A yapf update broke some formatting...re-running the linter

composer/algorithms/alibi/alibi.py

@@ -90,7 +90,7 @@ def apply_alibi(model: torch.nn.Module, heads_per_layer: int, max_sequence_lengt
     zero_and_freeze_expand_position_embeddings(model=model,
                                                attribute=position_embedding_attribute,
                                                new_embedding_length=max_sequence_length)
-    log.info(f" Position embedding expanded to sequence " f"length {max_sequence_length}, zeroed, and frozen")
+    log.info(f" Position embedding expanded to sequence length {max_sequence_length}, zeroed, and frozen")
 
     def convert_attention(module: torch.nn.Module, module_index: int = None):
         module = register_alibi(module=module, n_heads=heads_per_layer, max_token_length=max_sequence_length)

composer/datasets/hparams.py

@@ -71,7 +71,7 @@ class SyntheticHparamsMixin(hp.Hparams, abc.ABC):
             Ignored if :attr:`use_synthetic` is False. (Default: ``CONTIGUOUS_FORMAT``)
     """
 
-    use_synthetic: bool = hp.optional("Whether to use synthetic data. Defaults to False." "", default=False)
+    use_synthetic: bool = hp.optional("Whether to use synthetic data. Defaults to False.", default=False)
     synthetic_num_unique_samples: int = hp.optional("The number of unique samples to allocate memory for.", default=100)
     synthetic_device: str = hp.optional("Device to store the sample pool. Should be `cuda` or `cpu`. Defauls to `cpu`.",
                                         default="cpu")

composer/optim/pytorch_future.py

@@ -69,7 +69,7 @@ def __init__(self,
                  verbose=False,
                  interval='step'):
         if warmup_method not in ("constant", "linear"):
-            raise ValueError("Only 'constant' or 'linear' warmup_method accepted, but " "got {}".format(warmup_method))
+            raise ValueError("Only 'constant' or 'linear' warmup_method accepted, but got {}".format(warmup_method))
         self.warmup_factor = warmup_factor
         self.warmup_iters = warmup_iters
         self.warmup_method = warmup_method
@@ -84,7 +84,8 @@ def get_lr(self):
         """
 
         if not self._get_lr_called_within_step:
-            warnings.warn("To get the last learning rate computed by the scheduler, " "please use `get_last_lr()`.")
+            warnings.warn("To get the last learning rate computed by the scheduler, "
+                          "please use `get_last_lr()`.")
 
         if self.last_epoch == 0:
             return [group['lr'] * self.warmup_factor for group in self.optimizer.param_groups]