Add experimental support of fused decoder layer for llama2 (#11768)

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md

@@ -23,7 +23,7 @@ Go to https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-w
 Then go to **Device Manager**, find **Neural Processors** -> **Intel(R) AI Boost**.
 Right click and select **Update Driver**. And then manually select the folder unzipped from the driver.
 
-## Example: Predict Tokens using `generate()` API
+## Example 1: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel NPUs.
 ### 1. Install
 #### 1.1 Installation on Windows
@@ -81,3 +81,62 @@ Inference time: xxxx s
 --------------------------------------------------------------------------------
 done
 ```
+
+## Example 2: Predict Tokens using `generate()` API using multi processes
+In the example [llama2.py](./llama2.py), we show an experimental support for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimization and fused decoderlayer optimization on Intel NPUs.
+### 1. Install
+#### 1.1 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.10
+conda activate llm
+
+# install ipex-llm with 'all' option
+pip install --pre --upgrade ipex-llm[all]
+pip install --pre --upgrade bigdl-core-npu
+
+pip install transformers==4.40
+```
+
+### 2. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 2.1 Configurations for Windows
+
+> [!NOTE]
+> For optimal performance, we recommend running code in `conhost` rather than Windows Terminal:
+> - Press <kbd>Win</kbd>+<kbd>R</kbd> and input `conhost`, then press Enter to launch `conhost`.
+> - Run following command to use conda in `conhost`. Replace `<your conda install location>` with your conda install location.
+> ```
+> call <your conda install location>\Scripts\activate
+> ```
+
+**Following envrionment variables are required**:
+
+```cmd
+set BIGDL_USE_NPU=1
+```
+
+### 3. Running examples
+
+```
+torchrun --standalone --nnodes=1 --nproc-per-node=2  llama2.py
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (i.e. `meta-llama/Llama-2-7b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### Sample Output
+#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+
+```log
+First token cost: xxxx s, rest tokens cost average: xxxx s
+Inference time: xxxx s
+-------------------- Prompt --------------------
+Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun
+-------------------- Output --------------------
+<s> Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun and exciting experiences.
+
+One day, she decided to go on a journey to find a magical land that was said to be full of wonders
+```