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51 lines
1.8 KiB
51 lines
1.8 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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import paddle
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from paddle.static import InputSpec
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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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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 call_it(x):
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shape = paddle.shape(x)
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a = shape[0]
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b = shape[1]
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c = sinusoid_position_encoding(a, b)
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return c
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call_it(paddle.randn([8, 32]))
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m = paddle.jit.to_static(
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call_it, input_spec=[InputSpec([-1, -1], dtype=paddle.int32)])
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m(paddle.randn([8, 32]).astype(paddle.int32))
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