https://github.com/microsoft/minference

[NeurIPS'24 Spotlight] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
https://github.com/microsoft/minference

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[NeurIPS'24 Spotlight] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.

• Host: GitHub
• URL: https://github.com/microsoft/minference
• Owner: microsoft
• License: mit
• Created: 2024-05-22T12:59:47.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-11-25T16:18:19.000Z (about 2 months ago)
• Last Synced: 2024-11-28T12:06:29.184Z (about 1 month ago)
• Language: Python
• Homepage: https://aka.ms/MInference
• Size: 6.05 MB
• Stars: 808
• Watchers: 6
• Forks: 38
• Open Issues: 36
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Security: SECURITY.md
  • Support: SUPPORT.md

Awesome Lists containing this project

• Awesome-LLMOps - MInference - context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy. | | (Inference)
• Awesome-LLMOps - MInference - context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy. | | (Inference)

README

        


  

    

  



MInference: Million-Tokens Prompt Inference for Long-context LLMs




    | Project Page |

    Paper |

    HF Demo |

    SCBench |



https://github.com/microsoft/MInference/assets/30883354/52613efc-738f-4081-8367-7123c81d6b19

_Now, you can process **1M context 10x faster in a single A100** using Long-context LLMs like LLaMA-3-8B-1M, GLM-4-1M, with even **better accuracy**, try **MInference 1.0** right now!_

## News

- 🍩 [24/12/13] We are excited to announce the release of our KV cache-centric analysis work, [SCBench](https://aka.ms/SCBench), which evaluates long-context methods from a KV cache perspective.

- 🧤 [24/09/26] MInference has been accepted as **spotlight** at **NeurIPS'24**. See you in Vancouver!

- 👘 [24/09/16] We are pleased to announce the release of our KV cache offloading work, [RetrievalAttention](https://aka.ms/RetrievalAttention), which accelerates long-context LLM inference via vector retrieval.

- 🥤 [24/07/24] MInference support [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) now.

- 🪗 [24/07/07] Thanks @AK for sponsoring. You can now use MInference online in the [HF Demo](https://huggingface.co/spaces/microsoft/MInference) with ZeroGPU.

- 📃 [24/07/03] Due to an issue with arXiv, the PDF is currently unavailable there. You can find the paper at this [link](https://export.arxiv.org/pdf/2407.02490).

- 🧩 [24/07/03] We will present **MInference 1.0** at the _**Microsoft Booth**_ and _**ES-FoMo**_ at ICML'24. See you in Vienna!

## TL;DR

**MInference 1.0** leverages the dynamic sparse nature of LLMs' attention, which exhibits some static patterns, to speed up the pre-filling for long-context LLMs. It first determines offline which sparse pattern each head belongs to, then approximates the sparse index online and dynamically computes attention with the optimal custom kernels. This approach achieves up to a **10x speedup** for pre-filling on an A100 while maintaining accuracy.

- [MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://arxiv.org/abs/2407.02490) (NeurIPS'24 **spotlight**, ES-FoMo @ ICML'24)


  _Huiqiang Jiang†, Yucheng Li†, Chengruidong Zhang†, Qianhui Wu, Xufang Luo, Surin Ahn, Zhenhua Han, Amir H. Abdi, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu_

**SCBench** analyzes long-context methods from a **KV cache-centric perspective** across the full KV cache lifecycle (e.g., KV cache generation, compression, retrieval, and loading). It evaluates 12 tasks under two shared context modes, covering four categories of long-context capabilities: string retrieval, semantic retrieval, global information, and multi-task scenarios.

- [SCBench: A KV Cache-Centric Analysis of Long-Context Methods](https://arxiv.org/abs/2412.10319) (Under Review, ENLSP @ NeurIPS'24)


  _Yucheng Li, Huiqiang Jiang, Qianhui Wu, Xufang Luo, Surin Ahn, Chengruidong Zhang, Amir H. Abdi, Dongsheng Li, Jianfeng Gao, Yuqing Yang and Lili Qiu_

## 🎥 Overview

![Onepage of MInference](https://raw.githubusercontent.com/microsoft/MInference/main/images/MInference1_onepage.png)

![Onepage of SCBench](https://raw.githubusercontent.com/microsoft/MInference/main/images/SCBench_onepage.png)

