mirror of
https://github.com/ml-explore/mlx.git
synced 2025-09-05 07:34:42 +08:00
A bit of refactoring
This commit is contained in:
Angelos Katharopoulos
@@ -17,6 +17,66 @@ namespace mx = mlx::core;
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namespace nb = nanobind;
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using namespace nb::literals;
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namespace {
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struct PyCustomKernelFunction {
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PyCustomKernelFunction(mx::fast::CustomKernelFunction kernel, const char* tag)
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: kernel_(std::move(kernel)), tag_(tag) {}
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std::vector<mx::array> operator()(
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const std::vector<ScalarOrArray>& inputs_,
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const std::vector<mx::Shape>& output_shapes,
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const std::vector<mx::Dtype>& output_dtypes,
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std::tuple<int, int, int> grid,
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std::tuple<int, int, int> threadgroup,
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const std::optional<std::vector<std::pair<std::string, nb::object>>>&
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template_args_ = std::nullopt,
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std::optional<float> init_value = std::nullopt,
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bool verbose = false,
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mx::StreamOrDevice s = {}) const {
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std::vector<mx::array> inputs;
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for (const auto& value : inputs_) {
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inputs.push_back(to_array(value, std::nullopt));
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}
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std::vector<std::pair<std::string, mx::fast::TemplateArg>> template_args;
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if (template_args_) {
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for (const auto& [name, value] : template_args_.value()) {
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// Handle bool, int and dtype template args
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if (nb::isinstance<bool>(value)) {
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bool bool_val = nb::cast<bool>(value);
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template_args.emplace_back(name, bool_val);
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} else if (nb::isinstance<int>(value)) {
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int int_val = nb::cast<int>(value);
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template_args.emplace_back(name, int_val);
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} else if (nb::isinstance<mx::Dtype>(value)) {
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mx::Dtype dtype = nb::cast<mx::Dtype>(value);
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template_args.emplace_back(name, dtype);
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} else {
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std::ostringstream msg;
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msg << tag_
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<< " Invalid template argument. Must be `mlx.core.Dtype`, `int` or `bool`.";
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throw std::invalid_argument(msg.str());
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}
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}
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}
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return kernel_(
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inputs,
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output_shapes,
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output_dtypes,
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grid,
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threadgroup,
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template_args,
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init_value,
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verbose,
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s);
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}
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mx::fast::CustomKernelFunction kernel_;
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const char* tag_;
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};
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} // namespace
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void init_fast(nb::module_& parent_module) {
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auto m =
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parent_module.def_submodule("fast", "mlx.core.fast: fast operations");
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@@ -240,53 +300,7 @@ void init_fast(nb::module_& parent_module) {
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ensure_row_contiguous,
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atomic_outputs);
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return nb::cpp_function(
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[kernel = std::move(kernel)](
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const std::vector<ScalarOrArray>& inputs_,
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const std::vector<mx::Shape>& output_shapes,
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const std::vector<mx::Dtype>& output_dtypes,
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std::tuple<int, int, int> grid,
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std::tuple<int, int, int> threadgroup,
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const std::optional<
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std::vector<std::pair<std::string, nb::object>>>&
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template_args_ = std::nullopt,
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std::optional<float> init_value = std::nullopt,
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bool verbose = false,
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mx::StreamOrDevice s = {}) {
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std::vector<mx::array> inputs;
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for (const auto& value : inputs_) {
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inputs.push_back(to_array(value, std::nullopt));
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}
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std::vector<std::pair<std::string, mx::fast::TemplateArg>>
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template_args;
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if (template_args_) {
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for (const auto& [name, value] : template_args_.value()) {
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// Handle bool, int and dtype template args
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if (nb::isinstance<bool>(value)) {
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bool bool_val = nb::cast<bool>(value);
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template_args.emplace_back(name, bool_val);
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} else if (nb::isinstance<int>(value)) {
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int int_val = nb::cast<int>(value);
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template_args.emplace_back(name, int_val);
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} else if (nb::isinstance<mx::Dtype>(value)) {
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mx::Dtype dtype = nb::cast<mx::Dtype>(value);
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template_args.emplace_back(name, dtype);
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} else {
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throw std::invalid_argument(
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"[metal_kernel] Invalid template argument. Must be `mlx.core.Dtype`, `int` or `bool`.");
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}
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}
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}
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return kernel(
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inputs,
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output_shapes,
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output_dtypes,
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grid,
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threadgroup,
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template_args,
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init_value,
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verbose,
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s);
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},
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PyCustomKernelFunction(std::move(kernel), "[metal_kernel]"),
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nb::kw_only(),
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"inputs"_a,
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"output_shapes"_a,
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@@ -386,7 +400,123 @@ void init_fast(nb::module_& parent_module) {
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)pbdoc");
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m.def(
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"precompiled_custom_kernel",
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"cuda_kernel",
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[](const std::string& name,
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const std::vector<std::string>& input_names,
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const std::vector<std::string>& output_names,
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const std::string& source,
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const std::string& header,
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bool ensure_row_contiguous,
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int shared_mem) {
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auto kernel = mx::fast::cuda_kernel(
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name,
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input_names,
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output_names,
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source,
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header,
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ensure_row_contiguous,
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shared_mem);
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return nb::cpp_function(
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PyCustomKernelFunction(std::move(kernel), "[cuda_kernel]"),
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nb::kw_only(),
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"inputs"_a,
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"output_shapes"_a,
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"output_dtypes"_a,
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"grid"_a,
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"threadgroup"_a,
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"template"_a = nb::none(),
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"init_value"_a = nb::none(),
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"verbose"_a = false,
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"stream"_a = nb::none(),
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nb::sig(
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"def __call__(self, *, inputs: List[Union[scalar, array]], output_shapes: List[Sequence[int]], output_dtypes: List[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: Optional[List[Tuple[str, Union[bool, int, Dtype]]]] = None, init_value: Optional[float] = None, verbose: bool = false, stream: Union[None, Stream, Device] = None)"),
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R"pbdoc(
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Run the kernel.
