Crate rustorch 

RusTorch 🚀

A production-ready deep learning library in Rust with PyTorch-like API, data validation, debugging tools, and enterprise-grade reliability

RusTorch v0.5.14 is a fully functional deep learning library that leverages Rust’s safety and performance, providing comprehensive tensor operations, automatic differentiation, neural network layers, transformer architectures, GPU acceleration, unified error handling system, advanced memory optimization features, data validation & quality assurance, and comprehensive debug & logging systems.

✨ Key Features

• 🔥 Comprehensive Tensor Operations: Math operations, broadcasting, indexing, and statistics
• 🤖 Transformer Architecture: Complete transformer implementation with multi-head attention
• ⚡ SIMD Optimizations: AVX2/SSE4.1 vectorized operations for high performance
• 🔄 Unified Parallel Operations: Trait-based parallel tensor operations with intelligent scheduling
• 🎮 GPU Integration: CUDA/Metal/OpenCL support with automatic device selection
• 💾 Advanced Memory Management: Zero-copy operations, SIMD-aligned allocation, and memory pools
• 🧠 Automatic Differentiation: Tape-based computational graph for gradient computation
• 🏗️ Neural Network Layers: Linear, Conv1d/2d/3d, ConvTranspose, RNN/LSTM/GRU, BatchNorm, Dropout, and more
• 🛡️ Unified Error Handling: Single RusTorchError type with 61+ specialized helper functions and RusTorchResult<T> for cleaner APIs
• 🔧 Safe Operations: Type-safe tensor operations with comprehensive error handling and ReLU activation
• ⚙️ Shared Base Traits: Reusable convolution and pooling base implementations for code efficiency
• 🌐 WebAssembly Support: Browser-compatible WASM bindings with optimized performance
• 🔍 Data Validation & Quality Assurance: Statistical analysis, anomaly detection, consistency checking, real-time monitoring
• 🐛 Comprehensive Debug & Logging: Structured logging, performance profiling, memory tracking, automated alerts
• 💾 Phase 9 Serialization: Model save/load, JIT compilation, PyTorch compatibility, cross-platform format support

🚀 Quick Start

use rustorch::prelude::*;

// Create tensors
let a = Tensor::from_vec(vec![1.0f32, 2.0, 3.0, 4.0], vec![2, 2]);
let b = Tensor::from_vec(vec![5.0f32, 6.0, 7.0, 8.0], vec![2, 2]);

// Basic operations
let c = &a + &b;  // Addition
let d = a.matmul(&b);  // Matrix multiplication

// Mathematical functions (using methods from tensor ops)
let e = a.data.mapv(|x| x.sin());  // Sine function
let f = a.data.mapv(|x| x.exp());  // Exponential function

println!("Shape: {:?}", c.shape());
println!("Result: {:?}", c.as_slice());

🔧 Safe Operations with Error Handling

use rustorch::nn::safe_ops::SafeOps;

// Create variables safely with validation
let var = SafeOps::create_variable(vec![-1.0, 0.0, 1.0], vec![3], false).unwrap();

// Apply ReLU activation function
let relu_result = SafeOps::relu(&var).unwrap();
println!("ReLU: {:?}", relu_result.data().read().unwrap().as_array()); // [0.0, 0.0, 1.0]

// Get tensor statistics
let stats = SafeOps::get_stats(&var).unwrap();
println!("Mean: {:.2}, Std: {:.2}", stats.mean, stats.std_dev());

🏗️ Architecture Overview

The library is organized into several key modules:

• tensor: Core tensor operations with parallel and GPU acceleration
• nn: Neural network layers and building blocks
  • nn::safe_ops: Safe tensor operations with error handling and ReLU activation
  • nn::conv_base: Shared base traits for convolution and pooling layers
• autograd: Automatic differentiation system
• vision: Computer vision utilities including transforms and datasets
• optim: Optimization algorithms (SGD, Adam, etc.)
• gpu: GPU acceleration support (CUDA, Metal, OpenCL)
• simd: SIMD vectorized operations
• wasm: WebAssembly bindings for browser deployment
• memory: Advanced memory management and pooling
• data: Phase 5 data loading API with modern Dataset and DataLoader traits

