Revert "Fix FSDP resume Initialization issue" (#34193)

Revert "Fix FSDP resume Initialization issue (#34032)"

This reverts commit 4de1bdb.

src/transformers/trainer.py

@@ -273,39 +273,6 @@ def _get_fsdp_ckpt_kwargs():
         return {}
 
 
-def _init_fsdp(model, accelerator, device):
-    """
-    Initialize Fully Sharded Data Parallel (FSDP) for the model.
-
-    This function is needed to properly initialize FSDP when resuming from a checkpoint.
-    It runs a forward pass with dummy inputs to ensure FSDP is fully initialized.
-    See https://github.com/huggingface/transformers/issues/31892 for more details.
-
-    Args:
-        model: The model to initialize with FSDP.
-        accelerator: The Accelerator object.
-        device: The device to run the model on.
-
-    Returns:
-        The initialized FSDP model.
-    """
-    model = accelerator.prepare(model)
-    model.train()
-    with torch.no_grad():
-        # Run a forward pass with dummy inputs to initialize FSDP
-        dummy_input = {
-            name: torch.ones(
-                (1, 512),
-                dtype=torch.long,
-                device=device,
-            )
-            for name in model.forward.__code__.co_varnames
-            if name != "self"
-        }
-        _ = model(**dummy_input)
-    return model
-
-
 if TYPE_CHECKING:
     import optuna
 
@@ -634,10 +601,6 @@ def __init__(
                     " `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and"
                     " `model.to(xm.xla_device())` is performed before the optimizer creation in your script."
                 )
-
-        if self.is_fsdp_enabled:
-            self.model = _init_fsdp(self.model, self.accelerator, self.args.device)
-
         if (self.is_fsdp_xla_enabled or self.is_fsdp_enabled) and (
             self.optimizer is not None or self.lr_scheduler is not None
         ):

tests/trainer/test_trainer.py

@@ -4914,34 +4914,3 @@ def test_get_optimizer_group(self):
             param = next(model.parameters())
             group = trainer.get_optimizer_group(param)
             self.assertIn(param, group["params"])
-
-
-@require_torch_gpu
-@require_torch
-@require_accelerate
-class TestFSDPInitialization(unittest.TestCase):
-    def test_fsdp_initialization(self):
-        config = RegressionModelConfig(a=1, b=1, double_output=False)
-        model = RegressionPreTrainedModel(config)
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            training_args = TrainingArguments(
-                output_dir=tmp_dir,
-                fsdp=True,
-                fsdp_config={"min_num_params": 1},
-                no_cuda=True,
-            )
-            trainer = Trainer(model=model, args=training_args)
-
-            # Check for FSDP enabled
-            self.assertTrue(trainer.is_fsdp_enabled)
-
-            # Check if model is wrapped with FSDP
-            from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-
-            self.assertTrue(trainer.model, FSDP)
-
-            # Running a forward pass to ensure FSDP is initialized
-            dummy_input = torch.ones((1, 1), dtype=torch.float)
-            output = trainer.model(dummy_input)
-            self.assertTrue(output)