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facebookresearch / jepa

PyTorch code and models for V-JEPA self-supervised learning from video.
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ValueError: Default process group has not been initialized, please make sure to call init_process_group #55

Open MKaczkow opened 5 months ago

MKaczkow commented 5 months ago

First of all, thanks for providing this code 😄

tl;dr

I am getting ValueError when trying to run eval on iNat21 dataset with python -m evals.main --fname configs/evals/vitl16_inat.yaml --devices cuda:0 and running out of ideas how to fix it.

Config values

Full stacktrace

(venv) PS D:\__repos\jepa> python -m evals.main --fname configs/evals/vitl16_inat.yaml
INFO:root:called-params configs/evals/vitl16_inat.yaml
INFO:root:loaded params...
{   'data': {   'dataset_name': 'iNat21',
                'image_folder': 'inat',
                'num_classes': 10000,
                'resolution': 224,
                'root_path': 'D:\\__repos\\jepa\\data'},
    'eval_name': 'image_classification_frozen',
    'nodes': 8,
    'optimization': {   'batch_size': 16,
                        'final_lr': 0.0,
                        'lr': 0.001,
                        'num_epochs': 20,
                        'start_lr': 0.001,
                        'use_bfloat16': True,
                        'warmup': 0.0,
                        'weight_decay': 0.001},
    'pretrain': {   'checkpoint': 'vitl16.pth.tar',
                    'checkpoint_key': 'target_encoder',
                    'clip_duration': None,
                    'folder': 'D:\\__repos\\jepa\\models',
                    'frames_per_clip': 16,
                    'model_name': 'vit_large',
                    'patch_size': 16,
                    'tight_silu': False,
                    'tubelet_size': 2,
                    'uniform_power': True,
                    'use_sdpa': True,
                    'use_silu': False,
                    'write_tag': 'jepa'},
    'resume_checkpoint': False,
    'tag': 'inat-16f',
    'tasks_per_node': 8}
D:\__repos\jepa\venv\lib\site-packages\torch\distributed\distributed_c10d.py:608: UserWarning: Attempted to get default timeout for nccl backend, but NCCL support is not compiled
  warnings.warn("Attempted to get default timeout for nccl backend, but NCCL support is not compiled")
INFO:root:Rank: 0. Distributed training not available Distributed package doesn't have NCCL built in
INFO:root:Running... (rank: 0/1)
INFO:root:Running evaluation: image_classification_frozen
INFO:root:SLURM vars not set (distributed training not available)
INFO:root:Initialized (rank/world-size) 0/1
INFO:root:Loading pretrained model from D:\__repos\jepa\models\vitl16.pth.tar
VisionTransformer(
  (patch_embed): PatchEmbed3D(
    (proj): Conv3d(3, 1024, kernel_size=(2, 16, 16), stride=(2, 16, 16))
  )
  (blocks): ModuleList(
    (0-23): 24 x Block(
      (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=1024, out_features=3072, bias=True)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=1024, out_features=1024, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (norm2): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
      (mlp): MLP(
        (fc1): Linear(in_features=1024, out_features=4096, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=4096, out_features=1024, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): LayerNorm((1024,), eps=1e-06, elementwise_affine=True)
)
INFO:root:loaded pretrained model with msg: <All keys matched successfully>
INFO:root:loaded pretrained encoder from epoch: 300
 path: D:\__repos\jepa\models\vitl16.pth.tar
INFO:root:implementing auto-agument strategy
INFO:root:data-path D:\__repos\jepa\data\inat\train/
INFO:root:Initialized ImageFolder
INFO:root:ImageFolder dataset created
INFO:root:ImageFolder unsupervised data loader created
INFO:root:data-path D:\__repos\jepa\data\inat\val/
INFO:root:Initialized ImageFolder
INFO:root:ImageFolder dataset created
INFO:root:ImageFolder unsupervised data loader created
INFO:root:Dataloader created... iterations per epoch: 31250
INFO:root:Using AdamW
Process Process-1:
Traceback (most recent call last):
  File "C:\Users\Maciek\AppData\Local\Programs\Python\Python310\lib\multiprocessing\process.py", line 315, in _bootstrap
    self.run()
  File "C:\Users\Maciek\AppData\Local\Programs\Python\Python310\lib\multiprocessing\process.py", line 108, in run    self._target(*self._args, **self._kwargs)
  File "D:\__repos\jepa\evals\main.py", line 57, in process_main
    eval_main(params['eval_name'], args_eval=params)
  File "D:\__repos\jepa\evals\scaffold.py", line 22, in main
    return importlib.import_module(f'evals.{eval_name}.eval').main(
  File "D:\__repos\jepa\evals\image_classification_frozen\eval.py", line 201, in main
    classifier = DistributedDataParallel(classifier, static_graph=True)
  File "D:\__repos\jepa\venv\lib\site-packages\torch\nn\parallel\distributed.py", line 731, in __init__
    self.process_group = _get_default_group()
  File "D:\__repos\jepa\venv\lib\site-packages\torch\distributed\distributed_c10d.py", line 977, in _get_default_group
    raise ValueError(
ValueError: Default process group has not been initialized, please make sure to call init_process_group.
LangDaniel commented 5 months ago

you could just remove the lines initializing the DistributedDataParallel in app/vjepa/train.py , i.e. lines 295-297, as a quick fix.

MKaczkow commented 5 months ago

It didn't help, I am afraid, still getting: 

ValueError: Default process group has not been initialized, please make sure to call init_process_group.
LangDaniel commented 4 months ago

Did you also try to remove it from the eval scripts, i.e. line 201 in evals/image_classification_frozen/eval.py?

krstevskipetar commented 4 months ago

I faced this same issue using a single GPU on one machine, I got it working by changing the port and explicitly defining the rank and world size. For evaluation you can edit line 131 in evals/video_classification_frozen/eval.py to be world_size, rank = init_distributed(port=12321, rank_and_world_size=(0, 1))