[apl]

> gradient descent no longer has to be written by hand

Nobody's been writing derivatives by hand for 5+ years. All major frameworks (PyTorch, Tensorflow, MXNet, autodiff, Chainer, Theano, etc.) have decent to great automatic differentiation.

The differences and improvements are more subtle (easy parallelization/vectorization, higher-order gradients, good XLA support).

[Smerity]

For high performance CUDA kernels people still need to write derivatives by hand. I know this as for my own research, and for many production systems, I'd still need to write it myself. Many of my architectures wouldn't have been possible without writing the CUDA myself (Quasi-Recurrent Neural Network[1]) or using optimized hand written black boxes (cuDNN RNN). The lack of open optimized hand written CUDA kernels has actually been an impediment to progress in the field.

Automatic differentiation allows for great flexibility and composability but the performance is still far from good, even with the various JITs available. Jax seems to be one of the most flexible and optimized for many use cases for now however.

[1]: https://github.com/salesforce/pytorch-qrnn

[shoyer]

Right, you still need to write derivative rules by hand for the primitive operations of an auto-diff system. Automatic differentiation provides composition, it doesn't solve the root mathematical problem of differentiating operations at the lowest level.

So yes, if need a new primitive to add an efficient CUDA kernel, you will probably also have to write its derivative manually too. JAX has a few shortcuts that occasionally make this easier but fundamentally it has the same challenge as any auto-diff system.

[backpropaganda]

But is autodiff combined with a blackbox jit a real solution? The jit either works for your new model or it does not. If it does not, you can do pretty much nothing about it, other than ping jax authors or get your own hands dirty with jax internal code. Why is noone working on a usable low-level framework, where I can implement QRNN or more complicated stuff without relying on a black-box jit? Jax could have chosen to be this, but instead is a fancy solution to a non-problem.

[6gvONxR4sf7o]

How has your experience with CUDA been? Is it as painful as it appears at first glance? I've done a ton of python and C, and yet whenever I look at C++ code, it just screams stay away.

But I have some almost-reasonably-performant pytorch that I'd rather not just use as a cash burning machine, so it looks like it might be time to dive into CUDA :-\