Expose DistributedSampler RNG seed argument

Relies on mosaicml/composer#3724.

llmfoundry/data/finetuning/dataloader.py

@@ -63,6 +63,7 @@ def build_finetuning_dataloader(
     prefetch_factor: int = 2,
     persistent_workers: bool = True,
     timeout: int = 0,
+    shuffle_seed: int = 0,
 ) -> DataSpec:
     """Builds a finetuning dataloader for training or evaluating.
 
@@ -168,6 +169,9 @@ def build_finetuning_dataloader(
         timeout (int, optional): If positive, the timeout value for collecting a batch from workers.
             Should always be non-negative. The default is 0. This argument is passed directly to the
             pytorch :class:`DataLoader`.
+        shuffle_seed (int, optional): Initialization value for the random number generator of the
+            distributed sampler. Only relevant if `dataset.shuffle=True`. The default is 0. This
+            argument is passed directly to the PyTorch :class:`DistributedSampler`.
         See :class:`DataLoader` for standard argument options to the pytorch
             dataloader, such as `drop_last`, `num_workers`, etc.
 
@@ -336,6 +340,7 @@ def build_finetuning_dataloader(
             replication_factor if replication_factor > 1 else None,
             rank=dist.get_global_rank() //
             replication_factor if replication_factor > 1 else None,
+            seed=shuffle_seed,
         )
 
     assert streaming_dataset is not None  # for pyright