Reshard Collective replaces the serialized weight-resharding sequence with a parallel flow. The policy model is updated only on the training GPUs. Inference takes place on separate GPUs.

During the process, training trajectories are generated dynamically while inference runs with the current policy model. This reduces the waiting time between iterations and increases training efficiency. The system needs to update the weights periodically to maintain consistency between the models. The approach depends on a disaggregated setup with separate GPUs, which requires specific infrastructure to work. For developers looking to optimize weight resharding, Reshard Collective offers an alternative to the traditional serialized flow, allowing greater flexibility and performance.

An 8-by-8 weight matrix is split by rows across two training GPUs and by columns across four inference GPUs. Each inference G
Image: developer.nvidia.com

Reshard Collective generates trajectories in real time during inference

Reshard Collective uses a disaggregated setup with separate GPUs and requires the system to update the weights periodically. This periodic update is essential to maintain consistency between the inference model and the training model. This allows the model to learn from real-time data without having to wait for a new full update. The approach may frustrate those looking to simplify the system architecture, since it requires a specific multi-GPU configuration.

Diagram showing four training GPUs holding column shards of a tensor and four inference GPUs each receiving a complete tensor
Image: developer.nvidia.com

Reshard Collective emerges as an innovative approach to speed up weight resharding in reinforcement learning, allowing inference and training to take place simultaneously on separate GPUs. The idea is to replace the serialized sequence of updates with a parallel flow, which can reduce response time and increase the process's efficiency. Reshard Collective is part of a larger trend of optimizing reinforcement learning algorithms, which seeks to balance accuracy with execution speed.

Reshard Collective speeds up weight resharding through parallelization

Instead of waiting for data to be processed one after another, the system splits the task among the GPUs. This allows inference and training to happen at the same time. The division of work reduces the communication overhead between the system's components. Reshard Collective is an innovative approach.

With information from developer.nvidia.com.

Sources

  1. Accelerating reinforcement learning weight refit with the reshard collectivedeveloper.nvidia.com
  2. Accelerating reinLforcement learning weight refit with the reshard collectivedeveloper.nvidia.com
  3. Simple linear regression using rmse as cost functionmedium.com
  4. Linear regression and logistic regression in machine learning a complete beginner friendly guidemedium.com

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