Torch Pipeline Parallelism

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Provides a comprehensive framework for scaling large language models beyond single-device memory limits by partitioning model layers across multiple GPU ranks. This skill guides developers through the complexities of All-Forward-All-Backward (AFAB) scheduling, microbatching, and efficient inter-rank tensor communication while ensuring stable gradient flow and addressing architecture-specific challenges like rotary position embeddings. It is an essential resource for engineering high-performance distributed training systems for massive transformer-based architectures.

Características Principales

• Multi-level verification strategies for distributed integration testing
• Implementation of All-Forward-All-Backward (AFAB) scheduling patterns
• Gradient flow management with activation caching for microbatches
• Layer-wise model partitioning across distributed GPU ranks
• 16 GitHub stars
• Standardized inter-rank tensor and shape communication protocols

Casos de Uso

• Training large language models that exceed the memory capacity of a single GPU
• Developing custom distributed training loops for transformer-based architectures
• Optimizing training throughput via microbatching and pipeline scheduling