Fixed UT and examples error

Signed-off-by: Cheng, Penghui <penghui.cheng@intel.com>

Author: PenghuiCheng

.github/workflows/test_inc.yml

@@ -30,8 +30,11 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
+        pip install cmake>=3.16
+        pip install py-cpuinfo
+        pip install torch==2.1.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu
         pip install .[neural-compressor,diffusers,tests]
-        pip install intel-extension-for-pytorch
+        pip install intel-extension-for-pytorch==2.1.100
     - name: Test with Pytest
       run: |
         pytest tests/neural_compressor/

examples/neural_compressor/language-modeling/run_clm.py

@@ -63,6 +63,8 @@
 if is_intel_extension_for_transformers_available():
     from intel_extension_for_transformers.transformers.utils.config import WeightOnlyQuantConfig
 
+    from optimum.intel.neural_compressor import ITREXAutoModelForCausalLM
+
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
@@ -147,7 +149,9 @@ class OptimizationArguments:
     )
     quantization_approach: str = field(
         default="dynamic",
-        metadata={"help": "Quantization approach. Supported approach are static, dynamic and aware_training."},
+        metadata={
+            "help": "Quantization approach. Supported approach are static, dynamic aware_training and weight_only."
+        },
     )
     smooth_quant: bool = field(
         default=False,
@@ -200,8 +204,12 @@ class OptimizationArguments:
         default=False,
         metadata={"help": "Whether or not to verify the loading of the quantized model."},
     )
+    bits: str = field(
+        default="4",
+        metadata={"help": "Bits number of weight for weight only quantization. 1~8 bits."},
+    )
     weight_dtype: str = field(
-        default="int8",
+        default="int4_clip",
         metadata={"help": "weight dtype for weight only quantization."},
     )
     group_size: int = field(
@@ -218,9 +226,24 @@ class OptimizationArguments:
     )
     quantization_methodology: str = field(
         default="RTN",
-        metadata={
-            "help": "Quantization methodology for weight only quantization. Choose from 'RTN', 'AWQ' and 'GPTQ'."
-        },
+        metadata={"help": "Quantization methodology for weight only quantization. Choose from 'RTN' and 'GPTQ'."},
+    )
+    gptq_percdamp: float = field(
+        default=0.01,
+        metadata={"help": "Percent of the average Hessian diagonal to use for dampening."},
+    )
+    gptq_block_size: int = field(
+        default=128,
+        metadata={"help": "Block size. sub weight matrix size to run GPTQ."},
+    )
+    gptq_nsamples: int = field(default=128, metadata={"help": "Number of calibration data samples."})
+    gptq_use_max_length: bool = field(
+        default=False,
+        metadata={"help": "Set all sequence length to be same length of args.gptq_pad_max_length"},
+    )
+    gptq_pad_max_length: int = field(
+        default=2048,
+        metadata={"help": "Calibration dataset sequence max length, this should align with your model config"},
     )
 
 
@@ -636,11 +659,21 @@ def compute_metrics(eval_preds):
                     )
                 if optim_args.apply_pruning or optim_args.apply_distillation:
                     raise ValueError("Weight only quantization and pruning or distillation cannot be combined.")
+                if optim_args.quantization_methodology == "GPTQ":
+                    algorithm_args = {
+                        "act_order": False,
+                        "percdamp": optim_args.gptq_percdamp,
+                        "block_size": optim_args.gptq_block_size,
+                        "nsamples": optim_args.gptq_nsamples,
+                        "use_max_length": optim_args.gptq_use_max_length,
+                        "pad_max_length": optim_args.gptq_pad_max_length,
+                    }
                 quantization_config = WeightOnlyQuantConfig(
                     weight_dtype=optim_args.weight_dtype,
                     group_size=optim_args.group_size,
                     scheme=optim_args.weight_only_scheme,
                     algorithm=optim_args.quantization_methodology,
+                    algorithm_args=algorithm_args if optim_args.quantization_methodology == "GPTQ" else None,
                 )
             else:
                 quantization_config = PostTrainingQuantConfig(
@@ -733,17 +766,20 @@ def compute_metrics(eval_preds):
         quantizer.quantize(
             quantization_config=quantization_config,
             save_directory=training_args.output_dir,
-            calibration_dataset=train_dataset
-            if optim_args.quantization_approach in ["static", "weight_only"]
-            else None,
-            batch_size=1
-            if optim_args.quantization_approach == "weight_only"
-            else training_args.per_device_train_batch_size,
+            calibration_dataset=(
+                train_dataset if optim_args.quantization_approach in ["static", "weight_only"] else None
+            ),
+            batch_size=(
+                1 if optim_args.quantization_approach == "weight_only" else training_args.per_device_train_batch_size
+            ),
         )
         trainer.model = quantizer._quantized_model
 
