diff --git a/python/paddle/distributed/passes/auto_parallel_amp.py b/python/paddle/distributed/passes/auto_parallel_amp.py
index ac533db098c61..a7782b6d8d130 100644
--- a/python/paddle/distributed/passes/auto_parallel_amp.py
+++ b/python/paddle/distributed/passes/auto_parallel_amp.py
@@ -232,6 +232,11 @@ def build_state(self):
         return is_train
 
     def _mark_black_white_ops(self, op, ops, block):
+        # deal auto_cast info
+        if not op.amp_options.enable:
+            self._op_fp16_dict[op.desc.original_id()] = False
+            return
+
         # ernie inference trick
         if op.type == "assign" and "array_" in op.input_arg_names[0]:
             self._op_fp16_dict[op.desc.original_id()] = False
diff --git a/python/paddle/distributed/passes/auto_parallel_fp16.py b/python/paddle/distributed/passes/auto_parallel_fp16.py
index 2985b4da290f4..92259dee3ae05 100644
--- a/python/paddle/distributed/passes/auto_parallel_fp16.py
+++ b/python/paddle/distributed/passes/auto_parallel_fp16.py
@@ -209,6 +209,9 @@ def _build_state(self):
         for block in self.program.blocks:
             self.resolute_tensor_dtype(block)
 
+        for block in self.program.blocks:
+            self.resolute_cast_op(block)
+
         # insert cast ops
         for block in self.program.blocks:
             self.cast_block(block)
@@ -296,6 +299,19 @@ def set_var_to_fp16(self, var_name, block):
         if var.dtype == core.VarDesc.VarType.FP32:
             var.desc.set_dtype(__target_dtype__)
 
+    def resolute_cast_op(self, block):
+        """
+        Deal the "cast_op" from "FP32" to "FP16" or "BF16" in the model.
+        """
+        for op in block.ops:
+            if op.type == "cast":
+                in_name = op.input('X')[0]
+                out_name = op.output('Out')[0]
+                in_var = block._find_var_recursive(in_name)
+                out_var = block._find_var_recursive(out_name)
+                op._set_attr("in_dtype", in_var.dtype)
+                op._set_attr("out_dtype", out_var.dtype)
+
     def resolute_tensor_dtype(self, block):
         for op in block.ops:
             # 'amp_options' flag has highest priority