mirror of
https://github.com/zebrajr/pytorch.git
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d790ec6de0
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46301 Test Plan: Imported from OSS Reviewed By: bertmaher Differential Revision: D24295281 Pulled By: ZolotukhinM fbshipit-source-id: a4f84c773029845065895a81f9d753a9c82a99e0
103 lines
3.6 KiB
C++
103 lines
3.6 KiB
C++
#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <memory>
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#include <string>
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// `TorchScript` offers a simple logging facility that can enabled by setting an
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// environment variable `PYTORCH_JIT_LOG_LEVEL`.
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// Logging is enabled on a per file basis. To enable logging in
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// `dead_code_elimination.cpp`, `PYTORCH_JIT_LOG_LEVEL` should be
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// set to `dead_code_elimination.cpp` or, simply, to `dead_code_elimination`
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// (i.e. `PYTORCH_JIT_LOG_LEVEL=dead_code_elimination`).
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// Multiple files can be logged by separating each file name with a colon `:` as
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// in the following example,
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// `PYTORCH_JIT_LOG_LEVEL=dead_code_elimination:guard_elimination`
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// There are 3 logging levels available for your use ordered by the detail level
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// from lowest to highest.
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// * `GRAPH_DUMP` should be used for printing entire graphs after optimization
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// passes
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// * `GRAPH_UPDATE` should be used for reporting graph transformations (i.e.
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// node deletion, constant folding, etc)
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// * `GRAPH_DEBUG` should be used for providing information useful for debugging
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// the internals of a particular optimization pass or analysis
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// The default logging level is `GRAPH_DUMP` meaning that only `GRAPH_DUMP`
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// statements will be enabled when one specifies a file(s) in
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// `PYTORCH_JIT_LOG_LEVEL`.
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// `GRAPH_UPDATE` can be enabled by prefixing a file name with an `>` as in
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// `>alias_analysis`.
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// `GRAPH_DEBUG` can be enabled by prefixing a file name with an `>>` as in
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// `>>alias_analysis`.
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// `>>>` is also valid and **currently** is equivalent to `GRAPH_DEBUG` as there
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// is no logging level that is higher than `GRAPH_DEBUG`.
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namespace torch {
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namespace jit {
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struct Node;
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struct Graph;
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enum class JitLoggingLevels {
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GRAPH_DUMP = 0,
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GRAPH_UPDATE,
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GRAPH_DEBUG,
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};
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std::string TORCH_API getHeader(const Node* node);
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std::string TORCH_API log_function(const std::shared_ptr<Graph>& graph);
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TORCH_API ::torch::jit::JitLoggingLevels jit_log_level();
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// Prefix every line in a multiline string \p IN_STR with \p PREFIX.
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TORCH_API std::string jit_log_prefix(
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const std::string& prefix,
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const std::string& in_str);
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TORCH_API std::string jit_log_prefix(
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::torch::jit::JitLoggingLevels level,
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const char* fn,
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int l,
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const std::string& in_str);
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TORCH_API bool is_enabled(
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const char* cfname,
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::torch::jit::JitLoggingLevels level);
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TORCH_API std::ostream& operator<<(
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std::ostream& out,
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::torch::jit::JitLoggingLevels level);
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#define JIT_LOG(level, ...) \
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if (is_enabled(__FILE__, level)) { \
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std::cerr << ::torch::jit::jit_log_prefix( \
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level, __FILE__, __LINE__, ::c10::str(__VA_ARGS__)); \
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}
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// tries to reconstruct original python source
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#define SOURCE_DUMP(MSG, G) \
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JIT_LOG( \
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::torch::jit::JitLoggingLevels::GRAPH_DUMP, \
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MSG, \
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"\n", \
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::torch::jit::log_function(G));
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// use GRAPH_DUMP for dumping graphs after optimization passes
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#define GRAPH_DUMP(MSG, G) \
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JIT_LOG( \
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::torch::jit::JitLoggingLevels::GRAPH_DUMP, MSG, "\n", (G)->toString());
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// use GRAPH_UPDATE for reporting graph transformations (i.e. node deletion,
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// constant folding, CSE)
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#define GRAPH_UPDATE(...) \
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JIT_LOG(::torch::jit::JitLoggingLevels::GRAPH_UPDATE, __VA_ARGS__);
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// use GRAPH_DEBUG to provide information useful for debugging a particular opt
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// pass
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#define GRAPH_DEBUG(...) \
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JIT_LOG(::torch::jit::JitLoggingLevels::GRAPH_DEBUG, __VA_ARGS__);
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} // namespace jit
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} // namespace torch
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