facebookincubator/AITemplate
AITemplate
AITemplate is an ahead-of-time deep learning compiler that translates PyTorch neural networks into standalone C++ source code. It functions as a PyTorch to C++ compiler and a GPU kernel fusion engine, producing self-contained executable binaries that run inference without requiring a Python interpreter or deep learning framework runtime.
The project generates optimized CUDA and HIP C++ code specifically for NVIDIA TensorCores and AMD MatrixCores. It focuses on maximizing throughput for half-precision floating-point operations through a system that combines multiple neural network operators into single GPU kernels to minimize memory overhead and latency.
The toolset covers GPU inference acceleration and high performance computing, providing capabilities for custom GPU operator development and the mapping of graph nodes to hardware-specific templates. It includes utility support for benchmarking inference performance and visualizing model optimizations.
Features
- GPU-Accelerated Inference - Provides a compiler that translates PyTorch models into optimized C++ code for high-performance GPU inference on NVIDIA and AMD hardware.
- GPU Kernel Fusions - Implements specialized GPU kernel fusion to combine multiple neural network operators and minimize memory overhead.
- Fused Neural Modules - Fuses linear layers and activation functions into a single GPU kernel to reduce memory bandwidth overhead.
- Fused GPU Kernel Composition - Combines multiple parallel or sequential operators into a single GPU kernel to reduce memory traffic and overhead.
- Neural Network Layer Fusions - Combines multiple neural network operators into single GPU kernels to minimize memory overhead and latency.
- Inference Performance Optimization - Executes floating-point operations using hardware-accelerated cores to achieve peak performance across diverse architectures.
- Model Inference Accelerators - Translates PyTorch models into an optimized inference engine with support for partial acceleration of unsupported operators.
- Standalone Binaries - Translates PyTorch models into standalone executable binaries that run without requiring a heavy Python runtime or external libraries.
- GPU Kernel Development - Provides the ability to define new neural network primitives and map them to hardware-specific code generation templates.
- AOT Deep Learning Compilers - Functions as an ahead-of-time compiler creating self-contained binaries for model inference without a framework runtime.
- GPU Kernel Code Generators - Converts computation graphs into optimized GPU source code to accelerate model inference on graphics processors.
- PyTorch to C++ Compilers - Translates PyTorch neural networks into standalone C++ source code for high-performance inference.
- Source-to-C Transpilers - Transforms neural network models into standalone C++ source code for high-performance inference on hardware accelerators.
- C Code Generators - Translates neural network computation graphs into standalone C++ source code to remove framework runtime dependencies.
- CUDA and HIP Code Generators - Generates optimized CUDA and HIP C++ code specifically tailored for NVIDIA TensorCores and AMD MatrixCores.
- Inference Primitive Extensions - Provides a modular system to build high-performance primitives for new model types and complex operator fusions.
- Operator Primitive Definitions - Uses a system of graph nodes and header files to define new operators and backend generation logic.
- Quantized Inference Accelerators - Maximizes throughput for half-precision floating-point (FP16) operations on hardware-accelerated GPU cores.
- Standalone Binaries - Produces self-contained executable binaries that run model inference without needing a Python interpreter or external runtimes.
- Standalone Model Binaries - Produces portable, self-contained binaries that include weights and runtime for independent execution.
- Compiler Codegen Extensions - Allows the creation of new operators or fused kernels by defining graph nodes and backend generation logic.
- High-Performance Computing - Implements specialized C++ code targeting TensorCore and MatrixCore hardware to minimize inference latency in production environments.
- Custom Graph Operations - Implements custom neural network operations by mapping graph nodes to hardware-specific code generation templates.
- Hardware Code Generation Templates - Maps neural network graph nodes to specialized code generation templates optimized for TensorCores and MatrixCores.
- Model Serving & Deployment - Transforms neural networks into fast C++ inference code.
