Using Smart pointer to optimizer memory usage of dyGraph (#17768)
* for debug * test=develop, memory optimize for dygraph using shared_ptr * test=develop, fix travis ci showed error * test=develop, fix bug for recurrent usage of varbase * test=develop, init varbase when it need to be Adddependabot/pip/python/requests-2.20.0
Jiabin Yang
6 years ago
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# 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 paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.dygraph.nn import Embedding
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import paddle.fluid.framework as framework
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from paddle.fluid.optimizer import SGDOptimizer
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from paddle.fluid.dygraph.base import to_variable
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from test_imperative_base import new_program_scope
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import numpy as np
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import six
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class RecurrentTest(fluid.Layer):
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def __init__(self, name_scope):
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super(RecurrentTest, self).__init__(name_scope)
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def forward(self, in1, in2):
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out = fluid.layers.mul(in1, in2)
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sum_out = fluid.layers.reduce_sum(out)
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return sum_out, out
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class TestRecurrentFeed(unittest.TestCase):
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def test_recurrent_feed(self):
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seed = 90
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original_np1 = np.arange(1, 5).reshape(2, 2).astype("float32")
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original_np2 = np.arange(5, 9).reshape(2, 2).astype("float32")
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with fluid.dygraph.guard():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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original_in1 = to_variable(original_np1)
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original_in2 = to_variable(original_np2)
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rt = RecurrentTest("RecurrentTest")
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for i in range(3):
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sum_out, out = rt(original_in1, original_in2)
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original_in1 = out
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sum_out_value = sum_out.numpy()
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sum_out.backward()
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rt.clear_gradients()
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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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in1 = fluid.layers.data(
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name="inp1", shape=[2, 2], append_batch_size=False)
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in2 = fluid.layers.data(
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name="inp2", shape=[2, 2], append_batch_size=False)
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rt1 = RecurrentTest("RecurrentTest")
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static_sum_out, static_out = rt1(in1, in2)
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fluid.backward.append_backward(static_sum_out)
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exe = fluid.Executor(fluid.CPUPlace(
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) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
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fetch_list = [static_sum_out, static_out]
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for i in range(3):
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out = exe.run(
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fluid.default_main_program(),
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feed={"inp1": original_np1,
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"inp2": original_np2},
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fetch_list=fetch_list)
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static_out_value = out[1]
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static_sum_out = out[0]
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original_np1 = static_out_value
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self.assertTrue(np.array_equal(static_sum_out, sum_out_value))
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if __name__ == '__main__':
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unittest.main()
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