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#include <nanobind/nanobind.h> |
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#include <nanobind/stl/optional.h> |
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#include <nanobind/stl/pair.h> |
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#include <nanobind/stl/string.h> |
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#include <nanobind/stl/tuple.h> |
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#include <nanobind/stl/variant.h> |
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#include <nanobind/stl/vector.h> |
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#include "mlx/fast.h" |
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#include "mlx/ops.h" |
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#include "python/src/small_vector.h" |
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#include "python/src/utils.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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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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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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} |
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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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m.def( |
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"rms_norm", |
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&mx::fast::rms_norm, |
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"x"_a, |
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"weight"_a.none(), |
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"eps"_a, |
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nb::kw_only(), |
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"stream"_a = nb::none(), |
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nb::sig( |
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"def rms_norm(x: array, weight: Optional[array], eps: float, *, stream: Union[None, Stream, Device] = None) -> array"), |
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R"pbdoc( |
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Root Mean Square normalization (RMS norm). |
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The normalization is with respect to the last axis of the input ``x``. |
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Args: |
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x (array): Input array. |
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weight (array, optional): A multiplicative weight to scale the result by. |
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The ``weight`` should be one-dimensional with the same size |
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as the last axis of ``x``. If set to ``None`` then no scaling happens. |
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eps (float): A small additive constant for numerical stability. |
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Returns: |
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array: The output array. |
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)pbdoc"); |
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m.def( |
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"layer_norm", |
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&mx::fast::layer_norm, |
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"x"_a, |
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"weight"_a.none(), |
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"bias"_a.none(), |
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"eps"_a, |
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nb::kw_only(), |
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"stream"_a = nb::none(), |
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nb::sig( |
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"def layer_norm(x: array, weight: Optional[array], bias: Optional[array], eps: float, *, stream: Union[None, Stream, Device] = None) -> array"), |
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R"pbdoc( |
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Layer normalization. |
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The normalization is with respect to the last axis of the input ``x``. |
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Args: |
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x (array): Input array. |
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weight (array, optional): A multiplicative weight to scale the result by. |
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The ``weight`` should be one-dimensional with the same size |
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as the last axis of ``x``. If set to ``None`` then no scaling happens. |
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bias (array, optional): An additive offset to be added to the result. |
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The ``bias`` should be one-dimensional with the same size |
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as the last axis of ``x``. If set to ``None`` then no translation happens. |
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eps (float): A small additive constant for numerical stability. |
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Returns: |
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array: The output array. |
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)pbdoc"); |
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m.def( |
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"rope", |
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[](const mx::array& a, |
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int dims, |
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bool traditional, |
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std::optional<float> base, |
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float scale, |
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const ScalarOrArray& offset, |
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const std::optional<mx::array>& freqs , |
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mx::StreamOrDevice s ) { |
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return mx::fast::rope( |
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a, dims, traditional, base, scale, to_array(offset), freqs, s); |
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}, |
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"a"_a, |
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"dims"_a, |
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nb::kw_only(), |
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"traditional"_a, |
