MAINT: Update ruff version to ~0.6.1 (huggingface#1965)

Moving to ruff ~0.6.1. Changes:

- type comparisons now require is: str is str
- remove overridden class attribute active_adapter
- remove secondary import of fbd_cuda

Omit jupyter notebooks for now. We can think about adding that in a
separate PR.

.pre-commit-config.yaml

@@ -1,13 +1,13 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.1
+    rev: v0.6.1
     hooks:
       - id: ruff
         args:
           - --fix
       - id: ruff-format
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v0.6.1
     hooks:
       - id: check-merge-conflict
       - id: check-yaml

pyproject.toml

@@ -6,6 +6,7 @@ target-version = ['py38']
 [tool.ruff]
 target-version = "py38"
 line-length = 119
+extend-exclude = ["*.ipynb"]
 
 [tool.ruff.lint]
 extend-select = [

setup.py

@@ -21,7 +21,7 @@
 extras["quality"] = [
     "black",  # doc-builder has an implicit dependency on Black, see huggingface/doc-builder#434
     "hf-doc-builder",
-    "ruff~=0.4.8",
+    "ruff~=0.6.1",
 ]
 extras["docs_specific"] = [
     "black",  # doc-builder has an implicit dependency on Black, see huggingface/doc-builder#434

src/peft/tuners/boft/layer.py

@@ -91,7 +91,6 @@ def get_fbd_cuda():
                 # build_directory='/tmp/'  # for debugging
             )
             # extra_cuda_cflags = ['-std=c++14', '-ccbin=$$(which gcc-7)']) # cuda10.2 is not compatible with gcc9. Specify gcc 7
-            import fbd_cuda
     except Exception as e:
         warnings.warn(f"Failed to load the CUDA extension: {e}, check if ninja is available.")
         warnings.warn("Setting boft_n_butterfly_factor to 1 to speed up the finetuning process.")

src/peft/tuners/lora/model.py

@@ -553,9 +553,9 @@ def _check_add_weighted_adapter(
                 "Combining adapters with `target_modules` type being a mix of list/set and string is not supported."
             )
 
-        if target_module_types[0] == str:
+        if target_module_types[0] is str:
             new_target_modules = "|".join(f"({self.peft_config[adapter].target_modules})" for adapter in adapters)
-        elif target_module_types[0] == set:
+        elif target_module_types[0] is set:
             new_target_modules = reduce(
                 operator.or_, (self.peft_config[adapter].target_modules for adapter in adapters)
             )

src/peft/tuners/tuners_utils.py

@@ -506,8 +506,6 @@ class BaseTunerLayer(ABC):
             The name of the active adapter.
     """
 
-    active_adapter = None
-
     # All names of layers that may contain adapter (trainable) weights
     adapter_layer_names: tuple[str, ...] = ()
     # All names of other parameters that may contain adapter-related parameters

tests/test_mixed.py

@@ -122,7 +122,7 @@ def _check_mixed_outputs(self, model_cls, config0, config1, input, *, is_commuta
         # check the number of tuner layer types
         tuner_layers = [mod for mod in peft_model_01.modules() if isinstance(mod, BaseTunerLayer)]
         tuner_types = {type(tuner_layer) for tuner_layer in tuner_layers}
-        if type(config0) == type(config1):
+        if type(config0) is type(config1):
             assert len(tuner_types) == 1
         else:
             assert len(tuner_types) == 2
@@ -147,7 +147,7 @@ def _check_mixed_outputs(self, model_cls, config0, config1, input, *, is_commuta
         # check the number of tuner layer types
         tuner_layers = [mod for mod in peft_model_10.modules() if isinstance(mod, BaseTunerLayer)]
         tuner_types = {type(tuner_layer) for tuner_layer in tuner_layers}
-        if type(config0) == type(config1):
+        if type(config0) is type(config1):
             assert len(tuner_types) == 1
         else:
             assert len(tuner_types) == 2
@@ -166,7 +166,7 @@ def _check_mixed_outputs(self, model_cls, config0, config1, input, *, is_commuta
         # check the number of tuner layer types
         tuner_layers = [mod for mod in peft_model_10.modules() if isinstance(mod, BaseTunerLayer)]
         tuner_types = {type(tuner_layer) for tuner_layer in tuner_layers}
-        if type(config0) == type(config1):
+        if type(config0) is type(config1):
             assert len(tuner_types) == 1
         else:
             assert len(tuner_types) == 2

tests/test_tuners_utils.py

@@ -291,7 +291,7 @@ def _check_match_with_expected_target_modules(
         # compare the two models and assert that all layers are of the same type
         for name, actual_module in actual_model.named_modules():
             expected_module = expected_model_module_dict[name]
-            assert type(actual_module) == type(expected_module)
+            assert type(actual_module) is type(expected_module)
 
     def test_maybe_include_all_linear_layers_ia3_loha(self):
         model_id, initial_target_modules, expected_target_modules = (