example update for 3.x ipex sq

examples/.config/model_params_pytorch_3x.json

@@ -85,7 +85,7 @@
         "batch_size": 8
       },
       "gpt_j_ipex":{
-        "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/static_quant",
+        "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/static_quant/ipex",
         "dataset_location": "",
         "input_model": "",
         "main_script": "run_clm_no_trainer.py",
@@ -99,7 +99,7 @@
         "batch_size": 1
       },
       "llama2_7b_ipex":{
-        "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/static_quant",
+        "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/static_quant/ipex",
         "dataset_location": "",
         "input_model": "",
         "main_script": "run_clm_no_trainer.py",
@@ -113,7 +113,7 @@
         "batch_size": 1
       },
       "opt_125m_ipex":{
-        "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/static_quant",
+        "model_src_dir": "nlp/huggingface_models/language-modeling/quantization/static_quant/ipex",
         "dataset_location": "",
         "input_model": "",
         "main_script": "run_clm_no_trainer.py",

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/smooth_quant/requirements.txt

@@ -11,3 +11,4 @@ neural-compressor
 intel-extension-for-transformers
 lm_eval==0.4.2
 peft
+optimum-intel

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/smooth_quant/run_clm_no_trainer.py

@@ -162,15 +162,6 @@ def get_user_model():
         collate_fn=calib_evaluator.collate_batch,
     )
 
-    from neural_compressor.torch.quantization import SmoothQuantConfig
-
-    args.alpha = eval(args.alpha)
-    excluded_precisions = [] if args.int8_bf16_mixed else ["bf16"]
-    quant_config = SmoothQuantConfig(alpha=args.alpha, folding=False, excluded_precisions=excluded_precisions)
-
-    if re.search("gpt", user_model.config.model_type):
-        quant_config.set_local(torch.add, SmoothQuantConfig(w_dtype="fp32", act_dtype="fp32"))
-
     from neural_compressor.torch.algorithms.smooth_quant import move_input_to_device
     from tqdm import tqdm
 
@@ -189,16 +180,39 @@ def run_fn(model):
             if calib_iter >= args.calib_iters:
                 break
         return
-
+
+    def eval_func(model):
+        config = AutoConfig.from_pretrained(args.model)
+        setattr(model, "config", config)
+
+        from intel_extension_for_transformers.transformers.llm.evaluation.lm_eval import evaluate, LMEvalParser
+        eval_args = LMEvalParser(
+            model="hf",
+            user_model=model,
+            tokenizer=tokenizer,
+            batch_size=args.batch_size,
+            tasks=args.tasks,
+            device="cpu",
+        )
+        results = evaluate(eval_args)
+        if args.tasks == "wikitext":
+            return results["results"][args.tasks]["word_perplexity,none"]
+        else:
+            return results["results"][args.tasks]["acc,none"]
+
     from utils import get_example_inputs
 
     example_inputs = get_example_inputs(user_model, calib_dataloader)
 
-    from neural_compressor.torch.quantization import prepare, convert
-
-    user_model = prepare(model=user_model, quant_config=quant_config, example_inputs=example_inputs)
-    run_fn(user_model)
-    user_model = convert(user_model)
+    from neural_compressor.torch.quantization import SmoothQuantConfig, autotune, TuningConfig
+    tune_config = TuningConfig(config_set=SmoothQuantConfig.get_config_set_for_tuning())
+    user_model = autotune(
+        user_model, 
+        tune_config=tune_config,
+        eval_fn=eval_func,
+        run_fn=run_fn,
+        example_inputs=example_inputs,
+    )
     user_model.save(args.output_dir)
 
 
@@ -231,11 +245,10 @@ def run_fn(model):
     results = evaluate(eval_args)
     for task_name in args.tasks.split(","):
         if task_name == "wikitext":
-            acc = results["results"][task_name]["word_perplexity,none"]
+            print("Accuracy for %s is: %s" % (task_name, results["results"][task_name]["word_perplexity,none"]))
         else:
-            acc = results["results"][task_name]["acc,none"]
-    print("Accuracy: %.5f" % acc)
-    print("Batch size = %d" % args.batch_size)
+            print("Accuracy for %s is: %s" % (task_name, results["results"][task_name]["acc,none"]))
+
 
 if args.performance:
     user_model.eval()

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/static_quant/ipex/run_clm_no_trainer.py

@@ -164,9 +164,9 @@ def get_user_model():
     )
 
 
-    from neural_compressor.torch.quantization import get_default_static_config, StaticQuantConfig
-    quant_config =  get_default_static_config()
-    quant_config.excluded_precisions = [] if args.int8_bf16_mixed else ["bf16"]
+    from neural_compressor.torch.quantization import StaticQuantConfig
+    excluded_precisions = [] if args.int8_bf16_mixed else ["bf16"]
+    quant_config = StaticQuantConfig(excluded_precisions=excluded_precisions)
     if re.search("gpt", user_model.config.model_type):
         quant_config.set_local("add", StaticQuantConfig(w_dtype="fp32", act_dtype="fp32"))
 

neural_compressor/torch/quantization/autotune.py

@@ -81,7 +81,7 @@ def autotune(
     best_quant_model = None
     eval_func_wrapper = EvaluationFuncWrapper(eval_fn, eval_args)
     config_loader, tuning_logger, tuning_monitor = init_tuning(tuning_config=tune_config)
-    baseline: float = eval_func_wrapper.evaluate(model)
+    baseline: float = eval_func_wrapper.evaluate(deepcopy(model))
     tuning_monitor.set_baseline(baseline)
     tuning_logger.tuning_start()
     for trial_index, quant_config in enumerate(config_loader, 1):

neural_compressor/torch/quantization/config.py

@@ -1582,8 +1582,14 @@ def get_model_info(self, model: torch.nn.Module, example_inputs) -> List[Tuple[s
 
     @classmethod
     def get_config_set_for_tuning(cls) -> Union[None, "SmoothQuantConfig", List["SmoothQuantConfig"]]:
-        """Get the default configuration set for tuning."""
-        return SmoothQuantConfig(alpha=[0.1, 0.5], folding=[True, False], scale_sharing=[True, False])
+        import numpy as np
+
+        return SmoothQuantConfig(
+            alpha=np.arange(0.1, 1.0, 0.1).tolist(),
+            folding=[True, False],
+            scale_sharing=[True, False],
+            excluded_precisions=[["bf16"]],
+        )
 
 
 def get_default_sq_config() -> SmoothQuantConfig:

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

@@ -216,7 +216,7 @@ def test_static_quant_with_quantize_API(self):
     def test_static_quant_mixed_precision(self):
         fp32_model = copy.deepcopy(self.fp32_model)
         example_inputs = self.input
-        quant_config = get_default_static_config()
+        quant_config = StaticQuantConfig(excluded_precisions=["bf16"])
         prepared_model = prepare(fp32_model, quant_config=quant_config, example_inputs=example_inputs)
         run_fn(prepared_model)
         q_model = convert(prepared_model)
@@ -229,7 +229,6 @@ def test_static_quant_mixed_precision(self):
         q_model = convert(prepared_model)
         assert q_model is not None, "Quantization failed!"
 
-        quant_config.excluded_precisions = ["bf16"]
         prepared_model = prepare(fp32_model, quant_config=quant_config, example_inputs=example_inputs)
         run_fn(prepared_model)
         q_model = convert(prepared_model)