Gradient Checkpointing Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Chii chinonzi gradient cheki chinonyanya kutengesa kuitira kuchengetedza ndangariro?
   • Yakawedzera compute (recomputation) yekushandisa yakaderera ndangariro
   • Muenzaniso wakarurama wekukurumidza
   • Disk nzvimbo yetiweki bandwidth
   • Kururamisa kwehukuru hwebatch
2. Sei ma activation achiwanzo chengetwa panguva yekupfuura?
   • Kuti uzviratidze kumushandisi
   • Nekuti backpropagation inoda ivo kuti vaverenge ma gradients
   • Kuti ikurumidze kurodha data
   • Havatombodiwi
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. MuPyTorch, ndeipi chishandiso chinowanzoshandiswa kuisa gradient yekutarisa kune module?
   • torch.utils.checkpoint
   • torch.optim.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