Commits on Sep 9, 2024

1. Allow broadcasting across dimensions in eval mode; always require X t… …

   …o be at least 2d
   
   Summary:
   Context:
   
   A discussion on allowable shapes for transforms concluded:
   * We should not allow for broadcasting across the -1 dimension, so the first check in _check_shape should always happen.
   * The shapes always need to be broadcastable, so the torch.broadcast_shapes check in _check_shape should always happen.
   * We want to allow for broadcasting across the batch dimension in eval model, so the check that X has dimension of at least len(batch_shape) + 2 should only happen in training mode.
   * For clarity, we should disallow 1d X, even if broadcastable. BoTorch tends to be strict about requiring explicit dimensions, e.g. GPyTorchModel._validate_tensor_args, and that's a good thing because confusion about tensor dimensions causes a lot of pain.
   
   This diff:
   * Only checks that X has number of dimensions equal to 2 + the number of batch dimensions in training mode.
   * Disallows <2d X.
   
   Differential Revision: D62404492

   

   esantorella authored and facebook-github-bot committed Sep 9, 2024
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