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152 lines
5.1 KiB
152 lines
5.1 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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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.core as core
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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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class TestAddMMOp(OpTest):
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# test basic
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def setUp(self):
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self.op_type = "addmm"
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((100, 1)).astype(self.dtype),
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'X': np.random.random((100, 10)).astype(self.dtype),
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'Y': np.random.random((10, 20)).astype(self.dtype),
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}
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self.outputs = {
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'Out':
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self.inputs['Input'] + np.dot(self.inputs['X'], self.inputs['Y'])
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}
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def init_dtype_type(self):
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pass
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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(['Input', 'X', 'Y'], 'Out')
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def test_check_grad_x(self):
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self.check_grad(['X'], 'Out', no_grad_set=None)
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def test_check_grad_y(self):
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self.check_grad(['Y'], 'Out', no_grad_set=None)
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def test_check_grad_input(self):
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self.check_grad(['Input'], 'Out', no_grad_set=None)
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class TestAddMMOpError(unittest.TestCase):
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# test error
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of addmm_op must be Variable.
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input = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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x2 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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self.assertRaises(TypeError, paddle.addmm, input, x1, x2)
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# The input dtype of mul_op must be float32 or float64.
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input = fluid.layers.data(name='input', shape=[4], dtype="int32")
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="int32")
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x4 = fluid.layers.data(name='x4', shape=[4], dtype="int32")
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self.assertRaises(TypeError, paddle.addmm, input, x3, x4)
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class TestAddMMOp2(TestAddMMOp):
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# test alpha and beta
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def setUp(self):
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self.op_type = "addmm"
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((20, 30)).astype(self.dtype),
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'X': np.random.random((20, 6)).astype(self.dtype),
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'Y': np.random.random((6, 30)).astype(self.dtype),
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}
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self.attrs = {
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'Alpha': 0.1,
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'Beta': 1.0,
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}
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self.outputs = {'Out': self.attrs['Beta'] * self.inputs['Input'] + \
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self.attrs['Alpha'] * np.dot(self.inputs['X'], self.inputs['Y'])}
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class TestAddMMOp3(OpTest):
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# test broadcast
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def setUp(self):
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self.op_type = "addmm"
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'Input': np.random.random((1, 100)).astype(self.dtype),
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'X': np.random.random((20, 10)).astype(self.dtype),
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'Y': np.random.random((10, 100)).astype(self.dtype),
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}
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self.attrs = {
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'Alpha': 0.5,
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'Beta': 2.0,
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}
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self.outputs = {'Out': self.attrs['Beta'] * self.inputs['Input'] + \
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self.attrs['Alpha'] * np.dot(self.inputs['X'], self.inputs['Y'])}
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def init_dtype_type(self):
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pass
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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(['Input', 'X', 'Y'], 'Out')
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def test_check_grad_x(self):
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self.check_grad(['X'], 'Out', no_grad_set=None)
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def test_check_grad_y(self):
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self.check_grad(['Y'], 'Out', no_grad_set=None)
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def test_check_grad_input(self):
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self.check_grad(['Input'], 'Out', no_grad_set=None)
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class TestAddMMOp4(unittest.TestCase):
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def test_api_with_dygraph(self):
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np_input = np.random.random((20, 30)).astype(np.float32)
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np_x = np.random.random((20, 6)).astype(np.float32)
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np_y = np.random.random((6, 30)).astype(np.float32)
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with fluid.dygraph.guard():
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input = fluid.dygraph.to_variable(np_input)
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x = fluid.dygraph.to_variable(np_x)
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y = fluid.dygraph.to_variable(np_y)
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out = paddle.tensor.addmm(input, x, y)
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assert np.allclose(np_input + np.dot(np_x, np_y), out.numpy())
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if __name__ == "__main__":
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unittest.main()
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