diff --git a/python/paddle/fluid/dygraph/amp/auto_cast.py b/python/paddle/fluid/dygraph/amp/auto_cast.py
index 721d10d76b128639fa4d1b124bc002f8df5138af..1f644147a209bd067718fa568c8f8939314c329c 100644
--- a/python/paddle/fluid/dygraph/amp/auto_cast.py
+++ b/python/paddle/fluid/dygraph/amp/auto_cast.py
@@ -252,14 +252,26 @@ def check_models(models):
             )
 
 
+def _is_valid_optimizer(optimizer):
+    from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.dygraph_sharding_optimizer import (
+        DygraphShardingOptimizer,
+    )
+
+    return isinstance(
+        optimizer,
+        (
+            paddle.optimizer.Optimizer,
+            paddle.fluid.optimizer.Optimizer,
+            DygraphShardingOptimizer,
+        ),
+    )
+
+
 def check_optimizers(optimizers):
     for optimizer in optimizers:
-        if not isinstance(
-            optimizer,
-            (paddle.optimizer.Optimizer, paddle.fluid.optimizer.Optimizer),
-        ):
+        if not _is_valid_optimizer(optimizer):
             raise RuntimeError(
-                "Current train mode is pure fp16, optimizers should be paddle.optimizer.Optimizer or paddle.fluid.optimizer.Optimizer, but receive {}.".format(
+                "Current train mode is pure fp16, optimizers should be paddle.optimizer.Optimizer or paddle.fluid.optimizer.Optimizer or DygraphShardingOptimizer, but receive {}.".format(
                     type(optimizer)
                 )
             )
@@ -477,6 +489,20 @@ class StateDictHook:
                     state_dict[key] = param_applied
 
 
+def _set_multi_precision(optimizer, multi_precision):
+    from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.dygraph_sharding_optimizer import (
+        DygraphShardingOptimizer,
+    )
+
+    optimizer = (
+        optimizer._inner_optimizer
+        if isinstance(optimizer, DygraphShardingOptimizer)
+        else optimizer
+    )
+    if hasattr(optimizer, "_multi_precision"):
+        optimizer._multi_precision = multi_precision
+
+
 @dygraph_only
 def amp_decorate(
     models,
@@ -582,10 +608,7 @@ def amp_decorate(
     if optimizers is not None:
         # check optimizers
         optimizers_is_list = False
-        if isinstance(
-            optimizers,
-            (paddle.optimizer.Optimizer, paddle.fluid.optimizer.Optimizer),
-        ):
+        if _is_valid_optimizer(optimizers):
             optimizers_is_list = False
             optimizers = [optimizers]
             check_optimizers(optimizers)
@@ -596,13 +619,10 @@ def amp_decorate(
             raise TypeError(
                 "optimizers must be either a single optimizer or a list of optimizers."
             )
-        # supprot master_weight
-        for idx_opt in range(len(optimizers)):
-            if hasattr(optimizers[idx_opt], '_multi_precision'):
-                if master_weight is False:
-                    optimizers[idx_opt]._multi_precision = False
-                else:
-                    optimizers[idx_opt]._multi_precision = True
+        # support master_weight
+        use_multi_precision = not (master_weight is False)
+        for opt in optimizers:
+            _set_multi_precision(opt, use_multi_precision)
 
     if save_dtype is not None:
         if not (save_dtype in ['float16', 'bfloat16', 'float32', 'float64']):