Refine base config for 3.x (#1595)

* Refine base config for 3.x

* fixed group_dim is 0

---------

Signed-off-by: Mengni Wang <[email protected]>
Signed-off-by: yiliu30 <[email protected]>
Co-authored-by: yiliu30 <[email protected]>

Author: mengniwang95

neural_compressor/common/base_config.py

@@ -210,8 +210,9 @@ def to_dict(self):
 
     def get_params_dict(self):
         result = dict()
-        for param in self.params_list:
-            result[param] = getattr(self, param)
+        for param, value in self.__dict__.items():
+            if param not in ["_global_config", "_local_config", "_white_list"]:
+                result[param] = value
         return result
 
     @classmethod

neural_compressor/onnxrt/quantization/config.py

@@ -224,7 +224,8 @@ def __init__(
                            https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/quantization/quantize.py#L78
         """
         BaseConfig.__init__(self)
-        StaticQuantConfig.__init__(self, calibration_data_reader=None, **kwargs)
+        kwargs.update({"calibration_data_reader": None})
+        StaticQuantConfig.__init__(self, **kwargs)
         self.alpha = alpha
         self.folding = folding
         self.op_types = op_types

neural_compressor/torch/algorithms/weight_only/rtn.py

@@ -122,7 +122,10 @@ def rtn_quantize(
             continue
         logger.debug(f"RTN quantized module:{name, m}")
         logger.debug(log_msg)
-        weight = m.weight.t_().contiguous() if group_dim == 0 else m.weight
+        if group_dim == 0:
+            weight = m.weight.t_().contiguous()
+        else:
+            weight = m.weight
         if use_mse_search:
             quantile = search_clip(m, bits, group_size, scheme, dtype, use_full_range)
         if export_compressed_model:
@@ -169,6 +172,9 @@ def rtn_quantize(
                 full_range=use_full_range,
                 **double_quant_config,
             )
-            weight = weight.t_().contiguous() if group_dim == 0 else weight
+            if group_dim == 0:
+                # for group_dim is 0, we need to transpose the quantized tensor and module's weight back
+                weight = weight.t_().contiguous()
+                m.weight.t_().contiguous()
             m.weight.data.copy_(weight)
     return model