






'''simple docstring'''




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed

def 	__UpperCAmelCase       ( __magic_name__     ,      __magic_name__     ,      __magic_name__     ,      __magic_name__						)->    Dict:



			"""simple docstring"""



			for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters()						):
						if not param.requires_grad:
									continue
						if not did_step:
									# Grads should not be in sync
									assert (
									    torch.allclose(param.grad     ,      grad_param.grad						) is False
									), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
						else:
									# Grads should be in sync
									assert (
									    torch.allclose(param.grad     ,      grad_param.grad						) is True
									), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''

def 	__UpperCAmelCase       ( __magic_name__     ,      __magic_name__     ,      __magic_name__     ,      __magic_name__     ,      __magic_name__=True						)->    Dict:



			"""simple docstring"""



			model.train()
			snake_case_				:					Optional[int]   =       model(lowerCAmelCase_						)
			snake_case_				:					Any   =       F.mse_loss(lowerCAmelCase_     ,      target.to(output.device						)						)
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(lowerCAmelCase_						)

def 	__UpperCAmelCase       ( __magic_name__     ,      __magic_name__=False						)->    Union[str, Any]:



			"""simple docstring"""



			set_seed(42						)
			snake_case_				:					Optional[Any]   =       RegressionModel()
			snake_case_				:					Dict   =       deepcopy(lowerCAmelCase_						)
			snake_case_				:					Union[str, Any]   =       RegressionDataset(length=80						)
			snake_case_				:					List[Any]   =       DataLoader(lowerCAmelCase_     ,      batch_size=16						)
			model.to(accelerator.device						)
			if sched:
						snake_case_				:					List[str]   =       AdamW(params=model.parameters()     ,      lr=1E-3						)
						snake_case_				:					str   =       AdamW(params=ddp_model.parameters()     ,      lr=1E-3						)
						snake_case_				:					str   =       LambdaLR(lowerCAmelCase_     ,      lr_lambda=lambda __magic_name__						: epoch**0.65						)
						snake_case_				:					Any   =       LambdaLR(lowerCAmelCase_     ,      lr_lambda=lambda __magic_name__						: epoch**0.65						)
			# Make a copy of `model`
			if sched:
						snake_case_				:					str   =       accelerator.prepare(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
			else:
						snake_case_				:					str   =       accelerator.prepare(lowerCAmelCase_     ,      lowerCAmelCase_						)
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Dict   =       get_training_setup(lowerCAmelCase_						)
			# Use a single batch
			snake_case_				:					Any   =       next(iter(lowerCAmelCase_						)						).values()
			for iteration in range(3						):
						# Gather the distributed inputs and targs for the base model
						snake_case_				:					int   =       accelerator.gather((ddp_input, ddp_target)						)
						snake_case_				:					Dict   =       input.to(accelerator.device						), target.to(accelerator.device						)
						# Perform our initial ground truth step in non "DDP"
						step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(lowerCAmelCase_						):
												step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						else:
									# Sync grads
									step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters()						):
									if not param.requires_grad:
												continue
									assert torch.allclose(
									    param.grad     ,      ddp_param.grad						), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration						)
						snake_case_				:					Dict   =       ddp_input[torch.randperm(len(lowerCAmelCase_						)						)]

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					int   =       get_training_setup(lowerCAmelCase_						)
			# Use a single batch
			snake_case_				:					int   =       next(iter(lowerCAmelCase_						)						).values()
			for iteration in range(3						):
						# Gather the distributed inputs and targs for the base model
						snake_case_				:					Optional[int]   =       accelerator.gather((ddp_input, ddp_target)						)
						snake_case_				:					Dict   =       input.to(accelerator.device						), target.to(accelerator.device						)
						# Perform our initial ground truth step in non "DDP"
						step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(lowerCAmelCase_						):
												step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						else:
									# Sync grads
									step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters()						):
									if not param.requires_grad:
												continue
									if iteration % 2 == 0:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad     ,      ddp_param.grad						) is False
												), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
									else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad     ,      ddp_param.grad						) is True
												), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration						)
						snake_case_				:					List[Any]   =       ddp_input[torch.randperm(len(lowerCAmelCase_						)						)]

def 	__UpperCAmelCase       ( __magic_name__=False     ,      __magic_name__=False						)->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       Accelerator(
			    split_batches=lowerCAmelCase_     ,      dispatch_batches=lowerCAmelCase_     ,      gradient_accumulation_steps=2						)
			# Test that context manager behaves properly
			snake_case_				:					Union[str, Any]   =       get_training_setup(lowerCAmelCase_						)
			for iteration, batch in enumerate(lowerCAmelCase_						):
						snake_case_				:					Union[str, Any]   =       batch.values()
						# Gather the distributed inputs and targs for the base model
						snake_case_				:					List[Any]   =       accelerator.gather((ddp_input, ddp_target)						)
						snake_case_				:					Dict   =       input.to(accelerator.device						), target.to(accelerator.device						)
						# Perform our initial ground truth step in non "DDP"
						step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(lowerCAmelCase_						):
									step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters()						):
									if not param.requires_grad:
												continue
									if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCAmelCase_						) - 1):
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad     ,      ddp_param.grad						) is True
												), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
									else:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad     ,      ddp_param.grad						) is False
												), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration						)
						snake_case_				:					str   =       ddp_input[torch.randperm(len(lowerCAmelCase_						)						)]
			GradientState._reset_state()

