[hybrid performance] optim the grad fuse for pipeline mode by sorting…

… the grad by dtype (#35070)

python/paddle/fluid/optimizer.py

@@ -5216,6 +5216,9 @@ def _accumulate_gradients_with_fuse(self, main_block, fp16, fused_size):
         if len(grad_param_pairs) == 0:
             return
 
+        grad_param_pairs = self._sort_grad_param_by_dtype(main_block,
+                                                          grad_param_pairs)
+
         grad_param_segments = []
         merged_suffix = '@MERGED@FP16' if fp16 else '@MERGED'
         dtype = paddle.float16 if fp16 else paddle.float32
@@ -5409,6 +5412,24 @@ def _accumulate_gradients_with_fuse(self, main_block, fp16, fused_size):
 
         return fused_merged_gradients
 
+    def _sort_grad_param_by_dtype(self, main_block, grad_param_pairs):
+        # sort the grad param paris by the dtype
+        fp16_pairs = []
+        fp32_pairs = []
+        other_pairs = []
+        for pairs in grad_param_pairs:
+            dtype = main_block.var(pairs[0]).dtype
+            if dtype == paddle.float32:
+                fp32_pairs.append(pairs)
+            elif dtype == paddle.float16:
+                fp16_pairs.append(pairs)
+            else:
+                other_pairs.append(pairs)
+        sorted_pairs = fp16_pairs
+        sorted_pairs.extend(fp32_pairs)
+        sorted_pairs.extend(other_pairs)
+        return sorted_pairs
+
     def _get_var_size(self, var):
         dtype_to_size = {
             core.VarDesc.VarType.FP16: 2,