Developer Documentation
=======================
MLX provides a open and flexible backend to which users may add operations
and specialized implementations without much hassle. While the library supplies
efficient operations that can be used and composed for any number of
applications, there may arise cases where new functionalities or highly
optimized implementations are needed. For such cases, you may design and
implement your own operations that link to and build on top of :mod:`mlx.core`.
We will introduce the inner-workings of MLX and go over a simple example to
learn the steps involved in adding new operations to MLX with your own CPU
and GPU implementations.
Introducing the Example
-----------------------
Let's say that you would like an operation that takes in two arrays,
``x`` and ``y``, scales them both by some coefficients ``alpha`` and ``beta``
respectively, and then adds them together to get the result
``z = alpha * x + beta * y``. Well, you can very easily do that by just
writing out a function as follows:
.. code-block:: python
import mlx.core as mx
def simple_axpby(x: mx.array, y: mx.array, alpha: float, beta: float) -> mx.array:
return alpha * x + beta * y
This function performs that operation while leaving the implementations and
differentiation to MLX.
However, you work with vector math libraries often and realize that the
``axpby`` routine defines the same operation ``Y = (alpha * X) + (beta * Y)``.
You would really like the part of your applications that does this operation
on the CPU to be very fast - so you decide that you want it to rely on the
``axpby`` routine provided by the Accelerate_ framework. Continuing to impose
our assumptions on to you, let's also assume that you want to learn how add
your own implementation for the gradients of your new operation while going
over the ins-and-outs of the MLX framework.
Well, what a coincidence! You are in the right place. Over the course of this
example, we will learn:
* The structure of the MLX library from the frontend API to the backend implementations.
* How to implement your own CPU backend that redirects to Accelerate_ when appropriate (and a fallback if needed).
* How to implement your own GPU implementation using metal.
* How to add your own ``vjp`` and ``jvp``.
* How to build your implementations, link them to MLX, and bind them to python.
Operations and Primitives
-------------------------
In one sentence, operations in MLX build the computation graph, and primitives
provide the rules for evaluation and transformations of said graph. Let's start
by discussing operations in more detail.
Operations
^^^^^^^^^^^
Operations are the frontend functions that operate on arrays. They are defined
in the C++ API (:ref:`cpp_ops`) and then we provide bindings to these
operations in the Python API (:ref:`ops`).
We would like an operation, :meth:`axpby` that takes in two arrays ``x`` and ``y``,
and two scalars, ``alpha`` and ``beta``. This is how we would define it in the
C++ API:
.. code-block:: C++
/**
* Scale and sum two vectors element-wise
* z = alpha * x + beta * y
*
* Follow numpy style broadcasting between x and y
* Inputs are upcasted to floats if needed
**/
array axpby(
const array& x, // Input array x
const array& y, // Input array y
const float alpha, // Scaling factor for x
const float beta, // Scaling factor for y
StreamOrDevice s = {} // Stream on which to schedule the operation
);
This operation itself can call other operations within it if needed. So, the
simplest way to go about implementing this operation would be do so in terms
of existing operations.
.. code-block:: C++
array axpby(
const array& x, // Input array x
const array& y, // Input array y
const float alpha, // Scaling factor for x
const float beta, // Scaling factor for y
StreamOrDevice s /* = {} */ // Stream on which to schedule the operation
) {
// Scale x and y on the provided stream
auto ax = multiply(array(alpha), x, s);
auto by = multiply(array(beta), y, s);
// Add and return
return add(ax, by, s);
}
However, as we discussed earlier, this is not our goal. The operations themselves
do not contain the implementations that act on the data, nor do they contain the
rules of transformations. Rather, they are an easy to use interface that build
on top of the building blocks we call :class:`Primitive`.
