Fix broken test

language_interpolation/lightning_datamodule.py

@@ -246,12 +246,11 @@ def test_dataloader(self) -> DataLoader:
         )
 
 
-class TransformerMixin :
-
+class TransformerMixin:
     def collate_fn(self, batch) -> tuple[Tensor, Tensor, list[int]]:
         # TODO: this does not make sense to me
         # The max size includes the output
-        print('something seems wrong here. Fix')
+
         max_size = max(self._max_size, batch[0][0].shape[0])
         this_size = random.randint(1, max_size - 1)
         final_features = torch.stack([sample[0][:this_size] for sample in batch])
@@ -270,7 +269,7 @@ def collate_fn(self, batch) -> tuple[Tensor, Tensor, list[int]]:
         return self.normalize(final_features), final_targets, final_indexes
 
 
-class MambaMixin :
+class MambaMixin:
     def collate_fn(self, batch) -> tuple[Tensor, Tensor, list[int]]:
         # The targets are just the features shifted by 1
         # The max size includes the output
@@ -290,7 +289,6 @@ def collate_fn(self, batch) -> tuple[Tensor, Tensor, list[int]]:
         return final_features, final_targets, final_indexes
 
 
-
 class SequenceDataModule(pl.LightningDataModule):
     def __init__(
         self,
@@ -328,9 +326,9 @@ def __init__(
         """
         super().__init__()
         self._characters_per_feature = characters_per_feature
-        
+
         self._max_features = max_features
-        
+
         self._targets = targets
         self._batch_size = batch_size
         self._num_workers = num_workers
@@ -455,8 +453,10 @@ def test_dataloader(self) -> DataLoader:
             collate_fn=self.collate_fn,
         )
 
-class TransformerDataModule(TransformerMixin, SequenceDataModule) :
+
+class TransformerDataModule(TransformerMixin, SequenceDataModule):
     pass
 
+
 class MambaDataModule(MambaMixin, SequenceDataModule):
-    pass
+    pass

language_interpolation/networks.py

@@ -24,7 +24,7 @@
 import time
 from lion_pytorch import Lion
 from high_order_layers_torch.sparse_optimizers import SparseLion
-from language_interpolation.state_space_network import Mamba, ModelArgs
+from language_interpolation.state_space_network import Mamba
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.DEBUG)
@@ -863,18 +863,6 @@ def select_network(cfg: DictConfig, device: str = None):
         widths, output_sizes = tail_focus.compute_sizes(cfg.net.features)
         logger.info(f"TailFocusNetwork widths {widths} output_sizes {output_sizes}")
     elif cfg.net.model_type == "mamba":
-        model_args = ModelArgs(
-            d_model=cfg.net.d_model,
-            n_layer=cfg.net.n_layer,
-            vocab_size=cfg.net.vocab_size,
-            d_state=cfg.net.d_state,
-            expand=cfg.net.expand,
-            dt_rank=cfg.net.dt_rank,
-            d_conv=cfg.net.d_conv,
-            pad_vocab_size_multiple=cfg.net.pad_vocab_size_multiple,
-            conv_bias=cfg.net.conv_bias,
-            bias=cfg.net.bias,
-        )
         model = Mamba(
             d_model=cfg.net.d_model,
             n_layer=cfg.net.n_layer,