252 lines
5.9 KiB
Markdown
252 lines
5.9 KiB
Markdown
# RustyTorch++ Feature Overview
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**Production-Ready GPU-Accelerated ML Framework in Pure Rust**
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---
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## Core Infrastructure
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### rtx-tensor
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GPU-native tensor library with PyTorch-compatible API
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- GPU-first design with zero-copy operations
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- Advanced indexing and broadcasting
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- Native GPU memory management
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### rtx-runtime
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GPU runtime system with CUDA and Metal backends
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- CUDA/ROCm/Metal backend support
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- Dynamic compilation and tensor cores
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- Adaptive scheduling
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### rtx-autograd
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Tape-based automatic differentiation engine
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- Reverse-mode AD with gradient checkpointing
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- Higher-order derivatives
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- Zero-overhead abstractions
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### rtx-memory
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Advanced memory management system
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- Zero-copy buffers and memory-mapped tensors
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- NUMA-aware allocation
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- Huge page support
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### rtx-kernel
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GPU kernel primitives and abstractions
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- Custom CUDA/Metal kernels
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- Automatic optimization
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- Hardware abstraction layer
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### rtx-bindings
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Multi-language bindings and interoperability
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- Python bindings via PyO3
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- C API for FFI
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- ONNX/DLPack interop
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---
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## Training & Optimization
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### rtx-transformers
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Complete transformer training infrastructure
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- Multi-query attention (MQA), grouped-query attention (GQA)
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- Sliding window attention
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- Modern architectures: LLaMA, GPT, BERT, T5
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### rtx-distributed
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Multi-node distributed training
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- NCCL/RCCL integration
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- Fault tolerance and elastic recovery
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- Topology optimization
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### rtx-rl
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Reinforcement Learning with Human Feedback (RLHF)
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- PPO trainer
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- Reward modeling
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- Preference learning
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### rtx-compress
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Model compression and optimization
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- Pruning (structured and unstructured)
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- Knowledge distillation
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- LoRA fine-tuning
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- INT8/INT4 quantization
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### rtx-flash-attention
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Flash Attention implementation
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- Memory-efficient attention
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- Extended context support
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- GPU-optimized kernels
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### rtx-preprocessing
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GPU-accelerated data preprocessing
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- Memory-mapped loading
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- Distributed sharding
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- Intelligent prefetching
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---
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## Model Architectures
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### rtx-vision
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Vision Transformers and computer vision models
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- ViT (Base/Large/Huge)
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- ConvNeXt
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- Patch embedding and augmentation
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### rtx-multimodal
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Multimodal model architectures
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- Vision-language models
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- Audio transformers
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- Cross-modal attention
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### rtx-diffuse
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Diffusion models with advanced sampling
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- DDIM sampling
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- Classifier-free guidance
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- Dynamic thresholding
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### rtx-timeseries
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GPU-accelerated time series analysis
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- ARIMA, Prophet integration
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- State-space models
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- Forecasting pipelines
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---
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## Production & Deployment
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### rtx-serving-api
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HTTP/gRPC serving layer
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- REST APIs and gRPC streaming
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- WebSocket support
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- Load balancing and health monitoring
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### rtx-inference
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High-performance inference engine
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- Continuous batching
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- Speculative decoding
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- Paged KV cache
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### rtx-streaming
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Real-time model streaming
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- Sub-millisecond latency
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- Backpressure handling
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- Stream metrics
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### rtx-edge
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Edge computing framework
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- ARM/RISC-V support
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- WebAssembly compilation
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- Microcontroller deployment
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### rtx-security
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Enterprise security and privacy
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- Differential privacy
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- Secure aggregation
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---
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## GPU Backends
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### CUDA Support
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- cudarc 0.18.1 integration
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- cuBLAS for matrix operations
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- cuDNN for neural network primitives
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- Custom PTX kernel compilation
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### Apple Metal Support
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- Native Metal GPU backend for Apple Silicon (M1/M2/M3/M4)
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- Metal Performance Shaders (MPS) for GEMM
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- Custom MSL compute kernels
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- Unified memory with zero-copy CPU/GPU access
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### Supported Metal Operations
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- Matrix multiplication via MPS GEMM
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- Element-wise operations (add, sub, mul, div)
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- Activation functions (ReLU, sigmoid, tanh, GELU, SiLU)
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- Trigonometric functions (sin, cos) for Fourier features
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- Reduction operations (sum, mean, max, min)
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---
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## Advanced Features
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### Mixture of Experts (MoE)
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- Metal-accelerated MoE on Apple Silicon
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- SwitchTransformer, ExpertChoice, TokenChoice routing
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- GPU expert dispatch with capacity management
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- Load balancing with Z-loss regularization
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### Mamba/State Space Models (SSM)
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- Metal-accelerated selective scan
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- O(n) complexity alternative to O(n^2) attention
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- Causal convolution with fused activation
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- Hybrid Mamba-Transformer support
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### Speculative Decoding
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- 2-3x inference speedup
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- GPU token scoring with temperature scaling
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- Top-k selection and draft verification
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- KV-cache management
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### Neural Architecture Search (NAS)
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- DARTS algorithm
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- PC-DARTS with 60% memory reduction
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- Hardware-aware NAS
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- Multi-objective Pareto optimization
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---
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## Workspace Structure
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RustyTorch++ is organized as a Cargo workspace with 60+ crates:
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```
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crates/
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├── core/ # 9 Core Infrastructure Crates
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├── training/ # 11 Training & Optimization Crates
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├── models/ # 6 Model Architecture Crates
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├── production/ # 9 Production & Deployment Crates
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├── specialized/ # 8 Specialized Computing Crates
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├── tooling/ # 12 Development & Tooling Crates
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└── meta/ # 4 User-Facing Meta-Crates
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```
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---
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## Performance Highlights
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### GPU Performance (RTX 4090)
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| Benchmark | RustyTorch++ GPU | PyTorch GPU | Speedup |
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|-----------|------------------|-------------|---------|
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| Forward Pass (200 pts) | 41us | 97us | 2.35x |
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| Training Step (200 pts) | 127us | ~600us | 4.7x |
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| Training Throughput | ~8,000 steps/sec | ~1,600 steps/sec | 5x |
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### Apple Silicon Performance
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| Chip | GPU Cores | Memory BW | FP32 TFLOPS |
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|------|-----------|-----------|-------------|
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| M1 Max | 32 | 400 GB/s | ~10.4 |
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| M3 Max | 40 | 400 GB/s | ~14.2 |
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| M4 Max | 40 | 546 GB/s | ~18 |
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---
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## Key Differentiators
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### vs PyTorch
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- Memory safety via Rust's type system
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- No Python GIL overhead
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- Compile-time error detection
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- 2-4x faster training on comparable hardware
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### vs Other Rust ML Frameworks
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- Real GPU acceleration (CUDA + Metal)
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- Modern architectures (Transformers, Diffusion, MoE)
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- Complete training pipeline, not just inference
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- Active development
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---
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*RustyTorch++: Where memory safety meets machine learning.*
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