You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
127 lines
4.7 KiB
127 lines
4.7 KiB
# 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
|
|
|
|
|
|
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()
|
|
self.assertTrue(
|
|
np.allclose(pd_result, np_result),
|
|
"\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()
|
|
self.assertTrue(
|
|
np.allclose(pd_result, np_result),
|
|
"\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)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|