rebase

docs/build/html/_sources/usage/numpy.rst

@@ -21,11 +21,13 @@ Let's convert an array to NumPy and back.
 
 .. note::
 
-    Since NumPy does not support ``bfloat16`` arrays, you will need to convert to ``float16`` or ``float32`` first:
-    ``np.array(a.astype(mx.float32))``.
-    Otherwise, you will receive an error like: ``Item size 2 for PEP 3118 buffer format string does not match the dtype V item size 0.``
+    Since NumPy does not support ``bfloat16`` arrays, you will need to convert
+    to ``float16`` or ``float32`` first: ``np.array(a.astype(mx.float32))``.
+    Otherwise, you will receive an error like: ``Item size 2 for PEP 3118
+    buffer format string does not match the dtype V item size 0.``
 
-By default, NumPy copies data to a new array. This can be prevented by creating an array view:
+By default, NumPy copies data to a new array. This can be prevented by creating
+an array view:
 
 .. code-block:: python
 
@@ -35,10 +37,16 @@ By default, NumPy copies data to a new array. This can be prevented by creating
   a_view[0] = 1
   print(a[0].item()) # 1
 
-A NumPy array view is a normal NumPy array, except that it does not own its memory.
-This means writing to the view is reflected in the original array.
+.. note::
 
-While this is quite powerful to prevent copying arrays, it should be noted that external changes to the memory of arrays cannot be reflected in gradients.
+    NumPy arrays with type ``float64`` will be default converted to MLX arrays
+    with type ``float32``.
+
+A NumPy array view is a normal NumPy array, except that it does not own its
+memory. This means writing to the view is reflected in the original array.
+
+While this is quite powerful to prevent copying arrays, it should be noted that
+external changes to the memory of arrays cannot be reflected in gradients.
 
 Let's demonstrate this in an example:
 
@@ -56,11 +64,12 @@ Let's demonstrate this in an example:
 
 
 The function ``f`` indirectly modifies the array ``x`` through a memory view.
-However, this modification is not reflected in the gradient, as seen in the last line outputting ``1.0``,
-representing the gradient of the sum operation alone.
-The squaring of ``x`` occurs externally to MLX, meaning that no gradient is incorporated.
-It's important to note that a similar issue arises during array conversion and copying.
-For instance, a function defined as ``mx.array(np.array(x)**2).sum()`` would also result in an incorrect gradient,
+However, this modification is not reflected in the gradient, as seen in the
+last line outputting ``1.0``, representing the gradient of the sum operation
+alone.  The squaring of ``x`` occurs externally to MLX, meaning that no
+gradient is incorporated.  It's important to note that a similar issue arises
+during array conversion and copying.  For instance, a function defined as
+``mx.array(np.array(x)**2).sum()`` would also result in an incorrect gradient,
 even though no in-place operations on MLX memory are executed.
 
 PyTorch
@@ -71,7 +80,8 @@ PyTorch
    PyTorch Support for :obj:`memoryview` is experimental and can break for
    multi-dimensional arrays. Casting to NumPy first is advised for now.
 
-PyTorch supports the buffer protocol, but it requires an explicit :obj:`memoryview`.
+PyTorch supports the buffer protocol, but it requires an explicit
+:obj:`memoryview`.
 
 .. code-block:: python
 
@@ -82,7 +92,8 @@ PyTorch supports the buffer protocol, but it requires an explicit :obj:`memoryvi
   b = torch.tensor(memoryview(a))
   c = mx.array(b.numpy())
 
-Conversion from PyTorch tensors back to arrays must be done via intermediate NumPy arrays with ``numpy()``.
+Conversion from PyTorch tensors back to arrays must be done via intermediate
+NumPy arrays with ``numpy()``.
 
 JAX
 ---
@@ -100,7 +111,8 @@ JAX fully supports the buffer protocol.
 TensorFlow
 ----------
 
-TensorFlow supports the buffer protocol, but it requires an explicit :obj:`memoryview`.
+TensorFlow supports the buffer protocol, but it requires an explicit
+:obj:`memoryview`.
 
 .. code-block:: python