support for restoring ipex model from json

neural_compressor/utils/pytorch.py

@@ -464,3 +464,50 @@ def load(checkpoint_dir=None, model=None, layer_wise=False, history_cfg=None, **
             assert len(mismatch_log.unexpected_keys) == 0, "Loading state_dict failed: {}".format(mismatch_log)
     util.get_embedding_contiguous(model)
     return model
+
+
+def recover_model_from_json(model, json_file_path, example_inputs):
+    """Recover ipex model from JSON file.
+
+    Args:
+        model (object): fp32 model need to do quantization.
+        json_file_path (json): configuration JSON file for ipex.
+        example_inputs (tuple or torch.Tensor or dict): example inputs that will be passed to the ipex function.
+
+    Returns:
+        (object): quantized model
+    """
+    from ..utils.utility import LazyImport
+
+    ipex = LazyImport("intel_extension_for_pytorch")
+    from torch.ao.quantization.observer import MinMaxObserver
+
+    qconfig = ipex.quantization.get_smooth_quant_qconfig_mapping(alpha=0.5, act_observer=MinMaxObserver())
+    if isinstance(example_inputs, dict):
+        model = ipex.quantization.prepare(model, qconfig, example_kwarg_inputs=example_inputs, inplace=True)
+    else:
+        model = ipex.quantization.prepare(model, qconfig, example_inputs=example_inputs, inplace=True)
+    model.load_qconf_summary(qconf_summary=json_file_path)
+    model = ipex.quantization.convert(model, inplace=True)
+    with torch.no_grad():
+        try:
+            if isinstance(example_inputs, dict):
+                # pylint: disable=E1120,E1123
+                model = torch.jit.trace(model, example_kwarg_inputs=example_inputs)
+            else:
+                model = torch.jit.trace(model, example_inputs)
+            model = torch.jit.freeze(model.eval())
+        except:
+            if isinstance(example_inputs, dict):
+                # pylint: disable=E1120,E1123
+                model = torch.jit.trace(model, example_kwarg_inputs=example_inputs, strict=False, check_trace=False)
+            else:
+                model = torch.jit.trace(model, example_inputs, strict=False)
+            model = torch.jit.freeze(model.eval())
+            if isinstance(example_inputs, dict):
+                model(**example_inputs)
+                model(**example_inputs)
+            else:
+                model(example_inputs)
+                model(example_inputs)
+    return model

test/algorithm/test_smooth_quant.py

@@ -880,6 +880,26 @@ def calib_func(model):
                 calib_func=calib_func,
             )
             q_model.save("saved")
+            # test recover_model_from_json
+            from neural_compressor.utils.pytorch import recover_model_from_json
+
+            tmp_model = copy.deepcopy(fp32_model)
+
+            ipex_model = recover_model_from_json(tmp_model, "./saved/best_configure.json", example_inputs=input_ids)
+            inc_output = q_model.model(input_ids)
+            ipex_output = ipex_model(input_ids)
+            self.assertTrue(torch.allclose(inc_output, ipex_output, atol=1e-05))
+
+            example_tuple = (input_ids,)
+            ipex_model = recover_model_from_json(tmp_model, "./saved/best_configure.json", example_inputs=example_tuple)
+            ipex_output = ipex_model(input_ids)
+            self.assertTrue(torch.allclose(inc_output, ipex_output, atol=1e-05))
+
+            example_dict = {"x": input_ids}
+            ipex_model = recover_model_from_json(tmp_model, "./saved/best_configure.json", example_inputs=example_dict)
+            ipex_output = ipex_model(input_ids)
+            self.assertTrue(torch.allclose(inc_output, ipex_output, atol=1e-05))
+
             # compare ipex and inc quantization
             with open("saved/best_configure.json", "r") as f:
                 inc_config_json = json.load(f)