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neural_compressor/torch/algorithms/mx_quant/__init__.py

@@ -14,4 +14,4 @@
 
 # pylint:disable=import-error
 
-from .mx import mx_quantize
+from .mx import mx_quantize

neural_compressor/torch/algorithms/mx_quant/mx.py

@@ -18,14 +18,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .utils import quantize_elemwise_op, quantize_mx_op
 from typing import Dict, Tuple
-from neural_compressor.torch.utils import register_algo, set_module
+
+from neural_compressor.common.logger import Logger
 from neural_compressor.common.utility import MX_QUANT
 from neural_compressor.torch.quantization.config import MXQuantConfig
-from neural_compressor.common.logger import Logger
+from neural_compressor.torch.utils import register_algo, set_module
+
+from .utils import quantize_elemwise_op, quantize_mx_op
 
 logger = Logger().get_logger()
+
+
 class MXLinearFunction(Function):
     @staticmethod
     def forward(ctx, input, weight, bias=None, mx_specs=None):
@@ -54,6 +58,7 @@ def forward(ctx, input, weight, bias=None, mx_specs=None):
 
         return output
 
+
 class MXLinear(torch.nn.Linear):
     def __init__(
         self,
@@ -72,14 +77,10 @@ def __init__(
     def apply_mx_specs(self):
         if self.mx_specs is not None:
             if self.mx_specs.get("out_dtype", "float32") != "float32":
-                self.weight.data = quantize_elemwise_op(
-                    self.weight.data, mx_specs=self.mx_specs
-                )
+                self.weight.data = quantize_elemwise_op(self.weight.data, mx_specs=self.mx_specs)
 
                 if self.bias is not None:
-                    self.bias.data = quantize_elemwise_op(
-                        self.bias.data, mx_specs=self.mx_specs
-                    )
+                    self.bias.data = quantize_elemwise_op(self.bias.data, mx_specs=self.mx_specs)
 
             # MX quantize everything along input size
             self.weight.data = quantize_mx_op(
@@ -96,9 +97,10 @@ def append_name(self, postfix):
     def forward(self, inputs):
         if self.mx_none:
             return super().forward(inputs)
-        
+
         return MXLinearFunction.apply(inputs, self.weight, self.bias, self.mx_specs)
 
+
 def mx_quantize(
     model,
     config={},
@@ -150,4 +152,4 @@ def mx_quantize(
             return new_module
         else:
             set_module(model, name, new_module)
-    return model
+    return model