cleanup code comments _compute_numerical_gradient (#117484)
cleanup code comments for ` _compute_numerical_gradient`:
- reference parameters passed
- indicate that central difference approximation is used
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117484
Approved by: https://github.com/soulitzer
diff --git a/torch/autograd/gradcheck.py b/torch/autograd/gradcheck.py
index 3fdc828..f2e6aa2 100644
--- a/torch/autograd/gradcheck.py
+++ b/torch/autograd/gradcheck.py
@@ -349,8 +349,8 @@
def _compute_numerical_gradient(fn, entry, v, norm_v, nbhd_checks_fn):
- # Performs finite differencing by perturbing `entry` in-place by `v` and
- # returns the gradient of each of the outputs wrt to x at idx.
+ # Computes numerical directional derivative as finite difference
+ # of function `fn` at input `entry`, perturbed by vector `v`.
if _is_sparse_compressed_tensor(entry):
# sparse compressed tensors don't implement sub/add/copy_
# yet. However, in non-masked semantics context entry and v
@@ -373,7 +373,7 @@
def compute(a, b):
nbhd_checks_fn(a, b)
- ret = (b - a) / (2 * norm_v)
+ ret = (b - a) / (2 * norm_v) # use central difference approx
return ret.detach().reshape(-1)
return tuple(compute(a, b) for (a, b) in zip(outa, outb))
