fix some typo colossalai/shardformer (#4160)

colossalai/shardformer/README.md

@@ -252,7 +252,7 @@ class ModelSharder:
 
     def shard(self) -> None:
         """
-        Shard model with parallelelism with the help of pre-processing, replace_model_class, replace_module, and post-processing.
+        Shard model with parallelism with the help of pre-processing, replace_model_class, replace_module, and post-processing.
         """
         ...
 

colossalai/shardformer/layer/loss.py

@@ -48,13 +48,13 @@ class DistCrossEntropy(Function):
 
         # [down, up) => false, other device and -100 => true
         delta = (global_vocab_size + world_size - 1) // world_size
-        down_shreshold = rank * delta
-        up_shreshold = down_shreshold + delta
-        mask = (target < down_shreshold) | (target >= up_shreshold)
-        masked_target = target.clone() - down_shreshold
+        down_threshold = rank * delta
+        up_threshold = down_threshold + delta
+        mask = (target < down_threshold) | (target >= up_threshold)
+        masked_target = target.clone() - down_threshold
         masked_target[mask] = 0
 
-        # reshape the logist and target
+        # reshape the logits and target
         # reshape the vocab_logits to [bath_size * seq_len, vocab_size]
         # reshape the labels to [bath_size * seq_len]
         logits_2d = vocab_logits.view(-1, partition_vocab_size)
@@ -79,7 +79,7 @@ class DistCrossEntropy(Function):
         loss = torch.where(target == ignore_index, 0.0, torch.log(sum_exp_logits) - pred_logits)
         loss = torch.sum(loss).div_(torch.sum(loss != 0.0))
 
-        # caculate the softmax
+        # calculate the softmax
         exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
         ctx.save_for_backward(exp_logits, mask, masked_target_1d)
 

colossalai/shardformer/policies/basepolicy.py

@@ -66,7 +66,7 @@ class Policy(ABC):
     like BertPolicy for Bert Model or OPTPolicy for OPT model.
 
     Shardformer has provided many built-in sharding policies for the mainstream models. You can use the
-    built-in policies by setting `policy = None`, which is already the default arguemnt for `Shardformer.optimize`.
+    built-in policies by setting `policy = None`, which is already the default argument for `Shardformer.optimize`.
     If you want to define your own policy, you can inherit from this class and overwrite the methods you want to modify.
     """
 

colossalai/shardformer/shard/sharder.py

@@ -73,7 +73,7 @@ class ModelSharder(object):
             layer (torch.nn.Module): The object of layer to shard
             origin_cls (Union[str, torch.nn.Module]): The origin layer class or a string of layer class name.
             attr_replacement (Dict): The attribute dict to modify
-            param_replacement (List[Callable]): The function list to get parameter shard information in polic
+            param_replacement (List[Callable]): The function list to get parameter shard information in policy
             sub_module_replacement (List[Callable]): The function list to get sub module shard information in policy
         """
         if (isinstance(origin_cls, str) and origin_cls == module.__class__.__name__) or \