[Trainer] ignore_save_lr_and_optim

paddlenlp/trainer/trainer.py

@@ -2100,36 +2100,38 @@ def _save_checkpoint(self, model, metrics=None):
         else:
             self.save_model(output_dir)
 
-        optimizer_name = _add_variant(OPTIMIZER_NAME, self.args.optimizer_name_suffix)
+        # only save model state dict, ignore optimizer and scheduler
+        if not self.args.ignore_save_lr_and_optim:
+            optimizer_name = _add_variant(OPTIMIZER_NAME, self.args.optimizer_name_suffix)
 
-        if self.args.use_hybrid_parallel:
-            if self.dp_group.rank <= 0:
-                os.makedirs(output_dir, exist_ok=True)
-                logger.info("Saving optimizer files.")
-                if self.args.unified_checkpoint:
-                    save_unified_optimizer(
-                        self.args,
-                        self.model,
-                        self.optimizer,
-                        output_dir,
-                        safe_serialization=True,
-                    )
-                else:
-                    self._save_ckpt_func(
-                        self.optimizer.state_dict(),
-                        os.path.join(output_dir, optimizer_name),
-                    )
+            if self.args.use_hybrid_parallel:
+                if self.dp_group.rank <= 0:
+                    os.makedirs(output_dir, exist_ok=True)
+                    logger.info("Saving optimizer files.")
+                    if self.args.unified_checkpoint:
+                        save_unified_optimizer(
+                            self.args,
+                            self.model,
+                            self.optimizer,
+                            output_dir,
+                            safe_serialization=True,
+                        )
+                    else:
+                        self._save_ckpt_func(
+                            self.optimizer.state_dict(),
+                            os.path.join(output_dir, optimizer_name),
+                        )
 
-        if self.args.should_save:
-            if not self.args.use_hybrid_parallel:
-                logger.info("Saving optimizer files.")
-                self._save_ckpt_func(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
+            if self.args.should_save:
+                if not self.args.use_hybrid_parallel:
+                    logger.info("Saving optimizer files.")
+                    self._save_ckpt_func(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
 
-            # FIXME: maybe only save one copy
-            paddle.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
+                # FIXME: maybe only save one copy
+                paddle.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
 
-            if self.do_grad_scaling:
-                paddle.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
+                if self.do_grad_scaling:
+                    paddle.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
 
         self.runtime_timer.stop()
         # Determine the new best metric / best model checkpoint

paddlenlp/trainer/training_args.py

@@ -751,6 +751,10 @@ class TrainingArguments:
         default=False,
         metadata={"help": "whether to ignore load optimizer and scheduler."},
     )
+    ignore_save_lr_and_optim: Optional[bool] = field(
+        default=False,
+        metadata={"help": "whether to ignore save optimizer and scheduler."},
+    )
     force_reshard_pp: Optional[bool] = field(
         default=False,
         metadata={"help": "reshard pp even if pp degree in the model and pp degree in script match"},

tests/fixtures/llm/finetune.yaml

@@ -26,6 +26,8 @@ finetune:
     save_total_limit: 1
     tensor_parallel_degree: 1
     pipeline_parallel_degree: 1
+    ignore_save_lr_and_optim: 1
+
   default:
     llama:
       model_name_or_path: __internal_testing__/tiny-random-llama

tests/fixtures/llm/pretrain.yaml

@@ -18,6 +18,7 @@ pretrain:
     use_flash_attention: 0
     use_fused_rms_norm: 0
     continue_training: 1
+
   default:
     llama:
       model_name_or_path: __internal_testing__/tiny-random-llama

tests/fixtures/llm/ptq.yaml

@@ -12,6 +12,7 @@ ptq:
     eval_with_do_generation: false
     do_ptq: true
     ptq_step: 4
+
   default:
     llama:
       model_name_or_path: __internal_testing__/tiny-fused-llama-inference5.2