diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index b6115e6e49..0000000000
--- a/.gitattributes
+++ /dev/null
@@ -1,2 +0,0 @@
-*.py linguist-language=python
-*.ipynb linguist-documentation
diff --git a/pyro/distributions/transforms/affine_autoregressive.py b/pyro/distributions/transforms/affine_autoregressive.py
index 74ac7cada9..ceb85d8a39 100644
--- a/pyro/distributions/transforms/affine_autoregressive.py
+++ b/pyro/distributions/transforms/affine_autoregressive.py
@@ -217,8 +217,8 @@ def _inverse_stable(self, y):
             mean, logit_scale = self.arn(torch.stack(x, dim=-1))
             inverse_scale = 1 + torch.exp(-logit_scale[..., idx] - self.sigmoid_bias)
             x[idx] = inverse_scale * y[..., idx] + (1 - inverse_scale) * mean[..., idx]
-            self._cached_log_scale = inverse_scale
 
+        self._cached_log_scale = self.logsigmoid(logit_scale + self.sigmoid_bias)
         x = torch.stack(x, dim=-1)
         return x
 
diff --git a/tests/distributions/test_transforms.py b/tests/distributions/test_transforms.py
index ed7dd031d6..384156ee6b 100644
--- a/tests/distributions/test_transforms.py
+++ b/tests/distributions/test_transforms.py
@@ -95,16 +95,19 @@ def nonzero(x):
             assert lower_sum == float(0.0)
 
     def _test_inverse(self, shape, transform):
+        # Test g^{-1}(g(x)) = x
+        # NOTE: Calling _call and _inverse directly bypasses caching
         base_dist = dist.Normal(torch.zeros(shape), torch.ones(shape))
-
         x_true = base_dist.sample(torch.Size([10]))
         y = transform._call(x_true)
-
-        # Cache is empty, hence must be calculating inverse afresh
+        J_1 = transform.log_abs_det_jacobian(x_true, y)
         x_calculated = transform._inverse(y)
-
+        J_2 = transform.log_abs_det_jacobian(x_true, y)
         assert (x_true - x_calculated).abs().max().item() < self.delta
 
+        # Test that Jacobian after inverse op is same as after forward
+        assert (J_1 - J_2).abs().max().item() < self.delta
+
     def _test_shape(self, base_shape, transform):
         base_dist = dist.Normal(torch.zeros(base_shape), torch.ones(base_shape))
         sample = dist.TransformedDistribution(base_dist, [transform]).sample()