Chainer and potentially CuPy (which was extracted from Chainer to be independent) were around before PyTorch as it served as inspiration for PyTorch. I feel like that's a good motivation for diversity in packages and ecosystems regardless of your feelings otherwise.
Along with a colleague I used CuPy in first Chainer and then PyTorch for implementing the Quasi-Recurrent Neural Network (QRNN) which at the time was far faster than even NVIDIA's optimized cuDNN LSTM whilst getting the same (or better) performance for many tasks.
CuPy at the time was both the easiest and most Pythonic of potential solutions for that problem - even if it did involve writing CUDA in Python strings =]
n.b. Our use case was literally pushing state of the art in research - CuPy is even more Pythonic if you're hitting more standard use cases.