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144 lines
4.4 KiB
144 lines
4.4 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 unittest
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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import gradient_checker
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from decorator_helper import prog_scope
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paddle.enable_static()
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class TestMulGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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prog = fluid.Program()
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with fluid.program_guard(prog):
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x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
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y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
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z = layers.mul(x=x, y=y)
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gradient_checker.grad_check([x, y], z, place=place)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestMulDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not inlcude -1.
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x_shape = [7, 11]
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y_shape = [11, 9]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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y = layers.data('y', y_shape, False, dtype)
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y.persistable = True
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out = layers.mul(x, y)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestMatmulDoubleGradCheck(unittest.TestCase):
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def setUp(self):
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self.init_test()
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def init_test(self):
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self.x_shape = [2]
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self.y_shape = [2]
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self.transpose_x = False
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self.transpose_y = False
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@prog_scope()
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def func(self, place):
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eps = 0.005
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dtype = np.float64
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typename = "float64"
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x = layers.create_parameter(
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dtype=typename, shape=self.x_shape, name='x')
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y = layers.create_parameter(
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dtype=typename, shape=self.y_shape, name='y')
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out = layers.matmul(
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x, y, self.transpose_x, self.transpose_y, name='out')
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x_arr = np.random.uniform(-1, 1, self.x_shape).astype(dtype)
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y_arr = np.random.uniform(-1, 1, self.y_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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def TestMatmulDoubleGradCheckCase1(TestMatmulDoubleGradCheck):
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def init_test(self):
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self.x_shape = [2, 3]
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self.y_shape = [3, 2]
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self.transpose_x = True
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self.transpose_y = True
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def TestMatmulDoubleGradCheckCase2(TestMatmulDoubleGradCheck):
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def init_test(self):
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self.x_shape = [2, 4, 3]
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self.y_shape = [2, 4, 5]
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self.transpose_x = True
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self.transpose_y = False
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def TestMatmulDoubleGradCheckCase3(TestMatmulDoubleGradCheck):
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def init_test(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = [2, 3, 3, 5]
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self.transpose_x = False
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self.transpose_y = True
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def TestMatmulDoubleGradCheckCase4(TestMatmulDoubleGradCheck):
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def init_test(self):
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self.x_shape = [2, 3, 4]
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self.y_shape = [4, 3]
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self.transpose_x = False
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self.transpose_y = False
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if __name__ == "__main__":
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unittest.main()
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