73 lines
3.9 KiB
Markdown
73 lines
3.9 KiB
Markdown
# Falcon
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Falcon models on [Intel GPUs](../README.md). For illustration purposes, we utilize the [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) as a reference Falcon model.
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## 0. 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#recommended-requirements) for more information.
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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 Falcon 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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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 bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install einops # additional package required for falcon-7b-instruct to conduct generation
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```
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### 2. (Optional) Download Model and Replace File
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If you select the Falcon model ([tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)), please note that their code (`modelling_RW.py`) does not support KV cache at the moment. To address issue, we have provided updated file ([falcon-7b-instruct/modelling_RW.py](./falcon-7b-instruct/modelling_RW.py)), which can be used to achieve the best performance using BigDL-LLM INT4 optimizations with KV cache support.
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#### 2.1 Download Model
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You could use the following code to download [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) with a specific snapshot id. Please note that the `modelling_RW.py` files that we provide are based on these specific commits.
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```python
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from huggingface_hub import snapshot_download
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# for tiiuae/falcon-7b-instruct
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model_path = snapshot_download(repo_id='tiiuae/falcon-7b-instruct',
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revision="c7f670a03d987254220f343c6b026ea0c5147185",
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cache_dir="dir/path/where/model/files/are/downloaded")
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print(f'tiiuae/falcon-7b-instruct checkpoint is downloaded to {model_path}')
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```
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#### 2.2 Replace `modelling_RW.py`
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For `tiiuae/falcon-7b-instruct`, you should replace the `modelling_RW.py` with [falcon-7b-instruct/modelling_RW.py](./falcon-7b-instruct/modelling_RW.py).
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### 3. 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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### 4. 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 Falcon model (e.g. `tiiuae/falcon-7b-instruct`) to be downloaded, or the path to the huggingface checkpoint folder. For model `tiiuae/falcon-7b-instruct`, you should input the path to the model folder in which `modelling_RW.py` has been replaced.
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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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#### Sample Output
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#### [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<human> What is AI? <bot>
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-------------------- Output --------------------
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<human> What is AI? <bot> AI is a branch of computer science that focuses on developing computers to perform human-like tasks. <human> What are some examples of these tasks?
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```
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