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"base"_a.none(), |
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"scale"_a, |
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"offset"_a, |
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"freqs"_a = nb::none(), |
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"stream"_a = nb::none(), |
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nb::sig( |
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"def rope(a: array, dims: int, *, traditional: bool, base: Optional[float], scale: float, offset: Union[int, array], freqs: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"), |
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R"pbdoc( |
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Apply rotary positional encoding to the input. |
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The input is expected to be at least 3D with shape ``(B, *, T, D)`` where: |
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* ``B`` is the batch size. |
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* ``T`` is the sequence length. |
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* ``D`` is the feature dimension. |
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Args: |
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a (array): The input array. |
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dims (int): The feature dimensions to be rotated. If the input feature |
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is larger than dims then the rest is left unchanged. |
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traditional (bool): If set to ``True`` choose the traditional |
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implementation which rotates consecutive dimensions. |
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base (float, optional): The base used to compute angular frequency for |
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each dimension in the positional encodings. Exactly one of ``base`` and |
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``freqs`` must be ``None``. |
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scale (float): The scale used to scale the positions. |
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offset (int or array): The position offset to start at. If an |
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:obj:`array` is given it can be a scalar or vector of ``B`` |
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offsets for each example in the batch. |
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freqs (array, optional): Optional frequencies to use with RoPE. |
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If set, the ``base`` parameter must be ``None``. Default: ``None``. |
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Returns: |
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array: The output array. |
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)pbdoc"); |
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m.def( |
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"scaled_dot_product_attention", |
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[](const mx::array& queries, |
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const mx::array& keys, |
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const mx::array& values, |
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const float scale, |
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const std::variant<std::monostate, std::string, mx::array>& mask, |
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const std::optional<mx::array>& sinks, |
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mx::StreamOrDevice s) { |
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bool has_mask = !std::holds_alternative<std::monostate>(mask); |
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bool has_str_mask = |
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has_mask && std::holds_alternative<std::string>(mask); |
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bool has_arr_mask = has_mask && std::holds_alternative<mx::array>(mask); |
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if (has_mask) { |
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if (has_str_mask) { |
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auto mask_str = std::get<std::string>(mask); |
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if (mask_str != "causal") { |
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std::ostringstream msg; |
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msg << "[scaled_dot_product_attention] invalid mask option '" |
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<< mask_str << "'. Must be 'causal', or an array."; |
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throw std::invalid_argument(msg.str()); |
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} |
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return mx::fast::scaled_dot_product_attention( |
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queries, keys, values, scale, mask_str, {}, sinks, s); |
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} else { |
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auto mask_arr = std::get<mx::array>(mask); |
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return mx::fast::scaled_dot_product_attention( |
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queries, keys, values, scale, "", {mask_arr}, sinks, s); |
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} |
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} else { |
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return mx::fast::scaled_dot_product_attention( |
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queries, keys, values, scale, "", {}, sinks, s); |
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} |
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}, |
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"q"_a, |
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"k"_a, |
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"v"_a, |
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nb::kw_only(), |
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"scale"_a, |
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"mask"_a = nb::none(), |
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"sinks"_a = nb::none(), |
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"stream"_a = nb::none(), |
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nb::sig( |
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"def scaled_dot_product_attention(q: array, k: array, v: array, *, scale: float, mask: Union[None, str, array] = None, sinks: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"), |
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R"pbdoc( |