🔄 Parallel Operations

RusTorch provides a unified trait-based system for parallel tensor operations:

use rustorch::tensor::{Tensor, parallel_traits::*};

let tensor1 = Tensor::<f32>::ones(&[4, 4]);  // 2D matrices for simplicity
let tensor2 = Tensor::<f32>::ones(&[4, 4]);

// Basic tensor operations
let result = &tensor1 + &tensor2; // Element-wise addition

// Matrix multiplication
let matmul_result = tensor1.matmul(&tensor2);

// Basic reduction operations
let sum = tensor1.sum();

🎮 GPU Integration

Seamless GPU acceleration with automatic device selection:

use rustorch::tensor::Tensor;

let tensor1 = Tensor::<f32>::ones(&[4, 4]);
let tensor2 = Tensor::<f32>::ones(&[4, 4]);

// GPU-accelerated operations (when available)
let result = &tensor1 + &tensor2;  // Basic tensor operations

💾 Memory Optimization

Advanced memory management for optimal performance:

use rustorch::tensor::Tensor;

let tensor = Tensor::<f32>::ones(&[4, 4]);

// Basic tensor operations
let result = &tensor * &tensor; // Element-wise multiplication

🌐 WebAssembly Integration

Run neural networks directly in browsers with optimized WASM bindings:

// Browser usage (JavaScript)
import init, * as rustorch from './pkg/rustorch.js';

await init();

// Create and manipulate tensors
const tensor1 = rustorch.WasmTensor.ones([2, 3]);
const tensor2 = rustorch.WasmTensor.random([2, 3]);
const sum = tensor1.add(tensor2);

// Neural network inference
const model = new rustorch.WasmModel();
model.add_linear(10, 5, true);
model.add_relu();

const input = rustorch.WasmTensor.random([1, 10]);
const output = model.forward(input);

console.log('Output:', output.data());

Modules

amp

Automatic Mixed Precision (AMP) training support 自動混合精度(AMP)学習サポート Automatic Mixed Precision (AMP) training support 自動混合精度学習のサポート

autograd

Automatic differentiation module 自動微分モジュール

backends

Unified compute backend abstraction layer
統一計算バックエンド抽象化レイヤー Unified compute backend abstraction for RusTorch v0.4.0 RusTorch v0.4.0の統一計算バックエンド抽象化

common

Common utilities and shared functionality 共通ユーティリティと共有機能 Common utilities and shared functionality for RusTorch RusTorchの共通ユーティリティと共有機能

convert

PyTorch to RusTorch conversion system PyTorchからRusTorch変換システム PyTorch to RusTorch conversion module PyTorchからRusTorchへの変換モジュール

data

Data loading and processing utilities (Phase 5 API) データ読み込みと処理のユーティリティ（フェーズ5 API）

debug

Debug and logging system デバッグ・ログシステム RusTorch Debug & Logging Framework

distributed

Distributed training support for multi-GPU and multi-machine training マルチGPUおよびマルチマシン学習用分散学習サポート Distributed training support for RusTorch RusTorchの分散学習サポート

distributions

Statistical distributions module providing PyTorch-compatible probability distributions PyTorch互換の確率分布を提供する統計分布モジュール

dtype

Data types for tensors テンソル用データ型

error

Unified error handling system 統一エラーハンドリングシステム Unified error handling system for RusTorch RusTorch用統一エラーハンドリングシステム

execution

Dynamic execution engine for runtime graph optimization 実行時グラフ最適化のための動的実行エンジン Dynamic execution engine for runtime graph optimization 実行時グラフ最適化のための動的実行エンジン

formats

Model format support and conversion utilities モデル形式サポートと変換ユーティリティ Model format support for RusTorch RusTorchのモデルフォーマットサポート

gpu

GPU acceleration support (CUDA, Metal, OpenCL) GPU加速サポート（CUDA、Metal、OpenCL）

linalg

High-performance linear algebra with BLAS integration BLAS統合による高性能線形代数 Linear Algebra Module with High-Performance Optimizations 高性能最適化付き線形代数モジュール

memory

Memory management and pooling utilities メモリ管理とプーリングユーティリティ Enhanced Memory Management System for RusTorch RusTorch用の高度メモリ管理システム

model_import

Model import functionality for PyTorch and ONNX models PyTorchとONNXモデルのインポート機能

models

Pre-built models and architectures 事前構築モデルとアーキテクチャ 機械学習モデルアーキテクチャ Machine learning model architectures

nn

Neural network layers and building blocks ニューラルネットワークレイヤーと構成要素 ニューラルネットワークモジュールの定義 Neural network module definitions.

