[Experimental] Prefix Caching Support

[caoshiyi]

add prefix caching support

Section 1 (Basic Functionality):

• Test on single request for llama model (no cpu swap)
• Test on batches where there are requests with prefix and without prefix (no cpu swap)
• Benchmark performance for batched analytics tasks (no cpu swap)
• Alibi (Thanks @DouHappy )
• Clean code

Todo:
Automatic Prefix Caching Support -- SGLang RadixAttention

[DouHappy]

It seems that the prefix has not updated its physical block?

I tested on the meta-llama/Llama-2-70b-chat-hf and baichuan2-13b-chat, but it seems to have no acceleration effect.
Then I added a logger at the 358th row of worker.py to check if prefix updated its physics block.

logger's output is as follow:
------start generating------
prefix length: 592
block size: 16
Processed prompts: 0%| | 0/500 [00:00<?, ?it/s][36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:31 worker.py:358] prefix_block_tables: [[], [], [], [], []]
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:36 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 44x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:42 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 44x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:47 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 44x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:52 worker.py:358] prefix_block_tables: [[], [], [], []]
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:52 worker.py:358] prefix_block_tables: []
[36m(RayWorker pid=1138290)[0m INFO 12-05 10:46:51 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 39x across cluster][0m
Processed prompts: 0%| | 1/500 [00:23<3:18:23, 23.86s/it][36m(RayWorker pid=1138290)[0m INFO 12-05 10:46:52 worker.py:358] prefix_block_tables: [[], [], [], []][32m [repeated 3x across cluster][0m
[36m(RayWorker pid=1138290)[0m INFO 12-05 10:46:55 worker.py:358] prefix_block_tables: [][32m [repeated 59x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:46:57 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 21x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:02 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 44x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:08 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 44x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:13 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 44x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:15 worker.py:358] prefix_block_tables: [[], [], []]
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:16 worker.py:358] prefix_block_tables: []
Processed prompts: 43%|████▎ | 217/500 [00:45<00:50, 5.65it/s][36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:16 worker.py:358] prefix_block_tables: [[], []]
Processed prompts: 44%|████▍ | 219/500 [00:45<00:49, 5.65it/s][36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:17 worker.py:358] prefix_block_tables: [[]]
[36m(RayWorker pid=1138290)[0m INFO 12-05 10:47:15 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 19x across cluster][0m
Processed prompts: 44%|████▍ | 220/500 [00:47<00:53, 5.24it/s][36m(RayWorker pid=1138290)[0m INFO 12-05 10:47:15 worker.py:358] prefix_block_tables: [[], [], []][32m [repeated 3x across cluster][0m
[36m(RayWorker pid=1138290)[0m INFO 12-05 10:47:18 worker.py:358] prefix_block_tables: [][32m [repeated 59x across cluster][0m
[36m(RayWorker pid=1138290)[0m INFO 12-05 10:47:16 worker.py:358] prefix_block_tables: [[], []][32m [repeated 3x across cluster][0m
[36m(RayWorker pid=1138290)[0m INFO 12-05 10:47:17 worker.py:358] prefix_block_tables: [[]][32m [repeated 3x across cluster][0m
[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:23 worker.py:358] prefix_block_tables: [[], [], [], [], []][32m [repeated 41x across cluster][0m
Processed prompts: 100%|██████████| 500/500 [00:54<00:00, 9.13it/s]cost time 56.1341028213501
saving output

[36m(RayWorker pid=1138291)[0m INFO 12-05 10:47:26 worker.py:358] prefix_block_tables: []

By the way, My prompts are generated by this script:

# generate test prompt
test_table = "|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||"

prompt_template = '''\
You are a helpful assistant in recongnizes the content of tables in markdown format. Here is a table as follows. You need to answer my question about the table.
# Table
{}

# Question
What' s the content in the ({},{}) cells
'''
with open('prompt.txt', 'w') as outer:
    for row in range(50):
        for column in range(10):
            tmp_str = prompt_template.format(test_table, row + 1, column + 1)
            tmp_str
            # outer.write(f"{tmp_str}")
            print(tmp_str.replace("\n", "\\n"), file=outer)

Maybe there are some bugs in my test?

[caoshiyi]

@DouHappy Can you try with calling llm.generate() with one prompt first to warmup? prefix_block_tables=[[],[],[]] indicates that the kv cache for the prefix part hasn't been computed yet. Also, can you share your testing script?

[DouHappy]

@DouHappy Can you try with calling llm.generate() with one prompt first to warmup? prefix_block_tables=[[],[],[]] indicates that the kv cache for the prefix part hasn't been computed yet. Also, can you share your testing script?

