ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 69976) of binary:

[abose17]

I am dealing with a problem with using DataParallel and DistributedDataParallel to parallelize my GNN model into multiple GPUS. The data baching works fine with the NeighborLoader but it shows the problem only after wrapping the model of both of the above-mentioned class objects. Following is the code snippet and error log of my implementation. Please take a look at that and suggest me a solution.

slack: https://torchgeometricco.slack.com/archives/CRAR9HK2P/p1663872051009069

def train(optimizer, loader, model, data, plot=False):
    train_accuracies, test_accuracies = list(), list()
    for epoch in range(2):
            model.train() ## Can be moved above the for loop
            for i, batch in enumerate(loader):
                    optimizer.zero_grad()
                    out = model(batch)

def run(dataset, model, runs, epochs, lr, weight_decay, early_stopping,
        device):
    dist.iniprocess_group('nccl', rank = 0, world_size=torch.cuda.device_count())  ### NEW Line Added ###
    """model = DistributedDataParallel(model)  ### NEW Line Added ###"""
    model = DataParallel(model)

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    data = dataset[0].to(device)

    neigh_loader = NeighborLoader(
        data,
        num_neighbors=[10]*2,
        batch_size=128,
        input_nodes=data.train_mask,
    )
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
    train(optimizer, neigh_loader, model, data, plot=True)

Namespace(dataset='ctiRaw', device_idx=0, runs=10, early_stopping=50, epochs=20, kernel_size=8, lr=0.1, weight_decay=0.01, hidden=16, dropout=0.5)
args.data_fp->  /graph/../ctiRaw
Used for CTI dataset checking->  Data(x=[23113, 23113], edge_index=[2, 303074], y=[23113], n_id=[23113], train_mask=[5000], test_mask=[2500], val_mask=[614], edge_attr=[303074, 2])
 data.train_mask size->  torch.Size([5000])  data.train_mask ->  tensor([    1,     9,    10,  ..., 23101, 23109, 23112], device='cuda:0')  data.test_mask size->  torch.Size([2500])
batch information->  Data(x=[4361, 23113], edge_index=[2, 7235], y=[4361], n_id=[4361], train_mask=[5000], test_mask=[2500], val_mask=[614], edge_attr=[7235, 2], batch_size=128)
Traceback (most recent call last):
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/main_gat.py", line 97, in <module>
    run(ctData, GAT(ctData), args.runs, args.epochs, args.lr,
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/train_eval_scratch.py", line 161, in run
    train(optimizer, neigh_loader, model, data, plot=True)
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/train_eval_scratch.py", line 90, in train
    out = model(batch)
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch_geometric/nn/data_parallel.py", line 56, in forward
    data = Batch.from_data_list(
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch_geometric/data/batch.py", line 72, in from_data_list
    add_batch=not isinstance(data_list[0], Batch),
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch_geometric/data/data.py", line 371, in __getitem__
    return self._store[key]
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch_geometric/data/storage.py", line 70, in __getitem__
    return self._mapping[key]
KeyError: 0
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 69976) of binary: /anaconda3/envs/pyTorch_Geo/bin/python
Traceback (most recent call last):
  File "/anaconda3/envs/pyTorch_Geo/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==1.10.1', 'console_scripts', 'torchrun')())
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 345, in wrapper
    return f(*args, **kwargs)
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/run.py", line 719, in main
    run(args)
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/run.py", line 710, in run
    elastic_launch(
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 259, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
graph.main_gat FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-09-22_13:06:12
  host      : Kaos5-3
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 69976)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html

[EdisonLeeeee]

Not quite sure. The problem might be that DataParallel accepts a list of data while you just passed a single data batch. How about using DistributedDataParallel instead? Take a look at this example.

[abose17]

Thanks, Edison. DistributedDataParallel returns the following error

Namespace(dataset='ctiRaw', device_idx=0, runs=10, early_stopping=50, epochs=20, kernel_size=8, lr=0.1, weight_decay=0.01, hidden=16, dropout=0.5)
args.data_fp->  /Graph_Convolution_Core/pytorch_geo_Monet/graph/../ctiRaw
Used for CTI dataset checking->  Data(x=[23113, 23113], edge_index=[2, 303074], y=[23113], n_id=[23113], train_mask=[5000], test_mask=[2500], val_mask=[614], edge_attr=[303074, 2])
Traceback (most recent call last):
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/main_gat.py", line 97, in <module>
    run(ctData, GAT(ctData), args.runs, args.epochs, args.lr,
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/train_eval_scratch.py", line 140, in run
    dist.iniprocess_group('nccl', rank = 0, world_size=torch.cuda.device_count())  ### NEW Line Added ###
AttributeError: module 'torch.distributed' has no attribute 'iniprocess_group'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 85635) of binary: /anaconda3/envs/pyTorch_Geo/bin/python
Traceback (most recent call last):
  File "/anaconda3/envs/pyTorch_Geo/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==1.10.1', 'console_scripts', 'torchrun')())
  File /anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 345, in wrapper
    return f(*args, **kwargs)
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/run.py", line 719, in main
    run(args)
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/run.py", line 710, in run
    elastic_launch(
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 259, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
graph.main_gat FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-09-23_16:34:15
  host      : Kaos5-3
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 85635)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html

Without using iniprocess_group() for DistributedDataParallel, I noticed the following error

