



'''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  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						str  )      ->   Union[str, Any]:
							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  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict=True  )      ->   Any:
							model.train()
							UpperCAmelCase :       List[Any] 							=		model(_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		F.mse_loss(_lowerCAmelCase   ,      target.to(output.device  )  )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						List[str]=False  )      ->   str:
							set_seed(42  )
							UpperCAmelCase :       Union[str, Any] 							=		RegressionModel()
							UpperCAmelCase :       Dict 							=		deepcopy(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		RegressionDataset(length=80  )
							UpperCAmelCase :       Tuple 							=		DataLoader(_lowerCAmelCase   ,      batch_size=16  )
							model.to(accelerator.device  )
							if sched:
														UpperCAmelCase :       Optional[int] 							=		AdamW(params=model.parameters()   ,      lr=1e-3  )
														UpperCAmelCase :       str 							=		AdamW(params=ddp_model.parameters()   ,      lr=1e-3  )
														UpperCAmelCase :       Union[str, Any] 							=		LambdaLR(_lowerCAmelCase   ,      lr_lambda=lambda _lowerCAmelCase  : epoch**0.6_5  )
														UpperCAmelCase :       int 							=		LambdaLR(_lowerCAmelCase   ,      lr_lambda=lambda _lowerCAmelCase  : epoch**0.6_5  )
							# Make a copy of `model`
							if sched:
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       str 							=		accelerator.prepare(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							else:
														UpperCAmelCase							,		UpperCAmelCase :       int 							=		accelerator.prepare(_lowerCAmelCase   ,      _lowerCAmelCase  )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader


def  snake_case_     (					_lowerCAmelCase  :						Any  )      ->   Optional[Any]:
							# Test when on a single CPU or GPU that the context manager does nothing
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		get_training_setup(_lowerCAmelCase  )
							# Use a single batch
							UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		next(iter(_lowerCAmelCase  )  ).values()
							for iteration in range(3  ):
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		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  )
														UpperCAmelCase :       str 							=		ddp_input[torch.randperm(len(_lowerCAmelCase  )  )]


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   int:
							# Test on distributed setup that context manager behaves properly
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		get_training_setup(_lowerCAmelCase  )
							# Use a single batch
							UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		next(iter(_lowerCAmelCase  )  ).values()
							for iteration in range(3  ):
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		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  )
														UpperCAmelCase :       Optional[Any] 							=		ddp_input[torch.randperm(len(_lowerCAmelCase  )  )]


def  snake_case_     (					_lowerCAmelCase  :						List[Any]=False   ,      _lowerCAmelCase  :						Optional[int]=False  )      ->   Dict:
							UpperCAmelCase :       Union[str, Any] 							=		Accelerator(
							    split_batches=_lowerCAmelCase   ,      dispatch_batches=_lowerCAmelCase   ,      gradient_accumulation_steps=2  )
							# Test that context manager behaves properly
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		get_training_setup(_lowerCAmelCase  )
							for iteration, batch in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		batch.values()
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		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  )
														UpperCAmelCase :       List[Any] 							=		ddp_input[torch.randperm(len(_lowerCAmelCase  )  )]
							GradientState._reset_state()


def  snake_case_     (					_lowerCAmelCase  :						List[str]=False   ,      _lowerCAmelCase  :						str=False  )      ->   Optional[int]:
							UpperCAmelCase :       Dict 							=		Accelerator(
							    split_batches=_lowerCAmelCase   ,      dispatch_batches=_lowerCAmelCase   ,      gradient_accumulation_steps=2  )
							# Test that context manager behaves properly
							UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		get_training_setup(_lowerCAmelCase   ,      _lowerCAmelCase  )
							for iteration, batch in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		batch.values()
														# Gather the distributed inputs and targs for the base model
														UpperCAmelCase							,		UpperCAmelCase :       str 							=		accelerator.gather((ddp_input, ddp_target)  )
														UpperCAmelCase							,		UpperCAmelCase :       Union[str, 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"""
														UpperCAmelCase :       str 							=		(((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  snake_case_     (					)      ->   Tuple:
							UpperCAmelCase :       Optional[Any] 							=		Accelerator()

							UpperCAmelCase :       List[str] 							=		RegressionDataset(length=80  )
							UpperCAmelCase :       str 							=		DataLoader(_lowerCAmelCase   ,      batch_size=16  )
							UpperCAmelCase :       List[Any] 							=		RegressionDataset(length=96  )
							UpperCAmelCase :       List[Any] 							=		DataLoader(_lowerCAmelCase   ,      batch_size=16  )
							UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		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  snake_case_     (					)      ->   Optional[int]:
							UpperCAmelCase :       int 							=		Accelerator()
							UpperCAmelCase :       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  snake_case_     (					_lowerCAmelCase  :						Optional[int]  )      ->   Tuple:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase		:   Optional[int]										=       5_0000
_UpperCAmelCase		:   Dict										=       5000

_UpperCAmelCase				,						_UpperCAmelCase		:   Optional[int]										=       os.path.split(__file__)
_UpperCAmelCase		:   List[str]										=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(UpperCamelCase__      ):
														snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(0   ,							len(UpperCamelCase__      )   ,							UpperCamelCase__      ):
														snake_case_							=    dataset[i : i + batch_size]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(UpperCamelCase__      ):
																					snake_case_							=    dataset[i]





@get_duration
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							with dataset.formatted_as(type=UpperCamelCase__      ):
														for i in range(0   ,							UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    dataset[i : i + batch_size]





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    {'num examples': SPEED_TEST_N_EXAMPLES}
							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
							    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]

							snake_case_							=    [
							    (read, {'length': SMALL_TEST}),
							    (read, {'length': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
							    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
							    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
							    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('generating dataset'      )
														snake_case_							=    datasets.Features(
														    {'list': datasets.Sequence(datasets.Value('float32'      )      ), 'numbers': datasets.Value('float32'      )}      )
														snake_case_							=    generate_example_dataset(
														    os.path.join(UpperCamelCase__   ,							'dataset.arrow'      )   ,							UpperCamelCase__   ,							num_examples=UpperCamelCase__   ,							seq_shapes={'list': (100,)}   ,							)
														print('first set of iterations'      )
														for func, kwargs in functions:
																					print(func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(UpperCamelCase__   ,							**UpperCamelCase__      )

														print('shuffling dataset'      )
														snake_case_							=    dataset.shuffle()
														print('Second set of iterations (after shuffling'      )
														for func, kwargs in functions_shuffled:
																					print('shuffled '   ,							func.__name__   ,							str(UpperCamelCase__      )      )
																					snake_case_							=    func(
																					    UpperCamelCase__   ,							**UpperCamelCase__      )

							with open(UpperCamelCase__   ,							'wb'      ) as f:
														f.write(json.dumps(UpperCamelCase__      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
					benchmark_iterating()




