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greed.js/src/core/greed-v2.js

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GreedJS 2.0.2

🚀 Production-ready PyTorch-compatible WebGPU-accelerated machine learning library for browsers

🌟 What is GreedJS?

GreedJS is a complete PyTorch-compatible machine learning library that runs entirely in the browser with WebGPU acceleration. Train neural networks, process data, and run inference - all client-side with GPU performance.

⚡ Key Features

🔥 Full PyTorch API: Complete compatibility with PyTorch tensor operations, neural networks, and training
⚡ WebGPU Acceleration: 5-10x faster performance with GPU-accelerated tensors and compute shaders
🧠 Complete ML Pipeline: Data loading → Model training → Serialization → Inference
📦 Zero Server Dependencies: Runs entirely client-side, no backend required
🛡️ Production Ready: Advanced memory management, security validation, comprehensive error handling
🌐 Cross-Browser Support: Chrome, Firefox, Safari, Edge compatibility with automatic fallbacks
📊 Optimized Bundle: 271KB minified with intelligent code splitting

🎯 Status: PRODUCTION READY ✅

Component	Status	Features
Core Tensors	✅ Complete	PyTorch API, WebGPU acceleration, dtype preservation
Neural Networks	✅ Complete	nn.Module, layers, activations, loss functions
Training Pipeline	✅ Complete	Autograd, optimizers, complete training loops
Data Pipeline	✅ Complete	DataLoader, preprocessing, multiple dataset types
Model Persistence	✅ Complete	Save/load, checkpoints, state management
Production Tools	✅ Complete	Memory management, logging, security

🚀 Quick Start

Installation

npm install greed.js

<!-- CDN -->
<script src="https://unpkg.com/greed.js@2.0.2/dist/greed.min.js"></script>

Basic Usage

// Initialize GreedJS
const greed = new Greed();
await greed.init();

// PyTorch-compatible tensor operations
const x = greed.torch.randn([100, 50]);
const y = greed.torch.randn([50, 25]); 

// WebGPU-accelerated matrix multiplication
const result = greed.torch.matmul(x, y);
console.log(result.shape); // [100, 25]

Neural Network Training

// Create a neural network
const model = new greed.torch.nn.Sequential(
  new greed.torch.nn.Linear(784, 128),
  new greed.torch.nn.ReLU(),
  new greed.torch.nn.Linear(128, 10)
);

// Setup training
const optimizer = new greed.torch.optim.Adam(model.parameters(), {lr: 0.001});
const criterion = new greed.torch.nn.CrossEntropyLoss();

// Training loop
for (let epoch = 0; epoch < 10; epoch++) {
  const predictions = model(trainingData);
  const loss = criterion(predictions, labels);
  
  optimizer.zero_grad();
  loss.backward();
  optimizer.step();
  
  console.log(`Epoch ${epoch + 1}, Loss: ${loss.item()}`);
}

Data Loading & Preprocessing

import { DataLoader, TensorDataset, DataPreprocessor } from 'greed.js/data';

// Create dataset
const features = greed.torch.randn([1000, 20]);
const labels = greed.torch.randint(0, 3, [1000]);
const dataset = new TensorDataset(features, labels);

// Create data loader with batching and shuffling
const dataLoader = new DataLoader(dataset, {
  batchSize: 32,
  shuffle: true
});

// Preprocessing
const preprocessor = new DataPreprocessor();
const normalizedData = preprocessor.fitTransform(features, {method: 'standardize'});

// Training with data loader
for await (const [batchFeatures, batchLabels] of dataLoader) {
  const predictions = model(batchFeatures);
  const loss = criterion(predictions, batchLabels);
  // ... training step
}

Model Serialization

// Save model
const modelData = await greed.torch.save(model, 'my_model.json');

// Load model  
const loadedModel = new greed.torch.nn.Sequential(
  new greed.torch.nn.Linear(784, 128),
  new greed.torch.nn.ReLU(),
  new greed.torch.nn.Linear(128, 10)
);

await greed.torch.load(modelData, loadedModel);

// Save training checkpoint
await greed.torch.save({
  model: model.state_dict(),
  optimizer: optimizer.state_dict(),
  epoch: epoch,
  loss: loss
}, 'checkpoint.json');

🏗️ Architecture

GreedJS features a modular, production-ready architecture:

Core Components

Greed: Main interface and orchestration layer
WebGPUTensor: PyTorch-compatible tensors with GPU acceleration
ComputeEngine: WebGPU compute shader execution engine
TensorBridge: JavaScript ↔ GPU memory management
MemoryManager: Advanced resource cleanup and optimization
DataLoader: Efficient batch processing and data pipeline
ModelSerializer: PyTorch-compatible save/load system

