optionally configure sample packing for evals (#589)

src/axolotl/utils/trainer.py

@@ -117,6 +117,10 @@ class AxolotlTrainingArguments(TrainingArguments):
         default=False,
         metadata={"help": "Use sample packing for efficient training."},
     )
+    eval_sample_packing: Optional[bool] = field(
+        default=None,
+        metadata={"help": "Use sample packing for efficient evals."},
+    )
     sample_packing_efficiency: float = field(
         default=1.0,
         metadata={"help": "Sample packing efficiency for calculating batch length."},
@@ -212,7 +216,11 @@ class AxolotlTrainer(Trainer):
     def _get_eval_sampler(
         self, eval_dataset: Dataset
     ) -> Optional[torch.utils.data.Sampler]:
-        if self.args.world_size > 1 and self.args.sample_packing:
+        if (
+            self.args.world_size > 1
+            and self.args.sample_packing
+            and self.args.eval_sample_packing is not False
+        ):
             return SequentialDistributedSampler(
                 eval_dataset,
                 num_replicas=self.args.world_size,
@@ -241,7 +249,7 @@ class AxolotlTrainer(Trainer):
     def get_eval_dataloader(
         self, eval_dataset: Optional[Dataset] = None
     ) -> Union[DataLoader, MultipackDistributedDataloader]:
-        if self.args.sample_packing:
+        if self.args.sample_packing and self.args.eval_sample_packing is not False:
             eval_dataset = (
                 eval_dataset if eval_dataset is not None else self.eval_dataset
             )
@@ -659,6 +667,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         else "cosine",
         weight_decay=cfg.weight_decay if cfg.weight_decay is not None else 0.0,
         sample_packing=cfg.sample_packing if cfg.sample_packing else False,
+        eval_sample_packing=cfg.eval_sample_packing,
         sample_packing_seq_len_multiplier=cfg.micro_batch_size,
         relora_steps=cfg.relora_steps,
         relora_warmup_steps=cfg.relora_warmup_steps,