Primitives
^^^^^^^^^^^
A :class:`Primitive` is part of the computation graph of an :class:`array`. It
defines how to create an output given a set of input :class:`array` . Further,
a :class:`Primitive` is a class that contains rules on how it is evaluated
on the CPU or GPU, and how it acts under transformations such as ``vjp`` and
``jvp``. These words on their own can be a bit abstract, so lets take a step
back and go to our example to give ourselves a more concrete image.
.. code-block:: C++
class Axpby : public Primitive {
public:
explicit Axpby(Stream stream, float alpha, float beta)
: Primitive(stream), alpha_(alpha), beta_(beta){};
/**
* A primitive must know how to evaluate itself on the CPU/GPU
* for the given inputs and populate the output array.
*
* To avoid unnecessary allocations, the evaluation function
* is responsible for allocating space for the array.
*/
void eval_cpu(const std::vector<array>& inputs, array& out) override;
void eval_gpu(const std::vector<array>& inputs, array& out) override;
/** The Jacobian-vector product. */
array jvp(
const std::vector<array>& primals,
const std::vector<array>& tangents,
const std::vector<int>& argnums) override;
/** The vector-Jacobian product. */
std::vector<array> vjp(
const std::vector<array>& primals,
const array& cotan,
const std::vector<int>& argnums) override;
/**
* 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.
*/
std::pair<array, int> vmap(
const std::vector<array>& inputs,
const std::vector<int>& axes) override;
/** Print the primitive. */
void print(std::ostream& os) override {
os << "Axpby";
}
/** Equivalence check **/
bool is_equivalent(const Primitive& other) const override;
private:
float alpha_;
float beta_;
/** Fall back implementation for evaluation on CPU */
void eval(const std::vector<array>& inputs, array& out);
};
The :class:`Axpby` class derives from the base :class:`Primitive` class and
follows the above demonstrated interface. :class:`Axpby` treats ``alpha`` and
``beta`` as parameters. It then provides implementations of how the array ``out``
is produced given ``inputs`` through :meth:`Axpby::eval_cpu` and
:meth:`Axpby::eval_gpu`. Further, it provides rules of transformations in
:meth:`Axpby::jvp`, :meth:`Axpby::vjp`, and :meth:`Axpby::vmap`.
Using the Primitives
^^^^^^^^^^^^^^^^^^^^^
Operations can use this :class:`Primitive` to add a new :class:`array` to
the computation graph. An :class:`array` can be constructed by providing its
data type, shape, the :class:`Primitive` that computes it, and the
:class:`array` inputs that are passed to the primitive.
Let's re-implement our operation now in terms of our :class:`Axpby` primitive.
.. code-block:: C++
array axpby(
const array& x, // Input array x
const array& y, // Input array y
const float alpha, // Scaling factor for x
const float beta, // Scaling factor for y
StreamOrDevice s /* = {} */ // Stream on which to schedule the operation
) {
// Promote dtypes between x and y as needed
auto promoted_dtype = promote_types(x.dtype(), y.dtype());
// Upcast to float32 for non-floating point inputs x and y
auto out_dtype = is_floating_point(promoted_dtype)
? promoted_dtype
: promote_types(promoted_dtype, float32);
// Cast x and y up to the determined dtype (on the same stream s)
auto x_casted = astype(x, out_dtype, s);
auto y_casted = astype(y, out_dtype, s);
// Broadcast the shapes of x and y (on the same stream s)
auto broadcasted_inputs = broadcast_arrays({x_casted, y_casted}, s);
auto out_shape = broadcasted_inputs[0].shape();
// Construct the array as the output of the Axpby primitive
// with the broadcasted and upcasted arrays as inputs
return array(
/* const std::vector<int>& shape = */ out_shape,
/* Dtype dtype = */ out_dtype,
/* std::unique_ptr<Primitive> primitive = */
std::make_unique<Axpby>(to_stream(s), alpha, beta),
/* const std::vector<array>& inputs = */ broadcasted_inputs);
}
This operation now handles the following:
#. Upcast inputs and resolve the output data type.
