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timer.py
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executable file
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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import time
from numpy import mean
from deepspeed.utils.logging import log_dist
from deepspeed.accelerator import get_accelerator
from deepspeed import comm as dist
try:
import psutil
PSUTILS_INSTALLED = True
except ImportError:
PSUTILS_INSTALLED = False
pass
class CudaEventTimer(object):
def __init__(self, start_event: get_accelerator().Event, end_event: get_accelerator().Event):
self.start_event = start_event
self.end_event = end_event
def get_elapsed_msec(self):
get_accelerator().current_stream().wait_event(self.end_event)
self.end_event.synchronize()
return self.start_event.elapsed_time(self.end_event)
class SynchronizedWallClockTimer:
"""Group of timers. Borrowed from Nvidia Megatron code"""
class Timer:
"""Timer."""
def __init__(self, name):
self.name_ = name
self.started_ = False
self.event_timers = []
self.start_event = None
self.elapsed_records = None
def start(self):
"""Start the timer."""
assert not self.started_, f"{self.name_} timer has already been started"
self.start_event = get_accelerator().Event(enable_timing=True)
self.start_event.record()
self.started_ = True
def stop(self, reset=False, record=False):
"""Stop the timer."""
assert self.started_, "timer is not started"
end_event = get_accelerator().Event(enable_timing=True)
end_event.record()
self.event_timers.append(CudaEventTimer(self.start_event, end_event))
self.start_event = None
self.started_ = False
def _get_elapsed_msec(self):
self.elapsed_records = [et.get_elapsed_msec() for et in self.event_timers]
self.event_timers.clear()
return sum(self.elapsed_records)
def reset(self):
"""Reset timer."""
self.started_ = False
self.start_event = None
self.elapsed_records = None
self.event_timers.clear()
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._get_elapsed_msec()
# 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):
self.elapsed(reset=False)
return trim_mean(self.elapsed_records, 0.1)
def __init__(self):
self.timers = {}
def get_timers(self):
return 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(get_accelerator().memory_allocated() / (1024 * 1024 * 1024))
max_alloc = "max_mem_allocated: {:.4f} GB".format(get_accelerator().max_memory_allocated() /
(1024 * 1024 * 1024))
cache = "cache_allocated: {:.4f} GB".format(get_accelerator().memory_cached() / (1024 * 1024 * 1024))
max_cache = "max_cache_allocated: {:.4f} GB".format(get_accelerator().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={dist.get_rank()} time (ms)"
for name in names:
if name in self.timers:
elapsed_time = (self.timers[name].elapsed(reset=reset) / 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,
start_step=2,
steps_per_output=50,
monitor_memory=False,
logging_fn=None,
):
from deepspeed.utils import logger
self.start_time = 0
self.end_time = 0
self.started = False
self.batch_size = 1 if batch_size is None else batch_size
self.start_step = start_step
self.epoch_count = 0
self.micro_step_count = 0
self.global_step_count = 0
self.total_elapsed_time = 0
self.step_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.micro_step_count = 0
def _init_timer(self):
self.initialized = True
def start(self):
self._init_timer()
self.started = True
if self.global_step_count >= self.start_step:
get_accelerator().synchronize()
self.start_time = time.time()
def stop(self, global_step=False, report_speed=True):
if not self.started:
return
self.started = False
self.micro_step_count += 1
if global_step:
self.global_step_count += 1
if self.start_time > 0:
get_accelerator().synchronize()
self.end_time = time.time()
duration = self.end_time - self.start_time
self.total_elapsed_time += duration
self.step_elapsed_time += duration
if global_step:
if report_speed and self.global_step_count % self.steps_per_output == 0:
self.logging(
"epoch={}/micro_step={}/global_step={}, RunningAvgSamplesPerSec={}, CurrSamplesPerSec={}, "
"MemAllocated={}GB, MaxMemAllocated={}GB".format(
self.epoch_count,
self.micro_step_count,
self.global_step_count,
self.avg_samples_per_sec(),
self.batch_size / self.step_elapsed_time,
round(get_accelerator().memory_allocated() / 1024**3, 2),
round(get_accelerator().max_memory_allocated() / 1024**3, 2),
))
if self.monitor_memory:
virt_mem = psutil.virtual_memory()
swap = psutil.swap_memory()
self.logging("epoch={}/micro_step={}/global_step={}, vm %: {}, swap %: {}".format(
self.epoch_count,
self.micro_step_count,
self.global_step_count,
virt_mem.percent,
swap.percent,
))
self.step_elapsed_time = 0
def avg_samples_per_sec(self):
if self.global_step_count > 0:
total_step_offset = self.global_step_count - self.start_step
avg_time_per_step = self.total_elapsed_time / total_step_offset
# training samples per second
return self.batch_size / 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)
# Account for edge case of empty list
if len(data) == 0:
return 0
data.sort()
k = int(round(n * (trim_percent)))
return mean(data[k:n - k])