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解决无法多卡分布式训练的问题 #65

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解决无法多卡分布式训练的问题 #65

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26 changes: 19 additions & 7 deletions src/cell_load/data_modules/samplers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import numpy as np
from torch.utils.data import Sampler, Subset
import torch.distributed as dist
from torch.utils.data.distributed import DistributedSampler

from ..dataset import MetadataConcatDataset, PerturbationDataset
from ..utils.data_utils import H5MetadataCache
Expand Down Expand Up @@ -75,9 +76,14 @@ def __init__(

# Create caches for all unique H5 files.
self.metadata_caches = {}
for subset in self.dataset.datasets:
base_dataset: PerturbationDataset = subset.dataset
self.metadata_caches[base_dataset.h5_path] = base_dataset.metadata_cache
if isinstance(self.dataset, DistributedSampler):
for subset in self.dataset.dataset.datasets:
base_dataset: PerturbationDataset = subset.dataset
self.metadata_caches[base_dataset.h5_path] = base_dataset.metadata_cache
else :
for subset in self.dataset.datasets:
base_dataset: PerturbationDataset = subset.dataset
self.metadata_caches[base_dataset.h5_path] = base_dataset.metadata_cache

# Create batches using the code-based grouping.
self.sentences = self._create_sentences()
Expand Down Expand Up @@ -253,10 +259,16 @@ def _create_sentences(self) -> list[list[int]]:
"""
global_offset = 0
all_batches = []
for subset in self.dataset.datasets:
subset_batches = self._process_subset(global_offset, subset)
all_batches.extend(subset_batches)
global_offset += len(subset)
if isinstance(self.dataset, DistributedSampler):
for subset in self.dataset.dataset.datasets:
subset_batches = self._process_subset(global_offset, subset)
all_batches.extend(subset_batches)
global_offset += len(subset)
else:
for subset in self.dataset.datasets:
subset_batches = self._process_subset(global_offset, subset)
all_batches.extend(subset_batches)
global_offset += len(subset)
np.random.shuffle(all_batches)

return all_batches
Expand Down