


import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
		import torch

UpperCAmelCase__     			=							logging.get_logger(__name__)
def        UpperCAmelCase_      (							)  ->    str:







	"""simple docstring"""


	return importlib.util.find_spec('''deepspeed'''		) is not None


if is_accelerate_available() and is_deepspeed_available():
		from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
		# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
		# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
		from builtins import object as DeepSpeedConfig







class    lowerCamelCase__     ( lowerCAmelCase):





					def __init__(self	,  UpperCAmelCase )  ->     str:
						# set global weakref object
						set_hf_deepspeed_config(self )
						dep_version_check('''accelerate''' )
						dep_version_check('''deepspeed''' )
						super().__init__(UpperCAmelCase )







class    lowerCamelCase__     ( lowerCAmelCase):





					def __init__(self	,  UpperCAmelCase )  ->     Union[str, Any]:
						super().__init__(UpperCAmelCase )
						_lowercase							   =None
						_lowercase							   =[]





					def   __A      (self )  ->     Optional[int]:
						if self._dtype is None:
							raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype''' )
						return self._dtype





					def   __A      (self	,  UpperCAmelCase )  ->     Union[str, Any]:
						_lowercase							   =self.get_value(UpperCAmelCase )
						if val is None:
							return False
						else:
							return val == "auto"





					def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None	,  UpperCAmelCase=True )  ->     str:
						_lowercase   ,					_lowercase							   =self.find_config_node(UpperCAmelCase )
						if config is None:
							return

						if config.get(UpperCAmelCase ) == "auto":
							_lowercase							   =hf_val
							return

						if not must_match:
							return

						_lowercase							   =config.get(UpperCAmelCase )
						if ds_val is not None and ds_val != hf_val:
							self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}" )

					SCREAMING_SNAKE_CASE__       =    partialmethod(lowerCAmelCase     ,							must_match=lowerCAmelCase)





					def   __A      (self	,  UpperCAmelCase )  ->     Dict:
						_lowercase							   =args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
						self.fill_match(
						    '''train_micro_batch_size_per_gpu'''	,  args.per_device_train_batch_size	,  '''per_device_train_batch_size''' )
						self.fill_match('''gradient_accumulation_steps'''	,  args.gradient_accumulation_steps	,  '''gradient_accumulation_steps''' )
						self.fill_match('''train_batch_size'''	,  UpperCAmelCase	,  '''train_batch_size (calculated)''' )
						self.fill_match('''gradient_clipping'''	,  args.max_grad_norm	,  '''max_grad_norm''' )

						self.fill_match('''optimizer.params.lr'''	,  args.learning_rate	,  '''learning_rate''' )
						self.fill_match('''optimizer.params.betas'''	,  [args.adam_betaa, args.adam_betaa]	,  '''adam_beta1+adam_beta2''' )
						self.fill_match('''optimizer.params.eps'''	,  args.adam_epsilon	,  '''adam_epsilon''' )
						self.fill_match('''optimizer.params.weight_decay'''	,  args.weight_decay	,  '''weight_decay''' )

						self.fill_only('''scheduler.params.warmup_min_lr'''	,  0 )  # not a trainer arg
						self.fill_match('''scheduler.params.warmup_max_lr'''	,  args.learning_rate	,  '''learning_rate''' )
						# total_num_steps - will get set in trainer_config_finalize

						# fp16
						if args.fpaa or args.fpaa_full_eval:
							_lowercase							   ='''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
						else:
							_lowercase							   =None

						if args.save_on_each_node:
							# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
							_lowercase							   =self.config.get('''checkpoint'''	,  {} )
							_lowercase							   =args.save_on_each_node

						# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
						# any here unless the user did the work
						self.fill_match(
						    '''fp16.enabled'''	,  ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')	,  '''fp16|fp16_full_eval+fp16_backend(amp)'''	,  )

						# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
						# ZeRO features
						self.fill_match('''amp.enabled'''	,  fpaa_backend == '''apex'''	,  '''fp16+fp16_backend(apex)''' )
						self.fill_match('''amp.opt_level'''	,  args.fpaa_opt_level	,  '''fp16_opt_level''' )

						self.fill_match('''bf16.enabled'''	,  (args.bfaa or args.bfaa_full_eval)	,  '''bf16|bf16_full_eval''' )

						# deepspeed's default mode is fp16 unless there is a config that says differently
						if self.is_true('''bf16.enabled''' ):
							_lowercase							   =torch.bfloataa
						elif self.is_false('''fp16.enabled''' ):
							_lowercase							   =torch.floataa
						else:
							_lowercase							   =torch.floataa





					def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Union[str, Any]:
						_lowercase							   =[
						    '''zero_optimization.reduce_bucket_size''',
						    '''zero_optimization.stage3_prefetch_bucket_size''',
						    '''zero_optimization.stage3_param_persistence_threshold''',
						]
						_lowercase							   =[x for x in hidden_size_based_keys if self.is_auto(UpperCAmelCase )]

						if len(UpperCAmelCase ) > 0:
							if hasattr(model.config	,  '''hidden_size''' ):
								_lowercase							   =model.config.hidden_size
							elif hasattr(model.config	,  '''hidden_sizes''' ):
								# if there are many hidden sizes pick the largest one
								_lowercase							   =max(model.config.hidden_sizes )
							else:
								raise ValueError(
								    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
								    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
								    f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
								    '''`auto` values for these keys with an integer value of your choice.''' )

							self.fill_only('''zero_optimization.reduce_bucket_size'''	,  hidden_size * hidden_size )
							if self.is_zeroa():
								# automatically assign the optimal config values based on model config
								self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''	,  0.9 * hidden_size * hidden_size )
								self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''	,  1_0 * hidden_size )

