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67 lines
2.8 KiB
67 lines
2.8 KiB
3 years ago
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# 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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from paddle import Tensor
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__all__ = ["sinusoid_position_encoding", "scaled_position_encoding"]
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def sinusoid_position_encoding(num_positions: int,
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feature_size: int,
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omega: float=1.0,
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start_pos: int=0,
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dtype=None) -> paddle.Tensor:
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# return tensor shape (num_positions, feature_size)
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# NOTE: to be compatible with paddle's to_static, we cannnot raise
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# an exception here, take care of it by yourself
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# if (feature_size % 2 != 0):
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# raise ValueError("size should be divisible by 2")
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dtype = dtype or paddle.get_default_dtype()
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channel = paddle.arange(0, feature_size, 2, dtype=dtype)
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index = paddle.arange(start_pos, start_pos + num_positions, 1, dtype=dtype)
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p = (paddle.unsqueeze(index, -1) *
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omega) / (10000.0**(channel / float(feature_size)))
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encodings = paddle.zeros([num_positions, feature_size], dtype=dtype)
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encodings[:, 0::2] = paddle.sin(p)
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encodings[:, 1::2] = paddle.cos(p)
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return encodings
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def scaled_position_encoding(num_positions: int,
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feature_size: int,
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omega: Tensor,
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start_pos: int=0,
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dtype=None) -> Tensor:
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# omega: Tensor (batch_size, )
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# return tensor shape (batch_size, num_positions, feature_size)
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# consider renaming this as batched positioning encoding
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if (feature_size % 2 != 0):
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raise ValueError("size should be divisible by 2")
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dtype = dtype or paddle.get_default_dtype()
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channel = paddle.arange(0, feature_size, 2, dtype=dtype)
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index = paddle.arange(
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start_pos, start_pos + num_positions, 1, dtype=omega.dtype)
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batch_size = omega.shape[0]
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omega = paddle.unsqueeze(omega, [1, 2])
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p = (paddle.unsqueeze(index, -1) *
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omega) / (10000.0**(channel / float(feature_size)))
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encodings = paddle.zeros(
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[batch_size, num_positions, feature_size], dtype=dtype)
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# it is nice to have fancy indexing and inplace operations
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encodings[:, :, 0::2] = paddle.sin(p)
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encodings[:, :, 1::2] = paddle.cos(p)
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return encodings
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