#. Broadcast the inputs and resolve the output shape.
#. Construct the primitive :class:`Axpby` using the given stream, ``alpha``, and ``beta``.
#. Construct the output :class:`array` using the primitive and the inputs.
Implementing the Primitive
--------------------------
No computation happens when we call the operation alone. In effect, the
operation only builds the computation graph. When we evaluate the output
array, MLX schedules the execution of the computation graph, and calls
:meth:`Axpby::eval_cpu` or :meth:`Axpby::eval_gpu` depending on the
stream/device specified by the user.
.. warning::
When :meth:`Primitive::eval_cpu` or :meth:`Primitive::eval_gpu` are called,
no memory has been allocated for the output array. It falls on the implementation
of these functions to allocate memory as needed
Implementing the CPU Backend
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Let's start by trying to implement a naive and generic version of
:meth:`Axpby::eval_cpu`. We declared this as a private member function of
:class:`Axpby` earlier called :meth:`Axpby::eval`.
Our naive method will go over each element of the output array, find the
corresponding input elements of ``x`` and ``y`` and perform the operation
pointwise. This is captured in the templated function :meth:`axpby_impl`.
.. code-block:: C++
template <typename T>
void axpby_impl(
const array& x,
const array& y,
array& out,
float alpha_,
float beta_) {
// We only allocate memory when we are ready to fill the output
// malloc_or_wait synchronously allocates available memory
// There may be a wait executed here if the allocation is requested
// under memory-pressured conditions
out.set_data(allocator::malloc_or_wait(out.nbytes()));
// Collect input and output data pointers
const T* x_ptr = x.data<T>();
const T* y_ptr = y.data<T>();
T* out_ptr = out.data<T>();
// Cast alpha and beta to the relevant types
T alpha = static_cast<T>(alpha_);
T beta = static_cast<T>(beta_);
// Do the element-wise operation for each output
for (size_t out_idx = 0; out_idx < out.size(); out_idx++) {
// Map linear indices to offsets in x and y
auto x_offset = elem_to_loc(out_idx, x.shape(), x.strides());
auto y_offset = elem_to_loc(out_idx, y.shape(), y.strides());
// We allocate the output to be contiguous and regularly strided
// (defaults to row major) and hence it doesn't need additional mapping
out_ptr[out_idx] = alpha * x_ptr[x_offset] + beta * y_ptr[y_offset];
}
}
Now, we would like our implementation to be able to do this pointwise operation
for all incoming floating point arrays. Accordingly, we add dispatches for
``float32``, ``float16``, ``bfloat16`` and ``complex64``. We throw an error
if we encounter an unexpected type.
.. code-block:: C++
/** Fall back implementation for evaluation on CPU */
void Axpby::eval(const std::vector<array>& inputs, array& out) {
// Check the inputs (registered in the op while constructing the out array)
assert(inputs.size() == 2);
auto& x = inputs[0];
auto& y = inputs[1];
// Dispatch to the correct dtype
if (out.dtype() == float32) {
return axpby_impl<float>(x, y, out, alpha_, beta_);
} else if (out.dtype() == float16) {
return axpby_impl<float16_t>(x, y, out, alpha_, beta_);
} else if (out.dtype() == bfloat16) {
return axpby_impl<bfloat16_t>(x, y, out, alpha_, beta_);
} else if (out.dtype() == complex64) {
return axpby_impl<complex64_t>(x, y, out, alpha_, beta_);
} else {
throw std::runtime_error(
"Axpby is only supported for floating point types.");
}
}
We have a fallback implementation! Now, to do what we are really here to do.
