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#pragma once
#include <cstddef>
#include <cstdint>
#include <list>
#include <string>
#include <unordered_map>
#include <vector>
#include <ATen/record_function.h>
#include <c10/macros/Macros.h>
#include <c10/util/hash.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/source_range.h>
#include <optional>
// TODO: replace with pytorch/rfcs#43 when it is ready.
#define SOFT_ASSERT(cond, ...) \
[&]() -> bool { \
if (C10_UNLIKELY(!(cond))) { \
torch::profiler::impl::logSoftAssert( \
__func__, \
__FILE__, \
static_cast<uint32_t>(__LINE__), \
#cond, \
::c10::str(__VA_ARGS__)); \
if (torch::profiler::impl::softAssertRaises()) { \
TORCH_INTERNAL_ASSERT(cond, __VA_ARGS__); \
} else { \
TORCH_WARN_ONCE(__VA_ARGS__); \
} \
return false; \
} \
return true; \
}()
namespace torch::profiler::impl {
TORCH_API bool softAssertRaises();
TORCH_API void setSoftAssertRaises(std::optional<bool> value);
TORCH_API void logSoftAssert(
const char* func,
const char* file,
uint32_t line,
const char* cond,
const char* args);
TORCH_API inline void logSoftAssert(
const char* func,
const char* file,
uint32_t line,
const char* cond,
::c10::detail::CompileTimeEmptyString args) {
logSoftAssert(func, file, line, cond, (const char*)args);
}
TORCH_API void logSoftAssert(
const char* func,
const char* file,
uint32_t line,
const char* cond,
const std::string& args);
using shape =
std::variant<std::vector<int64_t>, std::vector<std::vector<int64_t>>>;
constexpr int TENSOR_LIST_DISPLAY_LENGTH_LIMIT = 30;
std::string getNvtxStr(
const char* name,
int64_t sequence_nr,
const std::vector<std::vector<int64_t>>& shapes,
at::RecordFunctionHandle op_id = 0,
const std::list<std::pair<at::RecordFunctionHandle, int>>& input_op_ids =
{});
struct TORCH_API FileLineFunc {
std::string filename;
size_t line;
std::string funcname;
};
struct TORCH_API SaveNcclMetaConfig {
bool truncate;
bool introspectMetadata;
bool introspectInputs;
bool introspectOutputs;
// Default constructor with default values
SaveNcclMetaConfig()
: truncate(true),
introspectMetadata(true),
introspectInputs(false),
introspectOutputs(false) {}
SaveNcclMetaConfig(
bool truncate,
bool introspectMetadata,
bool introspectInputs,
bool introspectOutputs)
: truncate(truncate),
introspectMetadata(introspectMetadata),
introspectInputs(introspectInputs),
introspectOutputs(introspectOutputs) {}
};
TORCH_API std::vector<FileLineFunc> prepareCallstack(
const std::vector<jit::StackEntry>& cs);
TORCH_API std::vector<std::string> callstackStr(
const std::vector<FileLineFunc>& cs);
TORCH_API std::string stacksToStr(
const std::vector<std::string>& stacks,
const char* delim);
TORCH_API std::vector<std::vector<int64_t>> inputSizes(
const at::RecordFunction& fn,
const bool flatten_list_enabled = false);
TORCH_API std::string variantShapesToStr(const std::vector<shape>& shapes);
TORCH_API std::string shapesToStr(
const std::vector<std::vector<int64_t>>& shapes);
TORCH_API std::string strListToStr(const std::vector<std::string>& types);
TORCH_API std::string inputOpIdsToStr(
const std::list<std::pair<at::RecordFunctionHandle, int>>& input_op_ids);
TORCH_API std::string ivalueToStr(const c10::IValue& val, bool isString);
TORCH_API std::string ivalueListToStr(const std::vector<c10::IValue>& list);
TORCH_API std::vector<std::string> inputTypes(const at::RecordFunction& fn);
std::unordered_map<std::string, c10::IValue> TORCH_API
saveExtraArgs(const at::RecordFunction& fn);
std::unordered_map<std::string, std::string> TORCH_API saveNcclMeta(
const at::RecordFunction& fn,
const SaveNcclMetaConfig& config = SaveNcclMetaConfig());
int getTensorStartHint(const at::Tensor& t);
bool checkFunctionOutputsForLogging(const at::RecordFunction& fn);
bool checkFunctionInputsForLogging(const at::RecordFunction& fn);
std::pair<bool, std::variant<int, std::vector<int>>> findStartAddrForTensors(
const c10::IValue& val);
uint64_t TORCH_API computeFlops(
const std::string& op_name,
const std::unordered_map<std::string, c10::IValue>& extra_args);
std::string shapeToStr(const std::vector<int64_t>& shape);
template <typename T>
class TORCH_API GlobalStateManager {
public:
static GlobalStateManager& singleton() {
/* library-local */ static GlobalStateManager singleton_;
return singleton_;
}
static void push(std::shared_ptr<T>&& state) {
if (singleton().state_) {
LOG(WARNING) << "GlobalStatePtr already exists!";
} else {
singleton().state_ = std::move(state);
}
}
static auto* get() {
return singleton().state_.get();
}
static std::shared_ptr<T> pop() {
auto out = singleton().state_;
singleton().state_.reset();
return out;
}
private:
GlobalStateManager() = default;
std::shared_ptr<T> state_;
};
struct HashCombine {
template <typename T0, typename T1>
size_t operator()(const std::pair<T0, T1>& i) {
return c10::get_hash((*this)(i.first), (*this)(i.second));
}
template <typename... Args>
size_t operator()(const std::tuple<Args...>& i) {
return c10::get_hash(i);
}
template <typename T>
size_t operator()(const T& i) {
return c10::get_hash(i);
}
};
#ifdef USE_DISTRIBUTED
constexpr auto kCommsName = "Collective name";
constexpr auto kDtype = "dtype";
constexpr auto kInMsgNelems = "In msg nelems";
constexpr auto kOutMsgNelems = "Out msg nelems";
constexpr auto kInSplit = "In split size";
constexpr auto kOutSplit = "Out split size";
constexpr auto kGlobalRankStart = "Global rank start";
constexpr auto kGlobalRankStride = "Global rank stride";
constexpr auto kGroupSize = "Group size";
constexpr auto kProcessGroupName = "Process Group Name";
constexpr auto kProcessGroupDesc = "Process Group Description";
constexpr auto kGroupRanks = "Process Group Ranks";
constexpr auto kRank = "Rank";
constexpr auto kP2pSrc = "Src Rank";
constexpr auto kP2pDst = "Dst Rank";
constexpr auto kInTensorsStart = "Input Tensors start";
constexpr auto kOutTensorsStart = "Output Tensors start";
#endif // USE_DISTRIBUTED
} // namespace torch::profiler::impl
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