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#pragma once
#include <torch/csrc/onnx/diagnostics/generated/rules.h>
#include <torch/csrc/utils/pybind.h>
#include <string>
namespace torch {
namespace onnx {
namespace diagnostics {
/**
* @brief Level of a diagnostic.
* @details The levels are defined by the SARIF specification, and are not
* modifiable. For alternative categories, please use Tag instead.
* @todo Introduce Tag to C++ api.
*/
enum class Level : uint8_t {
kNone,
kNote,
kWarning,
kError,
};
static constexpr const char* const kPyLevelNames[] = {
"NONE",
"NOTE",
"WARNING",
"ERROR",
};
// Wrappers around Python diagnostics.
// TODO: Move to .cpp file in following PR.
inline py::object _PyDiagnostics() {
return py::module::import("torch.onnx._internal.diagnostics");
}
inline py::object _PyEngine() {
return _PyDiagnostics().attr("engine");
}
inline py::object _PyContext() {
return _PyDiagnostics().attr("context");
}
inline py::object _PyRule(Rule rule) {
return _PyDiagnostics().attr("rules").attr(
kPyRuleNames[static_cast<uint32_t>(rule)]);
}
inline py::object _PyLevel(Level level) {
return _PyDiagnostics().attr("levels").attr(
kPyLevelNames[static_cast<uint32_t>(level)]);
}
inline void Diagnose(
Rule rule,
Level level,
std::vector<std::string> messageArgs = {}) {
py::object py_rule = _PyRule(rule);
py::object py_level = _PyLevel(level);
py::object py_context = _PyContext();
py::dict kwargs = py::dict();
// TODO: statically check that size of messageArgs matches with rule.
kwargs["message_args"] = messageArgs;
py_context.attr("diagnose")(py_rule, py_level, **kwargs);
}
} // namespace diagnostics
} // namespace onnx
} // namespace torch
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