

'''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__				)			->			Optional[int]:
       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				)			->			Any:
       model.train()
       A_						=      model(UpperCAmelCase__				)
       A_						=      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				)			->			List[Any]:
       set_seed(42				)
       A_						=      RegressionModel()
       A_						=      deepcopy(UpperCAmelCase__				)
       A_						=      RegressionDataset(length=80				)
       A_						=      DataLoader(UpperCAmelCase__,       batch_size=16				)
       model.to(accelerator.device				)
       if sched:
              A_						=      AdamW(params=model.parameters(),       lr=1e-3				)
              A_						=      AdamW(params=ddp_model.parameters(),       lr=1e-3				)
              A_						=      LambdaLR(UpperCAmelCase__,       lr_lambda=lambda UpperCAmelCase__				: epoch**0.65				)
              A_						=      LambdaLR(UpperCAmelCase__,       lr_lambda=lambda UpperCAmelCase__				: epoch**0.65				)
       # Make a copy of `model`
       if sched:
              A_					,			A_					,			A_					,			A_						=      accelerator.prepare(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
       else:
              A_					,			A_						=      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__				)			->			Tuple:
       # Test when on a single CPU or GPU that the context manager does nothing
       A_					,			A_					,			A_						=      get_training_setup(UpperCAmelCase__				)
       # Use a single batch
       A_					,			A_						=      next(iter(UpperCAmelCase__				)				).values()
       for iteration in range(3				):
              # Gather the distributed inputs and targs for the base model
              A_					,			A_						=      accelerator.gather((ddp_input, ddp_target)				)
              A_					,			A_						=      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(13_37 + iteration				)
              A_						=      ddp_input[torch.randperm(len(UpperCAmelCase__				)				)]
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[str]:
       # Test on distributed setup that context manager behaves properly
       A_					,			A_					,			A_						=      get_training_setup(UpperCAmelCase__				)
       # Use a single batch
       A_					,			A_						=      next(iter(UpperCAmelCase__				)				).values()
       for iteration in range(3				):
              # Gather the distributed inputs and targs for the base model
              A_					,			A_						=      accelerator.gather((ddp_input, ddp_target)				)
              A_					,			A_						=      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(13_37 + iteration				)
              A_						=      ddp_input[torch.randperm(len(UpperCAmelCase__				)				)]
def       UpperCAmelCase__    (      UpperCAmelCase__=False,       UpperCAmelCase__=False				)			->			int:
       A_						=      Accelerator(
           split_batches=UpperCAmelCase__,       dispatch_batches=UpperCAmelCase__,       gradient_accumulation_steps=2				)
       # Test that context manager behaves properly
       A_					,			A_					,			A_						=      get_training_setup(UpperCAmelCase__				)
       for iteration, batch in enumerate(UpperCAmelCase__				):
              A_					,			A_						=      batch.values()
              # Gather the distributed inputs and targs for the base model
              A_					,			A_						=      accelerator.gather((ddp_input, ddp_target)				)
              A_					,			A_						=      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(13_37 + iteration				)
              A_						=      ddp_input[torch.randperm(len(UpperCAmelCase__				)				)]
       GradientState._reset_state()
def       UpperCAmelCase__    (      UpperCAmelCase__=False,       UpperCAmelCase__=False				)			->			str:
       A_						=      Accelerator(
           split_batches=UpperCAmelCase__,       dispatch_batches=UpperCAmelCase__,       gradient_accumulation_steps=2				)
       # Test that context manager behaves properly
       A_					,			A_					,			A_					,			A_					,			A_					,			A_					,			A_						=      get_training_setup(UpperCAmelCase__,       UpperCAmelCase__				)
       for iteration, batch in enumerate(UpperCAmelCase__				):
              A_					,			A_						=      batch.values()
              # Gather the distributed inputs and targs for the base model
              A_					,			A_						=      accelerator.gather((ddp_input, ddp_target)				)
              A_					,			A_						=      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'''
              A_						=      (((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(13_37 + iteration				)
       GradientState._reset_state()
def       UpperCAmelCase__    (      )			->			Any:
       A_						=      Accelerator()

       A_						=      RegressionDataset(length=80				)
       A_						=      DataLoader(UpperCAmelCase__,       batch_size=16				)
       A_						=      RegressionDataset(length=96				)
       A_						=      DataLoader(UpperCAmelCase__,       batch_size=16				)
       A_					,			A_						=      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__    (      )			->			Dict:
       A_						=      Accelerator()
       A_						=      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__				)			->			int:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
      main()



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:
       if num < 0:
              return False

       A_						=      num
       A_						=      0
       while num > 0:
              A_						=      rev_num * 10 + (num % 10)
              num //= 10

       return num_copy == rev_num


if __name__ == "__main__":
      import doctest

      doctest.testmod()
