add gemma example (#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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Xin Qiu
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# Gemma
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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.
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## Requirements
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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.
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**Important: According to Gemma's requirement, please make sure you have installed `transformers==4.38.0` to run the example.**
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## Example: Predict Tokens using `generate()` API
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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.
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### 1. Install
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#### 1.1 Installation on Linux
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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#).
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After installing conda, create a Python environment for BigDL-LLM:
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```bash
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conda create -n llm python=3.9 # recommend to use Python 3.9
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
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pip install transformers==4.38.0
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```
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#### 1.2 Installation on Windows
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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 libuv
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
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pip install transformers==4.38.0
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```
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### 2. Configures OneAPI environment variables
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#### 2.1 Configurations for Linux
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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#### 2.2 Configurations for Windows
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```cmd
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call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
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```
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> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
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### 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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#### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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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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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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#### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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set BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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<details>
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<summary>For Intel Arc™ A300-Series or Pro A60</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For other Intel dGPU Series</summary>
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There is no need to set further environment variables.
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</details>
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> 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.
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### 4. Running examples
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```bash
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python ./generate.py --prompt 'What is AI?'
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```
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In the example, several arguments can be passed to satisfy your requirements:
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- `--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'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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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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#### 2.3 Sample Output
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#### [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
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```log
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Inference time: xxxx s
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-------------------- Output --------------------
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user
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What is AI?
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model
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**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,
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```
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#### [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
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```log
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Inference time: xxxx s
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-------------------- Output --------------------
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user
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What is AI?
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model
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**Artificial intelligence (AI)** is the simulation of human cognitive functions, such as learning, reasoning, and problem-solving, by machines. AI systems are designed
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```
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@ -0,0 +1,71 @@
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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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from bigdl.llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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# The instruction-tuned models use a chat template that must be adhered to for conversational use.
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# see https://huggingface.co/google/gemma-7b-it#chat-template.
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chat = [
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{ "role": "user", "content": "What is AI?" },
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]
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Gemma model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="google/gemma-7b-it",
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help='The huggingface repo id for the Gemma (e.g. `google/gemma-7b-it` and `google/gemma-7b-it`) 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="What is AI?",
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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 = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True,
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use_cache=True)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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chat[0]['content'] = args.prompt
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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# start inference
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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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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, 'Output', '-'*20)
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print(output_str)
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@ -0,0 +1,138 @@
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# Gemma
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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.
|
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|
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## 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
|
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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
|
||||
|
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# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
|
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pip install transformers==4.38.0
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```
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|
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#### 1.2 Installation on Windows
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We suggest using conda to manage environment:
|
||||
```bash
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conda create -n llm python=3.9 libuv
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conda activate llm
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||||
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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# According to Gemma's requirement, please make sure you are using a stable version of Transformers, 4.38.0 or newer.
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pip install transformers==4.38.0
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```
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### 2. Configures OneAPI environment variables
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#### 2.1 Configurations for Linux
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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#### 2.2 Configurations for Windows
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```cmd
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call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
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```
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> 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
|
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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>
|
||||
|
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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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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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#### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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set BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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<details>
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<summary>For Intel Arc™ A300-Series or Pro A60</summary>
|
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|
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For other Intel dGPU Series</summary>
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There is no need to set further environment variables.
|
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</details>
|
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|
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> 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.
|
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### 4. Running examples
|
||||
|
||||
```bash
|
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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`.
|
||||
|
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#### 2.3 Sample Output
|
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#### [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
|
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```log
|
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Inference time: xxxx s
|
||||
-------------------- Output --------------------
|
||||
user
|
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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)
|
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```log
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Inference time: xxxx s
|
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-------------------- Output --------------------
|
||||
user
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What is AI?
|
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model
|
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**Artificial intelligence (AI)** is the simulation of human cognitive functions, such as learning, reasoning, and problem-solving, by machines. AI systems are designed
|
||||
```
|
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@ -0,0 +1,80 @@
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#
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# 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.
|
||||
#
|
||||
|
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import torch
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import time
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import argparse
|
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|
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from bigdl.llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
|
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|
||||
# 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?" },
|
||||
]
|
||||
|
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if __name__ == '__main__':
|
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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",
|
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help='The huggingface repo id for the Gemma (e.g. `google/gemma-7b-it` and `google/gemma-7b-it`) 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="What is AI?",
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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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|
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# Load model in 4 bit,
|
||||
# which convert the relevant layers in the model into INT4 format
|
||||
# When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
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# This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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optimize_model=True,
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trust_remote_code=True,
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use_cache=True)
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model = model.to('xpu')
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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chat[0]['content'] = args.prompt
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
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# ipex model needs a warmup, then inference time can be accurate
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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|
||||
# 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)
|
||||
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)
|
||||
Loading…
Reference in a new issue