swap the data collator for evals if not using sample packing (#1076)

* swap the data collator for evals if not using sample packing

* drop last from dataloader to help with issues with evals

src/axolotl/core/trainer_builder.py

@@ -1,3 +1,4 @@
+# pylint: disable=too-many-lines
 """
 Builder for the training args and trainer
 """
@@ -137,10 +138,19 @@ class AxolotlTrainer(Trainer):
     args = None  # type: AxolotlTrainingArguments
     tag_names = ["axolotl"]
 
-    def __init__(self, *args, num_epochs=1, bench_data_collator=None, **kwargs):
+    def __init__(
+        self,
+        *_args,
+        num_epochs=1,
+        bench_data_collator=None,
+        eval_data_collator=None,
+        **kwargs
+    ):
         self.num_epochs = num_epochs
         self.bench_data_collator = bench_data_collator
-        super().__init__(*args, **kwargs)
+        self.eval_data_collator = eval_data_collator
+        super().__init__(*_args, **kwargs)
+        self.train_data_collator = self.data_collator
 
     def create_scheduler(
         self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
@@ -239,6 +249,16 @@ def get_train_dataloader(self) -> DataLoader:
         return super().get_train_dataloader()
 
     def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
+        if self.args.sample_packing and self.args.eval_sample_packing is False:
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.eval_data_collator
+            )
+            dataloader = super().get_eval_dataloader(eval_dataset)
+            self.data_collator = (  # pylint: disable=attribute-defined-outside-init
+                self.train_data_collator
+            )
+            return dataloader
+
         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
@@ -269,6 +289,7 @@ def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoa
             return self.accelerator.prepare_data_loader(
                 DataLoader(eval_dataset, **dataloader_params)
             )
+
         return super().get_eval_dataloader(eval_dataset)
 
     def _get_bench_sampler(
@@ -651,6 +672,12 @@ def build(self, total_num_steps):
             training_arguments_kwargs[
                 "dataloader_prefetch_factor"
             ] = self.cfg.dataloader_prefetch_factor
+        if self.cfg.dataloader_drop_last is not None:
+            training_arguments_kwargs[
+                "dataloader_drop_last"
+            ] = self.cfg.dataloader_drop_last
+        elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
+            training_arguments_kwargs["dataloader_drop_last"] = True
 
         if self.cfg.val_set_size == 0:
             # no eval set, so don't eval
@@ -831,6 +858,9 @@ def build(self, total_num_steps):
             eval_dataset=self.eval_dataset,
             args=training_args,
             data_collator=self.build_collator(training_args, **data_collator_kwargs),
+            eval_data_collator=self.build_collator(
+                training_args, is_eval=True, **data_collator_kwargs
+            ),
             bench_data_collator=transformers.DataCollatorForSeq2Seq(
                 self.tokenizer,
                 return_tensors="pt",
@@ -851,14 +881,22 @@ def build(self, total_num_steps):
 
         return trainer
 
-    def build_collator(self, training_args: AxolotlTrainingArguments, **kwargs):
+    def build_collator(
+        self, training_args: AxolotlTrainingArguments, is_eval=False, **kwargs
+    ):
         if training_args.pretraining:
             return None
 
         if self.cfg.model_config_type == "mamba":
             return MambaDataCollator(tokenizer=self.tokenizer)
 
-        if training_args.sample_packing:
+        use_batch_sampler_collator = False
+        if is_eval is False and training_args.sample_packing:
+            use_batch_sampler_collator = True
+        if is_eval and training_args.eval_sample_packing:
+            use_batch_sampler_collator = True
+
+        if use_batch_sampler_collator:
             return BatchSamplerDataCollatorForSeq2Seq(
                 self.tokenizer,
                 return_tensors="pt",