wehub-resource-sync
154 lines
4.5 KiB
Python
154 lines
4.5 KiB
Python
# Copyright (c) 2018 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 math
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import unittest
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import numpy as np
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from op_test import OpTest
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def add_position_encoding(input, alpha=1.0, beta=1.0):
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batch_size = input.shape[0]
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max_length = input.shape[1]
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enc_size = input.shape[2]
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out = np.copy(input)
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half_shape = int(enc_size / 2)
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for i in range(batch_size):
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for j in range(max_length):
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for k in range(half_shape):
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val = (
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j / pow(10000.0, k * 1.0 / (half_shape - 1))
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if half_shape > 1
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else j / 10000.0
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)
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out[i, j, k] = input[i, j, k] * alpha + math.sin(val) * beta
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out[i, j, half_shape + k] = (
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input[i, j, half_shape + k] * alpha + math.cos(val) * beta
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)
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return out
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class TestAddPositionEncodingTensorOp(OpTest):
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"""
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This class is to test the AddPositionEncodingOp
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"""
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def setUp(self):
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"""
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the prepared section for add position encoding op
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"""
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self.op_type = "add_position_encoding"
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self.dtype = np.float64
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self.init_input_output()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'alpha': self.alpha, 'beta': self.beta}
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def test_check_output(self):
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"""
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check the correctness of output
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"""
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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"""
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check the correctness of grad
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"""
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self.check_grad(['X'], 'Out', check_dygraph=False)
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def init_input_output(self):
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"""
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init the input and output for test cases
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"""
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self.alpha = 0.6
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self.beta = 0.5
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self.x = np.random.uniform(0.1, 1, [2, 15, 4]).astype(self.dtype)
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self.out = add_position_encoding(self.x, self.alpha, self.beta)
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class TestAddPositionEncodingDenseTensorOp(OpTest):
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"""
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This class is to test the AddPositionEncodingDenseTensorOp
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"""
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def setUp(self):
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"""
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the prepared section for add position encoding DenseTensor op
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"""
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self.op_type = "add_position_encoding"
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self.dtype = np.float64
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self.init_input_output()
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self.inputs = {
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'X': (self.x, self.lod),
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}
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self.outputs = {'Out': (self.out, self.lod)}
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self.attrs = {'alpha': self.alpha, 'beta': self.beta}
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def test_check_output(self):
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"""
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check the correctness of output
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"""
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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"""
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check the correctness of grad
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"""
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self.check_grad(['X'], 'Out', check_dygraph=False)
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def init_input_output(self):
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"""
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init the input and output for test cases
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"""
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self.alpha = 0.6
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self.beta = 0.5
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self.x = np.random.uniform(0.1, 1, [20, 6]).astype(self.dtype)
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self.lod = [[13, 7]]
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self.out = np.copy(self.x)
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batch_size = len(self.lod[0])
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enc_size = self.x.shape[1]
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start = 0
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half_shape = int(enc_size / 2)
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for i in range(batch_size):
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max_length = self.lod[0][i]
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for j in range(max_length):
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for k in range(half_shape):
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val = (
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j / pow(10000.0, k * 1.0 / (half_shape - 1))
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if half_shape > 1
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else j / 10000.0
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)
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pos = start + j
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self.out[pos, k] = (
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self.x[pos, k] * self.alpha + math.sin(val) * self.beta
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)
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self.out[pos, half_shape + k] = (
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self.x[pos, half_shape + k] * self.alpha
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+ math.cos(val) * self.beta
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)
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start += max_length
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if __name__ == '__main__':
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unittest.main()
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