[CK_TILE] Support for elementwise kernel

CHANGELOG.md

@@ -19,6 +19,7 @@ Documentation for Composable Kernel available at [https://rocm.docs.amd.com/proj
 * Added logit soft-capping support for fMHA forward kernels.
 * Added benchmarking support for tile engine GEMM.
 * Added rotating buffer feature for CK_Tile GEMM.
+* Added support for elementwise kernel.
 
 ### Optimized
 

example/ck_tile/19_elementwise/CMakeLists.txt

@@ -0,0 +1,10 @@
+# Elementwise example targets 2D inputs
+set(TARGET_NAME_2D_INPUT tile_example_elementwise)
+add_executable(${TARGET_NAME_2D_INPUT} elementwise_example.cpp)
+
+# Elementwise unary example targets 2D inputs
+set(TARGET_NAME_2D_INPUT_UNARY tile_example_elementwise_unary)
+add_executable(${TARGET_NAME_2D_INPUT_UNARY} elementwise_example_unary.cpp)
+
+set(TARGET_NAME_2D_INPUT_TRANSPOSE tile_example_elementwise_transpose)
+add_executable(${TARGET_NAME_2D_INPUT_TRANSPOSE} elementwise_example_transpose.cpp)

example/ck_tile/19_elementwise/elementwise_example.cpp

@@ -0,0 +1,211 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck_tile/core/arch/arch.hpp"
+#include "ck_tile/host.hpp"
+#include "ck_tile/ops/elementwise.hpp"
+#include "ck_tile/host/reference/reference_elementwise.hpp"
+
+auto create_args(int argc, char* argv[])
+{
+    ck_tile::ArgParser arg_parser;
+    arg_parser.insert("m", "1024", "m dimension")
+        .insert("n", "1024", "n dimension")
+        .insert("stride", "-1", "stride per row, if -1 then equal to n")
+        .insert("v", "1", "cpu validation or not")
+        .insert("prec", "fp16", "precision")
+        .insert("warmup", "10", "cold iter")
+        .insert("repeat", "50", "hot iter");
+
+    bool result = arg_parser.parse(argc, argv);
+    return std::make_tuple(result, arg_parser);
+}
+
+template <typename DataType>
+bool run(const ck_tile::ArgParser& arg_parser)
+{
+    ck_tile::index_t M      = arg_parser.get_int("m");
+    ck_tile::index_t N      = arg_parser.get_int("n");
+    ck_tile::index_t stride = arg_parser.get_int("stride");
+
+    // If stride is negative (default -1), set it to N, assuming a dense row-major layout.
+    if(stride < 0)
+        stride = N;
+    std::string data_type = arg_parser.get_str("prec");
+    int do_validation     = arg_parser.get_int("v");
+    int warmup            = arg_parser.get_int("warmup");
+    int repeat            = arg_parser.get_int("repeat");
+
+    if(stride < N)
+    {
+        throw std::runtime_error("stride must be >= N");
+    }
+
+    // Define type aliases for clarity.
+    // XDataType: Data type of the input tensors.
+    // ComputeDataType: Data type used for intermediate computations (often float for precision).
+    // YDataType: Data type of the output tensor.
+    // XElementwiseOperation: The specific elementwise operation to perform (e.g., Add, Mul).
+    using XDataType = DataType;
+    using ComputeDataType =
+        float; // Using float for intermediate calculations can improve numerical stability.
+    using YDataType             = DataType;
+    using XElementwiseOperation = ck_tile::element_wise::Add;
+
+    // 1. Initialize the input data on the host (CPU).
+    // HostTensor is a utility to manage tensor data on the CPU.
+    // The first argument is the shape (dimensions) of the tensor {M, N}.
+    // The second argument is the strides {stride, 1} for row-major layout.
+    // 'x_host_a' and 'x_host_b' are the two input tensors for the elementwise operation.
+    ck_tile::HostTensor<XDataType> x_host_a({M, N}, {stride, 1});
+    ck_tile::HostTensor<XDataType> x_host_b({M, N}, {stride, 1});
+    ck_tile::HostTensor<YDataType> y_host({M, N}, {stride, 1});
+    ck_tile::HostTensor<YDataType> y_validation({M, N}, {stride, 1});
+
+    std::vector<ck_tile::index_t> shape = {M, N};
+
+    // Fill the host tensors with random data.
