# Copyright (c) 2020 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. import unittest import paddle import numpy as np import paddle.fluid as fluid import paddle.fluid.dygraph as dg from paddle.fluid.framework import _test_eager_guard class TestComplexMatMulLayer(unittest.TestCase): def setUp(self): self._dtypes = ["float32", "float64"] self._places = [fluid.CPUPlace()] if fluid.core.is_compiled_with_cuda(): self._places.append(fluid.CUDAPlace(0)) def compare_by_basic_api(self, x, y, np_result): for place in self._places: with dg.guard(place): x_var = dg.to_variable(x) y_var = dg.to_variable(y) result = paddle.matmul(x_var, y_var) pd_result = result.numpy() np.testing.assert_allclose( pd_result, np_result, rtol=1e-05, err_msg= '\nplace: {}\npaddle diff result:\n {}\nnumpy diff result:\n {}\n' .format(place, pd_result[~np.isclose(pd_result, np_result)], np_result[~np.isclose(pd_result, np_result)])) def compare_op_by_basic_api(self, x, y, np_result): for place in self._places: with dg.guard(place): x_var = dg.to_variable(x) y_var = dg.to_variable(y) result = x_var.matmul(y_var) pd_result = result.numpy() np.testing.assert_allclose( pd_result, np_result, rtol=1e-05, err_msg= '\nplace: {}\npaddle diff result:\n {}\nnumpy diff result:\n {}\n' .format(place, pd_result[~np.isclose(pd_result, np_result)], np_result[~np.isclose(pd_result, np_result)])) def test_complex_xy(self): for dtype in self._dtypes: x = np.random.random( (2, 3, 4, 5)).astype(dtype) + 1J * np.random.random( (2, 3, 4, 5)).astype(dtype) y = np.random.random( (2, 3, 5, 4)).astype(dtype) + 1J * np.random.random( (2, 3, 5, 4)).astype(dtype) np_result = np.matmul(x, y) self.compare_by_basic_api(x, y, np_result) self.compare_op_by_basic_api(x, y, np_result) def test_complex_x_real_y(self): for dtype in self._dtypes: x = np.random.random( (2, 3, 4, 5)).astype(dtype) + 1J * np.random.random( (2, 3, 4, 5)).astype(dtype) y = np.random.random((2, 3, 5, 4)).astype(dtype) np_result = np.matmul(x, y) # float -> complex type promotion self.compare_by_basic_api(x, y, np_result) self.compare_op_by_basic_api(x, y, np_result) def test_real_x_complex_y(self): for dtype in self._dtypes: x = np.random.random((2, 3, 4, 5)).astype(dtype) y = np.random.random( (2, 3, 5, 4)).astype(dtype) + 1J * np.random.random( (2, 3, 5, 4)).astype(dtype) np_result = np.matmul(x, y) # float -> complex type promotion self.compare_by_basic_api(x, y, np_result) self.compare_op_by_basic_api(x, y, np_result) # for coverage def test_complex_xy_gemv(self): for dtype in self._dtypes: x = np.random.random( (2, 1, 100)).astype(dtype) + 1J * np.random.random( (2, 1, 100)).astype(dtype) y = np.random.random((100)).astype(dtype) + 1J * np.random.random( (100)).astype(dtype) np_result = np.matmul(x, y) self.compare_by_basic_api(x, y, np_result) self.compare_op_by_basic_api(x, y, np_result) # for coverage def test_complex_xy_gemm(self): for dtype in self._dtypes: x = np.random.random( (1, 2, 50)).astype(dtype) + 1J * np.random.random( (1, 2, 50)).astype(dtype) y = np.random.random( (1, 50, 2)).astype(dtype) + 1J * np.random.random( (1, 50, 2)).astype(dtype) np_result = np.matmul(x, y) self.compare_by_basic_api(x, y, np_result) self.compare_op_by_basic_api(x, y, np_result) def test_eager(self): with _test_eager_guard(): self.test_complex_xy_gemm() self.test_complex_xy_gemv() self.test_real_x_complex_y() self.test_complex_x_real_y() self.test_complex_xy() if __name__ == '__main__': unittest.main()