Revert "Remove netsec as no longer needed"

This reverts commit 8f3276d.

Author: gooishin

usecases/netsec/openvino-bert/README.md

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+# OpenVINO™ Bert Model Benchmarking
+Benchmark OpenVINO™  bert model
+
+## Steps
+### 1. Setup OpenVINO™
+Setup OpenVINO™
+```bash
+./setup_ov.sh
+```
+Installation is completed when you see this message:
+> ✓ OpenVINO™  configured
+
+### 2. Benchmark 
+
+Benchmark bert-base-cased model (FP32)
+```bash
+#Python virtual environment to testing bert openvino model
+source bert_ov_venv/bin/activate
+
+#CPU
+numactl -C 0 benchmark_app -m models/bert-base-cased.xml -d CPU -hint latency -shape "[1, 512]"
+#GPU
+numactl -C 0 benchmark_app -m models/bert-base-cased.xml -d GPU -hint latency -shape "[1, 512]"
+
+#Deactivate virtual environment
+deactivate
+
+```
+
+Benchmark quantized bert-base-cased model (INT8)
+```bash
+
+#Python virtual environment to testing bert openvino model
+source bert_ov_venv/bin/activate
+
+#CPU
+numactl -C 0 benchmark_app -m models/quantized_bert_base_cased.xml -d CPU -hint latency -shape "[1, 512]"
+#GPU
+numactl -C 0 benchmark_app -m models/quantized_bert_base_cased.xml -d GPU -hint latency -shape "[1, 512]"
+
+#Deactivate virtual environment
+deactivate
+```

usecases/netsec/openvino-bert/requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cpu
+nncf==2.7.0
+transformers==4.38.0
+openvino==2023.2.0
+evaluate==0.4.1
+datasets==2.15.0
+torch==2.2.0 

usecases/netsec/openvino-bert/setup_ov.sh

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+#!/bin/bash
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -e
+
+# symbol
+S_VALID="✓"
+#S_INVALID="✗"
+
+# verify current user
+if [ "$EUID" -eq 0 ]; then
+    echo "Must not run with sudo or root user"
+    exit 1
+fi
+
+CURRENT_DIR=$(pwd)
+
+install_packages(){
+    local PACKAGES=("$@")
+    local INSTALL_REQUIRED=0
+    for PACKAGE in "${PACKAGES[@]}"; do
+        INSTALLED_VERSION=$(dpkg-query -W -f='${Version}' "$PACKAGE" 2>/dev/null || true)
+        LATEST_VERSION=$(apt-cache policy "$PACKAGE" | grep Candidate | awk '{print $2}')
+        
+        if [ -z "$INSTALLED_VERSION" ] || [ "$INSTALLED_VERSION" != "$LATEST_VERSION" ]; then
+            echo "$PACKAGE is not installed or not the latest version."
+            INSTALL_REQUIRED=1
+        fi
+    done
+    if [ $INSTALL_REQUIRED -eq 1 ]; then
+        sudo -E apt update
+        sudo -E apt install -y "${PACKAGES[@]}"
+    fi
+}
+
+verify_dependencies_ov(){
+    echo -e "# Verifying OV dependencies"
+    DEPENDENCIES_PACKAGES=(
+        zip
+        wget
+        numactl
+        python3-pip
+        python3-venv
+        python3-opencv
+    )
+    install_packages "${DEPENDENCIES_PACKAGES[@]}"
+    echo "$S_VALID Dependencies installed";
+}
+
+#verify gpu
+verify_gpu() {
+    mkdir "$HOME"/neo
+    cd "$HOME"/neo
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.15985.7/intel-igc-core_1.0.15985.7_amd64.deb
+    wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.15985.7/intel-igc-opencl_1.0.15985.7_amd64.deb
+    wget https://github.com/intel/compute-runtime/releases/download/24.05.28454.6/intel-level-zero-gpu-dbgsym_1.3.28454.6_amd64.ddeb
+    wget https://github.com/intel/compute-runtime/releases/download/24.05.28454.6/intel-level-zero-gpu_1.3.28454.6_amd64.deb
+    wget https://github.com/intel/compute-runtime/releases/download/24.05.28454.6/intel-opencl-icd-dbgsym_24.05.28454.6_amd64.ddeb
+    wget https://github.com/intel/compute-runtime/releases/download/24.05.28454.6/intel-opencl-icd_24.05.28454.6_amd64.deb
+    wget https://github.com/intel/compute-runtime/releases/download/24.05.28454.6/libigdgmm12_22.3.11_amd64.deb
+    sudo dpkg -i ./*.deb
+}
+
+verify_ov() {
+    cd "$CURRENT_DIR"
+    python3 -m venv bert_ov_venv
+    # shellcheck source=/dev/null
+    source bert_ov_venv/bin/activate
+    pip install -U pip
+    pip install -r requirements.txt
+    python torch-to-ov.py
+}
+
+
+setup() {
+    verify_dependencies_ov
+    verify_gpu
+    verify_ov
+    
+    echo -e "\n# Status"
+    echo "$S_VALID OpenVINO™ configured"
+}
+
+setup

