torch-pipeline-parallelism

What problem does it solve?

This Skill addresses the challenge of training large language models that exceed the memory capacity of a single GPU by providing a structured approach to implementing PyTorch pipeline parallelism.

Core Features & Use Cases

• Model Partitioning: Distributes model layers across multiple GPUs.
• Inter-Rank Communication: Manages tensor and gradient flow between stages.
• AFAB Scheduling: Implements the All-Forward-All-Backward execution strategy.
• Use Case: When training a multi-billion parameter LLM, this skill helps partition the model across a cluster of GPUs, enabling training that would otherwise be impossible.

Quick Start

Implement PyTorch pipeline parallelism for distributed LLM training using the provided guidance.