[Distributed] metric calculation supports tp logits

Author: SylarTiaNII

llm/finetune_generation.py

@@ -140,7 +140,7 @@ def main():
         if not training_args.autotuner_benchmark:
             model = AutoModelForCausalLMPipe.from_pretrained(
                 model_args.model_name_or_path,
-                tensor_parallel_output=False,
+                tensor_parallel_output=training_args.tensor_parallel_output,
                 tensor_parallel_degree=training_args.tensor_parallel_degree,
                 tensor_parallel_rank=training_args.tensor_parallel_rank,
                 use_flash_attention=model_args.use_flash_attention,
@@ -152,7 +152,7 @@ def main():
             # NOTE(gongenlei): new add autotuner_benchmark
             model_config = AutoConfig.from_pretrained(
                 model_args.model_name_or_path,
-                tensor_parallel_output=False,
+                tensor_parallel_output=training_args.tensor_parallel_output,
                 tensor_parallel_degree=training_args.tensor_parallel_degree,
                 tensor_parallel_rank=training_args.tensor_parallel_rank,
                 dtype=dtype,
@@ -163,7 +163,7 @@ def main():
     else:
         model_config = AutoConfig.from_pretrained(
             model_args.model_name_or_path,
-            tensor_parallel_output=False,
+            tensor_parallel_output=training_args.tensor_parallel_output,
             tensor_parallel_degree=training_args.tensor_parallel_degree,
             tensor_parallel_rank=training_args.tensor_parallel_rank,
             dtype=dtype,

llm/utils.py

@@ -211,6 +211,12 @@ def prediction_step(
             # keepdim in order to maintain the same shape as logits
             if isinstance(logits, (list, tuple)):
                 logits = logits[0]
+            if self.args.tensor_parallel_degree > 1 and self.args.tensor_parallel_output:
+                hcg = fleet.get_hybrid_communicate_group()
+                model_parallel_group = hcg.get_model_parallel_group()
+                gathered_logits = []
+                dist.all_gather(gathered_logits, logits, group=model_parallel_group)
+                logits = paddle.concat(gathered_logits, axis=0)
             return (loss, logits.argmax(axis=-1, keepdim=True), labels)
 
         loss = None

paddlenlp/trainer/training_args.py

@@ -793,6 +793,10 @@ class TrainingArguments:
         default=False,
         metadata={"help": "whether to run distributed training in auto parallel mode"},
     )
+    tensor_parallel_output: Optional[bool] = field(
+        default=False,
+        metadata={"help": "whether to output logits in distributed status"},
+    )
 
     def __post_init__(self):
         env_local_rank = int(os.environ.get("PADDLE_RANK_IN_NODE", -1))