wehub-resource-sync
130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# 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 unittest
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import numpy as np
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from op_test import get_places
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import paddle
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import paddle.base.dygraph as dg
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class TestComplexMatMulLayer(unittest.TestCase):
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def setUp(self):
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self._dtypes = ["float32", "float64"]
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self._places = get_places()
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def compare_by_basic_api(self, x, y, np_result):
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for place in self._places:
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with dg.guard(place):
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x_var = paddle.to_tensor(x)
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y_var = paddle.to_tensor(y)
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result = paddle.matmul(x_var, y_var)
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pd_result = result.numpy()
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np.testing.assert_allclose(
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pd_result,
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np_result,
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rtol=1e-05,
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err_msg=f'\nplace: {place}\npaddle diff result:\n {pd_result[~np.isclose(pd_result, np_result)]}\nnumpy diff result:\n {np_result[~np.isclose(pd_result, np_result)]}\n',
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)
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def compare_op_by_basic_api(self, x, y, np_result):
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for place in self._places:
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with dg.guard(place):
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x_var = paddle.to_tensor(x)
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y_var = paddle.to_tensor(y)
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result = x_var.matmul(y_var)
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pd_result = result.numpy()
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np.testing.assert_allclose(
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pd_result,
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np_result,
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rtol=1e-05,
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err_msg=f'\nplace: {place}\npaddle diff result:\n {pd_result[~np.isclose(pd_result, np_result)]}\nnumpy diff result:\n {np_result[~np.isclose(pd_result, np_result)]}\n',
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)
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def test_complex_xy(self):
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for dtype in self._dtypes:
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x = np.random.random((2, 3, 4, 5)).astype(
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dtype
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) + 1j * np.random.random((2, 3, 4, 5)).astype(dtype)
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y = np.random.random((2, 3, 5, 4)).astype(
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dtype
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) + 1j * np.random.random((2, 3, 5, 4)).astype(dtype)
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np_result = np.matmul(x, y)
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self.compare_by_basic_api(x, y, np_result)
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self.compare_op_by_basic_api(x, y, np_result)
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def test_complex_x_real_y(self):
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for dtype in self._dtypes:
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x = np.random.random((2, 3, 4, 5)).astype(
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dtype
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) + 1j * np.random.random((2, 3, 4, 5)).astype(dtype)
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y = np.random.random((2, 3, 5, 4)).astype(dtype)
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np_result = np.matmul(x, y)
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# float -> complex type promotion
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self.compare_by_basic_api(x, y, np_result)
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self.compare_op_by_basic_api(x, y, np_result)
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def test_real_x_complex_y(self):
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for dtype in self._dtypes:
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x = np.random.random((2, 3, 4, 5)).astype(dtype)
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y = np.random.random((2, 3, 5, 4)).astype(
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dtype
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) + 1j * np.random.random((2, 3, 5, 4)).astype(dtype)
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np_result = np.matmul(x, y)
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# float -> complex type promotion
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self.compare_by_basic_api(x, y, np_result)
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self.compare_op_by_basic_api(x, y, np_result)
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# for coverage
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def test_complex_xy_gemv(self):
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for dtype in self._dtypes:
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x = np.random.random((2, 1, 100)).astype(
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dtype
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) + 1j * np.random.random((2, 1, 100)).astype(dtype)
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y = np.random.random(100).astype(dtype) + 1j * np.random.random(
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100
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).astype(dtype)
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np_result = np.matmul(x, y)
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self.compare_by_basic_api(x, y, np_result)
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self.compare_op_by_basic_api(x, y, np_result)
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# for coverage
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def test_complex_xy_gemm(self):
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for dtype in self._dtypes:
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x = np.random.random((1, 2, 50)).astype(
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dtype
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) + 1j * np.random.random((1, 2, 50)).astype(dtype)
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y = np.random.random((1, 50, 2)).astype(
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dtype
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) + 1j * np.random.random((1, 50, 2)).astype(dtype)
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np_result = np.matmul(x, y)
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self.compare_by_basic_api(x, y, np_result)
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self.compare_op_by_basic_api(x, y, np_result)
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if __name__ == '__main__':
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unittest.main()
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