MInference 1.0

Accelerates long-context pre-fill processing for large language models

CommonProductProgrammingNatural Language ProcessingMachine Learning

MInference 1.0 is a sparse computation method aimed at accelerating the pre-fill stage of long sequence processing. It implements a dynamic sparse attention method for long-context large language models (LLMs) by identifying three unique patterns in the long context attention matrix, accelerating the pre-fill stage for 1M token prompts while maintaining the capabilities of LLMs, especially retrieval capabilities.

Dynamic sparse attention method
accelerating the pre-fill stage for long-context LLMs
boosting processing speed up to 10 times.
Divides dynamic sparse attention into three modes: A-shape
Vertical-Slash
and Block-Sparse
and designs Kernel-Aware Sparse Pattern Search algorithm to find the optimal head mode.
Introduces online approximation methods and optimized GPU kernels to accelerate LLM inference with minimal overhead.
Presents a best-practice inference code library
enabling 1M token pre-fill inference for LLaMA-style models on a single A100.

MInference 1.0 is designed for researchers and developers working with large datasets and long-context information
particularly in natural language processing and machine learning. By optimizing the use of computational resources
it enables large language models to process and generate text faster
making it suitable for applications requiring efficient text generation and retrieval capabilities.

In question answering (QA) tasks
MInference 1.0 can quickly retrieve and generate accurate answers.
In programming tasks
MInference 1.0 can assist developers in quickly writing and understanding code.
In multi-hop QA tasks

Step 1: Visit the online demo of MInference 1.0 or download the code.
Step 2: Configure the required environment and dependencies according to the documentation.
Step 3: Load your long-context data or model.
Step 4: Use MInference 1.0's API or command-line tools to perform pre-fill processing on the data.
Step 5: Run the optimized inference process and observe the processing speed and result quality.

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