fix load best

paddlenlp/trainer/trainer.py

@@ -89,6 +89,7 @@
 from ..transformers.model_utils import (
     PretrainedModel,
     _add_variant,
+    _load_state_dict_into_model,
     load_sharded_checkpoint,
     unwrap_model,
 )
@@ -149,6 +150,7 @@
 from .utils import reshard as reshard_util
 from .utils.async_save import AsyncSaver
 from .utils.helper import (  # nested_truncate,
+    broadcast_dataset_rank0_model,
     broadcast_dp_optimizer,
     broadcast_moe_optimizer,
     distributed_concat,
@@ -1161,6 +1163,10 @@ def fused_allreduce_gradients_no_sync(paramlist, hcg):
                         self.state.best_model_checkpoint,
                         safe_serialization=True,
                     )
+                    if self.args.sharding_parallel_degree > 1 or self.args.data_parallel_degree > 1:
+                        state_dict = broadcast_dataset_rank0_model(self.model.state_dict())
+                    if self.args.dataset_rank > 0:
+                        _load_state_dict_into_model(self.model, state_dict, "")
                 else:
                     weight_name = PADDLE_WEIGHTS_NAME
                     best_model_path = os.path.join(
@@ -1203,6 +1209,10 @@ def _load_best_model_from_peft_checkpoint(self):
                 self.state.best_model_checkpoint,
                 safe_serialization=True,
             )
+            if self.args.sharding_parallel_degree > 1 or self.args.data_parallel_degree > 1:
+                state_dict = broadcast_dataset_rank0_model(self.model.get_trainable_state_dict())
+            if self.args.dataset_rank > 0:
+                _load_state_dict_into_model(self.model, state_dict, "")
             return
 
         convert_tp = False

paddlenlp/trainer/utils/helper.py

@@ -309,3 +309,21 @@ def _broadcast_moe_optimizer_state(state_dict):
         state_dict = base_state_dict
         del base_state_dict
     return state_dict
+
+
+def broadcast_dataset_rank0_model(state_dict):
+    if paddle.distributed.get_world_size() <= 1:
+        return state_dict
+
+    logger.info("Start broadcast model in sharding group or data parallel group.")
+    hcg = fleet.get_hybrid_communicate_group()
+    sharding_group = hcg.get_sharding_parallel_group()
+    dp_group = hcg.get_data_parallel_group()
+
+    if sharding_group.nranks > 1:
+        for k in state_dict.keys():
+            dist.broadcast(state_dict[k], src=hcg.get_sharding_parallel_group_src_rank(), group=sharding_group)
+    if dp_group.nranks > 1:
+        for k in state_dict.keys():
+            dist.broadcast(state_dict[k], src=hcg.get_data_parallel_group_src_rank(), group=dp_group)
+    return state_dict