Fix example inputs issue for IPEX smoothquant (#864)

Signed-off-by: changwangss <chang1.wang@intel.com>

neural_compressor/adaptor/pytorch.py

@@ -1269,7 +1269,8 @@ def smooth_quant(self, model, dataloader, calib_iter, tune_cfg=None, alpha=0.5,
 
         if not hasattr(self, 'sq') or force_re_smooth:
             from .torch_utils.smooth_quant import TorchSmoothQuant
-            self.sq = TorchSmoothQuant(model._model, dataloader=dataloader, q_func=self.q_func)
+            self.sq = TorchSmoothQuant(model._model, dataloader=dataloader, \
+                                          example_inputs=self.example_inputs, q_func=self.q_func)
         kwargs = {}  ##different backends may have different default values
         if op_types != None:
             kwargs["op_types"] = op_types
@@ -2986,7 +2987,8 @@ def _get_quantizable_ops_recursively(self, model, prefix, quantizable_ops):
                 ipex_conf.save(self.ipex_config_path)
             else:
                 if self.approach in ['post_training_static_quant', 'post_training_auto_quant']:
-                    assert self.q_dataloader is not None, "IPEX need q_dataloader to prepare the model"
+                    assert self.q_dataloader or self.example_inputs, \
+                            "IPEX need q_dataloader or example_inputs to prepare the model"
                     from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver, QConfig
                     if self.version.release >= Version("2.1").release:
                         # HistogramObserver will cause a performance issue.

neural_compressor/adaptor/torch_utils/smooth_quant.py

@@ -174,7 +174,7 @@ class TorchSmoothQuant:
     to recover the weights if needed
     """
 
-    def __init__(self, model, dataloader, q_func=None, traced_model=None):
+    def __init__(self, model, dataloader, example_inputs=None, q_func=None, traced_model=None):
         """
         :param model: Torch model :param dataloader: Calibration dataloader :param traced_model: A specific model
         shares the same architecture as the model and could be traced by torch.jit. If not supplied, we use model
@@ -187,6 +187,7 @@ def __init__(self, model, dataloader, q_func=None, traced_model=None):
         self.device = device
         self.dtype = dtype
         self.dataloader = dataloader
+        self.example_inputs = example_inputs
         self.q_func = q_func
         self.input_values = {}
         self.output_values = {}
@@ -752,10 +753,12 @@ def _trace(self, op_types):
         no_absorb_layers: A list saving the layers which could not find the absorb layer
         """
         tg = GraphTrace()
-        for idx, input in enumerate(self.dataloader):
-            example_inputs = input
-            break
-        absorb_to_layer, no_absorb_layers = tg.get_absorb_to_layer(self.traced_model, example_inputs, op_types)
+        if self.example_inputs is None:
+            assert self.dataloader, "Please provide dataloader or example_inputs"
+            for idx, input in enumerate(self.dataloader):
+                self.example_inputs = input
+                break
+        absorb_to_layer, no_absorb_layers = tg.get_absorb_to_layer(self.traced_model, self.example_inputs, op_types)
         return absorb_to_layer, no_absorb_layers