Traceback (most recent call last):
  File "/var/lib/jenkins/pytorch/test/test_binary_ufuncs.py", line 483, in test_batch_vs_slicing
    self.assertEqual(expected, actual)
  File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4284, in assertEqual
    raise error_metas.pop()[0].to_error(  # type: ignore[index]
AssertionError: Tensor-likes are not close!

Mismatched elements: 647 / 16384 (3.9%)
Greatest absolute difference: 11380077887488.0 at index (31, 186) (up to 1e-05 allowed)
Greatest relative difference: 4.084608917764854e-06 at index (23, 352) (up to 1.3e-06 allowed)

To execute this test, run the following from the base repo dir:
    PYTORCH_TEST_WITH_ROCM=1 python test/test_binary_ufuncs.py TestBinaryUfuncsCUDA.test_batch_vs_slicing___rpow___cuda_complex64

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

Traceback (most recent call last):
  File "/var/lib/jenkins/pytorch/test/test_binary_ufuncs.py", line 483, in test_batch_vs_slicing
    self.assertEqual(expected, actual)
  File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4284, in assertEqual
    raise error_metas.pop()[0].to_error(  # type: ignore[index]
AssertionError: Tensor-likes are not close!

Mismatched elements: 647 / 16384 (3.9%)
Greatest absolute difference: 11380077887488.0 at index (31, 186) (up to 1e-05 allowed)
Greatest relative difference: 4.084608917764854e-06 at index (23, 352) (up to 1.3e-06 allowed)

To execute this test, run the following from the base repo dir:
    PYTORCH_TEST_WITH_ROCM=1 python test/test_binary_ufuncs.py TestBinaryUfuncsCUDA.test_batch_vs_slicing___rpow___cuda_complex64

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

Traceback (most recent call last):
  File "/var/lib/jenkins/pytorch/test/test_binary_ufuncs.py", line 483, in test_batch_vs_slicing
    self.assertEqual(expected, actual)
  File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/testing/_internal/common_utils.py", line 4284, in assertEqual
    raise error_metas.pop()[0].to_error(  # type: ignore[index]
AssertionError: Tensor-likes are not close!

Mismatched elements: 647 / 16384 (3.9%)
Greatest absolute difference: 11380077887488.0 at index (31, 186) (up to 1e-05 allowed)
Greatest relative difference: 4.084608917764854e-06 at index (23, 352) (up to 1.3e-06 allowed)

To execute this test, run the following from the base repo dir:
    PYTORCH_TEST_WITH_ROCM=1 python test/test_binary_ufuncs.py TestBinaryUfuncsCUDA.test_batch_vs_slicing___rpow___cuda_complex64

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0

Traceback (most recent call last):
  File "/var/lib/jenkins/pytorch/test/test_torch.py", line 188, in test_int64_upsample3d
    torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')
  File "/opt/conda/envs/py_3.12/lib/python3.12/site-packages/torch/nn/functional.py", line 4815, in interpolate
    return torch._C._nn.upsample_nearest3d(input, output_size, scale_factors)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch.OutOfMemoryError: HIP out of memory. Tried to allocate 56.25 GiB. GPU 0 has a total capacity of 63.98 GiB of which 51.75 GiB is free. Of the allocated memory 10.25 GiB is allocated by PyTorch, and 0 bytes is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/var/lib/jenkins/pytorch/test/test_torch.py", line 190, in test_int64_upsample3d
    self.fail(f"Unexpected exception raised: {e}")
  File "/opt/conda/envs/py_3.12/lib/python3.12/unittest/case.py", line 715, in fail
    raise self.failureException(msg)
AssertionError: Unexpected exception raised: HIP out of memory. Tried to allocate 56.25 GiB. GPU 0 has a total capacity of 63.98 GiB of which 51.75 GiB is free. Of the allocated memory 10.25 GiB is allocated by PyTorch, and 0 bytes is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

To execute this test, run the following from the base repo dir:
    PYTORCH_TEST_WITH_ROCM=1 python test/test_torch.py TestTorchDeviceTypeCUDA.test_int64_upsample3d_cuda_bfloat16

This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0