prune back configs

[eellison]

Stack from ghstack (oldest at bottom):

• -> prune back configs #126570

We had a previous PR that added configs for an internal model. Running the below script on output from autotuning, we can prune back the added configs with negligible perf loss: P1365917790.

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang

[pytorch-bot]

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[nmacchioni]

Great! Wondering if you got a perf run with this new set of configs?

[eellison]

Yea, just did inference since training wasn't as affected when in your benchmark run:

Note - base commit slightly different, obviously don't expect speedups here, but shouldn't be any large slowdowns.

[eellison]

@pytorchbot merge

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Merge started

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[pytorchmergebot]

Merge failed

Reason: 11 jobs have failed, first few of them are: inductor / linux-jammy-cpu-py3.8-gcc11-inductor / test (inductor_torchbench_cpu_smoketest_perf, 1, 1, linux.24xl.spr-metal), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_distributed, 1, 1, linux.g5.12xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (dynamic_inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable)

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[eellison]

@pytorchbot rebase

[pytorchmergebot]

@pytorchbot started a rebase job onto refs/remotes/origin/viable/strict. Check the current status here

[pytorchmergebot]

Successfully rebased gh/eellison/652/orig onto refs/remotes/origin/viable/strict, please pull locally before adding more changes (for example, via ghstack checkout https://github.com/pytorch/pytorch/pull/126570)

[eellison]

@pytorchbot merge

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Merge started

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[pytorchmergebot]

Merge failed

Reason: 18 jobs have failed, first few of them are: inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_distributed, 1, 1, linux.g5.12xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm, 2, 2, linux.g5.4xlarge.nvidia.gpu, unstable)

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[nmacchioni]

@eellison force merge should be alright here, we know perf is good and I don't see how this could break things.

[eellison]

@pytorchbot merge --force "already tested, no risk of causing ci errors"

[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.

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