ignore git warning 'globally' (#6833)

* ignore git warning 'globally'

* improve comment

test/prototype_transforms_kernel_infos.py

@@ -2,7 +2,6 @@ import decimal
 import functools
 import itertools
 import math
-import re
 
 import numpy as np
 import pytest
@@ -159,12 +158,6 @@ KERNEL_INFOS.extend(
         KernelInfo(
             F.horizontal_flip_bounding_box,
             sample_inputs_fn=sample_inputs_horizontal_flip_bounding_box,
-            test_marks=[
-                TestMark(
-                    ("TestKernels", "test_scripted_vs_eager"),
-                    pytest.mark.filterwarnings(f"ignore:{re.escape('operator() profile_node %72')}:UserWarning"),
-                )
-            ],
         ),
         KernelInfo(
             F.horizontal_flip_mask,
@@ -2045,17 +2038,11 @@ def sample_inputs_convert_dtype_video():
         yield ArgsKwargs(video_loader)
 
 
-_common_convert_dtype_marks = [
-    TestMark(
+skip_dtype_consistency = TestMark(
     ("TestKernels", "test_dtype_and_device_consistency"),
     pytest.mark.skip(reason="`convert_dtype_*` kernels convert the dtype by design"),
     condition=lambda args_kwargs: args_kwargs.args[0].dtype != args_kwargs.kwargs.get("dtype", torch.float32),
-    ),
-    TestMark(
-        ("TestKernels", "test_scripted_vs_eager"),
-        pytest.mark.filterwarnings(f"ignore:{re.escape('operator() profile_node %')}:UserWarning"),
-    ),
-]
+)
 
 KERNEL_INFOS.extend(
     [
@@ -2065,7 +2052,7 @@ KERNEL_INFOS.extend(
             reference_fn=reference_convert_dtype_image_tensor,
             reference_inputs_fn=reference_inputs_convert_dtype_image_tensor,
             test_marks=[
-                *_common_convert_dtype_marks,
+                skip_dtype_consistency,
                 TestMark(
                     ("TestKernels", "test_against_reference"),
                     pytest.mark.xfail(reason="Conversion overflows"),
@@ -2083,7 +2070,9 @@ KERNEL_INFOS.extend(
         KernelInfo(
             F.convert_dtype_video,
             sample_inputs_fn=sample_inputs_convert_dtype_video,
-            test_marks=_common_convert_dtype_marks,
+            test_marks=[
+                skip_dtype_consistency,
+            ],
         ),
     ]
 )

test/test_prototype_transforms_functional.py

 import math
 import os
+import re
 
 import numpy as np
 import PIL.Image
@@ -26,6 +27,15 @@ def script(fn):
         raise AssertionError(f"Trying to `torch.jit.script` '{fn.__name__}' raised the error above.") from error
 
 
+# Scripting a function often triggers a warning like
+# `UserWarning: operator() profile_node %$INT1 : int[] = prim::profile_ivalue($INT2) does not have profile information`
+# with varying `INT1` and `INT2`. Since these are uninteresting for us and only clutter the test summary, we ignore
+# them.
+ignore_jit_warning_no_profile = pytest.mark.filterwarnings(
+    f"ignore:{re.escape('operator() profile_node %')}:UserWarning"
+)
+
+
 def make_info_args_kwargs_params(info, *, args_kwargs_fn, test_id=None):
     args_kwargs = list(args_kwargs_fn(info))
     idx_field_len = len(str(len(args_kwargs)))
@@ -87,6 +97,7 @@ class TestKernels:
         condition=lambda info: info.reference_fn is not None,
     )
 
+    @ignore_jit_warning_no_profile
     @sample_inputs
     @pytest.mark.parametrize("device", cpu_and_gpu())
     def test_scripted_vs_eager(self, info, args_kwargs, device):
@@ -218,6 +229,7 @@ class TestDispatchers:
         condition=lambda info: features.Image in info.kernels,
     )
 
+    @ignore_jit_warning_no_profile
     @image_sample_inputs
     @pytest.mark.parametrize("device", cpu_and_gpu())
     def test_scripted_smoke(self, info, args_kwargs, device):
@@ -230,6 +242,7 @@ class TestDispatchers:
 
     # TODO: We need this until the dispatchers below also have `DispatcherInfo`'s. If they do, `test_scripted_smoke`
     #  replaces this test for them.
+    @ignore_jit_warning_no_profile
     @pytest.mark.parametrize(
         "dispatcher",
         [