Linear-BN Fusion: add precondition check (#119264)
Fixes #118990
The root cause is due to `out_features` of Linear not matching `num_features` of BatchNorm, resulting in shape mismatch while computing `fused_w`, and `fused_b`. This can happen for linear-bn folding because linear layer operates over the last dim, `(*, H_in)`, while bn layer operates over the channel dim, `(N, C_in, H, W)`.
To preserve the shapes of the original linear weight and bias in linear-bn folding, check linear `out_features` match bn `num_features`. If they don't match, bn `num_features` need to be 1 to broadcast.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119264
Approved by: https://github.com/eellison
