Key-Value(KV) Cache Quantization

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 lmdeploy

Usage

Applying 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.

Offline inference

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)

Serving

lmdeploy serve api_server internlm/internlm2_5-7b-chat --quant-policy 8

Evaluation

We 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.

Performance

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