+    // FillUniformDistribution populates the tensor with values from a uniform distribution,
+    // within an interval.
+    ck_tile::FillUniformDistribution<XDataType>{0.f, 5.f}(x_host_a);
+    ck_tile::FillUniformDistribution<XDataType>{0.f, 5.f}(x_host_b);
+
+    // 2. Create device memory buffers
+    // DeviceMem allocates memory on the GPU.
+    // The size is determined by the total number of elements and the size of DataType.
+    ck_tile::DeviceMem x_buf_a(x_host_a.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem x_buf_b(x_host_b.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem y_buf(y_host.get_element_space_size_in_bytes());
+
+    // Copy data from host input tensors to device buffers.
+    x_buf_a.ToDevice(x_host_a.data());
+    x_buf_b.ToDevice(x_host_b.data());
+
+    // 3. Configure the kernel execution parameters.
+    // Dividing the problem into blocktile, warptile, and vector
+    // The blocktile is the size of the tile processed by a single work group (also called thread
+    // block). The warptile is the size of the tile processed by a single wavefront (also called
+    // warp). The vector is the size of the tile processed by a single work item (also called
+    // thread). The problem is divided into blocks of size BlockTile. Each block is further divided
+    // into wavefronts of size WarpTile. Each wavefront is composed of 64 work items (on AMD; 32
+    // threads on NVIDIA). Each work item in a wavefront processes one vector's worth of elements.
+    // Note that WarpTile/Vector should be 64 for CDNA (because there are 64 work items per
+    // wavefront).
+    using BlockTile = ck_tile::sequence<2048>; // How many elements are handled by a block tile (the
+                                               // tensor is divided into blocks of this size)
+    using BlockWarps = ck_tile::sequence<8>; // How many concurrent wavefronts are in a block (each
+                                             // wavefront will cover some part of the block tile)
+
+    // WarpTile: Defines the size of the data sub-tile processed by a single wavefront.
+    // This should be consistent with BlockTile and BlockWarps.
+    // If BlockTile is 2048 and BlockWarps is 8, then WarpTile could be 2048/8 = 256.
+    // However, this example uses 64, meaning each wavefront processes 64 elements, and multiple
+    // such wavefront operations might be needed to cover the BlockTile, or the BlockTile is
+    // distributed differently.
+    // The current configuration (BlockTile=2048, BlockWarps=8, WarpTile=64) implies that
+    // each wavefront processes 64 elements, and 8 wavefronts process 8*64 = 512 elements
+    // concurrently. Since 512 is not equal to 2048, it means that warptile(s) will need to iterate
+    // over multiple times over different set of elements to cover the entire BlockTile.
+    using WarpTile = ck_tile::sequence<64>;
+
+    // Vector: Defines the number of elements processed by a single work item in one operation.
+    // If Vector is sequence<1>, each work item handles one element at a time from its assigned
+    // WarpTile portion. If WarpTile is 64 and warpSize is 64 (common), then each work item in the
+    // wavefront processes one element. If Vector is > 1, it implies vectorized load/store/compute
+    // operations per work item.
+    using Vector = ck_tile::sequence<1>;
+
+    // 4. Create the kernel
+
+    // ElementWiseShape bundles these tiling parameters.
+    // It calculates derived properties like threads per wavefront, repeats, and total block size.
+    using Shape = ck_tile::ElementWiseShape<BlockWarps, BlockTile, WarpTile, Vector>;
+
+    // ElementWisePipelineProblem encapsulates all necessary information for the elementwise kernel:
+    // - Data types (input, compute, output).
+    // - Shape traits (tiling configuration).
+    // - The specific elementwise operation (e.g., Add).
