[nfc] fix typo colossalai/ applications/ (#3831)

* fix typo colossalai/autochunk auto_parallel amp

* fix typo colossalai/auto_parallel nn utils etc.

* fix typo colossalai/auto_parallel autochunk fx/passes  etc.

* fix typo docs/

* change placememt_policy to placement_policy in docs/ and examples/

* fix typo colossalai/ applications/

applications/Chat/coati/ray/src/detached_replay_buffer.py

@@ -34,7 +34,7 @@ class DetachedReplayBuffer:
         '''
         Workers in the same tp group share this buffer and need same sample for one step.
             Therefore a held_sample should be returned tp_world_size times before it could be dropped.
-            worker_state records wheter a worker got the held_sample
+            worker_state records whether a worker got the held_sample
         '''
         self.tp_world_size = tp_world_size
         self.worker_state = [False] * self.tp_world_size

applications/Chat/coati/ray/src/experience_maker_holder.py

@@ -22,7 +22,7 @@ from .utils import is_rank_0, get_strategy_from_args, set_dist_env
 class ExperienceMakerHolder:
     '''
     Args:
-        detached_trainer_name_list: str list to get ray actor handleskkk
+        detached_trainer_name_list: str list to get ray actor handles
         strategy: 
         experience_batch_size: batch size of generated experience
         kl_coef: the coefficient of kl divergence loss

applications/Chat/coati/ray/src/pipeline_strategy.py

@@ -26,7 +26,7 @@ rpc_is_initialized = _is_current_rpc_agent_set
 class PipelineModel(torch.nn.Module):
     '''
     Actor has 2 kinds of jobs: forward and generate. 
-        better to just pipelinize the inner model
+        better to just pipeline the inner model
     '''
     def __init__(self,
                  model: torch.nn.Module,

applications/Chat/evaluate/evaluator.py

@@ -119,7 +119,7 @@ class Evaluator(object):
             jdump(all_evaluations,
                   os.path.join(evaluation_results_save_path, f"{model_name_list[0]}_evaluation_results.json"))
 
-            # Start to calculate scores and save statictics.
+            # Start to calculate scores and save statistics.
             evaluation_statistics_save_path = os.path.join(base_save_path, "evaluation_statistics")
             gpt_evaluate.save_gpt35_evaluation_statistics(model_name_list[0], all_evaluations,
                                                           evaluation_statistics_save_path)

applications/Chat/evaluate/metrics.py

@@ -111,7 +111,7 @@ def calculate_precision_recall_f1(preds: list, targets: list) -> dict:
     The calculation of precision, recall and f1-score is realized by counting
     the number f overlaps between the preds and target. The comparison length
     limited by the shorter one of preds and targets. This design is mainly
-    considered for classifiction and extraction categories.
+    considered for classification and extraction categories.
     """
     precision_recall_f1 = {"precision": 0, "recall": 0, "f1_score": 0}
     precision_scores = []
@@ -138,7 +138,7 @@ def calculate_precision_recall_f1(preds: list, targets: list) -> dict:
 
 def precision(preds: list, targets: list) -> dict:
     """Calculate Precision Metric
-    (design for classifiction and extraction categories)
+    (design for classification and extraction categories)
 
     Calculating precision by counting the number of overlaps between the preds and target.
     """
@@ -149,7 +149,7 @@ def precision(preds: list, targets: list) -> dict:
 
 def recall(preds: list, targets: list) -> dict:
     """Calculate Recall Metric
-    (design for classifiction and extraction categories)
+    (design for classification and extraction categories)
 
     Calculating recall by counting the number of overlaps between the preds and target.
     """
@@ -160,7 +160,7 @@ def recall(preds: list, targets: list) -> dict:
 
 def F1_score(preds: list, targets: list) -> dict:
     """Calculate F1-score Metric
-    (design for classifiction and extraction categories)
+    (design for classification and extraction categories)
 
     Calculating f1-score by counting the number of overlaps between the preds and target.
     """

colossalai/auto_parallel/tensor_shard/node_handler/matmul_handler.py

@@ -206,7 +206,7 @@ class Broadcaster(BmmTransform):
                     # e.g. [1, 2, 4] x [4, 4, 8] -> [4, 2, 8]
                     # the dim 0 of [1, 2, 4] is multiplied to 4
                     tensor_shape[dim_idx] = 1
-                elif broadcast_type == BroadcastType.PADDDING:
+                elif broadcast_type == BroadcastType.PADDING:
                     # if the dim is padded
                     # we remove its sharding
                     tensor_shape[dim_idx] = None

colossalai/auto_parallel/tensor_shard/utils/broadcast.py

@@ -21,7 +21,7 @@ __all__ = [
 
 class BroadcastType(Enum):
     EQUAL = auto()
-    PADDDING = auto()
+    PADDING = auto()
     MULTIPLE = auto()
 
 
@@ -69,18 +69,18 @@ def get_broadcast_dim_info(logical_shape, physical_shape):
     for i in range(logical_num_dims):
         # get the trailing dim size
         logical_dim_idx = logical_num_dims - i - 1
-        phyiscal_dim_idx = physical_num_dims - i - 1
+        physical_dim_idx = physical_num_dims - i - 1
         logical_dim_size = logical_shape[logical_dim_idx]
 
-        if phyiscal_dim_idx >= 0:
-            physical_dim_size = physical_shape[phyiscal_dim_idx]
+        if physical_dim_idx >= 0:
+            physical_dim_size = physical_shape[physical_dim_idx]
 
             if physical_dim_size == logical_dim_size:
                 logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.EQUAL
             elif physical_dim_size == 1 and physical_dim_size != logical_dim_size:
                 logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.MULTIPLE
         else:
-            logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.PADDDING
+            logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.PADDING
 
     return logical_dim_broadcast_info
 
@@ -117,7 +117,7 @@ def recover_sharding_spec_for_broadcast_shape(logical_sharding_spec: ShardingSpe
     for shape_dim, mesh_dim in logical_dim_partition.items():
         logical_broadcast_type = logical_dim_broadcast_info[shape_dim]
 
-        if logical_broadcast_type == BroadcastType.PADDDING or logical_broadcast_type == BroadcastType.MULTIPLE:
+        if logical_broadcast_type == BroadcastType.PADDING or logical_broadcast_type == BroadcastType.MULTIPLE:
             removed_dims.extend(mesh_dim)
         else:
             # get the corresponding physical dim