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fbf7eec86d
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/50602 Test Plan: Imported from OSS Reviewed By: ZolotukhinM Differential Revision: D25931371 fbshipit-source-id: cf6bc58c419a1dc0018639596b304a3a05e38360
40 lines
1.4 KiB
C++
40 lines
1.4 KiB
C++
#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <string>
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#include <unordered_map>
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// `TorchScript` offers a simple optimization limit checker
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// that can be configured through environment variable `PYTORCH_JIT_OPT_LIMIT`.
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// The purpose is to limit how many optimization you can make per pass.
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// This is useful for debugging any passes.
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// Opt limit checker is enabled on a per file basis (hence per pass). For
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// example, in `constant_propagation.cpp`, `PYTORCH_JIT_OPT_LIMIT` should be set
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// to `constant_propagation=<opt_limt>` or, simply, to
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// `constant_propagation=<opt_limit>` where <opt_limit> is the number of
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// optimizations you want to make for the pass. (i.e.
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// `PYTORCH_JIT_OPT_LIMIT="constant_propagation=<opt_limit>"`).
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// Multiple files can be configured by separating each file name with a colon
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// `:` as in the following example,
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// `PYTORCH_JIT_OPT_LIMIT="constant_propagation=<opt_limit>:dead_code_elimination=<opt_limit>"`
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// You can call opt limiter by calling JIT_OPT_ALLOWED. It will return true if
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// we haven't reached the optimization limit yet. Otherwise, it will return
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// false. Typical usage:
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// if (!JIT_OPT_ALLOWED) {
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// GRAPH_DUMP(...); //supplied from jit_log
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// return;
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// }
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namespace torch {
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namespace jit {
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TORCH_API bool opt_limit(const char* pass_name);
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#define JIT_OPT_ALLOWED opt_limit(__FILE__)
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} // namespace jit
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} // namespace torch
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