diff --git a/composer/algorithms/gradient_clipping/gradient_clipping.py b/composer/algorithms/gradient_clipping/gradient_clipping.py
index accb88daf6..973d12914a 100644
--- a/composer/algorithms/gradient_clipping/gradient_clipping.py
+++ b/composer/algorithms/gradient_clipping/gradient_clipping.py
@@ -14,7 +14,6 @@
 from composer.core import Algorithm, Event, State
 from composer.loggers import Logger
 from composer.models import ComposerModel
-from composer.utils import using_torch_2
 
 log = logging.getLogger(__name__)
 
@@ -44,38 +43,24 @@ def apply_gradient_clipping(model: Union[ComposerModel, torch.nn.Module], clippi
             raise RuntimeError('To use FSDP with Composer, you must use torch>=1.13.0.')
         from torch.distributed.fsdp import FullyShardedDataParallel
 
-        is_torch_2_0 = using_torch_2()
-
         for module in model.modules():
-            if isinstance(module, FullyShardedDataParallel):
-                # We can only call grad clip on the parent instance, so we iterate through all
-                # modules and try grad clipping and FSDP will throw an exception if we
-                # clip any gradients that aren't a parent module
-                try:
-                    if clipping_type == 'norm':
-                        module.clip_grad_norm_(max_norm=clipping_threshold)
-                    elif clipping_type == 'value':
-                        module.clip_grad_norm_(max_norm=clipping_threshold, norm_type=float('inf'))
-                    else:
-                        raise ValueError(f"clipping type must be 'norm' or 'value' with FSDP not {clipping_type}")
-                except (AssertionError, RuntimeError) as e:
-                    if (('clip_grad_norm should only be called on the root (parent) instance' == str(e) and
-                         not is_torch_2_0) or
-                        ('`clip_grad_norm_()` should only be called on the root FSDP instance' == str(e) and
-                         is_torch_2_0)):
-                        continue
-                    else:
-                        raise
-        return
-    parameters = model.parameters()
-    if clipping_type == 'adaptive':
-        _apply_agc(parameters, clipping_threshold=clipping_threshold)
-    elif clipping_type == 'norm':
-        torch.nn.utils.clip_grad_norm_(parameters, max_norm=clipping_threshold)
-    elif clipping_type == 'value':
-        torch.nn.utils.clip_grad_value_(parameters, clip_value=clipping_threshold)
+            if isinstance(module, FullyShardedDataParallel) and module.check_is_root():
+                if clipping_type == 'norm':
+                    module.clip_grad_norm_(max_norm=clipping_threshold)
+                elif clipping_type == 'value':
+                    module.clip_grad_norm_(max_norm=clipping_threshold, norm_type=float('inf'))
+                else:
+                    raise ValueError(f"clipping type must be 'norm' or 'value' with FSDP not {clipping_type}")
     else:
-        raise ValueError(f"clipping_type must be 'adaptive', 'norm', or 'value' not {clipping_type} ")
+        parameters = model.parameters()
+        if clipping_type == 'adaptive':
+            _apply_agc(parameters, clipping_threshold=clipping_threshold)
+        elif clipping_type == 'norm':
+            torch.nn.utils.clip_grad_norm_(parameters, max_norm=clipping_threshold)
+        elif clipping_type == 'value':
+            torch.nn.utils.clip_grad_value_(parameters, clip_value=clipping_threshold)
+        else:
+            raise ValueError(f"clipping_type must be 'adaptive', 'norm', or 'value' not {clipping_type} ")
 
 
 def _apply_agc(