The high-level interface of KeOps is the
LazyTensor wrappers, which allows users to perform efficient, semi-symbolic computations on very large NumPy arrays or PyTorch tensors respectively. As displayed on this website’s front page, this new tensor type may be used with very little overhead:
# Create two arrays with 3 columns and a (huge) number of lines, on the GPU import torch x = torch.randn(1000000, 3, requires_grad=True).cuda() y = torch.randn(2000000, 3).cuda() # Turn our Tensors into KeOps symbolic variables: from pykeops.torch import LazyTensor x_i = LazyTensor( x[:,None,:] ) # x_i.shape = (1e6, 1, 3) y_j = LazyTensor( y[None,:,:] ) # y_j.shape = ( 1, 2e6,3) # We can now perform large-scale computations, without memory overflows: D_ij = ((x_i - y_j)**2).sum(dim=2) # Symbolic (1e6,2e6,1) matrix of squared distances K_ij = (- D_ij).exp() # Symbolic (1e6,2e6,1) Gaussian kernel matrix # And come back to vanilla PyTorch Tensors or NumPy arrays using # reduction operations such as .sum(), .logsumexp() or .argmin(). # Here, the kernel density estimation a_i = sum_j exp(-|x_i-y_j|^2) # is computed using a CUDA online map-reduce routine that has a linear # memory footprint and outperforms standard PyTorch implementations # by two orders of magnitude. a_i = K_ij.sum(dim=1) # Genuine torch.cuda.FloatTensor, a_i.shape = (1e6, 1), g_x = torch.autograd.grad((a_i ** 2).sum(), [x]) # KeOps supports autograd!
Starting with the KeOps 101 tutorial,
most examples in our gallery
going through this collection of real-life demos is probably
the best way of getting familiar with the KeOps user interface.