Refine base Quantizer (#1760)

Refine base Quantizer class
---------

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
Signed-off-by: yiliu30 <yi4.liu@intel.com>
Co-authored-by: yuwenzho <yuwen.zhou@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

neural_compressor/torch/algorithms/base_algorithm.py

@@ -14,45 +14,41 @@
 
 from abc import ABC, abstractmethod
 from collections import OrderedDict
-from typing import Any
+from typing import Any, Optional
 
 import torch
 
 from neural_compressor.torch.utils import Mode
 
 
 class Quantizer(ABC):
-    """The base quantizer for all algorithm quantizers."""
+    """The base quantizer for all algorithm quantizers.
 
-    def __init__(self, tune_cfg: OrderedDict = {}):
-        """Init a Quantizer object.
+    The `Quantizer` unifies the interfaces across various quantization algorithms, including GPTQ, RTN, etc.
+    Given a float model, `Quantizer` apply the quantization algorithm to the model according to the `quant_config`.
 
-        Args:
-            tune_cfg (OrderedDict, optional): quantization config for ops. Defaults to {}.
-                Take weight-only quantization as an example,
-                    tune_cfg={
-                        'fc2':
-                            {
-                                'dtype': 'int',
-                                'bits': 4,
-                                'group_size': 32,
-                                'scheme': 'sym'
-                            }
-                    }
-        """
-        self.tune_cfg = tune_cfg
+    To implement a new quantization algorithm,, inherit from `Quantizer` and implement the following methods:
+        - `prepare`: prepare a given model for convert.
+        - `convert`: convert a prepared model to a quantized model.
+    Note: `quantize` and `execute` are optional for new quantization algorithms.
+    """
 
-    @abstractmethod
-    def quantize(self, model: torch.nn.Module, *args: Any, **kwargs: Any):
-        """Quantizes a given torch model.
+    def __init__(self, quant_config: Optional[Any] = None):
+        """Init a Quantizer object.
 
         Args:
-            model (torch.nn.Module): The torch model to be quantized.
-
-        Returns:
-            A quantized model.
+            quant_config : Specifies how to apply the algorithm on the given model.
+            The format of `quant_config` can be defined by `Quantized` itself.
+            For example, `quant_config` can be a dictionary as below:
+                quant_config={
+                'fc2':{
+                    'dtype': 'int',
+                    'bits': 4,
+                    'group_size': 32,
+                    'scheme': 'sym'
+                    }}
         """
-        raise NotImplementedError("{} doesn't implement `quantize` function.".format(self.__class__.__name__))
+        self.quant_config = quant_config
 
     @abstractmethod
     def prepare(self, model: torch.nn.Module, *args: Any, **kwargs: Any):
@@ -80,6 +76,30 @@ def convert(self, model: torch.nn.Module, *args: Any, **kwargs: Any):
         """
         raise NotImplementedError("{} doesn't implement `convert` function. ".format(self.__class__.__name__))
 
+    def quantize(self, model: torch.nn.Module, *args: Any, **kwargs: Any):
+        """Quantizes a given float model.
+
+        Args:
+            model (torch.nn.Module): The float model to be quantized.
+
+        Returns:
+            A quantized model.
+        """
+        run_fn = kwargs.get("run_fn", None)
+        run_args = kwargs.get("run_args", None)
+        assert run_fn is not None, (
+            "Can't find run_func. Please provide run_func to quantize API "
+            "or overwrite quantize member function in your Quantizer class."
+        )
+
+        model = self.prepare(model, *args, **kwargs)
+        if run_args:
+            run_fn(model, *run_args)
+        else:
+            run_fn(model)
+        model = self.convert(model, *args, **kwargs)
+        return model
+
     def execute(self, model: torch.nn.Module, mode, *args: Any, **kwargs: Any):  # pragma: no cover
         """Execute according to mode.
 

neural_compressor/torch/algorithms/static_quant/static_quant.py

@@ -46,13 +46,13 @@
 
 
 class StaticQuantQuantizer(Quantizer):
-    def __init__(self, tune_cfg: OrderedDict = {}):
+    def __init__(self, quant_config: OrderedDict = {}):
         """Init a StaticQuantQuantizer object.
 
         Args:
-            tune_cfg (OrderedDict, optional): quantization config for ops. Defaults to {}.
+            quant_config (OrderedDict, optional): quantization config for ops. Defaults to {}.
         """
-        super().__init__(tune_cfg)
+        super().__init__(quant_config)
 
     def prepare(self, model, example_inputs, inplace=True, *args, **kwargs):
         """Prepares a given model for quantization.
@@ -71,7 +71,7 @@ def prepare(self, model, example_inputs, inplace=True, *args, **kwargs):
             model, example_inputs
         )
         # update json file in ipex_config_path; map ipex op_name to pt op_name
-        user_cfg = cfg_to_qconfig(self.tune_cfg, cfgs, op_infos_from_cfgs, output_tensor_id_op_name)
+        user_cfg = cfg_to_qconfig(self.quant_config, cfgs, op_infos_from_cfgs, output_tensor_id_op_name)
         model.eval()
 
         # Check save_qconf_summary part is a workaround for IPEX bug.
@@ -126,23 +126,6 @@ def convert(self, model, example_inputs, inplace=True, *args, **kwargs):
         model.save = MethodType(save, model)
         return model
 
-    def quantize(self, model, example_inputs, run_fn, inplace=True, *args, **kwargs):
-        """Quantizes a given torch model.
-
-        Args:
-            model: A float model to be quantized.
-            example_inputs: Used to trace torch model.
-            run_fn: a calibration function for calibrating the model.
-            inplace: Whether to carry out model transformations in-place. Defaults to True.
-
-        Returns:
-            A quantized model.
-        """
-        model = self.prepare(model, example_inputs=example_inputs, inplace=inplace)
-        run_fn(model)
-        model = self.convert(model, example_inputs=example_inputs, inplace=inplace)
-        return model
-
 
 def _ipex_post_quant_process(model, example_inputs, inplace=False):
     """Convert to a jit model.

neural_compressor/torch/quantization/algorithm_entry.py

@@ -155,7 +155,7 @@ def static_quant_entry(
     inplace = kwargs.get("inplace", True)
     assert example_inputs is not None, "Please provide example_inputs for static quantization."
 
-    quantizer = StaticQuantQuantizer(tune_cfg=quant_config_mapping)
+    quantizer = StaticQuantQuantizer(quant_config=quant_config_mapping)
     model = quantizer.execute(model, mode=mode, run_fn=run_fn, example_inputs=example_inputs, inplace=inplace)
     return model