LLM: add chatglm2 example for Arc (#8741)
* add chatglm2 example * update * fix readme
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Ruonan Wang
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@ -3,8 +3,11 @@ You can use BigDL-LLM to run almost every Huggingface Transformer models with IN
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## Recommended Requirements
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To apply Intel® Arc™ A-Series Graphics acceleration, there’re several steps for tools installation and environment preparation.
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Step 1, only Linux system is supported now, Ubuntu 22.04 is prefered.
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Step 2, please refer to our [drive installation](https://dgpu-docs.intel.com/installation-guides/index.html#intel-arc-gpus) for general purpose GPU capabilities.
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Step 3, you also need to download and install [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html). OneMKL and DPC++ compiler are needed, others are optional.
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## Best Known Configuration on Linux
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@ -0,0 +1,67 @@
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# ChatGLM2
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on ChatGLM2 models on any Intel® Arc™ A-Series Graphics. For illustration purposes, we utilize the [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) as a reference ChatGLM2 model.
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## 0. Requirements
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To run these examples with BigDL-LLM on Intel® Arc™ A-Series Graphics, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
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## Example 1: Predict Tokens using `generate()` API
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In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel® Arc™ A-Series Graphics.
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### 1. Install
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.9
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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```
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### 2. Configures OneAPI environment variables
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Run
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For optimal performance on Arc, it is recommended to set several environment variables.
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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```
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the ChatGLM2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm2-6b'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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#### Sample Output
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#### [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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问：AI是什么？
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答：
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-------------------- Output --------------------
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问：AI是什么？
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答： AI指的是人工智能,是一种能够通过学习和推理来执行任务的计算机程序。它可以模仿人类的思维方式,做出类似人类的决策,并且具有自主学习、自我
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```
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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问：What is AI?
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答：
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-------------------- Output --------------------
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问：What is AI?
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答： Artificial Intelligence (AI) refers to the ability of a computer or machine to perform tasks that typically require human-like intelligence, such as understanding language, recognizing patterns
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```
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@ -0,0 +1,73 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import time
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import argparse
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import numpy as np
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from bigdl.llm.transformers import AutoModel
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from transformers import AutoTokenizer
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import intel_extension_for_pytorch as ipex
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to https://huggingface.co/THUDM/chatglm2-6b/blob/main/modeling_chatglm.py#L1007
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CHATGLM_V2_PROMPT_FORMAT = "问：{prompt}\n\n答："
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for ChatGLM2 model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/chatglm2-6b",
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help='The huggingface repo id for the ChatGLM2 model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default="AI是什么？",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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model = AutoModel.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=False,
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trust_remote_code=True)
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model = model.half().to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = CHATGLM_V2_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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st = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with BigDL-LLM INT4 optimizations
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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torch.xpu.synchronize()
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end = time.time()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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