UFMT formatting on test/distributions, test/error_messages, test/forward_backward_compatability

[arunppsg]

Partiall addresses #123062

UFMT formatting on

• test/distributions
• test/error_messages, test/forward_backward_compatability

cc @albanD

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/123527

• 📄 Preview Python docs built from this PR
• 📄 Preview C++ docs built from this PR
• ❓ Need help or want to give feedback on the CI? Visit the bot commands wiki or our office hours

Note: Links to docs will display an error until the docs builds have been completed.

❌ 1 New Failure

As of commit ca7ced7 with merge base 7c23fed ():

NEW FAILURE - The following job has failed:

• Lint / pr-sanity-checks (gh)
  Process completed with exit code 1.

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[arunppsg]

fixed the following lint errors in e29c8ec

[ezyang]

export will conflict with #123520

[ezyang]

has nontrivial changes

diff --git b/test/export/test_passes.py a/test/export/test_passes.py
index 79cfbd68086..dae9e203747 100644
--- b/test/export/test_passes.py
+++ a/test/export/test_passes.py
@@ -651,7 +651,7 @@ def forward(self, arg_0):
     cos = torch.ops.aten.cos.default(add);  add = None
     sum_2 = torch.ops.aten.sum.default(cos);  cos = None
     submod_3 = self.submod_1
-    wrap_with_set_grad_enabled = torch._higher_order_ops.wrap.wrap_with_set_grad_enabled(False, submod_3, sum_1, sum_2);  submod_3 = sum_1 = sum_2 = None
+    wrap_with_set_grad_enabled = torch._higher_order_ops.wrap.wrap_with_set_grad_enabled(False, submod_3, sum_1, sum_2);  submod_3 = sum_1 = sum_2 = None  # noqa: B950
     add_1 = wrap_with_set_grad_enabled[0]
     add_2 = wrap_with_set_grad_enabled[1];  wrap_with_set_grad_enabled = None
     sub = torch.ops.aten.sub.Tensor(add_1, 1)
@@ -667,13 +667,13 @@ def forward(self, arg_0):
     x, = fx_pytree.tree_flatten_spec(([arg_0], {}), self._in_spec)
     add = torch.ops.aten.add.Tensor(x, 1);  x = None
     submod_5 = self.submod_1
-    wrap_with_set_grad_enabled = torch._higher_order_ops.wrap.wrap_with_set_grad_enabled(True, submod_5, add);  submod_5 = add = None
+    wrap_with_set_grad_enabled = torch._higher_order_ops.wrap.wrap_with_set_grad_enabled(True, submod_5, add);  submod_5 = add = None  # noqa: B950
     sum_1 = wrap_with_set_grad_enabled[0]
     sum_2 = wrap_with_set_grad_enabled[1];  wrap_with_set_grad_enabled = None
     add_1 = torch.ops.aten.add.Tensor(sum_1, 1);  sum_1 = None
     add_2 = torch.ops.aten.add.Tensor(sum_2, 1);  sum_2 = None
     submod_6 = self.submod_3
-    wrap_with_set_grad_enabled_1 = torch._higher_order_ops.wrap.wrap_with_set_grad_enabled(True, submod_6, add_1, add_2);  submod_6 = None
+    wrap_with_set_grad_enabled_1 = torch._higher_order_ops.wrap.wrap_with_set_grad_enabled(True, submod_6, add_1, add_2);  submod_6 = None  # noqa: B950
     sub = wrap_with_set_grad_enabled_1[0]
     sub_1 = wrap_with_set_grad_enabled_1[1];  wrap_with_set_grad_enabled_1 = None
     return pytree.tree_unflatten((add_1, add_2, sub, sub_1), self._out_spec)

You can't put the noqa directly in multistring line, has to go at end.

[ezyang]

will fail ci

[arunppsg]

dropped changes to export as it is already covered on #123520

[ezyang]

Diff is now

diff --git b/test/distributions/test_distributions.py a/test/distributions/test_distributions.py
index 3bb3e4fa5ae..01c598b427d 100644
--- b/test/distributions/test_distributions.py
+++ a/test/distributions/test_distributions.py
@@ -2787,9 +2787,7 @@ class TestDistributions(DistributionsTestCase):
             scipy.stats.multivariate_normal(
                 mean.detach().numpy(), cov.detach().numpy()
             ),
-            "LowRankMultivariateNormal(loc={}, cov_factor={}, cov_diag={})".format(
-                mean, cov_factor, cov_diag
-            ),
+            f"LowRankMultivariateNormal(loc={mean}, cov_factor={cov_factor}, cov_diag={cov_diag})",
             multivariate=True,
         )
 
@@ -5959,9 +5957,7 @@ class TestNumericalStability(DistributionsTestCase):
             expected_value,
             atol=atol,
             rtol=0,
-            msg="Incorrect value for tensor type: {}. Expected = {}, Actual = {}".format(
-                type(x), expected_value, log_pdf
-            ),
+            msg=f"Incorrect value for tensor type: {type(x)}. Expected = {expected_value}, Actual = {log_pdf}",
         )
         if expected_gradient is not None:
             self.assertEqual(
@@ -5969,9 +5965,7 @@ class TestNumericalStability(DistributionsTestCase):
                 expected_gradient,
                 atol=atol,
                 rtol=0,
-                msg="Incorrect gradient for tensor type: {}. Expected = {}, Actual = {}".format(
-                    type(x), expected_gradient, p.grad
-                ),
+                msg=f"Incorrect gradient for tensor type: {type(x)}. Expected = {expected_gradient}, Actual = {p.grad}",
             )
 
     def test_bernoulli_gradient(self):

which looks acceptable

[ezyang]

@pytorchbot merge

[pytorchmergebot]

Merge started

Your change will be merged once all checks pass (ETA 0-4 Hours).

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

Check the merge workflow status
here

[pytorchmergebot]

Merge failed

Reason: 1 mandatory check(s) failed. The first few are:

• Lint / pr-sanity-checks

Dig deeper by viewing the failures on hud

Details for Dev Infra team

Raised by workflow job

Failing merge rule: Core Maintainers

[ezyang]

@pytorchbot merge -i

[pytorchmergebot]

Merge started

Your change will be merged while ignoring the following 1 checks: Lint / pr-sanity-checks

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

Check the merge workflow status
here