Refine clip_by_global_norm (#38209)

* refine clip

* delete unused code

* refine logic for clip

python/paddle/fluid/clip.py

@@ -479,29 +479,30 @@ class ClipGradByGlobalNorm(ClipGradBase):
         sum_dtype = 'float64' if len(sum_square_list) > 0 else "float32"
         global_norm_var = []
         if len(sum_square_list_fp16) > 0:
-            global_norm_var_fp16 = layers.concat(sum_square_list_fp16)
-            global_norm_var_fp16 = layers.reduce_sum(global_norm_var_fp16)
+            global_norm_var_fp16 = paddle.add_n(sum_square_list_fp16)
             global_norm_var.append(global_norm_var_fp16.astype(sum_dtype))
         if len(sum_square_list_fp32) > 0:
-            global_norm_var_fp32 = layers.concat(sum_square_list_fp32)
-            global_norm_var_fp32 = layers.reduce_sum(global_norm_var_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:
                 global_norm_var.append(global_norm_var_fp32.astype(sum_dtype))
         if len(sum_square_list) > 0:
-            global_norm_var_fp64 = layers.concat(sum_square_list)
-            global_norm_var_fp64 = layers.reduce_sum(global_norm_var_fp64)
+            global_norm_var_fp64 = paddle.add_n(sum_square_list)
             global_norm_var.append(global_norm_var_fp64)
-        global_norm_var = layers.concat(global_norm_var)
-        global_norm_var = layers.reduce_sum(global_norm_var)
+        global_norm_var = paddle.add_n(global_norm_var)
         global_norm_var = layers.sqrt(global_norm_var)
         max_global_norm = layers.fill_constant(
             shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)
+
+        # only when global_norm_var > max_global_norm, grad need clip
+        need_clip = False
+        if global_norm_var > max_global_norm:
+            need_clip = True
+
+        if need_clip:
             clip_var = layers.elementwise_div(
-            x=max_global_norm,
-            y=layers.elementwise_max(
-                x=global_norm_var, y=max_global_norm))
+                x=max_global_norm, y=global_norm_var)
         for p, g in params_grads:
             if g is None:
                 continue
@@ -509,10 +510,14 @@ class ClipGradByGlobalNorm(ClipGradBase):
                 params_and_grads.append((p, g))
                 continue
             # TODO(wangxi): use inplace elementwise_mul
+            if need_clip:
                 clip_input = (clip_var.astype('float16')
-                          if g.dtype == core.VarDesc.VarType.FP16 else clip_var)
+                              if g.dtype == core.VarDesc.VarType.FP16 else
+                              clip_var)
                 new_grad = layers.elementwise_mul(x=g, y=clip_input)
                 params_and_grads.append((p, new_grad))
+            else:
+                params_and_grads.append((p, g))
 
         return params_and_grads