airllm

AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card. No quantization, distillation, pruning or other model compression techniques that would result in degraded model performance are needed.

AirLLM优化inference内存，4GB单卡GPU可以运行70B大语言模型推理。不需要任何损失模型性能的量化和蒸馏，剪枝等模型压缩。

Updates

[2023/12/03] added support of ChatGLM, QWen!

[2023/12/02] added support for safetensors. Now support all top 10 models in open llm leaderboard.

[2023/12/01] airllm 2.0. Support compressions: 3x run time speed up!

[2023/11/20] airllm Initial verion!

Quickstart

1. install package

First, install airllm pip package.

首先安装airllm包。

pip install airllm

如果找不到package，可能是因为默认的镜像问题。可以尝试制定原始镜像：

pip install -i https://pypi.org/simple/ airllm

2. Inference

Then, initialize AirLLMLlama2, pass in the huggingface repo ID of the model being used, or the local path, and inference can be performed similar to a regular transformer model.

然后，初始化AirLLMLlama2，传入所使用模型的huggingface repo ID，或者本地路径即可类似于普通的transformer模型进行推理。

(You can can also specify the path to save the splitted layered model through layer_shards_saving_path when init AirLLMLlama2.

如果需要指定另外的路径来存储分层的模型可以在初始化AirLLMLlama2是传入参数：layer_shards_saving_path。)

from airllm import AirLLMLlama2

MAX_LENGTH = 128
# could use hugging face model repo id:
model = AirLLMLlama2("garage-bAInd/Platypus2-70B-instruct")

# or use model's local path...
#model = AirLLMLlama2("/home/ubuntu/.cache/huggingface/hub/models--garage-bAInd--Platypus2-70B-instruct/snapshots/b585e74bcaae02e52665d9ac6d23f4d0dbc81a0f")

input_text = [
        'What is the capital of United States?',
        #'I like',
    ]

input_tokens = model.tokenizer(input_text,
    return_tensors="pt", 
    return_attention_mask=False, 
    truncation=True, 
    max_length=MAX_LENGTH, 
    padding=True)
           
generation_output = model.generate(
    input_tokens['input_ids'].cuda(), 
    max_new_tokens=20,
    use_cache=True,
    return_dict_in_generate=True)

output = model.tokenizer.decode(generation_output.sequences[0])

print(output)

Note: During inference, the original model will first be decomposed and saved layer-wise. Please ensure there is sufficient disk space in the huggingface cache directory.

注意：推理过程会首先将原始模型按层分拆，转存。请保证huggingface cache目录有足够的磁盘空间。

3. Model Compression - 3x Inference Speed Up!

We just added model compression based on block-wise quantization based model compression. Which can further speed up the inference speed for up to 3x , with almost ignorable accuracy loss! (see more performance evaluation and why we use block-wise quantization in this paper)

how to enalbe model compression speed up:

Step 1. make sure you have bitsandbytes installed by pip install -U bitsandbytes
Step 2. make sure airllm verion later than 2.0.0: pip install -U airllm
Step 3. when initialize the model, passing the argument compression ('4bit' or '8bit'):

model = AirLLMLlama2("garage-bAInd/Platypus2-70B-instruct",
                     compression='4bit' # specify '8bit' for 8-bit block-wise quantization 
                    )

4. All supported configurations

When initialize the model, we support the following configurations:

compression: supported options: 4bit, 8bit for 4-bit or 8-bit block-wise quantization, or by default None for no compression
profiling_mode: supported options: True to output time consumptions or by default False
layer_shards_saving_path: optionally another path to save the splitted model

5. Supported Models

HF open llm leaderboard top models

Including but not limited to the following: (Most of the open models are based on llama2, so should be supported by default)

@12/01/23

Rank	Model	Supported	Model Class
1	TigerResearch/tigerbot-70b-chat-v2	✅	AirLLMLlama2
2	upstage/SOLAR-0-70b-16bit	✅	AirLLMLlama2
3	ICBU-NPU/FashionGPT-70B-V1.1	✅	AirLLMLlama2
4	sequelbox/StellarBright	✅	AirLLMLlama2
5	bhenrym14/platypus-yi-34b	✅	AirLLMLlama2
6	MayaPH/GodziLLa2-70B	✅	AirLLMLlama2
7	01-ai/Yi-34B	✅	AirLLMLlama2
8	garage-bAInd/Platypus2-70B-instruct	✅	AirLLMLlama2
9	jondurbin/airoboros-l2-70b-2.2.1	✅	AirLLMLlama2
10	chargoddard/Yi-34B-Llama	✅	AirLLMLlama2

opencompass leaderboard top models

Including but not limited to the following: (Most of the open models are based on llama2, so should be supported by default)

@12/01/23

Rank	Model	Supported	Model Class
1	GPT-4	closed.ai😓	N/A
2	TigerResearch/tigerbot-70b-chat-v2	✅	AirLLMLlama2
3	THUDM/chatglm3-6b-base	✅	AirLLMChatGLM
4	Qwen/Qwen-14B	✅	AirLLMQWen
5	01-ai/Yi-34B	✅	AirLLMLlama2
6	ChatGPT	closed.ai😓	N/A
7	OrionStarAI/OrionStar-Yi-34B-Chat	✅	AirLLMLlama2
8	Qwen/Qwen-14B-Chat	✅	AirLLMQWen
9	Duxiaoman-DI/XuanYuan-70B	✅	AirLLMLlama2
10	internlm/internlm-20b	⏰(adding, to accelerate😀)
26	baichuan-inc/Baichuan2-13B-Chat	⏰(adding, to accelerate😀)

example of other models (ChatGLM, QWen, etc):

ChatGLM:

from airllm import AirLLMChatGLM
MAX_LENGTH = 128
model = AirLLMChatGLM("THUDM/chatglm3-6b-base")
input_text = ['What is the capital of China?',]
input_tokens = model.tokenizer(input_text,
    return_tensors="pt", 
    return_attention_mask=False, 
    truncation=True, 
    max_length=MAX_LENGTH, 
    padding=True)
generation_output = model.generate(
    input_tokens['input_ids'].cuda(), 
    max_new_tokens=5,
    use_cache= True,
    return_dict_in_generate=True)
model.tokenizer.decode(generation_output.sequences[0])

QWen:

from airllm import AirLLMQWen
MAX_LENGTH = 128
model = AirLLMQWen("Qwen/Qwen-7B")
input_text = ['What is the capital of China?',]
input_tokens = model.tokenizer(input_text,
    return_tensors="pt", 
    return_attention_mask=False, 
    truncation=True, 
    max_length=MAX_LENGTH)
generation_output = model.generate(
    input_tokens['input_ids'].cuda(), 
    max_new_tokens=5,
    use_cache=True,
    return_dict_in_generate=True)
model.tokenizer.decode(generation_output.sequences[0])

Acknowledgement

A lot of the code are based on SimJeg's great work in the Kaggle exam competition. Big shoutout to SimJeg:

GitHub account @SimJeg, the code on Kaggle, the associated discussion.

FAQ

1. MetadataIncompleteBuffer

safetensors_rust.SafetensorError: Error while deserializing header: MetadataIncompleteBuffer

If you run into this error, most possible cause is you run out of disk space. The process of splitting model is very disk-consuming. See this. You may need to extend your disk space, clear huggingface .cache and rerun.

如果你碰到这个error，很有可能是空间不足。可以参考一下这个可能需要扩大硬盘空间，删除huggingface的.cache，然后重新run。

Contribution

Welcome contribution, ideas and discussions!

If you find it useful, please ⭐ or buy me a coffee! 🙏

README.md Unescape Escape