use distributed sampler, avoid accelerate prepare

src/axolotl/utils/dataloader.py

@@ -109,7 +109,6 @@ class MultipackDistributedDataloader:
         seq_max_length: int = 2048,
         batch_size: int = 1,
         sampler: Union[Sampler, DistributedSampler] = None,
-        seed: int = 0,
     ):
         # Dataset
         self.dataset = dataset
@@ -127,19 +126,10 @@ class MultipackDistributedDataloader:
         self.num_replicas = 1
         self.rank = 0
 
-        # Seed
-        self.seed = seed
-
-        # Epoch
-        self.epoch = 0
-
         # statistics
         self.eff_total_used = 0
         self.eff_total_slots = 0
 
-    def set_epoch(self, epoch: int):
-        self.epoch = epoch
-
     def generate_batches(self, set_stats=False):
         if self.sampler:
             indices = [idx for idx in self.sampler]

src/axolotl/utils/trainer.py

@@ -14,7 +14,7 @@ import transformers
 from datasets import Dataset
 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
 
@@ -87,18 +87,26 @@ 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]:
+        if self.args.world_size > 1 and self.args.sample_packing:
+            return DistributedSampler(
+                self.train_dataset,
+                num_replicas=self.args.world_size,
+                rank=self.args.process_index,
+                seed=self.args.seed,
+            )
+        return super()._get_train_sampler()
+
     def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
         if self.args.sample_packing:
             train_sampler = self._get_train_sampler()
 
-            return self.accelerator.prepare(
-                MultipackDistributedDataloader(
-                    self.train_dataset,
-                    batch_size=self._train_batch_size,
-                    seq_max_length=self.args.max_seq_length,
-                    collate_fn=self.data_collator,
-                    sampler=train_sampler,
-                )
+            return MultipackDistributedDataloader(
+                self.train_dataset,
+                batch_size=self._train_batch_size,
+                seq_max_length=self.args.max_seq_length,
+                collate_fn=self.data_collator,
+                sampler=train_sampler,
             )
         return super().get_train_dataloader()
 
@@ -278,7 +286,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
 
     training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
-        max_steps=total_num_steps,  # this is helpful in case we don't actually know total # of steps
+        # max_steps=total_num_steps,  # this is helpful in case we don't actually know total # of steps
         per_device_train_batch_size=cfg.micro_batch_size,
         per_device_eval_batch_size=cfg.eval_batch_size
         if cfg.eval_batch_size is not None