wehub-resource-sync
214 lines
8.2 KiB
Python
214 lines
8.2 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_device_place
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import paddle
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class TestMultiplyApi(unittest.TestCase):
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def _run_static_graph_case(self, x_data, y_data):
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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paddle.enable_static()
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x = paddle.static.data(
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name='x', shape=x_data.shape, dtype=x_data.dtype
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)
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y = paddle.static.data(
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name='y', shape=y_data.shape, dtype=y_data.dtype
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)
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res = paddle.inner(x, y)
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place = get_device_place()
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exe = paddle.static.Executor(place)
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outs = exe.run(
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paddle.static.default_main_program(),
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feed={'x': x_data, 'y': y_data},
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fetch_list=[res],
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)
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res = outs[0]
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return res
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def _run_dynamic_graph_case(self, x_data, y_data):
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paddle.disable_static()
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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res = paddle.inner(x, y)
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return res.numpy()
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def test_multiply_static_case1(self):
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# test static computation graph: 3-d array
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x_data = np.random.rand(2, 10, 10).astype(np.float64)
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y_data = np.random.rand(2, 5, 10).astype(np.float64)
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res = self._run_static_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_static_case2(self):
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# test static computation graph: 2-d array
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x_data = np.random.rand(200, 5).astype(np.float64)
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y_data = np.random.rand(50, 5).astype(np.float64)
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res = self._run_static_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_static_case3(self):
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# test static computation graph: 1-d array
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x_data = np.random.rand(50).astype(np.float64)
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y_data = np.random.rand(50).astype(np.float64)
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res = self._run_static_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_dynamic_case1(self):
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# test dynamic computation graph: 3-d array
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x_data = np.random.rand(5, 10, 10).astype(np.float64)
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y_data = np.random.rand(2, 10).astype(np.float64)
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_dynamic_case2(self):
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# test dynamic computation graph: 2-d array
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x_data = np.random.rand(20, 50).astype(np.float64)
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y_data = np.random.rand(50).astype(np.float64)
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_dynamic_case3(self):
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# test dynamic computation graph: Scalar
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x_data = np.random.rand(20, 10).astype(np.float32)
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y_data = np.random.rand(1).astype(np.float32).item()
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_dynamic_case4(self):
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# test dynamic computation graph: 2-d array Complex
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x_data = np.random.rand(20, 50).astype(
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np.float64
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) + 1j * np.random.rand(20, 50).astype(np.float64)
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y_data = np.random.rand(50).astype(np.float64) + 1j * np.random.rand(
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50
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).astype(np.float64)
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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def test_multiply_dynamic_case5(self):
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# test dynamic computation graph: 3-d array Complex
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x_data = np.random.rand(5, 10, 10).astype(
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np.float64
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) + 1j * np.random.rand(5, 10, 10).astype(np.float64)
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y_data = np.random.rand(2, 10).astype(np.float64) + 1j * np.random.rand(
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2, 10
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).astype(np.float64)
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.inner(x_data, y_data), rtol=1e-05)
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class TestMultiplyError(unittest.TestCase):
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def test_errors_static_case1(self):
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# test static computation graph: dtype can not be int8
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[100], dtype=np.int8)
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y = paddle.static.data(name='y', shape=[100], dtype=np.int8)
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self.assertRaises(TypeError, paddle.inner, x, y)
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def test_errors_static_case2(self):
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# test static computation graph: inputs must be broadcastable
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = paddle.static.data(name='x', shape=[20, 50], dtype=np.float64)
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y = paddle.static.data(name='y', shape=[20], dtype=np.float64)
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self.assertRaises(ValueError, paddle.inner, x, y)
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def test_errors_dynamic_case1(self):
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# test dynamic computation graph: inputs must be broadcastable
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x_data = np.random.rand(20, 5)
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y_data = np.random.rand(10, 2)
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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self.assertRaisesRegex(
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ValueError,
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"After performing an optional transpose",
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paddle.inner,
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x,
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y,
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)
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def test_errors_dynamic_case2(self):
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# test dynamic computation graph: dtype must be Tensor type
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x_data = np.random.randn(200).astype(np.float64)
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y_data = np.random.randn(200).astype(np.float64)
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y = paddle.to_tensor(y_data)
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self.assertRaisesRegex(
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Exception, r"matmul\(\): argument", paddle.inner, x_data, y
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)
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def test_errors_dynamic_case3(self):
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# test dynamic computation graph: dtype must be Tensor type
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x_data = np.random.randn(200).astype(np.float64)
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y_data = np.random.randn(200).astype(np.float64)
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x = paddle.to_tensor(x_data)
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self.assertRaisesRegex(
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Exception, r"matmul\(\): argument", paddle.inner, x, y_data
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)
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def test_errors_dynamic_case4(self):
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# test dynamic computation graph: dtype must be Tensor type
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x_data = np.random.randn(200).astype(np.float32)
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y_data = np.random.randn(200).astype(np.float32)
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self.assertRaisesRegex(
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Exception,
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r"matmul\(\): argument",
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paddle.inner,
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x_data,
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y_data,
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)
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class TestMultiplyApi_ZeroSize(unittest.TestCase):
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def _test_case(self, x_shape, y_shape):
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paddle.disable_static()
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x_data = np.random.rand(*x_shape).astype(np.float64)
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y_data = np.random.rand(*y_shape).astype(np.float64)
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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x.stop_gradient = False
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y.stop_gradient = False
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res = paddle.inner(x, y)
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np.testing.assert_allclose(
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res.numpy(), np.inner(x_data, y_data), rtol=1e-05
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)
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loss = paddle.sum(res)
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loss.backward()
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np.testing.assert_allclose(x.grad.shape, x.shape)
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def test_case(self):
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self._test_case([5, 10, 0], [2, 0])
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self._test_case([0], [0])
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self._test_case([0, 0], [1, 0])
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self._test_case([0, 0], [0, 0])
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self._test_case([0], [1, 0])
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self._test_case([5, 1, 1], [1, 0, 1])
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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