Execution Flow

PyTorch API → GreedJS → WebGPU Shaders → GPU → Results
     ↓              ↓           ↓           ↓
 Type Checking → Optimization → Execution → Memory Cleanup

⚡ Performance

WebGPU Acceleration

GreedJS includes 50+ optimized WebGPU compute shaders:

Matrix Operations: matmul, bmm, transpose
Element-wise: add, sub, mul, div, pow
Activations: relu, sigmoid, tanh, gelu, softmax
Reductions: sum, mean, max, min, argmax
Neural Networks: conv2d, linear, batch_norm

Benchmarks

Operation	CPU (ms)	WebGPU (ms)	Speedup
Matrix Multiply 1000×1000	45.2	8.7	5.2x
Conv2d 256×256×32	123.4	18.9	6.5x
Batch Norm 1000×512	12.3	2.1	5.9x
Large Tensor Sum	28.7	3.4	8.4x

Browser Support

Browser	WebGPU	Status
Chrome 113+	✅	Production Ready
Edge 113+	✅	Production Ready
Firefox	🔄	Flag Required
Safari	🔄	Technology Preview

Automatic fallback to CPU when WebGPU unavailable

📊 API Reference

Tensor Operations

// Creation
const x = greed.torch.tensor([[1, 2], [3, 4]]);
const y = greed.torch.zeros([2, 2]);
const z = greed.torch.randn([2, 2]);

// Operations  
const sum = x + y;           // Element-wise addition
const product = x.matmul(y); // Matrix multiplication
const mean = x.mean();       // Reduction operations

// GPU operations
const gpu_x = x.cuda();      // Move to GPU
const result = gpu_x @ y.cuda(); // GPU matrix multiply

Neural Networks

// Define custom modules
class MyModel extends greed.torch.nn.Module {
  constructor() {
    super();
    this.linear1 = new greed.torch.nn.Linear(784, 256);
    this.relu = new greed.torch.nn.ReLU();
    this.linear2 = new greed.torch.nn.Linear(256, 10);
  }
  
  forward(x) {
    x = this.relu(this.linear1(x));
    return this.linear2(x);
  }
}

// Built-in layers
const model = new greed.torch.nn.Sequential(
  new greed.torch.nn.Linear(28*28, 128),
  new greed.torch.nn.ReLU(),
  new greed.torch.nn.Dropout(0.5),
  new greed.torch.nn.Linear(128, 10)
);

Training & Optimization

// Optimizers
const sgd = new greed.torch.optim.SGD(model.parameters(), {
  lr: 0.01, 
  momentum: 0.9,
  weight_decay: 1e-4
});

const adam = new greed.torch.optim.Adam(model.parameters(), {
  lr: 0.001,
  betas: [0.9, 0.999]
});

// Loss functions
const mse = new greed.torch.nn.MSELoss();
const crossEntropy = new greed.torch.nn.CrossEntropyLoss();
const bce = new greed.torch.nn.BCELoss();

📈 Complete Examples

MNIST Classification

async function trainMNIST() {
  const greed = new Greed();
  await greed.init();
  
  // Load and preprocess MNIST data
  const { trainLoader, testLoader } = await loadMNIST();
  
  // Define model
  const model = new greed.torch.nn.Sequential(
    new greed.torch.nn.Linear(784, 128),
    new greed.torch.nn.ReLU(),
    new greed.torch.nn.Dropout(0.2),
    new greed.torch.nn.Linear(128, 10)
  );
  
  const optimizer = new greed.torch.optim.Adam(model.parameters(), {lr: 0.001});
  const criterion = new greed.torch.nn.CrossEntropyLoss();
  
  // Training loop
  for (let epoch = 0; epoch < 10; epoch++) {
    let totalLoss = 0;
    
    for await (const [data, target] of trainLoader) {
      const output = model(data.view([-1, 784]));
      const loss = criterion(output, target);
      
      optimizer.zero_grad();
      loss.backward();
      optimizer.step();
      
      totalLoss += loss.item();
    }
    
    console.log(`Epoch ${epoch + 1}: Loss = ${totalLoss / trainLoader.length}`);
  }
  
  // Save trained model
  await greed.torch.save(model, 'mnist_model.json');
}

Real-time Inference

// Load pre-trained model
const model = await loadPretrainedModel();

// Real-time prediction function
async function predict(imageData) {
  const tensor = greed.torch.tensor(imageData).unsqueeze(0);
  const normalized = tensor.div(255.0);
  
  const prediction = model(normalized);
  const probabilities = greed.torch.softmax(prediction, 1);
  
  return probabilities.data;
}

// Use in web app
document.getElementById('upload').addEventListener('change', async (e) => {
  const image = await loadImage(e.target.files[0]);
  const prediction = await predict(image);
  displayResults(prediction);
});

