Skip python matmul benchmarks that fail due to OOM#3174
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!build --matmul-bench |
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Inspired by #3174 Previously we were manually resetting the cuda cache whenever the usage was above 80%. This is not ideal since we could have 79% usage and a test that requires 25% and that would fail. We also might clear the cache unnecessarily sometimes: e.g. we are using 81% but only need a few percent for the remainder of tests. This PR cleans this up by introducing a new test decorator @retry_on_oom_or_skip_test. This decorator must be placed innermost, underneath the other decorators. It will execute the test inside a try block. If the test fails due to torch.OutOfMemoryError, we clear the cuda cache and retry the test. If it fails again due to torch.OutOfMemoryError, then we skip the test.
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Inspired by #3174 This is an alternative to #3238. Previously we were manually resetting the cuda cache whenever the usage was above 80%. This is not ideal since we could have 79% usage and a test that requires 25% and that would fail. We also might clear the cache unnecessarily sometimes: e.g. we are using 81% but only need a few percent for the remainder of tests. This PR cleans this up by introducing a new test decorator `@retry_on_oom_or_skip_test`. This decorator must be placed innermost, underneath the other decorators. It will execute the test inside a try block. If the test fails due to `torch.OutOfMemoryError`, we clear the cuda cache and retry the test. If it fails again due to `torch.OutOfMemoryError`, then we skip the test. I updated the python benchmarks to apply this decorator automatically, and to remove the manual `clear_cuda_cache()` calls.
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I'm adding this skip so that we can soon expect to run python matmul benchmarks without any failures. Some of these tests use extreme sizes needing over 200GB of memory. Instead of removing these tests, I try to run all tests and skip the ones that fail due to torch OOM error.