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### What does this PR do? Type of change: new feature <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> - Add Conv3D implicit GEMM kernel with BF16 WMMA tensor cores and fused NVFP4 activation quantization for video diffusion VAE layers - Integrate into _QuantConv3d via QuantModuleRegistry — automatically dispatched when NVFP4 quantization is applied to nn.Conv3d - Move kernel from `experimental/conv/ to modelopt/torch/kernels/conv/`; move tests to `tests/gpu/torch/quantization/kernels/` ### Testing <!-- Mention how have you tested your change if applicable. --> - Added test cases to measure the difference between cuDNN and our CUDA implicit GEMM kernel - Added an NVFP4 fake quantization test using CUDA code ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ <!--- Mandatory --> - Did you write any new necessary tests?: ✅ <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Per-backbone quantization/export in a single run with per-backbone checkpoints and backbone-aware quant filters * Configurable NVFP4 block-size via CLI/config; improved NVFP4 Conv3D inference path and Wan 2.2 quantization support * **Bug Fixes** * Video-model calibration now respects extra params and forces video decoding during calibration * **Documentation** * Added comprehensive Conv3D implicit‑GEMM kernel documentation; removed experimental Conv3D prototype docs/benchmark * **Tests** * New Wan 2.2 quantization/export tests and expanded Conv3D/FP4 kernel test coverage <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com>