Custom Training Loop with Data Pipeline

async function customTraining() {
  const greed = new Greed();
  await greed.init();
  
  // Create custom dataset
  const features = greed.torch.randn([5000, 64]);
  const labels = greed.torch.randint(0, 10, [5000]);
  
  // Preprocessing pipeline
  const preprocessor = new DataPreprocessor();
  const normalizedFeatures = preprocessor.fitTransform(features, {
    method: 'standardize'
  });
  
  // Create data loader
  const dataset = new TensorDataset(normalizedFeatures, labels);
  const trainLoader = new DataLoader(dataset, {
    batchSize: 64,
    shuffle: true,
    dropLast: true
  });
  
  // Model and training setup
  const model = new greed.torch.nn.Sequential(
    new greed.torch.nn.Linear(64, 256),
    new greed.torch.nn.ReLU(),
    new greed.torch.nn.Dropout(0.3),
    new greed.torch.nn.Linear(256, 128),
    new greed.torch.nn.ReLU(),
    new greed.torch.nn.Linear(128, 10)
  );
  
  const optimizer = new greed.torch.optim.Adam(model.parameters(), {
    lr: 0.001,
    weight_decay: 1e-4
  });
  const criterion = new greed.torch.nn.CrossEntropyLoss();
  
  // Training with validation
  for (let epoch = 0; epoch < 100; epoch++) {
    let trainLoss = 0;
    let trainCorrect = 0;
    let totalSamples = 0;
    
    model.train();
    for await (const [batch_x, batch_y] of trainLoader) {
      // Forward pass
      const outputs = model(batch_x);
      const loss = criterion(outputs, batch_y);
      
      // Backward pass
      optimizer.zero_grad();
      loss.backward();
      optimizer.step();
      
      // Statistics
      trainLoss += loss.item();
      const predicted = greed.torch.argmax(outputs, 1);
      trainCorrect += predicted.eq(batch_y).sum().item();
      totalSamples += batch_x.shape[0];
    }
    
    const avgLoss = trainLoss / trainLoader.length;
    const accuracy = (trainCorrect / totalSamples) * 100;
    
    console.log(`Epoch ${epoch + 1}/100:`);
    console.log(`  Loss: ${avgLoss.toFixed(4)}`);
    console.log(`  Accuracy: ${accuracy.toFixed(2)}%`);
    
    // Save checkpoint every 10 epochs
    if ((epoch + 1) % 10 === 0) {
      await greed.torch.save({
        epoch: epoch + 1,
        model_state_dict: model.state_dict(),
        optimizer_state_dict: optimizer.state_dict(),
        loss: avgLoss,
        accuracy: accuracy
      }, `checkpoint_epoch_${epoch + 1}.json`);
    }
  }
}

🧪 Testing

Comprehensive test suite with 95%+ coverage:

# Run all tests
npm test

# Run specific test suites
npm run test:core        # Core tensor operations
npm run test:nn          # Neural network modules  
npm run test:training    # Training pipeline
npm run test:data        # Data loading
npm run test:serialization # Model save/load

# Browser tests
npm run test:browser     # Cross-browser compatibility
npm run test:webgpu      # WebGPU acceleration
npm run test:performance # Performance benchmarks

Interactive Test Suite

Open tests/html/index.html for interactive browser testing with visual results and performance monitoring.

🔧 Development

# Setup
git clone https://github.com/adityakhalkar/greed.git
cd greed
npm install

# Development
npm run dev          # Development server with hot reload
npm run build        # Production build
npm run test         # Run test suite
npm run lint         # Code linting

🤝 Contributing

We welcome contributions! Areas of focus:

WebGPU Optimizations: New compute shaders and performance improvements
PyTorch Compatibility: Additional operations and API coverage
Browser Support: Expanding WebGPU compatibility
Documentation: Examples, tutorials, and API docs

See CONTRIBUTING.md for detailed guidelines.

📄 License

Dual-licensed under AGPL v3.0 (open source) and commercial licenses.

Open Source: Free for research, education, and open-source projects
Commercial: Contact khalkaraditya8@gmail.com for proprietary use

🙏 Acknowledgments

PyTorch Team: For the incredible ML framework
WebGPU Working Group: For GPU acceleration standards
Pyodide Project: For Python-in-browser runtime
Open Source Community: For continuous feedback and contributions

GreedJS - Bringing PyTorch and WebGPU acceleration to every web browser! 🚀