code: import gzip
  import json
  import os
  import tempfile
  from enum import Enum
  from typing import Any, Callable, Iterable, Optional
  from warnings import warn
  
  import torch
  import torch.autograd.profiler as prof
  from torch.autograd import kineto_available, ProfilerActivity
  
  
  class ProfilerAction(Enum):
      """
      Profiler actions that can be taken at the specified intervals
      """
      NONE = 0
      WARMUP = 1
      RECORD = 2
      RECORD_AND_SAVE = 3
  
  
  def schedule(*, wait: int, warmup: int, active: int, repeat: int = 0, skip_first: int = 0) -> Callable:
      """
      Returns a callable that can be used as profiler ``schedule`` argument. The profiler will skip
      the first ``skip_first`` steps, then wait for ``wait`` steps, then do the warmup for the next ``warmup`` steps,
      then do the active recording for the next ``active`` steps and then repeat the cycle starting with ``wait`` steps.
      The optional number of cycles is specified with the ``repeat`` parameter, the zero value means that
      the cycles will continue until the profiling is finished.
      """
      def schedule_fn(step: int) -> ProfilerAction:
          assert step >= 0
          if step < skip_first:
              return ProfilerAction.NONE
          else:
              step -= skip_first
          num_steps = wait + warmup + active
          if repeat > 0 and step / num_steps >= repeat:
              return ProfilerAction.NONE
          mod_step = step % num_steps
          if mod_step < wait:
              return ProfilerAction.NONE
          elif mod_step < wait + warmup:
              return ProfilerAction.WARMUP
          else:
              return ProfilerAction.RECORD if mod_step < num_steps - 1 \
                  else ProfilerAction.RECORD_AND_SAVE
      assert wait >= 0 and warmup >= 0 and active > 0 and \
             repeat >= 0 and skip_first >= 0, "Invalid profiler schedule arguments"
      if warmup == 0:
          warn("Profiler won't be using warmup, this can skew profiler results")
      return schedule_fn
  
  
  def _default_schedule_fn(_: int) -> ProfilerAction:
      """
      Default profiler behavior - immediately starts recording the events,
      keeps doing it on every profiler step.
      """
      return ProfilerAction.RECORD
  
  def tensorboard_trace_handler(dir_name: str, worker_name: Optional[str] = None, use_gzip: bool = False):
      """
      Outputs tracing files to directory of ``dir_name``, then that directory can be
      directly delivered to tensorboard as logdir.
      ``worker_name`` should be unique for each worker in distributed scenario,
      it will be set to '[hostname]_[pid]' by default.
      """
      import os
      import socket
      import time
  
      def handler_fn(prof) -> None:
          nonlocal worker_name
          if not os.path.isdir(dir_name):
              try:
                  os.makedirs(dir_name, exist_ok=True)
              except Exception:
                  raise RuntimeError("Can't create directory: " + dir_name)
          if not worker_name:
              worker_name = "{}_{}".format(socket.gethostname(), str(os.getpid()))
          file_name = "{}.{}.pt.trace.json".format(worker_name, int(time.time() * 1000))
          if use_gzip:
              file_name = file_name + '.gz'
          prof.export_chrome_trace(os.path.join(dir_name, file_name))
      return handler_fn
  
  def supported_activities():
      """
      Returns a set of supported profiler tracing activities.
  
      Note: profiler uses CUPTI library to trace on-device CUDA kernels.
      In case when CUDA is enabled but CUPTI is not available, passing
      ``ProfilerActivity.CUDA`` to profiler results in using the legacy CUDA
      profiling code (same as in the legacy ``torch.autograd.profiler``).
      This, in turn, results in including CUDA time in the profiler table output,
      but not in the JSON trace.
      """
      return torch.autograd._supported_activities()
  
  
  class profile(object):
      """Profiler context manager.
  
      Args:
          activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
              ``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
              Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
          schedule (callable): callable that takes step (int) as a single parameter and returns
              ``ProfilerAction`` value that specifies the profiler action to perform at each step.
          on_trace_ready (callable): callable that is called at each step when ``schedule``
              returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling.
          record_shapes (bool): save information about operator's input shapes.
          profile_memory (bool): track tensor memory allocation/deallocation.
          with_stack (bool): record source information (file and line number) for the ops.
          with_flops (bool): use formula to estimate the FLOPS of specific operators
              (matrix multiplication and 2D convolution).
          use_cuda (bool):
              .. deprecated:: 1.8.1
                  use ``activities`` instead.
  
      .. note::
          Use :func:`~torch.profiler.schedule` to generate the callable schedule.
          Non-default schedules are useful when profiling long training jobs
          and allow the user to obtain multiple traces at the different iterations
          of the training process.
          The default schedule simply records all the events continuously for the
          duration of the context manager.
  
