[fix] Broadcast optimizer state using broadcast_dp without shard-resh…

paddlenlp/trainer/trainer.py

@@ -582,7 +582,7 @@
                         weights_index_file,
                     ]
                 ):
-                    raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint}")
+                    raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint} -- {weights_file}")
 
                 logger.info(f"Loading model from {resume_from_checkpoint} .")
 
@@ -2237,7 +2237,7 @@
                             safe_serialization=True,
                         )
                     else:
-                        if self.dp_group.rank > 0:
+                        if self.args.data_parallel_rank > 0 and self.args.use_expert_parallel:
                             self._save_ckpt_func(
                                 self._filter_moe_no_sync_optimizer_params(), os.path.join(output_dir, OPTIMIZER_NAME)
                             )
@@ -2525,7 +2525,9 @@
         if self.args.local_rank != -1:
             dist.barrier()
         if self.args.use_expert_parallel:
-            opt_state_dict = broadcast_moe_optimizer(opt_state_dict)
+            opt_state_dict = broadcast_moe_optimizer(
+                opt_state_dict, broadcast_dp=not self.args.should_load_sharding_stage1_model
+            )
         else:
             if not self.args.should_load_sharding_stage1_model:
                 opt_state_dict = broadcast_dp_optimizer(opt_state_dict)

paddlenlp/trainer/utils/helper.py

@@ -228,7 +228,7 @@
     return state_dict
 
 
-def broadcast_moe_optimizer(state_dict):
+def broadcast_moe_optimizer(state_dict, broadcast_dp=True):
 
     try:
         hcg = fleet.get_hybrid_communicate_group()
@@ -270,7 +270,10 @@
         base_state_dict.update(buf[2])
         return base_state_dict
 
-    base_state_dict = _broadcast_moe_optimizer_state(state_dict)
+    if broadcast_dp:
+        base_state_dict = broadcast_dp_optimizer(state_dict)
+    else:
+        base_state_dict = _broadcast_moe_optimizer_state(state_dict)
     if data_parallel_rank > 0:
         master_weight = state_dict.pop("master_weights", {})
         base_state_dict.update(state_dict)