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Args:
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inputs (List[array]): The inputs passed to the CUDA kernel.
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output_shapes (List[Sequence[int]]): The list of shapes for each output in ``output_names``.
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output_dtypes (List[Dtype]): The list of data types for each output in ``output_names``.
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grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with.
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For compatibility with :func:`metal_kernel` the grid is in threads and not in threadblocks.
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threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use.
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template (List[Tuple[str, Union[bool, int, Dtype]]], optional): Template arguments.
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These will be added as template arguments to the kernel definition. Default: ``None``.
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init_value (float, optional): Optional value to use to initialize all of the output arrays.
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By default, output arrays are uninitialized. Default: ``None``.
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verbose (bool, optional): Whether to print the full generated source code of the kernel
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when it is run. Default: ``False``.
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stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``.
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Returns:
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List[array]: The list of output arrays.)pbdoc");
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},
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"name"_a,
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"input_names"_a,
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"output_names"_a,
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"source"_a,
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"header"_a = "",
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"ensure_row_contiguous"_a = true,
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"shared_memory"_a = 0,
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R"pbdoc(
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A jit-compiled custom CUDA kernel defined from a source string.
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This is the CUDA equivalent of :ref:`custom_metal_kernels`.
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Args:
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name (str): Name for the kernel.
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input_names (List[str]): The parameter names of the inputs in the
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function signature.
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output_names (List[str]): The parameter names of the outputs in the
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function signature.
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source (str): Source code. This is the body of a function in CUDA,
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the function signature will be automatically generated.
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header (str): Header source code to include before the main function.
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Useful for helper functions or includes that should live outside of
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the main function body.
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ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous
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before the kernel runs. Default: ``True``.
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shared_memory (int): The dynamic shared memory to request for the
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kernel. A value of 0 means no dynamic shared memory. Default: ``0``.
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Returns:
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Callable ``cuda_kernel``.
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Example:
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.. code-block:: python
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def exp_elementwise(a: mx.array):
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source = '''
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auto elem = cooperative_groups::this_grid().thread_rank();
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T tmp = inp[elem];
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out[elem] = exp(tmp);
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'''
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kernel = mx.fast.cuda_kernel(
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name="myexp",
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input_names=["inp"],
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output_names=["out"],
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source=source
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)
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outputs = kernel(
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inputs=[a],
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template=[("T", mx.float32)],
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grid=(a.size, 1, 1),
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threadgroup=(256, 1, 1),
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output_shapes=[a.shape],
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output_dtypes=[a.dtype],
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verbose=True,
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)
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return outputs[0]
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a = mx.random.normal(shape=(16, 16)).astype(mx.float16)
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b = exp_elementwise(a)
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assert mx.allclose(b, mx.exp(a))
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)pbdoc");
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m.def(
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"precompiled_cuda_kernel",
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[](const std::string& name,
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const nb::bytes compiled_source,
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const std::vector<ScalarOrArray>& inputs_,
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@@ -420,14 +550,14 @@ void init_fast(nb::module_& parent_module) {
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std::string vtype_name =
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nb::cast<std::string>(vtype.attr("__name__"));
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std::ostringstream msg;
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msg << "[precompiled_custom_kernel] Invalid scalar argument type. "
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msg << "[precompiled_cuda_kernel] Invalid scalar argument type. "
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<< "Received " << vtype_name
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<< " but must be one of bool, int or float";
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throw std::invalid_argument(msg.str());
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}
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}
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return mx::fast::precompiled_custom_kernel(
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return mx::fast::precompiled_cuda_kernel(
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name,
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std::string(
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static_cast<const char*>(compiled_source.data()),
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@@ -457,5 +587,27 @@ void init_fast(nb::module_& parent_module) {
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"ensure_row_contiguous"_a = false,
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"stream"_a = nb::none(),
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R"pbdoc(
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Run a precompiled CUDA kernel defined from PTX or cubin.
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This op is still experimental and various parts of the API may change.
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Args:
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name (str): Name for the kernel
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compiled_source (bytes): The precompiled kernel in raw bytes.
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inputs (List[array]): The inputs passed to the CUDA kernel.
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output_shapes (List[Sequence[int]]): The list of shapes for each output.
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output_dtypes (List[Dtype]): The list of data types for each output.
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scalars (List[Union[bool, int, float]]): A list of scalar arguments to
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pass to the kernel.
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grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with.
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For compatibility with :func:`metal_kernel` the grid is in threads and not in threadblocks.
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threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use.
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shared_memory (int): The dynamic shared memory to request for the
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kernel. A value of 0 means no dynamic shared memory. Default: ``0``.
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init_value (float, optional): Optional value to use to initialize all of the output arrays.
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By default, output arrays are uninitialized. Default: ``None``.
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ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous
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before the kernel runs. Default: ``False``.
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stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``.
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)pbdoc");
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}
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