+    using Problem = ck_tile::ElementWisePipelineProblem<XDataType,
+                                                        ComputeDataType,
+                                                        YDataType,
+                                                        Shape,
+                                                        XElementwiseOperation>;
+
+    // ElementWiseKernel refers to the GPU kernel class
+    using Kernel = ck_tile::ElementWiseKernel<Problem, ck_tile::ElementWiseDefaultPolicy>;
+
+    // Compute flattened size
+    ck_tile::index_t total_elements = 1;
+    for(auto d : shape)
+        total_elements *= d;
+
+    // kBlockSize: The number of work items in a GPU workgroup (thread block).
+    // This is often a multiple of the wavefront size, 64 on CDNA.
+    // Here, it's explicitly set to 512. This should be consistent with Shape::kBlockSize.
+    // Shape::kBlockSize would be BlockWarps * warpSize (e.g., 8 * 64 = 512).
+    constexpr ck_tile::index_t kBlockSize =
+        ck_tile::get_warp_size() * BlockWarps::at(ck_tile::number<0>{});
+
+    // kBlockPerCu: Hint for how many workgroups can be scheduled per Compute Unit (CU).
+    // This can influence occupancy and performance.
+    constexpr ck_tile::index_t kBlockPerCu = 1;
+
+    // kGridSize: Calculates the total number of workgroups required to process all elements.
+    // Each workgroup is responsible for 'elements_per_block' elements.
+    // To ensure all elements are covered, especially when 'total_elements' is not perfectly
+    // divisible by 'elements_per_block', using ceiling division.
+    constexpr ck_tile::index_t elements_per_block = BlockTile::at(ck_tile::number<0>{});
+    ck_tile::index_t kGridSize = (total_elements + elements_per_block - 1) / elements_per_block;
+
+    std::cout << "grid size = " << kGridSize << std::endl;
+    std::cout << "Total elements = " << total_elements << std::endl;
+
+    auto input_tensors = ck_tile::make_tuple(static_cast<XDataType*>(x_buf_a.GetDeviceBuffer()),
+                                             static_cast<XDataType*>(x_buf_b.GetDeviceBuffer()));
+
+    // 4. Run the kernel
+    float ave_time = launch_kernel(ck_tile::stream_config{nullptr, true, 0, warmup, repeat},
+                                   ck_tile::make_kernel<kBlockSize, kBlockPerCu>(
+                                       Kernel{},
+                                       kGridSize,
+                                       kBlockSize,
+                                       0,
+                                       ck_tile::make_tuple(M, N), // Input size
+                                       ck_tile::make_tuple(N, 1), // Input Stride
+                                       ck_tile::make_tuple(N, 1), // Output Stride
+                                       input_tensors,
+                                       static_cast<YDataType*>(y_buf.GetDeviceBuffer())));
+
+    std::cout << "Average time: " << ave_time << " ms" << std::endl;
+
+    // 5. Verify the output
+    bool pass = true;
+    if(do_validation)
+    {
+        y_buf.FromDevice(y_validation.data());
+        auto op = [](const auto& v0, const auto& v1) { return v0 + v1; };
+
+        ck_tile::reference_binary_elementwise<XDataType, XDataType, YDataType, ComputeDataType>(
+            x_host_a, x_host_b, y_host, op);
+
+        pass = ck_tile::check_err(
+            y_validation, y_host, "Elementwise Add Error: Incorrect results!", 0.01, 0.01);
+    }
+
+    return pass;
+}
+
+int main(int argc, char* argv[])
+{
+    auto [result, arg_parser] = create_args(argc, argv);
+    if(!result)
+        return -1;
+
+    const std::string data_type = arg_parser.get_str("prec");
+    if(data_type == "fp16")
+    {
+        return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
+    }
+
+    return -3;
+}

