Kediska Tababarka Saxda ah ee Isku-dhafan

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. In an FP16 training recipe that keeps an FP32 master weight, why retain it?
   • Si loo ilaaliyo saxnaanta cusboonaysiinta miisaanka yar ee 16-bit laga yaabo inay wareegaan
   • Si aad faylka moodelku uga dhigto mid ka yar diskka
   • Si loo ogolaado tababar la'aan GPU-da
   • Si loo sireeyo cabbirada tusaalaha
2. Dhibaato noocee ah ayay miisaanka lumisku ku xalliyaa tababarka FP16?
   • Qaababka ku habboon xogta
   • Gradients hoos ugu qulqulaya eber sababtoo ah kala duwanaanta FP16 waa cidhiidhi
   • GPU-yada waxaa ka dhamaanaya booska diskooga
   • Xogta aayar ka soo rarista saxanka
3. Waa maxay faa'iidada ay bfloat16 ka leedahay FP16?
   • Waxay isticmaashaa xoogaa yar oo wadarta guud
   • Waxay kaydisaa qoraalka halkii lambarro
   • Waxay leedahay jibbaaro ballaadhan oo la mid ah kan FP32, taasoo yaraynaysa baahida miisaanka luminta
   • Waxa kaliya oo ku shaqeeya CPU-yada
4. For many supported Tensor Core matrix operations, how are partial sums commonly accumulated?
   • In integer arithmetic with no rounding
   • In text before converting to FP16
   • In FP16 only, which prevents every numerical error
   • In FP32, which reduces rounding error compared with low-precision accumulation
5. What is a common benefit of using 16-bit values for supported training operations?
   • They can reduce tensor memory and improve throughput, with gains depending on hardware and workload
   • They guarantee higher model accuracy
   • They remove the need to compute gradients
   • They always double end-to-end training speed
6. Which current PyTorch APIs are commonly used for automatic mixed-precision training?
   • torch.cuda.amp only, which is the recommended new namespace
   • torch.amp.autocast and, for FP16 gradient scaling, torch.amp.GradScaler
   • torch.load and torch.save
   • torch.nn.DataParallel and torch.manual_seed
7. Why does bfloat16 often need less loss scaling than FP16?
   • It has more mantissa bits than FP32
   • It stores gradients as integers
   • It has an exponent range like FP32, so very small gradients are less likely to underflow
   • It never rounds values
8. What does autocast do in automatic mixed precision?
   • Converts every tensor and operation to FP16 unconditionally
   • Replaces the optimizer with a different algorithm
   • Guarantees identical bitwise results to FP32
   • Selects operation dtypes so supported operations can use lower precision while others retain safer precision