My test script:

# %%
# generate test prompt
test_table = "|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||"

prompt_template = '''\
You are a helpful assistant in recongnizes the content of tables in markdown format. Here is a table as fellows. You need to answer my question about the table.
# Table
{}

# Question
What' s the content in the ({},{}) cells
'''
with open('prompt.txt', 'w') as outer:
    for row in range(50):
        for column in range(10):
            tmp_str = prompt_template.format(test_table, row + 1, column + 1)
            tmp_str
            # outer.write(f"{tmp_str}")
            print(tmp_str.replace("\n", "\\n"), file=outer)

# %%
import time
import datetime
import os

from vllm import LLM
from vllm import SamplingParams

import torch

def test_prefix(llm = None, sampling_params=None, prompts=None, prefix_len=None, save_file=None):
    # set sampling_params
    if sampling_params == None:
        sampling_params = SamplingParams(temperature=0)

    print("------start generating------")
    start_time = time.time()
    # whether use Prefix
    if prefix_len != None:
        print("warmup")
        outputs = llm.generate(prompts[0], sampling_params=sampling_params, prefix_pos=[prefix_len])
        # start inference
        outputs = llm.generate(prompts, sampling_params=sampling_params, prefix_pos=[prefix_len] * len(prompts))
    else:
        outputs = llm.generate(prompts, sampling_params=sampling_params)

    end_time = time.time()
    print(f"cost time {end_time - start_time}")

    if save_file != None:
        print("saving output......")
        for output in outputs:
            print(output, file=save_file)
        print(f"output saved in {save_file.name} {datetime.datetime.now()}")

# %%
# set gpus
os.environ['CUDA_VISIBLE_DEVICES']="0,1"
# init model and sampling parames
tensor_parallel_size = len(os.getenv('CUDA_VISIBLE_DEVICES').split(','))
# set baichuan model
model = "/data/images/llms/models--baichuan-inc--Baichuan2-13B-Chat"
# model = "/data/images/llms/chatdoc-llama-2-70b-chat-hf-checkpoint-8000"

# Create an LLM.
llm = LLM(model=model, tokenizer_mode='auto', trust_remote_code=True, tensor_parallel_size=tensor_parallel_size)

# %%
# get prompts
prompts = []
with open("prompt.txt", 'r') as reader:
    prompts = reader.readlines()[:500]

with open("output.txt", 'w') as f:
    test_prefix(llm=llm,
                prompts=prompts[:50],
                prefix_len=591,
                save_file=f
                )

I find a bug that when using two GPUs is slower than single GPU. Prefix‘s state 'on_gpu' is always False before prepare_inputs() When using two GPUs. and it works nice on single gpu. It mean multi_query_cached_kv_attention never be used when running on multi-gpus. My last test is also pass on single gpu.

one gpu with prefix
------start generating------
Processed prompts: 100%|██████████| 500/500 [00:22<00:00, 22.55it/s]cost time 23.27279233932495
saving output

one gpu without prefix
------start generating------
Processed prompts: 100%|██████████| 500/500 [01:21<00:00,  6.16it/s]cost time 82.11750793457031
saving output

but it cost about 60s on two gpus.
Although It is very fast, I can't got the right output when I use prefix.

[yudian0504]

when tp>1, seq_group_metadata.prefix here is copied by ray workers, so on_gpu=true won't work on multi gpus.

[yudian0504]

cc. @DouHappy

[DouHappy]

Thank you for your reply. This help me a lot.

[zhuohan123]

Thanks for the great work! Can you also merge with the latest main branch as well? I will test the PR after the merge.

[zhuohan123]

Consider adding another example just for prefix caching?

[franklyd]

Thanks a lot for this great feature!
I tried it with the latest caoshiyi:prefix, but I found that there's no speed improvement. (one V100 GPU, tested with Baichuan2-13B-chat model)

Hi @DouHappy , did you observe any speed improvement afterwards?

[DouHappy]

Thanks a lot for this great feature! I tried it with the latest caoshiyi:prefix, but I found that there's no speed improvement. (one V100 GPU, tested with Baichuan2-13B-chat model)

Hi @DouHappy , did you observe any speed improvement afterwards?

Yes，i got observe speed up. Could you should me your test script? Maybe you forgot warmup? BTW, I am trying to introduce prefix but only chinese version now. See this vLLM-prefix浅析（System Prompt，大模型推理加速） @franklyd

[zhuohan123]

@franklyd @DouHappy There was a bug in my refactor. If you try now, you should be able to see speedups.

[zhuohan123]

LGTM! Thanks for the great work! Pushed some style refactors by myself.

[HaiShaw]

@zhuohan123

I think this merge now invalidates FP8 KV cache (#2279).
Look at kernels in prefix_prefill.py, when FP8 KV cache is ON, K/V and K_cache/V_cache are different types now.

Please let know what is the best way to move forward, thanks!

[gangooteli]

Thanks a lot for this great feature! I tried it with the latest caoshiyi:prefix, but I found that there's no speed improvement. (one V100 GPU, tested with Baichuan2-13B-chat model)
Hi @DouHappy , did you observe any speed improvement afterwards?

Yes，i got observe speed up. Could you should me your test script? Maybe you forgot warmup? BTW, I am trying to introduce prefix but only chinese version now. See this vLLM-prefix浅析（System Prompt，大模型推理加速） @franklyd

Could you provide a test script for the speedup?

[ksjadeja]

Could you provide a test script for the speedup?

+1

[AlpinDale]

Hi @HaiShaw

Triton doesn't seem to support mixed precision dot product, so this kernel here fails if the k is uint8 and q is another precision. I've been trying to find a solution to this problem, but coming up with blanks. Do you have any ideas on how to approach this?

[chenxu2048]

Hi @HaiShaw

Triton doesn't seem to support mixed precision dot product, so this kernel here fails if the k is uint8 and q is another precision. I've been trying to find a solution to this problem, but coming up with blanks. Do you have any ideas on how to approach this?

Hi, @AlpinDale. Are you using prefix caching with FP8 KVCache? PyTorch and Triton used by vLLM could not support FP8 KVCache. Here are more information about prefix caching and FP8 KVCache in #3234.