Add Comprehensive Distributed Training Support

[athul-22]

Problem Statement

Currently, Burn lacks comprehensive support for distributed training across multiple devices and nodes, which is essential for training large-scale deep learning models efficiently. While Burn has excellent multi-backend support and thread-safe building blocks, there's no native distributed training infrastructure comparable to PyTorch's torch.distributed or TensorFlow's distributed strategies.

Current State

• Burn has thread-safe modules that can be sent between threads
• Individual backends support GPU acceleration
• No native support for:
  • Multi-node training coordination
  • Gradient synchronization across devices
  • Data parallel training strategies
  • Model parallel training for large models

Proposed Solution

Implement a comprehensive distributed training system that includes:

Core Components

1. Distributed Backend Abstraction

   • Communication primitives (AllReduce, AllGather, Broadcast, etc.)
   • Support for different communication backends (NCCL, Gloo, MPI)
   • Cross-platform compatibility

2. Training Strategies

   • Data Parallel: Distribute batches across multiple devices
   • Model Parallel: Split large models across devices
   • Pipeline Parallel: Stage-wise model execution
   • Hybrid strategies for complex scenarios

3. Synchronization Mechanisms

   • Gradient accumulation and averaging
   • Parameter synchronization
   • Asynchronous and synchronous training modes

4. Launch and Coordination

   • Process group management
   • Device topology detection
   • Fault tolerance and recovery

Implementation Details

// Example API design
use burn::distributed::{DistributedConfig, DistributedLearner};

let config = DistributedConfig::new()
    .with_strategy(DataParallel)
    .with_world_size(4)
    .with_backend(NcclBackend::new());

let learner = DistributedLearner::new(model, config)
    .build(&device);

// Training loop with automatic gradient synchronization
learner.train(dataloader);

Benefits

• Scalability: Enable training of larger models and datasets
• Performance: Leverage multiple GPUs/nodes for faster training
• Ecosystem Growth: Make Burn competitive with PyTorch/TensorFlow for large-scale ML
• Research Enablement: Support cutting-edge research requiring distributed computation

Technical Considerations

• Leverage Burn's existing thread-safe architecture
• Maintain backend agnosticism (work with WGPU, CUDA, etc.)
• Ensure compatibility with existing Burn APIs
• Optimize for both speed and memory efficiency

Success Criteria

• Basic data parallel training across multiple GPUs
• Multi-node training support
• Integration with existing Burn backends
• Comprehensive documentation and examples
• Performance benchmarks showing scaling efficiency
• Test suite covering various distributed scenarios

Related Work

• PyTorch Distributed: torch.distributed
• TensorFlow Distributed: tf.distribute.Strategy
• Horovod: Framework-agnostic distributed training

This feature would significantly enhance Burn's capabilities for large-scale machine learning workloads and make it more competitive in the enterprise and research markets.

[laggui]

Hey thanks for your interest!

This is a duplicate of #3261, though your issue is a bit more detailed.. perhaps even too big in scope 😅

There is some ongoing work on collective operations.