mirror of
https://github.com/ml-explore/mlx.git
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339 lines
11 KiB
C++
339 lines
11 KiB
C++
// Copyright © 2024 Apple Inc.
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#include <nanobind/nanobind.h>
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#include <nanobind/stl/map.h>
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#include <nanobind/stl/optional.h>
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#include <nanobind/stl/string.h>
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#include <nanobind/stl/vector.h>
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#include <fstream>
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#include "mlx/array.h"
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#include "mlx/export.h"
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#include "mlx/graph_utils.h"
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#include "python/src/trees.h"
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namespace mx = mlx::core;
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namespace nb = nanobind;
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using namespace nb::literals;
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std::pair<std::vector<mx::array>, std::map<std::string, mx::array>>
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validate_and_extract_inputs(
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const nb::args& args,
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const nb::kwargs& kwargs,
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const std::string& prefix) {
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auto maybe_throw = [&prefix](bool valid) {
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if (!valid) {
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throw std::invalid_argument(
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prefix +
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" Inputs can either be a variable "
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"number of positional and keyword arrays or a single tuple "
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"and/or dictionary of arrays.");
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}
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};
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std::vector<mx::array> args_;
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std::map<std::string, mx::array> kwargs_;
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if (args.size() == 0) {
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// No args so kwargs must be keyword arrays
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maybe_throw(nb::try_cast(kwargs, kwargs_));
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} else if (args.size() > 0 && nb::isinstance<mx::array>(args[0])) {
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// Args are positional arrays and kwargs are keyword arrays
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maybe_throw(nb::try_cast(args, args_));
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maybe_throw(nb::try_cast(kwargs, kwargs_));
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} else if (args.size() == 1) {
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// - args[0] can be a tuple or list or arrays or a dict
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// with string keys and array values
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// - kwargs should be empty
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maybe_throw(kwargs.size() == 0);
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if (!nb::try_cast(args[0], args_)) {
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maybe_throw(nb::try_cast(args[0], kwargs_));
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}
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} else if (args.size() == 2) {
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// - args[0] can be a tuple or list of arrays
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// - args[1] can be a dict of string keys with array values.
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// - kwargs should be empty
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maybe_throw(kwargs.size() == 0);
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maybe_throw(nb::try_cast(args[0], args_));
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maybe_throw(nb::try_cast(args[1], kwargs_));
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} else {
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maybe_throw(false);
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}
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return {args_, kwargs_};
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}
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int py_function_exporter_tp_traverse(
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PyObject* self,
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visitproc visit,
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void* arg);
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class PyFunctionExporter {
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public:
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PyFunctionExporter(mx::FunctionExporter exporter, nb::handle dep)
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: exporter_(std::move(exporter)), dep_(dep) {}
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~PyFunctionExporter() {
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nb::gil_scoped_acquire gil;
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}
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PyFunctionExporter(const PyFunctionExporter&) = delete;
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PyFunctionExporter& operator=(const PyFunctionExporter&) = delete;
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PyFunctionExporter& operator=(const PyFunctionExporter&&) = delete;
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PyFunctionExporter(PyFunctionExporter&& other)
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: exporter_(std::move(other.exporter_)), dep_(std::move(other.dep_)) {}
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void close() {
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exporter_.close();
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}
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void operator()(
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const std::vector<mx::array>& args,
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const std::map<std::string, mx::array>& kwargs) {
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exporter_(args, kwargs);
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}
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friend int py_function_exporter_tp_traverse(PyObject*, visitproc, void*);
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private:
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mx::FunctionExporter exporter_;
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nb::handle dep_;
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};
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int py_function_exporter_tp_traverse(
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PyObject* self,
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visitproc visit,
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void* arg) {
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auto* p = nb::inst_ptr<PyFunctionExporter>(self);
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Py_VISIT(p->dep_.ptr());
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Py_VISIT(Py_TYPE(self));
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return 0;
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}
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PyType_Slot py_function_exporter_slots[] = {
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{Py_tp_traverse, (void*)py_function_exporter_tp_traverse},
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{0, 0}};
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auto wrap_export_function(nb::callable fun) {
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return [fun = std::move(fun)](
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const std::vector<mx::array>& args_,
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const std::map<std::string, mx::array>& kwargs_) {
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auto kwargs = nb::dict();
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kwargs.update(nb::cast(kwargs_));
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auto args = nb::tuple(nb::cast(args_));
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auto outputs = fun(*args, **kwargs);
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std::vector<mx::array> outputs_;
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if (nb::isinstance<mx::array>(outputs)) {
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outputs_.push_back(nb::cast<mx::array>(outputs));
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} else if (!nb::try_cast(outputs, outputs_)) {
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throw std::invalid_argument(
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"[export_function] Outputs can be either a single array "
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"a tuple or list of arrays.");
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}
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return outputs_;
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};
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}
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void init_export(nb::module_& m) {
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m.def(
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"export_function",
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[](const std::string& file,
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const nb::callable& fun,
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const nb::args& args,
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bool shapeless,
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const nb::kwargs& kwargs) {
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auto [args_, kwargs_] =
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validate_and_extract_inputs(args, kwargs, "[export_function]");
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mx::export_function(
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file, wrap_export_function(fun), args_, kwargs_, shapeless);
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},
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"file"_a,
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"fun"_a,
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"args"_a,
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nb::kw_only(),
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"shapeless"_a = false,
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"kwargs"_a,
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R"pbdoc(
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Export a function to a file.
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Example input arrays must be provided to export a function. The example
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inputs can be variable ``*args`` and ``**kwargs`` or a tuple of arrays
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and/or dictionary of string keys with array values.
