llama.cpp/ggml/src/ggml-opencl/ggml-opencl.cpp

#define CL_TARGET_OPENCL_VERSION 220
#define CL_USE_DEPRECATED_OPENCL_1_2_APIS

// suppress warnings in CL headers for GCC and Clang
#pragma GCC diagnostic ignored "-Woverlength-strings"
#ifdef __clang__
#pragma GCC diagnostic ignored "-Wgnu-anonymous-struct"
#endif

#include "ggml-opencl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include "ggml-backend-impl.h"
#include "ggml.h"

#include <CL/cl.h>

#include <string.h>

#include <cstddef>
#include <cstdint>
#include <atomic>
#include <fstream>
#include <limits>
#include <vector>
#include <string>
#include <cmath>

#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))

#define UNUSED(x) (void)(x)

#define CL_CHECK(err)                                               \
    do {                                                            \
        cl_int err_ = (err);                                        \
        if (err_ != CL_SUCCESS) {                                   \
            GGML_LOG_ERROR("ggml_opencl: %s error %d at %s:%d\n",  \
                #err, err_, __FILE__, __LINE__);                    \
            GGML_ASSERT(0);                                         \
        }                                                           \
    } while (0)

//------------------------------------------------------------------------------
// OpenCL
//------------------------------------------------------------------------------

bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor);

enum GPU_FAMILY {
    ADRENO,
    INTEL,
    UNKNOWN,
};

enum ADRENO_GPU_GEN {
    ADRENO_UNKNOWN,
    A7X,
    A8X,
    X1E,
};

static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) {
    if (strstr(device_name, "730") ||
        strstr(device_name, "740") ||
        strstr(device_name, "750")) {
        return ADRENO_GPU_GEN::A7X;
    }

    if (strstr(device_name, "830")) {
        return ADRENO_GPU_GEN::A8X;
    }

    if (strstr(device_name, "X1")) {
        return ADRENO_GPU_GEN::X1E;
    }

    return ADRENO_GPU_GEN::ADRENO_UNKNOWN;
}

static int get_adreno_cl_compiler_version(const char *driver_version) {
    std::string driver_ver_str(driver_version);
    size_t compiler_ver_pos = driver_ver_str.find("E031");
    size_t compiler_ver_len = 13;
    size_t compiler_ver_offset = 5;

    if (compiler_ver_pos == std::string::npos) {
        compiler_ver_pos = driver_ver_str.find("DX");
        if (compiler_ver_pos == std::string::npos) {
            return -1;
        }
        compiler_ver_len = 11;
        compiler_ver_offset = 3;
    }

    std::string compiler_ver_str = driver_ver_str.substr(compiler_ver_pos, compiler_ver_len);
    std::string major_ver_str = compiler_ver_str.substr(compiler_ver_offset, 2);
    return std::atoi(major_ver_str.c_str());
}

// backend device context
struct ggml_backend_opencl_device_context {
    cl_platform_id platform;
    std::string platform_name;

    cl_device_id device;
    std::string device_name;
};

// backend context
struct ggml_backend_opencl_context {
    cl_device_id device;
    std::string device_name;

    std::string driver_version;

    GPU_FAMILY gpu_family;
    ADRENO_GPU_GEN adreno_gen;

    cl_int alignment;
    size_t max_alloc_size;
    bool fp16_support;

    int adreno_wave_size;

    cl_context context;
    cl_command_queue queue;

    cl_program program;
    cl_program program_1;
    cl_program program_2;

    cl_kernel kernel_add, kernel_add_row;
    cl_kernel kernel_mul, kernel_mul_row;
    cl_kernel kernel_scale;
    cl_kernel kernel_silu, kernel_silu_4;
    cl_kernel kernel_gelu, kernel_gelu_4;
    cl_kernel kernel_relu;
    cl_kernel kernel_clamp;
    cl_kernel kernel_norm;
    cl_kernel kernel_rms_norm;
    cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
    cl_kernel kernel_soft_max, kernel_soft_max_4;
    cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
    cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
    cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
    cl_kernel kernel_mul_mat_f32_f32;
    cl_kernel kernel_mul_mat_f16_f16;
    cl_kernel kernel_mul_mat_f16_f32_1row;
    cl_kernel kernel_mul_mat_f16_f32;
    cl_kernel kernel_mul_mat_f16_f32_l4;
    cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
    cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0, kernel_mul_mat_q4_0_f32_flat;
    cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
    cl_kernel kernel_convert_block_q4_0_noshuffle, kernel_mul_mat_q4_0_f32_flat_v0,
              kernel_mul_mat_q4_0_f32_flat_img_v0;
    cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
    cl_kernel kernel_mul_mv_q6_K_f32;

#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
    // Transpose kernels
    cl_program program_transpose_32;
    cl_program program_transpose_32_16;
    cl_program program_transpose_16;
    cl_kernel kernel_transpose_32;
    cl_kernel kernel_transpose_32_16;
    cl_kernel kernel_transpose_16;

    cl_mem A_s_d_max;            // max scale buffer size for transpose
    cl_mem A_q_d_max;            // max weight buffer size for transpose
    cl_mem B_d_max;              // max activation buffer size for transpose