     if optim_args.apply_quantization and optim_args.verify_loading:
-        loaded_model = INCModelForCausalLM.from_pretrained(training_args.output_dir)
+        if optim_args.quantization_approach == "weight_only":
+            loaded_model = ITREXAutoModelForCausalLM.from_pretrained(training_args.output_dir)
+        else:
+            loaded_model = INCModelForCausalLM.from_pretrained(training_args.output_dir)
         tokens = tokenizer("This is a sample input", return_tensors="pt")
         with torch.no_grad():
             original_model_outputs = trainer.model(**tokens)

examples/neural_compressor/text-generation/run_generation.py

@@ -368,9 +368,9 @@ def calibration_fn(p_model):
 
     args.length = adjust_length_to_model(
         args.length,
-        max_sequence_length=model.config.max_position_embeddings
-        if hasattr(model.config, "max_position_embeddings")
-        else 0,
+        max_sequence_length=(
+            model.config.max_position_embeddings if hasattr(model.config, "max_position_embeddings") else 0
+        ),
     )
     logger.info(args)
 

optimum/intel/neural_compressor/__init__.py

@@ -12,7 +12,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from ..utils.import_utils import is_diffusers_available
+from ..utils.import_utils import is_diffusers_available, is_intel_extension_for_transformers_available
 from .configuration import INCConfig
 from .modeling_base import (
     INCModel,
@@ -32,3 +32,7 @@
 
 if is_diffusers_available():
     from .modeling_diffusion import INCStableDiffusionPipeline
+
+
+if is_intel_extension_for_transformers_available():
+    from .modeling_base import ITREXAutoModelForCausalLM

optimum/intel/neural_compressor/configuration.py

@@ -35,7 +35,7 @@ class INCConfig(BaseConfig):
 
     def __init__(
         self,
-        quantization: Optional[Union[Dict, _BaseQuantizationConfig, "WeightOnlyQuantConfig"]] = None,
+        quantization: Optional[Union[Dict, _BaseQuantizationConfig]] = None,
         pruning: Optional[Union[Dict, _BaseQuantizationConfig]] = None,
         distillation: Optional[Union[Dict, _BaseQuantizationConfig]] = None,
         save_onnx_model: bool = False,
@@ -50,7 +50,7 @@ def __init__(
         self.save_onnx_model = save_onnx_model
 
     @staticmethod
-    def _create_quantization_config(config):
+    def _create_quantization_config(config: Union[Dict, _BaseQuantizationConfig]):
         # TODO : add activations_dtype and weights_dtype
         if isinstance(config, _BaseQuantizationConfig):
             approach = _quantization_model[config.approach]

optimum/intel/neural_compressor/modeling_base.py

@@ -43,7 +43,7 @@
 from optimum.intel.generation import BaseModelForCausalLM
 
 from ...modeling_base import OptimizedModel
-from ..utils.import_utils import _torch_version, is_torch_version
+from ..utils.import_utils import _torch_version, is_intel_extension_for_transformers_available, is_torch_version
 from .configuration import INCConfig
 from .utils import WEIGHTS_NAME
 
@@ -63,6 +63,14 @@
 """
 
 
+if is_intel_extension_for_transformers_available():
+    from intel_extension_for_transformers.transformers.modeling import AutoModelForCausalLM as ITREX_WOQ_MODEL
+
+    class ITREXAutoModelForCausalLM(ITREX_WOQ_MODEL):
+        auto_model_class = AutoModelForCausalLM
+        export_feature = "text-generation"
+
+
 class INCModel(OptimizedModel):
     auto_model_class = AutoModel
     export_feature = "feature-extraction"

optimum/intel/neural_compressor/quantization.py

@@ -15,6 +15,7 @@
 import copy
 import inspect
 import logging
+import types
 import warnings
 from enum import Enum
 from itertools import chain
@@ -79,6 +80,7 @@
 
 if is_intel_extension_for_transformers_available():
     from intel_extension_for_transformers.llm.quantization.utils import convert_to_quantized_model
+    from intel_extension_for_transformers.transformers.modeling.modeling_auto import save_low_bit
     from intel_extension_for_transformers.transformers.utils.config import WeightOnlyQuantConfig
 
