rustytorch/FEATURES.md

RustyTorch++ Feature Overview

Production-Ready GPU-Accelerated ML Framework in Pure Rust

---

Core Infrastructure

rtx-tensor

GPU-native tensor library with PyTorch-compatible API

• GPU-first design with zero-copy operations
• Advanced indexing and broadcasting
• Native GPU memory management

rtx-runtime

GPU runtime system with CUDA and Metal backends

• CUDA/ROCm/Metal backend support
• Dynamic compilation and tensor cores
• Adaptive scheduling

rtx-autograd

Tape-based automatic differentiation engine

• Reverse-mode AD with gradient checkpointing
• Higher-order derivatives
• Zero-overhead abstractions

rtx-memory

Advanced memory management system

• Zero-copy buffers and memory-mapped tensors
• NUMA-aware allocation
• Huge page support

rtx-kernel

GPU kernel primitives and abstractions

• Custom CUDA/Metal kernels
• Automatic optimization
• Hardware abstraction layer

rtx-bindings

Multi-language bindings and interoperability

• Python bindings via PyO3
• C API for FFI
• ONNX/DLPack interop

---

Training & Optimization

rtx-transformers

Complete transformer training infrastructure

• Multi-query attention (MQA), grouped-query attention (GQA)
• Sliding window attention
• Modern architectures: LLaMA, GPT, BERT, T5

rtx-distributed

Multi-node distributed training

• NCCL/RCCL integration
• Fault tolerance and elastic recovery
• Topology optimization

rtx-rl

Reinforcement Learning with Human Feedback (RLHF)

• PPO trainer
• Reward modeling
• Preference learning

rtx-compress

Model compression and optimization

• Pruning (structured and unstructured)
• Knowledge distillation
• LoRA fine-tuning
• INT8/INT4 quantization

rtx-flash-attention

Flash Attention implementation

• Memory-efficient attention
• Extended context support
• GPU-optimized kernels

rtx-preprocessing

GPU-accelerated data preprocessing

• Memory-mapped loading
• Distributed sharding
• Intelligent prefetching

---

Model Architectures

rtx-vision

Vision Transformers and computer vision models

• ViT (Base/Large/Huge)
• ConvNeXt
• Patch embedding and augmentation

rtx-multimodal

Multimodal model architectures

• Vision-language models
• Audio transformers
• Cross-modal attention

rtx-diffuse

Diffusion models with advanced sampling

• DDIM sampling
• Classifier-free guidance
• Dynamic thresholding

rtx-timeseries

GPU-accelerated time series analysis

• ARIMA, Prophet integration
• State-space models
• Forecasting pipelines

---

Production & Deployment

rtx-serving-api

HTTP/gRPC serving layer

• REST APIs and gRPC streaming
• WebSocket support
• Load balancing and health monitoring

rtx-inference

High-performance inference engine

• Continuous batching
• Speculative decoding
• Paged KV cache

rtx-streaming

Real-time model streaming

• Sub-millisecond latency
• Backpressure handling
• Stream metrics

rtx-edge

Edge computing framework

• ARM/RISC-V support
• WebAssembly compilation
• Microcontroller deployment

rtx-security

Enterprise security and privacy

• Differential privacy
• Secure aggregation

---

GPU Backends

CUDA Support

• cudarc 0.18.1 integration
• cuBLAS for matrix operations
• cuDNN for neural network primitives
• Custom PTX kernel compilation

Apple Metal Support

• Native Metal GPU backend for Apple Silicon (M1/M2/M3/M4)
• Metal Performance Shaders (MPS) for GEMM
• Custom MSL compute kernels
• Unified memory with zero-copy CPU/GPU access

Supported Metal Operations

• Matrix multiplication via MPS GEMM
• Element-wise operations (add, sub, mul, div)
• Activation functions (ReLU, sigmoid, tanh, GELU, SiLU)
• Trigonometric functions (sin, cos) for Fourier features
• Reduction operations (sum, mean, max, min)

---

Advanced Features

Mixture of Experts (MoE)

• Metal-accelerated MoE on Apple Silicon
• SwitchTransformer, ExpertChoice, TokenChoice routing
• GPU expert dispatch with capacity management
• Load balancing with Z-loss regularization

Mamba/State Space Models (SSM)

• Metal-accelerated selective scan
• O(n) complexity alternative to O(n^2) attention
• Causal convolution with fused activation
• Hybrid Mamba-Transformer support

Speculative Decoding

• 2-3x inference speedup
• GPU token scoring with temperature scaling
• Top-k selection and draft verification
• KV-cache management

Neural Architecture Search (NAS)

• DARTS algorithm
• PC-DARTS with 60% memory reduction
• Hardware-aware NAS
• Multi-objective Pareto optimization

---

Workspace Structure

RustyTorch++ is organized as a Cargo workspace with 60+ crates:

crates/
├── core/           # 9 Core Infrastructure Crates
├── training/       # 11 Training & Optimization Crates
├── models/         # 6 Model Architecture Crates
├── production/     # 9 Production & Deployment Crates
├── specialized/    # 8 Specialized Computing Crates
├── tooling/        # 12 Development & Tooling Crates
└── meta/           # 4 User-Facing Meta-Crates

---

Performance Highlights

GPU Performance (RTX 4090)

Benchmark	RustyTorch++ GPU	PyTorch GPU	Speedup
Forward Pass (200 pts)	41us	97us	2.35x
Training Step (200 pts)	127us	~600us	4.7x
Training Throughput	~8,000 steps/sec	~1,600 steps/sec	5x

Apple Silicon Performance

Chip	GPU Cores	Memory BW	FP32 TFLOPS
M1 Max	32	400 GB/s	~10.4
M3 Max	40	400 GB/s	~14.2
M4 Max	40	546 GB/s	~18

---

Key Differentiators

vs PyTorch

• Memory safety via Rust's type system
• No Python GIL overhead
• Compile-time error detection
• 2-4x faster training on comparable hardware

vs Other Rust ML Frameworks

• Real GPU acceleration (CUDA + Metal)
• Modern architectures (Transformers, Diffusion, MoE)
• Complete training pipeline, not just inference
• Active development

---

RustyTorch++: Where memory safety meets machine learning.