Open sunkun1997 opened 1 month ago
Hi @sunkun1997 - can you please share more information on your setup, ds_config, ds_report, and sample repro script?
repro script I just use the https://github.com/microsoft/DeepSpeedExamples/tree/master/training/pipeline_parallelism example, but in order to fit our environment, I need to make a slight modification. In our environment, each node has four environment variables: the number of nodes WORLD_SIZE, the node rank RANK, the master node ip MASTER_ADDR, the port MASTER_PORT. So I modified run.sh
gpu=8
n=$(($WORLD_SIZE * $gpu))
start_rank=$(($RANK * $gpu))
end_rank=$((($RANK + 1) * $gpu))
for ((i=$start_rank; i<$end_rank; i++))
do
{
LOCAL_RANK=$i WORLD_SIZE=$n MASTER_ADDR=$MASTER_ADDR MASTER_PORT=$MASTER_PORT python train.py \
--p $gpu \
--steps=200
}&
done
wait
And modified the main of train.py
if __name__ == '__main__':
import json
args = get_args()
with open('./ds_config.json', 'r') as f:
args.deepspeed_config = json.loads(f.read())
args.local_rank = int(os.environ['LOCAL_RANK'])
args.world_size = int(os.environ['WORLD_SIZE'])
deepspeed.init_distributed(dist_backend=args.backend, rank=args.local_rank,
world_size=args.world_size, auto_mpi_discovery=False)
torch.cuda.set_device(args.local_rank % 8)
if args.pipeline_parallel_size == 0:
train_base(args)
else:
train_pipe(args)
Then Each node run the run.sh. ds_report raise Cuda error
Traceback (most recent call last):
File "train.py", line 165, in <module>
train_pipe(args)
File "train.py", line 131, in train_pipe
net = PipelineModule(layers=join_layers(net),
File "/home/ray/anaconda3/lib/python3.8/site-packages/deepspeed/runtime/pipe/module.py", line 201, in __init__
self.to(get_accelerator().device_name(self.local_rank))
File "/home/ray/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1145, in to
return self._apply(convert)
File "/home/ray/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 797, in _apply
module._apply(fn)
File "/home/ray/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 820, in _apply
param_applied = fn(param)
File "/home/ray/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1143, in convert
return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
RuntimeError: CUDA error: invalid device ordinal
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
By the way, If I modify the start train.py with
LOCAL_RANK=$((i % $gpu)) RANK=$i WORLD_SIZE=$n MASTER_ADDR=$MASTER_ADDR MASTER_PORT=$MASTER_PORT python train.py
and modify the start distributed environment with
deepspeed.init_distributed(dist_backend="nccl", rank=args.rank, world_size=args.world_size, auto_mpi_discovery=False)
.
It looks like the nodes can't communicate with each other and raise
Traceback (most recent call last):
File "train.py", line 166, in <module>
train_pipe(args)
File "train.py", line 137, in train_pipe
trainset = cifar_trainset(args.local_rank)
File "train.py", line 30, in cifar_trainset
dist.barrier()
File "/home/ray/anaconda3/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 3328, in barrier
work = default_pg.barrier(opts=opts)
torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1275, internal error, NCCL version 2.14.3
ncclInternalError: Internal check failed.
Describe the bug When I trained the model with two nodes for pipeline parallel tasks, each node has eight graphics cards. So the incoming LOCAL_RANK of Node One is 8-17, and the line 201 of deepspeed/runtime/pipe/module. py
self. to (get_accelerator().device_name (self.local_rank))
. Here local_rank does not match the graphics card numbers 0-7, so CUDA error is raised