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- # 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 numpy as np
-
- import paddle
- from paddle.pir_utils import test_with_pir_api
-
-
- class TestMultiplyApi(unittest.TestCase):
- def _run_static_graph_case(self, x_data, y_data):
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- paddle.enable_static()
- x = paddle.static.data(
- name='x', shape=x_data.shape, dtype=x_data.dtype
- )
- y = paddle.static.data(
- name='y', shape=y_data.shape, dtype=y_data.dtype
- )
- res = paddle.inner(x, y)
-
- place = (
- paddle.CUDAPlace(0)
- if paddle.is_compiled_with_cuda()
- else paddle.CPUPlace()
- )
- exe = paddle.static.Executor(place)
- outs = exe.run(
- paddle.static.default_main_program(),
- feed={'x': x_data, 'y': y_data},
- fetch_list=[res],
- )
- res = outs[0]
- return res
-
- def _run_dynamic_graph_case(self, x_data, y_data):
- paddle.disable_static()
- x = paddle.to_tensor(x_data)
- y = paddle.to_tensor(y_data)
- res = paddle.inner(x, y)
- return res.numpy()
-
- @test_with_pir_api
- def test_multiply_static_case1(self):
- # test static computation graph: 3-d array
- x_data = np.random.rand(2, 10, 10).astype(np.float64)
- y_data = np.random.rand(2, 5, 10).astype(np.float64)
- res = self._run_static_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- @test_with_pir_api
- def test_multiply_static_case2(self):
- # test static computation graph: 2-d array
- x_data = np.random.rand(200, 5).astype(np.float64)
- y_data = np.random.rand(50, 5).astype(np.float64)
- res = self._run_static_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- @test_with_pir_api
- def test_multiply_static_case3(self):
- # test static computation graph: 1-d array
- x_data = np.random.rand(50).astype(np.float64)
- y_data = np.random.rand(50).astype(np.float64)
- res = self._run_static_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- def test_multiply_dynamic_case1(self):
- # test dynamic computation graph: 3-d array
- x_data = np.random.rand(5, 10, 10).astype(np.float64)
- y_data = np.random.rand(2, 10).astype(np.float64)
- res = self._run_dynamic_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- def test_multiply_dynamic_case2(self):
- # test dynamic computation graph: 2-d array
- x_data = np.random.rand(20, 50).astype(np.float64)
- y_data = np.random.rand(50).astype(np.float64)
- res = self._run_dynamic_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- def test_multiply_dynamic_case3(self):
- # test dynamic computation graph: Scalar
- x_data = np.random.rand(20, 10).astype(np.float32)
- y_data = np.random.rand(1).astype(np.float32).item()
- res = self._run_dynamic_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- def test_multiply_dynamic_case4(self):
- # test dynamic computation graph: 2-d array Complex
- x_data = np.random.rand(20, 50).astype(
- np.float64
- ) + 1j * np.random.rand(20, 50).astype(np.float64)
- y_data = np.random.rand(50).astype(np.float64) + 1j * np.random.rand(
- 50
- ).astype(np.float64)
- res = self._run_dynamic_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
- def test_multiply_dynamic_case5(self):
- # test dynamic computation graph: 3-d array Complex
- x_data = np.random.rand(5, 10, 10).astype(
- np.float64
- ) + 1j * np.random.rand(5, 10, 10).astype(np.float64)
- y_data = np.random.rand(2, 10).astype(np.float64) + 1j * np.random.rand(
- 2, 10
- ).astype(np.float64)
- res = self._run_dynamic_graph_case(x_data, y_data)
- np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
-
-
- class TestMultiplyError(unittest.TestCase):
- @test_with_pir_api
- def test_errors_static_case1(self):
- # test static computation graph: dtype can not be int8
- paddle.enable_static()
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- x = paddle.static.data(name='x', shape=[100], dtype=np.int8)
- y = paddle.static.data(name='y', shape=[100], dtype=np.int8)
- self.assertRaises(TypeError, paddle.inner, x, y)
-
- @test_with_pir_api
- def test_errors_static_case2(self):
- # test static computation graph: inputs must be broadcastable
- paddle.enable_static()
- with paddle.static.program_guard(
- paddle.static.Program(), paddle.static.Program()
- ):
- x = paddle.static.data(name='x', shape=[20, 50], dtype=np.float64)
- y = paddle.static.data(name='y', shape=[20], dtype=np.float64)
- self.assertRaises(ValueError, paddle.inner, x, y)
-
- def test_errors_dynamic_case1(self):
- # test dynamic computation graph: inputs must be broadcastable
- x_data = np.random.rand(20, 5)
- y_data = np.random.rand(10, 2)
- x = paddle.to_tensor(x_data)
- y = paddle.to_tensor(y_data)
- self.assertRaises(Exception, paddle.inner, x, y)
-
- def test_errors_dynamic_case2(self):
- # test dynamic computation graph: dtype must be Tensor type
- x_data = np.random.randn(200).astype(np.float64)
- y_data = np.random.randn(200).astype(np.float64)
- y = paddle.to_tensor(y_data)
- self.assertRaises(Exception, paddle.inner, x_data, y)
-
- def test_errors_dynamic_case3(self):
- # test dynamic computation graph: dtype must be Tensor type
- x_data = np.random.randn(200).astype(np.float64)
- y_data = np.random.randn(200).astype(np.float64)
- x = paddle.to_tensor(x_data)
- self.assertRaises(Exception, paddle.inner, x, y_data)
-
- def test_errors_dynamic_case4(self):
- # test dynamic computation graph: dtype must be Tensor type
- x_data = np.random.randn(200).astype(np.float32)
- y_data = np.random.randn(200).astype(np.float32)
- self.assertRaises(Exception, paddle.inner, x_data, y_data)
-
-
- if __name__ == '__main__':
- paddle.enable_static()
- unittest.main()
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