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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/Optional.h>
#include <torch/csrc/Export.h>
#include <torch/csrc/jit/frontend/source_range.h>
#ifndef _WIN32
#include <ctime>
#endif
#if defined(C10_IOS) && defined(C10_MOBILE)
#include <sys/time.h> // for gettimeofday()
#endif
#if defined(__i386__) || defined(__x86_64__) || defined(__amd64__)
#define C10_RDTSC
#if defined(_MSC_VER)
#include <intrin.h>
#elif defined(__CUDACC__) || defined(__HIPCC__)
#undef C10_RDTSC
#elif defined(__clang__)
// `__rdtsc` is available by default.
// NB: This has to be first, because Clang will also define `__GNUC__`
#elif defined(__GNUC__)
#include <x86intrin.h>
#else
#undef C10_RDTSC
#endif
#endif
// TODO: replace with pytorch/rfcs#43 when it is ready.
#define SOFT_ASSERT(cond, ...) \
[&]() -> bool { \
if (C10_UNLIKELY(!(cond))) { \
if (torch::profiler::impl::softAssertRaises()) { \
TORCH_INTERNAL_ASSERT(cond, __VA_ARGS__); \
} else { \
TORCH_WARN(__VA_ARGS__); \
} \
return false; \
} \
return true; \
}()
namespace torch {
namespace profiler {
namespace impl {
TORCH_API bool softAssertRaises();
TORCH_API void setSoftAssertRaises(c10::optional<bool> value);
using time_t = int64_t;
using steady_clock_t = std::conditional<
std::chrono::high_resolution_clock::is_steady,
std::chrono::high_resolution_clock,
std::chrono::steady_clock>::type;
inline time_t getTimeSinceEpoch() {
auto now = std::chrono::system_clock::now().time_since_epoch();
return std::chrono::duration_cast<std::chrono::nanoseconds>(now).count();
}
inline time_t getTime(bool allow_monotonic = false) {
#if defined(C10_IOS) && defined(C10_MOBILE)
// clock_gettime is only available on iOS 10.0 or newer. Unlike OS X, iOS
// can't rely on CLOCK_REALTIME, as it is defined no matter if clock_gettime
// is implemented or not
struct timeval now;
gettimeofday(&now, NULL);
return static_cast<time_t>(now.tv_sec) * 1000000000 +
static_cast<time_t>(now.tv_usec) * 1000;
#elif defined(_WIN32) || defined(__MACH__)
return std::chrono::duration_cast<std::chrono::nanoseconds>(
steady_clock_t::now().time_since_epoch())
.count();
#else
// clock_gettime is *much* faster than std::chrono implementation on Linux
struct timespec t {};
auto mode = CLOCK_REALTIME;
if (allow_monotonic) {
mode = CLOCK_MONOTONIC;
}
clock_gettime(mode, &t);
return static_cast<time_t>(t.tv_sec) * 1000000000 +
static_cast<time_t>(t.tv_nsec);
#endif
}
// We often do not need to capture true wall times. If a fast mechanism such
// as TSC is available we can use that instead and convert back to epoch time
// during post processing. This greatly reduce the clock's contribution to
// profiling.
// http://btorpey.github.io/blog/2014/02/18/clock-sources-in-linux/
// https://quick-bench.com/q/r8opkkGZSJMu9wM_XTbDouq-0Io
// TODO: We should use
// `https://github.com/google/benchmark/blob/main/src/cycleclock.h`
inline auto getApproximateTime() {
#if defined(C10_RDTSC)
return static_cast<uint64_t>(__rdtsc());
#else
return getTime();
#endif
}
using approx_time_t = decltype(getApproximateTime());
static_assert(
std::is_same<approx_time_t, int64_t>::value ||
std::is_same<approx_time_t, uint64_t>::value,
"Expected either int64_t (`getTime`) or uint64_t (some TSC reads).");
// Convert `getCount` results to Nanoseconds since unix epoch.
class ApproximateClockToUnixTimeConverter final {
public:
ApproximateClockToUnixTimeConverter();
std::function<time_t(approx_time_t)> makeConverter();
struct UnixAndApproximateTimePair {
time_t t_;
approx_time_t approx_t_;
};
static UnixAndApproximateTimePair measurePair();
private:
static constexpr size_t replicates = 1001;
using time_pairs = std::array<UnixAndApproximateTimePair, replicates>;
time_pairs measurePairs();
time_pairs start_times_;
};
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;
};
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 shapesToStr(
const std::vector<std::vector<int64_t>>& shapes);
TORCH_API std::string dtypesToStr(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::vector<std::string> inputTypes(const at::RecordFunction& fn);
std::unordered_map<std::string, c10::IValue> TORCH_API
saveExtraArgs(const at::RecordFunction& fn);
uint64_t TORCH_API computeFlops(
const std::string& op_name,
const std::unordered_map<std::string, c10::IValue>& extra_args);
template <typename T>
class TORCH_API GlobalStateManager {
public:
static GlobalStateManager& singleton() {
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_;
};
} // namespace impl
} // namespace profiler
} // namespace torch
namespace torch {
namespace autograd {
namespace profiler {
using torch::profiler::impl::computeFlops;
using torch::profiler::impl::getTime;
} // namespace profiler
} // namespace autograd
} // namespace torch
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