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232 lines
9.5 KiB
232 lines
9.5 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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from __future__ import print_function
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import paddle
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import paddle.tensor as tensor
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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import numpy as np
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import unittest
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class TestMultiplyAPI(unittest.TestCase):
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"""TestMultiplyAPI."""
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def __run_static_graph_case(self, x_data, y_data, axis=-1):
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with program_guard(Program(), Program()):
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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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y = paddle.static.data(
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name='y', shape=y_data.shape, dtype=y_data.dtype)
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res = tensor.multiply(x, y, axis=axis)
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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outs = exe.run(fluid.default_main_program(),
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feed={'x': x_data,
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'y': y_data},
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fetch_list=[res])
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res = outs[0]
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return res
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def __run_static_graph_case_with_numpy_input(self, x_data, y_data, axis=-1):
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with program_guard(Program(), Program()):
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paddle.enable_static()
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res = tensor.multiply(x_data, y_data, axis=axis)
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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outs = exe.run(fluid.default_main_program(),
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feed={'x': x_data,
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'y': y_data},
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fetch_list=[res])
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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, axis=-1):
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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.multiply(x, y, axis=axis)
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return res.numpy()
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def __run_dynamic_graph_case_with_numpy_input(self, x_data, y_data,
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axis=-1):
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paddle.disable_static()
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res = paddle.multiply(x_data, y_data, axis=axis)
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return res.numpy()
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def test_multiply(self):
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"""test_multiply."""
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np.random.seed(7)
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# test static computation graph: 1-d array
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x_data = np.random.rand(200)
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y_data = np.random.rand(200)
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res = self.__run_static_graph_case(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test static computation graph: 1-d array
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x_data = np.random.rand(200)
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y_data = np.random.rand(200)
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res = self.__run_static_graph_case_with_numpy_input(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test static computation graph: 2-d array
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x_data = np.random.rand(2, 500)
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y_data = np.random.rand(2, 500)
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res = self.__run_static_graph_case(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test static computation graph with_primitives: 2-d array
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x_data = np.random.rand(2, 500)
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y_data = np.random.rand(2, 500)
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res = self.__run_static_graph_case_with_numpy_input(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test static computation graph: broadcast
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x_data = np.random.rand(2, 500)
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y_data = np.random.rand(500)
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res = self.__run_static_graph_case(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test static computation graph with_primitives: broadcast
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x_data = np.random.rand(2, 500)
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y_data = np.random.rand(500)
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res = self.__run_static_graph_case_with_numpy_input(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test static computation graph: broadcast with axis
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x_data = np.random.rand(2, 300, 40)
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y_data = np.random.rand(300)
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res = self.__run_static_graph_case(x_data, y_data, axis=1)
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expected = np.multiply(x_data, y_data[..., np.newaxis])
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self.assertTrue(np.allclose(res, expected))
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# test static computation graph with_primitives: broadcast with axis
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x_data = np.random.rand(2, 300, 40)
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y_data = np.random.rand(300)
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res = self.__run_static_graph_case_with_numpy_input(
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x_data, y_data, axis=1)
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expected = np.multiply(x_data, y_data[..., np.newaxis])
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self.assertTrue(np.allclose(res, expected))
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# test dynamic computation graph: 1-d array
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x_data = np.random.rand(200)
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y_data = np.random.rand(200)
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res = self.__run_dynamic_graph_case(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test dynamic numpy input computation graph: 1-d array
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x_data = np.random.rand(200)
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y_data = np.random.rand(200)
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res = self.__run_dynamic_graph_case_with_numpy_input(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test dynamic computation graph: 2-d array
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x_data = np.random.rand(20, 50)
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y_data = np.random.rand(20, 50)
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res = self.__run_dynamic_graph_case(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test dynamic numpy input computation graph: 1-d array
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x_data = np.random.rand(20, 50)
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y_data = np.random.rand(20, 50)
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res = self.__run_dynamic_graph_case_with_numpy_input(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test dynamic computation graph: broadcast
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x_data = np.random.rand(2, 500)
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y_data = np.random.rand(500)
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res = self.__run_dynamic_graph_case(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test dynamic computation graph with numpy tensor: broadcast
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x_data = np.random.rand(2, 500)
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y_data = np.random.rand(500)
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res = self.__run_dynamic_graph_case_with_numpy_input(x_data, y_data)
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self.assertTrue(np.allclose(res, np.multiply(x_data, y_data)))
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# test dynamic computation graph: broadcast with axis
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x_data = np.random.rand(2, 300, 40)
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y_data = np.random.rand(300)
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res = self.__run_dynamic_graph_case(x_data, y_data, axis=1)
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expected = np.multiply(x_data, y_data[..., np.newaxis])
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self.assertTrue(np.allclose(res, expected))
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# test dynamic computation graph with numpy tensor: broadcast with axis
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x_data = np.random.rand(2, 300, 40)
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y_data = np.random.rand(300)
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res = self.__run_dynamic_graph_case_with_numpy_input(
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x_data, y_data, axis=1)
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expected = np.multiply(x_data, y_data[..., np.newaxis])
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self.assertTrue(np.allclose(res, expected))
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class TestMultiplyError(unittest.TestCase):
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"""TestMultiplyError."""
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def test_errors(self):
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"""test_errors."""
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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 program_guard(Program(), Program()):
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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, tensor.multiply, x, y)
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# test static computation graph: inputs must be broadcastable
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with program_guard(Program(), Program()):
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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, tensor.multiply, x, y)
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np.random.seed(7)
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# test dynamic computation graph: dtype can not be int8
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paddle.disable_static()
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x_data = np.random.randn(200).astype(np.int8)
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y_data = np.random.randn(200).astype(np.int8)
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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.assertRaises(RuntimeError, paddle.multiply, x, y)
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# test dynamic computation graph: inputs must be broadcastable
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x_data = np.random.rand(200, 5)
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y_data = np.random.rand(200)
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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.assertRaises(ValueError, paddle.multiply, x, y)
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# test dynamic computation graph: inputs must be broadcastable(python)
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x_data = np.random.rand(200, 5)
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y_data = np.random.rand(200)
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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.assertRaises(ValueError, paddle.multiply, x, y)
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# test dynamic computation graph: dtype must be same
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x_data = np.random.randn(200).astype(np.int64)
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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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y = paddle.to_tensor(y_data)
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self.assertRaises(TypeError, paddle.multiply, x, y)
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if __name__ == '__main__':
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unittest.main()
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