
#
# 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_  (						*__lowerCAmelCase	) ->						str:


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


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

__lowerCAmelCase					:			Optional[int]            =		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
 __lowerCAmelCase					:			str            =		dist.get_rank()
 __lowerCAmelCase					:			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




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers import AutoModelForCausalLM


@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Any	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Optional[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[int]	    =       tokenizer.decode(greedy_ids[0]				)

          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				,       streamer=_snake_case				)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          __lowercase		:			List[Any]	    =       cs.out[:-1]

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Optional[Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Tuple	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(greedy_ids[0]				)

          __lowercase		:			int	    =       TextIteratorStreamer(_snake_case				)
          __lowercase		:			int	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[Any]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()
          __lowercase		:			Optional[int]	    =       ''''''
          for new_text in streamer:
              streamer_text += new_text

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			int	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			str	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			List[Any]	    =       greedy_ids[:, input_ids.shape[1] :]
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(new_greedy_ids[0]				)

          with CaptureStdout() as cs:
              __lowercase		:			int	    =       TextStreamer(_snake_case				,       skip_prompt=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				,       streamer=_snake_case				)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          __lowercase		:			int	    =       cs.out[:-1]

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Any				):
          # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
          # with actual models -- the dummy models' tokenizers are not aligned with their models, and
          # `skip_special_tokens=True` has no effect on them
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''distilgpt2'''				)
          __lowercase		:			List[str]	    =       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(_snake_case				)
          __lowercase		:			str	    =       -1

          __lowercase		:			Dict	    =       torch.ones((1, 5)				,       device=_snake_case				).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				,       skip_special_tokens=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=1				,       do_sample=_snake_case				,       streamer=_snake_case				)

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          __lowercase		:			Dict	    =       cs.out[:-1]  # Remove the final "\n"
          __lowercase		:			Any	    =       tokenizer(_snake_case				,       return_tensors='''pt'''				)
          self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Union[str, Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			Dict	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       TextIteratorStreamer(_snake_case				,       timeout=0.0_01				)
          __lowercase		:			Tuple	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[int]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(_snake_case				):
              __lowercase		:			List[Any]	    =       ''''''
              for new_text in streamer:
                  streamer_text += new_text


