# Copyright (c) Facebook, Inc. and its affiliates. import logging import numpy as np from itertools import count from typing import List, Tuple import torch import tqdm from fvcore.common.timer import Timer from detectron2.utils import comm from .build import build_batch_data_loader from .common import DatasetFromList, MapDataset from .samplers import TrainingSampler logger = logging.getLogger(__name__) class _EmptyMapDataset(torch.utils.data.Dataset): """ Map anything to emptiness. """ def __init__(self, dataset): self.ds = dataset def __len__(self): return len(self.ds) def __getitem__(self, idx): _ = self.ds[idx] return [0] def iter_benchmark( iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60 ) -> Tuple[float, List[float]]: """ Benchmark an iterator/iterable for `num_iter` iterations with an extra `warmup` iterations of warmup. End early if `max_time_seconds` time is spent on iterations. Returns: float: average time (seconds) per iteration list[float]: time spent on each iteration. Sometimes useful for further analysis. """ num_iter, warmup = int(num_iter), int(warmup) iterator = iter(iterator) for _ in range(warmup): next(iterator) timer = Timer() all_times = [] for curr_iter in tqdm.trange(num_iter): start = timer.seconds() if start > max_time_seconds: num_iter = curr_iter break next(iterator) all_times.append(timer.seconds() - start) avg = timer.seconds() / num_iter return avg, all_times class DataLoaderBenchmark: """ Some common benchmarks that help understand perf bottleneck of a standard dataloader made of dataset, mapper and sampler. """ def __init__( self, dataset, *, mapper, sampler=None, total_batch_size, num_workers=0, max_time_seconds: int = 90, ): """ Args: max_time_seconds (int): maximum time to spent for each benchmark other args: same as in `build.py:build_detection_train_loader` """ if isinstance(dataset, list): dataset = DatasetFromList(dataset, copy=False, serialize=True) if sampler is None: sampler = TrainingSampler(len(dataset)) self.dataset = dataset self.mapper = mapper self.sampler = sampler self.total_batch_size = total_batch_size self.num_workers = num_workers self.per_gpu_batch_size = self.total_batch_size // comm.get_world_size() self.max_time_seconds = max_time_seconds def _benchmark(self, iterator, num_iter, warmup, msg=None): avg, all_times = iter_benchmark(iterator, num_iter, warmup, self.max_time_seconds) if msg is not None: self._log_time(msg, avg, all_times) return avg, all_times def _log_time(self, msg, avg, all_times, distributed=False): percentiles = [np.percentile(all_times, k, interpolation="nearest") for k in [1, 5, 95, 99]] if not distributed: logger.info( f"{msg}: avg={1.0/avg:.1f} it/s, " f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, " f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s." ) return avg_per_gpu = comm.all_gather(avg) percentiles_per_gpu = comm.all_gather(percentiles) if comm.get_rank() > 0: return for idx, avg, percentiles in zip(count(), avg_per_gpu, percentiles_per_gpu): logger.info( f"GPU{idx} {msg}: avg={1.0/avg:.1f} it/s, " f"p1={percentiles[0]:.2g}s, p5={percentiles[1]:.2g}s, " f"p95={percentiles[2]:.2g}s, p99={percentiles[3]:.2g}s." ) def benchmark_dataset(self, num_iter, warmup=5): """ Benchmark the speed of taking raw samples from the dataset. """ def loader(): while True: for k in self.sampler: yield self.dataset[k] self._benchmark(loader(), num_iter, warmup, "Dataset Alone") def benchmark_mapper(self, num_iter, warmup=5): """ Benchmark the speed of taking raw samples from the dataset and map them in a single process. """ def loader(): while True: for k in self.sampler: yield self.mapper(self.dataset[k]) self._benchmark(loader(), num_iter, warmup, "Single Process Mapper (sec/sample)") def benchmark_workers(self, num_iter, warmup=10): """ Benchmark the dataloader by tuning num_workers to [0, 1, self.num_workers]. """ candidates = [0, 1] if self.num_workers not in candidates: candidates.append(self.num_workers) dataset = MapDataset(self.dataset, self.mapper) for n in candidates: loader = build_batch_data_loader( dataset, self.sampler, self.total_batch_size, num_workers=n, ) self._benchmark( iter(loader), num_iter * max(n, 1), warmup * max(n, 1), f"DataLoader ({n} workers, bs={self.per_gpu_batch_size})", ) del loader def benchmark_IPC(self, num_iter, warmup=10): """ Benchmark the dataloader where each worker outputs nothing. This eliminates the IPC overhead compared to the regular dataloader. PyTorch multiprocessing's IPC only optimizes for torch tensors. Large numpy arrays or other data structure may incur large IPC overhead. """ n = self.num_workers dataset = _EmptyMapDataset(MapDataset(self.dataset, self.mapper)) loader = build_batch_data_loader( dataset, self.sampler, self.total_batch_size, num_workers=n ) self._benchmark( iter(loader), num_iter * max(n, 1), warmup * max(n, 1), f"DataLoader ({n} workers, bs={self.per_gpu_batch_size}) w/o comm", ) def benchmark_distributed(self, num_iter, warmup=10): """ Benchmark the dataloader in each distributed worker, and log results of all workers. This helps understand the final performance as well as the variances among workers. It also prints startup time (first iter) of the dataloader. """ gpu = comm.get_world_size() dataset = MapDataset(self.dataset, self.mapper) n = self.num_workers loader = build_batch_data_loader( dataset, self.sampler, self.total_batch_size, num_workers=n ) timer = Timer() loader = iter(loader) next(loader) startup_time = timer.seconds() logger.info("Dataloader startup time: {:.2f} seconds".format(startup_time)) comm.synchronize() avg, all_times = self._benchmark(loader, num_iter * max(n, 1), warmup * max(n, 1)) del loader self._log_time( f"DataLoader ({gpu} GPUs x {n} workers, total bs={self.total_batch_size})", avg, all_times, True, )