zhangyiss/mlx
1
0
Fork 0
mirror of https://github.com/ml-explore/mlx.git synced 2025-06-24 09:21:16 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Update docs for extensions (#2004)

Browse Source
This commit is contained in:
Yi Wang 2025-03-25 18:35:03 -07:00 committed by GitHub
parent eda7a7b43e
commit 0da8506552
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 7 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
docs/src/dev/extensions.rst
View File

@ -93,9 +93,9 @@ Primitives
^^^^^^^^^^^
A :class:`Primitive` is part of the computation graph of an :class:`array`. It
defines how to create outputs arrays given a input arrays. Further, a
defines how to create output arrays given input arrays. Further, a
:class:`Primitive` has methods to run on the CPU or GPU and for function
transformations such as ``vjp`` and ``jvp``. Lets go back to our example to be
transformations such as ``vjp`` and ``jvp``. Let's go back to our example to be
more concrete:
.. code-block:: C++
@ -128,7 +128,7 @@ more concrete:
/** The vector-Jacobian product. */
std::vector<array> vjp(
const std::vector<array>& primals,
const array& cotan,
const std::vector<array>& cotangents,
const std::vector<int>& argnums,
const std::vector<array>& outputs) override;
@ -469,7 +469,7 @@ one we just defined:
const std::vector<array>& tangents,
const std::vector<int>& argnums) {
// Forward mode diff that pushes along the tangents
// The jvp transform on the primitive can built with ops
// The jvp transform on the primitive can be built with ops
// that are scheduled on the same stream as the primitive
// If argnums = {0}, we only push along x in which case the
@ -481,7 +481,7 @@ one we just defined:
auto scale_arr = array(scale, tangents[0].dtype());
return {multiply(scale_arr, tangents[0], stream())};
}
// If, argnums = {0, 1}, we take contributions from both
// If argnums = {0, 1}, we take contributions from both
// which gives us jvp = tangent_x * alpha + tangent_y * beta
else {
return {axpby(tangents[0], tangents[1], alpha_, beta_, stream())};
@ -735,7 +735,7 @@ Let's look at a simple script and its results:
print(f"c shape: {c.shape}")
print(f"c dtype: {c.dtype}")
print(f"c correct: {mx.all(c == 6.0).item()}")
print(f"c is correct: {mx.all(c == 6.0).item()}")
Output:
@ -743,7 +743,7 @@ Output:
c shape: [3, 4]
c dtype: float32
c correctness: True
c is correct: True
Results
^^^^^^^