quick start for windows with gpu (intel#10221)

* quick start for windows igpu

* Update install_windows_gpu.md

* Update install_windows_gpu.md

* Update install_windows_gpu.md

* Update install_windows_gpu.md

* Update install_windows_gpu.md

* Update install_windows_gpu.md

* update the demo.py

* Update install_windows_gpu.md

* Update install_windows_gpu.md

* fix image position typo

* Update install_windows_gpu.md

* update pip install command

---------

Co-authored-by: Shengsheng Huang <[email protected]>

Author: ivy-lv11

docs/readthedocs/source/doc/LLM/Quickstart/install_windows_gpu.md

@@ -0,0 +1,96 @@
+# Install BigDL-LLM on Windows for Intel GPU
+
+This guide applies to Intel Core Ultra and Core 12 - 14 gen integrated GPUs, as well as Intel Arc Series GPU.
+
+## Install GPU driver
+
+* Download and Install Visual Studio 2022 Community Edition from the [official Microsoft Visual Studio website](https://visualstudio.microsoft.com/downloads/). Ensure you select the **Desktop development with C++ workload** during the installation process.
+   
+    > Note: The installation could take around 15 minutes, and requires at least 7GB of free disk space.  
+    > If you accidentally skip adding the **Desktop development with C++ workload** during the initial setup, you can add it afterward by navigating to **Tools > Get Tools and Features...**. Follow the instructions on [this Microsoft guide](https://learn.microsoft.com/en-us/cpp/build/vscpp-step-0-installation?view=msvc-170#step-4---choose-workloads)  to update your installation.
+    > 
+    > <img src="https://llm-assets.readthedocs.io/en/latest/_images/quickstart_windows_gpu_1.png" alt="image-20240221102252560" width=100%; />
+
+* Download and install the latest GPU driver from the [official Intel download page](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html). A system reboot is necessary to apply the changes after the installation is complete.
+   
+    > Note: the process could take around 10 minutes. After reboot, check for the **Intel Arc Control** application to verify the driver has been installed correctly. If the installation was successful, you should see the **Arc Control** interface similar to the figure below
+
+    > <img src="https://llm-assets.readthedocs.io/en/latest/_images/quickstart_windows_gpu_3.png" width=80%; />
+
+* To monitor your GPU's performance and status, you can use either use the **Windows Task Manager** (see the left side of the figure below) or the **Arc Control** application (see the right side of the figure below) or :
+    >  <img src="https://llm-assets.readthedocs.io/en/latest/_images/quickstart_windows_gpu_4.png"  width=70%; />
+
+## Setup Python Environment
+
+* Visit [Miniconda installation page](https://docs.anaconda.com/free/miniconda/), download the **Miniconda installer for Windows**, and follow the instructions to complete the installation.
+
+  > <img src="https://llm-assets.readthedocs.io/en/latest/_images/quickstart_windows_gpu_5.png"  width=50%; />
+
+* After installation, open the **Anaconda Prompt**, create a new python environment `llm`:
+  ```bash
+  conda create -n llm python=3.9 libuv
+  ```
+* Activate the newly created environment `llm`:
+  ```bash
+  conda activate llm
+  ```
+ 
+## Install oneAPI 
+
+* With the `llm` environment active, use `pip` to install the **OneAPI Base Toolkit**:
+  ```bash
+  pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0
+  ```
+  
+## Install `bigdl-llm`
+
+* With the `llm` environment active, use `pip` to install `bigdl-llm` for GPU: 
+  ```bash
+  pip install --pre --upgrade bigdl-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
+  ```
+  > Note: If there are network issues when installing IPEX, refer to [this guide](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html#install-bigdl-llm-from-wheel) for more details. 
+
+* You can verfy if bigdl-llm is successfully by simply importing a few classes from the library. For example, in the Python interactive shell, execute the following import command:
+  ```python
+  from bigdl.llm.transformers import AutoModel,AutoModelForCausalLM
+  ```
+
+## A quick example
+* Next step you can start play with a real LLM. We use [phi-1.5](https://huggingface.co/microsoft/phi-1_5) (an 1.3B model) for demostration. You can copy/paste the following code in a python script and run it. 
+> Note: to use phi-1.5, you may need to update your transformer version to 4.37.0.  
+> ```
+> pip install -U transformers==4.37.0 
+> ```
+> Note: when running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
+> This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
+
+   ```python
+   import torch
+   from bigdl.llm.transformers import AutoModelForCausalLM
+   from transformers import AutoTokenizer, GenerationConfig
+   generation_config = GenerationConfig(use_cache = True)
+   
+   tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
+   # load Model using bigdl-llm and load it to GPU
+   model = AutoModelForCausalLM.from_pretrained(
+       "microsoft/phi-1_5", load_in_4bit=True, cpu_embedding=True, trust_remote_code=True)
+   model = model.to('xpu')
+
+   # Format the prompt
+   question = "What is AI?"
+   prompt = " Question:{prompt}\n\n Answer:".format(prompt=question)
+   # Generate predicted tokens
+   with torch.inference_mode():
+       input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
+       output = model.generate(input_ids, do_sample=False, max_new_tokens=32, generation_config = generation_config).cpu()
+       output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+       print(output_str)
+   ```
+
+* An example output on the laptop equipped with i7 11th Gen Intel Core CPU and Iris Xe Graphics iGPU looks like below. 
+
+```
+Question:What is AI?
+Answer: AI stands for Artificial Intelligence, which is the simulation of human intelligence in machines.
+```
+