Honestly, I didn't spend enough time delving in to the Python overhead, especially in terms of the framework. Most of it would be an issue of my own causing however rather than the framework's. The original code I wrote was never written with data loading / saving in mind as the source for speed issues so I avoided what would have been premature optimization at the time.
Some of the slowdowns now just seem silly and aren't even listed in the per epoch timings: PyTorch doesn't have an asynchronous torch.save(). This means that if you save your model after each epoch, and the model save takes a few seconds, you're increasing your per epoch timings 5-10% just by saving the damn thing!
Regarding FP16, PyTorch supports, and there's even a pull request that updates the examples repo with FP16 support for language modeling and ImageNet. It's not likely to be merged as it greatly complicates a codebase that's meant primarily for teaching purposes but it's lovely to look at. I also think many of the FP16 issues will get a general wrapper and they'll become far more agnostic to the end user. For the most part they're all outlined in NVIDIA / Baidu's "Mixed Precision Training" paper. Might be useful for DeepLearning4j to go through the most common heavy throughput use cases and get them running (just as an example of how to work around issues really) if customers were using P100s/V100s?
I'm really interested in exploring the FP16 aspect as the QRNN, especially for single GPU, is sitting at basically 100% utilization, with almost all the time spent on matrix multiplications. FP16 is about the only way to speed it up at that stage. This gets a tad more complicated regardless as the CUDA kernel is not written in FP16 (and is not easy to do so) but even converting FP16->FP32->(QRNN element-wise CUDA kernel)->FP16 ("pseudo" FP16) should still be a crazy speedup. I tested that on the P100 and it took per epoch AWD-QRNN from ~28 seconds to ~18.
- PyTorch async save issue: https://github.com/pytorch/pytorch/issues/1567
- PyTorch FP16 examples pull request: https://github.com/pytorch/examples/pull/203
- "Mixed Precision Training": https://arxiv.org/abs/1710.03740