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112 lines
3.7 KiB
112 lines
3.7 KiB
# Copyright (c) 2018 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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import paddle.fluid.core as core
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from op_test import OpTest
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class TestMulOp(OpTest):
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def setUp(self):
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self.op_type = "mul"
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self.inputs = {
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'X': np.random.random((32, 84)).astype("float32"),
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'Y': np.random.random((84, 100)).astype("float32")
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}
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self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X"))
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
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class TestMulOp2(OpTest):
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def setUp(self):
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self.op_type = "mul"
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self.inputs = {
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'X': np.random.random((15, 4, 12, 10)).astype("float32"),
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'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32")
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}
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self.attrs = {'x_num_col_dims': 2, 'y_num_col_dims': 2}
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result = np.dot(self.inputs['X'].reshape(15 * 4, 12 * 10),
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self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9))
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result = result.reshape(15, 4, 8, 2, 9)
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self.outputs = {'Out': result}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=0.5, no_grad_set=set('X'))
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
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class TestFP16MulOp1(OpTest):
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def setUp(self):
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self.op_type = "mul"
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x = np.random.random((32, 84)).astype("float16")
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y = np.random.random((84, 100)).astype("float16")
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self.inputs = {'X': x.view(np.uint16), 'Y': y.view(np.uint16)}
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self.outputs = {'Out': np.dot(x, y)}
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=1e-1)
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class TestFP16MulOp2(OpTest):
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def setUp(self):
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self.op_type = "mul"
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x = np.random.random((15, 4, 12, 10)).astype("float16")
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y = np.random.random((4, 30, 8, 2, 9)).astype("float16")
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self.inputs = {'X': x.view(np.uint16), 'Y': y.view(np.uint16)}
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self.attrs = {
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'x_num_col_dims': 2,
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'y_num_col_dims': 2,
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}
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result = np.dot(
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x.reshape(15 * 4, 12 * 10), y.reshape(4 * 30, 8 * 2 * 9))
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result = result.reshape(15, 4, 8, 2, 9)
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self.outputs = {'Out': result}
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-1)
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if __name__ == "__main__":
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unittest.main()
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