diff --git a/deepspeed/runtime/zero/partition_parameters.py b/deepspeed/runtime/zero/partition_parameters.py
index d4df1c4647467c3d7deb2cb4c67ca00159aa2307..db986c747970f58df0e2e488e7d216306b9dd8b4 100755
--- a/deepspeed/runtime/zero/partition_parameters.py
+++ b/deepspeed/runtime/zero/partition_parameters.py
@@ -800,9 +800,9 @@ class Init(InsertPostInitMethodToModuleSubClasses):
                 f'"nvme_path" in DeepSpeed Config cannot be None if remote device is {OffloadDeviceEnum.nvme}'
 
     def _post_init_method(self, module):
-        #see_memory_usage(f"Before converting parmas in {module.__class__.__name__}", force=False)
+        #see_memory_usage(f"Before converting params in {module.__class__.__name__}", force=False)
         print_rank_0(f'Converting Params in {module.__class__.__name__}', force=False)
-        see_memory_usage(f"Before converting and partitioning parmas in {module.__class__.__name__}", force=False)
+        see_memory_usage(f"Before converting and partitioning params in {module.__class__.__name__}", force=False)
 
         global param_count
         for name, param in module.named_parameters(recurse=False):
@@ -825,7 +825,7 @@ class Init(InsertPostInitMethodToModuleSubClasses):
 
                 param.partition()
         see_memory_usage(
-            f"Param count {param_count}. After converting and partitioning parmas in {module.__class__.__name__}",
+            f"Param count {param_count}. After converting and partitioning params in {module.__class__.__name__}",
             force=False)
 
     def _convert_to_deepspeed_param(self, param):
@@ -1404,7 +1404,7 @@ class Init(InsertPostInitMethodToModuleSubClasses):
             partition_size = param.ds_tensor.ds_numel
             start = self.get_partition_rank() * partition_size
             end = start + partition_size
-            #print_rank_0("REduce scatter was executed for praam {param.ds_id}")
+            #print_rank_0("REduce scatter was executed for param {param.ds_id}")
             if start < param.ds_numel and end > param.ds_numel:
                 elements = param.ds_numel - start
                 param.grad.view(-1).narrow(0, start, elements).copy_(reduced_partition.narrow(0, 0, elements))
diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py
index 5918fe25d74e37b6150dabf40f2a3863d8de4dfa..c5359a8272823a19ece394e3f4a2658890976afb 100644
--- a/deepspeed/runtime/zero/stage3.py
+++ b/deepspeed/runtime/zero/stage3.py
@@ -892,7 +892,7 @@ class DeepSpeedZeroOptimizer_Stage3(ZeROOptimizer):
             else:
                 self.fp32_partitioned_groups_flat[i].grad = gradient_buffer.narrow(0, 0, num_elements)
 
-            # Initialize the optimizer states with the flattended fp32 partition.
+            # Initialize the optimizer states with the flattened fp32 partition.
             if not is_adagrad:
                 self._optimizer_step(i)
 
@@ -906,7 +906,7 @@ class DeepSpeedZeroOptimizer_Stage3(ZeROOptimizer):
                 f'[End] Initialize optimizer states {i} / {num_subgroups} subgroups, num_elems: {num_elements}, swappable opt/param:{swappable_optimizer_subgroup}/{swappable_param_subgroup}',
                 force=False)
 
-        # Initialize the optimizer states with the flattended fp32 partition.
+        # Initialize the optimizer states with the flattened fp32 partition.
         if is_adagrad:
             self.optimizer = torch.optim.Adagrad(self.fp32_partitioned_groups_flat, **self.optimizer.defaults)
 
diff --git a/deepspeed/runtime/zero/stage_1_and_2.py b/deepspeed/runtime/zero/stage_1_and_2.py
index 67fdcba65ea55eadc997122e147be555e34f1f57..8f15b87af0e2a0e8d534f6c2902af4eaae462d6c 100755
--- a/deepspeed/runtime/zero/stage_1_and_2.py
+++ b/deepspeed/runtime/zero/stage_1_and_2.py
@@ -611,7 +611,7 @@ class DeepSpeedZeroOptimizer(ZeROOptimizer):
             self.single_partition_of_fp32_groups[i].grad = get_accelerator().pin_memory(
                 single_grad_partition) if self.cpu_offload else single_grad_partition
 
-        # Initialize the optimizer states with the flattended fp32 partition.
+        # Initialize the optimizer states with the flattened fp32 partition.
         # State initialization for the Adagrad optimizer occurs at construction as opposed to other optimizers
         # which do lazy initialization of the state at the first call to step.
         if isinstance(self.optimizer, torch.optim.Adagrad):