PyTorch CUDA Graphs

Note

This section provides comprehensive guidance for using CUDA Graphs in PyTorch, covering integration, best practices, and common challenges.

This chapter focuses on using CUDA Graphs in PyTorch. While previous chapters covered CUDA graph fundamentals, this section addresses the practical challenges of integrating graphs into real-world PyTorch code: eliminating synchronization, handling dynamic patterns, and navigating PyTorch-specific constraints.

What This Chapter Covers

🐍 PyTorch Integration

PyTorch’s CUDA graph APIs (torch.cuda.CUDAGraph, torch.cuda.graph(), torch.cuda.make_graphed_callables()) and basic usage patterns.

PyTorch CUDA Graph Integration

⚡ Transformer Engine & Megatron-LM

Megatron-LM’s built-in CUDA graph support (CudaGraphManager and FullCudaGraphWrapper) for distributed training.

Transformer Engine and Megatron-LM CUDA Graph Support

✅ Best Practices

Systematic approach to adopting CUDA graphs (quantifying benefits, choosing scope, verifying correctness, and writing compatible code).

Best Practices for PyTorch CUDA Graphs

🔇 Writing Sync-Free Code

Eliminating CPU-GPU synchronization—a prerequisite for CUDA graph capture.

Writing Sync-Free Code

🔄 Handling Dynamic Patterns

Adapting dynamic control flow, tensors, scalars, and shapes to static graph execution.

Handling Dynamic Patterns

✔️ Quick Checklist

Pre-capture checklist to verify your code is CUDA Graph compatible.

Quick Checklist

Quick Navigation

🎯 Your Situation	📍 Start Here
New to CUDA graphs in PyTorch	PyTorch Integration
Ready to capture	Quick Checklist
Capture fails with sync errors	Writing Sync-Free Code
Wrong results or model divergence	Handling Dynamic Patterns
Need adoption strategy guidance	Best Practices
Debugging capture/runtime failures	Troubleshooting

What’s Next?

Begin with PyTorch Integration to learn PyTorch’s CUDA graph APIs and integration patterns.