





"""simple docstring"""


#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def      UpperCamelCase							(*SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """r"""     ) as fh:
												fcntl.flock(SCREAMING_SNAKE_CASE							,     fcntl.LOCK_EX     )
												try:
																		print(*SCREAMING_SNAKE_CASE     )
												finally:
																		fcntl.flock(SCREAMING_SNAKE_CASE							,     fcntl.LOCK_UN     )


__magic_name__       :					str						=			int(os.environ["""LOCAL_RANK"""])
torch.cuda.set_device(local_rank)
__magic_name__       :					Optional[int]						=			torch.device("""cuda""", local_rank)
__magic_name__       :					List[Any]						=			socket.gethostname()

__magic_name__       :					Dict						=			f'''[{hostname}-{local_rank}]'''

try:
		# test distributed
		dist.init_process_group("""nccl""")
		dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
		dist.barrier()

		# test cuda is available and can allocate memory
		torch.cuda.is_available()
		torch.ones(1).cuda(local_rank)

		# global rank
		__magic_name__       :					Any						=			dist.get_rank()
		__magic_name__       :					Optional[Any]						=			dist.get_world_size()

		printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

		dist.barrier()
		if rank == 0:
				printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
		printflock(f'''{gpu} is broken''')
		raise





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MegaForCausalLM',
     'MegaForMaskedLM',
     'MegaForMultipleChoice',
     'MegaForQuestionAnswering',
     'MegaForSequenceClassification',
     'MegaForTokenClassification',
     'MegaModel',
     'MegaPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mega import (
      MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MegaForCausalLM,
      MegaForMaskedLM,
      MegaForMultipleChoice,
      MegaForQuestionAnswering,
      MegaForSequenceClassification,
      MegaForTokenClassification,
      MegaModel,
      MegaPreTrainedModel,
  )

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
