# 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,
)