README.md

<div align="center">
  <img src="resources/lmdeploy-logo.png" width="450"/>

English | [简体中文](README_zh-CN.md)

</div>

<p align="center">
    👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">Twitter</a>, <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=internwx" target="_blank">WeChat</a>
</p>

______________________________________________________________________

## News 🎉

- \[2023/08\] TurboMind supports 4-bit quantization and inference.
- \[2023/07\] TurboMind supports Llama-2 70B with GQA.
- \[2023/07\] TurboMind supports Llama-2 7B/13B.
- \[2023/07\] TurboMind supports tensor-parallel inference of InternLM.

______________________________________________________________________

## Introduction

LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the [MMRazor](https://github.com/open-mmlab/mmrazor) and [MMDeploy](https://github.com/open-mmlab/mmdeploy) teams. It has the following core features:

- **Efficient Inference Engine (TurboMind)**: Based on [FasterTransformer](https://github.com/NVIDIA/FasterTransformer), we have implemented an efficient inference engine - TurboMind, which supports the inference of LLaMA and its variant models on NVIDIA GPUs.

- **Interactive Inference Mode**: By caching the k/v of attention during multi-round dialogue processes, it remembers dialogue history, thus avoiding repetitive processing of historical sessions.

- **Multi-GPU Model Deployment and Quantization**: We provide comprehensive model deployment and quantification support, and have been validated at different scales.

- **Persistent Batch Inference**: Further optimization of model execution efficiency.

![PersistentBatchInference](https://github.com/InternLM/lmdeploy/assets/67539920/e3876167-0671-44fc-ac52-5a0f9382493e)

## Performance

**Case I**: output token throughput with fixed input token and output token number (1, 2048)

**Case II**: request throughput with real conversation data

Test Setting: LLaMA-7B, NVIDIA A100(80G)

The output token throughput of TurboMind exceeds 2000 tokens/s, which is about 5% - 15% higher than DeepSpeed overall and outperforms huggingface transformers by up to 2.3x.
And the request throughput of TurboMind is 30% higher than vLLM.

![benchmark](https://github.com/InternLM/lmdeploy/assets/4560679/7775c518-608e-4e5b-be73-7645a444e774)

## Quick Start

### Installation

Install lmdeploy with pip ( python 3.8+) or [from source](./docs/en/build.md)

```shell
pip install lmdeploy
```

### Deploy InternLM

#### Get InternLM model

```shell
# 1. Download InternLM model

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/internlm/internlm-chat-7b /path/to/internlm-chat-7b

# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1

# 2. Convert InternLM model to turbomind's format, which will be in "./workspace" by default
python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b

```

#### Inference by TurboMind

```shell
python -m lmdeploy.turbomind.chat ./workspace
```

> **Note**<br />
> When inferring with FP16 precision, the InternLM-7B model requires at least 15.7G of GPU memory overhead on TurboMind. <br />
> It is recommended to use NVIDIA cards such as 3090, V100, A100, etc.
> Disable GPU ECC can free up 10% memory, try `sudo nvidia-smi --ecc-config=0` and reboot system.

> **Note**<br />
> Tensor parallel is available to perform inference on multiple GPUs. Add `--tp=<num_gpu>` on `chat` to enable runtime TP.

#### Serving with gradio

```shell
python3 -m lmdeploy.serve.gradio.app ./workspace
```

![](https://github.com/InternLM/lmdeploy/assets/67539920/08d1e6f2-3767-44d5-8654-c85767cec2ab)

#### Serving with Triton Inference Server

Launch inference server by:

```shell
bash workspace/service_docker_up.sh
```

Then, you can communicate with the inference server by command line,

```shell
python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
```

or webui,

```shell
python3 -m lmdeploy.serve.gradio.app {server_ip_addresss}:33337
```

For the deployment of other supported models, such as LLaMA, LLaMA-2, vicuna and so on, you can find the guide from [here](docs/en/serving.md)

### Inference with PyTorch

For detailed instructions on Inference pytorch models, see [here](docs/en/pytorch.md).

#### Single GPU

```shell
python3 -m lmdeploy.pytorch.chat $NAME_OR_PATH_TO_HF_MODEL \
    --max_new_tokens 64 \
    --temperture 0.8 \
    --top_p 0.95 \
    --seed 0
```

#### Tensor Parallel with DeepSpeed

```shell
deepspeed --module --num_gpus 2 lmdeploy.pytorch.chat \
    $NAME_OR_PATH_TO_HF_MODEL \
    --max_new_tokens 64 \
    --temperture 0.8 \
    --top_p 0.95 \
    --seed 0
```

You need to install deepspeed first to use this feature.

```
pip install deepspeed
```

## Quantization

### Step 1. Obtain Quantization Parameters

First, run the quantization script to obtain the quantization parameters.

> After execution, various parameters needed for quantization will be stored in `$WORK_DIR`; these will be used in the following steps..

```
python3 -m lmdeploy.lite.apis.calibrate \
  --model $HF_MODEL \
  --calib_dataset 'c4' \             # Calibration dataset, supports c4, ptb, wikitext2, pileval
  --calib_samples 128 \              # Number of samples in the calibration set, if memory is insufficient, you can appropriately reduce this
  --calib_seqlen 2048 \              # Length of a single piece of text, if memory is insufficient, you can appropriately reduce this
  --work_dir $WORK_DIR \             # Folder storing Pytorch format quantization statistics parameters and post-quantization weight

```

### Step 2. Actual Model Quantization

`LMDeploy` supports INT4 quantization of weights and INT8 quantization of KV Cache. Run the corresponding script according to your needs.

#### Weight INT4 Quantization

LMDeploy uses AWQ algorithm for model weight quantization

> Requires input from the $WORK_DIR of step 1, and the quantized weights will also be stored in this folder.

```
python3 -m lmdeploy.lite.apis.auto_awq \
  --w_bits 4 \                       # Bit number for weight quantization
  --w_sym False \                    # Whether to use symmetric quantization for weights
  --w_group_size 128 \               # Group size for weight quantization statistics
  --work_dir $WORK_DIR \             # Directory saving quantization parameters from Step 1
```

#### KV Cache INT8 Quantization

In fp16 mode, kv_cache int8 quantization can be enabled, and a single card can serve more users.
First execute the quantization script, and the quantization parameters are stored in the `workspace/triton_models/weights` transformed by `deploy.py`.

```
python3 -m lmdeploy.lite.apis.kv_qparams \
  --work_dir $WORK_DIR \             # Directory saving quantization parameters from Step 1
  --turbomind_dir $TURBOMIND_DIR \
  --kv_sym False \                   # Whether to use symmetric or asymmetric quantization.
  --num_tp 1 \                       # The number of GPUs used for tensor parallelism
```

Then adjust `workspace/triton_models/weights/config.ini`

- `use_context_fmha` changed to 0, means off
- `quant_policy` is set to 4. This parameter defaults to 0, which means it is not enabled

Here is [quantization test results](./docs/en/quantization.md).

> **Warning**<br />
> runtime Tensor Parallel for quantilized model is not available. Please setup `--tp` on `deploy` to enable static TP.

## Contributing

We appreciate all contributions to LMDeploy. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.

## Acknowledgement

- [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)
- [llm-awq](https://github.com/mit-han-lab/llm-awq)

## License

This project is released under the [Apache 2.0 license](LICENSE).