refine load API for 3.x ipex backend (#1755)

Signed-off-by: Cheng, Zixuan <zixuan.cheng@intel.com>

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/habana_fp8/run_llm.py

@@ -143,7 +143,7 @@ def calib_func(model):
 
 if args.load:
     from neural_compressor.torch.quantization import load
-    user_model = load(user_model, "saved_results")
+    user_model = load("saved_results", user_model)
 
 
 if args.approach in ["dynamic", "static"] or args.load:

neural_compressor/common/utils/save_load.py

@@ -19,7 +19,7 @@
 import os
 
 
-def save_config_mapping(config_mapping, qconfig_file_path):
+def save_config_mapping(config_mapping, qconfig_file_path):  # pragma: no cover
     """Save config mapping to json file.
 
     Args:
@@ -36,7 +36,7 @@ def save_config_mapping(config_mapping, qconfig_file_path):
         json.dump(per_op_qconfig, f, indent=4)
 
 
-def load_config_mapping(qconfig_file_path, config_name_mapping):
+def load_config_mapping(qconfig_file_path, config_name_mapping):  # pragma: no cover
     """Reload config mapping from json file.
 
     Args:

neural_compressor/torch/quantization/load_entry.py

@@ -24,15 +24,23 @@
 }
 
 
-def load(model, output_dir="./saved_results"):
+def load(output_dir="./saved_results", model=None):
     from neural_compressor.common.base_config import ConfigRegistry
 
     qconfig_file_path = os.path.join(os.path.abspath(os.path.expanduser(output_dir)), "qconfig.json")
-    config_mapping = load_config_mapping(qconfig_file_path, ConfigRegistry.get_all_configs()["torch"])
-    model.qconfig = config_mapping
-    # select load function
-    config_object = config_mapping[next(iter(config_mapping))]
-    if isinstance(config_object, FP8Config):
-        from neural_compressor.torch.algorithms.habana_fp8 import load
-
-        return load(model, output_dir)
+    with open(qconfig_file_path, "r") as f:
+        per_op_qconfig = json.load(f)
+    if " " in per_op_qconfig.keys():  # ipex qconfig format: {' ': {'q_op_infos': {'0': {'op_type': ...
+        from neural_compressor.torch.algorithms.static_quant import load
+
+        return load(output_dir)
+
+    else:  # FP8
+        config_mapping = load_config_mapping(qconfig_file_path, ConfigRegistry.get_all_configs()["torch"])
+        model.qconfig = config_mapping
+        # select load function
+        config_object = config_mapping[next(iter(config_mapping))]
+        if isinstance(config_object, FP8Config):
+            from neural_compressor.torch.algorithms.habana_fp8 import load
+
+            return load(model, output_dir)

test/3x/torch/quantization/habana_fp8/test_fp8.py

@@ -153,7 +153,7 @@ def calib_func(model):
         from neural_compressor.torch.quantization import load
 
         m = copy.deepcopy(self.model)
-        m = load(m, "saved_results")
+        m = load("saved_results", m)
         recovered_out = m(inp)
         assert (recovered_out == fp8_out).all(), "Unexpected result. Please double check."
         assert isinstance(m.fc1, FP8Linear), "Unexpected result. Please double check."

test/3x/torch/quantization/test_smooth_quant.py

@@ -133,7 +133,8 @@ def test_sq_save_load(self):
         q_model.save("saved_results")
         inc_out = q_model(example_inputs)
 
-        from neural_compressor.torch.algorithms.smooth_quant import load, recover_model_from_json
+        from neural_compressor.torch.algorithms.smooth_quant import recover_model_from_json
+        from neural_compressor.torch.quantization import load
 
         # load using saved model
         loaded_model = load("saved_results")

test/3x/torch/quantization/test_static_quant.py

@@ -153,7 +153,7 @@ def run_fn(model):
         assert torch.allclose(inc_out, ipex_out, atol=2e-02), "Unexpected result. Please double check."
         q_model.save("saved_results")
 
-        from neural_compressor.torch.algorithms.static_quant import load
+        from neural_compressor.torch.quantization import load
 
         # load
         loaded_model = load("saved_results")