def 	__UpperCAmelCase       ( __magic_name__=False     ,      __magic_name__=False						)->    Dict:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       Accelerator(
			    split_batches=lowerCAmelCase_     ,      dispatch_batches=lowerCAmelCase_     ,      gradient_accumulation_steps=2						)
			# Test that context manager behaves properly
			snake_case_				:					int   =       get_training_setup(lowerCAmelCase_     ,      lowerCAmelCase_						)
			for iteration, batch in enumerate(lowerCAmelCase_						):
						snake_case_				:					Dict   =       batch.values()
						# Gather the distributed inputs and targs for the base model
						snake_case_				:					Any   =       accelerator.gather((ddp_input, ddp_target)						)
						snake_case_				:					List[Any]   =       input.to(accelerator.device						), target.to(accelerator.device						)
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCAmelCase_						)):
									if split_batches:
												sched.step()
									else:
												for _ in range(accelerator.num_processes						):
															sched.step()
						opt.zero_grad()
						# Perform gradient accumulation under wrapper
						with accelerator.accumulate(lowerCAmelCase_						):
									step_model(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
									ddp_opt.step()
									ddp_sched.step()
									ddp_opt.zero_grad()

						# Learning rates should be the same
						assert (
						    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
						), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
						snake_case_				:					Optional[Any]   =       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCAmelCase_						))
						if accelerator.num_processes > 1:
									check_model_parameters(lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_     ,      lowerCAmelCase_						)
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration						)
			GradientState._reset_state()

def 	__UpperCAmelCase       ( )->    str:



			"""simple docstring"""



			snake_case_				:					int   =       Accelerator()

			snake_case_				:					Optional[int]   =       RegressionDataset(length=80						)
			snake_case_				:					Tuple   =       DataLoader(lowerCAmelCase_     ,      batch_size=16						)
			snake_case_				:					Optional[int]   =       RegressionDataset(length=96						)
			snake_case_				:					Tuple   =       DataLoader(lowerCAmelCase_     ,      batch_size=16						)
			snake_case_				:					Optional[int]   =       accelerator.prepare(lowerCAmelCase_     ,      lowerCAmelCase_						)
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(lowerCAmelCase_						):
						assert id(accelerator.gradient_state.active_dataloader						) == id(lowerCAmelCase_						)
						if iteration < len(lowerCAmelCase_						) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(lowerCAmelCase_						):
															assert id(accelerator.gradient_state.active_dataloader						) == id(lowerCAmelCase_						)
															if batch_num < len(lowerCAmelCase_						) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
															else:
																		assert accelerator.gradient_state.end_of_dataloader
						else:
									assert accelerator.gradient_state.end_of_dataloader
			assert accelerator.gradient_state.active_dataloader is None

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Dict   =       Accelerator()
			snake_case_				:					Optional[Any]   =       accelerator.state
			if state.local_process_index == 0:
						print("**Test `accumulate` gradient accumulation with dataloader break**"						)
			test_dataloader_break()
			if state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print("**Test NOOP `no_sync` context manager**"						)
						test_noop_sync(lowerCAmelCase_						)
			if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
						if state.local_process_index == 0:
									print("**Test Distributed `no_sync` context manager**"						)
						test_distributed_sync(lowerCAmelCase_						)
			if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
									for dispatch_batches in [True, False]:
												if state.local_process_index == 0:
															print(
															    "**Test `accumulate` gradient accumulation, "     ,      F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''     ,      )
												test_gradient_accumulation(lowerCAmelCase_     ,      lowerCAmelCase_						)

    # Currently will break on torch 2.0 +, need to investigate why
			if is_torch_version("<"     ,      "2.0"						) or state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print(
									    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "     ,      "`split_batches=False`, `dispatch_batches=False`**"     ,      )
						test_gradient_accumulation_with_opt_and_scheduler()
						if state.distributed_type == DistributedType.MULTI_GPU:
									for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
															if not split_batch and not dispatch_batches:
																		continue
															if state.local_process_index == 0:
																		print(
																		    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "     ,      F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''     ,      )
															test_gradient_accumulation_with_opt_and_scheduler(lowerCAmelCase_     ,      lowerCAmelCase_						)

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			main()


if __name__ == "__main__":
		main()












'''simple docstring'''




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       torch.nn.Linear(10       ,							10				)
										snake_case_				:					Dict   =       torch.optim.SGD(model.parameters()       ,							0.1				)
										snake_case_				:					Tuple   =       Accelerator()
										snake_case_				:					Optional[Any]   =       accelerator.prepare(lowerCAmelCase__				)
										try:
													pickle.loads(pickle.dumps(lowerCAmelCase__				)				)
										except Exception as e:
													self.fail(F'''Accelerated optimizer pickling failed with {e}'''				)
										AcceleratorState._reset_state()




