remove restriction for autoquant int8wo bs>32

[HDCharles]

Summary: it seems the mixed_mm kernels have been optimized in pytorch and are performing well at larger batchsizes.

Test Plan:

see CI

Reviewers:

Subscribers:

Tasks:

Tags:

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1208

• 📄 Preview Python docs built from this PR

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✅ You can merge normally! (5 Unrelated Failures)

As of commit 2293549 with merge base 2761917 ():

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• Run Float8 Tests / test (SM-89, linux.g6.4xlarge.experimental.nvidia.gpu, --pre torch --index-url https://download.p... / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

BROKEN TRUNK - The following jobs failed but was present on the merge base:

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

• Run Regression Tests / test (CUDA 2.3, linux.g5.12xlarge.nvidia.gpu, torch==2.3.0, cuda, 12.1) / linux-job (gh) (trunk failure)
• Run Regression Tests / test (CUDA 2.4, linux.g5.12xlarge.nvidia.gpu, torch==2.4.0, cuda, 12.1) / linux-job (gh) (trunk failure)
• Run Regression Tests / test (CUDA 2.5, linux.g5.12xlarge.nvidia.gpu, torch==2.5.0 --index-url https://download.pytorch.o... / linux-job (gh) (trunk failure)
• Run Regression Tests / test (CUDA Nightly, linux.g5.12xlarge.nvidia.gpu, --pre torch --index-url https://download.pytorc... / linux-job (gh) (trunk failure)

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