      .. note::
          Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard:
  
          ``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)``
  
          After profiling, result files can be found in the specified directory. Use the command:
  
          ``tensorboard --logdir dir_name``
  
          to see the results in TensorBoard.
          For more information, see
          `PyTorch Profiler TensorBoard Plugin <https://github.com/pytorch/kineto/tree/master/tb_plugin>`__
  
      .. note::
          Enabling shape and stack tracing results in additional overhead.
          When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
          that may further prevent certain optimizations that depend on the reference count and introduce
          extra tensor copies.
  
      Examples:
  
      .. code-block:: python
  
          with torch.profiler.profile(
              activities=[
                  torch.profiler.ProfilerActivity.CPU,
                  torch.profiler.ProfilerActivity.CUDA,
              ]
          ) as p:
              code_to_profile()
          print(p.key_averages().table(
              sort_by="self_cuda_time_total", row_limit=-1))
  
      Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions:
  
      .. code-block:: python
  
          # Non-default profiler schedule allows user to turn profiler on and off
          # on different iterations of the training loop;
          # trace_handler is called every time a new trace becomes available
          def trace_handler(prof):
              print(prof.key_averages().table(
                  sort_by="self_cuda_time_total", row_limit=-1))
              # prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")
  
          with torch.profiler.profile(
              activities=[
                  torch.profiler.ProfilerActivity.CPU,
                  torch.profiler.ProfilerActivity.CUDA,
              ],
  
              # In this example with wait=1, warmup=1, active=2,
              # profiler will skip the first step/iteration,
              # start warming up on the second, record
              # the third and the forth iterations,
              # after which the trace will become available
              # and on_trace_ready (when set) is called;
              # the cycle repeats starting with the next step
  
              schedule=torch.profiler.schedule(
                  wait=1,
                  warmup=1,
                  active=2),
              on_trace_ready=trace_handler
              # on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
              # used when outputting for tensorboard
              ) as p:
                  for iter in range(N):
                      code_iteration_to_profile(iter)
                      # send a signal to the profiler that the next iteration has started
                      p.step()
      """
      def __init__(
              self,
              *,
              activities: Optional[Iterable[ProfilerActivity]] = None,
              schedule: Optional[Callable[[int], ProfilerAction]] = None,
              on_trace_ready: Optional[Callable[..., Any]] = None,
              record_shapes: bool = False,
              profile_memory: bool = False,
              with_stack: bool = False,
              with_flops: bool = False,
              # deprecated:
              use_cuda: Optional[bool] = None):
          if activities:
              self.activities = set(activities)
          else:
              self.activities = supported_activities()
  
          if use_cuda is not None:
              warn("use_cuda is deprecated, use activities argument instead")
              if use_cuda:
                  self.activities.add(ProfilerActivity.CUDA)
              elif ProfilerActivity.CUDA in self.activities:
                  self.activities.remove(ProfilerActivity.CUDA)
  
          assert len(self.activities) > 0, "No valid profiler activities found"
  
          if schedule:
              self.schedule = schedule
              # add step markers into the trace and table view
              self.record_steps = True
          else:
              self.schedule = _default_schedule_fn
              self.record_steps = False
          self.on_trace_ready = on_trace_ready
          self.record_shapes = record_shapes
          self.with_flops = with_flops
          self.profile_memory = profile_memory
          self.with_stack = with_stack
          self.step_num = 0
          self.current_action = self.schedule(self.step_num)
          self.profiler: Optional[prof.profile] = None
          self.step_rec_fn: Optional[prof.record_function] = None
  
      def __enter__(self):
          self.start()
          return self
  
      def __exit__(self, exc_type, exc_val, exc_tb):
          self.stop()
  
      def start(self):
          self._enter_actions()
          if self.record_steps:
              self.step_rec_fn = prof.record_function("ProfilerStep#" + str(self.step_num))
              self.step_rec_fn.__enter__()
  
      def stop(self):
          if self.record_steps and self.step_rec_fn:
              self.step_rec_fn.__exit__(None, None, None)
          self._exit_actions()
  
      def step(self):
          """
          Signals the profiler that the next profiling step has started.
          """
          if self.record_steps and self.step_rec_fn:
              self.step_rec_fn.__exit__(None, None, None)
          prev_action = self.current_action
          self.step_num += 1
          self.current_action = self.schedule(self.step_num)
  
          if self.current_action == ProfilerAction.NONE:
              if prev_action == ProfilerAction.NONE:
                  pass
              elif prev_action == ProfilerAction.WARMUP:
                  warn("Incorrect schedule: WARMUP followed by NONE")
                  self._start_trace()
                  self._stop_trace()
              elif prev_action == ProfilerAction.RECORD:
                  warn("Incorrect schedule: RECORD followed by NONE")
                  self._stop_trace()
              else:
                  assert prev_action == ProfilerAction.RECORD_AND_SAVE
                  self._stop_trace()
                  if self.on_trace_ready:
                      self.on_trace_ready(self)
          elif self.current_action == ProfilerAction.WARMUP:
              if prev_action == ProfilerAction.NONE:
                  self._start_warmup()
              elif prev_action == ProfilerAction.WARMUP:
                  pass
              elif prev_action == ProfilerAction.RECORD:
                  warn("Incorrect schedule: RECORD followed by WARMUP")
                  self._stop_trace()
              else:
                  assert prev_action == ProfilerAction.RECORD_AND_SAVE
                  self._stop_trace()
                  if self.on_trace_ready:
                      self.on_trace_ready(self)
                  self._start_warmup()
          elif self.current_action in \
                  [ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE]:
              if prev_action == ProfilerAction.NONE:
                  self._start_warmup()
                  self._start_trace()
              elif prev_action == ProfilerAction.WARMUP:
                  self._start_trace()
              elif prev_action == ProfilerAction.RECORD:
                  pass
              else:
                  assert prev_action == ProfilerAction.RECORD_AND_SAVE
                  self._stop_trace()
                  if self.on_trace_ready:
                      self.on_trace_ready(self)
                  self._start_warmup()
                  self._start_trace()
  
