formatting

Summary

Signed-off-by: HDCharles <[email protected]>

Author: HDCharles

src/llmcompressor/modifiers/awq/base.py

@@ -7,7 +7,6 @@
 from compressed_tensors.utils import (
     align_modules,
     get_execution_device,
-    get_lowest_common_ancestor_name,
     match_modules_set,
     match_named_modules,
     update_offload_parameter,
@@ -32,7 +31,8 @@
 from llmcompressor.utils.fsdp.helpers import get_fsdp_parent
 from llmcompressor.utils.helpers import calibration_forward_context
 from llmcompressor.utils.pytorch.module import (
-    get_module_to_name_dict, get_layer_by_name
+    get_layer_by_name,
+    get_module_to_name_dict,
 )
 
 __all__ = ["AWQModifier"]
@@ -329,7 +329,7 @@ def _set_resolved_mappings(self, model: Module) -> None:
             for smooth_layers, *nested_balance_layers in match_modules_set(
                 model, (mapping.smooth_layer, *mapping.balance_layers), self.ignore
             ):
-                if len(smooth_layers)>1:
+                if len(smooth_layers) > 1:
                     raise ValueError(
                         "AWQ needs to match a single smoothlayer for each mapping but "
                         f"got {[module_to_name.get(s) for s in smooth_layers]}"
@@ -738,6 +738,7 @@ def _check_layers_are_compatible(
             return False
     return True
 
+
 def get_lowest_ancestor_with_avoid(name: str, model: Module, avoid=torch.nn.Module):
     """
     get lowest ancestor that is not the avoided class/type
@@ -756,6 +757,7 @@ def get_lowest_ancestor_with_avoid(name: str, model: Module, avoid=torch.nn.Modu
             return name, ancestor
         name = ".".join(name.split(".")[:-1])
 
+
 def _pseudo_quantize_tensor(
     w: torch.Tensor, symmetric: bool = False, bit_width: int = 8, group_size: int = -1
 ):

src/llmcompressor/modifiers/smoothquant/base.py

@@ -204,8 +204,8 @@ def _resolve_mappings(self, model: Module) -> List[SmoothQuantMapping]:
             for *nested_balance_layers, smooth_layers in match_modules_set(
                 model, tree_leaves(mapping), self.ignore
             ):
-                if len(smooth_layers)>1:
-                    raise ValueError (
+                if len(smooth_layers) > 1:
+                    raise ValueError(
                         "SmoothQuant must match a single smooth layer for each mapping"
                         f" but got {[module_to_name.get(s) for s in smooth_layers]}"
                         f" for mapping: {mapping}"