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accross -> across (#183)

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Víctor Aguilar 2023-12-15 13:46:50 -08:00 committed by GitHub
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4 changed files with 4 additions and 4 deletions
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2
docs/src/dev/extensions.rst
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@ -150,7 +150,7 @@ back and go to our example to give ourselves a more concrete image.
const std::vector<int>& argnums) override;
/**
* The primitive must know how to vectorize itself accross
* The primitive must know how to vectorize itself across
* the given axes. The output is a pair containing the array
* representing the vectorized computation and the axis which
* corresponds to the output vectorized dimension.

2
examples/extensions/axpby/axpby.h
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@ -58,7 +58,7 @@ class Axpby : public Primitive {
const std::vector<int>& argnums) override;
/**
* The primitive must know how to vectorize itself accross
* The primitive must know how to vectorize itself across
* the given axes. The output is a pair containing the array
* representing the vectorized computation and the axis which
* corresponds to the output vectorized dimension.

2
mlx/primitives.h
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@ -72,7 +72,7 @@ class Primitive {
const std::vector<int>& argnums);
/**
* The primitive must know how to vectorize itself accross
* The primitive must know how to vectorize itself across
* the given axes. The output is a pair containing the array
* representing the vectorized computation and the axis which
* corresponds to the output vectorized dimension.

2
python/mlx/nn/layers/normalization.py
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@ -97,7 +97,7 @@ class GroupNorm(Module):
where :math:`\gamma` and :math:`\beta` are learned per feature dimension
parameters initialized at 1 and 0 respectively. However, the mean and
variance are computed over the spatial dimensions and each group of
features. In particular, the input is split into num_groups accross the
features. In particular, the input is split into num_groups across the
feature dimension.
The feature dimension is assumed to be the last dimension and the dimensions