          if self.record_steps:
              self.step_rec_fn = prof.record_function("ProfilerStep#" + str(self.step_num))
              self.step_rec_fn.__enter__()
  
      def export_chrome_trace(self, path: str):
          """
          Exports the collected trace in Chrome JSON format.
          """
          assert self.profiler
          if path.endswith('.gz'):
              fp = tempfile.NamedTemporaryFile('w+t', suffix='.json', delete=False)
              fp.close()
              retvalue = self.profiler.export_chrome_trace(fp.name)
              with open(fp.name) as fin:
                  with gzip.open(path, 'wt') as fout:
                      fout.writelines(fin)
              os.remove(fp.name)
              return retvalue
          else:
              return self.profiler.export_chrome_trace(path)
  
      def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
          """Save stack traces in a file in a format suitable for visualization.
  
          Args:
              path (str): save stacks file to this location;
              metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total"
  
          .. note::
              Example of using FlameGraph tool:
  
              - git clone https://github.com/brendangregg/FlameGraph
              - cd FlameGraph
              - ./flamegraph.pl --title "CPU time" --countname "us." profiler.stacks > perf_viz.svg
          """
          assert self.profiler
          return self.profiler.export_stacks(path, metric)
  
      def key_averages(self, group_by_input_shape: bool = False, group_by_stack_n: int = 0):
          """Averages events, grouping them by operator name and (optionally) input shapes and
          stack.
  
          .. note::
              To use shape/stack functionality make sure to set record_shapes/with_stack
              when creating profiler context manager.
          """
          assert self.profiler
          return self.profiler.key_averages(group_by_input_shape, group_by_stack_n)
  
      def events(self):
          """
          Returns the list of unaggregated profiler events,
          to be used in the trace callback or after the profiling is finished
          """
          assert self.profiler
          return self.profiler.function_events
  
      def add_metadata(self, key: str, value: str):
          """
          Adds a user defined metadata with a string key and a string value
          into the trace file
          """
          wrapped_value = "\"" + value.replace('"', '\\"') + "\""
          torch.autograd._add_metadata_json(key, wrapped_value)
  
      def add_metadata_json(self, key: str, value: str):
          """
          Adds a user defined metadata with a string key and a valid json value
          into the trace file
          """
          torch.autograd._add_metadata_json(key, value)
  
      def _get_distributed_info(self):
          import torch.distributed as dist
          if not dist.is_available() or not dist.is_initialized():
              return None
  
          return {
              "backend": dist.get_backend(),
              "rank": dist.get_rank(),
              "world_size": dist.get_world_size()
          }
  
      def _enter_actions(self):
          if self.current_action == ProfilerAction.WARMUP:
              self._start_warmup()
          elif self.current_action in \
                  [ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE]:
              self._start_warmup()
              self._start_trace()
  
      def _exit_actions(self):
          if self.current_action == ProfilerAction.WARMUP:
              self._start_trace()
              self._stop_trace()
          elif self.current_action in \
                  [ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE]:
              self._stop_trace()
              if self.on_trace_ready:
                  self.on_trace_ready(self)
  
      def _start_warmup(self):
          self.profiler = prof.profile(
              use_cuda=(ProfilerActivity.CUDA in self.activities),
              use_cpu=(ProfilerActivity.CPU in self.activities),
              record_shapes=self.record_shapes,
              with_flops=self.with_flops,
              profile_memory=self.profile_memory,
              with_stack=self.with_stack,
              use_kineto=True,
          )
          self.profiler._prepare_trace()
  
      def _start_trace(self):
          assert self.profiler is not None
          self.profiler._start_trace()
  
          if kineto_available():
              dist_info = self._get_distributed_info()
              if dist_info:
                  self.add_metadata_json("distributedInfo", json.dumps(dist_info))
  
      def _stop_trace(self):
          assert self.profiler is not None
          self.profiler.__exit__(None, None, None)
  
apis: torch.distributed.get_backend torch.distributed.get_rank torch.autograd._add_metadata_json torch.distributed.get_world_size torch.distributed.is_available torch.distributed.is_initialized torch.autograd._supported_activities torch.autograd.kineto_available torch.autograd.profiler.profile