non_blocking=True and process the GPU data
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 25/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python, pytorch
- Domain
- machine-learning
Research direction
The issue only provides a CUDA-stream code excerpt; start by reviewing the shown non_blocking=True transfers and subsequent normalization in the PyTorch context. Done means documenting whether the ordering is correct and identifying any synchronization change required.
Written by the indexing model from the issue text.
Description
with torch.cuda.stream(self.stream):
self.next_input = self.next_input.cuda(non_blocking=True)
self.next_target = self.next_target.cuda(non_blocking=True)
# more code for the alternative if record_stream() doesn't work:
# copy_ will record the use of the pinned source tensor in this side stream.
# self.next_input_gpu.copy_(self.next_input, non_blocking=True)
# self.next_target_gpu.copy_(self.next_target, non_blocking=True)
# self.next_input = self.next_input_gpu
# self.next_target = self.next_target_gpu
# With Amp, it isn't necessary to manually convert data to half.
# if args.fp16:
# self.next_input = self.next_input.half()
# else:
self.next_input = self.next_input.float()
self.next_input = self.next_input.sub_(self.mean).div_(self.std)
In this code, when put data to cuda using non_blocking=True. but after that normalize the data without confirm the uploading data processing is done. Is that correct?
(My language is poor, so I don't know whether the explanation is clear )
- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
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