kv_int8.md

# KV Cache Quantization and Test Results

For the LLaMa-7B fp16 model with a maximum length of 2048, the server requires approximately 1030MB of GPU memory to store kv_cache for each concurrent session created. This means that even an A100 80G can only serve a limited number of users.

To reduce runtime GPU memory usage, we have implemented PTQ quantization for kv cache, using the following formula:

```bash
zp = (min+max) / 2
scale = (max-min) / 255
quant: q = round( (f-zp) / scale)
dequant: f = q * scale + zp
```

## How to Enable KV Cache INT8

### **Step One**

Convert the Hugging Face model format to the TurboMind inference format to create a workspace directory.

```bash
python3 -m lmdeploy.serve.turbomind.deploy internlm-chat-7b /path/to/internlm-chat-7b
```

If you already have a workspace directory, skip this step.

### **Step Two**

Get the quantization parameters.

```bash
python3 -m lmdeploy.lite.apis.kv_qparams \
  --work_dir /path/to/internlm-chat-7b  \             # Directory of the Hugging Face model
  --turbomind_dir workspace/trition_models/weights/ \ # Directory to save the quantization parameters
  --kv_sym False \                                    # Symmetric or asymmetric quantization, default is False
  --num_tp 1  \                                       # Number of GPUs used for Tensor parallelization, keep it consistent with deploy.py
```

`kv_qparams` will generate fp32 scaling factors in the `weights` directory. The file format is a binary produced by `numpy.tofile`.

You can also first set `turbomind_dir` to a private directory, then copy the scaling factors into `workspace/trition_models/weights/`.

### **Step Three**

Modify `workspace/trition_models/weights/config.ini`:

- Set use_context_fmha to 0, which means turning off flashattention
- Set quant_policy to 4. This means enabling kv_cache int8

This is because there are two versions of flashattention, v1 and v2, and kv_cache int8 has also previously realized the symmetric version.

Considering there are four combinations of kernels needed to be implemented, premature optimization when the algorithm is uncertain can be disastrous for software.

### **Step Four**

Test the chat performance.

```bash
python3 -m lmdeploy.turbomind.chat ./workspace
```

## GPU Memory Test

The test object is the [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) model.
Testing method:

1. Use `deploy.py` to convert the model, modify the maximum concurrency in the `workspace` configuration; adjust the number of requests in `llama_config.ini`.
2. Compile and run `bin/llama_triton_example` to obtain the GPU memory situation of the fp16 version under different batch_size.
3. Enable quantization, re-run `bin/llama_triton_example` to obtain the GPU memory situation of the int8 version under different batch_size.

Below shows the comparison of GPU memory between the two versions:

| batch_size | fp16 memory(MiB) | int8 memory(MiB) | diff(MiB) |
| :--------: | :--------------: | :--------------: | :-------: |
|     8      |      22337       |      18241       |   -4096   |
|     16     |      30593       |      22369       |   -8224   |
|     32     |      47073       |      30625       |  -16448   |
|     48     |      63553       |      38881       |  -24672   |

Compared to directly quantizing Weight (such as [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa/)), we have done a comparative estimation of memory growth in the 7B model for both methods, with some data from [llama.cpp](https://github.com/ggerganov/llama.cpp).

![](../../resources/batch_memory.png)

As can be seen, the fp16 version requires 1030MB of GPU memory for each concurrency, so quantizing kv_cache can significantly reduce the rate of increase of runtime memory.

## Accuracy Test

The test object is the [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) command model.

Below is the result of PTQ quantization of `kCacheKVInt8` method with only 128 randomly selected data from the c4 dataset. The accuracy was tested using [opencompass](https://github.com/InternLM/opencompass) before and after quantization.

|     task      |     dataset     |    metric     | int8  | fp16  | diff  |
| :-----------: | :-------------: | :-----------: | :---: | :---: | :---: |
|   Language    |   winogrande    |   accuracy    | 60.77 | 61.48 | -0.71 |
|   Knowledge   |       nq        |     score     | 2.69  | 2.60  | +0.09 |
|   Reasoning   |      gsm8k      |   accuracy    | 33.28 | 34.72 | -1.44 |
|   Reasoning   |       bbh       | naive_average | 20.12 | 20.51 | -0.39 |
| Understanding | openbookqa_fact |   accuracy    | 82.40 | 82.20 | +0.20 |
| Understanding |   eprstmt-dev   |   accuracy    | 90.62 | 88.75 | +1.87 |
|    Safety     |   crows_pairs   |   accuracy    | 32.56 | 31.43 | +1.13 |

Note that both `kCacheKVInt8` and `WeightInt4` methods can be enabled at the same time.