The PaddlePaddle framework 3.0 has recently released significant updates, introducing unified dynamic and static automatic parallel technology to streamline the development process for large-scale distributed model training and enhance development efficiency.
The new version supports four-dimensional and even five-dimensional hybrid parallel technologies, including data parallelism, tensor model parallelism, pipeline parallelism, and grouped parameter slicing parallelism, effectively improving the efficiency of distributed training for large models. Addressing the complexity of multi-dimensional hybrid parallel development, PaddlePaddle has proposed an automatic parallel technology solution. By using tensor slicing syntax markers, the framework can automatically infer distributed slicing states and add communication operators, significantly reducing the difficulty of developing distributed training.
The automatic parallel principles of PaddlePaddle 3.0 include key aspects such as distributed tensor representation, slicing inference, and slicing transformation, supporting reshuffling capabilities that allow for distributed tensor transformations across ProcessMesh. Additionally, the framework offers a unified dynamic and static execution mode, supporting transitions from dynamic graphs to static graphs, balancing development convenience and runtime efficiency.
In terms of performance optimization, PaddlePaddle 3.0 supports various strategies such as operator fusion, pipeline orchestration scheduling, communication-computation overlap, and communication fusion, which can be enabled through configuration options to further enhance distributed training performance.
PaddlePaddle Official Website: https://www.paddlepaddle.org.cn/