Remember we wanted to use the ``axpby`` routine provided by the Accelerate_
framework? Well, there are 3 complications to keep in mind:
#. Accelerate does not provide implementations of ``axpby`` for half precision
floats. We can only direct to it for ``float32`` types
#. Accelerate assumes the inputs ``x`` and ``y`` are contiguous and all elements
have fixed strides between them. Possibly due to broadcasts and transposes,
we aren't guaranteed that the inputs fit this requirement. We can
only direct to Accelerate if both ``x`` and ``y`` are row contiguous or
column contiguous.
#. Accelerate performs the routine ``Y = (alpha * X) + (beta * Y)`` inplace.
MLX expects to write out the answer to a new array. We must copy the elements
of ``y`` into the output array and use that as an input to ``axpby``
Let's write out an implementation that uses Accelerate in the right conditions.
It must simply allocate data for the output, copy elements of ``y`` into it,
and then call the :meth:`catlas_saxpby` from accelerate.
.. code-block:: C++
template <typename T>
void axpby_impl_accelerate(
const array& x,
const array& y,
array& out,
float alpha_,
float beta_) {
// Accelerate library provides catlas_saxpby which does
// Y = (alpha * X) + (beta * Y) in place
// To use it, we first copy the data in y over to the output array
// This specialization requires both x and y be contiguous in the same mode
// i.e: corresponding linear indices in both point to corresponding elements
// The data in the output array is allocated to match the strides in y
// such that x, y, and out are contiguous in the same mode and
// no transposition is needed
out.set_data(
allocator::malloc_or_wait(y.data_size() * out.itemsize()),
y.data_size(),
y.strides(),
y.flags());
// We then copy over the elements using the contiguous vector specialization
copy_inplace(y, out, CopyType::Vector);
// Get x and y pointers for catlas_saxpby
const T* x_ptr = x.data<T>();
T* y_ptr = out.data<T>();
T alpha = static_cast<T>(alpha_);
T beta = static_cast<T>(beta_);
// Call the inplace accelerate operator
catlas_saxpby(
/* N = */ out.size(),
/* ALPHA = */ alpha,
/* X = */ x_ptr,
/* INCX = */ 1,
/* BETA = */ beta,
/* Y = */ y_ptr,
/* INCY = */ 1);
}
Great! But what about the inputs that do not fit the criteria for accelerate?
Luckily, we can always just direct back to :meth:`Axpby::eval`.
With this in mind, lets finally implement our :meth:`Axpby::eval_cpu`.
.. code-block:: C++
/** Evaluate primitive on CPU using accelerate specializations */
void Axpby::eval_cpu(const std::vector<array>& inputs, array& out) {
assert(inputs.size() == 2);
auto& x = inputs[0];
auto& y = inputs[1];
// Accelerate specialization for contiguous single precision float arrays
if (out.dtype() == float32 &&
((x.flags().row_contiguous && y.flags().row_contiguous) ||
(x.flags().col_contiguous && y.flags().col_contiguous))) {
axpby_impl_accelerate<float>(x, y, out, alpha_, beta_);
return;
}
// Fall back to common backend if specializations are not available
eval(inputs, out);
}
We have now hit a milestone! Just this much is enough to run the operation
:meth:`axpby` on a CPU stream!
If you do not plan on running the operation on the GPU or using transforms on
computation graphs that contain :class:`Axpby`, you can stop implementing the
primitive here and enjoy the speed-ups you get from the Accelerate library.
Implementing the GPU Backend
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Apple silicon devices address their GPUs using the Metal_ shading language, and
all GPU kernels in MLX are written using metal.
.. note::
Here are some helpful resources if you are new to metal!
* A walkthrough of the metal compute pipeline: `Metal Example`_
* Documentation for metal shading language: `Metal Specification`_
* Using metal from C++: `Metal-cpp`_
Let's keep the GPU algorithm simple. We will launch exactly as many threads
as there are elements in the output. Each thread will pick the element it needs
from ``x`` and ``y``, do the pointwise operation, and then update its assigned
element in the output.