    // Gemm and Gemv related programs, kernels, etc
    cl_program program_CL_gemm;
    cl_program program_CL_gemv_general;
    cl_program program_CL_gemv_4096_1_11008;
    cl_program program_CL_gemv_4096_1_4096;
    cl_program program_CL_gemv_11008_1_4096;
    cl_program program_CL_gemv_32000_1_4096;
    cl_kernel CL_mul_mat_Ab_Bi_8x4;
    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general;
    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008;
    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
    cl_kernel CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
#endif // GGML_OPENCL_USE_ADRENO_KERNELS
};

static ggml_backend_device                 g_ggml_backend_opencl_device;
static ggml_backend_opencl_device_context  g_ggml_ctx_dev_main {
    /*.platform         =*/ nullptr,
    /*.platform_nane    =*/ "",
    /*.device           =*/ nullptr,
    /*.device_name      =*/ "",
};

static int ggml_backend_opencl_n_devices = 0;

// Profiling
#ifdef GGML_OPENCL_PROFILING
struct ProfilingInfo {
    std::string op_name;
    std::string kernel_name;
    // Kernel execution time in nanoseconds.
    cl_ulong duration_ns;
    // Global and local work sizes.
    size_t global_size[3];
    size_t local_size[3];
    // Op output size.
    size_t output_size[4];
};

std::vector<ProfilingInfo> g_profiling_info;
#endif

inline std::string read_file(const std::string &path) {
  std::ifstream ifs(path);
  if (!ifs) {
    return "";
  }
  std::string text;
  ifs.seekg(0, std::ios::end);
  text.resize(ifs.tellg());
  ifs.seekg(0, std::ios::beg);
  ifs.read(&text[0], text.size());
  return text;
}

static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts) {
    cl_program p;
    char *program_log;
    size_t program_size;
    size_t log_size;
    int err;

    program_size = strlen(program_buffer);

    p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
    if(err < 0) {
        GGML_LOG_ERROR("OpenCL error creating program");
        exit(1);
    }

    err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
    if(err < 0) {
        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
        program_log = (char*) malloc(log_size + 1);
        program_log[log_size] = '\0';
        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
        GGML_LOG_ERROR("ggml_opencl: kernel compile error:\n\n%s\n", program_log);
        free(program_log);
        exit(1);
    }

    return p;
}

static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    static bool initialized = false;
    static ggml_backend_opencl_context *backend_ctx = nullptr;

    if (initialized) {
        return backend_ctx;
    }

    ggml_backend_opencl_device_context *dev_ctx = (ggml_backend_opencl_device_context *)dev->context;
    GGML_ASSERT(dev_ctx);
    GGML_ASSERT(dev_ctx->platform == nullptr);
    GGML_ASSERT(dev_ctx->device == nullptr);
    GGML_ASSERT(backend_ctx == nullptr);

    initialized = true;
    backend_ctx = new ggml_backend_opencl_context();
    backend_ctx->gpu_family = GPU_FAMILY::UNKNOWN;

    cl_int err;

#ifdef GGML_PROFILE_OPENCL
    GGML_LOG_INFO("ggml_opencl: OpenCL profiling enabled\n");
#endif

    struct cl_device;
    struct cl_platform {
        cl_platform_id id;
        unsigned number;
        char name[128];
        char vendor[128];
        struct cl_device * devices;
        unsigned n_devices;
        struct cl_device * default_device;
    };

    struct cl_device {
        struct cl_platform * platform;
        cl_device_id id;
        unsigned number;
        cl_device_type type;
        char name[128];
    };

    enum { NPLAT = 16, NDEV = 16 };

    struct cl_platform platforms[NPLAT];
    unsigned n_platforms = 0;
    struct cl_device devices[NDEV];
    unsigned n_devices = 0;
    struct cl_device * default_device = NULL;

    cl_platform_id platform_ids[NPLAT];
    if (clGetPlatformIDs(NPLAT, platform_ids, &n_platforms) != CL_SUCCESS) {
        GGML_LOG_ERROR("ggml_opencl: plaform IDs not available.\n");
        return backend_ctx;
    }

    for (unsigned i = 0; i < n_platforms; i++) {
        struct cl_platform * p = &platforms[i];
        p->number = i;
        p->id = platform_ids[i];
        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL));
        CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL));

        cl_device_id device_ids[NDEV];
        cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices);
        if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) {
            p->n_devices = 0;
        } else {
            CL_CHECK(clGetDeviceIDsError);
        }
        p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL;
        p->default_device = NULL;

        for (unsigned j = 0; j < p->n_devices; j++) {
            struct cl_device * d = &devices[n_devices];
            d->number = n_devices++;
            d->id = device_ids[j];
            d->platform = p;
            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL));
            CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL));

            if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) {
                p->default_device = d;
            }
        }

        if (default_device == NULL && p->default_device != NULL) {
            default_device = p->default_device;
        }
    }

    if (n_devices == 0) {
        GGML_LOG_ERROR("ggml_opencl: could find any OpenCL devices.\n");
        return backend_ctx;
    }

    char * user_platform_string = getenv("GGML_OPENCL_PLATFORM");
    char * user_device_string = getenv("GGML_OPENCL_DEVICE");
    int user_platform_number = -1;
    int user_device_number = -1;

    unsigned n;
    if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) {
        user_platform_number = (int)n;
    }
    if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) {
        user_device_number = (int)n;
    }
    if (user_platform_number != -1 && user_device_number != -1) {
        cl_platform* platform = &platforms[user_platform_number];
        if ((unsigned)user_device_number >= platform->n_devices) {
            GGML_LOG_ERROR("ggml_opencl: invalid device number %d\n", user_device_number);
            exit(1);
        }
        default_device = &platform->devices[user_device_number];
    } else {

