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102 lines
3.4 KiB
102 lines
3.4 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 paddle.fluid.op import Operator
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from op_test import OpTest
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class TestSGDOp(OpTest):
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def setUp(self):
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self.op_type = "sgd"
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w = np.random.random((102, 105)).astype("float32")
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g = np.random.random((102, 105)).astype("float32")
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lr = np.array([0.1]).astype("float32")
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self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr}
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self.outputs = {'ParamOut': w - lr * g}
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def test_check_output(self):
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self.check_output()
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class TestSparseSGDOp(unittest.TestCase):
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def check_with_place(self, place):
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scope = core.Scope()
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# create and initialize Grad Variable
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height = 10
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rows = [0, 4, 7]
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row_numel = 12
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grad_selected_rows = scope.var('Grad').get_selected_rows()
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grad_selected_rows.set_height(height)
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grad_selected_rows.set_rows(rows)
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np_array = np.ones((len(rows), row_numel)).astype("float32")
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np_array[0, 0] = 2.0
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np_array[2, 8] = 4.0
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grad_tensor = grad_selected_rows.get_tensor()
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grad_tensor.set(np_array, place)
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# create and initialize Param Variable
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param = scope.var('Param').get_tensor()
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param_array = np.full((height, row_numel), 5.0).astype("float32")
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param.set(param_array, place)
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# create and initialize LeraningRate Variable
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lr = scope.var('LearningRate').get_tensor()
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lr_array = np.full((1), 2.0).astype("float32")
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lr.set(lr_array, place)
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# create and run sgd operator
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sgd_op = Operator(
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"sgd",
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Param='Param',
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Grad='Grad',
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ParamOut='Param',
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LearningRate='LearningRate')
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sgd_op.run(scope, place)
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# get and compare result
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result_array = np.array(param)
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# rows[0] = 0, 5.0 - 2.0 * 2.0
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self.assertAlmostEqual(1.0, result_array[rows[0], 0])
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# rows[0] = 0, 5.0 - 2.0 * 1.0
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self.assertAlmostEqual(3.0, result_array[rows[0], 2])
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# 5.0 - 2.0 * 0.0
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self.assertAlmostEqual(5.0, result_array[1, 0])
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# rows[1] = 4, 5.0 - 2.0 * 1.0
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self.assertAlmostEqual(3.0, result_array[rows[1], 10])
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# 5.0 - 2.0 * 0.0
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self.assertAlmostEqual(5.0, result_array[5, 8])
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# rows[2] = 7, 5.0 - 2.0 * 1.0
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self.assertAlmostEqual(3.0, result_array[rows[2], 1])
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# rows[2] = 7, 5.0 - 2.0 * 4.0
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self.assertAlmostEqual(-3.0, result_array[rows[2], 8])
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def test_sparse_sgd(self):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(core.CUDAPlace(0))
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for place in places:
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self.check_with_place(place)
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if __name__ == "__main__":
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unittest.main()
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