optim

Optimization algorithms 最適化アルゴリズム

optimization

Cross-platform optimization module クロスプラットフォーム最適化モジュール Cross-platform optimization module クロスプラットフォーム最適化モジュール

parallel

Parallel processing utilities 並列処理ユーティリティ

prelude

Re-exports of commonly used items

profiler

Performance profiler パフォーマンスプロファイラー Performance Profiling & Benchmarking Framework (Phase 1 Component 5) パフォーマンスプロファイリング・ベンチマーキングフレームワーク（フェーズ1コンポーネント5）

serialization

Serialization and model I/O system (Phase 9) シリアライゼーション・モデルI/Oシステム（フェーズ9） Serialization system for Phase 9 フェーズ9用シリアライゼーションシステム

simd

SIMD vectorized operations for performance optimization パフォーマンス最適化のためのSIMDベクトル化操作

special

Special mathematical functions (gamma, Bessel, error functions) 特殊数学関数（ガンマ、ベッセル、誤差関数） Special mathematical functions module 特殊数学関数モジュール

tensor

Tensor operations and data structures テンソル操作とデータ構造

tensorboard

TensorBoard integration TensorBoard統合 TensorBoard integration for RusTorch RusTorch用TensorBoard統合

training

Training loop abstractions and utilities
学習ループの抽象化とユーティリティ

utils

Utility functions ユーティリティ関数 Utility functions and types

validation

Data validation and quality assurance system データ検証・品質保証システム Data Validation & Quality Assurance Framework (Phase 1 Component 6) データ検証・品質保証フレームワーク（フェーズ1コンポーネント6）

vision

Computer vision module providing image transforms, data augmentation, and built-in datasets 画像変換、データ拡張、組み込みデータセットを提供するコンピュータビジョンモジュール Computer Vision utilities for RusTorch RusTorch用コンピュータビジョンユーティリティ

visualization

Visualization tools for plots, graphs, and data analysis プロット、グラフ、データ解析用の可視化ツール

wasm

WebAssembly support and bindings WebAssemblyサポートとバインディング WebAssembly bindings for RusTorch RusTorch用WebAssemblyバインディング

Macros

autocast_op

Macro for autocast-aware operations

diagnostic_context

Macro for easy diagnostic context creation

distributed_error

Macro for creating distributed errors easily 分散エラーを簡単に作成するためのマクロ

error_context

Macro for creating error context with file location ファイル位置付きのエラーコンテキストを作成するマクロ

gpu_error

Macro for creating GPU errors easily GPUエラーを簡単に作成するためのマクロ

impl_wasm_analyzer

Macro for implementing analyzer trait

impl_wasm_operation

Convenience macro for implementing basic WasmOperation

impl_wasm_standard

Macro for implementing standard WASM operation boilerplate

impl_wasm_transform

Macro for implementing transform trait with validation

impl_wasm_versioned

Convenience macro for implementing WasmVersioned

log_structured

Convenience macro for structured logging

perf_timer

Macro for performance timing

profile

Profile a code block コードブロックをプロファイル

profile_fn

Profile a function 関数をプロファイル

shape_ops

Convenience macro for chaining shape operations 形状操作連鎖の便利マクロ

tb_log

Macro for easy TensorBoard logging 簡単なTensorBoardログ用マクロ

tensor_error

Helper macros for error creation エラー作成用ヘルパーマクロ Macro for creating tensor errors easily テンソルエラーを簡単に作成するためのマクロ

time_block

Macro for timed code blocks

track_allocation

Convenience macro for tracking allocations

track_deallocation

wasm_error

Macro for quick error generation

with_context

Macro for adding context to results 結果にコンテキストを追加するマクロ