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256 lines
8.7 KiB
256 lines
8.7 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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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], [-1, -1]]), [[2]], fluid.CPUPlace())
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x1 = fluid.create_lod_tensor(
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np.array([[-1, -1], [-1, -1]]), [[2]], fluid.CPUPlace())
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x2 = fluid.create_lod_tensor(
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np.array([[-1, -1], [-1, -1]]), [[2]], 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(
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name='input',
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shape=[4, 4],
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dtype="int32",
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append_batch_size=False)
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x3 = fluid.layers.data(
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name='x3', shape=[4, 4], dtype="int32", append_batch_size=False)
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x4 = fluid.layers.data(
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name='x4', shape=[4, 4], dtype="int32", append_batch_size=False)
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self.assertRaises(TypeError, paddle.addmm, input, x3, x4)
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# x and y dimension mismatch
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x5 = fluid.layers.data(
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name='x5',
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shape=[4, 5],
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dtype="float32",
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append_batch_size=False)
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x6 = fluid.layers.data(
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name='x6',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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self.assertRaises(ValueError, paddle.addmm, input, x5, x6)
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# input and x are not broadcastable
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x7 = fluid.layers.data(
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name='x7',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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x8 = fluid.layers.data(
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name='x8',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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input1 = fluid.layers.data(
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name='input1',
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shape=[2, 4],
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dtype="float32",
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append_batch_size=False)
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self.assertRaises(ValueError, paddle.addmm, input1, x7, x8)
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# input and x are not broadcastable
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x9 = fluid.layers.data(
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name='x9',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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x10 = fluid.layers.data(
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name='x10',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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input2 = fluid.layers.data(
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name='input2',
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shape=[1, 2],
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dtype="float32",
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append_batch_size=False)
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self.assertRaises(ValueError, paddle.addmm, input2, x9, x10)
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x11 = fluid.layers.data(
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name='x11',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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x12 = fluid.layers.data(
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name='x12',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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input3 = fluid.layers.data(
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name='input3',
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shape=[4, 2],
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dtype="float32",
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append_batch_size=False)
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self.assertRaises(ValueError, paddle.addmm, input3, x11, x12)
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x13 = fluid.layers.data(
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name='x13',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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x14 = fluid.layers.data(
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name='x14',
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shape=[4, 4],
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dtype="float32",
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append_batch_size=False)
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input4 = fluid.layers.data(
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name='input4',
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shape=[3, 1],
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dtype="float32",
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append_batch_size=False)
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self.assertRaises(ValueError, paddle.addmm, input4, x13, x14)
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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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'''
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class TestAddMMAPI(unittest.TestCase):
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def test_api_error(self):
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data_x = np.ones((2, 2)).astype(np.float32)
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data_y = np.ones((2, 2)).astype(np.float32)
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data_input = np.ones((2, 2)).astype(np.float32)
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paddle.disable_static()
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def test_error1():
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data_x_wrong = np.ones((2, 3)).astype(np.float32)
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x = paddle.to_tensor(data_x_wrong)
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y = paddle.to_tensor(data_y)
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input = paddle.to_tensor(data_input)
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out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )
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self.assertRaises(ValueError, test_error1)
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'''
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if __name__ == "__main__":
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unittest.main()
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