梯度检查点测验

当您参加测验时，就会出现答案和解释。

1. 为了节省内存，梯度检查点主要交换什么？
   • 额外计算（重新计算）以减少内存使用
   • 速度模型精度
   • 网络带宽的磁盘空间
   • 批量精度
2. 为什么激活通常在前向传递过程中存储？
   • 将它们显示给用户
   • 因为反向传播需要它们来计算梯度
   • 加快数据加载速度
   • 它们从来都不是真正需要的
3. In a simplified chain of N similarly sized layers, what can checkpoint boundaries spaced about sqrt(N) layers apart achieve in the textbook schedule?
   • It stores no activations and performs no recomputation
   • It guarantees constant memory for every model graph
   • Activation storage can scale on the order of sqrt(N) checkpoints
   • It makes optimizer-state memory scale as sqrt(N)
4. What most determines gradient-checkpointing runtime overhead in a practical model?
   • Only the model’s file extension
   • A fixed 20–30% slowdown for every model
   • The number of labels in the dataset
   • Which operations are recomputed, checkpoint placement, graph structure, and hardware
5. 在 PyTorch 中，哪个实用程序通常用于将梯度检查点应用于模块？
   • torch.utils.checkpoint
   • 火炬优化SGD
   • torch.nn.Dropout
   • torch.cuda.empty_cache
6. What happens to operations inside a checkpointed region during backward?
   • Their gradients are skipped entirely
   • Their forward computations are rerun to regenerate needed activations
   • Their weights are permanently discarded
   • They are executed only during inference
7. Which training techniques can be combined with activation checkpointing to reduce different memory costs?
   • Only increasing the number of labels
   • Turning off backpropagation
   • FSDP for sharded model state and mixed precision for lower-precision tensors
   • Replacing the model with a tokenizer
8. What is selective activation checkpointing intended to choose?
   • Which training labels are removed
   • Which model outputs are published
   • Which GPU becomes the tokenizer
   • Which operations to save and which to recompute based on their memory and runtime costs