# Hands-on Training and Inference with Grok 300B

This documentation introduces the process of finetuning and inferencing the Grok-MoE 300B model using an 8-GPU environment.

## Table of Contents
- [Environment Setup](#environment-setup)
- [Finetuning](#finetuning)
- [Inference](#inference)

## Environment Setup
```shell
git clone https://github.com/modelscope/swift.git
cd swift
pip install -e '.[llm]'
```

## Finetuning

### Experiment Environment

- GPU: 8 x A100 80G
- Docker Image: ModelScope official image version 1.13.1
- peft: 0.10.0

### Dataset Preparation

Grok is a base model, so we used the [DuReader Question Generation dataset](https://www.modelscope.cn/datasets/modelscope/DuReader_robust-QG/summary) as the training set. This dataset contains around 15,000 examples. With a max-length of 512, there are about 10,000 training examples (average length 305±92 tokens).

### Model Preparation

For the Grok model, we used the version provided by [ColossalAI](https://www.modelscope.cn/models/colossalai/grok-1-pytorch/summary), and additionally prepared a [tokenizer conforming to the transformers standard](https://www.modelscope.cn/models/AI-ModelScope/grok-1-tokenizer/summary).

### Training

Since the Grok model is too large, device_map and deepspeed zero3 non-offload are unable to run training. Therefore, in this experiment, we used the LoRA + deepspeed zero3 offload mode to run the training. The complete training script is as follows:

```shell
# cd examples/pytorch/llm first
nproc_per_node=8

PYTHONPATH=../../.. \
torchrun \
    --nproc_per_node=$nproc_per_node \
    --master_port 29500 \
    llm_sft.py \
    --model_type grok-1 \
    --sft_type lora \
    --tuner_backend peft \
    --dtype bf16 \
    --output_dir output \
    --ddp_backend nccl \
    --dataset dureader-robust-zh \
    --train_dataset_sample -1 \
    --num_train_epochs 1 \
    --max_length 512 \
    --check_dataset_strategy warning \
    --lora_rank 8 \
    --lora_alpha 32 \
    --lora_dropout 0.05 \
    --lora_dtype AUTO \
    --lora_target_modules DEFAULT \
    --gradient_checkpointing true \
    --batch_size 2 \
    --weight_decay 0.1 \
    --learning_rate 1e-4 \
    --gradient_accumulation_steps $(expr 16 / $nproc_per_node) \
    --max_grad_norm 0.5 \
    --warmup_ratio 0.03 \
    --eval_steps 100 \
    --save_steps 100 \
    --save_total_limit 2 \
    --logging_steps 10 \
    --deepspeed zero3-offload \
```

The complete training files can be found [here](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/grok-1/lora_ddp_ds).

Here are some benchmarks from the training process:

| Metric        | Value                                                        |
|---------------|------------------------------------------------------------|
| GPU Memory Usage | 8 * 21G                                                    |
| Training Speed   | 45s/it                                                      |
| Total Iterations | 340 (10000(dataset_length)/16(gradient_accumulation)/2(batch_size)) |

<img src="../../resources/image-20240329122854204.png" alt="image-20240329122854204" style="zoom: 33%;" />

Since the GPU memory usage is below 24G, training should theoretically be possible on RTX 3090/A10 environments.

<img src="../../resources/grok_train_loss.png" alt="train_loss (1)" style="zoom:33%;" />

<img src="../../resources/grok_train_acc.png" alt="train_acc" style="zoom:33%;" />

The training took about 4 hours.

### Inference

The SWIFT framework currently does not support deepspeed inference, so we still use transformers' device_map for inference support. However, since the model is too large, some layers will be offloaded to the CPU, which will cause errors when loading LoRA during inference. Therefore, we patched the peft implementation (the original Linear module on the meta device does not affect LoRA, and dynamically move LoRA weights to the device during runtime).

The inference script is as follows:

```shell
# cd examples/pytorch/llm first
PYTHONPATH=../../.. \
python llm_infer.py \
    --ckpt_dir output/grok-1/vx-xxx-xxx/checkpoint-xxx \
    --dtype bf16 \
    --load_dataset_config true \
    --max_new_tokens 64 \
    --do_sample true \
    --dtype bf16 \
    --eval_human false \
    --merge_lora false \
```

Inference result:

```text
[PROMPT]Task: Question Generation
Context: 我个人感觉是吕颂贤版,剧情和原著差别不大,虽然TVB演员颜值和风光没有大陆的好。但是香港特区人口和地域的限制,只能注重在演员的演技方面发挥很出色,楼主看过大陆排《笑傲江湖》吧!在台词上表现的很生硬没有香港的注重神色配台词,比如杜燕歌把吕颂贤表情和性格几乎和原著差别不大。武打几乎沿用徐克和程小东动作的风格很注重实际技巧,没有大陆版的在武打场面依靠电脑特效表现的太夸张了。李亚鹏版的武打动作和导演还是香港的元彬,大陆毕竟还是在武侠剧起步的比较晚,主要是还是靠明星大腕压阵而香港却是恰恰相反。
Answer: 吕颂贤版
Question:[OUTPUT]笑傲江湖哪个版本好看</s>

[LABELS]笑傲江湖哪个版本好看
--------------------------------------------------
[PROMPT]Task: Question Generation
Context: 这位朋友你好,女性出现妊娠反应一般是从6-12周左右,也就是女性怀孕1个多月就会开始出现反应,第3个月的时候,妊辰反应基本结束。 而大部分女性怀孕初期都会出现恶心、呕吐的感觉,这些症状都是因人而异的,除非恶心、呕吐的非常厉害,才需要就医,否则这些都是刚怀孕的的正常症状。1-3个月的时候可以观察一下自己的皮肤,一般女性怀孕初期可能会产生皮肤色素沉淀或是腹壁产生妊娠纹,特别是在怀孕的后期更加明显。 还有很多女性怀孕初期会出现疲倦、嗜睡的情况。怀孕三个月的时候,膀胱会受到日益胀大的子宫的压迫,容量会变小,所以怀孕期间也会有尿频的现象出现。月经停止也是刚怀孕最容易出现的症状,只要是平时月经正常的女性,在性行为后超过正常经期两周,就有可能是怀孕了。 如果你想判断自己是否怀孕,可以看看自己有没有这些反应。当然这也只是多数人的怀孕表现,也有部分女性怀孕表现并不完全是这样,如果你无法确定自己是否怀孕,最好去医院检查一下。
Answer: 6-12周
Question:[OUTPUT]怀孕几个月开始反应</s>

[LABELS]怀孕多久会有反应
--------------------------------------------------
```
