I want to create a tensor with torch.zeros based on the shape of an input to the function. Then I want to vectorize the function with torch.vmap.

Something like this:

poly_batched = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
def polycompanion(polynomial): deg = polynomial.shape[-1] - 2 companion = torch.zeros((deg+1, deg+1)) companion[1:,:-1] = torch.eye(deg) companion[:,-1] = -1. * polynomial[:-1] / polynomial[-1] return companion
polycompanion_vmap = torch.vmap(polycompanion)
print(polycompanion_vmap(poly_batched))

The problem is that the batched version will not work, because companion won’t be a BatchedTensor, unlike polynomial, which was the input.

There is a workaround:

poly_batched = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
def polycompanion(polynomial,companion): deg = companion.shape[-1] - 1 companion[1:,:-1] = torch.eye(deg) companion[:,-1] = -1. * polynomial[:-1] / polynomial[-1] return companion
polycompanion_vmap = torch.vmap(polycompanion)
print(polycompanion_vmap(poly_batched, torch.zeros(poly_batched.shape[0],poly_batched.shape[-1]-1, poly_batched.shape[-1]-1)))

Output:

tensor([[[ 0.0000, 0.0000, -0.2500], [ 1.0000, 0.0000, -0.5000], [ 0.0000, 1.0000, -0.7500]], [[ 0.0000, 0.0000, -0.2500], [ 1.0000, 0.0000, -0.5000], [ 0.0000, 1.0000, -0.7500]]])

But this is ugly.

Is there a solution for this? Will this be supported in the future?

Note: If you use torch.zeros_like on an input to the function it works and creates BatchedTensor but this doesn’t help me here.

Thanks in advance for the help!

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