usecases/netsec/openvino-bert/torch-to-ov.py

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import sys
+import time
+from pathlib import Path
+from zipfile import ZipFile
+from typing import Iterable
+from typing import Any
+
+import datasets
+import numpy as np
+import nncf
+from nncf.parameters import ModelType
+import openvino as ov
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+
+
+MODEL_DIR = "models"
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+MAX_SEQ_LENGTH = 512
+
+
+def load_model(inputs, input_info):
+    try: 
+        ir_model_xml = Path(MODEL_DIR) / "bert-base-cased.xml"
+        core = ov.Core()
+
+        torch_model = BertForSequenceClassification.from_pretrained('bert-base-cased')
+        torch_model.eval
+
+        # Convert the PyTorch model to OpenVINO IR FP32.
+        if not ir_model_xml.exists():
+            model = ov.convert_model(torch_model, example_input=inputs, input=input_info)
+            ov.save_model(model, str(ir_model_xml))
+        else:
+            model = core.read_model(ir_model_xml)
+
+        return model
+    except Exception as e:
+        print(f"Error in load_model: {e}")
+        sys.exit(1)
+
+def create_data_source():
+    try: 
+        raw_dataset = datasets.load_dataset('glue', 'mrpc', split='validation')
+        tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+
+        def _preprocess_fn(examples):
+            texts = (examples['sentence1'], examples['sentence2'])
+            result = tokenizer(*texts, padding='max_length', max_length=MAX_SEQ_LENGTH, truncation=True)
+            result['labels'] = examples['label']
+            return result
+        processed_dataset = raw_dataset.map(_preprocess_fn, batched=True, batch_size=1)
+
+        return processed_dataset
+    except Exception as e:
+        print(f"Error in create_data_source: {e}")
+        sys.exit(1)
+
+def nncf_quantize(model, inputs):
+    try:
+        INPUT_NAMES = [key for key in inputs.keys()]
+        data_source = create_data_source()
+
+        def transform_fn(data_item):
+            """
+            Extract the model's input from the data item.
+            The data item here is the data item that is returned from the data source per iteration.
+            This function should be passed when the data item cannot be used as model's input.
+            """
+            inputs = {
+                name: np.asarray([data_item[name]], dtype=np.int64) for name in INPUT_NAMES
+            }
+            return inputs
+
+        calibration_dataset = nncf.Dataset(data_source, transform_fn)
+        # Quantize the model. By specifying model_type, we specify additional transformer patterns in the model.
+        quantized_model = nncf.quantize(model, calibration_dataset,
+                                        model_type=ModelType.TRANSFORMER)
+
+
+        compressed_model_xml = Path(MODEL_DIR) / "quantized_bert_base_cased.xml"
+        ov.save_model(quantized_model, compressed_model_xml)
+    except Exception as e:
+        print(f"Error in nncf_quantize: {e}")
+        sys.exit(1)
+
+if __name__ == '__main__':
+    input_shape = ov.PartialShape([1, 512])
+    input_info = [("input_ids", input_shape, np.int64),("attention_mask", input_shape, np.int64),("token_type_ids", input_shape, np.int64)]
+    default_input = torch.ones(1, MAX_SEQ_LENGTH, dtype=torch.int64)
+    inputs = {
+        "input_ids": default_input,
+        "attention_mask": default_input,
+        "token_type_ids": default_input,
+    }
+
+    model = load_model(inputs, input_info)
+    quantized_model = nncf_quantize(model, inputs)