Try to deprecate unstable python memory optimize (#18983)
* deprecate python memory optimize, test=develop * remove memory_optimize in unittests, test=develop * add unittests to deprecated interfaces, test=developpadding_in_crf
Zeng Jinle
6 years ago
committed by
GitHub
parent
5a80cc8431
commit
c194b0c835
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# Copyright (c) 2019 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 paddle.fluid as fluid
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import unittest
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from simple_nets import simple_fc_net
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class DeprecatedMemoryOptimizationInterfaceTest(unittest.TestCase):
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def setUp(self):
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self.method = fluid.memory_optimize
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def build_network(self, call_interface):
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startup_prog = fluid.Program()
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main_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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with fluid.unique_name.guard():
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loss = simple_fc_net()
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opt = fluid.optimizer.Adam(learning_rate=1e-3)
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opt.minimize(loss)
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if call_interface:
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self.method(main_prog)
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return main_prog
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def assert_program_equal(self, prog1, prog2):
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block_num = prog1.num_blocks
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self.assertEquals(block_num, prog2.num_blocks)
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for block_id in range(block_num):
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block1 = prog1.block(block_id)
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block2 = prog2.block(block_id)
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self.assertEquals(len(block1.ops), len(block2.ops))
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for op1, op2 in zip(block1.ops, block2.ops):
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self.assertEquals(op1.input_arg_names, op2.input_arg_names)
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self.assertEquals(op1.output_arg_names, op2.output_arg_names)
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self.assertEquals(len(block1.vars), len(block2.vars))
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for var1 in block1.vars.values():
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self.assertTrue(var1.name in block2.vars)
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var2 = block2.vars.get(var1.name)
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self.assertEquals(var1.name, var2.name)
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def test_main(self):
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prog1 = self.build_network(False)
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prog2 = self.build_network(True)
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self.assert_program_equal(prog1, prog2)
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class ReleaseMemoryTest(DeprecatedMemoryOptimizationInterfaceTest):
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def setUp(self):
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self.method = fluid.release_memory
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if __name__ == '__main__':
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unittest.main()
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@ -1,118 +0,0 @@
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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.layers as layers
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import paddle.fluid.optimizer as optimizer
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from paddle.fluid.framework import Program, program_guard
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from paddle.fluid.transpiler import memory_optimize
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def _get_vars(prog):
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assert (isinstance(prog, Program))
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all_vars = set()
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for op in prog.global_block().ops:
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all_vars.update(op.input_arg_names)
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all_vars.update(op.output_arg_names)
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return all_vars
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class TestControlFlowGraph(unittest.TestCase):
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def setUp(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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x = layers.data(name='x', shape=[13], dtype='float32')
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y_predict = layers.fc(input=x, size=1, act=None)
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y = layers.data(name='y', shape=[1], dtype='float32')
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cost = layers.square_error_cost(input=y_predict, label=y)
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avg_cost = layers.mean(cost)
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opt = optimizer.SGD(learning_rate=0.001)
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opt = opt.minimize(avg_cost)
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self.program = program
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def test_control_flow_graph(self):
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result_program = self.program.clone()
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memory_optimize(self.program)
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old_vars = _get_vars(self.program)
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new_vars = _get_vars(result_program)
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self.assertTrue(old_vars != new_vars)
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class TestMemoryTranspiler2(unittest.TestCase):
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def setUp(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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x = layers.data(name='x', shape=[13], dtype='float32')
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fc = layers.fc(input=x, size=10, act=None)
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reshape = layers.reshape(x=fc, shape=[-1, 2, 5])
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fc = layers.reshape(x=reshape, shape=[-1, 5, 2])
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y_predict = layers.fc(input=fc, size=1, act=None)
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y = layers.data(name='y', shape=[1], dtype='float32')
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cost = layers.square_error_cost(input=y_predict, label=y)
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avg_cost = layers.mean(cost)
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opt = optimizer.SGD(learning_rate=0.001)
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opt.minimize(avg_cost)
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self.skip_set = set([cost.name, fc.name])
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self.program = program
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def test_inplace_ops(self):
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result_program = self.program.clone()
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memory_optimize(self.program)
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old_vars = _get_vars(self.program)
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new_vars = _get_vars(result_program)
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self.assertTrue(old_vars != new_vars)
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def test_skip_opt(self):
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result_program = self.program.clone()
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memory_optimize(self.program, skip_opt_set=self.skip_set)
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old_vars = _get_vars(self.program)
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new_vars = _get_vars(result_program)
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self.assertTrue(old_vars != new_vars)
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class TestMemoryTranspiler3(unittest.TestCase):
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def setUp(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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word = fluid.layers.data(name='word', shape=[1], dtype='int64')
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emb = [
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fluid.layers.embedding(
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word, size=[65536, 256], param_attr='emb') for _ in range(6)
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]
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left = emb.pop(0)
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while len(emb) != 0:
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right = emb.pop(0)
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left = fluid.layers.concat([left, right])
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emb = fluid.layers.mean(left)
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fluid.backward.append_backward(emb)
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self.program = program
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def test_cascade_reuse(self):
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block = self.program.block(0)
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# variable reuse in programdesc
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# TODO(dzhwinter): confirm cascade strategy. disable temporialy
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self.assertTrue("concat_4.tmp_0@GRAD" in block.vars)
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# self.assertTrue("concat_3.tmp_0@GRAD" not in block.vars)
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# self.assertTrue("concat_2.tmp_0@GRAD" not in block.vars)
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# self.assertTrue("concat_1.tmp_0@GRAD" not in block.vars)
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# self.assertTrue("concat_0.tmp_0@GRAD" not in block.vars)
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if __name__ == "__main__":
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unittest.main()
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