# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Mask module."""
import paddle
[docs]def subsequent_mask(size, dtype=paddle.bool):
"""Create mask for subsequent steps (size, size).
Args:
size (int):
size of mask
dtype (paddle.dtype):
result dtype
Return:
Tensor:
>>> subsequent_mask(3)
[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]
"""
ret = paddle.ones([size, size], dtype=dtype)
return paddle.tril(ret)
[docs]def target_mask(ys_in_pad, ignore_id, dtype=paddle.bool):
"""Create mask for decoder self-attention.
Args:
ys_pad (Tensor):
batch of padded target sequences (B, Lmax)
ignore_id (int):
index of padding
dtype (paddle.dtype):
result dtype
Return:
Tensor: (B, Lmax, Lmax)
"""
ys_mask = ys_in_pad != ignore_id
m = subsequent_mask(ys_mask.shape[-1]).unsqueeze(0)
return ys_mask.unsqueeze(-2) & m