        struct cl_device * selected_devices = devices;
        unsigned n_selected_devices = n_devices;

        if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) {
            for (unsigned i = 0; i < n_platforms; i++) {
                struct cl_platform * p = &platforms[i];
                if (strstr(p->name, user_platform_string) != NULL ||
                    strstr(p->vendor, user_platform_string) != NULL) {
                    user_platform_number = (int)i;
                    break;
                }
            }
            if (user_platform_number == -1) {
                GGML_LOG_ERROR("ggml_opencl: no platform matching '%s' was found.\n", user_platform_string);
                exit(1);
            }
        }
        if (user_platform_number != -1) {
            struct cl_platform * p = &platforms[user_platform_number];
            selected_devices = p->devices;
            n_selected_devices = p->n_devices;
            default_device = p->default_device;
            if (n_selected_devices == 0) {
                GGML_LOG_ERROR("ggml_opencl: selected platform '%s' does not have any devices.\n", p->name);
                exit(1);
            }
        }

        if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) {
            for (unsigned i = 0; i < n_selected_devices; i++) {
                struct cl_device * d = &selected_devices[i];
                if (strstr(d->name, user_device_string) != NULL) {
                    user_device_number = d->number;
                    break;
                }
            }
            if (user_device_number == -1) {
                GGML_LOG_ERROR("ggml_opencl: no device matching '%s' was found.\n", user_device_string);
                exit(1);
            }
        }
        if (user_device_number != -1) {
            selected_devices = &devices[user_device_number];
            n_selected_devices = 1;
            default_device = &selected_devices[0];
        }

        GGML_ASSERT(n_selected_devices > 0);

        if (default_device == NULL) {
            default_device = &selected_devices[0];
        }
    }

    GGML_LOG_INFO("ggml_opencl: selecting platform: '%s'\n", default_device->platform->name);
    GGML_LOG_INFO("ggml_opencl: selecting device: '%s'\n", default_device->name);
    if (default_device->type != CL_DEVICE_TYPE_GPU) {
        GGML_LOG_WARN("ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name);
    }

    dev_ctx->platform = default_device->platform->id;
    dev_ctx->device = default_device->id;
    backend_ctx->device = default_device->id;

    if (strstr(default_device->name, "Adreno")) {
        backend_ctx->gpu_family = GPU_FAMILY::ADRENO;
        backend_ctx->adreno_gen = get_adreno_gpu_gen(default_device->name);

        // Default wave size is 128, A8x uses 64.
        if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A8X) {
            backend_ctx->adreno_wave_size = 64;
        } else if (backend_ctx->adreno_gen == ADRENO_GPU_GEN::A7X ||
                   backend_ctx->adreno_gen == ADRENO_GPU_GEN::X1E) {
            backend_ctx->adreno_wave_size = 128;
        } else {
            backend_ctx->adreno_wave_size = 128;
            GGML_LOG_WARN("ggml_opencl: Unsupported Adreno GPU: %s, "
                "using wave size %d, "
                "may not work as expected\n",
                backend_ctx->device_name.c_str(), backend_ctx->adreno_wave_size);
        }
    } else if (strstr(default_device->name, "Intel")) {
        backend_ctx->gpu_family = GPU_FAMILY::INTEL;
    } else {
        GGML_LOG_ERROR("Unsupported GPU: %s\n", default_device->name);
        backend_ctx->gpu_family = GPU_FAMILY::UNKNOWN;
        return backend_ctx;
    }

#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
    if (backend_ctx->gpu_family != GPU_FAMILY::ADRENO) {
        GGML_LOG_ERROR("ggml_opencl: Adreno-specific kernels should not be enabled for non-Adreno GPUs; "
            "run on an Adreno GPU or recompile with CMake option `-DGGML_OPENCL_USE_ADRENO_KERNELS=OFF`\n");
        return backend_ctx;
    }
#endif

    // Populate backend device name
    dev_ctx->platform_name = default_device->platform->name;
    dev_ctx->device_name = default_device->name;
    backend_ctx->device_name = default_device->name;

    // A local ref of cl_device_id for convenience
    cl_device_id device = backend_ctx->device;

    // Check device OpenCL version, OpenCL 2.0 or above is required
    size_t device_ver_str_size;
    clGetDeviceInfo(device, CL_DEVICE_VERSION, 0, NULL, &device_ver_str_size);
    char *device_ver_buffer = (char *)alloca(device_ver_str_size + 1);
    clGetDeviceInfo(device, CL_DEVICE_VERSION, device_ver_str_size, device_ver_buffer, NULL);
    device_ver_buffer[device_ver_str_size] = '\0';
    GGML_LOG_INFO("ggml_opencl: device OpenCL version: %s\n", device_ver_buffer);

    if (strstr(device_ver_buffer, "OpenCL 2") == NULL &&
        strstr(device_ver_buffer, "OpenCL 3") == NULL) {
        GGML_LOG_ERROR("ggml_opencl: OpenCL 2.0 or above is required\n");
        return backend_ctx;
    }

