Add call stack info during compile time (#19067)
* Add call stack info during runtime and compile time test=develop * Rename operator_call_stack test=develop * Add unit test test=develop * follow comment test=developpadding_in_crf
chengduo
6 years ago
committed by
GitHub
parent
a99bc64c63
commit
21440b4d69
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/framework/op_call_stack.h"
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/attribute.h"
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#include "paddle/fluid/framework/op_proto_maker.h"
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namespace paddle {
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namespace framework {
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void InsertCallStackInfo(const std::string &type, const AttributeMap &attrs,
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platform::EnforceNotMet *exception) {
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if (attrs.count("sub_block") != 0) {
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return;
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}
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auto &callstack = boost::get<std::vector<std::string>>(
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attrs.at(OpProtoAndCheckerMaker::OpCreationCallstackAttrName()));
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if (callstack.empty()) {
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return;
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}
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std::ostringstream sout;
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sout << "Invoke operator " << type << " error.\n";
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sout << "Python Call stacks: \n";
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for (auto &line : callstack) {
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sout << line;
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}
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sout << "C++ Call stacks: \n";
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sout << exception->err_str_;
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exception->err_str_ = sout.str();
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}
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} // namespace framework
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} // namespace paddle
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/type_defs.h"
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#include "paddle/fluid/platform/enforce.h"
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namespace paddle {
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namespace framework {
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void InsertCallStackInfo(const std::string &type, const AttributeMap &attrs,
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platform::EnforceNotMet *exception);
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} // namespace framework
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} // namespace paddle
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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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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class TestRunTimeException(OpTest):
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def test_run_time_exception(self):
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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train_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(train_program, startup_program):
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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fluid.layers.one_hot(input=label, depth=100)
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def _run_program():
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x = np.random.random(size=(10)).astype('int64')
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exe.run(train_program, feed={"label": x})
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self.assertRaises(core.EnforceNotMet, _run_program)
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class TestCompileTimeException(OpTest):
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def test_compile_time_exception(self):
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self.assertRaises(core.EnforceNotMet, self.build_model)
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def build_model(self):
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train_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(train_program, startup_program):
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label = fluid.layers.data(
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name="label", shape=[1], dtype="int64", append_batch_size=False)
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fluid.layers.one_hot(input=label, depth=100)
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if __name__ == '__main__':
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unittest.main()
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