## 🎯 Quick Start

### Requirements

- Torch

- FlashAttention-2 (Optional)

- Triton

- **Transformers >= 4.46.0**

To get started with MInference, simply install it using pip:

```bash

pip install minference

```

### Supported Efficient Methods

You can get the complete list of supported efficient methods by running the following code:

```python

from minference import MInferenceConfig

supported_attn_types = MInferenceConfig.get_available_attn_types()

supported_kv_types = MInferenceConfig.get_available_kv_types()

```

Currently, we support the following long-context methods:

- **[① KV Cache Generation]:** [MInference](https://arxiv.org/abs/2407.02490), [FlexPrefill](https://openreview.net/forum?id=OfjIlbelrT), [A-shape](https://arxiv.org/abs/2309.17453), [Tri-shape](https://arxiv.org/abs/2412.10319), [MInference w/ static](https://arxiv.org/abs/2407.02490), [Dilated](https://arxiv.org/abs/2004.05150), [Strided](https://arxiv.org/abs/1904.10509)

- **[② KV Cache Compression]:** [StreamingLLM](https://arxiv.org/abs/2309.17453), [SnapKV](https://arxiv.org/abs/2404.14469), [PyramidKV](https://arxiv.org/abs/2406.02069), [KIVI](https://arxiv.org/abs/2402.02750)

- **[③ KV Cache Retrieval]:** [CacheBlend](https://arxiv.org/abs/2405.16444)

- **[④ KV Cache Loading]:** [Quest](https://arxiv.org/abs/2406.10774), [RetrievalAttention](https://arxiv.org/abs/2409.10516)

For more details about the KV cache lifecycle, please refer to [**SCBench**](https://arxiv.org/abs/2412.10319). Note that some modes are supported by vLLM, while all modes are supported by HF.

### Supported Models

General *MInference* **supports any decoding LLMs**, including LLaMA-style models, and Phi models.

We have adapted nearly all open-source long-context LLMs available in the market.

If your model is not on the supported list, feel free to let us know in the issues, or you can follow [the guide](https://github.com/microsoft/MInference/blob/main/experiments) to manually generate the sparse heads config.

You can get the complete list of supported LLMs by running:

```python

from minference import get_support_models

get_support_models()

```

Currently, we support the following LLMs:

- LLaMA-3.1: [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)

- LLaMA-3: [gradientai/Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k), [gradientai/Llama-3-8B-Instruct-Gradient-1048k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k), [gradientai/Llama-3-8B-Instruct-Gradient-4194k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k), [gradientai/Llama-3-70B-Instruct-Gradient-262k](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-262k), [gradientai/Llama-3-70B-Instruct-Gradient-1048k](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k)

- GLM-4: [THUDM/glm-4-9b-chat-1m](https://huggingface.co/THUDM/glm-4-9b-chat-1m)

- Yi: [01-ai/Yi-9B-200K](https://huggingface.co/01-ai/Yi-9B-200K)

- Phi-3: [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)

- Qwen2: [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)

### How to use MInference

for HF,

```diff

from transformers import pipeline

+from minference import MInference

pipe = pipeline("text-generation", model=model_name, torch_dtype="auto", device_map="auto")

# Patch MInference Module,

# If you use the local path, please use the model_name from HF when initializing MInference.

+minference_patch = MInference("minference", model_name)

+pipe.model = minference_patch(pipe.model)

pipe(prompt, max_length=10)

```

for vLLM,

> For now, please use vllm>=0.4.1

```diff

from vllm import LLM, SamplingParams

+ from minference import MInference

llm = LLM(model_name, enforce_eager=True, max_model_len=128_000, enable_chunked_prefill=False)

# Patch MInference Module,

# If you use the local path, please use the model_name from HF when initializing MInference.

+minference_patch = MInference("vllm", model_name)

+llm = minference_patch(llm)

outputs = llm.generate(prompts, sampling_params)

```

for vLLM w/ TP,

1. Copy `minference_patch_vllm_tp` and `minference_patch_vllm_executor` from `minference/patch.py` to the end of the `Worker` class in `vllm/worker/worker.py`. Make sure to indent `minference_patch_vllm_tp`.

