memory leak for cpu (#21174)
* add fake init for the trainer, fix large memory hold in the trainer * do not merge recv vars from a remote endpoint, test=develop * add recv and save op, merge slice var in one op, save memory * remove hsigmoid with pull sparse, test=developpaddle_tiny_install
tangwei12
6 years ago
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03133c2c58
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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 traceback
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import math
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import collections
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import six
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import unittest
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import numpy as np
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import gc
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gc.set_debug(gc.DEBUG_COLLECTABLE)
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import paddle.fluid as fluid
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from test_dist_transpiler import TranspilerTest
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class TestFakeInit(TranspilerTest):
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def net_conf(self):
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dict_size, embedding_size, neg_num = 10000, 8, 5
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input_word = fluid.layers.data(
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name="input_word", shape=[1], dtype='int64', lod_level=1)
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true_word = fluid.layers.data(
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name='true_label', shape=[1], dtype='int64', lod_level=1)
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neg_word = fluid.layers.data(
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name="neg_label", shape=[1], dtype='int64', lod_level=1)
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inputs = [input_word, true_word, neg_word]
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init_width = 0.5 / embedding_size
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input_emb = fluid.layers.embedding(
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input=inputs[0],
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is_sparse=True,
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size=[dict_size, embedding_size],
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param_attr=fluid.ParamAttr(
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name='emb',
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initializer=fluid.initializer.Uniform(-init_width, init_width)))
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true_emb_w = fluid.layers.embedding(
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input=inputs[1],
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is_sparse=True,
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size=[dict_size, embedding_size],
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param_attr=fluid.ParamAttr(
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name='emb_w',
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initializer=fluid.initializer.Constant(value=0.0)))
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true_emb_b = fluid.layers.embedding(
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input=inputs[1],
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is_sparse=True,
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size=[dict_size, 1],
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param_attr=fluid.ParamAttr(
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name='emb_b',
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initializer=fluid.initializer.Constant(value=0.0)))
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neg_word_reshape = fluid.layers.reshape(inputs[2], shape=[-1, 1])
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neg_word_reshape.stop_gradient = True
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neg_emb_w = fluid.layers.embedding(
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input=neg_word_reshape,
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is_sparse=True,
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size=[dict_size, embedding_size],
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param_attr=fluid.ParamAttr(
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name='emb_w', learning_rate=1.0))
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neg_emb_w_re = fluid.layers.reshape(
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neg_emb_w, shape=[-1, neg_num, embedding_size])
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neg_emb_b = fluid.layers.embedding(
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input=neg_word_reshape,
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is_sparse=True,
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size=[dict_size, 1],
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param_attr=fluid.ParamAttr(
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name='emb_b', learning_rate=1.0))
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neg_emb_b_vec = fluid.layers.reshape(neg_emb_b, shape=[-1, neg_num])
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true_logits = fluid.layers.elementwise_add(
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fluid.layers.reduce_sum(
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fluid.layers.elementwise_mul(input_emb, true_emb_w),
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dim=1,
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keep_dim=True),
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true_emb_b)
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input_emb_re = fluid.layers.reshape(
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input_emb, shape=[-1, 1, embedding_size])
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neg_matmul = fluid.layers.matmul(
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input_emb_re, neg_emb_w_re, transpose_y=True)
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neg_matmul_re = fluid.layers.reshape(neg_matmul, shape=[-1, neg_num])
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neg_logits = fluid.layers.elementwise_add(neg_matmul_re, neg_emb_b_vec)
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# nce loss
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label_ones = fluid.layers.fill_constant_batch_size_like(
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true_logits, shape=[-1, 1], value=1.0, dtype='float32')
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label_zeros = fluid.layers.fill_constant_batch_size_like(
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true_logits, shape=[-1, neg_num], value=0.0, dtype='float32')
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true_xent = fluid.layers.sigmoid_cross_entropy_with_logits(true_logits,
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label_ones)
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neg_xent = fluid.layers.sigmoid_cross_entropy_with_logits(neg_logits,
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label_zeros)
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cost = fluid.layers.elementwise_add(
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fluid.layers.reduce_sum(
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true_xent, dim=1),
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fluid.layers.reduce_sum(
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neg_xent, dim=1))
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avg_cost = fluid.layers.reduce_mean(cost)
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sgd_optimizer = fluid.optimizer.SGD(
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learning_rate=fluid.layers.exponential_decay(
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learning_rate=1.0,
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decay_steps=2100,
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decay_rate=0.1,
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staircase=True))
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sgd_optimizer.minimize(avg_cost)
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def transpiler_test_impl(self):
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trainer, startup = self.get_trainer()
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fake_init_ops = []
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for op in startup.global_block().ops:
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if op.type == "fake_init":
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fake_init_ops.append(op)
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self.assertEqual(len(fake_init_ops), 3)
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if __name__ == "__main__":
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unittest.main()
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