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.. warning::
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This is part of an experimental API which is likely to
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change in future versions of MLX. Functions exported with older
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versions of MLX may not be compatible with future versions.
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Args:
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file (str): File path to export the function to.
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fun (Callable): A function which takes as input zero or more
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:class:`array` and returns one or more :class:`array`.
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*args (array): Example array inputs to the function.
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shapeless (bool, optional): Whether or not the function allows
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inputs with variable shapes. Default: ``False``.
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**kwargs (array): Additional example keyword array inputs to the
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function.
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Example:
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.. code-block:: python
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def fun(x, y):
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return x + y
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x = mx.array(1)
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y = mx.array([1, 2, 3])
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mx.export_function("fun.mlxfn", fun, x, y=y)
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)pbdoc");
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m.def(
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"import_function",
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[](const std::string& file) {
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return nb::cpp_function(
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[fn = mx::import_function(file)](
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const nb::args& args, const nb::kwargs& kwargs) {
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auto [args_, kwargs_] = validate_and_extract_inputs(
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args, kwargs, "[import_function::call]");
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return nb::tuple(nb::cast(fn(args_, kwargs_)));
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});
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},
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"file"_a,
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nb::sig("def import_function(file: str) -> Callable"),
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R"pbdoc(
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Import a function from a file.
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The imported function can be called either with ``*args`` and
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``**kwargs`` or with a tuple of arrays and/or dictionary of string
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keys with array values. Imported functions always return a tuple of
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arrays.
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.. warning::
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This is part of an experimental API which is likely to
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change in future versions of MLX. Functions exported with older
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versions of MLX may not be compatible with future versions.
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Args:
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file (str): The file path to import the function from.
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Returns:
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Callable: The imported function.
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Example:
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>>> fn = mx.import_function("function.mlxfn")
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>>> out = fn(a, b, x=x, y=y)[0]
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>>>
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>>> out = fn((a, b), {"x": x, "y": y}[0]
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)pbdoc");
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nb::class_<PyFunctionExporter>(
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m,
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"FunctionExporter",
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nb::type_slots(py_function_exporter_slots),
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R"pbdoc(
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A context managing class for exporting multiple traces of the same
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function to a file.
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Make an instance of this class by calling fun:`mx.exporter`.
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)pbdoc")
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.def("close", &PyFunctionExporter::close)
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.def("__enter__", [](PyFunctionExporter& exporter) { return &exporter; })
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.def(
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"__exit__",
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[](PyFunctionExporter& exporter,
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const std::optional<nb::object>&,
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const std::optional<nb::object>&,
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const std::optional<nb::object>&) { exporter.close(); },
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"exc_type"_a = nb::none(),
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"exc_value"_a = nb::none(),
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"traceback"_a = nb::none())
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.def(
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"__call__",
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[](PyFunctionExporter& exporter,
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const nb::args& args,
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const nb::kwargs& kwargs) {
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auto [args_, kwargs_] =
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validate_and_extract_inputs(args, kwargs, "[export_function]");
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exporter(args_, kwargs_);
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});
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m.def(
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"exporter",
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[](const std::string& file, nb::callable fun, bool shapeless) {
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return PyFunctionExporter{
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mx::exporter(file, wrap_export_function(fun), shapeless), fun};
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},
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"file"_a,
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"fun"_a,
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nb::kw_only(),
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"shapeless"_a = false,
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R"pbdoc(
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Make a callable object to export multiple traces of a function to a file.
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.. warning::
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This is part of an experimental API which is likely to
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change in future versions of MLX. Functions exported with older
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versions of MLX may not be compatible with future versions.
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Args:
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file (str): File path to export the function to.
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shapeless (bool, optional): Whether or not the function allows
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inputs with variable shapes. Default: ``False``.
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Example:
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.. code-block:: python
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def fun(*args):
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return sum(args)
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with mx.exporter("fun.mlxfn", fun) as exporter:
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exporter(mx.array(1))
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exporter(mx.array(1), mx.array(2))
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exporter(mx.array(1), mx.array(2), mx.array(3))
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)pbdoc");
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m.def(
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"export_to_dot",
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[](nb::object file, const nb::args& args, const nb::kwargs& kwargs) {
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std::vector<mx::array> arrays =
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tree_flatten(nb::make_tuple(args, kwargs));
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mx::NodeNamer namer;
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for (const auto& n : kwargs) {
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namer.set_name(
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nb::cast<mx::array>(n.second), nb::cast<std::string>(n.first));
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}
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if (nb::isinstance<nb::str>(file)) {
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std::ofstream out(nb::cast<std::string>(file));
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mx::export_to_dot(out, std::move(namer), arrays);
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} else if (nb::hasattr(file, "write")) {
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std::ostringstream out;
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mx::export_to_dot(out, std::move(namer), arrays);
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auto write = file.attr("write");
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write(out.str());
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} else {
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throw std::invalid_argument(
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"[export_to_dot] Accepts file-like objects or strings "
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"to be used as filenames.");
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}
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},
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"file"_a,
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"args"_a,
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"kwargs"_a,
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R"pbdoc(
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Export a graph to DOT format for visualization.
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A variable number of output arrays can be provided for exporting
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The graph exported will recursively include all unevaluated inputs of
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the provided outputs.
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Args:
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file (str): The file path to export to.
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*args (array): The output arrays.
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**kwargs (dict[str, array]): Provide some names for arrays in the
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graph to make the result easier to parse.
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Example:
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>>> a = mx.array(1) + mx.array(2)
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>>> mx.export_to_dot("graph.dot", a)
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>>> x = mx.array(1)
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>>> y = mx.array(2)
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>>> mx.export_to_dot("graph.dot", x + y, x=x, y=y)
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)pbdoc");
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}
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