fixes and go back to distributed sampler since batch sampler won't work

src/axolotl/utils/data.py

@@ -427,7 +427,7 @@ def load_prepare_datasets(
             + "|"
             + "train"
             + "|"
-            + cfg.seed
+            + str(cfg.seed or 42)
         )
         to_hash_test = (
             dataset._fingerprint  # pylint: disable=protected-access
@@ -436,7 +436,7 @@ def load_prepare_datasets(
             + "|"
             + "test"
             + "|"
-            + cfg.seed
+            + str(cfg.seed or 42)
         )
         train_fingerprint = hashlib.md5(
             to_hash_train.encode(), usedforsecurity=False
@@ -449,7 +449,7 @@ def load_prepare_datasets(
             dataset = dataset.train_test_split(
                 test_size=cfg.val_set_size,
                 shuffle=False,
-                seed=cfg.seed,
+                seed=cfg.seed or 42,
                 train_new_fingerprint=train_fingerprint,
                 test_new_fingerprint=test_fingerprint,
             )
@@ -458,7 +458,7 @@ def load_prepare_datasets(
             dataset = dataset.train_test_split(
                 test_size=cfg.val_set_size,
                 shuffle=False,
-                seed=cfg.seed,
+                seed=cfg.seed or 42,
                 train_new_fingerprint=train_fingerprint,
                 test_new_fingerprint=test_fingerprint,
             )

src/axolotl/utils/trainer.py

@@ -16,7 +16,7 @@ import transformers
 from datasets import Dataset, set_caching_enabled
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from torch.utils.data import DataLoader, RandomSampler
+from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
 from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_pt_utils import get_parameter_names
 
@@ -159,23 +159,20 @@ class AxolotlTrainer(Trainer):
                 return super().create_scheduler(num_training_steps, optimizer)
         return self.lr_scheduler
 
-    # def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+    def _get_train_sampler(self) -> 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:
-    #         return DistributedSampler(
+            return DistributedSampler(
-    #             self.train_dataset,
+                self.train_dataset,
-    #             num_replicas=self.args.world_size,
+                num_replicas=self.args.world_size,
-    #             rank=self.args.process_index,
+                rank=self.args.process_index,
-    #             seed=self.args.seed,
+                seed=self.args.seed,
-    #         )
+            )
-    #     return super()._get_train_sampler()
+        return super()._get_train_sampler()
 
     def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
         if self.args.sample_packing:
             train_sampler = self._get_train_sampler()
-            #  If set to True, the dataloader prepared is only iterated through on the
+            return self.accelerator.prepare(
-            #  main process and then the batches are split and broadcast to each process
-            self.accelerator.dispatch_batches = True
-            return self.accelerator.prepare_data_loader(
                 MultipackDistributedDataloader(
                     self.train_dataset,
                     batch_size=self._train_batch_size,
@@ -197,10 +194,7 @@ class AxolotlTrainer(Trainer):
                 eval_dataset if eval_dataset is not None else self.eval_dataset
             )
             eval_sampler = self._get_eval_sampler(eval_dataset)
-            #  If set to True, the datalaoder prepared is only iterated through on the
+            return self.accelerator.prepare(
-            #  main process and then the batches are split and broadcast to each process
-            self.accelerator.dispatch_batches = True
-            return self.accelerator.prepare_data_loader(
                 MultipackDistributedDataloader(
                     eval_dataset,
                     batch_size=self.args.eval_batch_size,