.. code-block:: C++
template <typename T>
[[kernel]] void axpby_general(
device const T* x [[buffer(0)]],
device const T* y [[buffer(1)]],
device T* out [[buffer(2)]],
constant const float& alpha [[buffer(3)]],
constant const float& beta [[buffer(4)]],
constant const int* shape [[buffer(5)]],
constant const size_t* x_strides [[buffer(6)]],
constant const size_t* y_strides [[buffer(7)]],
constant const int& ndim [[buffer(8)]],
uint index [[thread_position_in_grid]]) {
// Convert linear indices to offsets in array
auto x_offset = elem_to_loc(index, shape, x_strides, ndim);
auto y_offset = elem_to_loc(index, shape, y_strides, ndim);
// Do the operation and update the output
out[index] =
static_cast<T>(alpha) * x[x_offset] + static_cast<T>(beta) * y[y_offset];
}
We then need to instantiate this template for all floating point types and give
each instantiation a unique host name so we can identify the right kernel for
each data type.
.. code-block:: C++
#define instantiate_axpby(type_name, type) \
template [[host_name("axpby_general_" #type_name)]] \
[[kernel]] void axpby_general<type>( \
device const type* x [[buffer(0)]], \
device const type* y [[buffer(1)]], \
device type* out [[buffer(2)]], \
constant const float& alpha [[buffer(3)]], \
constant const float& beta [[buffer(4)]], \
constant const int* shape [[buffer(5)]], \
constant const size_t* x_strides [[buffer(6)]], \
constant const size_t* y_strides [[buffer(7)]], \
constant const int& ndim [[buffer(8)]], \
uint index [[thread_position_in_grid]]);
instantiate_axpby(float32, float);
instantiate_axpby(float16, half);
instantiate_axpby(bfloat16, bfloat16_t);
instantiate_axpby(complex64, complex64_t);
This kernel will be compiled into a metal library ``mlx_ext.metallib`` as we
will see later in :ref:`Building with CMake`. In the following example, we
assume that the library ``mlx_ext.metallib`` will always be co-located with
the executable/ shared-library calling the :meth:`register_library` function.
The :meth:`register_library` function takes the library's name and potential
path (or in this case, a function that can produce the path of the metal
library) and tries to load that library if it hasn't already been registered
by the relevant static :class:`mlx::core::metal::Device` object. This is why,
it is important to package your C++ library with the metal library. We will
go over this process in more detail later.
The logic to determine the kernel, set the inputs, resolve the grid dimensions
and dispatch it to the GPU are contained in :meth:`Axpby::eval_gpu` as shown
below.
.. code-block:: C++
/** Evaluate primitive on GPU */
void Axpby::eval_gpu(const std::vector<array>& inputs, array& out) {
// Prepare inputs
assert(inputs.size() == 2);
auto& x = inputs[0];
auto& y = inputs[1];
// Each primitive carries the stream it should execute on
// and each stream carries its device identifiers
auto& s = stream();
// We get the needed metal device using the stream
auto& d = metal::device(s.device);
// Allocate output memory
out.set_data(allocator::malloc_or_wait(out.nbytes()));
// Resolve name of kernel (corresponds to axpby.metal)
std::ostringstream kname;
kname << "axpby_" << "general_" << type_to_name(out);
// Make sure the metal library is available and look for it
// in the same folder as this executable if needed
d.register_library("mlx_ext", metal::get_colocated_mtllib_path);
// Make a kernel from this metal library
auto kernel = d.get_kernel(kname.str(), "mlx_ext");
// Prepare to encode kernel
auto compute_encoder = d.get_command_encoder(s.index);
compute_encoder->setComputePipelineState(kernel);
// Kernel parameters are registered with buffer indices corresponding to
// those in the kernel declaration at axpby.metal
int ndim = out.ndim();
size_t nelem = out.size();
// Encode input arrays to kernel
set_array_buffer(compute_encoder, x, 0);
set_array_buffer(compute_encoder, y, 1);
// Encode output arrays to kernel
set_array_buffer(compute_encoder, out, 2);
// Encode alpha and beta
compute_encoder->setBytes(&alpha_, sizeof(float), 3);
compute_encoder->setBytes(&beta_, sizeof(float), 4);
// Encode shape, strides and ndim
compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5);
compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6);
compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7);
compute_encoder->setBytes(&ndim, sizeof(int), 8);
// We launch 1 thread for each input and make sure that the number of
// threads in any given threadgroup is not higher than the max allowed
size_t tgp_size = std::min(nelem, kernel->maxTotalThreadsPerThreadgroup());
// Fix the 3D size of each threadgroup (in terms of threads)
MTL::Size group_dims = MTL::Size(tgp_size, 1, 1);
// Fix the 3D size of the launch grid (in terms of threads)
MTL::Size grid_dims = MTL::Size(nelem, 1, 1);
// Launch the grid with the given number of threads divided among
// the given threadgroups
compute_encoder->dispatchThreads(grid_dims, group_dims);
}
We can now call the :meth:`axpby` operation on both the CPU and the GPU!
