Open pytorch-bot[bot] opened 3 weeks ago
Another case of trunk flakiness has been found here. The list of platforms [linux] appears to contain all the recently affected platforms [linux]. Either the change didn't propogate fast enough or disable bot might be broken.
Another case of trunk flakiness has been found here. The list of platforms [linux] appears to contain all the recently affected platforms [linux]. Either the change didn't propogate fast enough or disable bot might be broken.
Platforms: linux
This test was disabled because it is failing in CI. See recent examples and the most recent trunk workflow logs.
Over the past 3 hours, it has been determined flaky in 30 workflow(s) with 90 failures and 30 successes.
Debugging instructions (after clicking on the recent samples link): DO NOT ASSUME THINGS ARE OKAY IF THE CI IS GREEN. We now shield flaky tests from developers so CI will thus be green but it will be harder to parse the logs. To find relevant log snippets:
test_assigning_back_deleter_fns_to_tensor
Sample error message
``` Traceback (most recent call last): File "/var/lib/jenkins/workspace/test/test_cuda.py", line 4670, in test_assigning_back_deleter_fns_to_tensor graph, outputs = cudagraphify(foo, [inp]) File "/var/lib/jenkins/workspace/test/test_cuda.py", line 4430, in cudagraphify fn(*inputs) File "/var/lib/jenkins/workspace/test/test_cuda.py", line 4666, in foo int8_cuda(LARGE_BUFFER) + x, torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 160.00 MiB. GPU 0 has a total capacity of 21.98 GiB of which 20.85 GiB is free. Process 474015 has 248.00 MiB memory in use. Process 474226 has 248.00 MiB memory in use. Process 474587 has 248.00 MiB memory in use. Process 474603 has 380.00 MiB memory in use. 120.00 MiB allowed; Of the allocated memory 52.00 MiB is allocated by PyTorch, and 6.00 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_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: python test/test_cuda.py TestBlockStateAbsorption.test_assigning_back_deleter_fns_to_tensor This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0 ```Test file path:
test_cuda.py
cc @ptrblck @msaroufim @clee2000