2. When calling VLLM, ensure `enable_chunked_prefill=False` is set.

3. Refer to the script in https://github.com/microsoft/MInference/blob/hjiang/support_vllm_tp/experiments/benchmarks/run_e2e_vllm_tp.sh

```diff

from vllm import LLM, SamplingParams

+ from minference import MInference

llm = LLM(model_name, enforce_eager=True, max_model_len=128_000, enable_chunked_prefill=False, tensor_parallel_size=2)

# Patch MInference Module,

# If you use the local path, please use the model_name from HF when initializing MInference.

+minference_patch = MInference("vllm", model_name)

+llm = minference_patch(llm)

outputs = llm.generate(prompts, sampling_params)

```

using only the kernel,

```python

from minference import vertical_slash_sparse_attention, block_sparse_attention, streaming_forward

attn_output = vertical_slash_sparse_attention(q, k, v, vertical_topk, slash)

attn_output = block_sparse_attention(q, k, v, topk)

attn_output = streaming_forward(q, k, v, init_num, local_window_num)

```

for a local gradio demo 

```bash

git clone https://huggingface.co/spaces/microsoft/MInference

cd MInference

pip install -r requirments.txt

pip install flash_attn

python app.py

```

For more details, please refer to our [Examples](https://github.com/microsoft/MInference/tree/main/examples) and [Experiments](https://github.com/microsoft/MInference/tree/main/experiments). You can find more information about the dynamic compiler PIT in this [paper](https://dl.acm.org/doi/10.1145/3600006.3613139) and on [GitHub](https://github.com/microsoft/SparTA/tree/pit_artifact).

## SCBench

> [!Note]

> - **datasets >= 2.15.0**

### Load Data

You can download and load the **SCBench** data through the Hugging Face datasets ([🤗 HF Repo](https://huggingface.co/datasets/microsoft/SCBench)):

```python

from datasets import load_dataset

datasets = ["scbench_kv", "scbench_prefix_suffix", "scbench_vt", "scbench_repoqa", "scbench_qa_eng", "scbench_qa_chn", "scbench_choice_eng", "scbench_many_shot", "scbench_summary", "scbench_mf", "scbench_summary_with_needles", "scbench_repoqa_and_kv"]

for dataset in datasets:

    data = load_dataset("microsoft/SCBench", dataset, split="test")

```

### Data Format

All data in **SCBench** are standardized to the following format:

```json

{

    "id": "Random id for each piece of data.",

    "context": "The long context required for the task, such as repo-code, long-document, and many-shot.",

    "multi_turns": [{"input": "multi-turn question.", "answer": "multi-turn reference answer."}],

}

```

### Experiments

We implement **Multi-Turn** and **Multi-Request** modes with HF and vLLM in [`GreedySearch`](https://github.com/microsoft/MInference/blob/yucheng/kvcompression/scbench/eval_utils.py#L1160) and [`GreedySearch_vllm`](https://github.com/microsoft/MInference/blob/yucheng/kvcompression/scbench/eval_utils.py#L1070) two class. Please refer the follow scripts to run the experiments.

for all methods,

```bash

cd scbench

# Single-GPU, in Multi-Turn Mode

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 CUDA_VISIBLE_DEVICES=0 VLLM_WORKER_MULTIPROC_METHOD=spawn bash scripts/run_all_tasks.sh meta-llama/Llama-3.1-8B-Instruct 1 multi-turn

# Multi-GPU, in Multi-Turn Mode

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 CUDA_VISIBLE_DEVICES=0,1 VLLM_WORKER_MULTIPROC_METHOD=spawn bash scripts/run_all_tasks.sh meta-llama/Llama-3.1-8B-Instruct 2 multi-turn

# Multi-GPU, in Multi-Request Mode

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 CUDA_VISIBLE_DEVICES=0,1 VLLM_WORKER_MULTIPROC_METHOD=spawn bash scripts/run_all_tasks.sh meta-llama/Llama-3.1-8B-Instruct 2 scdq

