Enable UFMT on test/test_cuda*.py

[shink]

Part of: #123062

Ran lintrunner on:

• test/test_cuda.py
• test/test_cuda_expandable_segments.py
• test/test_cuda_multigpu.py
• test/test_cuda_nvml_based_avail.py
• test/test_cuda_primary_ctx.py
• test/test_cuda_sanitizer.py
• test/test_cuda_trace.py

Detail:

$ lintrunner -a --take UFMT --all-files
ok No lint issues.
Successfully applied all patches.

cc @albanD

[pytorch-bot]

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/124352

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✅ No Failures

As of commit 5dcc005 with merge base 25c0d3f ():
💚 Looks good so far! There are no failures yet. 💚

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[ezyang]

need to resolve merge conflict

[ezyang]

is clean

[ezyang]

@pytorchbot merge

[pytorchmergebot]

Merge started

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[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]

seems to be failing lint

[shink]

@ezyang fixed, please check it out: 6e0e19d

[ezyang]

@pytorchbot merge

[pytorchmergebot]

Merge started

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[pytorchmergebot]

Merge failed

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

• Lint / lintrunner-noclang / linux-job

Dig deeper by viewing the failures on hud

Details for Dev Infra team

Raised by workflow job

Failing merge rule: Core Maintainers

[ezyang]

merge conflict now

[ezyang]

diff --git b/test/test_cuda.py a/test/test_cuda.py
index 27549eb82dd..9e9f6dc8cc1 100644
--- b/test/test_cuda.py
+++ a/test/test_cuda.py
@@ -2650,10 +2650,8 @@ exit(2)
                 # These stat checks are specific to the native allocator.
                 if share_mem != "Don't share":
                     self.assertEqual(
-                        reserved_no_sharing
-                        - torch.cuda.memory_stats()[
-                            "reserved_bytes.all.current"
-                        ],  # noqa: F821
+                        reserved_no_sharing  # noqa: F821
+                        - torch.cuda.memory_stats()["reserved_bytes.all.current"],
                         kSmallBuffer,
                     )
                 else:

lg

[ezyang]

@pytorchbot merge

[pytorchmergebot]

Merge failed

Reason: 1 mandatory check(s) are pending/not yet run. The first few are:

• EasyCLA

Dig deeper by viewing the pending checks on hud

Details for Dev Infra team

Raised by workflow job

Failing merge rule: Core Maintainers

[ezyang]

@pytorchbot merge -f "easycla go away little lily wants to play"

[pytorchmergebot]

Merge started

Your change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Please use -f as last resort and instead consider -i/--ignore-current to continue the merge ignoring current failures. This will allow currently pending tests to finish and report signal before the merge.

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) are pending/not yet run. The first few are:

• EasyCLA

Dig deeper by viewing the pending checks on hud

Details for Dev Infra team

Raised by workflow job

Failing merge rule: Core Maintainers

[ezyang]

sorry about all the conflicting. Rebase it again and that should resolve the easycla problem (which was an outage on github)

[shink]

@ezyang You are welcome. All conflicts are resolved, see eec97ab

diff:

diff --git a/test/test_cuda.py b/test/test_cuda.py
index fca8039a62..c9b6063322 100644
--- a/test/test_cuda.py
+++ b/test/test_cuda.py
@@ -2658,10 +2658,8 @@ exit(2)
                 # These stat checks are specific to the native allocator.
                 if share_mem != "Don't share":
                     self.assertEqual(
-                        reserved_no_sharing
-                        - torch.cuda.memory_stats()[
-                            "reserved_bytes.all.current"
-                        ],  # noqa: F821
+                        reserved_no_sharing  # noqa: F821
+                        - torch.cuda.memory_stats()["reserved_bytes.all.current"],
                         kSmallBuffer,
                     )
                 else:

[ezyang]

🤔 still conflicted

[shink]

fixed :p

diff:

+ lintrunner -a --take UFMT --all-files
Warning: Could not find a lintrunner config at: '.lintrunner.private.toml'. Continuing without using configuration file.
ok No lint issues.
Successfully applied all patches.
+ git diff 5dcc005748241f6f53d86b02b7b5169f19f692b9
diff --git a/test/test_cuda.py b/test/test_cuda.py
index 24acfb0dc2..96c62a408a 100644
--- a/test/test_cuda.py
+++ b/test/test_cuda.py
@@ -2663,8 +2663,10 @@ exit(2)
                 # These stat checks are specific to the native allocator.
                 if share_mem != "Don't share":
                     self.assertEqual(
-                        reserved_no_sharing  # noqa: F821
-                        - torch.cuda.memory_stats()["reserved_bytes.all.current"],
+                        reserved_no_sharing
+                        - torch.cuda.memory_stats()[
+                            "reserved_bytes.all.current"
+                        ],  # noqa: F821
                         kSmallBuffer,
                     )
                 else:

[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