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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; 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 * the given axes. The output is a pair containing the array
* representing the vectorized computation and the axis which * representing the vectorized computation and the axis which
* corresponds to the output vectorized dimension. * 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; 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 * the given axes. The output is a pair containing the array
* representing the vectorized computation and the axis which * representing the vectorized computation and the axis which
* corresponds to the output vectorized dimension. * 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); 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 * the given axes. The output is a pair containing the array
* representing the vectorized computation and the axis which * representing the vectorized computation and the axis which
* corresponds to the output vectorized dimension. * 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 where :math:`\gamma` and :math:`\beta` are learned per feature dimension
parameters initialized at 1 and 0 respectively. However, the mean and parameters initialized at 1 and 0 respectively. However, the mean and
variance are computed over the spatial dimensions and each group of 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. feature dimension.
The feature dimension is assumed to be the last dimension and the dimensions The feature dimension is assumed to be the last dimension and the dimensions