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import random
from datetime import datetime
import torch
from torch.utils.data import Sampler, Dataset
import torch.distributed as dist
from utils.dist import get_local_rank, get_local_size, get_rank, get_world_size
from .data_utils.sampler_utils import PrepareData
import logging
logger = logging.getLogger(__name__)
class DistributedSamplerLimited(Sampler):
def __init__(self, dataset: Dataset, num_replicas: int = None,
rank: int = None, shuffle: bool = True,
seed: int = 0, drop_last: bool = False, limited=-1) -> 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 rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_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
print(f'Dbg: distribeted sampler limited: rank={rank}, num_replicas={num_replicas}')
# 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
# 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(
# `type:ignore` is required because Dataset cannot provide a default __len__
# see NOTE in pytorch/torch/utils/data/sampler.py
(len(self.dataset) - self.num_replicas) / self.num_replicas # type: ignore
)
else:
self.num_samples = math.ceil(len(self.dataset) / self.num_replicas) # type: ignore
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
self.seed = seed
self.limited = limited
if self.limited > -1:
self.num_samples = min(self.limited, self.num_samples)
def __iter__(self):
if self.shuffle:
# deterministically shuffle based on epoch and seed
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
indices = torch.randperm(len(self.dataset), generator=g).tolist() # type: ignore
else:
indices = list(range(len(self.dataset))) # type: ignore
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]
if self.limited > -1 and len(indices) > self.limited:
print(f'Trim indices: {len(indices)} --> {self.limited}')
indices = indices[:self.limited]
assert len(indices) == self.num_samples
# shuffle subsample
if self.shuffle: # and self.epoch > 0:
# random.seed(self.seed + self.epoch)
random.seed(datetime.now())
random.shuffle(indices)
return iter(indices)
def __len__(self) -> int:
return self.num_samples
def set_epoch(self, epoch: int) -> None:
self.epoch = 0 # keep data unchanged
# self.epoch = epoch
class NodeSplitSampler(Sampler):
def __init__(self, dataset, shuffle, random_seed,
first_epoch_skip_shuffle=False,
prepare_data=True):
self.dataset = dataset
self.shuffle = shuffle
self.random_seed = random_seed
self.world_size = get_world_size()
self.local_size = get_local_size()
self.node_size = self.world_size // self.local_size
self.rank = get_rank()
self.node_idx = self.rank // self.local_size
self.local_rank = get_local_rank()
self.next_epoch_skip_shuffle = first_epoch_skip_shuffle
# only be used when shuffle = True and first_epoch_skip_shuffle = True
self.prepare_data = prepare_data
self.prepare = None
self.skip = 0
def get_index_on_node(self):
# there is no need to cache source_list as we only call this function
# once in the whole training life-time
source_list = self.dataset.get_composite_source_idx()
idx_split = list(enumerate(source_list))
idx_split = torch.tensor(idx_split)
if self.shuffle:
random_idx = self.get_shufle_idx(len(idx_split))
idx_split = idx_split[random_idx]
max_split = idx_split[:, 1].max() + 1
priority = self.get_shufle_idx(max_split)
sort_idx = torch.argsort(priority[idx_split[:, 1]])
idx_split = idx_split[sort_idx]
num_idx_on_node = (len(idx_split) + self.node_size - 1) // self.node_size
offset = num_idx_on_node * self.node_idx
offset_end = offset + num_idx_on_node
offset_end = min(offset_end, len(idx_split))
unique_split_index = list(
set(idx_split[offset:offset_end, 1].tolist()))
logger.info('calling get_index_on_node in NodeSplitSampler ....')
logger.info(f'dataset is {self.dataset.yaml_file}')
logger.info(unique_split_index)
if self.shuffle and self.next_epoch_skip_shuffle and self.prepare_data:
if get_local_rank() == 0:
self.prepare = PrepareData(
self.dataset,
prepare_t_versions=[],
fixed_samples_in_node=True)
for s in unique_split_index:
self.prepare.prepare(s)
return idx_split[offset:offset_end, 0]
def get_shufle_idx(self, n):
g = torch.Generator()
g.manual_seed(self.random_seed)
random_idx = torch.randperm(n, generator=g)
self.random_seed += 99
return random_idx
def get_index_on_rank(self, idx_on_node):
if self.shuffle:
if not self.next_epoch_skip_shuffle:
curr_idx_on_node = idx_on_node[
self.get_shufle_idx(len(idx_on_node))]
else:
curr_idx_on_node = idx_on_node
self.next_epoch_skip_shuffle = False
else:
curr_idx_on_node = idx_on_node
idx_rank_size = (
len(curr_idx_on_node) + self.local_size - 1) // self.local_size
offset = idx_rank_size * self.local_rank
offset_end = offset + idx_rank_size
offset_end = min(offset_end, len(curr_idx_on_node))
curr_idx_on_node = curr_idx_on_node.tolist()
for i in range(offset, offset_end):
yield curr_idx_on_node[i]
def skip(self, num):
self.skip = num
def __iter__(self):
self.curr_idx = 0
idx_on_node = self.get_index_on_node()
if self.skip > 0:
logging.info('we will skip {}'.format(self.skip))
while True:
for i in self.get_index_on_rank(idx_on_node):
if self.skip <= 0:
yield i
else:
self.skip -= 1
def __len__(self):
raise ValueError('should not be called')
class IterationBasedBatchSampler(torch.utils.data.sampler.BatchSampler):
"""
Wraps a BatchSampler, resampling from it until
a specified number of iterations have been sampled
"""
def __init__(self, batch_sampler, num_iterations, start_iter=0):
self.batch_sampler = batch_sampler
self.num_iterations = num_iterations
self.start_iter = start_iter
def __iter__(self):
iteration = self.start_iter
while iteration <= self.num_iterations:
# if the underlying sampler has a set_epoch method, like
# DistributedSampler, used for making each process see
# a different split of the dataset, then set it
if hasattr(self.batch_sampler.sampler, "set_epoch"):
self.batch_sampler.sampler.set_epoch(iteration)
for batch in self.batch_sampler:
iteration += 1
if iteration > self.num_iterations:
break
yield batch
def __len__(self):
return self.num_iterations
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