Add DistFieldSampler to benefit from page cache
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Yin Li
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ec46f41ba5
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@ -70,7 +70,7 @@ def add_common_args(parser):
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parser.add_argument('--batches', type=int, required=True,
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help='mini-batch size, per GPU in training or in total in testing')
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parser.add_argument('--loader-workers', default=-2, type=int,
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parser.add_argument('--loader-workers', default=-8, type=int,
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help='number of subprocesses per data loader. '
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'0 to disable multiprocessing; '
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'negative number to multiply by the batch size')
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@ -123,6 +123,14 @@ def add_train_args(parser):
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parser.add_argument('--seed', default=42, type=int,
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help='seed for initializing training')
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parser.add_argument('--div-data', action='store_true',
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help='enable data division among GPUs for better page caching. '
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'Only relevant if there are multiple crops in each field')
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parser.add_argument('--div-shuffle-dist', default=1, type=float,
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help='distance to further shuffle within each data division. '
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'Only relevant if there are multiple crops in each field. '
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'The order of each sample is randomly displaced by this value. '
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'Change this to balance cache locality and stochasticity')
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parser.add_argument('--dist-backend', default='nccl', type=str,
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choices=['gloo', 'nccl'], help='distributed backend')
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parser.add_argument('--log-interval', default=100, type=int,
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@ -1,2 +1,2 @@
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from .fields import FieldDataset
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from .sampler import GroupedRandomSampler
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from .sampler import DistFieldSampler
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@ -119,14 +119,16 @@ class FieldDataset(Dataset):
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'only support integer upsampling'
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self.scale_factor = scale_factor
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self.nsample = self.nfile * self.ncrop
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def __len__(self):
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return self.nfile * self.ncrop
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return self.nsample
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def __getitem__(self, idx):
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ifile, icrop = divmod(idx, self.ncrop)
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in_fields = [np.load(f, mmap_mode='r') for f in self.in_files[ifile]]
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tgt_fields = [np.load(f, mmap_mode='r') for f in self.tgt_files[ifile]]
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in_fields = [np.load(f) for f in self.in_files[ifile]]
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tgt_fields = [np.load(f) for f in self.tgt_files[ifile]]
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anchor = self.anchors[icrop]
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@ -184,7 +186,6 @@ def crop(fields, anchor, crop, pad, size):
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ind.append(i)
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x = x[tuple(ind)]
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x.setflags(write=True) # workaround numpy bug before 1.18
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new_fields.append(x)
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@ -1,31 +1,77 @@
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from itertools import chain
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import torch
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import torch.distributed as dist
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from torch.utils.data import Sampler
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class GroupedRandomSampler(Sampler):
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"""Sample randomly within each group of samples and sequentially from group
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to group.
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class DistFieldSampler(Sampler):
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"""Distributed sampler for field data, useful for multiple crops
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This behaves like a simple random sampler by default
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Stochastic training on fields with multiple crops puts burden on the IO.
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A node may load files of the whole field but only need a small part of it.
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Numpy memmap can load part of the field, but can also be very slow (even
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slower than reading the whole thing)
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`div_data` enables data file division among GPUs when `shuffle=True`.
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For field with multiple crops, it helps IO by benefiting from the page
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cache, but limits stochasticity.
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Increase `div_shuffle_dist` can mitigate this by shuffling the order of
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samples within the specified distance.
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When `div_data=False` this sampler behaves similar to `DistributedSampler`,
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except for the chunky (rather than strided) subsample slicing.
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Like `DistributedSampler`, `set_epoch()` should be called at the beginning
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of each epoch during training.
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"""
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def __init__(self, data_source, group_size=None):
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self.data_source = data_source
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self.sample_size = len(data_source)
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def __init__(self, dataset, shuffle,
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div_data=False, div_shuffle_dist=0):
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self.rank = dist.get_rank()
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self.world_size = dist.get_world_size()
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if group_size is None:
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group_size = self.sample_size
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self.group_size = group_size
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self.dataset = dataset
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self.nsample = len(dataset)
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self.nfile = dataset.nfile
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self.ncrop = dataset.ncrop
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self.shuffle = shuffle
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self.div_data = div_data
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self.div_shuffle_dist = div_shuffle_dist
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def __iter__(self):
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starts = range(0, self.sample_size, self.group_size)
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sizes = [self.group_size] * (len(starts) - 1)
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sizes.append(self.sample_size - starts[-1])
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if self.shuffle:
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# deterministically shuffle based on epoch
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g = torch.Generator()
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g.manual_seed(self.epoch)
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return iter(chain(*[
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(start + torch.randperm(size)).tolist()
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for start, size in zip(starts, sizes)
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]))
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if self.div_data:
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# shuffle files
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ind = torch.randperm(self.nfile, generator=g)
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ind = ind[:, None] * self.ncrop + torch.arange(self.ncrop)
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ind = ind.flatten()
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# displace crops with respect to files
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dis = torch.rand((self.nfile, self.ncrop),
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generator=g) * self.div_shuffle_dist
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loc = torch.arange(self.nfile)
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loc = loc[:, None] + dis
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loc = loc.flatten() % self.nfile # periodic in files
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dis_ind = loc.argsort()
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# shuffle crops
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ind = ind[dis_ind].tolist()
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else:
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ind = torch.randperm(self.nsample, generator=g).tolist()
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else:
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ind = list(range(self.nsample))
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start = self.rank * len(self)
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stop = start + len(self)
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ind = ind[start:stop]
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return iter(ind)
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def __len__(self):
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return self.sample_size
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return self.nsample // self.world_size
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def set_epoch(self, epoch):
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self.epoch = epoch
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@ -10,11 +10,10 @@ import torch.optim as optim
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import torch.distributed as dist
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from torch.multiprocessing import spawn
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data.distributed import DistributedSampler
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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from .data import FieldDataset, GroupedRandomSampler
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from .data import FieldDataset, DistFieldSampler
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from .data.figures import plt_slices
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from . import models
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from .models import narrow_cast, resample, Lag2Eul
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@ -72,7 +71,9 @@ def gpu_worker(local_rank, node, args):
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pad=args.pad,
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scale_factor=args.scale_factor,
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)
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train_sampler = DistributedSampler(train_dataset, shuffle=True)
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train_sampler = DistFieldSampler(train_dataset, shuffle=True,
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div_data=args.div_data,
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div_shuffle_dist=args.div_shuffle_dist)
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train_loader = DataLoader(
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train_dataset,
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batch_size=args.batches,
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@ -100,7 +101,9 @@ def gpu_worker(local_rank, node, args):
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pad=args.pad,
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scale_factor=args.scale_factor,
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)
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val_sampler = DistributedSampler(val_dataset, shuffle=False)
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val_sampler = DistFieldSampler(val_dataset, shuffle=False,
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div_data=args.div_data,
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div_shuffle_dist=args.div_shuffle_dist)
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val_loader = DataLoader(
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val_dataset,
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batch_size=args.batches,
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9
setup.py
9
setup.py
@ -1,5 +1,4 @@
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from setuptools import setup
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from setuptools import find_packages
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from setuptools import setup, find_packages
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setup(
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name='map2map',
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@ -8,6 +7,9 @@ setup(
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author='Yin Li et al.',
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author_email='eelregit@gmail.com',
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packages=find_packages(),
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scripts=[
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'scripts/m2m.py',
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],
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python_requires='>=3.6',
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install_requires=[
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'torch>=1.2',
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@ -17,7 +19,4 @@ setup(
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extras_require={
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"visualization": ["tensorboard"],
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},
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scripts=[
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'scripts/m2m.py',
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]
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)
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