Awesome

Chunk Attention

ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
Paper: https://arxiv.org/abs/2205.14135

Example: Integrate ChunkAttn into Production LLMs

We provide an example in chunk-attn package (src/chunk_attn/models/llama_hf/modeling_llama.py) to show how to integrate ChunkAttn into production LLMs. The example is built on top of Huggingface Llama and vLLM's optimized kernels (layer normalization and rotary embedding), but the attention module is substituted by ChunkAttn. The following code demonstrates how to use the example to create LLaMA model with ChunkAttn and run asynchronous inference.

from chunk_attn.models.llama_hf import LlamaConfig, LlamaTokenizer, LlamaForCausalLM
from chunk_attn.models.model_host import ModelHost

model_path = 'openlm-research/open_llama_7b'    
tokenizer = LlamaTokenizer.from_pretrained(model_path)
config = LlamaConfig.from_pretrained(model_path)
config.torch_dtype = torch.float16
config.torch_device = torch.device('cuda:0')       

model = LlamaForCausalLM.from_pretrained(
    model_path,
    config=config,
    torch_dtype=config.torch_dtype,
    device_map=config.torch_device)
kv_caches = model.create_kv_caches()

with ModelHost(model, kv_caches, max_batch_size=16) as host:
    futures = []
    system_prompt = 'You are a chatbot and you are talking to an user. The user will ask you a question and you will answer it. You should answer the question in a friendly way. Do not use offensive words.'
    for i in range(16):
        prompt_tokens = tokenizer(f'{system_prompt}\nQ{i}: What is black hole?\nA: ', return_tensors="pt").input_ids.tolist()[0]
        f = host.predict_async(prompt_tokens, 32)
        futures.append(f)
    
    for f in futures:
        print(tokenizer.decode(f.get_result()) + '\n')

Build from Source

GPU Kernel (BLAS=CUDA)

pip install mypy xformers vllm

# Find your PyTorch installation path by:
python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)'

# Linux
cmake -S . -B build -DTORCH=</path/to/python>/site-packages/torch -DUSE_MKL=OFF -DUSE_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build

# Windows
cmake -S . -B build -DTORCH=</path/to/python>/site-packages/torch -DUSE_MKL=OFF -DUSE_CUDA=ON
cmake --build build --config RelWithDebInfo

python tests/test_chunk_attn_cuda.py

CPU Kernel (BLAS=MKL) [Experimental]

# Find your PyTorch installation path by:
python -c 'import torch.utils; print(torch.utils.cmake_prefix_path)'

# Linux
# sudo apt install intel-oneapi-mkl-devel
cmake -S . -B build -DTORCH=</path/to/python>/site-packages/torch -DUSE_MKL=ON -DUSE_CUDA=OFF -DMKL_LINK=static -DMKL_THREADING=sequential -DCMAKE_BUILD_TYPE=Release
cmake --build build

# Windows
cmake -S . -B build -DTORCH=</path/to/python>/site-packages/torch -DUSE_MKL=ON -DUSE_CUDA=OFF -DMKL_LINK=static -DMKL_THREADING=sequential
cmake --build build --config RelWithDebInfo

python tests/test_chunk_attn_cpu.py

MKL CMake options(default*):

• -DMKL_LINK=[static,dynamic*]
• -DMKL_THREADING=[sequential,parallel*]

Tips

Using PyTorch Context Manager

PyTorch has a context manager which can take care of the device transfer automatically. ChunkAttn is compatible with this context manager.

from chunk_attn import Attention

with torch.device('cuda'):
    attn = Attention(n_heads=32, d_head=128, chunk_size=32)

In C++, you can use c10::DeviceGuard to switch the device. It is a RAII guard that sets a certain default device in its constructor, and changes it back to the device that was originally active upon destruction.

#include "c10/core/DeviceGuard.h"

{
    c10::DeviceGuard guard(torch::Device(torch::kCUDA, 1));
    Attention attn(32, 128, 32);
}

Add nvcc to PATH if not already

export PATH="/usr/local/cuda/bin:$PATH"

Change HuggingFace Cache Path

export TRANSFORMERS_CACHE=/mnt/huggingface/

Citation

@misc{ye2024chunkattention,
      title={ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition}, 
      author={Lu Ye and Ze Tao and Yong Huang and Yang Li},
      year={2024},
      eprint={2402.15220},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}