Enhance woq model loading & support hf woq model loading

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

neural_compressor/torch/quantization/load_entry.py

@@ -31,28 +31,47 @@
 }
 
 
-def load(output_dir="./saved_results", model=None):
-    from neural_compressor.common.base_config import ConfigRegistry
+def load(model_name_or_path="./saved_results", model=None, format="default", *hf_model_args, **hf_model_kwargs):
+    """Load quantized model.
 
-    qconfig_file_path = os.path.join(os.path.abspath(os.path.expanduser(output_dir)), "qconfig.json")
-    with open(qconfig_file_path, "r") as f:
-        per_op_qconfig = json.load(f)
+    Args:
+        model_name_or_path (str, optional): local path where quantized weights or model are saved
+            or huggingface model id. Defaults to "./saved_results".
+        model (torch.nn.Module, optional): original model. Require to pass when loading INC WOQ quantized model
+            or loading FP8 model. Defaults to None.
+        format (str, optional): 'defult' for loading INC quantized model.
+            'huggingface' now only for loading huggingface WOQ causal language model. Defaults to "default".
 
-    if " " in per_op_qconfig.keys():  # ipex qconfig format: {' ': {'q_op_infos': {'0': {'op_type': ...
-        from neural_compressor.torch.algorithms.static_quant import load
+    Returns:
+        torch.nn.Module: quantized model
+    """
+    if format == "default":
+        from neural_compressor.common.base_config import ConfigRegistry
+        from neural_compressor.torch.algorithms.static_quant import load as static_quant_load
+        from neural_compressor.torch.algorithms.weight_only.save_load import load as woq_load
+        from neural_compressor.torch.algorithms.habana_fp8 import load as habana_fp8_load
 
-        return load(output_dir)
-    else:
-        config_mapping = load_config_mapping(qconfig_file_path, ConfigRegistry.get_all_configs()["torch"])
-        # select load function
-        config_object = config_mapping[next(iter(config_mapping))]
-        if isinstance(config_object, (RTNConfig, GPTQConfig, AWQConfig, TEQConfig, AutoRoundConfig)):  # WOQ
-            from neural_compressor.torch.algorithms.weight_only.save_load import load
+        qconfig_file_path = os.path.join(os.path.abspath(os.path.expanduser(model_name_or_path)), "qconfig.json")
+        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': ...
+            return static_quant_load(model_name_or_path)
+        else:
+            config_mapping = load_config_mapping(qconfig_file_path, ConfigRegistry.get_all_configs()["torch"])
+            # select load function
+            config_object = config_mapping[next(iter(config_mapping))]
 
-            return load(output_dir)
+            if isinstance(config_object, (RTNConfig, GPTQConfig, AWQConfig, TEQConfig, AutoRoundConfig)):  # WOQ
+                return woq_load(model_name_or_path, model=model, format=format)
 
-        model.qconfig = config_mapping
-        if isinstance(config_object, FP8Config):  # FP8
-            from neural_compressor.torch.algorithms.habana_fp8 import load
+            model.qconfig = config_mapping
+            if isinstance(config_object, FP8Config):  # FP8
+                return habana_fp8_load(model, model_name_or_path)
+    elif format == "huggingface":
+        # now only support load huggingface WOQ causal language model
+        from neural_compressor.torch.algorithms.weight_only.save_load import load as woq_load
 
-            return load(model, output_dir)  # pylint: disable=E1121
+        return woq_load(model_name_or_path, format=format, *hf_model_args, **hf_model_kwargs)
+    else:
+        raise ValueError("`format` in load function can only be 'huggingface' or 'default', but get {}".format(format))

test/3x/torch/quantization/weight_only/test_autoround.py

@@ -148,7 +148,7 @@ def test_save_and_load(self):
         from neural_compressor.torch.quantization import load
 
         # loading compressed model
-        loaded_model = load("saved_results")
+        loaded_model = load("saved_results", model=copy.deepcopy(self.gptj))
         loaded_out = loaded_model(self.inp)[0]
         assert torch.allclose(inc_out, loaded_out), "Unexpected result. Please double check."
 

test/3x/torch/quantization/weight_only/test_awq.py

@@ -131,7 +131,7 @@ def calib_func(model):
         from neural_compressor.torch.quantization import load
 
         # loading compressed model
-        loaded_model = load("saved_results")
+        loaded_model = load("saved_results", model=copy.deepcopy(self.tiny_gptj))
         loaded_out = loaded_model(self.example_inputs)[0]
         assert torch.allclose(inc_out, loaded_out), "Unexpected result. Please double check."
         assert isinstance(loaded_model.lm_head, WeightOnlyLinear), "loading compressed model failed."

test/3x/torch/quantization/weight_only/test_gptq.py

@@ -254,7 +254,7 @@ def test_save_and_load(self):
         from neural_compressor.torch.quantization import load
 
         # loading compressed model
-        loaded_model = load("saved_results")
+        loaded_model = load("saved_results", model=copy.deepcopy(self.tiny_gptj))
         loaded_out = loaded_model(self.example_inputs)[0]
         assert torch.allclose(inc_out, loaded_out), "Unexpected result. Please double check."
         assert isinstance(

test/3x/torch/quantization/weight_only/test_load_woq_hf_model.py

@@ -0,0 +1,17 @@
+import torch
+from transformers import AutoTokenizer
+from neural_compressor.torch.utils import accelerator
+
+device = accelerator.current_device_name()
+
+class TestHFModelLoad:
+    def setup_class(self):
+        self.model_name = "TheBloke/TinyLlama-1.1B-python-v0.1-GPTQ"
+        self.example_inputs = torch.tensor([[10, 20, 30, 40, 50, 60]], dtype=torch.long).to(device)
+
+    def test_load_hf_woq_model(self):
+        from neural_compressor.torch.quantization import load
+
+        qmodel = load(self.model_name, format="huggingface")
+        output = qmodel(self.example_inputs)[0]
+        assert len(output) > 0, "Not loading the model correctly"

test/3x/torch/quantization/weight_only/test_rtn.py

@@ -290,7 +290,7 @@ def test_save_and_load(self):
         from neural_compressor.torch.quantization import load
 
         # loading compressed model
-        loaded_model = load("saved_results")
+        loaded_model = load("saved_results", model=copy.deepcopy(self.tiny_gptj))
         loaded_out = loaded_model(self.example_inputs)[0]
         assert torch.allclose(inc_out, loaded_out), "Unexpected result. Please double check."
         assert isinstance(loaded_model.lm_head, WeightOnlyLinear), "loading compressed model failed."

test/3x/torch/quantization/weight_only/test_teq.py

@@ -141,7 +141,7 @@ def test_save_and_load(self):
         from neural_compressor.torch.quantization import load
 
         # loading compressed model
-        loaded_model = load("saved_results")
+        loaded_model = load("saved_results", model=copy.deepcopy(self.gptj))
         loaded_out = loaded_model(self.example_inputs)[0]
         assert torch.allclose(inc_out, loaded_out), "Unexpected result. Please double check."