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Minor gradcheck update to reduce computations (#45757)

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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45757

Test Plan: Imported from OSS

Reviewed By: glaringlee

Differential Revision: D24137143

Pulled By: anjali411

fbshipit-source-id: e0174ec03d93b1fedf27baa72c3542dac0b70058
This commit is contained in:
anjali411 2020-10-06 13:56:47 -07:00 committed by Facebook GitHub Bot
parent e154b36685
commit a3662fa78c
1 changed files with 5 additions and 7 deletions
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12
torch/autograd/gradcheck.py
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@ -102,13 +102,11 @@ def get_numerical_jacobian(fn, input, target=None, eps=1e-3, grad_out=1.0):
d[d_idx] = grad_out.conjugate() * conj_w_d + grad_out * w_d.conj()
elif ds_dx.is_complex(): # R -> C
# w_d = conj_w_d = 0.5 * ds_dx
dL_dz_conj = 0.5 * (grad_out.conjugate() * ds_dx + grad_out * ds_dx.conj())
# The above formula is derived for a C -> C function that's a part of
# bigger function with real valued output. From separate calculations,
# it can be verified that the gradient for R -> C function
# equals to real value of the result obtained from the generic formula for
# C -> C functions used above.
d[d_idx] = torch.real(dL_dz_conj)
# dL_dz_conj = 0.5 * [grad_out.conj() * ds_dx + grad_out * ds_dx.conj()]
# = 0.5 * [grad_out.conj() * ds_dx + (grad_out.conj() * ds_dx).conj()]
# = 0.5 * 2 * real(grad_out.conj() * ds_dx)
# = real(grad_out.conj() * ds_dx)
d[d_idx] = torch.real(grad_out.conjugate() * ds_dx)
else: # R -> R
d[d_idx] = ds_dx * grad_out