mirror of https://github.com/hpcaitech/ColossalAI
131 lines
3.8 KiB
Python
131 lines
3.8 KiB
Python
import time
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import torch
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from .WandbLog import TensorboardLog
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_GLOBAL_TIMERS = None
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_GLOBAL_TENSORBOARD_WRITER = None
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def set_global_variables(launch_time, tensorboard_path):
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_set_timers()
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_set_tensorboard_writer(launch_time, tensorboard_path)
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def _set_timers():
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"""Initialize timers."""
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global _GLOBAL_TIMERS
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_ensure_var_is_not_initialized(_GLOBAL_TIMERS, "timers")
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_GLOBAL_TIMERS = Timers()
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def _set_tensorboard_writer(launch_time, tensorboard_path):
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"""Set tensorboard writer."""
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global _GLOBAL_TENSORBOARD_WRITER
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_ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER, "tensorboard writer")
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if torch.distributed.get_rank() == 0:
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_GLOBAL_TENSORBOARD_WRITER = TensorboardLog(tensorboard_path + f"/{launch_time}", launch_time)
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def get_timers():
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"""Return timers."""
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_ensure_var_is_initialized(_GLOBAL_TIMERS, "timers")
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return _GLOBAL_TIMERS
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def get_tensorboard_writer():
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"""Return tensorboard writer. It can be None so no need
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to check if it is initialized."""
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return _GLOBAL_TENSORBOARD_WRITER
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def _ensure_var_is_initialized(var, name):
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"""Make sure the input variable is not None."""
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assert var is not None, "{} is not initialized.".format(name)
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def _ensure_var_is_not_initialized(var, name):
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"""Make sure the input variable is not None."""
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assert var is None, "{} is already initialized.".format(name)
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class _Timer:
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"""Timer."""
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def __init__(self, name):
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self.name_ = name
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self.elapsed_ = 0.0
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self.started_ = False
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self.start_time = time.time()
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def start(self):
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"""Start the timer."""
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# assert not self.started_, 'timer has already been started'
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torch.cuda.synchronize()
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self.start_time = time.time()
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self.started_ = True
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def stop(self):
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"""Stop the timer."""
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assert self.started_, "timer is not started"
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torch.cuda.synchronize()
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self.elapsed_ += time.time() - self.start_time
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self.started_ = False
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def reset(self):
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"""Reset timer."""
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self.elapsed_ = 0.0
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self.started_ = False
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def elapsed(self, reset=True):
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"""Calculate the elapsed time."""
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started_ = self.started_
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# If the timing in progress, end it first.
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if self.started_:
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self.stop()
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# Get the elapsed time.
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elapsed_ = self.elapsed_
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# Reset the elapsed time
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if reset:
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self.reset()
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# If timing was in progress, set it back.
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if started_:
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self.start()
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return elapsed_
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class Timers:
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"""Group of timers."""
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def __init__(self):
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self.timers = {}
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def __call__(self, name):
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if name not in self.timers:
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self.timers[name] = _Timer(name)
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return self.timers[name]
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def write(self, names, writer, iteration, normalizer=1.0, reset=False):
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"""Write timers to a tensorboard writer"""
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# currently when using add_scalars,
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# torch.utils.add_scalars makes each timer its own run, which
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# pollutes the runs list, so we just add each as a scalar
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assert normalizer > 0.0
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for name in names:
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value = self.timers[name].elapsed(reset=reset) / normalizer
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writer.add_scalar(name + "-time", value, iteration)
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def log(self, names, normalizer=1.0, reset=True):
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"""Log a group of timers."""
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assert normalizer > 0.0
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string = "time (ms)"
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for name in names:
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elapsed_time = self.timers[name].elapsed(reset=reset) * 1000.0 / normalizer
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string += " | {}: {:.2f}".format(name, elapsed_time)
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if torch.distributed.is_initialized():
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if torch.distributed.get_rank() == (torch.distributed.get_world_size() - 1):
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print(string, flush=True)
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else:
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print(string, flush=True)
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