





import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory






def lowerCamelCase__    (							)      ->				Dict:
   raise RuntimeError('''CUDA out of memory.'''       )

class 		SCREAMING_SNAKE_CASE__				(       nn.Module  ):


      def __init__(self       :      List[Any]      ):





         """simple docstring"""



         super().__init__()
         __snake_case							       =   nn.Linear(3				,			4      )
         __snake_case							       =   nn.BatchNormad(4      )
         __snake_case							       =   nn.Linear(4				,			5      )


      def    a (self       :      Optional[Any]				,			a__       :      List[str]      ):





         """simple docstring"""



         return self.lineara(self.batchnorm(self.lineara(a__      )      )      )






class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   []

         @find_executable_batch_size(starting_batch_size=128      )
         def mock_training_loop_function(a__       :      Any      ):
            nonlocal batch_sizes
            batch_sizes.append(a__      )
            if batch_size != 8:
               raise_fake_out_of_memory()

         mock_training_loop_function()
         self.assertListEqual(a__				,			[128, 64, 32, 16, 8]      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   []

         @find_executable_batch_size(starting_batch_size=128      )
         def mock_training_loop_function(a__       :      str				,			a__       :      Tuple      ):
            nonlocal batch_sizes
            batch_sizes.append(a__      )
            if batch_size != 8:
               raise_fake_out_of_memory()
            return batch_size, arga

         __snake_case       ,	__snake_case							       =   mock_training_loop_function('''hello'''      )
         self.assertListEqual(a__				,			[128, 64, 32, 16, 8]      )
         self.assertListEqual([bs, arga]				,			[8, '''hello''']      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=0      )
         def mock_training_loop_function(a__       :      Optional[int]      ):
            pass

         with self.assertRaises(a__      ) as cm:
            mock_training_loop_function()
            self.assertIn('''No executable batch size found, reached zero.'''				,			cm.exception.args[0]      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=16      )
         def mock_training_loop_function(a__       :      str      ):
            if batch_size > 0:
               raise_fake_out_of_memory()
            pass

         with self.assertRaises(a__      ) as cm:
            mock_training_loop_function()
            self.assertIn('''No executable batch size found, reached zero.'''				,			cm.exception.args[0]      )


      def    a (self       :      int      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=128      )
         def mock_training_loop_function(a__       :      int				,			a__       :      Tuple				,			a__       :      int      ):
            if batch_size != 8:
               raise raise_fake_out_of_memory()

         with self.assertRaises(a__      ) as cm:
            mock_training_loop_function(128				,			'''hello'''				,			'''world'''      )
            self.assertIn('''Batch size was passed into `f`'''				,			cm.exception.args[0]      )
            self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''				,			cm.exception.args[0]      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         @find_executable_batch_size(starting_batch_size=16      )
         def mock_training_loop_function(a__       :      Optional[Any]      ):
            raise ValueError('''Oops, we had an error!'''      )

         with self.assertRaises(a__      ) as cm:
            mock_training_loop_function()
            self.assertIn('''Oops, we had an error!'''				,			cm.exception.args[0]      )


      @require_cuda
      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   torch.cuda.memory_allocated()
         __snake_case							       =   ModelForTest()
         model.cuda()
         self.assertGreater(torch.cuda.memory_allocated()				,			a__      )
         __snake_case							       =   release_memory(a__      )
         self.assertEqual(torch.cuda.memory_allocated()				,			a__      )






#
# 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        a						(      *_UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		with open(_UpperCAmelCase ,   '''r'''						) as fh:
				fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_EX						)
				try:
						print(*_UpperCAmelCase						)
				finally:
						fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_UN						)


__A   			=int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__A   			=torch.device("cuda", local_rank)
__A   			=socket.gethostname()

__A   			=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
				__A   			=dist.get_rank()
				__A   			=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