A few things to note about MLX and metal before moving on. MLX keeps track
of the active ``compute_encoder``. We rely on :meth:`d.get_command_encoder`
to give us the active metal compute command encoder instead of building a
new one and calling :meth:`compute_encoder->end_encoding` at the end.
MLX keeps adding kernels (compute pipelines) to the active command encoder
until some specified limit is hit or the compute encoder needs to be flushed
for synchronization. MLX also handles enqueuing and committing the associated
command buffers as needed. We suggest taking a deeper dive into
:class:`metal::Device` if you would like to study this routine further.
Primitive Transforms
^^^^^^^^^^^^^^^^^^^^^
Now that we have come this far, let's also learn how to add implementations to
transformations in a :class:`Primitive`. These transformations can be built on
top of our operations, including the one we just defined now. Which then gives
us the following :meth:`Axpby::jvp` and :meth:`Axpby::vjp` implementations.
.. code-block:: C++
/** The Jacobian-vector product. */
array Axpby::jvp(
const std::vector<array>& primals,
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
// that are scheduled on the same stream as the primitive
// If argnums = {0}, we only push along x in which case the
// jvp is just the tangent scaled by alpha
// Similarly, if argnums = {1}, the jvp is just the tangent
// scaled by beta
if (argnums.size() > 1) {
auto scale = argnums[0] == 0 ? alpha_ : beta_;
auto scale_arr = array(scale, tangents[0].dtype());
return multiply(scale_arr, tangents[0], stream());
}
// 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());
}
}
.. code-block:: C++
/** The vector-Jacobian product. */
std::vector<array> Axpby::vjp(
const std::vector<array>& primals,
const array& cotan,
const std::vector<int>& argnums) {
// Reverse mode diff
std::vector<array> vjps;
for (auto arg : argnums) {
auto scale = arg == 0 ? alpha_ : beta_;
auto scale_arr = array(scale, cotan.dtype());
vjps.push_back(multiply(scale_arr, cotan, stream()));
}
return vjps;
}
Finally, you need not have a transformation fully defined to start using your
own :class:`Primitive`.
.. code-block:: C++
/** Vectorize primitive along given axis */
std::pair<array, int> Axpby::vmap(
const std::vector<array>& inputs,
const std::vector<int>& axes) {
throw std::runtime_error("Axpby has no vmap implementation.");
}
Building and Binding
--------------------
Let's look at the overall directory structure first.