     Config = Union[PostTrainingQuantConfig, WeightOnlyQuantConfig]
@@ -185,6 +187,9 @@ def quantize(
         save_directory = Path(save_directory)
         save_directory.mkdir(parents=True, exist_ok=True)
         save_onnx_model = kwargs.pop("save_onnx_model", False)
+        device = kwargs.pop("device", "cpu")
+        use_cpu = True if device == torch.device("cpu") or device == "cpu" else False
+        use_xpu = True if (isinstance(device, torch.device) and device.type == "xpu") or device == "xpu" else False
 
         if save_onnx_model and (isinstance(self._original_model, ORTModel) or weight_only):
             save_onnx_model = False
@@ -217,7 +222,10 @@ def quantize(
                     f"For weight-only quantization, `quantization_config` should be an instance of `WeightOnlyQuantConfig`, but got: {type(quantization_config)} instead."
                 )
 
-            if calibration_dataset is None and ("GPTQ" in algo or "AWQ" in algo):
+            if algo not in ["RTN", "GPTQ"]:
+                raise ValueError("Weight-only quantization is only support RTN and GPTQ algorithm now!")
+
+            if calibration_dataset is None and quantization_config.tokenizer is None and ("GPTQ" in algo):
                 raise ValueError(
                     "Weight-only quantization needs a calibration dataset for both GPTQ and AWQ methodologies."
                 )
@@ -278,10 +286,24 @@ def quantize(
             )
 
         if not isinstance(quantization_config, PostTrainingQuantConfig):
-            self._quantized_model = convert_to_quantized_model(self._original_model, quantization_config)
+            if use_cpu:
+                # will remove after intel-extension-for-transformers 1.3.3 released
+                quantization_config.device = "cpu"
+                quantization_config.post_init()
+            elif use_xpu:
+                # will remove after intel-extension-for-transformers 1.3.3 released
+                quantization_config.device = "xpu"
+                quantization_config.post_init_xpu()
+            self._quantized_model = convert_to_quantized_model(
+                self._original_model, quantization_config, device=quantization_config.device
+            )
+            # will remove after intel-extension-for-transformers 1.3.3 released
+            if hasattr(quantization_config, "calib_dataloader"):
+                quantization_config.calib_dataloader = None
+            self._quantized_model.quantization_config = quantization_config
+            self._quantized_model.save_pretrained = types.MethodType(save_low_bit, self._quantized_model)
             # Save the quantized model
-            output_path = save_directory.joinpath(file_name or default_name)
-            self._quantized_model.save_pretrained(output_path)
+            self._quantized_model.save_pretrained(save_directory)
         else:
             if isinstance(self._original_model.config, PretrainedConfig):
                 self._original_model.config.backend = quantization_config.backend

setup.py

@@ -34,7 +34,7 @@
     "rjieba",
     "timm",
     "invisible-watermark>=0.2.0",
-    "cmake>=3.16",
+    # Will remove after intel-extension-for-transformers 1.3.3 released.
     "intel-extension-for-transformers>=1.3",
     "peft",
     "auto-gptq",

tests/neural_compressor/test_optimization.py

@@ -45,7 +45,7 @@
     set_seed,
 )
 from utils_tests import SEED, INCTestMixin, _generate_dataset
-from optimum.intel.utils.import_utils import is_torch_version
+from optimum.intel.utils.import_utils import is_torch_version, is_intel_extension_for_transformers_available
 
 
 from optimum.intel import (
@@ -60,11 +60,13 @@
     INCSeq2SeqTrainer,
     INCStableDiffusionPipeline,
 )
-from intel_extension_for_transformers.transformers.utils.config import WeightOnlyQuantConfig
 from optimum.intel.utils.constant import DIFFUSION_WEIGHTS_NAME
 from optimum.onnxruntime import ORTModelForCausalLM, ORTModelForSequenceClassification
 from optimum.pipelines import ORT_SUPPORTED_TASKS
 