example/ck_tile/19_elementwise/elementwise_example_transpose.cpp

@@ -0,0 +1,150 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck_tile/host.hpp"
+#include "ck_tile/ops/elementwise.hpp"
+#include "ck_tile/host/reference/reference_transpose.hpp"
+
+auto create_args(int argc, char* argv[])
+{
+    ck_tile::ArgParser arg_parser;
+    arg_parser.insert("m", "1024", "m dimension of input")
+        .insert("n", "1024", "n dimension of input")
+        .insert("stride_in", "-1", "stride for input M dim, if -1 then equal to n")
+        .insert("v", "1", "cpu validation or not")
+        .insert("prec", "fp16", "precision")
+        .insert("warmup", "10", "cold iter")
+        .insert("repeat", "50", "hot iter");
+
+    bool result = arg_parser.parse(argc, argv);
+    return std::make_tuple(result, arg_parser);
+}
+
+template <typename DataType>
+bool run(const ck_tile::ArgParser& arg_parser)
+{
+    ck_tile::index_t M         = arg_parser.get_int("m");
+    ck_tile::index_t N         = arg_parser.get_int("n");
+    ck_tile::index_t stride_in = arg_parser.get_int("stride_in");
+
+    if(stride_in < 0)
+        stride_in = N; // Dense input: stride for M dim is N
+    std::string data_type = arg_parser.get_str("prec");
+    int do_validation     = arg_parser.get_int("v");
+    int warmup            = arg_parser.get_int("warmup");
+    int repeat            = arg_parser.get_int("repeat");
+
+    if(stride_in < N)
+    {
+        throw std::runtime_error("stride_in must be >= N");
+    }
+
+    using XDataType       = DataType;
+    using ComputeDataType = float;
+    using YDataType       = DataType;
+    // Use PassThrough operation for transposition (data is moved, not changed)
+    using XElementwiseOperation = ck_tile::element_wise::PassThrough;
+
+    // 1. Initialize the input data on the host (CPU).
+    // Input x_host_a: M x N
+    // Output y_host: N x M (transposed)
+    ck_tile::HostTensor<XDataType> x_host_a({M, N}, {stride_in, 1});
+    // Output tensor y_host will have dimensions N x M.
+    // Assuming dense output, its stride for the N dimension will be M.
+    ck_tile::index_t stride_out_dim0 = M;
+    ck_tile::HostTensor<YDataType> y_host({N, M}, {stride_out_dim0, 1});
+    ck_tile::HostTensor<YDataType> y_validation({N, M}, {stride_out_dim0, 1});
+
+    // The logical shape for the element-wise operation kernel is based on the input tensor's
+    // elements.
+    std::vector<ck_tile::index_t> op_shape_vec = {M, N};
+    auto op_lengths                            = ck_tile::make_tuple(M, N); // Lens for the kernel
+
+    ck_tile::FillUniformDistribution<XDataType>{0.f, 5.f}(x_host_a);
+
+    // 2. Create device memory buffers
+    ck_tile::DeviceMem x_buf_a(x_host_a.get_element_space_size_in_bytes());
+    ck_tile::DeviceMem y_buf(y_host.get_element_space_size_in_bytes()); // y_host is N x M
+
+    x_buf_a.ToDevice(x_host_a.data());
+
+    // 3. Configure the kernel execution parameters.
+    using BlockTile  = ck_tile::sequence<2048>;
+    using BlockWarps = ck_tile::sequence<8>;
+    using WarpTile   = ck_tile::sequence<64>;
+    using Vector     = ck_tile::sequence<1>;
+
+    using Shape = ck_tile::ElementWiseShape<BlockWarps, BlockTile, WarpTile, Vector>;
+
+    // Problem definition for a single input tensor
+    using Problem = ck_tile::ElementWisePipelineProblem<XDataType,
+                                                        ComputeDataType,
+                                                        YDataType,
+                                                        Shape,
+                                                        XElementwiseOperation>;
+
+    using Kernel = ck_tile::ElementWiseKernel<Problem, ck_tile::ElementWiseDefaultPolicy>;
+
+    ck_tile::index_t total_elements = M * N;
+
+    constexpr ck_tile::index_t kBlockSize         = 64 * BlockWarps::at(ck_tile::number<0>{});
+    constexpr ck_tile::index_t kBlockPerCu        = 1;
+    constexpr ck_tile::index_t elements_per_block = BlockTile::at(ck_tile::number<0>{});
+    ck_tile::index_t kGridSize = (total_elements + elements_per_block - 1) / elements_per_block;
+
+    std::cout << "Input M=" << M << ", N=" << N << ", StrideIn=" << stride_in << std::endl;
+    std::cout << "Output N=" << N << ", M=" << M << ", StrideOut=" << stride_out_dim0 << std::endl;
+    std::cout << "Grid size = " << kGridSize << ", BlockSize = " << kBlockSize << std::endl;
+    std::cout << "Total elements = " << total_elements << std::endl;
+
+    // Input tensors tuple (single input)
+    auto input_tensors = ck_tile::make_tuple(static_cast<XDataType*>(x_buf_a.GetDeviceBuffer()));
+    // Input strides tuple (tuple of tuples, one for each input)
+    auto input_strides = ck_tile::make_tuple(stride_in, 1);
+    // Output strides (for N x M tensor, dense)
+    auto output_strides = ck_tile::make_tuple(1, stride_out_dim0);
+
+    // 4. Run the kernel
+    float ave_time = launch_kernel(ck_tile::stream_config{nullptr, true, 0, warmup, repeat},
+                                   ck_tile::make_kernel<kBlockSize, kBlockPerCu>(
+                                       Kernel{},
+                                       kGridSize,
+                                       kBlockSize,
+                                       0,             // Shared memory
+                                       op_lengths,    // Logical dimensions for the operation (M, N)
+                                       input_strides, // Strides for input tensor(s)
+                                       output_strides, // Strides for output tensor (N, M)
+                                       input_tensors,
+                                       static_cast<YDataType*>(y_buf.GetDeviceBuffer())));
+
+    std::cout << "Average time: " << ave_time << " ms" << std::endl;
+
+    // 5. Verify the output
+    bool pass = true;
+    if(do_validation)
+    {
+        y_buf.FromDevice(y_validation.data()); // Copy result from device to y_validation
+        ck_tile::reference_transpose_elementwise<XDataType, YDataType>(
+            x_host_a, y_host); // Compute reference on host
+        pass = ck_tile::check_err(
+            y_validation, y_host, "Transpose Error: Incorrect results!", 0.01, 0.01);
+    }
+
+    return pass;
+}
+
+int main(int argc, char* argv[])
+{
+    auto [result, arg_parser] = create_args(argc, argv);
+    if(!result)
+        return -1;
+
+    const std::string data_type = arg_parser.get_str("prec");
+    if(data_type == "fp16")
+    {
+        return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
+    }
+
+    std::cerr << "Unsupported data type: " << data_type << std::endl;
+    return -3;
+}