Namespace(dataset='ctiRaw', device_idx=0, runs=10, early_stopping=50, epochs=20, kernel_size=8, lr=0.1, weight_decay=0.01, hidden=16, dropout=0.5)
args.data_fp->  /Graph_Convolution_Core/pytorch_geo_Monet/graph/../ctiRaw
Used for CTI dataset checking->  Data(x=[23113, 23113], edge_index=[2, 303074], y=[23113], n_id=[23113], train_mask=[5000], test_mask=[2500], val_mask=[614], edge_attr=[303074, 2])
Traceback (most recent call last):
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/main_gat.py", line 97, in <module>
    run(ctData, GAT(ctData), args.runs, args.epochs, args.lr,
  File "/Graph_Convolution_Core/pytorch_geo_Monet/graph/train_eval_scratch.py", line 141, in run
    model = DistributedDataParallel(model)  ### NEW Line Added ###"""
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/nn/parallel/distributed.py", line 530, in __init__
    self.process_group = _get_default_group()
  File "/anaconda3/envs/pyTorch_Geo/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 410, in _get_default_group
    raise RuntimeError(
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 85739) of binary: /home/abose/anaconda3/envs/pyTorch_Geo/bin/python
Traceback (most recent call last):

[EdisonLeeeee]

Sorry for the late reply. I found there is a typo - it should be init_process_group instead of iniprocess_group.

[abose17]

@EdisonLeeeee , @rusty1s I am facing another problem using the DistributedDataParallel code while running on 8 GPUS let me share the error. Could you please tell me what is wrong right now? All of these following codes are running perfectly in my Single GPU machine but not in the high configuration GPU clusters!!

(dl) root@211c0e5f7017:/data# python -m graph.main_gmm_train_eval_multi_gpu
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Let's use 8 GPUs!
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
[W socket.cpp:558] [c10d] The client socket has failed to connect to [localhost]:12345 (errno: 99 - Cannot assign requested address).
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Entered
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
data set information->  Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708], edge_attr=[10556, 2])
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  1433  out_channels->  16  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
in_channels->  16  out_channels->  7  dim->  2  kernel_size->  8  separate_gaussians->  False  root_weight->  True
Traceback (most recent call last):
  File "/root/miniconda3/envs/dl/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/miniconda3/envs/dl/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/data/graph/main_gmm_train_eval_multi_gpu.py", line 140, in <module>
    mp.spawn(run, args=(world_size, dataset), nprocs=world_size, join=True)
  File "/root/miniconda3/envs/dl/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/root/miniconda3/envs/dl/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
    while not context.join():
  File "/root/miniconda3/envs/dl/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 160, in join
    raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:

-- Process 2 terminated with the following error:
Traceback (most recent call last):
  File "/root/miniconda3/envs/dl/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap
    fn(i, *args)
  File "/data/graph/main_gmm_train_eval_multi_gpu.py", line 94, in run
    model = DistributedDataParallel(model, device_ids=[rank])
  File "/root/miniconda3/envs/dl/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 646, in __init__
    _verify_param_shape_across_processes(self.process_group, parameters)
  File "/root/miniconda3/envs/dl/lib/python3.8/site-packages/torch/distributed/utils.py", line 89, in _verify_param_shape_across_processes
    return dist._verify_params_across_processes(process_group, tensors, logger)
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1659484810403/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, unhandled system error, NCCL version 2.10.3
ncclSystemError: System call (e.g. socket, malloc) or external library call failed or device error. It can be also caused by unexpected exit of a remote peer, you can check NCCL warnings for failure reason and see if there is connection closure by a peer.

(dl) root@211c0e5f7017:/data# python -m graph.main_gatGmm_train_eval_multi_gpu
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=2, lr=0.01, runs=10, weight_decay=0.0005)
Let's use 8 GPUs!
/root/miniconda3/envs/dl/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 2 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Bus error (core dumped)

(dl) root@211c0e5f7017:/data# python -m graph.main_gcn_CTI_multi_gpu
Namespace(dataset='ctiRaw', device_idx=0, dropout=0.5, early_stopping=50, epochs=10, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Let's use 8 GPUs!
/root/miniconda3/envs/dl/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 2 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Bus error (core dumped)

(dl) root@211c0e5f7017:/data# python -m graph.main_gcn_train_eval_multi_gpu
Namespace(dataset='CORA', device_idx=0, dropout=0.5, early_stopping=50, epochs=2, hidden=16, kernel_size=8, lr=0.1, runs=10, weight_decay=0.01)
Let's use 8 GPUs!
/root/miniconda3/envs/dl/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 2 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Bus error (core dumped)

[abose17]

If I run this in a single remote GPU python -m graph.main_gcn_CTI_multi_gpu shows the same problem

/root/miniconda3/envs/dl/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 2 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
Bus error (core dumped)

It runs okay in my local GPU machine. Even your code in this (https://github.com/pyg-team/pytorch_geometric/blob/master/examples/multi_gpu/distributed_sampling.py) does not work in multi GPU setup and shows the same problem mentioned above.

[rusty1s]

Sorry for the late reply. I don't have any good ideas :( Can you confirm that the same script runs fine when using an MLP instead of a GNN?

[wieczorek1990]

Spam. W dniu czw., 29.12.2022 o 01:18 SusanCollins3351 ***@***.***> napisał(a):

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