    // Check driver version
    size_t driver_version_str_size;
    clGetDeviceInfo(device, CL_DRIVER_VERSION, 0, NULL, &driver_version_str_size);
    char *driver_version = (char *)alloca(driver_version_str_size + 1);
    clGetDeviceInfo(device, CL_DRIVER_VERSION, driver_version_str_size, driver_version, NULL);
    driver_version[driver_version_str_size] = '\0';
    GGML_LOG_INFO("ggml_opencl: OpenCL driver: %s\n", driver_version);
    backend_ctx->driver_version = driver_version;

    int adreno_cl_compiler_version = get_adreno_cl_compiler_version(driver_version);
    bool has_vector_subgroup_broadcast =
        adreno_cl_compiler_version >= 47 || adreno_cl_compiler_version == 17;
    GGML_LOG_INFO("ggml_opencl: vector subgroup broadcast support: %s\n",
        has_vector_subgroup_broadcast ? "true" : "false");

    size_t ext_str_size;
    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size);
    char *ext_buffer = (char *)alloca(ext_str_size + 1);
    clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
    ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
    // Check if ext_buffer contains cl_khr_fp16
    backend_ctx->fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
    GGML_LOG_INFO("ggml_opencl: device FP16 support: %s\n", backend_ctx->fp16_support ? "true" : "false");

    // fp16 is required
    if (!backend_ctx->fp16_support) {
        GGML_LOG_ERROR("ggml_opencl: device does not support FP16\n");
        return backend_ctx;
    }

    // If OpenCL 3.0 is supported, then check for cl_khr_subgroups, which becomes
    // optional in OpenCL 3.0 (cl_khr_subgroup is mandatory in OpenCL 2.x)
    if (strstr(device_ver_buffer, "OpenCL 3") &&
        strstr(ext_buffer, "cl_khr_subgroups") == NULL &&
        strstr(ext_buffer, "cl_intel_subgroups") == NULL) {
        GGML_LOG_ERROR("ggml_opencl: device does not support subgroups (cl_khr_subgroups or cl_intel_subgroups) "
            "(note that subgroups is an optional feature in OpenCL 3.0)\n");
        return backend_ctx;
    }

    CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &backend_ctx->alignment, NULL));
    GGML_LOG_INFO("ggml_opencl: mem base addr align: %u\n", backend_ctx->alignment);

    clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &backend_ctx->max_alloc_size, NULL);
    GGML_LOG_INFO("ggml_opencl: max mem alloc size: %zu MB\n", backend_ctx->max_alloc_size/1024/1024);

    // Check SVM.
    cl_device_svm_capabilities svm_caps;
    CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_SVM_CAPABILITIES, sizeof(cl_device_svm_capabilities), &svm_caps, 0));
    GGML_LOG_INFO("ggml_opencl: SVM coarse grain buffer support: %s\n",
        svm_caps & CL_DEVICE_SVM_COARSE_GRAIN_BUFFER ? "true" : "false");
    GGML_LOG_INFO("ggml_opencl: SVM fine grain buffer support: %s\n",
        svm_caps & CL_DEVICE_SVM_FINE_GRAIN_BUFFER ? "true" : "false");
    GGML_LOG_INFO("ggml_opencl: SVM fine grain system support: %s\n",
        svm_caps & CL_DEVICE_SVM_FINE_GRAIN_SYSTEM ? "true" : "false");
    GGML_LOG_INFO("ggml_opencl: SVM atomics support: %s\n",
        svm_caps & CL_DEVICE_SVM_ATOMICS ? "true" : "false");

    // Print out configurations
#ifdef GGML_OPENCL_SOA_Q
    GGML_LOG_INFO("ggml_opencl: flattening quantized weights representation as struct of arrays (GGML_OPENCL_SOA_Q)\n");
#endif // GGML_OPENCL_SOA_Q

#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
    GGML_LOG_INFO("ggml_opencl: using kernels optimized for Adreno (GGML_OPENCL_USE_ADRENO_KERNELS)\n");
#endif // GGML_OPENCL_USE_ADRENO_KERNELS

    cl_context_properties properties[] = {
        (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)dev_ctx->platform, 0
    };

    CL_CHECK((backend_ctx->context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err));

    // A local ref of cl_context for convenience
    cl_context context = backend_ctx->context;

    //CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err),
    //    (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err :
    //    (queue = clCreateCommandQueue(context, device, 0, &err), err)
    //)));
    cl_command_queue_properties command_queue_props = 0;
#ifdef GGML_OPENCL_PROFILING
    command_queue_props |= CL_QUEUE_PROFILING_ENABLE;
#endif
    CL_CHECK((backend_ctx->queue = clCreateCommandQueue(context, device, command_queue_props, &err), err));

#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string kernel_src {
        #include "ggml-opencl.cl.h"
    };
#else
    const std::string kernel_src = read_file("ggml-opencl.cl");
#endif

    std::string compile_opts =
        "-cl-std=CL2.0 -cl-mad-enable -cl-unsafe-math-optimizations "
        "-cl-finite-math-only -cl-fast-relaxed-math ";
    backend_ctx->program = build_program_from_source(context, device, kernel_src.c_str(), compile_opts);

