[nfc] fix some typo with colossalai/device colossalai/tensor/ etc.

colossalai/device/device_mesh.py

@@ -59,7 +59,7 @@ def __init__(self,
         # 2. directly supply the logical mesh id
         assert mesh_shape is None or logical_mesh_id is None, \
             "Only one of mesh_shape and logical_mesh_id can be specified." \
-            "Logical mesh IDs are obtained from either mesh_shape + phyiscal_mesh_id or directly from the user-supplied logical_mesh_id"
+            "Logical mesh IDs are obtained from either mesh_shape + physical_mesh_id or directly from the user-supplied logical_mesh_id"
 
         if logical_mesh_id is None:
             self._mesh_shape = mesh_shape
@@ -74,7 +74,7 @@ def __init__(self,
         assert torch.equal(torch.unique(self._physical_mesh_id), torch.unique(self.logical_mesh_id)), \
             "physical and logical mesh IDs should contain the same elements, please check if you have consistent physical_mesh_id and logical_mesh_id."
         assert torch.unique(self._physical_mesh_id).numel() == self._physical_mesh_id.numel(), \
-            "Found duplicate IDs in the phyiscal_mesh_id and this is not allowed, please check your physical_mesh_id again."
+            "Found duplicate IDs in the physical_mesh_id and this is not allowed, please check your physical_mesh_id again."
         assert torch.unique(self.logical_mesh_id).numel() == self.logical_mesh_id.numel(), \
             "Found duplicate IDs in the logical_mesh_id and this is not allowed, please check your logical_mesh_id again."
 
@@ -118,7 +118,7 @@ def __init__(self,
         self._global_rank_of_current_process = None
         self._is_initialized = False
 
-        # attribute used to inidicate whether this objectd
+        # attribute used to indicate whether this object
         # is created using DeviceMesh.from_process_group
         # this attribute can be used to do some check in methods
         # such get_process_group as no global rank information
@@ -395,7 +395,7 @@ def _collate_global_ranks_in_same_process_group(self, global_rank):
         Example:
 
         ```python
-        sphysical_mesh_id = torch.arange(0, 16)
+        physical_mesh_id = torch.arange(0, 16)
         mesh_shape = (4, 4)
 
         # logical mesh will look like
@@ -438,7 +438,7 @@ def _collate_global_ranks_in_same_process_group(self, global_rank):
             # the _local_rank refers to the local rank of the current process
             for _local_rank in range(self.logical_mesh_id.shape[dim]):
 
-                # if this dimension is not initailized yet,
+                # if this dimension is not initialized yet,
                 # initialize it with an empty array
                 if dim not in processes_in_the_same_process_group:
                     processes_in_the_same_process_group[dim] = []
@@ -447,7 +447,7 @@ def _collate_global_ranks_in_same_process_group(self, global_rank):
                 process_coordinates = self._global_to_local_rank_mapping[global_rank].copy()
 
                 # replace the local rank in the given dimension with the
-                # lcoal rank of the current process iterated
+                # local rank of the current process iterated
                 process_coordinates[dim] = _local_rank
                 processes_in_the_same_process_group[dim].append(process_coordinates)
 

colossalai/tensor/d_tensor/comm_spec.py

@@ -28,7 +28,7 @@ class CommSpec:
     to determine the buffer shape, and logical_process_axis
 
     Argument:
-        comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec.
+        comm_pattern(CollectiveCommPattern): describe the communication method used in this spec.
         process_group_dict(Dict): A dict which contains the process groups used to apply this CommSpec.
         gather_dim(int, Optional): The gather_dim of the tensor will be gathered.
         shard_dim(int, Optional): The shard_dim of the tensor will be sharded.

colossalai/tensor/shape_consistency.py

@@ -339,7 +339,7 @@ def get_all_mix_gather_spec(self, source_spec: ShardingSpec,
         RS01 -> RR
         '''
         valid_spec_dict = {}
-        comm_pathern = CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD
+        comm_pattern = CollectiveCommPattern.MIXGATHER_FWD_SPLIT_BWD
         tensor_dims = len(source_spec.entire_shape)
         for f_index in range(tensor_dims - 1):
             for b_index in range(f_index + 1, tensor_dims):
@@ -362,7 +362,7 @@ def get_all_mix_gather_spec(self, source_spec: ShardingSpec,
                         b_target_pair = (b_index, [])
 
                 gather_dim, logical_process_axes = mix_gather_simulator(f_target_pair, b_target_pair)
-                comm_spec = CommSpec(comm_pathern,
+                comm_spec = CommSpec(comm_pattern,
                                      sharding_spec=source_spec,
                                      gather_dim=gather_dim,
                                      logical_process_axis=logical_process_axes,

tests/kit/model_zoo/transformers/t5.py

@@ -43,7 +43,7 @@ def data_gen_for_t5_model():
 # output transform function
 output_transform_fn = lambda x: x
 
-# define loss funciton
+# define loss function
 loss_fn_for_t5_model = lambda x: x.last_hidden_state.mean()
 loss_fn_for_encoder_only = lambda x: x.last_hidden_state.mean()
 loss_fn_for_conditional_generation = lambda x: x.loss

tests/test_booster/test_plugin/test_torch_ddp_plugin.py

@@ -64,7 +64,7 @@ def check_torch_ddp_no_sync():
     model = DummyModel()
     criterion = lambda x: x.mean()
     optimizer = SGD(model.parameters(), lr=1e-3)
-    # create a custom dasetset with 0 to 10
+    # create a custom dataset with 0 to 10
     dataset = torch.arange(0, 10)
     train_dataloader = plugin.prepare_dataloader(dataset, batch_size=2)
     model, optimizer, criterion, train_dataloader, _ = booster.boost(model,

tests/test_booster/test_plugin/test_torch_fsdp_plugin.py

@@ -15,7 +15,7 @@
 from tests.kit.model_zoo import model_zoo
 
 
-# test baisc fsdp function
+# test basic fsdp function
 def run_fn(model_fn, data_gen_fn, output_transform_fn):
     plugin = TorchFSDPPlugin()
     booster = Booster(plugin=plugin)