Torch Tensor Parallelism

소개

Expert guidance for implementing high-performance tensor-parallel linear layers in PyTorch, focusing on column-parallel and row-parallel patterns. It provides a structured workflow for sharding weight matrices, managing distributed communication across multiple ranks, and ensuring correct gradient flow. By emphasizing rigorous verification steps and manual tracing, this skill helps developers avoid common pitfalls such as shape mismatches and truncated file writes in distributed training environments.

주요 기능

• Instruction on all-gather and all-reduce communication patterns
• Implementation patterns for ColumnParallelLinear and RowParallelLinear
• 16 GitHub stars
• Guidance on weight sharding and bias handling across distributed ranks
• Strategies to prevent truncated file writes and gradient flow issues
• Manual tracing techniques for numeric verification

사용 사례

• Debugging complex shape mismatches in distributed linear layers
• Implementing custom layers within torch.distributed frameworks
• Developing large-scale models that require multi-GPU model parallelism