diff --git a/python/paddle/fluid/clip.py b/python/paddle/fluid/clip.py
index d803de22606e16d4e66e8d02db4a06ed8afb3ef4..2f38cd978889b6e44d0a9c82b8644067e6af29a7 100644
--- a/python/paddle/fluid/clip.py
+++ b/python/paddle/fluid/clip.py
@@ -420,6 +420,20 @@ def _allow_pure_fp16_global_norm_clip(*args):
         return old_value
 
 
+_allow_pure_bf16_global_norm_clip_flag = False
+
+
+def _allow_pure_bf16_global_norm_clip(*args):
+    global _allow_pure_bf16_global_norm_clip_flag
+    if len(args) == 0:
+        return _allow_pure_bf16_global_norm_clip_flag
+    else:
+        assert len(args) == 1 and isinstance(args[0], bool)
+        old_value = _allow_pure_bf16_global_norm_clip_flag
+        _allow_pure_bf16_global_norm_clip_flag = args[0]
+        return old_value
+
+
 class ClipGradByGlobalNorm(ClipGradBase):
     r"""
     Given a list of Tensor :math:`t\_list` , calculate the global norm for the elements of all tensors in
@@ -584,6 +598,7 @@ class ClipGradByGlobalNorm(ClipGradBase):
         params_and_grads = []
         sum_square_list = []
         sum_square_list_fp16 = []
+        sum_square_list_bf16 = []
         sum_square_list_fp32 = []
         with framework.name_scope('gradient_clip'):
             for p, g in params_grads:
@@ -598,18 +613,27 @@ class ClipGradByGlobalNorm(ClipGradBase):
                         merge_grad = layers.get_tensor_from_selected_rows(
                             merge_grad
                         )
+
                     sum_square = _squared_l2_norm(merge_grad)
                     if sum_square.dtype == core.VarDesc.VarType.FP16:
                         sum_square_list_fp16.append(sum_square)
+                    elif sum_square.dtype == core.VarDesc.VarType.BF16:
+                        sum_square_list_bf16.append(sum_square)
                     elif sum_square.dtype == core.VarDesc.VarType.FP32:
                         sum_square_list_fp32.append(sum_square)
                     else:
                         sum_square_list.append(sum_square)
 
+            if len(sum_square_list_fp16) > 0 and len(sum_square_list_bf16) > 0:
+                raise NotSupportedError(
+                    'FP16 and BF16 are not supported at the same time.'
+                )
+
             # all parameters have been filterd out
             if (
                 len(sum_square_list)
                 + len(sum_square_list_fp16)
+                + len(sum_square_list_bf16)
                 + len(sum_square_list_fp32)
                 == 0
             ):
@@ -620,7 +644,7 @@ class ClipGradByGlobalNorm(ClipGradBase):
 
                 global_norm_var = []
                 if len(sum_square_list_fp16) > 0:
-                    global_norm_var_fp16 = layers.sums(sum_square_list_fp16)
+                    global_norm_var_fp16 = paddle.add_n(sum_square_list_fp16)
                     if (
                         sum_square_list_fp32
                         or sum_square_list
@@ -631,8 +655,20 @@ class ClipGradByGlobalNorm(ClipGradBase):
                         )
                     else:
                         global_norm_var.append(global_norm_var_fp16)
+                if len(sum_square_list_bf16) > 0:
+                    global_norm_var_bf16 = paddle.add_n(sum_square_list_bf16)
+                    if (
+                        sum_square_list_fp32
+                        or sum_square_list
+                        or not _allow_pure_bf16_global_norm_clip()
+                    ):
+                        global_norm_var.append(
+                            global_norm_var_bf16.astype(sum_dtype)
+                        )
+                    else:
+                        global_norm_var.append(global_norm_var_bf16)
                 if len(sum_square_list_fp32) > 0:
-                    global_norm_var_fp32 = layers.sums(sum_square_list_fp32)
+                    global_norm_var_fp32 = paddle.add_n(sum_square_list_fp32)
                     if sum_dtype == 'float32':
                         global_norm_var.append(global_norm_var_fp32)
                     else:
@@ -641,23 +677,24 @@ class ClipGradByGlobalNorm(ClipGradBase):
                         )
                 if len(sum_square_list) > 0:
                     # fp64
-                    global_norm_var_other_dtype = layers.sums(sum_square_list)
+                    global_norm_var_other_dtype = paddle.add_n(sum_square_list)
                     global_norm_var.append(global_norm_var_other_dtype)
 
                 global_norm_var = (
-                    layers.sums(global_norm_var)
+                    paddle.add_n(global_norm_var)
                     if len(global_norm_var) > 1
                     else global_norm_var[0]
                 )
-                global_norm_var = layers.sqrt(x=global_norm_var)
-                max_global_norm = layers.fill_constant(
-                    shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm
+
+                global_norm_var = paddle.sqrt(x=global_norm_var)
+                max_global_norm = paddle.full(
+                    shape=[1],
+                    dtype=global_norm_var.dtype,
+                    fill_value=self.clip_norm,
                 )
-                scale_var = layers.elementwise_div(
+                scale_var = paddle.divide(
                     x=max_global_norm,
-                    y=layers.elementwise_max(
-                        x=max_global_norm, y=global_norm_var
-                    ),
+                    y=paddle.maximum(x=max_global_norm, y=global_norm_var),
                 )
             param_new_grad_name_dict = dict()
             for p, g in params_grads:
@@ -671,9 +708,8 @@ class ClipGradByGlobalNorm(ClipGradBase):
                     new_g = _cast_to_mp_type_if_enabled(g)
                     # inplace
                     scale_input = (
-                        scale_var.astype('float16')
-                        if new_g.dtype == core.VarDesc.VarType.FP16
-                        and scale_var.dtype != core.VarDesc.VarType.FP16
+                        scale_var.astype(new_g.dtype)
+                        if scale_var.dtype != new_g.dtype
                         else scale_var
                     )
                     # NOTE(Yuang Liu): For pure dp with gradient merge, the p and g