Initial implementation of vmap #389
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josevalim
reviewed
May 9, 2021
| {res, cache} | ||
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| %{} -> | ||
| {res, cache} = vectorize(op, args, in_axes, expr, cache) |
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You are missing a traverse_args somewhere that would effectively make this recursive. :)
josevalim
reviewed
May 9, 2021
nx/lib/nx/defn/kernel.ex
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| @@ -233,6 +233,13 @@ defmodule Nx.Defn.Kernel do | |||
| grad | |||
| end | |||
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| @doc """ | |||
| Vectorizes `fun` over given `args`. | |||
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Can we add some examples and more explanation? :) I am struggling to wrap my head around the current API. I assume that fun receives the vectorized tensor? Are there any restrictions on args? What are the possible values for in_axes?
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Perhaps handling dot is a good example of how the code will change before and after with and without vmap. :)
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Just so everyone is on the same page, I talked to @seanmor5 and we will try to implement this as tensor axes metadata instead of a transform. |
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WIP
This is an attempt to resolve #174. I've implemented
vmapin basically the same style asgradexcept it supports multiple vectorized arguments. We don't includeout_axeslike Jax, but that's easy to add either with an additionaltransposeor to propagate during the expression transformation.There are still a lot of checks and validations that need to be done:
I wanted to open this to collect feedback on the foundation before moving forward with implementing some additional batch rules. Based on this PR, we'd then have to revisit some Nx implementations to support batching. Off the top of my head I believe most of the LinAlg operators will need to support batched matrix operations.
I think we can also discuss the possibility of implementing automatic vectorization based on input names (e.g.
:batchname is always treated as a batch dimension).