mirror of
https://github.com/ml-explore/mlx.git
synced 2025-12-16 01:49:05 +08:00
binary => binary_two in binary_two.cu
This commit is contained in:
@@ -19,7 +19,7 @@ namespace cg = cooperative_groups;
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template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
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__global__ void
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binary_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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binary_two_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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IdxT index = cg::this_grid().thread_rank();
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int remaining = size - index * N_READS;
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if (remaining <= 0) {
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@@ -50,7 +50,7 @@ binary_ss(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
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__global__ void
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binary_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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binary_two_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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IdxT index = cg::this_grid().thread_rank();
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int remaining = size - index * N_READS;
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if (remaining <= 0) {
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@@ -83,7 +83,7 @@ binary_sv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
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__global__ void
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binary_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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binary_two_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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IdxT index = cg::this_grid().thread_rank();
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int remaining = size - index * N_READS;
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if (remaining <= 0) {
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@@ -116,7 +116,7 @@ binary_vs(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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template <typename Op, typename In, typename Out, typename IdxT, int N_READS>
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__global__ void
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binary_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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binary_two_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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IdxT index = cg::this_grid().thread_rank();
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int remaining = size - index * N_READS;
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if (remaining <= 0) {
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@@ -149,7 +149,7 @@ binary_vv(const In* a, const In* b, Out* out_a, Out* out_b, IdxT size) {
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}
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template <typename Op, typename In, typename Out, typename IdxT, int NDIM>
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__global__ void binary_g_nd(
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__global__ void binary_two_g_nd(
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const In* a,
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const In* b,
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Out* out_a,
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@@ -169,7 +169,7 @@ __global__ void binary_g_nd(
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}
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template <typename Op, typename In, typename Out, typename IdxT>
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__global__ void binary_g(
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__global__ void binary_two_g(
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const In* a,
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const In* b,
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Out* out_a,
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@@ -190,7 +190,7 @@ __global__ void binary_g(
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}
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template <typename Op, typename In, typename Out>
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constexpr bool supports_binary_op() {
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constexpr bool supports_binary_two_op() {
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if (std::is_same_v<Op, DivMod>) {
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return std::is_same_v<In, Out> &&
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(std::is_integral_v<Out> || is_floating_v<Out>);
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@@ -201,7 +201,7 @@ constexpr bool supports_binary_op() {
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} // namespace cu
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template <typename Op>
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void binary_op_gpu_inplace(
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void binary_two_op_gpu_inplace(
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const std::vector<array>& inputs,
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std::vector<array>& outputs,
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std::string_view op,
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@@ -228,7 +228,7 @@ void binary_op_gpu_inplace(
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dispatch_all_types(out_a.dtype(), [&](auto out_type_tag) {
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using CTYPE_IN = MLX_GET_TYPE(in_type_tag);
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using CTYPE_OUT = MLX_GET_TYPE(out_type_tag);
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if constexpr (cu::supports_binary_op<Op, CTYPE_IN, CTYPE_OUT>()) {
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if constexpr (cu::supports_binary_two_op<Op, CTYPE_IN, CTYPE_OUT>()) {
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using InType = cuda_type_t<CTYPE_IN>;
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using OutType = cuda_type_t<CTYPE_OUT>;
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@@ -248,8 +248,12 @@ void binary_op_gpu_inplace(
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int ndim = shape.size();
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if (ndim <= 3) {
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dispatch_1_2_3(ndim, [&](auto dims_constant) {
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auto kernel = cu::
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binary_g_nd<Op, InType, OutType, IdxT, dims_constant()>;
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auto kernel = cu::binary_two_g_nd<
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Op,
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InType,
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OutType,
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IdxT,
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dims_constant()>;
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auto [num_blocks, block_dims] =
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get_launch_args(kernel, out_a, large());
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encoder.add_kernel_node(
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@@ -266,7 +270,7 @@ void binary_op_gpu_inplace(
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const_param<dims_constant()>(b_strides));
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});
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} else {
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auto kernel = cu::binary_g<Op, InType, OutType, IdxT>;
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auto kernel = cu::binary_two_g<Op, InType, OutType, IdxT>;
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auto [num_blocks, block_dims] =
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get_launch_args(kernel, out_a, large());
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encoder.add_kernel_node(
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@@ -289,13 +293,13 @@ void binary_op_gpu_inplace(
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using IdxT = std::conditional_t<large(), int64_t, int32_t>;
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// TODO: Choose optimized value based on type size.
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constexpr int N_READS = 4;
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auto kernel = cu::binary_ss<Op, InType, OutType, IdxT, N_READS>;
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auto kernel = cu::binary_two_ss<Op, InType, OutType, IdxT, N_READS>;
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if (bopt == BinaryOpType::ScalarVector) {
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kernel = cu::binary_sv<Op, InType, OutType, IdxT, N_READS>;
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kernel = cu::binary_two_sv<Op, InType, OutType, IdxT, N_READS>;
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} else if (bopt == BinaryOpType::VectorScalar) {
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kernel = cu::binary_vs<Op, InType, OutType, IdxT, N_READS>;
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kernel = cu::binary_two_vs<Op, InType, OutType, IdxT, N_READS>;
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} else if (bopt == BinaryOpType::VectorVector) {
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kernel = cu::binary_vv<Op, InType, OutType, IdxT, N_READS>;
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kernel = cu::binary_two_vv<Op, InType, OutType, IdxT, N_READS>;
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}
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auto [num_blocks, block_dims] = get_launch_args(
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kernel,
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@@ -327,7 +331,7 @@ void binary_op_gpu_inplace(
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}
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template <typename Op>
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void binary_op_gpu(
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void binary_two_op_gpu(
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const std::vector<array>& inputs,
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std::vector<array>& outputs,
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std::string_view op,
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@@ -337,7 +341,7 @@ void binary_op_gpu(
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auto bopt = get_binary_op_type(a, b);
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set_binary_op_output_data(a, b, outputs[0], bopt);
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set_binary_op_output_data(a, b, outputs[1], bopt);
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binary_op_gpu_inplace<Op>(inputs, outputs, op, s);
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binary_two_op_gpu_inplace<Op>(inputs, outputs, op, s);
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}
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void DivMod::eval_gpu(
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@@ -345,7 +349,7 @@ void DivMod::eval_gpu(
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std::vector<array>& outputs) {
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nvtx3::scoped_range r("DivMod::eval_gpu");
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auto& s = outputs[0].primitive().stream();
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binary_op_gpu<cu::DivMod>(inputs, outputs, get_primitive_string(this), s);
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binary_two_op_gpu<cu::DivMod>(inputs, outputs, get_primitive_string(this), s);
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}
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} // namespace mlx::core
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