Add gpu gguf example (#9603)

* add gpu gguf example

* some fixes

* address kai's comments

* address json's comments

README.md

@@ -13,7 +13,7 @@
 
 ### Latest update
 - [2023/12] `bigdl-llm` now supports [FP8 and FP4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types) on Intel ***GPU***.
-- [2023/11] Initial support for directly loading [GGUF](python/llm/example/CPU/GGUF-Models/llama2), [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ) and [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ) models in to `bigdl-llm` is available.
+- [2023/11] Initial support for directly loading [GGUF](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF), [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ) and [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ) models in to `bigdl-llm` is available.
 - [2023/11] Initial support for [vLLM continuous batching](python/llm/example/CPU/vLLM-Serving) is availabe on Intel ***CPU***.
 - [2023/11] Initial support for [vLLM continuous batching](python/llm/example/GPU/vLLM-Serving) is availabe on Intel ***GPU***.
 - [2023/10] [QLoRA finetuning](python/llm/example/CPU/QLoRA-FineTuning) on Intel ***CPU*** is available.

python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md

@@ -5,6 +5,9 @@ In this directory, you will find examples on how to load GGUF model into `bigdl-
 ## Requirements
 To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
+**Important: Please make sure you have installed `transformers==4.33.0` to run the example.**
+
+
 ## Example: Load gguf model using `from_gguf()` API
 In the example [generate.py](./generate.py), we show a basic use case to load a GGUF LLaMA2 model into `bigdl-llm` using `from_gguf()` API, with BigDL-LLM optimizations.
 
@@ -17,6 +20,7 @@ conda create -n llm python=3.9 # recommend to use Python 3.9
 conda activate llm
 
 pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
+pip install transformers==4.33.0  # upgrade transformers
 ```
 
 ### 2. Run
@@ -50,7 +54,7 @@ In the example, several arguments can be passed to satisfy your requirements:
 - `--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
+#### 2.4 Sample Output
 #### [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main)
 ```log
 Inference time: xxxx s

python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md

@@ -0,0 +1,77 @@
+# Loading GGUF models
+In this directory, you will find examples on how to load GGUF model into `bigdl-llm`. For illustration purposes, we utilize the [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main) and [llama-2-7b-chat.Q4_1.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main) as reference LLaMA2 GGUF models.
+>Note: Only LLaMA2 family models are currently supported
+
+## Requirements
+To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
+
+**Important: Please make sure you have installed `transformers==4.33.0` to run the example.**
+
+## Example: Load gguf model using `from_gguf()` API
+In the example [generate.py](./generate.py), we show a basic use case to load a GGUF LLaMA2 model into `bigdl-llm` using `from_gguf()` API, with BigDL-LLM optimizations.
+
+### 1. Install
+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==2.0.110+xpu as default
+# you can install specific ipex/torch version for your need
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install transformers==4.33.0  # upgrade transformers
+```
+
+### 2. Configures OneAPI environment variables
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Run
+
+For optimal performance on Arc, it is recommended to set several environment variables.
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+```
+python ./generate.py --model <path_to_gguf_model> --prompt 'What is AI?'
+```
+
+More information about arguments can be found in [Arguments Info](#33-arguments-info) section. The expected output can be found in [Sample Output](#34-sample-output) section.
+
+#### 3.3 Arguments Info
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--model`: path to GGUF model, it should be a file with name like `llama-2-7b-chat.Q4_0.gguf`
+- `--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`.
+
+#### 3.4 Sample Output
+#### [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI is a term used to describe a type of computer software that is designed to perform tasks that typically require human intelligence, such as visual perception, speech
+```
+
+#### [llama-2-7b-chat.Q4_1.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/tree/main)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+Artificial intelligence (AI) is the field of study focused on creating machines that can perform tasks that typically require human intelligence, such as understanding language,
+```

python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/generate.py

@@ -0,0 +1,63 @@
+#
+# 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 intel_extension_for_pytorch as ipex
+import time
+import argparse
+
+from transformers import LlamaTokenizer
+from bigdl.llm.transformers import AutoModelForCausalLM
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
+LLAMA2_PROMPT_FORMAT = """### HUMAN:
+{prompt}
+
+### RESPONSE:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
+    parser.add_argument('--model', type=str, required=True,
+                        help='Path to a gguf model')
+    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.model
+
+    # Load gguf model and vocab, then convert them to bigdl-llm model and huggingface tokenizer
+    model, tokenizer = AutoModelForCausalLM.from_gguf(model_path)
+    model = model.to('xpu')
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
+        st = time.time()
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        torch.xpu.synchronize()
+        end = time.time()
+        output = output.cpu()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)