PyTorch for WebGPU

Oddly, two tests fail for me with Brave (Version 1.51.118 / Chromium: 113.0.5672.126 (arm64)) on macOS Ventura 13.3.1

- pow([0], [0]) gradient, with "Expected «-Infinity» to be close to «0» (diff: < 0.0000005)"

- xlogy([0], [0.30000001192092896]) gradient with "Expected «0» to be close to «-1.2039728164672852»"

>>activa+S4
https://praeclarum.org/webgpu-torch/tests/

This is a dumb question but... are GPUs really that much faster than CPUs specifically at the math functions tested on this page?

xlogy trunc tan/tanh sub square sqrt sin/sinc/silu/sinh sign sigmoid sqrt/rsqrt round relu reciprocal rad2deg pow positive neg mul logaddexp/logaddexp2 log/log1p/log10/log2 ldexp hypot frac floor expm1 exp2 exp div deg2rad cos/cosh copysign ceil atan/atan2 asinh/asin add acosh/acos abs

Those are the types of math GPUs are good at? I thought they were better at a different kind of math, like matrices or something?

>>Muffin+7k
GPUs are usually not faster at doing the operation, but excel at doing the operation in parallel on a gazillion elements. Matrix math is mostly additions and multiplications.

>>nicoco+7l
The main advantage is parallelism, but on top of that, common math operations are hardware accelerated on the GPU, so should run indeed faster just by being run on the GPU.

zlacker