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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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"""Mask module."""
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import paddle
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def subsequent_mask(size, dtype=paddle.bool):
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"""Create mask for subsequent steps (size, size).
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Args:
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size (int): size of mask
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dtype (paddle.dtype): result dtype
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Return:
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Tensor:
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>>> subsequent_mask(3)
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[[1, 0, 0],
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[1, 1, 0],
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[1, 1, 1]]
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"""
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ret = paddle.ones([size, size], dtype=dtype)
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return paddle.tril(ret)
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def target_mask(ys_in_pad, ignore_id, dtype=paddle.bool):
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"""Create mask for decoder self-attention.
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Args:
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ys_pad (Tensor): batch of padded target sequences (B, Lmax)
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ignore_id (int): index of padding
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dtype (paddle.dtype): result dtype
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Return:
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Tensor: (B, Lmax, Lmax)
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"""
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ys_mask = ys_in_pad != ignore_id
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m = subsequent_mask(ys_mask.shape[-1]).unsqueeze(0)
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return ys_mask.unsqueeze(-2) & m
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