| extensions
| ├── axpby
| │ ├── axpby.cpp
| │ ├── axpby.h
| │ └── axpby.metal
| ├── mlx_sample_extensions
| │ └── __init__.py
| ├── bindings.cpp
| ├── CMakeLists.txt
| └── setup.py
* ``extensions/axpby/`` defines the C++ extension library
* ``extensions/mlx_sample_extensions`` sets out the structure for the
associated python package
* ``extensions/bindings.cpp`` provides python bindings for our operation
* ``extensions/CMakeLists.txt`` holds CMake rules to build the library and
python bindings
* ``extensions/setup.py`` holds the ``setuptools`` rules to build and install
the python package
Binding to Python
^^^^^^^^^^^^^^^^^^
We use PyBind11_ to build a Python API for the C++ library. Since bindings
for all needed components such as `mlx.core.array`, `mlx.core.stream`, etc.
are already provided, adding our :meth:`axpby` becomes very simple!
.. code-block:: C++
PYBIND11_MODULE(mlx_sample_extensions, m) {
m.doc() = "Sample C++ and metal extensions for MLX";
m.def(
"axpby",
&axpby,
"x"_a,
"y"_a,
py::pos_only(),
"alpha"_a,
"beta"_a,
py::kw_only(),
"stream"_a = py::none(),
R"pbdoc(
Scale and sum two vectors element-wise
``z = alpha * x + beta * y``
Follows numpy style broadcasting between ``x`` and ``y``
Inputs are upcasted to floats if needed
Args:
x (array): Input array.
y (array): Input array.
alpha (float): Scaling factor for ``x``.
beta (float): Scaling factor for ``y``.
Returns:
array: ``alpha * x + beta * y``
)pbdoc");
}
Most of the complexity in the above example comes from additional bells and
whistles such as the literal names and doc-strings.
.. warning::
:mod:`mlx.core` needs to be imported before importing
:mod:`mlx_sample_extensions` as defined by the pybind11 module above to
ensure that the casters for :mod:`mlx.core` components like
:class:`mlx.core.array` are available.
.. _Building with CMake:
Building with CMake
^^^^^^^^^^^^^^^^^^^^
Building the C++ extension library itself is simple, it only requires that you
``find_package(MLX CONFIG)`` and then link it to your library.
.. code-block:: cmake
# Add library
add_library(mlx_ext)
# Add sources
target_sources(
mlx_ext
PUBLIC
${CMAKE_CURRENT_LIST_DIR}/axpby/axpby.cpp
)
# Add include headers
target_include_directories(
mlx_ext PUBLIC ${CMAKE_CURRENT_LIST_DIR}
)
# Link to mlx
target_link_libraries(mlx_ext PUBLIC mlx)
We also need to build the attached metal library. For convenience, we provide a
:meth:`mlx_build_metallib` function that builds a ``.metallib`` target given
sources, headers, destinations, etc. (defined in ``cmake/extension.cmake`` and
automatically imported with MLX package).
Here is what that looks like in practice!
.. code-block:: cmake
# Build metallib
if(MLX_BUILD_METAL)
mlx_build_metallib(
TARGET mlx_ext_metallib
TITLE mlx_ext
SOURCES ${CMAKE_CURRENT_LIST_DIR}/axpby/axpby.metal
INCLUDE_DIRS ${PROJECT_SOURCE_DIR} ${MLX_INCLUDE_DIRS}
OUTPUT_DIRECTORY ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}
)
add_dependencies(
mlx_ext
mlx_ext_metallib
)
endif()
Finally, we build the Pybind11_ bindings
.. code-block:: cmake
pybind11_add_module(
mlx_sample_extensions
${CMAKE_CURRENT_LIST_DIR}/bindings.cpp
)
target_link_libraries(mlx_sample_extensions PRIVATE mlx_ext)
if(BUILD_SHARED_LIBS)
target_link_options(mlx_sample_extensions PRIVATE -Wl,-rpath,@loader_path)
endif()
Building with ``setuptools``
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Once we have set out the CMake build rules as described above, we can use the
build utilities defined in :mod:`mlx.extension` for a simple build process.
