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# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/bucketsampler.py
import itertools
import math
import random
from random import shuffle
from typing import Iterator
from typing import Optional
from typing import TypeVar
import torch
import torch.distributed as dist
from torch.utils.data import Dataset
from torch.utils.data import Sampler
__all__ = [
"DistributedBucketSampler",
]
T_co = TypeVar("T_co", covariant=True)
class DistributedBucketSampler(Sampler[T_co]):
r"""
sort the dataset wrt. input length
divide samples into buckets
sort within buckets
divide buckets into batches
sort batches
"""
def __init__(
self,
dataset: Dataset,
num_replicas: Optional[int] = None,
rank: Optional[int] = None,
shuffle: bool = True,
seed: int = 0,
drop_last: bool = False,
batch_size: int = 32,
) -> None:
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size() if torch.cuda.is_available() else 1
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank() if torch.cuda.is_available() else 0
if torch.cuda.is_available():
torch.cuda.set_device(rank)
if rank >= num_replicas or rank < 0:
raise ValueError(
"Invalid rank {}, rank should be in the interval"
" [0, {}]".format(rank, num_replicas - 1)
)
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
self.drop_last = drop_last
# If the dataset length is evenly divisible by # of replicas, then there
# is no need to drop any data, since the dataset will be split equally.
if (
self.drop_last and len(self.dataset) % self.num_replicas != 0
): # type: ignore[arg-type]
# Split to nearest available length that is evenly divisible.
# This is to ensure each rank receives the same amount of data when
# using this Sampler.
self.num_samples = math.ceil(
(len(self.dataset) - self.num_replicas)
/ self.num_replicas # type: ignore[arg-type]
)
else:
self.num_samples = math.ceil(
len(self.dataset) / self.num_replicas
) # type: ignore[arg-type]
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.seed = seed
self.batch_size = batch_size
self.id_with_length = self._get_sample_lengths()
self.id_buckets = self.make_buckets(bucket_width=2.0)
def _get_sample_lengths(self):
id_with_lengths = []
for i in range(len(self.dataset)):
id_with_lengths.append((i, self.dataset.get_sample_length(i)))
id_with_lengths.sort(key=lambda x: x[1])
return id_with_lengths
def make_buckets(self, bucket_width: float = 2.0):
buckets = []
cur = []
max_sec = bucket_width
for id, sec in self.id_with_length:
if sec < max_sec:
cur.append(id)
else:
buckets.append(cur)
cur = [id]
max_sec += bucket_width
if len(cur) > 0:
buckets.append(cur)
return buckets
def __iter__(self) -> Iterator[T_co]:
if self.shuffle:
# deterministically shuffle based on epoch and seed
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
random.seed(self.epoch + self.seed)
shuffled_bucket = []
for buc in self.id_buckets:
buc_copy = buc.copy()
shuffle(buc_copy)
shuffled_bucket.append(buc_copy)
grouped_batch_size = self.batch_size * self.num_replicas
shuffled_bucket = list(itertools.chain(*shuffled_bucket))
n_batch = int(math.ceil(len(shuffled_bucket) / grouped_batch_size))
batches = [
shuffled_bucket[b * grouped_batch_size : (b + 1) * grouped_batch_size]
for b in range(n_batch)
]
shuffle(batches)
indices = list(itertools.chain(*batches))
else:
# type: ignore[arg-type]
indices = list(range(len(self.dataset)))
if not self.drop_last:
# add extra samples to make it evenly divisible
padding_size = self.total_size - len(indices)
if padding_size <= len(indices):
indices += indices[:padding_size]
else:
indices += (indices * math.ceil(padding_size / len(indices)))[
:padding_size
]
else:
# remove tail of data to make it evenly divisible.
indices = indices[: self.total_size]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank : self.total_size : self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self) -> int:
return self.num_samples
def set_epoch(self, epoch: int) -> None:
r"""
Sets the epoch for this sampler. When :attr:`shuffle=True`, this ensures all replicas
use a different random ordering for each epoch. Otherwise, the next iteration of this
sampler will yield the same ordering.
Args:
epoch (int): Epoch number.
"""
self.epoch = epoch
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