOOL:
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return paddle.logical_and(mask1, mask2)
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else:
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return mask1 * mask2
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def future_mask(time_steps, dtype="bool"):
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"""Generate lower triangular mask.
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It is used at transformer decoder to prevent the decoder to see future
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information.
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Parameters
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----------
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time_steps : int
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Decoder time steps.
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dtype : str, optional
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The data type of the generate mask, by default "bool".
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Returns
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-------
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Tensor
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The generated mask.
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"""
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mask = paddle.tril(paddle.ones([time_steps, time_steps]))
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return paddle.cast(mask, dtype)
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