    // Non matmul kernels.
    CL_CHECK((backend_ctx->kernel_get_rows_f32       = clCreateKernel(backend_ctx->program, "kernel_get_rows_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_get_rows_f16       = clCreateKernel(backend_ctx->program, "kernel_get_rows_f16", &err), err));
    CL_CHECK((backend_ctx->kernel_get_rows_q4_0      = clCreateKernel(backend_ctx->program, "kernel_get_rows_q4_0", &err), err));
    CL_CHECK((backend_ctx->kernel_add                = clCreateKernel(backend_ctx->program, "kernel_add", &err), err));
    CL_CHECK((backend_ctx->kernel_add_row            = clCreateKernel(backend_ctx->program, "kernel_add_row", &err), err));
    CL_CHECK((backend_ctx->kernel_mul                = clCreateKernel(backend_ctx->program, "kernel_mul", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_row            = clCreateKernel(backend_ctx->program, "kernel_mul_row", &err), err));
    CL_CHECK((backend_ctx->kernel_scale              = clCreateKernel(backend_ctx->program, "kernel_scale", &err), err));
    CL_CHECK((backend_ctx->kernel_silu               = clCreateKernel(backend_ctx->program, "kernel_silu", &err), err));
    CL_CHECK((backend_ctx->kernel_silu_4             = clCreateKernel(backend_ctx->program, "kernel_silu_4", &err), err));
    CL_CHECK((backend_ctx->kernel_gelu               = clCreateKernel(backend_ctx->program, "kernel_gelu", &err), err));
    CL_CHECK((backend_ctx->kernel_gelu_4             = clCreateKernel(backend_ctx->program, "kernel_gelu_4", &err), err));
    CL_CHECK((backend_ctx->kernel_relu               = clCreateKernel(backend_ctx->program, "kernel_relu", &err), err));
    CL_CHECK((backend_ctx->kernel_clamp              = clCreateKernel(backend_ctx->program, "kernel_clamp", &err), err));
    CL_CHECK((backend_ctx->kernel_norm               = clCreateKernel(backend_ctx->program, "kernel_norm", &err), err));
    CL_CHECK((backend_ctx->kernel_rms_norm           = clCreateKernel(backend_ctx->program, "kernel_rms_norm", &err), err));
    CL_CHECK((backend_ctx->kernel_diag_mask_inf      = clCreateKernel(backend_ctx->program, "kernel_diag_mask_inf", &err), err));
    CL_CHECK((backend_ctx->kernel_diag_mask_inf_8    = clCreateKernel(backend_ctx->program, "kernel_diag_mask_inf_8", &err), err));
    CL_CHECK((backend_ctx->kernel_soft_max           = clCreateKernel(backend_ctx->program, "kernel_soft_max", &err), err));
    CL_CHECK((backend_ctx->kernel_soft_max_4         = clCreateKernel(backend_ctx->program, "kernel_soft_max_4", &err), err));
    CL_CHECK((backend_ctx->kernel_rope_norm_f32      = clCreateKernel(backend_ctx->program, "kernel_rope_norm_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_rope_norm_f16      = clCreateKernel(backend_ctx->program, "kernel_rope_norm_f16", &err), err));
    CL_CHECK((backend_ctx->kernel_rope_neox_f32      = clCreateKernel(backend_ctx->program, "kernel_rope_neox_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_rope_neox_f16      = clCreateKernel(backend_ctx->program, "kernel_rope_neox_f16", &err), err));
    CL_CHECK((backend_ctx->kernel_cpy_f16_f16        = clCreateKernel(backend_ctx->program, "kernel_cpy_f16_f16", &err), err));
    CL_CHECK((backend_ctx->kernel_cpy_f16_f32        = clCreateKernel(backend_ctx->program, "kernel_cpy_f16_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_cpy_f32_f16        = clCreateKernel(backend_ctx->program, "kernel_cpy_f32_f16", &err), err));
    CL_CHECK((backend_ctx->kernel_cpy_f32_f32        = clCreateKernel(backend_ctx->program, "kernel_cpy_f32_f32", &err), err));

    // Matmul kernels.
    CL_CHECK((backend_ctx->kernel_mul_mat_f32_f32        = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f32_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f16        = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f16", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_1row   = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f32_1row", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32        = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4     = clCreateKernel(backend_ctx->program, "kernel_mul_mat_f16_f32_l4", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32       = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_v     = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32_v", &err), err));

    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_flat  = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32_flat", &err), err));
    CL_CHECK((backend_ctx->kernel_convert_block_q4_0     = clCreateKernel(backend_ctx->program, "kernel_convert_block_q4_0", &err), err));
    CL_CHECK((backend_ctx->kernel_restore_block_q4_0     = clCreateKernel(backend_ctx->program, "kernel_restore_block_q4_0", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat = clCreateKernel(backend_ctx->program, "kernel_mul_mat_q4_0_f32_8x_flat", &err), err));

    // Load additional mulmat kernels.
#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string kernel_src_1 {
        #include "ggml-opencl_mm.cl.h"
    };
#else
    const std::string kernel_src_1 = read_file("ggml-opencl_mm.cl");
#endif
    backend_ctx->program_1 = build_program_from_source(context, device, kernel_src_1.c_str(), compile_opts);

    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_8x_flat      = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_1d_8x_flat", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_1d_16x_flat     = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_1d_16x_flat", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mv_q6_K_f32                  = clCreateKernel(backend_ctx->program_1, "kernel_mul_mv_q6_K_f32", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_flat_v0         = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_flat_v0", &err), err));
    CL_CHECK((backend_ctx->kernel_mul_mat_q4_0_f32_flat_img_v0     = clCreateKernel(backend_ctx->program_1, "kernel_mul_mat_q4_0_f32_flat_img_v0", &err), err));

