Fix is_dataset_splitted is_dataset_split

python/paddle/distributed/auto_parallel/api.py

@@ -3248,7 +3248,7 @@ class ShardDataloader:
     ShardDataloader converts a dataloader to a new dataloader which provided two capabilities:
     1. split dataloader by shard_dim to do data parallel.
     2. reshard the output of dataloader to distributed tensor.
-    if is_dataset_splitted is True, just need to do reshard.
+    if is_dataset_split is True, just need to do reshard.
 
     Args:
         dataloader (paddle.io.DataLoader): The dataloader to be sharded.
@@ -3262,7 +3262,7 @@ class ShardDataloader:
         shard_dims (list|tuple|str|int]): The mesh dimension to shard the dataloader.
             Users can specify the shard_dim of each mesh or specify a single shard_dim for all meshes.
             Default: None, which means the data loader will not be split, i.e. mp.
-        is_dataset_splitted (bool): Whether the dataset has been splitted.
+        is_dataset_split (bool): Whether the dataset has been splitted.
         dense_tensor_idx (list): A paired 2D list specifies the index of the dense_tensor in the output of dataloader.
             It allows users to identify which elements within each output batch are dense_tensor.
             first dense_tensor: the dense_tensor return by dataloader.
@@ -3277,13 +3277,13 @@ def __init__(
         meshes: ProcessMesh | list[ProcessMesh] | tuple[ProcessMesh],
         input_keys: list[str] | tuple[str] | None = None,
         shard_dims: list | tuple | str | int | None = None,
-        is_dataset_splitted: bool = False,
+        is_dataset_split: bool = False,
         dense_tensor_idx: list[list[int]] | None = None,
     ):
         # do some check
-        if is_dataset_splitted is True and shard_dims is None:
+        if is_dataset_split is True and shard_dims is None:
             raise ValueError(
-                "shard_dims must be set when is_dataset_splitted is True"
+                "shard_dims must be set when is_dataset_split is True"
             )
 
         self._meshes = to_list(meshes)
@@ -3310,7 +3310,7 @@ def __init__(
             dp_rank = mesh.get_rank_by_dim_and_process_id(shard_dim, process_id)
             dp_world_size = mesh.get_dim_size(shard_dim)
 
-        if is_dataset_splitted is True or shard_dims is None:
+        if is_dataset_split is True or shard_dims is None:
             self._dataloader = dataloader
             self.batch_size = dataloader.batch_sampler.batch_size
         else:
@@ -3566,15 +3566,15 @@ def shard_dataloader(
     meshes: ProcessMesh | Sequence[ProcessMesh],
     input_keys: Sequence[str] | None = None,
     shard_dims: Sequence[str] | Sequence[int] | str | int | None = None,
-    is_dataset_splitted: bool = False,
+    is_dataset_split: bool = False,
     dense_tensor_idx: list[list[int]] | None = None,
 ) -> ShardDataloader:
     """
     Convert the dataloader to a ShardDataloader which provided two capabilities:
     1. split dataloader by shard_dim to do data parallel if it it not None.
     2. reshard the output of dataloader to distributed tensor.
-    if is_dataset_splitted is True, it means that the dataset has been split by users, and just need to do reshard.
-    only if is_dataset_splitted is False and shard_dims is not None, it will do split.
+    if is_dataset_split is True, it means that the dataset has been split by users, and just need to do reshard.
+    only if is_dataset_split is False and shard_dims is not None, it will do split.
 
     Args:
         dataloader (paddle.io.DataLoader): The dataloader to be sharded. the output of dataloader
@@ -3591,7 +3591,7 @@ def shard_dataloader(
             The mesh dimension to shard the dataloader.
             Users can specify the shard_dim of each mesh or specify a single shard_dim for all meshes.
             Default: None, which means the data loader will not be split, i.e. mp.
-        is_dataset_splitted (bool): Whether the dataset has been splitted, Default: False.
+        is_dataset_split (bool): Whether the dataset has been splitted, Default: False.
         dense_tensor_idx (list): A paired 2D list specifies the index of the dense_tensor in the output of dataloader.
             It allows users to identify which elements within each output batch are dense_tensor.
             first dense_tensor: the dense_tensor return by dataloader.
@@ -3761,7 +3761,7 @@ def shard_dataloader(
         meshes,
         input_keys,
         shard_dims,
-        is_dataset_splitted,
+        is_dataset_split,
         dense_tensor_idx,
     )
 

test/auto_parallel/pir/while_unittest_pir.py

@@ -95,7 +95,7 @@ def create_data_loader(self):
             meshes=mesh,
             shard_dims="x",
             input_keys=["inputs", "label"],
-            is_dataset_splitted=True,
+            is_dataset_split=True,
         )
         return dist_dataloader