```

for single methods,

```bash

cd scbench

# Single-GPU, in Multi-Turn Mode, using attn_type: vllm, kv_type: dense

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 CUDA_VISIBLE_DEVICES=0 VLLM_WORKER_MULTIPROC_METHOD=spawn bash scripts/run_single_method.sh meta-llama/Llama-3.1-8B-Instruct 1 multi-turn vllm dense

# Multi-GPU, in Multi-Turn Mode, using attn_type: vllm, kv_type: dense

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 CUDA_VISIBLE_DEVICES=0,1 VLLM_WORKER_MULTIPROC_METHOD=spawn bash scripts/run_single_method.sh meta-llama/Llama-3.1-8B-Instruct 2 multi-turn vllm dense

# Multi-GPU, in Multi-Request Mode, using attn_type: vllm, kv_type: dense

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 CUDA_VISIBLE_DEVICES=0,1 VLLM_WORKER_MULTIPROC_METHOD=spawn bash scripts/run_single_method.sh meta-llama/Llama-3.1-8B-Instruct 2 scdq vllm dense

```

More details about **attn_type** and **kv_type**, please refer to this section: [Supported Efficient Methods](https://github.com/microsoft/MInference/tree/main?tab=readme-ov-file#supported-efficient-methods).

## FAQ

For more insights and answers, visit our [FAQ section](https://github.com/microsoft/MInference/blob/main/Transparency_FAQ.md).

**Q1: How to effectively evaluate the impact of dynamic sparse attention on the capabilities of long-context LLMs?**

To evaluate long-context LLM capabilities using models like LLaMA-3-8B-Instruct-1M and GLM-4-9B-1M, we tested: 1) context window with RULER, 2) general tasks with InfiniteBench, 3) retrieval tasks with Needle in a Haystack, and 4) language model prediction with PG-19.


We found traditional methods perform poorly in retrieval tasks, with difficulty levels as follows: KV retrieval > Needle in a Haystack > Retrieval.Number > Retrieval PassKey. The main challenge is the semantic difference between needles and the haystack. Traditional methods excel when this difference is larger, as in passkey tasks. KV retrieval requires higher retrieval capabilities since any key can be a target, and multi-needle tasks are even more complex.


We will continue to update our results with more models and datasets in future versions.

**Q2: Does this dynamic sparse attention pattern only exist in long-context LLMs that are not fully trained?**

Firstly, attention is dynamically sparse, a characteristic inherent to the mechanism. We selected state-of-the-art long-context LLMs, GLM-4-9B-1M and LLaMA-3-8B-Instruct-1M, with effective context windows of 64K and 16K. With MInference, these can be extended to 64K and 32K, respectively. We will continue to adapt our method to other advanced long-context LLMs and update our results, as well as explore the theoretical basis for this dynamic sparse attention pattern.

**Q3: Does this dynamic sparse attention pattern only exist in Auto-regressive LMs or RoPE based LLMs?**

Similar vertical and slash line sparse patterns have been discovered in BERT[1] and multi-modal LLMs[2]. Our analysis of T5's attention patterns, shown in the figure, reveals these patterns persist across different heads, even in bidirectional attention.


[1] SparseBERT: Rethinking the Importance Analysis in Self-Attention, ICML 2021.


[2] LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference, 2024.




    



Figure 1. The sparse pattern in T5 Encoder.


**Q4: What is the relationship between MInference, SSM, Linear Attention, and Sparse Attention?**

All four approaches (MInference, SSM, Linear Attention, and Sparse Attention) efficiently optimize attention complexity in Transformers, each introducing inductive bias differently. The latter three require training from scratch. Recent works like Mamba-2 and Unified Implicit Attention Representation unify SSM and Linear Attention as static sparse attention, with Mamba-2 itself being a block-wise sparse method. While these approaches show potential due to sparse redundancy in attention, static sparse attention may struggle with dynamic semantic associations in complex tasks. In contrast, dynamic sparse attention is better suited for managing these relationships.

## Citation

If you find MInference useful or relevant to your project and research, please kindly cite our paper:

```bibtex

@article{jiang2024minference,

    title={MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention},

    author={Jiang, Huiqiang and Li, Yucheng and Zhang, Chengruidong and Wu, Qianhui and Luo, Xufang and Ahn, Surin and Han, Zhenhua and Abdi, Amir H and Li, Dongsheng and Lin, Chin-Yew and Yang, Yuqing and Qiu, Lili},

    journal={arXiv preprint arXiv:2407.02490},

    year={2024}

}

```

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a

Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide

a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions

provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).

For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or

contact [[email protected]](mailto:[email protected]) with any additional questions or comments.

## Trademarks

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trademarks or logos is subject to and must follow

[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).

Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.

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