    // Load additional data conversion kernels.
#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string kernel_src_2 {
        #include "ggml-opencl_cvt.cl.h"
    };
#else
    const std::string kernel_src_2 = read_file("ggml-opencl_cvt.cl");
#endif
    backend_ctx->program_2 = build_program_from_source(context, device, kernel_src_2.c_str(), compile_opts);

    CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle     = clCreateKernel(backend_ctx->program_2, "kernel_convert_block_q4_0_noshuffle", &err), err));

    // Kernels for Adreno
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string transpose_32_src {
        #include "ggml-opencl_transpose_32.cl.h"
    };
#else
    const std::string transpose_32_src = read_file("ggml-opencl_transpose_32.cl");
#endif
    backend_ctx->program_transpose_32 = build_program_from_source(context, device, transpose_32_src.c_str(), compile_opts);
    CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose_32, "kernel_transpose_32", &err), err));

#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string transpose_32_16_src {
        #include "ggml-opencl_transpose_32_16.cl.h"
    };
#else
    const std::string transpose_32_16_src = read_file("ggml-opencl_transpose_32_16.cl");
#endif
    backend_ctx->program_transpose_32_16 = build_program_from_source(context, device, transpose_32_16_src.c_str(), compile_opts);
    CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose_32_16, "kernel_transpose_32_16", &err), err));

#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string transpose_16_src {
        #include "ggml-opencl_transpose_16.cl.h"
    };
#else
    const std::string transpose_16_src = read_file("ggml-opencl_transpose_16.cl");
#endif
    backend_ctx->program_transpose_16 = build_program_from_source(context, device, transpose_16_src.c_str(), compile_opts);
    CL_CHECK((backend_ctx->kernel_transpose_16 = clCreateKernel(backend_ctx->program_transpose_16, "kernel_transpose_16", &err), err));

    // Gemv general
    std::string CL_gemv_compile_opts =
        " -cl-std=CL2.0 "
        " -cl-mad-enable "
        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string kernel_src_CL_gemv_general {
        #include "ggml-opencl_gemv_noshuffle_general.cl.h"
    };
#else
    const std::string kernel_src_CL_gemv_general = read_file("ggml-opencl_gemv_noshuffle_general.cl");
#endif

    backend_ctx->program_CL_gemv_general = build_program_from_source(
        context, device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts);
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general = clCreateKernel(backend_ctx->program_CL_gemv_general, "kernel_gemv_noshuffle", &err), err));

    // Gemv 2048, 16384
    CL_gemv_compile_opts =
        " -cl-std=CL2.0 "
        " -cl-mad-enable "
        " -DLINE_STRIDE_A=2048 "
        " -DBLOCK_STRIDE_A=16384 "
        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string kernel_src_CL_gemv {
        #include "ggml-opencl_gemv_noshuffle.cl.h"
    };
#else
    const std::string kernel_src_CL_gemv = read_file("ggml-opencl_gemv_noshuffle.cl");
#endif

    backend_ctx->program_CL_gemv_4096_1_4096 = build_program_from_source(
        context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_4096, "kernel_gemv_noshuffle", &err), err));

    // Gemv 2048, 16384
    CL_gemv_compile_opts =
        " -cl-std=CL2.0 "
        " -cl-mad-enable "
        " -DLINE_STRIDE_A=2048 "
        " -DBLOCK_STRIDE_A=16384 "
        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }

    backend_ctx->program_CL_gemv_4096_1_11008 = build_program_from_source(
        context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_11008, "kernel_gemv_noshuffle", &err), err));

    // Gemv 5504, 44032
    CL_gemv_compile_opts =
        " -cl-std=CL2.0 "
        " -cl-mad-enable "
        " -DLINE_STRIDE_A=5504 "
        " -DBLOCK_STRIDE_A=44032 "
        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }

    backend_ctx->program_CL_gemv_11008_1_4096 = build_program_from_source(
        context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_11008_1_4096, "kernel_gemv_noshuffle", &err), err));

    // Gemv 16000, 128000
    CL_gemv_compile_opts =
        " -cl-std=CL2.0 "
        " -cl-mad-enable "
        " -DLINE_STRIDE_A=16000 "
        " -DBLOCK_STRIDE_A=128000 "
        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }

    backend_ctx->program_CL_gemv_32000_1_4096 = build_program_from_source(context, device, kernel_src_CL_gemv.c_str(), CL_gemv_compile_opts);
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_32000_1_4096, "kernel_gemv_noshuffle", &err), err));

    // Gemm
#ifdef GGML_OPENCL_EMBED_KERNELS
    const std::string kernel_src_CL_gemm {
        #include "ggml-opencl_mul_mat_Ab_Bi_8x4.cl.h"
    };
#else
    const std::string kernel_src_CL_gemm = read_file("ggml-opencl_mul_mat_Ab_Bi_8x4.cl");
#endif
    backend_ctx->program_CL_gemm = build_program_from_source(context, device, kernel_src_CL_gemm.c_str(), compile_opts);
    CL_CHECK((backend_ctx->CL_mul_mat_Ab_Bi_8x4 = clCreateKernel(backend_ctx->program_CL_gemm, "kernel_mul_mat_Ab_Bi_8x4", &err), err));

    // Allocate intermediate buffers and images
    size_t max_A_q_d_bytes = 311164928;
    size_t max_A_s_d_bytes = 38895616;
    size_t max_B_d_bytes = 45088768;

