[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

neural_compressor/common/base_config.py

@@ -224,7 +224,7 @@ def _post_init(self):
         self._is_initialized = True
 
     def __setattr__(self, name, value):
-        """Override the setattr function to propagate updates"""
+        """Override the setattr function to propagate updates."""
         super().__setattr__(name, value)
         if self._is_initialized and name in self.params_list:
             self._is_initialized = False

neural_compressor/torch/algorithms/layer_wise/utils.py

@@ -259,7 +259,7 @@ def load_first_layer_only(user_model, model_name):
         model_name (str): model name or path
     """
     for name, m in user_model.named_modules():
-        if ('layers' not in name or 'layers.0' in name) and len(name) > 0 and len(list(m.named_children())) == 0:
+        if ("layers" not in name or "layers.0" in name) and len(name) > 0 and len(list(m.named_children())) == 0:
             load_module(user_model, name, get_path(model_name), device="hpu" if is_hpu_available() else "cpu")
 
 
@@ -357,6 +357,7 @@ def clean_module_weight(module):
                 kwargs = submodule._parameters[n].__dict__
                 if is_hpu_available:
                     from habana_frameworks.torch.core import weight_sharing
+
                     if param_cls == weight_sharing.HabanaParameterWrapper:
                         try:
                             kwargs.pop("change_device_placement")

neural_compressor/torch/algorithms/weight_only/save_load.py

@@ -93,14 +93,15 @@ def save(model, output_dir="./saved_results", format=SaveLoadFormat.DEFAULT, **k
         qconfig_file_path = os.path.join(output_folder, QCONFIG_NAME)
         # saving process
         save_config_mapping(model.qconfig, qconfig_file_path)
-        if 'blockwise' in kwargs:
-            from neural_compressor.torch.algorithms.layer_wise import save_layers_in_shards_iteratively, LWQ_WORKSPACE
+        if "blockwise" in kwargs:
+            from neural_compressor.torch.algorithms.layer_wise import LWQ_WORKSPACE, save_layers_in_shards_iteratively
+
             checkpoints_folder = kwargs.get("blockwise_load_folder", None)
             if not checkpoints_folder:
                 checkpoints_folder = LWQ_WORKSPACE
             save_layers_in_shards_iteratively(checkpoints_folder, output_folder, layers_per_shard=8)
         else:
-            model_state_dict = model.state_dict() # if 'model_state_dict' not in kwargs else kwargs['model_state_dict']
+            model_state_dict = model.state_dict()  # if 'model_state_dict' not in kwargs else kwargs['model_state_dict']
             torch.save(model_state_dict, qmodel_weight_file_path)
             logger.info("Save quantized model weight to {}.".format(qmodel_weight_file_path))
         logger.info("Save configuration of quantized model to {}.".format(qconfig_file_path))

neural_compressor/torch/quantization/quantize.py

@@ -157,8 +157,11 @@ def prepare(
     else:
         model_info = quant_config.get_model_info(model=prepared_model)
 
-    if (hasattr(quant_config, "model_path") and quant_config.model_path == "" 
-       and hasattr(prepared_model, "name_or_path")):
+    if (
+        hasattr(quant_config, "model_path")
+        and quant_config.model_path == ""
+        and hasattr(prepared_model, "name_or_path")
+    ):
         quant_config.model_path = prepared_model.name_or_path
     configs_mapping = quant_config.to_config_mapping(model_info=model_info)
     logger.debug(configs_mapping)

neural_compressor/transformers/quantization/utils.py

@@ -629,8 +629,8 @@ def set_nontext_module_config(model, to_quant_block_names, config):
 def convert_to_GPTQ_checkpoints(model, quantization_config):
     from intel_extension_for_pytorch.nn.modules import WeightOnlyQuantizedLinear as ipex_cpu_linear
 
-    from neural_compressor.torch.utils import set_module
     from neural_compressor.torch.algorithms.weight_only.modules import INCWeightOnlyLinear
+    from neural_compressor.torch.utils import set_module
 
     dtype = "int4" if quantization_config.bits == 4 else "int8"
     bits = quantization_config.bits

test/3x/torch/requirements.txt

@@ -1,4 +1,5 @@
 auto_round
+datasets
 deepspeed @ git+https://github.com/HabanaAI/[email protected]
 expecttest
 intel_extension_for_pytorch
@@ -9,4 +10,3 @@ psutil
 pytest
 torchvision
 transformers
-datasets