+if is_intel_extension_for_transformers_available():
+    from optimum.intel.neural_compressor import ITREXAutoModelForCausalLM
+    from intel_extension_for_transformers.transformers.utils.config import WeightOnlyQuantConfig
 
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 set_seed(SEED)
@@ -200,63 +202,60 @@ def test_ipex_static_quantization_with_smoothquant(self, task, model_name, expec
                 load_ipex_model=True,
             )
 
+    @unittest.skipIf(
+        not is_intel_extension_for_transformers_available(), reason="Intel-extension-for-transformers not available!"
+    )
     def test_weight_only_quantization(self):
         model_name = "hf-internal-testing/tiny-random-GPTNeoForCausalLM"
         model = AutoModelForCausalLM.from_pretrained(model_name)
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         tokenizer.add_special_tokens({"pad_token": "[PAD]"})
-        calibration_dataset = _generate_dataset(quantizer, tokenizer, num_samples=2)
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             quantizer = INCQuantizer.from_pretrained(copy.deepcopy(model), task="text-generation")
+            calibration_dataset = _generate_dataset(quantizer, tokenizer, num_samples=2)
             quantization_config = WeightOnlyQuantConfig(weight_dtype="int8")
             q_model = quantizer.quantize(
                 quantization_config=quantization_config,
                 save_directory=tmp_dir,
             )
+            q_model = ITREXAutoModelForCausalLM.from_pretrained(tmp_dir)
             inp = torch.tensor([calibration_dataset[0]["input_ids"]])
             out = model(inp)[0]
             q_out = q_model(inp)[0]
             self.assertTrue(torch.all(torch.isclose(out, q_out, atol=5e-1)))
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             quantizer = INCQuantizer.from_pretrained(copy.deepcopy(model), task="text-generation")
+            calibration_dataset = _generate_dataset(quantizer, tokenizer, num_samples=2)
             quantization_config = WeightOnlyQuantConfig(
                 algorithm="GPTQ",
+                algorithm_args={
+                    "percdamp": 0.01,
+                    "act_order": False,
+                    "scheme": "sym",
+                },
                 weight_dtype="int4_clip",
             )
             q_model = quantizer.quantize(
                 quantization_config=quantization_config,
                 calibration_dataset=calibration_dataset,
                 save_directory=tmp_dir,
             )
+            q_model = ITREXAutoModelForCausalLM.from_pretrained(tmp_dir)
             inp = torch.tensor([calibration_dataset[0]["input_ids"]])
             out = model(inp)[0]
             q_out = q_model(inp)[0]
             self.assertTrue(torch.all(torch.isclose(out, q_out, atol=5e-1)))
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             quantizer = INCQuantizer.from_pretrained(copy.deepcopy(model), task="text-generation")
-            quantization_config = WeightOnlyQuantConfig(
-                algorithm="AWQ",
-                weight_dtype="int4_clip",
-            )
-            q_model = quantizer.quantize(
-                quantization_config=quantization_config,
-                calibration_dataset=calibration_dataset,
-                save_directory=tmp_dir,
-            )
-            inp = torch.tensor([calibration_dataset[0]["input_ids"]])
-            out = model(inp)[0]
-            q_out = q_model(inp)[0]
-            self.assertTrue(torch.all(torch.isclose(out, q_out, atol=5e-1)))
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            quantizer = INCQuantizer.from_pretrained(copy.deepcopy(model), task="text-generation")
+            calibration_dataset = _generate_dataset(quantizer, tokenizer, num_samples=2)
             q_model = quantizer.quantize(
                 weight_only=True,  # use RTN quantization method and NF4 weight data type is default.
                 save_directory=tmp_dir,
             )
+            q_model = ITREXAutoModelForCausalLM.from_pretrained(tmp_dir)
             inp = torch.tensor([calibration_dataset[0]["input_ids"]])
             out = model(inp)[0]
             q_out = q_model(inp)[0]

tests/openvino/test_modeling_basic.py

@@ -7,6 +7,7 @@
 This test is meant to run quickly with tiny test models. More extensive tests are in
 test_modeling.py.
 """
+
 # ruff: noqa
 
 import gc