A high-performance inference engine built in Rust for running Llama series models. This project leverages Rust's speed and safety to provide efficient text generation while offering a modular design that supports a range of neural network operators.
The inference system implements the core functionalities needed to run Llama models, including:
- Grouped Query Attention (GQA)
- RMSNorm (Root Mean Square Normalization)
- Masked Softmax
- SiLU (Swiglu variant) Activation
- RoPE (Rotary Positional Encoding)
Future enhancements will add support for operators such as GELU, Layer Norm, Multi-Query Attention (MQA), Multi-Linear Attention (MLA), and more.
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Efficient Inference Pipeline:
Utilizes a key-value cache for attention states and reuses pre-allocated buffers to optimize runtime performance. -
Core Neural Network Operators:
Implements essential operations for transformer models:- GQA: Efficient handling of grouped attention heads.
- RMSNorm: Stabilizes activations using RMS normalization.
- Masked Softmax: Customized softmax for attention with masking support.
- SiLU (Swiglu): Applies a variant of the SiLU activation function.
- RoPE: Implements rotary positional encoding to capture token order.
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Extensible Architecture:
Designed to easily incorporate additional operators and features as the project evolves.
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Rust and Cargo:
Install Rust using the instructions from rust-lang.org. -
Dependencies:
The project uses several external crates:- SafeTensors for loading model weights.
- Tokenizers for text tokenization.
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Clone the repository:
git clone <repository-url> cd <repository-directory>
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Build the project in release mode:
cargo build --release
- Additional Operator Support:
- Integrate operators such as GELU, Layer Norm, MQA, MLA, and others.
- Performance Optimizations:
- Improve speed and memory efficiency for handling larger models and longer sequences.
- Extended Model Compatibility:
- Adapt the system for other transformer-based models and configurations.