    CL_CHECK((backend_ctx->A_q_d_max = clCreateBuffer(context, 0, max_A_q_d_bytes, NULL, &err), err));
    CL_CHECK((backend_ctx->A_s_d_max = clCreateBuffer(context, 0, max_A_s_d_bytes, NULL, &err), err));
    CL_CHECK((backend_ctx->B_d_max   = clCreateBuffer(context, 0, max_B_d_bytes,   NULL, &err), err));
#endif // GGML_OPENCL_USE_ADRENO_KERNELS

    // For now we support a single devices
    ggml_backend_opencl_n_devices = 1;

    return backend_ctx;
}

static void ggml_cl2_free(void) {
#ifdef GGML_OPENCL_PROFILING
    FILE * fperf = fopen("cl_profiling.csv", "w");
    if (!fperf) {
        GGML_LOG_ERROR("Failed to open cl_profiling.csv\n");
        return;
    }

    float total_kernel_time = 0;
    fprintf(fperf, "op name, kernel name, duration (ms), global size, local size, output size\n");
    for (const ProfilingInfo & info : g_profiling_info) {
        total_kernel_time += info.duration_ns/1.e6f;
        fprintf(fperf, "%s,%s,%f,%zux%zux%zu,%zux%zux%zu,%zux%zux%zux%zu\n",
            info.op_name.c_str(), info.kernel_name.c_str(), info.duration_ns/1.e6f,
            info.global_size[0], info.global_size[1], info.global_size[2],
            info.local_size[0], info.local_size[2], info.local_size[2],
            info.output_size[0], info.output_size[1], info.output_size[2], info.output_size[3]);
    }
    fclose(fperf);

    GGML_LOG_INFO("ggml_opencl: total kernel time: %f\n", total_kernel_time);
#endif
}

//------------------------------------------------------------------------------
// Tensor extra management
//------------------------------------------------------------------------------
struct ggml_tensor_extra_cl {
    // The buffer object that holds the data.
    cl_mem data_device;
    // The offset into the buffer object. This is primarily for scratch buffer
    // and view operation.
    // NB: this offset no longer includes view offset (view_offs). Whenever this
    // offset is used, view_offs should be considered.
    cl_ulong offset;
    // The actual size of the cl_mem object. This is needed when returning the
    // block to the pool.
    size_t actual_size;

    void reset() {
        data_device = nullptr;
        offset = 0;
        actual_size = 0;
    }
};

// Additional tensor extra structs for quantized tensors.
// These tensors are loaded from files and should not be allocated in scratch --
// they should always be allocated from the pool. Hence, they do not have an
// `offset`, which indicate their locations in the scratch buffer.
struct ggml_tensor_extra_cl_q4_0 {
    // Quantized values.
    cl_mem q = nullptr;
    // Quantized values in image1d_buffer_t.
    cl_mem q_img = nullptr;
    // Scales.
    cl_mem d = nullptr;
    // Scales in image1d_buffer_t.
    cl_mem d_img = nullptr;
    // Size of quantized values.
    size_t size_q = 0;
    // Size of scales.
    size_t size_d = 0;

    ~ggml_tensor_extra_cl_q4_0() {
        reset();
    }

    void reset() {
        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
        // They must be properly released so that the original buffer can be
        // properly released to avoid memory leak.
        if (q != nullptr) {
            CL_CHECK(clReleaseMemObject(q));
            q = nullptr;
        }
        if (d != nullptr) {
            CL_CHECK(clReleaseMemObject(d));
            d = nullptr;
        }
        // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
        // enabled. They point to the images in ggml_backend_opencl_buffer_context.
        // So, there is no need to release them here.
        // TODO: initialize them for non SMALL_PATH path, or remove them.
        q_img = nullptr;
        d_img = nullptr;
        size_q = 0;
        size_d = 0;
    }
};

//------------------------------------------------------------------------------
// Backend API
//------------------------------------------------------------------------------

//
// backend
//
static const char * ggml_backend_opencl_name(ggml_backend_t backend) {
    return "OpenCL";

    UNUSED(backend);
}

static void ggml_backend_opencl_free(ggml_backend_t backend) {
    ggml_cl2_free();

    GGML_UNUSED(backend);
}

static void ggml_backend_opencl_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    GGML_UNUSED(backend);
    GGML_UNUSED(tensor);
    GGML_UNUSED(data);
    GGML_UNUSED(offset);
    GGML_UNUSED(size);
}

static void ggml_backend_opencl_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    GGML_UNUSED(backend);
    GGML_UNUSED(tensor);
    GGML_UNUSED(data);
    GGML_UNUSED(offset);
    GGML_UNUSED(size);
}

static bool ggml_backend_opencl_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
    GGML_UNUSED(backend);
    GGML_UNUSED(src);
    GGML_UNUSED(dst);
    return false;
}

static void ggml_backend_opencl_synchronize(ggml_backend_t backend) {
    GGML_UNUSED(backend);
}

static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
    for (int i = 0; i < cgraph->n_nodes; i++) {
        ggml_tensor * node = cgraph->nodes[i];

        if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
            continue;
        }

        bool ok = ggml_cl_compute_forward(backend, node);
        if (!ok) {
            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
        }
        GGML_ASSERT(ok);
    }

    return GGML_STATUS_SUCCESS;
}

static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
    GGML_UNUSED(dev);

