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206 lines
7.6 KiB
206 lines
7.6 KiB
# 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 paddle
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.dygraph as dg
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class TestComplexMatMulLayer(unittest.TestCase):
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def setUp(self):
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self._places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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self._places.append(fluid.CUDAPlace(0))
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def compare_by_complex_api(self, x, y):
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np_result = np.matmul(x, y)
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for place in self._places:
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with dg.guard(place):
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x_var = dg.to_variable(x)
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y_var = dg.to_variable(y)
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result = paddle.complex.matmul(x_var, y_var)
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self.assertTrue(np.allclose(result.numpy(), np_result))
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def compare_by_basic_api(self, x, y):
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np_result = np.matmul(x, y)
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for place in self._places:
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with dg.guard(place):
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x_var = fluid.core.VarBase(
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value=x,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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y_var = fluid.core.VarBase(
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value=y,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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result = paddle.matmul(x_var, y_var)
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self.assertTrue(np.allclose(result.numpy(), np_result))
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def compare_op_by_complex_api(self, x, y):
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np_result = np.matmul(x, y)
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for place in self._places:
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with dg.guard(place):
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x_var = dg.to_variable(x)
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y_var = dg.to_variable(y)
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result = x_var.matmul(y_var)
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self.assertTrue(np.allclose(result.numpy(), np_result))
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def compare_op_by_basic_api(self, x, y):
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np_result = np.matmul(x, y)
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for place in self._places:
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with dg.guard(place):
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x_var = fluid.core.VarBase(
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value=x,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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y_var = fluid.core.VarBase(
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value=y,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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result = x_var.matmul(y_var)
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self.assertTrue(np.allclose(result.numpy(), np_result))
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def test_complex_xy(self):
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x = np.random.random(
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(2, 3, 4, 5)).astype("float32") + 1J * np.random.random(
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(2, 3, 4, 5)).astype("float32")
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y = np.random.random(
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(2, 3, 5, 4)).astype("float32") + 1J * np.random.random(
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(2, 3, 5, 4)).astype("float32")
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self.compare_by_complex_api(x, y)
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self.compare_op_by_complex_api(x, y)
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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def test_complex_x(self):
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x = np.random.random(
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(2, 3, 4, 5)).astype("float32") + 1J * np.random.random(
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(2, 3, 4, 5)).astype("float32")
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y = np.random.random((2, 3, 5, 4)).astype("float32")
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self.compare_by_complex_api(x, y)
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self.compare_op_by_complex_api(x, y)
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def test_complex_y(self):
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x = np.random.random((2, 3, 4, 5)).astype("float32")
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y = np.random.random(
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(2, 3, 5, 4)).astype("float32") + 1J * np.random.random(
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(2, 3, 5, 4)).astype("float32")
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self.compare_by_complex_api(x, y)
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def test_complex_xy_128(self):
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x = np.random.random(
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(2, 3, 4, 5)).astype("float64") + 1J * np.random.random(
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(2, 3, 4, 5)).astype("float64")
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y = np.random.random(
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(2, 3, 5, 4)).astype("float64") + 1J * np.random.random(
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(2, 3, 5, 4)).astype("float64")
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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def test_complex_xy_gemv(self):
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x = np.random.random(
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(2, 1, 100)).astype("float32") + 1J * np.random.random(
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(2, 1, 100)).astype("float32")
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y = np.random.random((100)).astype("float32") + 1J * np.random.random(
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(100)).astype("float32")
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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x = np.random.random(
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(2, 1, 100)).astype("float64") + 1J * np.random.random(
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(2, 1, 100)).astype("float64")
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y = np.random.random((100)).astype("float64") + 1J * np.random.random(
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(100)).astype("float64")
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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def test_complex_xy_gemm_128(self):
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x = np.random.random(
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(1, 2, 50)).astype("float64") + 1J * np.random.random(
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(1, 2, 50)).astype("float64")
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y = np.random.random(
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(1, 50, 2)).astype("float64") + 1J * np.random.random(
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(1, 50, 2)).astype("float64")
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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class TestComplexMatMulLayerGEMM(unittest.TestCase):
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def setUp(self):
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self._places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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self._places.append(fluid.CUDAPlace(0))
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def compare_by_basic_api(self, x, y):
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np_result = np.matmul(x, y)
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for place in self._places:
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with dg.guard(place):
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x_var = fluid.core.VarBase(
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value=x,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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y_var = fluid.core.VarBase(
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value=y,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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result = paddle.matmul(x_var, y_var)
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self.assertTrue(np.allclose(result.numpy(), np_result))
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def compare_op_by_basic_api(self, x, y):
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np_result = np.matmul(x, y)
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for place in self._places:
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with dg.guard(place):
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x_var = fluid.core.VarBase(
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value=x,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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y_var = fluid.core.VarBase(
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value=y,
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place=place,
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persistable=False,
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zero_copy=None,
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name='')
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result = x_var.matmul(y_var)
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self.assertTrue(np.allclose(result.numpy(), np_result))
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def test_complex_xy_gemm_64(self):
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x = np.random.random(
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(1, 2, 50)).astype("float32") + 1J * np.random.random(
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(1, 2, 50)).astype("float32")
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y = np.random.random(
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(1, 50, 2)).astype("float32") + 1J * np.random.random(
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(1, 50, 2)).astype("float32")
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self.compare_by_basic_api(x, y)
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self.compare_op_by_basic_api(x, y)
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if __name__ == '__main__':
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unittest.main()
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