Pytorch训练优化技巧及其在Diffusion上的应用-23页_3mb

Summary of Diffusion Generative Models Training Optimizations

Introduction

This document summarizes the optimization techniques applied to the training of Stable Diffusion generative models, as outlined in the provided discussion. The optimizations focus on improving performance, efficiency, and throughput on NVIDIA GPUs by addressing bottlenecks in the training pipeline.

Key Optimization Details

• Dataloader Optimizations: Tuning parameters like num_workers and pin_memory significantly reduced training time per epoch. On an NVIDIA A100, time decreased from 2.93 seconds to 2.80 seconds, minimizing CPU overhead and enabling better resource utilization.

• Kernel Fusion with APEX: Replaced standard PyTorch modules with APEX-fused versions (e.g., FusedDense, FusedLayerNorm, FusedAdam). This reduced kernel execution time by lowering overhead and improving computational efficiency.

• Flash Attention: Leverages optimized attention mechanisms to reduce memory reads and writes, supporting A100 GPUs in fp16/bf16. Increased batch size from 8 to over 48, reducing training time and memory footprint (e.g., from 67G to 25G memory at batch=8).

• Multi-Stream EMA and Fused EMA: Utilized multiple CUDA streams and fusion techniques to overlap EMA computations with primary training, reducing bubbles caused by frequent memory operations and enhancing asynchronous execution.

• ZeRO Optimization: Partitioned gradients, parameters, and activations using ZeRO-2 to decrease GPU memory usage. This allowed batch size scaling from small values to 56, improving throughput and training speed by leveraging distributed computing.

Performance Metrics Summary

• Baseline performance (e.g., batch size=8) showed an average time per iteration of approximately 0.99 seconds with default configurations.
• Optimized training achieved a 2.9x speedup for batch size=8 (from 12.531 seconds to 3.98 seconds), increasing batch size to 56 for 2.96x speedup overall.
• Memory efficiency was notably improved, with FlashAttention reducing memory usage by about two-thirds and ZeRO further optimizing storage.

Conclusion

The optimizations demonstrated substantial improvements in Stable Diffusion training speed, batch size capacity, and memory efficiency on modern NVIDIA hardware, enabling more efficient model development and deployment.