Principle:Zai org CogVideo Distributed Training Setup
| Principle Metadata | |
|---|---|
| Name | Distributed_Training_Setup |
| Category | Infrastructure |
| Domains | Fine_Tuning, Diffusion_Models |
| Knowledge Sources | CogVideo Repository, CogVideoX Paper |
| Last Updated | 2026-02-10 00:00 GMT |
Overview
Distributed Training Setup is the principle of distributed training orchestration for scaling deep learning workloads across multiple GPUs using data parallelism and memory optimization.
Description
Distributed training setup involves configuring data-parallel training across GPUs via DDP or DeepSpeed ZeRO, handling gradient accumulation, mixed precision, and process group initialization. The Accelerator abstraction manages device placement, gradient synchronization, and checkpoint saving across distributed workers.
Key aspects of distributed training for video diffusion models include:
- Data Parallelism (DDP): The model is replicated on each GPU, and each GPU processes a different subset of the batch. Gradients are synchronized across GPUs after each backward pass.
- DeepSpeed ZeRO: Partitions optimizer states, gradients, and optionally parameters across GPUs to reduce per-GPU memory footprint.
- Gradient Accumulation: Simulates larger effective batch sizes by accumulating gradients over multiple micro-batches before performing an optimizer step.
- Mixed Precision: Uses lower-precision floating point (bf16 or fp16) for forward/backward passes while maintaining fp32 master weights for numerical stability.
- Process Group Initialization: NCCL backend setup for GPU-to-GPU communication with configurable timeout for large model synchronization.
Usage
Use when training CogVideoX models on multi-GPU setups or when single-GPU VRAM is insufficient. Video diffusion models typically require 40-80GB+ VRAM without optimization. Distributed training is essential for:
- Training on datasets that exceed single-GPU memory capacity.
- Reducing wall-clock training time through parallelism.
- Enabling training of the 5B parameter model which may not fit on a single GPU even with LoRA.
Theoretical Basis
Data Parallelism replicates the model on each GPU and splits batches. Each GPU computes gradients independently, then an all-reduce operation averages gradients across all workers. The effective batch size is per_gpu_batch_size * num_gpus * gradient_accumulation_steps.
DeepSpeed ZeRO (Zero Redundancy Optimizer) partitions state across workers:
- Stage 1: Partitions optimizer states (e.g., Adam's first and second moments) across GPUs.
- Stage 2: Additionally partitions gradients across GPUs.
- Stage 3: Additionally partitions model parameters across GPUs (most memory-efficient but highest communication overhead).
Mixed Precision halves memory for activations and weights:
- bf16: Recommended for CogVideoX-5B; maintains dynamic range similar to fp32.
- fp16: Only numerically stable for CogVideoX-2B; requires loss scaling to avoid underflow.
The NCCL (NVIDIA Collective Communication Library) backend provides optimized GPU-to-GPU communication primitives. A generous timeout (default 1800 seconds) is required for large model initialization where weight broadcasting can be slow.