amp & channels_last

[xoiga123]

channels_last:

• https://pytorch.org/blog/tensor-memory-format-matters/
• https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html
• https://github.com/pytorch/pytorch/issues/57806
• https://discuss.pytorch.org/t/why-does-pytorch-prefer-using-nchw/83637
• https://gist.github.com/mingfeima/595f63e5dd2ac6f87fdb47df4ffe4772
• memory format != dimension format

While PyTorch operators expect all tensors to be in Channels First (NCHW) dimension format, PyTorch operators support 3 output memory formats. Contiguous: Tensor memory is in the same order as the tensor’s dimensions. ChannelsLast: Irrespective of the dimension order, the 2d (image) tensor is laid out as an HWC or NHWC (N: batch, H: height, W: width, C: channels) tensor in memory. The dimensions could be permuted in any order.

amp:

• http://www.idris.fr/eng/ia/mixed-precision-eng.html
• https://pytorch.org/docs/stable/amp.html
• Had to remove last activation from segmentation head because of https://pytorch.org/docs/stable/amp.html#prefer-binary-cross-entropy-with-logits-over-binary-cross-entropy

extra:

• http://blog.ezyang.com/2019/05/pytorch-internals/
• https://discuss.huggingface.co/t/why-is-grad-norm-clipping-done-during-training-by-default/1866
• https://github.com/NVIDIA/apex/issues/221#issuecomment-478084841 Tensor loves the number 8

prefetch:

• https://www.jpatrickpark.com/post/prefetcher/
• https://github.com/NVIDIA/apex/blob/master/examples/imagenet/main_amp.py best code I've seen so far
• https://tigress-web.princeton.edu/~jdh4/PyTorchPerformanceTuningGuide_GTC2021.pdf best slide I've seen so far
• https://stackoverflow.com/questions/67085517/how-to-load-fetch-the-next-data-batches-for-the-next-epoch-during-the-current-e
• https://androidkt.com/create-dataloader-with-collate_fn-for-variable-length-input-in-pytorch/

Going to train from scratch to see what's good, with a working log this time. UPDATE 12/07/2022: Seems like the bottleneck is in dataloading, which takes an unholy amount of time even though I cached everything in RAM. Currently profiling CPU & GPU and trying out this dataloader which allegedly actually does prefetch. UPDATE: It all makes sense now, Pytorch's Dataloader can only prefetch batches in the current running epoch. For the next epoch, there is apparently no prefetch whatsoever.