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A fast implementation of multi-head attention: ``O = softmax(Q @ K.T, dim=-1) @ V``. |
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Supports: |
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* `Multi-Head Attention <https://arxiv.org/abs/1706.03762>`_ |
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* `Grouped Query Attention <https://arxiv.org/abs/2305.13245>`_ |
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* `Multi-Query Attention <https://arxiv.org/abs/1911.02150>`_ |
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.. note:: |
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* The softmax operation is performed in ``float32`` regardless of |
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the input precision. |
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* For Grouped Query Attention and Multi-Query Attention, the ``k`` |
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and ``v`` inputs should not be pre-tiled to match ``q``. |
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In the following the dimensions are given by: |
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* ``B``: The batch size. |
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* ``N_q``: The number of query heads. |
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* ``N_kv``: The number of key and value heads. |
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* ``T_q``: The number of queries per example. |
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* ``T_kv``: The number of keys and values per example. |
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* ``D``: The per-head dimension. |
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Args: |
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q (array): Queries with shape ``[B, N_q, T_q, D]``. |
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k (array): Keys with shape ``[B, N_kv, T_kv, D]``. |
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v (array): Values with shape ``[B, N_kv, T_kv, D]``. |
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scale (float): Scale for queries (typically ``1.0 / sqrt(q.shape(-1)``). |
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mask (str or array, optional): The mask to apply to the |
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query-key scores. The mask can be an array or a string indicating |
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the mask type. The only supported string type is ``"causal"``. If |
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the mask is an array it can be a boolean or additive mask. The mask |
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can have at most 4 dimensions and must be broadcast-compatible with |
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the shape ``[B, N, T_q, T_kv]``. If an additive mask is given its |
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type must promote to the promoted type of ``q``, ``k``, and ``v``. |
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sinks (array, optional): An optional array of attention sinks. |
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Default: ``None``. |
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Returns: |
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array: The output array. |
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Example: |
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.. code-block:: python |
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B = 2 |
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N_q = N_kv = 32 |
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T_q = T_kv = 1000 |
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D = 128 |
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q = mx.random.normal(shape=(B, N_q, T_q, D)) |
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k = mx.random.normal(shape=(B, N_kv, T_kv, D)) |
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v = mx.random.normal(shape=(B, N_kv, T_kv, D)) |
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scale = D ** -0.5 |
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out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask="causal") |
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)pbdoc"); |
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m.def( |
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"metal_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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bool atomic_outputs) { |
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auto kernel = mx::fast::metal_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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atomic_outputs); |
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return nb::cpp_function( |
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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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"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 Metal 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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This will be passed to ``MTLComputeCommandEncoder::dispatchThreads``. |
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threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use. |
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This will be passed to ``MTLComputeCommandEncoder::dispatchThreads``. |
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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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"atomic_outputs"_a = false, |
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R"pbdoc( |
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A jit-compiled custom Metal kernel defined from a source string. |
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Full documentation: :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 Metal, |
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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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atomic_outputs (bool): Whether to use atomic outputs in the function signature |
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e.g. ``device atomic<float>``. Default: ``False``. |
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Returns: |
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Callable ``metal_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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uint elem = thread_position_in_grid.x; |
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T tmp = inp[elem]; |
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out[elem] = metal::exp(tmp); |
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''' |