.. code-block:: python
from mlx import extension
from setuptools import setup
if __name__ == "__main__":
setup(
name="mlx_sample_extensions",
version="0.0.0",
description="Sample C++ and Metal extensions for MLX primitives.",
ext_modules=[extension.CMakeExtension("mlx_sample_extensions")],
cmdclass={"build_ext": extension.CMakeBuild},
packages = ["mlx_sample_extensions"],
package_dir = {"": "mlx_sample_extensions"},
package_data = {"mlx_sample_extensions" : ["*.so", "*.dylib", "*.metallib"]},
zip_safe=False,
python_requires=">=3.7",
)
.. note::
We treat ``extensions/mlx_sample_extensions`` as the package directory
even though it only contains a ``__init__.py`` to ensure the following:
* :mod:`mlx.core` is always imported before importing :mod:`mlx_sample_extensions`
* The C++ extension library and the metal library are co-located with the python
bindings and copied together if the package is installed
You can build inplace for development using
``python setup.py build_ext -j8 --inplace`` (in ``extensions/``)
This will result in a directory structure as follows:
| extensions
| ├── mlx_sample_extensions
| │ ├── __init__.py
| │ ├── libmlx_ext.dylib # C++ extension library
| │ ├── mlx_ext.metallib # Metal library
| │ └── mlx_sample_extensions.cpython-3x-darwin.so # Python Binding
| ...
When you try to install using the command ``python -m pip install .``
(in ``extensions/``), the package will be installed with the same structure as
``extensions/mlx_sample_extensions`` and the C++ and metal library will be
copied along with the python binding since they are specified as ``package_data``.
Usage
-----
After installing the extension as described above, you should be able to simply
import the python package and play with it as you would any other MLX operation!
Let's looks at a simple script and it's results!
.. code-block:: python
import mlx.core as mx
from mlx_sample_extensions import axpby
a = mx.ones((3, 4))
b = mx.ones((3, 4))
c = axpby(a, b, 4.0, 2.0, stream=mx.cpu)
print(f"c shape: {c.shape}")
print(f"c dtype: {c.dtype}")
print(f"c correctness: {mx.all(c == 6.0).item()}")
Output:
.. code-block::
c shape: [3, 4]
c dtype: float32
c correctness: True
Results
^^^^^^^^^^^^^^^^
Let's run a quick benchmark and see how our new ``axpby`` operation compares
with the naive :meth:`simple_axpby` we defined at first on the CPU.
.. code-block:: python
import mlx.core as mx
from mlx_sample_extensions import axpby
import time
mx.set_default_device(mx.cpu)
def simple_axpby(x: mx.array, y: mx.array, alpha: float, beta: float) -> mx.array:
return alpha * x + beta * y
M = 256
N = 512
x = mx.random.normal((M, N))
y = mx.random.normal((M, N))
alpha = 4.0
beta = 2.0
mx.eval((x, y))
def bench(f):
# Warm up
for i in range(100):
z = f(x, y, alpha, beta)
mx.eval(z)
# Timed run
s = time.time()
for i in range(5000):
z = f(x, y, alpha, beta)
mx.eval(z)
e = time.time()
return e - s
simple_time = bench(simple_axpby)
custom_time = bench(axpby)
print(f"Simple axpby: {simple_time:.3f} s | Custom axpby: {custom_time:.3f} s")
Results:
.. code-block::
Simple axpby: 0.114 s | Custom axpby: 0.109 s
We see some modest improvements right away!
This operation is now good to be used to build other operations,
in :class:`mlx.nn.Module` calls, and also as a part of graph
transformations such as :meth:`grad` and :meth:`simplify`!
Scripts
-------
.. admonition:: Download the code
The full example code is available in `mlx-examples <code>`_.
.. _Accelerate: https://developer.apple.com/documentation/accelerate/blas?language=objc
.. _Metal: https://developer.apple.com/documentation/metal?language=objc
.. _PyBind11: https://pybind11.readthedocs.io/en/stable/