Efficient Pipeline Training of Large Diffusion Models with DiffusionPipe

Centrala begrepp

DiffusionPipe is an efficient pipeline training system designed specifically for large diffusion models, which systematically determines optimized model partitioning, stages, and replication settings while applying innovative pipeline bubble filling techniques.

Sammanfattning

DiffusionPipe is a pipeline training system for large diffusion models that addresses the unique challenges posed by the structural characteristics and training procedures of diffusion models. The key highlights are:

Unified dynamic programming-based algorithm for optimized model partitioning that can handle various training scenarios, such as models with different numbers of backbones and models trained with self-conditioning.

Novel pipeline bubble filling strategy that fills the computation of non-trainable model parts into the bubble time of the pipeline training of the backbone(s), effectively eliminating pipeline bubbles. It efficiently partitions the non-trainable components and the input data for bubble filling, and addresses dependencies between the non-trainable part and the trainable part.

Effective handling of extra-long non-trainable layers that do not fit into individual pipeline bubbles, using a partial-batch processing design. Partial-batch layers' execution time can be precisely controlled by their input batch size, enabling them to be inserted into bubbles.

DiffusionPipe is implemented and compared to state-of-the-art data parallel and pipeline parallel training systems. Experimental results show that DiffusionPipe achieves up to 1.28x speedup over data parallel training and up to 1.41x speedup over existing pipeline parallel methods on representative diffusion models.

Statistik

Stable Diffusion v2.1 has 38-44% of the forward time of the non-trainable part compared to the forward and backward time of the trainable part.
ControlNet v1.0 has 76-89% of the forward time of the non-trainable part compared to the forward and backward time of the trainable part.
The proportion of synchronization in training iteration time can be up to 38.1% for Stable Diffusion v2.1 and 40.1% for ControlNet v1.0 when using 64 GPUs.

Citat

"DiffusionPipe systematically determines optimized model partitioning, stages, and replication settings while applying innovative pipeline bubble filling techniques."
"DiffusionPipe achieves up to 1.28x speedup over data parallel training and up to 1.41x speedup over existing pipeline parallel methods on representative diffusion models."

Viktiga insikter från

DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines

by Ye Tian,Zhen... på arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01248.pdf

Djupare frågor

How can DiffusionPipe's techniques be extended to other types of large neural network models beyond diffusion models

DiffusionPipe's techniques can be extended to other types of large neural network models by adapting the pipeline parallel training and pipeline bubble filling strategies to suit the specific characteristics of those models. For instance, in models with multiple backbones or complex dependencies between different components, the dynamic programming approach used in DiffusionPipe for optimal partitioning and device assignment can be modified to accommodate the unique structure of the new models. Additionally, the cross-iteration pipelining concept can be applied to models that involve iterative processes or feedback loops, enabling efficient utilization of computational resources across iterations. By customizing the pipeline scheduling and bubble filling algorithms based on the requirements of different neural network architectures, DiffusionPipe's techniques can be effectively applied to a broader range of models beyond diffusion models.

What are the potential limitations or drawbacks of the partial-batch processing approach used in DiffusionPipe

One potential limitation of the partial-batch processing approach used in DiffusionPipe is the need to carefully select the batch size for the partial batches to ensure optimal performance. If the batch size for the partial batches is too small, the overhead of handling the input and output data for these batches may outweigh the benefits of inserting them into the pipeline bubbles. Additionally, using irregular batch sizes for partial batches could lead to performance degradation due to suboptimal kernel execution on the hardware. Another drawback is the complexity of managing the input and output data splitting and concatenation for partial-batch layers, which may introduce additional computational overhead and potential synchronization issues. Overall, while partial-batch processing can improve pipeline bubble filling efficiency, it requires careful optimization and tuning to achieve the desired performance benefits without introducing unnecessary complexity.

How could DiffusionPipe's pipeline bubble filling strategies be further improved or generalized to handle a wider range of neural network architectures and training procedures

To further improve and generalize DiffusionPipe's pipeline bubble filling strategies for a wider range of neural network architectures and training procedures, several enhancements can be considered. One approach is to incorporate dynamic adjustment of the partial-batch sizes based on the computational workload of individual layers, allowing for more flexible utilization of idle time in pipeline bubbles. Additionally, developing adaptive algorithms that can automatically adjust the pipeline bubble filling schemes based on the specific characteristics of the model and the available resources could enhance the efficiency and effectiveness of the bubble filling process. Furthermore, exploring advanced scheduling techniques, such as reinforcement learning-based approaches or genetic algorithms, to optimize the pipeline bubble filling in a more adaptive and intelligent manner could lead to further improvements in handling diverse neural network architectures and training scenarios. By continuously refining and expanding the pipeline bubble filling strategies, DiffusionPipe can be better equipped to handle a wider variety of models and training setups with increased efficiency and effectiveness.