Torch Pipeline Parallelism

关于

Provides expert guidance for implementing pipeline parallelism in PyTorch to scale model training across multiple distributed ranks. This skill facilitates efficient model partitioning, activation caching, and inter-rank communication using optimized peer-to-peer operations. By addressing critical implementation pitfalls like broken gradient flows, improper shape handling, and missing output heads, it ensures that large models can be split across hardware while maintaining training stability and numerical accuracy compared to single-device baselines.

主要功能

• Standardized P2P communication patterns for activations and gradients
• Strategies for efficient model partitioning across distributed ranks
• 16 GitHub stars
• Implementation guidance for AFAB and 1F1B scheduling patterns
• Comprehensive verification checklists for loss and gradient accuracy
• Techniques for maintaining gradient flow and graph connectivity

使用场景

• Training large-scale models that exceed individual GPU memory limits
• Developing custom distributed training pipelines for specific architectures
• Optimizing multi-GPU communication and memory efficiency