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|
| import time |
| from numpy import mean |
| from deepspeed.utils.logging import log_dist |
| from deepspeed.accelerator import get_accelerator |
|
|
| FORWARD_MICRO_TIMER = 'fwd_microstep' |
| FORWARD_GLOBAL_TIMER = 'fwd' |
| BACKWARD_MICRO_TIMER = 'bwd_microstep' |
| BACKWARD_GLOBAL_TIMER = 'bwd' |
| BACKWARD_INNER_MICRO_TIMER = 'bwd_inner_microstep' |
| BACKWARD_INNER_GLOBAL_TIMER = 'bwd_inner' |
| BACKWARD_REDUCE_MICRO_TIMER = 'bwd_allreduce_microstep' |
| BACKWARD_REDUCE_GLOBAL_TIMER = 'bwd_allreduce' |
| STEP_MICRO_TIMER = 'step_microstep' |
| STEP_GLOBAL_TIMER = 'step' |
| TIME_EPSILON = 1e-6 |
|
|
| 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.use_host_timer = get_accelerator().use_host_timers() |
| self.start_event = None |
| self.elapsed_records = None |
| self.start_time = 0.0 |
| self.end_time = 0.0 |
|
|
| def start(self): |
| """Start the timer.""" |
| assert not self.started_, f"{self.name_} timer has already been started" |
| if self.use_host_timer: |
| self.start_time = time.time() |
| else: |
| event_class = get_accelerator().Event |
| self.start_event = event_class(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" |
| event_class = get_accelerator().Event |
| if self.use_host_timer: |
| self.end_time = time.time() |
| self.event_timers.append(self.end_time - self.start_time) |
| else: |
| event_class = get_accelerator().Event |
| end_event = event_class(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): |
| if self.use_host_timer: |
| self.elapsed_records = [et * 1000.0 for et in self.event_timers] |
| else: |
| 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 self.started_: |
| self.stop() |
| |
| elapsed_ = self._get_elapsed_msec() |
| |
| if reset: |
| self.reset() |
| |
| 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"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 NoopTimer: |
|
|
| class Timer: |
|
|
| def start(self): |
| ... |
|
|
| def reset(self): |
| ... |
|
|
| def stop(self, **kwargs): |
| ... |
|
|
| def elapsed(self, **kwargs): |
| return 0 |
|
|
| def mean(self): |
| return 0 |
|
|
| def __init__(self): |
| self.timer = self.Timer() |
|
|
| def __call__(self, name): |
| return self.timer |
|
|
| def get_timers(self): |
| return {} |
|
|
| def log(self, names, normalizer=1.0, reset=True, memory_breakdown=False, ranks=None): |
| ... |
|
|
| def get_mean(self, names, normalizer=1.0, reset=True): |
| ... |
|
|
|
|
| class ThroughputTimer: |
|
|
| def __init__(self, config, batch_size, start_step=2, steps_per_output=None, monitor_memory=False, logging_fn=None): |
| from deepspeed.utils import logger |
| self.config = config |
| 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): |
| if not self.config.enabled: |
| return |
| self._init_timer() |
| self.started = True |
| if self.global_step_count >= self.start_step: |
| if self.config.synchronized: |
| get_accelerator().synchronize() |
| self.start_time = time.time() |
|
|
| def _is_report_boundary(self): |
| if self.steps_per_output is None: |
| return False |
| return self.global_step_count % self.steps_per_output == 0 |
|
|
| def stop(self, global_step=False, report_speed=True): |
| if not self.config.enabled or 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: |
| if self.config.synchronized: |
| 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._is_report_boundary(): |
| 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 + TIME_EPSILON), |
| 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 |
| |
| 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 0.0 <= trim_percent <= 1.0 |
| n = len(data) |
| |
| if len(data) == 0: |
| return 0 |
| data.sort() |
| k = int(round(n * (trim_percent))) |
| return mean(data[k:n - k]) |
|
|