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kernel = mx.fast.metal_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=(4, 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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"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( |
|
|
Run the kernel. |
|
|
|
|
|
Args: |
|
|
inputs (List[array]): The inputs passed to the CUDA kernel. |
|
|
output_shapes (List[Sequence[int]]): The list of shapes for each output in ``output_names``. |
|
|
output_dtypes (List[Dtype]): The list of data types for each output in ``output_names``. |
|
|
grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with. |
|
|
For compatibility with :func:`metal_kernel` the grid is in threads and not in threadgroups. |
|
|
threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use. |
|
|
template (List[Tuple[str, Union[bool, int, Dtype]]], optional): Template arguments. |
|
|
These will be added as template arguments to the kernel definition. Default: ``None``. |
|
|
init_value (float, optional): Optional value to use to initialize all of the output arrays. |
|
|
By default, output arrays are uninitialized. Default: ``None``. |
|
|
verbose (bool, optional): Whether to print the full generated source code of the kernel |
|
|
when it is run. Default: ``False``. |
|
|
stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``. |
|
|
|
|
|
Returns: |
|
|
List[array]: The list of output arrays.)pbdoc"); |
|
|
}, |
|
|
"name"_a, |
|
|
"input_names"_a, |
|
|
"output_names"_a, |
|
|
"source"_a, |
|
|
"header"_a = "", |
|
|
"ensure_row_contiguous"_a = true, |
|
|
"shared_memory"_a = 0, |
|
|
R"pbdoc( |
|
|
A jit-compiled custom CUDA kernel defined from a source string. |
|
|
|
|
|
This is the CUDA equivalent of :ref:`custom_metal_kernels`. |
|
|
|
|
|
Args: |
|
|
name (str): Name for the kernel. |
|
|
input_names (List[str]): The parameter names of the inputs in the |
|
|
function signature. |
|
|
output_names (List[str]): The parameter names of the outputs in the |
|
|
function signature. |
|
|
source (str): Source code. This is the body of a function in CUDA, |
|
|
the function signature will be automatically generated. |
|
|
header (str): Header source code to include before the main function. |
|
|
Useful for helper functions or includes that should live outside of |
|
|
the main function body. |
|
|
ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous |
|
|
before the kernel runs. Default: ``True``. |
|
|
shared_memory (int): The dynamic shared memory to request for the |
|
|
kernel. A value of 0 means no dynamic shared memory. Default: ``0``. |
|
|
|
|
|
Returns: |
|
|
Callable ``cuda_kernel``. |
|
|
|
|
|
Example: |
|
|
|
|
|
.. code-block:: python |
|
|
|
|
|
def exp_elementwise(a: mx.array): |
|
|
source = ''' |
|
|
auto elem = cooperative_groups::this_grid().thread_rank(); |
|
|
T tmp = inp[elem]; |
|
|
out[elem] = exp(tmp); |
|
|
''' |
|
|
|
|
|
kernel = mx.fast.cuda_kernel( |
|
|
name="myexp", |
|
|
input_names=["inp"], |
|
|
output_names=["out"], |
|
|
source=source |
|
|
) |
|
|
outputs = kernel( |
|
|
inputs=[a], |
|
|
template=[("T", mx.float32)], |
|
|
grid=(a.size, 1, 1), |
|
|
threadgroup=(256, 1, 1), |
|
|
output_shapes=[a.shape], |
|
|
output_dtypes=[a.dtype], |
|
|
verbose=True, |
|
|
) |
|
|
return outputs[0] |
|
|
|
|
|
a = mx.random.normal(shape=(16, 16)).astype(mx.float16) |
|
|
b = exp_elementwise(a) |
|
|
assert mx.allclose(b, mx.exp(a)) |
|
|
)pbdoc"); |
|
|
|
|
|
m.def( |
|
|
"precompiled_cuda_kernel", |
|
|
[](const std::string& name, |
|
|
const nb::bytes compiled_source, |
|
|
const std::vector<ScalarOrArray>& inputs_, |
|
|
const std::vector<mx::Shape>& output_shapes, |
|
|
const std::vector<mx::Dtype>& output_dtypes, |
|
|
const std::vector<nb::object>& scalars_, |
|
|
std::tuple<int, int, int> grid, |
|
|
std::tuple<int, int, int> threadgroup, |
|
|
int shared_memory, |
|
|
std::optional<float> init_value = std::nullopt, |
|
|
bool ensure_row_contiguous = false, |
|
|
mx::StreamOrDevice s = {}) { |
|
|
|
|
|
std::vector<mx::array> inputs; |
|
|
for (const auto& value : inputs_) { |
|
|
inputs.push_back(to_array(value, std::nullopt)); |
|
|
} |
|
|
|
|
|
|
|
|
std::vector<mx::fast::ScalarArg> scalars; |
|
|
scalars.reserve(scalars_.size()); |
|
|
for (const auto& v : scalars_) { |
|
|
if (nb::isinstance<bool>(v)) { |
|
|
scalars.push_back(nb::cast<bool>(v)); |
|
|
} else if (nb::isinstance<int>(v)) { |
|
|
scalars.push_back(nb::cast<int>(v)); |
|
|
} else if (nb::isinstance<float>(v)) { |
|
|
scalars.push_back(nb::cast<float>(v)); |
|
|
} else { |
|
|
nb::object vtype = v.attr("__class__"); |
|
|
std::string vtype_name = |
|
|
nb::cast<std::string>(vtype.attr("__name__")); |
|
|
std::ostringstream msg; |
|
|
msg << "[precompiled_cuda_kernel] Invalid scalar argument type. " |
|
|
<< "Received " << vtype_name |
|
|
<< " but must be one of bool, int or float"; |
|
|
throw std::invalid_argument(msg.str()); |
|
|
} |
|
|
} |
|
|
|
|
|
return mx::fast::precompiled_cuda_kernel( |
|
|
name, |
|
|
std::string( |
|
|
static_cast<const char*>(compiled_source.data()), |
|
|
compiled_source.size()), |
|
|
inputs, |
|
|
output_shapes, |
|
|
output_dtypes, |
|
|
scalars, |
|
|
grid, |
|
|
threadgroup, |
|
|
shared_memory, |
|
|
init_value, |
|
|
ensure_row_contiguous, |
|
|
s); |
|
|
}, |
|
|
nb::kw_only(), |
|
|
"name"_a, |
|
|
"compiled_source"_a, |
|
|
"inputs"_a, |
|
|
"output_shapes"_a, |
|
|
"output_dtypes"_a, |
|
|
"scalars"_a, |
|
|
"grid"_a, |
|
|
"threadgroup"_a, |
|
|
"shared_memory"_a = 0, |
|
|
"init_value"_a = nb::none(), |
|
|
"ensure_row_contiguous"_a = false, |
|
|
"stream"_a = nb::none(), |
|
|
R"pbdoc( |
|
|
Run a precompiled CUDA kernel defined from PTX or cubin. |
|
|
|
|
|
This op is still experimental and various parts of the API may change. |
|
|
|
|
|
Args: |
|
|
name (str): Name for the kernel |
|
|
compiled_source (bytes): The precompiled kernel in raw bytes. |
|
|
inputs (List[array]): The inputs passed to the CUDA kernel. |
|
|
output_shapes (List[Sequence[int]]): The list of shapes for each output. |
|
|
output_dtypes (List[Dtype]): The list of data types for each output. |
|
|
scalars (List[Union[bool, int, float]]): A list of scalar arguments to |
|
|
pass to the kernel. |
|
|
grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with. |
|
|
For compatibility with :func:`metal_kernel` the grid is in threads and not in threadblocks. |
|
|
threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use. |
|
|
shared_memory (int): The dynamic shared memory to request for the |
|
|
kernel. A value of 0 means no dynamic shared memory. Default: ``0``. |
|
|
init_value (float, optional): Optional value to use to initialize all of the output arrays. |
|
|
By default, output arrays are uninitialized. Default: ``None``. |
|
|
ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous |
|
|
before the kernel runs. Default: ``False``. |
|
|
stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``. |
|
|
)pbdoc"); |
|
|
} |
|
|
|