								# scheduler
						self.fill_match('''scheduler.params.total_num_steps'''	,  UpperCAmelCase	,  '''num_training_steps (calculated)''' )
						self.fill_match('''scheduler.params.warmup_num_steps'''	,  args.get_warmup_steps(UpperCAmelCase )	,  '''warmup_steps''' )

						if len(self.mismatches ) > 0:
							_lowercase							   ='''\n'''.join(self.mismatches )
							raise ValueError(
							    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
							    f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'." )



UpperCAmelCase__     			=							None
def        UpperCAmelCase_      (							__snake_case		)  ->    Dict:







	"""simple docstring"""


	global _hf_deepspeed_config_weak_ref
	# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
	_lowercase							   =weakref.ref(__snake_case		)
def        UpperCAmelCase_      (							)  ->    str:







	"""simple docstring"""


	global _hf_deepspeed_config_weak_ref
	_lowercase							   =None
def        UpperCAmelCase_      (							)  ->    Union[str, Any]:







	"""simple docstring"""


	if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
		return _hf_deepspeed_config_weak_ref().is_zeroa()
	else:
		return False
def        UpperCAmelCase_      (							)  ->    Any:







	"""simple docstring"""


	if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
		return _hf_deepspeed_config_weak_ref().config
	else:
		return None
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)  ->    str:







	"""simple docstring"""


	from accelerate.utils import DummyOptim, DummyScheduler

	_lowercase							   =hf_deepspeed_config.config

	# Optimizer + Scheduler
	# Currently supported combos:
	# 1. DS scheduler + DS optimizer: Yes
	# 2. HF scheduler + HF optimizer: Yes
	# 3. DS scheduler + HF optimizer: Yes
	# 4. HF scheduler + DS optimizer: No
	#
	# Unless Offload is enabled in which case it's:
	# 1. DS scheduler + DS optimizer: Yes
	# 2. HF scheduler + HF optimizer: Mostly*
	# 3. DS scheduler + HF optimizer: Mostly*
	# 4. HF scheduler + DS optimizer: No
	#
	# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

	_lowercase							   =None
	if "optimizer" in config:
		if args.adafactor:
			raise ValueError(
			    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
			    '''Only one optimizer can be configured.'''		)
		_lowercase							   =DummyOptim(params=__snake_case		)
	else:
		if hf_deepspeed_config.is_offload():
			logger.info(
			    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
			    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''		)

		# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
		# But trainer uses AdamW by default.
		_lowercase							   =trainer.create_optimizer()
		# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
		_lowercase							   =True

	_lowercase							   =None
	if "scheduler" in config:
		_lowercase							   =DummyScheduler(__snake_case		)
	else:
		if isinstance(__snake_case  ,		__snake_case		):
			raise ValueError(
			    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
			    '''Please configure a scheduler in the DeepSpeed config.'''		)
		_lowercase							   =trainer.create_scheduler(num_training_steps=__snake_case  ,		optimizer=__snake_case		)

	return optimizer, lr_scheduler
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case=False		)  ->    Optional[int]:







	"""simple docstring"""


	from deepspeed.utils import logger as ds_logger

	_lowercase							   =trainer.model
	_lowercase							   =trainer.args

	_lowercase							   =trainer.accelerator.state.deepspeed_plugin.hf_ds_config

	# resume config update - some bits like `model` and `num_training_steps` only become available during train
	hf_deepspeed_config.trainer_config_finalize(__snake_case  ,		__snake_case  ,		__snake_case		)

	# set the Deepspeed log level consistent with the Trainer
	ds_logger.setLevel(args.get_process_log_level()		)

	if inference:
		# only Z3 makes sense for the inference
		if not hf_deepspeed_config.is_zeroa():
			raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''		)

		# in case the training config is re-used for inference
		hf_deepspeed_config.del_config_sub_tree('''optimizer'''		)
		hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''		)
		_lowercase   ,					_lowercase							   =None, None
		_lowercase							   =None
	else:
		_lowercase							   =None  # important for when deepspeed_init is used as re-init
		_lowercase							   =list(filter(lambda __snake_case		: p.requires_grad  ,		model.parameters()		)		)
		_lowercase   ,					_lowercase							   =deepspeed_optim_sched(
		    __snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)

	# keep for quick debug:
	# from pprint import pprint; pprint(config)

	return optimizer, lr_scheduler



def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    List[str]:







	"""simple docstring"""


	import glob

	_lowercase							   =sorted(glob.glob(F"{checkpoint_path}/global_step*"		)		)

	if len(__snake_case		) > 0:
		logger.info(F"Attempting to resume from {checkpoint_path}"		)
		# this magically updates self.optimizer and self.lr_scheduler
		_lowercase   ,					_lowercase							   =deepspeed_engine.load_checkpoint(
		    __snake_case  ,		load_optimizer_states=__snake_case  ,		load_lr_scheduler_states=__snake_case		)
		if load_path is None:
			raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"		)
	else:
		raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"		)





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

