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- """
- Copyright 2019 The Microsoft DeepSpeed Team
- """
- from numpy.core.numeric import count_nonzero
- from deepspeed.elasticity.elasticity import compute_elastic_config
- import time
- import torch
- from numpy import mean
- from deepspeed.utils.logging import log_dist
- from deepspeed.utils import logger
- try:
- import psutil
- PSUTILS_INSTALLED = True
- except ImportError:
- PSUTILS_INSTALLED = False
- pass
- class SynchronizedWallClockTimer:
- """Group of timers. Borrowed from Nvidia Megatron code"""
- class Timer:
- """Timer."""
- def __init__(self, name):
- self.name_ = name
- self.elapsed_ = 0.0
- self.started_ = False
- self.start_time = time.time()
- self.records = []
- def start(self):
- """Start the timer."""
- assert not self.started_, "timer has already been started"
- torch.cuda.synchronize()
- self.start_time = time.time()
- self.started_ = True
- def stop(self, reset=False, record=False):
- """Stop the timer."""
- assert self.started_, "timer is not started"
- torch.cuda.synchronize()
- if reset:
- self.elapsed_ = time.time() - self.start_time
- else:
- self.elapsed_ += time.time() - self.start_time
- self.started_ = False
- if record:
- self.records.append(self.elapsed_)
- def reset(self):
- """Reset timer."""
- self.elapsed_ = 0.0
- self.started_ = False
- self.acc_ = 0.0
- self.cnt_ = 0
- def elapsed(self, reset=True):
- """Calculate the elapsed time."""
- started_ = self.started_
- # If the timing in progress, end it first.
- if self.started_:
- self.stop()
- # Get the elapsed time.
- elapsed_ = self.elapsed_
- # Reset the elapsed time
- if reset:
- self.reset()
- # If timing was in progress, set it back.
- if started_:
- self.start()
- return elapsed_
- def mean(self):
- return trim_mean(self.records, 0.1)
- def __init__(self):
- self.timers = {}
- def __call__(self, name):
- if name not in self.timers:
- self.timers[name] = self.Timer(name)
- return self.timers[name]
- @staticmethod
- def memory_usage():
- alloc = "mem_allocated: {:.4f} GB".format(torch.cuda.memory_allocated() /
- (1024 * 1024 * 1024))
- max_alloc = "max_mem_allocated: {:.4f} GB".format(
- torch.cuda.max_memory_allocated() / (1024 * 1024 * 1024))
- cache = "cache_allocated: {:.4f} GB".format(torch.cuda.memory_cached() /
- (1024 * 1024 * 1024))
- max_cache = "max_cache_allocated: {:.4f} GB".format(
- torch.cuda.max_memory_cached() / (1024 * 1024 * 1024))
- return " | {} | {} | {} | {}".format(alloc, max_alloc, cache, max_cache)
- def log(self, names, normalizer=1.0, reset=True, memory_breakdown=False, ranks=None):
- """Log a group of timers."""
- assert normalizer > 0.0
- string = f"rank={torch.distributed.get_rank()} time (ms)"
- for name in names:
- if name in self.timers:
- elapsed_time = (self.timers[name].elapsed(reset=reset) * 1000.0 /
- normalizer)
- string += " | {}: {:.2f}".format(name, elapsed_time)
- log_dist(string, ranks=ranks or [0])
- def get_mean(self, names, normalizer=1.0, reset=True):
- """Get the mean of a group of timers."""
- assert normalizer > 0.0
- means = {}
- for name in names:
- if name in self.timers:
- elapsed_time = (self.timers[name].mean() * 1000.0 / normalizer)
- means[name] = elapsed_time
- return means
- class ThroughputTimer:
- def __init__(
- self,
- batch_size,
- num_workers,
- start_step=2,
- steps_per_output=50,
- monitor_memory=False,
- logging_fn=None,
- ):
- self.start_time = 0
- self.end_time = 0
- self.started = False
- self.batch_size = batch_size
- if batch_size is None:
- self.batch_size = 1
- self.num_workers = num_workers
- self.start_step = start_step
- self.epoch_count = 0
- self.local_step_count = 0
- self.total_step_count = 0
- self.total_elapsed_time = 0
- self.steps_per_output = steps_per_output
- self.monitor_memory = monitor_memory
- self.logging = logging_fn
- if self.logging is None:
- self.logging = logger.info
- self.initialized = False
- if self.monitor_memory and not PSUTILS_INSTALLED:
- raise ImportError("Unable to import 'psutils', please install package")
- def update_epoch_count(self):
- self.epoch_count += 1
- self.local_step_count = 0
- def _init_timer(self):
- self.initialized = True
- def start(self):
- self._init_timer()
- self.started = True
- if self.total_step_count >= self.start_step:
- torch.cuda.synchronize()
- self.start_time = time.time()
- def stop(self, report_speed=True):
- if not self.started:
- return
- self.started = False
- self.total_step_count += 1
- self.local_step_count += 1
- if self.total_step_count > self.start_step:
- torch.cuda.synchronize()
- self.end_time = time.time()
- duration = self.end_time - self.start_time
- self.total_elapsed_time += duration
- if self.local_step_count % self.steps_per_output == 0:
- if report_speed:
- self.logging("{}/{}, SamplesPerSec={}".format(
- self.epoch_count,
- self.local_step_count,
- self.avg_samples_per_sec(),
- ))
- if self.monitor_memory:
- virt_mem = psutil.virtual_memory()
- swap = psutil.swap_memory()
- self.logging("{}/{}, vm percent: {}, swap percent: {}".format(
- self.epoch_count,
- self.local_step_count,
- virt_mem.percent,
- swap.percent,
- ))
- def avg_samples_per_sec(self):
- if self.total_step_count > 0:
- samples_per_step = self.batch_size * self.num_workers
- total_step_offset = self.total_step_count - self.start_step
- avg_time_per_step = self.total_elapsed_time / total_step_offset
- # training samples per second
- return samples_per_step / avg_time_per_step
- return float("-inf")
- def trim_mean(data, trim_percent):
- """Compute the trimmed mean of a list of numbers.
- Args:
- data (list): List of numbers.
- trim_percent (float): Percentage of data to trim.
- Returns:
- float: Trimmed mean.
- """
- assert trim_percent >= 0.0 and trim_percent <= 1.0
- n = len(data)
- data.sort()
- k = int(round(n * (trim_percent)))
- return mean(data[k:n - k])
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