


'''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      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			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      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=True					):
			model.train()
			UpperCAmelCase__  :					Dict        =     model(UpperCamelCase__					)
			UpperCAmelCase__  :					List[Any]        =     F.mse_loss(UpperCamelCase__ ,      target.to(output.device					)					)
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__=False					):
			set_seed(4_2					)
			UpperCAmelCase__  :					Tuple        =     RegressionModel()
			UpperCAmelCase__  :					Optional[Any]        =     deepcopy(UpperCamelCase__					)
			UpperCAmelCase__  :					str        =     RegressionDataset(length=8_0					)
			UpperCAmelCase__  :					Dict        =     DataLoader(UpperCamelCase__ ,      batch_size=1_6					)
			model.to(accelerator.device					)
			if sched:
						UpperCAmelCase__  :					Dict        =     AdamW(params=model.parameters() ,      lr=1e-3					)
						UpperCAmelCase__  :					Tuple        =     AdamW(params=ddp_model.parameters() ,      lr=1e-3					)
						UpperCAmelCase__  :					Optional[Any]        =     LambdaLR(UpperCamelCase__ ,      lr_lambda=lambda UpperCamelCase__					: epoch**0.65					)
						UpperCAmelCase__  :					Tuple        =     LambdaLR(UpperCamelCase__ ,      lr_lambda=lambda UpperCamelCase__					: epoch**0.65					)
			# Make a copy of `model`
			if sched:
						UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     accelerator.prepare(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
			else:
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     accelerator.prepare(UpperCamelCase__ ,      UpperCamelCase__					)
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader







def     _UpperCamelCase      (		UpperCamelCase__					):
			# Test when on a single CPU or GPU that the context manager does nothing
			UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     get_training_setup(UpperCamelCase__					)
			# Use a single batch
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     next(iter(UpperCamelCase__					)					).values()
			for iteration in range(3					):
						# Gather the distributed inputs and targs for the base model
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Dict        =     accelerator.gather((ddp_input, ddp_target)					)
						UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     input.to(accelerator.device					), target.to(accelerator.device					)
						# Perform our initial ground truth step in non "DDP"
						step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(UpperCamelCase__					):
												step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						else:
									# Sync grads
									step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						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(1_3_3_7 + iteration					)
						UpperCAmelCase__  :					str        =     ddp_input[torch.randperm(len(UpperCamelCase__					)					)]







def     _UpperCamelCase      (		UpperCamelCase__					):
			# Test on distributed setup that context manager behaves properly
			UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     get_training_setup(UpperCamelCase__					)
			# Use a single batch
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     next(iter(UpperCamelCase__					)					).values()
			for iteration in range(3					):
						# Gather the distributed inputs and targs for the base model
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Dict        =     accelerator.gather((ddp_input, ddp_target)					)
						UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     input.to(accelerator.device					), target.to(accelerator.device					)
						# Perform our initial ground truth step in non "DDP"
						step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(UpperCamelCase__					):
												step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						else:
									# Sync grads
									step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						# 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(1_3_3_7 + iteration					)
						UpperCAmelCase__  :					List[str]        =     ddp_input[torch.randperm(len(UpperCamelCase__					)					)]







def     _UpperCamelCase      (		UpperCamelCase__=False ,      UpperCamelCase__=False					):
			UpperCAmelCase__  :					Optional[Any]        =     Accelerator(
			    split_batches=UpperCamelCase__ ,      dispatch_batches=UpperCamelCase__ ,      gradient_accumulation_steps=2					)
			# Test that context manager behaves properly
			UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     get_training_setup(UpperCamelCase__					)
			for iteration, batch in enumerate(UpperCamelCase__					):
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     batch.values()
						# Gather the distributed inputs and targs for the base model
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     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(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(UpperCamelCase__					):
									step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						# 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(UpperCamelCase__					) - 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(1_3_3_7 + iteration					)
						UpperCAmelCase__  :					Tuple        =     ddp_input[torch.randperm(len(UpperCamelCase__					)					)]
			GradientState._reset_state()







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

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__					)):
									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(UpperCamelCase__					):
									step_model(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
									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__  :					Any        =     (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__					))
						if accelerator.num_processes > 1:
									check_model_parameters(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1_3_3_7 + iteration					)
			GradientState._reset_state()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Optional[int]        =     Accelerator()

			UpperCAmelCase__  :					Optional[Any]        =     RegressionDataset(length=8_0					)
			UpperCAmelCase__  :					int        =     DataLoader(UpperCamelCase__ ,      batch_size=1_6					)
			UpperCAmelCase__  :					Tuple        =     RegressionDataset(length=9_6					)
			UpperCAmelCase__  :					Dict        =     DataLoader(UpperCamelCase__ ,      batch_size=1_6					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     accelerator.prepare(UpperCamelCase__ ,      UpperCamelCase__					)
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(UpperCamelCase__					):
						assert id(accelerator.gradient_state.active_dataloader					) == id(UpperCamelCase__					)
						if iteration < len(UpperCamelCase__					) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(UpperCamelCase__					):
															assert id(accelerator.gradient_state.active_dataloader					) == id(UpperCamelCase__					)
															if batch_num < len(UpperCamelCase__					) - 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      (		):
			UpperCAmelCase__  :					Dict        =     Accelerator()
			UpperCAmelCase__  :					Dict        =     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(UpperCamelCase__					)
			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(UpperCamelCase__					)
			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(UpperCamelCase__ ,      UpperCamelCase__					)

    # 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(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
		main()



'''simple docstring'''



import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__A	=object()

# For specifying empty leaf dict `{}`
__A	=object()







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     tuple((re.compile(x + """$"""					) for x in qs)					)
			for i in range(len(UpperCamelCase__					) - len(UpperCamelCase__					) + 1					):
						UpperCAmelCase__  :					Optional[Any]        =     [x.match(UpperCamelCase__					) for x, y in zip(UpperCamelCase__ ,      ks[i:]					)]
						if matches and all(UpperCamelCase__					):
									return True
			return False







def     _UpperCamelCase      (		UpperCamelCase__					):
			def replace(UpperCamelCase__ ,      UpperCamelCase__					):
						for rule, replacement in rules:
									if _match(UpperCamelCase__ ,      UpperCamelCase__					):
												return replacement
						return val

			return replace







def     _UpperCamelCase      (		):
			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp""" ,      UpperCamelCase__					)),
			    (("transformer", "wte", "embedding"), P("""mp""" ,      UpperCamelCase__					)),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__ ,      """mp"""					)),
			    (("attention", "out_proj", "kernel"), P("""mp""" ,      UpperCamelCase__					)),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(UpperCamelCase__ ,      """mp"""					)),
			    (("mlp", "c_fc", "bias"), P("""mp"""					)),
			    (("mlp", "c_proj", "kernel"), P("""mp""" ,      UpperCamelCase__					)),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =     _get_partition_rules()
			UpperCAmelCase__  :					Optional[int]        =     _replacement_rules(UpperCamelCase__					)
			UpperCAmelCase__  :					List[Any]        =     {k: _unmatched for k in flatten_dict(UpperCamelCase__					)}
			UpperCAmelCase__  :					Tuple        =     {k: replace(UpperCamelCase__ ,      UpperCamelCase__					) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(UpperCamelCase__					)					)