INT4/INT8 KV Cache

INT4/INT8 KV Cache#

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:

  1. data-free online quantization

  2. Supports all nvidia GPU models with Volta architecture (sm70) and above

  3. KV int8 quantization has almost lossless accuracy, and KV int4 quantization accuracy is within an acceptable range

  4. 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