



from math import ceil






def     a(      A       :    Optional[int] ,     A       :    int       )		->       Union[str, Any]:
					"""simple docstring"""





					a					     =   list(range(0 ,     _SCREAMING_SNAKE_CASE       )       )

					a					     =   [item for sublist in list(device_map.values()       ) for item in sublist]

					# Duplicate check
					a					     =   []
					for i in device_map_blocks:
										if device_map_blocks.count(_SCREAMING_SNAKE_CASE       ) > 1 and i not in duplicate_blocks:
															duplicate_blocks.append(_SCREAMING_SNAKE_CASE       )
    # Missing blocks
					a					     =   [i for i in blocks if i not in device_map_blocks]
					a					     =   [i for i in device_map_blocks if i not in blocks]

					if len(_SCREAMING_SNAKE_CASE       ) != 0:
										raise ValueError(
										    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
										    " These attention blocks were specified more than once: " + str(_SCREAMING_SNAKE_CASE       )       )
					if len(_SCREAMING_SNAKE_CASE       ) != 0:
										raise ValueError(
										    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
										    "blocks to a device on the device_map: " + str(_SCREAMING_SNAKE_CASE       )       )
					if len(_SCREAMING_SNAKE_CASE       ) != 0:
										raise ValueError(
										    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
										    + str(_SCREAMING_SNAKE_CASE       )       )







def     a(      A       :    List[str] ,     A       :    Optional[int]       )		->       List[str]:
					"""simple docstring"""





					a					     =   list(range(_SCREAMING_SNAKE_CASE       )       )
					a					     =   int(ceil(n_layers / len(_SCREAMING_SNAKE_CASE       )       )       )
					a					     =   [layers[i : i + n_blocks] for i in range(0 ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE       )]

					return dict(zip(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE       )       )




'''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   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					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   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	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
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, 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



