delete unused code

llm/run_embedding.py

@@ -16,7 +16,7 @@
 import sys
 
 import paddle
-from utils.argument import EmbeddingArgument, GenerateArgument
+from utils.argument import EmbeddingArgument
 
 from paddlenlp.data import DataCollatorForEmbedding
 from paddlenlp.datasets import EmbeddingIterableDataset, load_dataset
@@ -39,15 +39,14 @@
 
 
 def main():
-    parser = PdArgumentParser((GenerateArgument, ModelConfig, DataConfig, SFTConfig, EmbeddingArgument))
+    parser = PdArgumentParser((ModelConfig, DataConfig, SFTConfig, EmbeddingArgument))
     if len(sys.argv) >= 2 and sys.argv[1].endswith(".json"):
-        gen_args, model_args, data_args, training_args, embedding_args = parser.parse_json_file_and_cmd_lines()
+        model_args, data_args, training_args, embedding_args = parser.parse_json_file_and_cmd_lines()
     else:
-        gen_args, model_args, data_args, training_args, embedding_args = parser.parse_args_into_dataclasses()
+        model_args, data_args, training_args, embedding_args = parser.parse_args_into_dataclasses()
 
     training_args.print_config(model_args, "Model")
     training_args.print_config(data_args, "Data")
-    training_args.print_config(gen_args, "Generation")
 
     # Setup GPU & distributed training
     paddle.set_device(training_args.device)
@@ -57,12 +56,8 @@ def main():
         + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16 or training_args.bf16}"
     )
 
-    if (
-        training_args.pipeline_parallel_degree > 1
-        or training_args.tensor_parallel_degree > 1
-        or training_args.sharding_parallel_degree > 1
-    ):
-        raise ValueError("Now embedding training only supports in data parallel mode.")
+    if training_args.pipeline_parallel_degree > 1:
+        raise NotImplementedError("Cannot support pipeline parallel for Embedding training now.")
 
     # Detecting last checkpoint.
     last_checkpoint = None
@@ -263,11 +258,6 @@ def main():
     else:
         padding = True
 
-    if training_args.pipeline_parallel_degree > 1:
-        raise NotImplementedError("Cannot support pipeline parallel for Embedding training now.")
-    else:
-        metrics = compute_metrics
-
     data_collator_fn = DataCollatorForEmbedding(
         tokenizer=tokenizer,
         max_query_len=embedding_args.max_query_len,
@@ -284,7 +274,7 @@ def main():
         train_dataset=train_ds,
         eval_dataset=dev_ds,
         tokenizer=tokenizer,
-        compute_metrics=metrics,
+        compute_metrics=compute_metrics,
         data_collator=data_collator_fn,
     )
     trainable_parameters = [p for p in model.parameters() if not p.stop_gradient]
@@ -298,53 +288,10 @@ def main():
         elif last_checkpoint is not None:
             checkpoint = last_checkpoint
         train_result = trainer.train(resume_from_checkpoint=checkpoint)
-        if training_args.benchmark:
-            total_effective_tokens = (
-                sum([len(i["input_ids"]) for i in trainer.train_dataset]) * train_result.metrics["progress_or_epoch"]
-            )
-            effective_tokens_per_second = total_effective_tokens / train_result.metrics["train_runtime"]
-            logger.info(f"Effective_Tokens_per_second: {effective_tokens_per_second} ")
-            logger.info("Benchmark done.")
-        else:
-            if model_args.save_to_aistudio:
-                kwargs = {}
-                if model_args.aistudio_token is not None:
-                    kwargs["token"] = model_args.aistudio_token
-                # PEFT Model only save PEFT parameters, if pretrained model obtains from aistudio
-                if model_args.from_aistudio and (model_args.lora or model_args.prefix_tuning):
-                    kwargs["base_model"] = model_args.model_name_or_path
-                else:
-                    trainer.tokenizer.save_to_aistudio(
-                        repo_id=model_args.aistudio_repo_id,
-                        private=model_args.aistudio_repo_private,
-                        license=model_args.aistudio_repo_license,
-                        exist_ok=True,
-                        **kwargs,
-                    )
-                trainer.model.save_to_aistudio(
-                    repo_id=model_args.aistudio_repo_id,
-                    private=model_args.aistudio_repo_private,
-                    license=model_args.aistudio_repo_license,
-                    merge_tensor_parallel=training_args.tensor_parallel_degree > 1,
-                    exist_ok=True,
-                    **kwargs,
-                )
-
-            if not training_args.autotuner_benchmark:
-                trainer.save_model(merge_tensor_parallel=training_args.tensor_parallel_degree > 1)
-                trainer.log_metrics("train", train_result.metrics)
-                trainer.save_metrics("train", train_result.metrics)
-                trainer.save_state()
-
-    # Evaluation test set
-    if training_args.do_predict:
-        test_ds = load_dataset(
-            "json",
-            data_files=os.path.join(data_args.dataset_name_or_path, "test.json"),
-            lazy=data_args.lazy,
-        )[0]
-        eval_result = trainer.predict(test_ds).metrics
-        trainer.log_metrics("test", eval_result)
+        trainer.save_model(merge_tensor_parallel=training_args.tensor_parallel_degree > 1)
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
 
     # Evaluation dev set
     if training_args.do_eval: