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This PR adds
conv3d
support for the CPU backend. This is required for tasks such as inferencing text to video models.Since we are limited by the number of tensor dimensions, my idea was to unroll the depth dimension. The implementation flattens 3D input patches into columns, then performs a single matrix multiplication against the flattened kernel weights.
I did not seek for optimizations, and I'm not sure it is worth it (unless there are obvious ones), as this implementation will essentially serve as a ground truth to test against when implementing this op on other backends.
The correctness was checked against PyTorch's native
conv3d