Since v0.4.0, LMDeploy has supported online key-value (kv) cache quantization with int4 and int8 numerical precision, utilizing an asymmetric quantization method that is applied on a per-head, per-token basis. The original kv offline quantization method has been removed.
Intuitively, quantization is beneficial for increasing the number of kv block. Compared to fp16, the number of kv block for int4/int8 kv can be increased by 4 times and 2 times respectively. This means that under the same memory conditions, the system can support a significantly increased number of concurrent operations after kv quantization, thereby ultimately enhancing throughput.
However, quantization typically brings in some loss of model accuracy. We have used OpenCompass to evaluate the accuracy of several models after applying int4/int8 quantization. int8 kv keeps the accuracy while int4 kv has slight loss. The detailed results are presented in the Evaluation section. You can refer to the information and choose wisely based on your requirements.
LMDeploy inference with quantized kv supports the following NVIDIA GPU models:
- Volta architecture (sm70): V100
- Turing architecture (sm75): 20 series, T4
- Ampere architecture (sm80, sm86): 30 series, A10, A16, A30, A100
- Ada Lovelace architecture (sm89): 40 series
- Hopper architecture (sm90): H100, H200
In summary, LMDeploy kv quantization has the following advantages:
- data-free online quantization
- Supports all nvidia GPU models with Volta architecture (sm70) and above
- KV int8 quantization has almost lossless accuracy, and KV int4 quantization accuracy is within an acceptable range
- Efficient inference, with int8/int4 kv quantization applied to llama2-7b, RPS is improved by round 30% and 40% respectively compared to fp16
In the next section, we will take internlm2-chat-7b model as an example, introducing the usage of kv quantization and inference of lmdeploy. But before that, please ensure that lmdeploy is installed.
pip install lmdeployApplying kv quantization and inference via LMDeploy is quite straightforward. Simply set the quant_policy parameter.
LMDeploy specifies that quant_policy=4 stands for 4-bit kv, whereas quant_policy=8 indicates 8-bit kv.
from lmdeploy import pipeline, TurbomindEngineConfig
engine_config = TurbomindEngineConfig(quant_policy=8)
pipe = pipeline("internlm/internlm2_5-7b-chat", backend_config=engine_config)
response = pipe(["Hi, pls intro yourself", "Shanghai is"])
print(response)lmdeploy serve api_server internlm/internlm2_5-7b-chat --quant-policy 8We apply kv quantization of LMDeploy to several LLM models and utilize OpenCompass to evaluate the inference accuracy. The results are shown in the table below:
| - | - | - | llama2-7b-chat | - | - | internlm2-chat-7b | - | - | internlm2.5-chat-7b | - | - | qwen1.5-7b-chat | - | - |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| dataset | version | metric | kv fp16 | kv int8 | kv int4 | kv fp16 | kv int8 | kv int4 | kv fp16 | kv int8 | kv int4 | fp16 | kv int8 | kv int4 |
| ceval | - | naive_average | 28.42 | 27.96 | 27.58 | 60.45 | 60.88 | 60.28 | 78.06 | 77.87 | 77.05 | 70.56 | 70.49 | 68.62 |
| mmlu | - | naive_average | 35.64 | 35.58 | 34.79 | 63.91 | 64 | 62.36 | 72.30 | 72.27 | 71.17 | 61.48 | 61.56 | 60.65 |
| triviaqa | 2121ce | score | 56.09 | 56.13 | 53.71 | 58.73 | 58.7 | 58.18 | 65.09 | 64.87 | 63.28 | 44.62 | 44.77 | 44.04 |
| gsm8k | 1d7fe4 | accuracy | 28.2 | 28.05 | 27.37 | 70.13 | 69.75 | 66.87 | 85.67 | 85.44 | 83.78 | 54.97 | 56.41 | 54.74 |
| race-middle | 9a54b6 | accuracy | 41.57 | 41.78 | 41.23 | 88.93 | 88.93 | 88.93 | 92.76 | 92.83 | 92.55 | 87.33 | 87.26 | 86.28 |
| race-high | 9a54b6 | accuracy | 39.65 | 39.77 | 40.77 | 85.33 | 85.31 | 84.62 | 90.51 | 90.42 | 90.42 | 82.53 | 82.59 | 82.02 |
For detailed evaluation methods, please refer to this guide. Remember to pass quant_policy to the inference engine in the config file.
| model | kv type | test settings | RPS | v.s. kv fp16 |
|---|---|---|---|---|
| llama2-chat-7b | fp16 | tp1 / ratio 0.8 / bs 256 / prompts 10000 | 14.98 | 1.0 |
| - | int8 | tp1 / ratio 0.8 / bs 256 / prompts 10000 | 19.01 | 1.27 |
| - | int4 | tp1 / ratio 0.8 / bs 256 / prompts 10000 | 20.81 | 1.39 |
| llama2-chat-13b | fp16 | tp1 / ratio 0.9 / bs 128 / prompts 10000 | 8.55 | 1.0 |
| - | int8 | tp1 / ratio 0.9 / bs 256 / prompts 10000 | 10.96 | 1.28 |
| - | int4 | tp1 / ratio 0.9 / bs 256 / prompts 10000 | 11.91 | 1.39 |
| internlm2-chat-7b | fp16 | tp1 / ratio 0.8 / bs 256 / prompts 10000 | 24.13 | 1.0 |
| - | int8 | tp1 / ratio 0.8 / bs 256 / prompts 10000 | 25.28 | 1.05 |
| - | int4 | tp1 / ratio 0.8 / bs 256 / prompts 10000 | 25.80 | 1.07 |
The performance data is obtained by benchmark/profile_throughput.py