I recently ran into an application where I had to compute many inner products quickly (roughy 50k inner products in less than a second). I wanted a vector of inner products over the 50k vectors, or `[x1.T @ A @ x1, …, xn.T @ A @ xn]` with A.shape = (1k, 1k).
My first instinct was to look for a NumPy function to quickly compute this, such as np.inner. However, it looks like np.inner has some other behavior and I couldn’t get tensordot/einsum to work for me. Then a labmate pointed out that I can just do some slick matrix multiplication to compute the same quantity, `(X.T * A @ X.T).sum(axis=0)`. I opened [a PR] with this, and proposed that we define a new function called `inner_prods` for this. However, in the PR, @shoyer pointed out > The main challenge is to figure out how to transition the behavior of all >these operations, while preserving backwards compatibility. Quite likely, we >need to pick new names for these functions, though we should try to pick >something that doesn't suggest that they are second class alternatives. Do we choose new function names? Do we add a keyword arg that changes what np.inner returns? [a PR]:https://github.com/numpy/numpy/pull/7690
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