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124 lines
4.5 KiB
124 lines
4.5 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
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import collections
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.initializer import ConstantInitializer
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from paddle.fluid.param_attr import WeightNormParamAttr
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class TestWeightNormalization(unittest.TestCase):
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batch_size = 3
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hidden_size = 5
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data_desc = (['x', [10], 0], )
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@classmethod
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def setUpClass(cls):
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cls.set_program()
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@classmethod
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def set_program(cls):
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data = fluid.layers.data(
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name=cls.data_desc[0][0], shape=cls.data_desc[0][1])
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out = fluid.layers.fc(input=data,
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size=cls.hidden_size,
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param_attr=WeightNormParamAttr(
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dim=None,
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name='weight_norm_param',
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initializer=ConstantInitializer(1.0)),
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bias_attr=False,
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act=None)
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loss = fluid.layers.reduce_sum(out)
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fluid.backward.append_backward(loss=loss)
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cls.fetch_list = [
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'weight_norm_param_g', 'weight_norm_param_v',
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'weight_norm_param_g@GRAD'
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]
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def run_program(self):
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outputs = []
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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.set_inputs(place)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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output = exe.run(fluid.default_main_program(),
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feed=self.inputs,
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fetch_list=self.fetch_list,
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return_numpy=False)
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outputs.append(output)
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self.actual_outputs = outputs
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def set_data(self):
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self.data = collections.OrderedDict()
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for desc in self.data_desc:
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data_name = desc[0]
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data_shape = desc[1]
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data_lod_level = desc[2]
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data_lod = []
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for i in range(data_lod_level):
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lod_level_i = numpy.random.randint(
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low=1,
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high=5,
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size=self.batch_size
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if i == 0 else sum(lod_level_i)).tolist()
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data_lod.append(lod_level_i)
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data_value = numpy.random.random(
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size=[sum(data_lod[-1]) if data_lod else self.batch_size
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] + data_shape).astype('float32')
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self.data[data_name] = (data_value, data_lod)
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def set_inputs(self, place):
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self.inputs = {}
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for desc in self.data_desc:
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tensor = fluid.Tensor()
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tensor.set(self.data[desc[0]][0], place)
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if self.data[desc[0]][1]:
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tensor.set_recursive_sequence_lengths(self.data[desc[0]][1])
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self.inputs[desc[0]] = tensor
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def weight_normalize(self):
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v = numpy.ones((self.data[self.data_desc[0][0]][0].shape[-1],
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self.hidden_size))
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g = numpy.linalg.norm(v, axis=None, keepdims=True)
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w = g * v / numpy.linalg.norm(v, axis=None, keepdims=True)
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x = self.data[self.data_desc[0][0]][0]
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out = numpy.dot(x, w)
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g_grad = (numpy.dot(x.T, numpy.ones_like(out)) * (v / numpy.linalg.norm(
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v, axis=None, keepdims=True))).sum(axis=None, keepdims=True)
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return g, v, g_grad
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def test_weight_normalization(self):
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self.set_data()
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self.run_program()
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expect_output = self.weight_normalize()
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for actual_output in self.actual_outputs:
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[
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self.assertTrue(
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numpy.allclose(
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numpy.array(actual), expect, atol=0.001))
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for expect, actual in zip(expect_output, actual_output)
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]
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if __name__ == '__main__':
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unittest.main()
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