论文标题
简化Pytorch中的张量和网络修剪
Streamlining Tensor and Network Pruning in PyTorch
论文作者
论文摘要
为了将最先进的机器学习模型的大小爆炸爆炸，这可以归因于过度参数化的经验优势，并且由于必须在资源约束设备上部署快速，可持续性和私人的现场模型，因此该社区专注于诸如实质性，量化，量化，量化和蒸馏型策略等技术上。为了促进在Pytorch中采用神经网络修剪的常见界面，这项贡献描述了Pytorch Torch.nn.utils.pruness.prune模块的最新添加，该模块提供了共享的，开源的原始功能，以降低技术实施障碍，以降低模型的大小和培训，并在培训之前，培训和培训。我们介绍了模块的用户界面，阐明实现详细信息，说明了示例用法，并提出了将贡献功能扩展到新修剪方法的方法。
In order to contrast the explosion in size of state-of-the-art machine learning models that can be attributed to the empirical advantages of over-parametrization, and due to the necessity of deploying fast, sustainable, and private on-device models on resource-constrained devices, the community has focused on techniques such as pruning, quantization, and distillation as central strategies for model compression. Towards the goal of facilitating the adoption of a common interface for neural network pruning in PyTorch, this contribution describes the recent addition of the PyTorch torch.nn.utils.prune module, which provides shared, open source pruning functionalities to lower the technical implementation barrier to reducing model size and capacity before, during, and/or after training. We present the module's user interface, elucidate implementation details, illustrate example usage, and suggest ways to extend the contributed functionalities to new pruning methods.
