[B200] Fix flex-attention heuristic for test_tma_with_customer_kernel_options_cuda

[eqy]

Otherwise fails with

torch._inductor.exc.InductorError: RuntimeError: No valid triton configs. OutOfMemoryError: out of resource: triton_tem_fused__to_copy_ones_sort_sum_zeros_2 Required: 264224 Hardware limit: 232448 Reducing block sizes or `num_stages` may help.

cc @ptrblck @msaroufim @jerryzh168 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben @Chillee @drisspg @yanboliang @BoyuanFeng

[pytorch-bot]

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✅ You can merge normally! (2 Unrelated Failures)

As of commit 6c3f41c with merge base 14ecc03 ():

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• inductor / cuda12.8-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu) (gh) (trunk failure)
  torchrec_dlrm

UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:

• pull / cuda12.8-py3.10-gcc9-sm75 / test (pr_time_benchmarks, 1, 1, linux.g4dn.metal.nvidia.gpu, unstable) (gh) (#153987)
  RuntimeError: No CUDA GPUs are available

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[drisspg]

👀

[eqy]

@pytorchmergebot merge

[pytorchmergebot]

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