Add a BERT example using Intel Neural Compressor SmoothQuant

examples/bert/README.md

@@ -57,6 +57,26 @@ The workflow in [bert_inc_static_ptq_cpu.json](bert_inc_static_ptq_cpu.json) is
 #### Dynamic Quantization
 The workflow in [bert_inc_dynamic_ptq_cpu.json](bert_inc_dynamic_ptq_cpu.json) is similar to the above workflow, but specifically uses dynamic quantization instead of static/dynamic quantization.
 
+#### Run with SmoothQuant
+
+Quantizing activations in large language models (LLMs) with huge parameter sizes can be challenging due to the presence of outliers. The SmoothQuant method, introduced in this [paper](https://arxiv.org/abs/2211.10438), addresses this issue by transferring the quantization difficulty from activations to weights through a mathematically equivalent transformation by using a fixed-value $\alpha$ for the entire model. However, the distributions of activation outliers vary not only across different models but also across different layers within a model. To resolve this, Intel® Neural Compressor proposes a method to obtain layer-wise optimal $\alpha$ values with the ability to tune automatically. Please refer to this [link](https://github.com/intel/neural-compressor/blob/master/docs/source/smooth_quant.md) for more algorithm details.
+
+User can use SmoothQuant by setting `smooth_quant` in `recipes` as shown below. Refer to [bert_inc_smoothquant_ptq_cpu.json](bert_inc_smoothquant_ptq_cpu.json) for an example of SmoothQuant.
+
+```json
+"passes": {
+    "quantization": {
+        "type": "IncStaticQuantization",
+        "config": {
+            "recipes":{
+                "smooth_quant": true,
+                "smooth_quant_args": {"alpha": 0.5}
+            }
+        }
+    }
+}
+```
+
 ### BERT optimization with QAT Customized Training Loop on CPU
 This workflow performs BERT optimization on CPU with QAT Customized Training Loop. It performs the optimization pipeline:
 - *PyTorch Model -> PyTorch Model after QAT -> Onnx Model -> Transformers Optimized Onnx Model -> ONNX Runtime performance tuning*

examples/bert/bert_inc_ptq_cpu.json

@@ -56,6 +56,7 @@
         },
         "quantization": {
             "type": "IncQuantization",
+            "disable_search": true,
             "config": {
                 "approach": "SEARCHABLE_VALUES",
                 "user_script": "user_script.py",

examples/bert/bert_inc_smoothquant_ptq_cpu.json

@@ -0,0 +1,92 @@
+{
+    "input_model":{
+        "type": "PyTorchModel",
+        "config": {
+            "model_loader": "load_pytorch_origin_model",
+            "model_script": "user_script.py",
+            "io_config": {
+                "input_names": ["input_ids", "attention_mask", "token_type_ids"],
+                "input_shapes": [[1, 128], [1, 128], [1, 128]],
+                "input_types": ["int64", "int64", "int64"],
+                "output_names": ["output"]
+            }
+        }
+    },
+    "evaluators": {
+        "common_evaluator": {
+            "metrics":[
+                {
+                    "name": "accuracy",
+                    "type": "accuracy",
+                    "sub_types": [
+                        {"name": "accuracy_score", "priority": 1, "goal": {"type": "percent-max-degradation", "value": 2}}
+                    ],
+                    "user_config":{
+                        "post_processing_func": "post_process",
+                        "user_script": "user_script.py",
+                        "dataloader_func": "create_dataloader",
+                        "batch_size": 1
+                    }
+                },
+                {
+                    "name": "latency",
+                    "type": "latency",
+                    "sub_types": [
+                        {"name": "avg", "priority": 2, "goal": {"type": "percent-min-improvement", "value": 20}}
+                    ],
+                    "user_config":{
+                        "user_script": "user_script.py",
+                        "dataloader_func": "create_dataloader",
+                        "batch_size": 1
+                    }
+                }
+            ]
+        }
+    },
+    "passes": {
+        "conversion": {
+            "type": "OnnxConversion",
+            "config": {
+                "target_opset": 13
+            }
+        },
+        "transformers_optimization": {
+            "type": "OrtTransformersOptimization",
+            "config": {"model_type": "bert"}
+        },
+        "quantization": {
+            "type": "IncStaticQuantization",
+            "disable_search": true,
+            "config": {
+                "quant_format": "QOperator",
+                "user_script": "user_script.py",
+                "dataloader_func": "inc_glue_calibration_reader",
+                "recipes":{
+                    "smooth_quant": true,
+                    "smooth_quant_args": {"alpha": 0.7}
+                },
+                "metric": {
+                    "name": "accuracy",
+                    "type": "custom",
+                    "sub_types": [
+                        {"name": "accuracy_custom", "priority": 1, "higher_is_better": true, "goal": {"type": "percent-max-degradation", "value": 2}}
+                    ],
+                    "user_config":{
+                        "user_script": "user_script.py",
+                        "evaluate_func": "eval_accuracy",
+                        "batch_size": 1
+                    }
+                }
+            }
+        }
+    },
+    "engine": {
+        "search_strategy": {
+            "execution_order": "joint",
+            "search_algorithm": "exhaustive"
+        },
+        "evaluator": "common_evaluator",
+        "cache_dir": "cache",
+        "output_dir": "models/bert_inc_static_ptq_cpu"
+    }
+}