Applied formatter changes

Ran ruff formatter on the project (`check_dirs` in makefile) to fix format issues.

examples/nlp/text-generation/quantize_causal_lm_model.py

@@ -120,7 +120,9 @@ def main():
     torch_dtype = (
         torch.float16
         if args.load_dtype == "float16"
-        else torch.bfloat16 if args.load_dtype == "bfloat16" else torch.float32
+        else torch.bfloat16
+        if args.load_dtype == "bfloat16"
+        else torch.float32
     )
     model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch_dtype, low_cpu_mem_usage=True).to(
         device

examples/vision/object-detection/quantize_owl_model.py

@@ -12,7 +12,6 @@
 
 
 def detect(model, processor, image, texts):
-
     inputs = processor(text=texts, images=image, return_tensors="pt").to(model.device)
 
     # forward pass

optimum/quanto/library/extensions/extension.py

@@ -11,7 +11,6 @@
 
 
 class Extension(object):
-
     def __init__(
         self,
         name: str,

optimum/quanto/models/diffusers_models.py

@@ -42,7 +42,6 @@
 
 
 class QuantizedDiffusersModel(ModelHubMixin):
-
     BASE_NAME = "quanto"
     base_class = None
 
@@ -188,5 +187,4 @@ def _save_pretrained(self, save_directory: Path) -> None:
 
 
 class QuantizedPixArtTransformer2DModel(QuantizedDiffusersModel):
-
     base_class = PixArtTransformer2DModel

optimum/quanto/models/transformers_models.py

@@ -36,7 +36,6 @@
 
 
 class QuantizedTransformersModel(ModelHubMixin):
-
     BASE_NAME = "quanto"
     auto_class = None
 
@@ -178,5 +177,4 @@ def _save_pretrained(self, save_directory: Path) -> None:
 
 
 class QuantizedModelForCausalLM(QuantizedTransformersModel):
-
     auto_class = AutoModelForCausalLM

optimum/quanto/subpackage/commands/base.py

@@ -23,7 +23,6 @@
 
 @optimum_cli_subcommand()
 class QuantoCommand(BaseOptimumCLICommand):
-
     COMMAND = CommandInfo(name="quanto", help="Hugging Face models quantization tools")
     SUBCOMMANDS = (
         CommandInfo(

optimum/quanto/tensor/activations/qbytes.py

@@ -26,7 +26,6 @@
 
 
 class ActivationQBytesQuantizer(Function):
-
     @staticmethod
     def forward(ctx, base: torch.Tensor, qtype: qtype, scale: torch.Tensor) -> torch.Tensor:
         if qtype.bits != 8:

optimum/quanto/tensor/optimizers/absmax_optimizer.py

@@ -24,7 +24,6 @@
 
 
 class AbsmaxOptimizer(SymmetricOptimizer):
-
     def optimize(
         self, base: torch.Tensor, qtype: qtype, axis: Optional[int] = None
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:

optimum/quanto/tensor/optimizers/affine_optimizer.py

@@ -25,7 +25,6 @@
 
 
 class AffineOptimizer(Optimizer):
-
     def __call__(
         self,
         base: torch.Tensor,

optimum/quanto/tensor/optimizers/max_optimizer.py

@@ -24,7 +24,6 @@
 
 
 class MaxOptimizer(AffineOptimizer):
-
     def optimize(
         self, base: torch.Tensor, qtype: qtype, axis: int
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:

optimum/quanto/tensor/optimizers/optimizer.py

@@ -22,7 +22,6 @@
 
 
 class Optimizer(ABC):
-
     def __call__(
         self, base: torch.Tensor, bits: int, axis: int, group_size: Optional[int] = None
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:

optimum/quanto/tensor/optimizers/symmetric_optimizer.py

@@ -24,7 +24,6 @@
 
 
 class SymmetricOptimizer(Optimizer):
-
     def __call__(self, base: torch.Tensor, qtype: qtype, axis: Optional[int] = None) -> torch.Tensor:
         if axis not in [None, 0, -1]:
             raise ValueError("axis parameter must be None, 0 (first axis) or -1 (last axis)")

optimum/quanto/tensor/qbits.py

@@ -54,7 +54,6 @@ def backward(ctx, gO):
 
 
 class QBitsTensor(QTensor):
-
     def __init__(self, qtype, axis, group_size, size, stride, data, scale, shift, requires_grad=False):
         super().__init__(qtype, axis)
         self._data = data

optimum/quanto/tensor/qbytes.py

@@ -37,7 +37,6 @@ def backward(ctx, gO):
 
 
 class QBytesTensor(QTensor):
-
     def __init__(self, qtype, axis, size, stride, data, scale, requires_grad=False):
         super().__init__(qtype, axis)
         self._data = data

optimum/quanto/tensor/qtensor.py

@@ -30,7 +30,6 @@ def qfallback(callable, *args, **kwargs):
 
 
 class QTensor(torch.Tensor):
-
     def __init__(self, qtype, axis):
         self._qtype = qtype
         self._axis = axis

optimum/quanto/tensor/weights/marlin/fp8/qbits.py

@@ -61,7 +61,6 @@ def __new__(cls, qtype, axis, size, stride, data, scale, requires_grad=False):
         )
 
     def __init__(self, qtype, axis, size, stride, data, scale, requires_grad=False):
-
         assert axis == 0
         assert data.ndim == 2
 

optimum/quanto/tensor/weights/qbits.py

@@ -31,7 +31,6 @@
 
 
 class WeightsQBitsQuantizer(Function):
-
     @staticmethod
     def forward(
         ctx,

optimum/quanto/tensor/weights/qbytes.py

@@ -28,7 +28,6 @@
 
 
 class WeightQBytesQuantizer(Function):
-
     @staticmethod
     def forward(
         ctx, base: torch.Tensor, qtype: qtype, axis: int, scale: torch.Tensor, activation_qtype: qtype, optimized: bool

test/library/test_quantize.py

@@ -96,9 +96,7 @@ def test_affine_quantize(input_shape, dtype, qtype, axis, group_size, shift_mode
             "zeropoint": 6e-2,
             "float": 5e-2,
         },
-    }[
-        qtype
-    ][shift_mode]
+    }[qtype][shift_mode]
     if group_size is not None:
         qa = ungroup(qa, axis=axis, orig_shape=a.shape)
     assert_similar(a, qa, atol=atol)

test/quantize/test_quantize_patterns.py

@@ -36,7 +36,6 @@ def forward(self, inputs):
 
 
 class ClassificationModel(torch.nn.Module):
-
     def __init__(self, input_size, output_size, hidden_size, classes):
         super().__init__()
         self.model = MLP(input_size, output_size, hidden_size)

test/tensor/weights/test_weight_qbits_tensor_quantize.py

@@ -49,9 +49,7 @@ def test_weight_qbits_tensor_quantize(input_shape, dtype, qtype, axis, group_siz
             "zeropoint": 6e-2,
             "float": 5e-2,
         },
-    }[
-        qtype
-    ][shift_mode]
+    }[qtype][shift_mode]
     assert_similar(a, qa, atol=atol)