wehub-resource-sync
368 lines
14 KiB
Python
Executable File
368 lines
14 KiB
Python
Executable File
# 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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from paddle import static, tensor
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from paddle.base.framework import in_pir_mode
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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 static.program_guard(static.Program(), static.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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)
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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 = tensor.multiply(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.multiply(x, y)
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return res.numpy()
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def test_multiply(self):
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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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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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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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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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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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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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# test static computation graph: boolean
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x_data = np.random.choice([True, False], size=[200])
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y_data = np.random.choice([True, False], size=[200])
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res = self._run_static_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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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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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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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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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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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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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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# test dynamic computation graph: boolean
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x_data = np.random.choice([True, False], size=[200])
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y_data = np.random.choice([True, False], size=[200])
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, np.multiply(x_data, y_data), rtol=1e-05)
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class TestMultiplyError(unittest.TestCase):
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def test_errors(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 static.program_guard(static.Program(), static.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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if not in_pir_mode():
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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 static.program_guard(static.Program(), static.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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# test dynamic computation graph: dtype must be Tensor type
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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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y = paddle.to_tensor(y_data)
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self.assertRaises(ValueError, paddle.multiply, x_data, y)
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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.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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self.assertRaises(ValueError, paddle.multiply, x, y_data)
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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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x = paddle.to_tensor(x_data)
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self.assertRaises(ValueError, paddle.multiply, x, y_data)
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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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x = paddle.to_tensor(x_data)
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self.assertRaises(ValueError, paddle.multiply, x_data, y)
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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.assertRaises(ValueError, paddle.multiply, x_data, y_data)
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class TestMultiplyInplaceApi(TestMultiplyApi):
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def _run_static_graph_case(self, x_data, y_data):
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with static.program_guard(static.Program(), static.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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)
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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 = x.multiply_(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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with paddle.no_grad():
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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.multiply_(y)
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return x.numpy()
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class TestMultiplyInplaceError(unittest.TestCase):
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def test_errors(self):
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paddle.disable_static()
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# test dynamic computation graph: inputs must be broadcastable
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x_data = np.random.rand(3, 4)
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y_data = np.random.rand(2, 3, 4)
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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def multiply_shape_error():
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with paddle.no_grad():
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x.multiply_(y)
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self.assertRaises(ValueError, multiply_shape_error)
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paddle.enable_static()
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class TestMultiplyApiZeroSize(TestMultiplyApi):
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# only support the 0 size tensor
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def _test_grad(self, x_data, y_data):
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paddle.disable_static()
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x = paddle.to_tensor(x_data, stop_gradient=False)
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y = paddle.to_tensor(y_data, stop_gradient=False)
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z = paddle.multiply(x, y)
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loss = z.sum()
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loss.backward()
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np.testing.assert_allclose(
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x.grad.numpy(), np.zeros(self.x_shape).astype('float32'), rtol=1e-05
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)
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np.testing.assert_allclose(
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y.grad.numpy(), np.zeros(self.y_shape).astype('float32'), rtol=1e-05
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)
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def init_shapes(self):
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self.x_shape = [0, 4]
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self.y_shape = [0, 1]
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def test_multiply(self):
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np.random.seed(7)
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self.init_shapes()
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# test static computation graph
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x_data = np.random.rand(*(self.x_shape)).astype('float32')
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y_data = np.random.rand(*(self.y_shape)).astype('float32')
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expected_res = np.multiply(x_data, y_data)
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res = self._run_static_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, expected_res, rtol=1e-05)
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# test dynamic computation graph
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res = self._run_dynamic_graph_case(x_data, y_data)
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np.testing.assert_allclose(res, expected_res, rtol=1e-05)
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# test gradient
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self._test_grad(x_data, y_data)
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class TestMultiplyApiZeroSize1(TestMultiplyApiZeroSize):
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def init_shapes(self):
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self.x_shape = [6, 0]
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self.y_shape = [6, 0]
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class TestMultiplyApiZeroSize2(TestMultiplyApiZeroSize):
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def init_shapes(self):
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self.x_shape = [1, 8]
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self.y_shape = [0, 1]
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class TestMultiplyApiZeroSize3(TestMultiplyApiZeroSize):
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def init_shapes(self):
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self.x_shape = [5, 0]
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self.y_shape = [5, 1]
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class TestMultiplyApiBF16(unittest.TestCase):
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# Now only check the successful run of multiply with bfloat16 and backward.
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def setUp(self):
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paddle.device.set_device('cpu')
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def test_multiply(self):
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self.x_shape = [1, 1024, 32, 128]
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self.y_shape = [1, 1024, 1, 128]
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x = paddle.rand(self.x_shape, dtype='bfloat16')
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x.stop_gradient = False
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y = paddle.rand(self.y_shape, dtype='bfloat16')
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y.stop_gradient = False
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res = paddle.multiply(x, y)
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loss = res.sum()
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loss.backward()
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assert x.grad is not None
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assert x.grad.dtype == paddle.bfloat16
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assert y.grad is not None
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assert y.grad.dtype == paddle.bfloat16
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class TestMultiplyOutAndParamDecorator(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x_np = np.random.rand(3, 4).astype(np.float32)
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self.y_np = np.random.rand(3, 4).astype(np.float32)
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self.test_types = [
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# "decorator_input",
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# "decorator_other",
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# "decorator_both",
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"out",
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# "out_decorator",
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]
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def do_test(self, test_type):
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x = paddle.to_tensor(self.x_np, stop_gradient=False)
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y = paddle.to_tensor(self.y_np, stop_gradient=False)
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if test_type == 'raw':
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result = paddle.multiply(x, y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'decorator_input':
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result = paddle.multiply(input=x, y=y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'decorator_other':
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result = paddle.multiply(x, other=y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'decorator_both':
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result = paddle.multiply(input=x, other=y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'out':
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out = paddle.empty_like(x)
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out.stop_gradient = False
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paddle.multiply(x, y, out=out)
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out.mean().backward()
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return out, x.grad, y.grad
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elif test_type == 'out_decorator':
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out = paddle.empty_like(x)
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out.stop_gradient = False
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paddle.multiply(input=x, other=y, out=out)
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out.mean().backward()
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return out, x.grad, y.grad
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else:
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raise ValueError(f"Unknown test type: {test_type}")
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def test_all(self):
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out_std, x_grad_std, y_grad_std = self.do_test('raw')
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for test_type in self.test_types:
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out, x_grad, y_grad = self.do_test(test_type)
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np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-20)
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np.testing.assert_allclose(
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x_grad.numpy(), x_grad_std.numpy(), rtol=1e-20
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)
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np.testing.assert_allclose(
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y_grad.numpy(), y_grad_std.numpy(), rtol=1e-20
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)
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if __name__ == '__main__':
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unittest.main()
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