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docs fixes (#1263)

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Awni Hannun 2024-07-11 15:59:07 -07:00 committed by GitHub
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3 changed files with 14 additions and 11 deletions
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2
docs/src/python/array.rst
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@ -24,6 +24,7 @@ Array
array.any array.any
array.argmax array.argmax
array.argmin array.argmin
array.conj
array.cos array.cos
array.cummax array.cummax
array.cummin array.cummin
@ -57,3 +58,4 @@ Array
array.transpose array.transpose
array.T array.T
array.var array.var
array.view

17
python/src/ops.cpp
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@ -2559,7 +2559,7 @@ void init_ops(nb::module_& m) {
If unspecified, it defaults to ``-1``. If unspecified, it defaults to ``-1``.
Returns: Returns:
array: The `uint32`` array containing indices that partition the input. array: The ``uint32`` array containing indices that partition the input.
)pbdoc"); )pbdoc");
m.def( m.def(
"topk", "topk",
@ -4388,20 +4388,21 @@ void init_ops(nb::module_& m) {
Perform the Walsh-Hadamard transform along the final axis. Perform the Walsh-Hadamard transform along the final axis.
Equivalent to: Equivalent to:
```python
from scipy.linalg import hadamard
y = hadamard(len(x)) @ x .. code-block:: python
```
Supports sizes `n = m*2^k` where m in (1, 12, 20, 28) from scipy.linalg import hadamard
and 2^k <= 8192 for FP32 and 2^k <= 16384 for FP16/BF16.
y = hadamard(len(x)) @ x
Supports sizes ``n = m*2^k`` for ``m`` in ``(1, 12, 20, 28)`` and ``2^k
<= 8192`` for float32 and ``2^k <= 16384`` for float16/bfloat16.
Args: Args:
a (array): Input array or scalar. a (array): Input array or scalar.
scale (float): Scale the output by this factor. scale (float): Scale the output by this factor.
Returns: Returns:
array: The array with the new type. array: The transformed array.
)pbdoc"); )pbdoc");
} }

6
python/src/transforms.cpp
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@ -934,9 +934,9 @@ void init_transforms(nb::module_& m) {
This class is meant to be used as a function decorator. Instances are This class is meant to be used as a function decorator. Instances are
callables that behave identically to the wrapped function. However, when callables that behave identically to the wrapped function. However, when
a function transformation is used (e.g. computing gradients using a function transformation is used (e.g. computing gradients using
:func:`value_and_grad`) then the functions defined via :method:`vjp`, :func:`value_and_grad`) then the functions defined via
:method:`jvp` and :method:`vmap` are used instead of the default :meth:`custom_function.vjp`, :meth:`custom_function.jvp` and
transformation. :meth:`custom_function.vmap` are used instead of the default transformation.
Note, all custom transformations are optional. Undefined transformations Note, all custom transformations are optional. Undefined transformations
fall back to the default behaviour. fall back to the default behaviour.