Fixed calibration sampling size error and IPEX examples error (#264)

* Fixed calibration sampling size error
* Update training fit API docstring
* Fixed IPEX examples error

Signed-off-by: Cheng, Penghui <[email protected]>

Author: PenghuiCheng

neural_compressor/config.py

@@ -469,6 +469,8 @@ def calibration_sampling_size(self):
     @calibration_sampling_size.setter
     def calibration_sampling_size(self, sampling_size):
         if check_value('calibration_sampling_size', sampling_size, int):
+            if isinstance(sampling_size, int):
+                sampling_size =[sampling_size]
             self._calibration_sampling_size = sampling_size
 
     @property

neural_compressor/experimental/benchmark.py

@@ -497,6 +497,12 @@ def model(self, user_model):
             logger.warning("Force convert framework model to neural_compressor model.")
             self._model = NCModel(user_model, framework=self.framework)
         else:
+            # It is config of neural_compressor version < 2.0, no need in 2.0
+            if cfg.model.framework == "pytorch_ipex":
+                from neural_compressor.model.torch_model import IPEXModel
+                if not isinstance(user_model, IPEXModel):
+                    self._model = NCModel(user_model.model, framework=cfg.model.framework)
+                    return
             self._model = user_model
 
         # (TODO) ugly to set these params, but tensorflow need

neural_compressor/experimental/component.py

@@ -489,6 +489,13 @@ def model(self, user_model):
             logger.warning("Force convert framework model to neural_compressor model.")
             self._model = Model(user_model, framework=self.framework)
         else:
+            # It is config of neural_compressor version < 2.0, no need in 2.0
+            if self.cfg.model.framework == "pytorch_ipex":
+                from neural_compressor.model.torch_model import IPEXModel
+                if not isinstance(user_model, IPEXModel):
+                    self._model = Model(user_model.model, framework=self.cfg.model.framework)
+                    return
+
             self._model = user_model
 
         if 'tensorflow' in self.framework:

neural_compressor/training.py

@@ -35,22 +35,23 @@ class CompressionManager:
     examples:
         import neural_compressor.training.prepare_compression
         compression_manager = prepare_compression(conf, model)
+        compression_manager.callbacks.on_train_begin()
+        model = compression_manager.model
         train_loop:
-            compression_manager.on_train_begin()
             for epoch in range(epochs):
-                compression_manager.on_epoch_begin(epoch)
+                compression_manager.callbacks.on_epoch_begin(epoch)
                 for i, batch in enumerate(dataloader):
-                    compression_manager.on_step_begin(i)
+                    compression_manager.callbacks.on_step_begin(i)
                     ......
-                    output = compression_manager.model(batch)
+                    output = model(batch)
                     loss = ......
-                    loss = compression_manager.on_after_compute_loss(batch, output, loss)
+                    loss = compression_manager.callbacks.on_after_compute_loss(batch, output, loss)
                     loss.backward()
-                    compression_manager.on_before_optimizer_step()
+                    compression_manager.callbacks.on_before_optimizer_step()
                     optimizer.step()
-                    compression_manager.on_step_end()
-                compression_manager.on_epoch_end()
-            compression_manager.on_train_end()
+                    compression_manager.callbacks.on_step_end()
+                compression_manager.callbacks.on_epoch_end()
+        compression_manager.callbacks.on_train_end()
         compression_manager.save("path_to_save")
     """
     def __init__(self, component):