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feat: use torch.cdist to compute Gaussian kernel for performance improvement #1358

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feat: use torch.cdist to compute Gaussian kernel for performance improvement #1358

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14 changes: 1 addition & 13 deletions dowhy/causal_prediction/algorithms/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,20 +13,8 @@
import torch


def my_cdist(x1, x2):
x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
res = torch.addmm(x2_norm.transpose(-2, -1), x1, x2.transpose(-2, -1), alpha=-2).add_(x1_norm)
return res.clamp_min_(1e-30)


def gaussian_kernel(x, y, gamma):
D = my_cdist(x, y)
K = torch.zeros_like(D)

K.add_(torch.exp(D.mul(-gamma)))

return K
return torch.exp(torch.cdist(x, y, p=2.0).pow(2).clamp_min_(1e-30).mul(-gamma))


def mmd_compute(x, y, kernel_type, gamma):
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