[Fix] add seed to distributed sampler

mmselfsup/datasets/builder.py

@@ -108,7 +108,12 @@ def build_dataloader(dataset,
     rank, world_size = get_dist_info()
     if dist:
         sampler = DistributedSampler(
-            dataset, world_size, rank, shuffle=shuffle, replace=replace)
+            dataset,
+            world_size,
+            rank,
+            shuffle=shuffle,
+            replace=replace,
+            seed=seed)
         shuffle = False
         batch_size = samples_per_gpu
         num_workers = workers_per_gpu

mmselfsup/datasets/samplers/distributed_sampler.py

@@ -5,6 +5,8 @@
 from torch.utils.data import DistributedSampler as _DistributedSampler
 from torch.utils.data import Sampler
 
+from mmselfsup.utils import sync_random_seed
+
 
 class DistributedSampler(_DistributedSampler):
 
@@ -13,12 +15,21 @@ def __init__(self,
                  num_replicas=None,
                  rank=None,
                  shuffle=True,
-                 replace=False):
+                 replace=False,
+                 seed=0):
         super().__init__(dataset, num_replicas=num_replicas, rank=rank)
         self.shuffle = shuffle
         self.replace = replace
         self.unif_sampling_flag = False
 
+        # In distributed sampling, different ranks should sample
+        # non-overlapped data in the dataset. Therefore, this function
+        # is used to make sure that each rank shuffles the data indices
+        # in the same order based on the same seed. Then different ranks
+        # could use different indices to select non-overlapped data from the
+        # same data list.
+        self.seed = sync_random_seed(seed)
+
     def __iter__(self):
         # deterministically shuffle based on epoch
         if not self.unif_sampling_flag:
@@ -31,7 +42,11 @@ def __iter__(self):
     def generate_new_list(self):
         if self.shuffle:
             g = torch.Generator()
-            g.manual_seed(self.epoch)
+            # 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.
+            g.manual_seed(self.epoch + self.seed)
             if self.replace:
                 indices = torch.randint(
                     low=0,

mmselfsup/utils/__init__.py

@@ -3,6 +3,7 @@
 from .batch_shuffle import batch_shuffle_ddp, batch_unshuffle_ddp
 from .collect import dist_forward_collect, nondist_forward_collect
 from .collect_env import collect_env
+from .dist_utils import sync_random_seed
 from .distributed_sinkhorn import distributed_sinkhorn
 from .extractor import Extractor
 from .gather import concat_all_gather, gather_tensors, gather_tensors_batch
@@ -14,7 +15,8 @@
 __all__ = [
     'AliasMethod', 'batch_shuffle_ddp', 'batch_unshuffle_ddp',
     'dist_forward_collect', 'nondist_forward_collect', 'collect_env',
-    'distributed_sinkhorn', 'Extractor', 'concat_all_gather', 'gather_tensors',
-    'gather_tensors_batch', 'get_root_logger', 'find_latest_checkpoint',
-    'multi_gpu_test', 'single_gpu_test', 'setup_multi_processes'
+    'sync_random_seed', 'distributed_sinkhorn', 'Extractor',
+    'concat_all_gather', 'gather_tensors', 'gather_tensors_batch',
+    'get_root_logger', 'find_latest_checkpoint', 'multi_gpu_test',
+    'single_gpu_test', 'setup_multi_processes'
 ]

mmselfsup/utils/dist_utils.py

@@ -0,0 +1,44 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.distributed as dist
+from mmcv.runner import get_dist_info
+
+
+def sync_random_seed(seed=None, device='cuda'):
+    """Make sure different ranks share the same seed. All workers must call
+    this function, otherwise it will deadlock. This method is generally used in
+    `DistributedSampler`, because the seed should be identical across all
+    processes in the distributed group.
+
+    In distributed sampling, different ranks should sample non-overlapped
+    data in the dataset. Therefore, this function is used to make sure that
+    each rank shuffles the data indices in the same order based
+    on the same seed. Then different ranks could use different indices
+    to select non-overlapped data from the same data list.
+
+    Args:
+        seed (int, Optional): The seed. Default to None.
+        device (str): The device where the seed will be put on.
+            Default to 'cuda'.
+    Returns:
+        int: Seed to be used.
+    References:
+        .. [1] https://github.com/open-mmlab/mmdetection
+                    /blob/master/mmdet/core/utils/dist_utils.py
+    """
+    if seed is None:
+        seed = np.random.randint(2**31)
+    assert isinstance(seed, int)
+
+    rank, world_size = get_dist_info()
+
+    if world_size == 1:
+        return seed
+
+    if rank == 0:
+        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
+    else:
+        random_num = torch.tensor(0, dtype=torch.int32, device=device)
+    dist.broadcast(random_num, src=0)
+    return random_num.item()