Skip to content

[distributions] Fix broadcasting error in LogNormal, TransformedDistribution #7269

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ezyang merged 1 commit into from
May 4, 2018
Merged

[distributions] Fix broadcasting error in LogNormal, TransformedDistribution #7269

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 5 additions & 0 deletions test/test_distributions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1156,6 +1156,11 @@ def test_lognormal(self):
self._gradcheck_log_prob(LogNormal, (mean, 1.0))
self._gradcheck_log_prob(LogNormal, (0.0, std))

# check .log_prob() can broadcast.
dist = LogNormal(torch.zeros(4), torch.ones(2, 1, 1))
log_prob = dist.log_prob(torch.ones(3, 1))
self.assertEqual(log_prob.shape, (2, 3, 4))

@unittest.skipIf(not TEST_NUMPY, "NumPy not found")
def test_lognormal_logprob(self):
mean = torch.randn(5, 1, requires_grad=True)
Expand Down
8 changes: 4 additions & 4 deletions torch/distributions/transformed_distribution.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,12 +79,12 @@ def log_prob(self, value):
y = value
for transform in reversed(self.transforms):
x = transform.inv(y)
log_prob -= _sum_rightmost(transform.log_abs_det_jacobian(x, y),
event_dim - transform.event_dim)
log_prob = log_prob - _sum_rightmost(transform.log_abs_det_jacobian(x, y),
event_dim - transform.event_dim)
y = x

log_prob += _sum_rightmost(self.base_dist.log_prob(y),
event_dim - len(self.base_dist.event_shape))
log_prob = log_prob + _sum_rightmost(self.base_dist.log_prob(y),
event_dim - len(self.base_dist.event_shape))
return log_prob

def _monotonize_cdf(self, value):
Expand Down