add gemma example (intel-analytics#10224)

* add gemma gpu example * Update README.md * add cpu example * Update README.md * Update README.md * Update generate.py * Update generate.py
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diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma/README.md
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+# Gemma
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Google Gemma models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [google/gemma-7b-it ](https://huggingface.co/google/gemma-7b-it) and [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) as reference Gemma models.
+
+## Requirements
+To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+**Important: According to Gemma's requirement, please make sure you have installed `transformers==4.38.0` to run the example.**
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Gemma model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+
+# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
+pip install transformers==4.38.0
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9 libuv
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+
+# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
+pip install transformers==4.38.0
+```
+
+### 2. Configures OneAPI environment variables
+#### 2.1 Configurations for Linux
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+#### 2.2 Configurations for Windows
+```cmd
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+```
+> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A300-Series or Pro A60</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For other Intel dGPU Series</summary>
+
+There is no need to set further environment variables.
+
+</details>
+
+> Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```bash
+python ./generate.py --prompt 'What is AI?'
+```
+
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Gemma model (e.g. `google/gemma-7b-it` and `google/gemma-2b-it`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'google/gemma-7b-it'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### 2.3 Sample Output
+#### [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+user
+What is AI?
+model
+**Artificial Intelligence (AI)** is a field of computer science that involves the creation of intelligent machines capable of performing tasks typically requiring human intelligence, such as learning,
+```
+
+#### [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+user
+What is AI?
+model
+**Artificial intelligence (AI)** is the simulation of human cognitive functions, such as learning, reasoning, and problem-solving, by machines. AI systems are designed
+```
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma/generate.py b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma/generate.py
@@ -0,0 +1,71 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import torch
+import time
+import argparse
+
+from bigdl.llm.transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+# The instruction-tuned models use a chat template that must be adhered to for conversational use.
+# see https://huggingface.co/google/gemma-7b-it#chat-template.
+chat = [
+    { "role": "user", "content": "What is AI?" },
+]
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Gemma model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="google/gemma-7b-it",
+                        help='The huggingface repo id for the Gemma (e.g. `google/gemma-7b-it` and `google/gemma-7b-it`) to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 trust_remote_code=True,
+                                                 use_cache=True)
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        chat[0]['content'] = args.prompt
+        prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+        # start inference
+        st = time.time()
+        # if your selected model is capable of utilizing previous key/value attentions
+        # to enhance decoding speed, but has `"use_cache": false` in its model config,
+        # it is important to set `use_cache=True` explicitly in the `generate` function
+        # to obtain optimal performance with BigDL-LLM INT4 optimizations
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        end = time.time()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/gemma/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/gemma/README.md
@@ -0,0 +1,138 @@
+# Gemma
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Google Gemma models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [google/gemma-7b-it ](https://huggingface.co/google/gemma-7b-it) and [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) as reference Gemma models.
+
+## Requirements
+To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+**Important: According to Gemma's requirement, please make sure you have installed `transformers==4.38.0` to run the example.**
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Gemma model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+
+# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
+pip install transformers==4.38.0
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9 libuv
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+
+# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
+pip install transformers==4.38.0
+```
+
+### 2. Configures OneAPI environment variables
+#### 2.1 Configurations for Linux
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+#### 2.2 Configurations for Windows
+```cmd
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+```
+> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A300-Series or Pro A60</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For other Intel dGPU Series</summary>
+
+There is no need to set further environment variables.
+
+</details>
+
+> Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```bash
+python ./generate.py --prompt 'What is AI?'
+```
+
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Gemma model (e.g. `google/gemma-7b-it` and `google/gemma-2b-it`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'google/gemma-7b-it'`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### 2.3 Sample Output
+#### [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+user
+What is AI?
+model
+**Artificial Intelligence (AI)** is a field of computer science that involves the creation of intelligent machines capable of performing tasks typically requiring human intelligence, such as learning,
+```
+
+#### [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+user
+What is AI?
+model
+**Artificial intelligence (AI)** is the simulation of human cognitive functions, such as learning, reasoning, and problem-solving, by machines. AI systems are designed
+```