fix traced layer with non persistable vars, test=develop (#22552)
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# 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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import unittest
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import paddle.fluid as fluid
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import numpy as np
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import six
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import os
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class SimpleFCLayer(fluid.dygraph.Layer):
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def __init__(self, feature_size, batch_size, fc_size):
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super(SimpleFCLayer, self).__init__()
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self._linear = fluid.dygraph.Linear(feature_size, fc_size)
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self._offset = fluid.dygraph.to_variable(
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np.random.random((batch_size, fc_size)).astype('float32'))
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def forward(self, x):
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fc = self._linear(x)
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return fc + self._offset
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class TestTracedLayerRecordNonPersistableInput(unittest.TestCase):
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def test_main(self):
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traced_layer = None
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with fluid.dygraph.guard():
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feature_size = 3
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batch_size = 4
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fc_size = 2
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layer = SimpleFCLayer(feature_size, batch_size, fc_size)
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optimizer = fluid.optimizer.SGD(learning_rate=1e-3,
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parameter_list=layer.parameters())
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expected_persistable_vars = set([
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layer._linear.weight.name, layer._linear.bias.name,
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layer._offset.name
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])
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for _ in six.moves.range(10):
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in_x = fluid.dygraph.to_variable(
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np.random.random((batch_size, feature_size)).astype(
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'float32'))
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if traced_layer is None:
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dygraph_out, traced_layer = fluid.dygraph.TracedLayer.trace(
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layer, [in_x])
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else:
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dygraph_out = layer(in_x)
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dygraph_out_numpy = dygraph_out.numpy()
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static_out = traced_layer([in_x])[0]
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self.assertTrue(np.array_equal(dygraph_out_numpy, static_out))
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loss = fluid.layers.reduce_mean(dygraph_out)
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loss.backward()
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optimizer.minimize(loss)
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del layer
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program = traced_layer.program
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actual_persistable_vars = set()
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for var in program.list_vars():
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if var.persistable:
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actual_persistable_vars.add(var.name)
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self.assertEqual(actual_persistable_vars, expected_persistable_vars)
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dirname = './traced_layer_test_non_persistable_vars'
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traced_layer.save_inference_model(dirname=dirname)
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filenames = set([f for f in os.listdir(dirname) if f != '__model__'])
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self.assertEqual(filenames, expected_persistable_vars)
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if __name__ == '__main__':
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unittest.main()
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