    switch (op->op) {
        case GGML_OP_NONE:
            return true;
        case GGML_OP_GET_ROWS:
            switch (op->src[0]->type) {
                case GGML_TYPE_F32:
                case GGML_TYPE_F16:
                    return true;
                case GGML_TYPE_Q4_0:
#ifdef GGML_OPENCL_SOA_Q
                    // We do not support flattened Q4_0 (and possibly other Q's)
                    return false;
#else // GGML_OPENCL_SOA_Q
                    return true;
#endif // GGML_OPENCL_SOA_Q
                default:
                    return false;
            }
        case GGML_OP_CPY:
        case GGML_OP_DUP:
        case GGML_OP_CONT:
            switch (op->src[0]->type) {
                case GGML_TYPE_F32:
                    switch (op->type) {
                        case GGML_TYPE_F16:
                        case GGML_TYPE_F32:
                            return true;
                        default:
                            return false;
                    }
                case GGML_TYPE_F16:
                    switch (op->type) {
                        case GGML_TYPE_F16:
                        case GGML_TYPE_F32:
                            return true;
                        default:
                            return false;
                    }
                default:
                    return false;
            }
        case GGML_OP_ADD:
        case GGML_OP_SCALE:
        case GGML_OP_MUL:
            return true;
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
                case GGML_UNARY_OP_GELU:
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_RELU:
                   return ggml_is_contiguous(op->src[0]);
                default:
                    return false;
            }
        case GGML_OP_CLAMP:
        case GGML_OP_SOFT_MAX:
        case GGML_OP_NORM:
        case GGML_OP_RMS_NORM:
            return true;
        case GGML_OP_MUL_MAT:
            if (op->src[0]->type == GGML_TYPE_F16) {
                return true;
            } else if (op->src[0]->type == GGML_TYPE_F32) {
                return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
            } else if (op->src[0]->type == GGML_TYPE_Q4_0 ||
                       op->src[0]->type == GGML_TYPE_Q6_K) {
                return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
            }
            return false;
        case GGML_OP_RESHAPE:
        case GGML_OP_VIEW:
        case GGML_OP_PERMUTE:
        case GGML_OP_TRANSPOSE:
            return true;
        case GGML_OP_DIAG_MASK_INF:
            return op->ne[3] == 1;
        case GGML_OP_ROPE:
            return true;
        default:
            return false;
    }
}

// Forward declaration - implementation appears later in the file.
static const char * ggml_backend_opencl_buffer_type_get_name(ggml_backend_buffer_type_t buffer_type);

static ggml_guid_t ggml_backend_opencl_guid() {
    static ggml_guid guid = { 0xde, 0xe0, 0x70, 0xa2, 0x73, 0x4e, 0x4d, 0xbc, 0xb0, 0xc7, 0x4f, 0xd4, 0x6d, 0x4e, 0x90, 0xfe };
    return &guid;
}

static ggml_backend_i ggml_backend_opencl_i = {
    /* .get_name                = */ ggml_backend_opencl_name,
    /* .free                    = */ ggml_backend_opencl_free,
    /* .set_tensor_async        = */ NULL,  /* ggml_backend_opencl_set_tensor_async */
    /* .get_tensor_async        = */ NULL,  /* ggml_backend_opencl_get_tensor_async */
    /* .cpy_tensor_async        = */ NULL,  /* ggml_backend_opencl_cpy_tensor_async */
    /* .synchronize             = */ NULL,  /* ggml_backend_opencl_synchronize */
    /* .graph_plan_create       = */ NULL,
    /* .graph_plan_free         = */ NULL,
    /* .graph_plan_update       = */ NULL,
    /* .graph_plan_compute      = */ NULL,
    /* .graph_compute           = */ ggml_backend_opencl_graph_compute,
    /* .event_record            = */ NULL,
    /* .event_wait              = */ NULL,
};

ggml_backend_t ggml_backend_opencl_init(void) {
    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_opencl_reg(), 0);
    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(dev);

    ggml_backend_t backend = new ggml_backend {
        /* .guid      = */ ggml_backend_opencl_guid(),
        /* .interface = */ ggml_backend_opencl_i,
        /* .device    = */ dev,
        /* .context   = */ backend_ctx
    };

    return backend;
}

bool ggml_backend_is_opencl(ggml_backend_t backend) {
    return backend && backend->iface.get_name == ggml_backend_opencl_name;
}

//
// buffer
//
struct ggml_backend_opencl_buffer_context {
    // A buffer context can hold multiple cl_mem objects. This is for flattening
    // quantized weights and should be used with GGML_OPENCL_SMALL_ALLOC where
    // each tensor is allocated a separate buffer. When flattening is enabled
    // with small allocation, each tensor is backed by two cl_mem objects (for
    // quants and scales) packed into a backend_opencl_buffer.
    ggml_backend_opencl_buffer_context(cl_mem buf)
        : name("OpenCL") {
        buffer.push_back(buf);
    }

    ~ggml_backend_opencl_buffer_context() {
        for (cl_mem buf : buffer) {
            CL_CHECK(clReleaseMemObject(buf));
        }
        for (cl_mem im : img) {
            CL_CHECK(clReleaseMemObject(im));
        }

        // Delete all extras to trigger their destructors
        for (ggml_tensor_extra_cl * e : temp_tensor_extras) {
            delete e;
        }
        for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) {
            delete e;
        }
        for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0) {
            delete e;
        }
        for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
            delete e;
        }
    }

    ggml_tensor_extra_cl * ggml_opencl_alloc_temp_tensor_extra() {
        ggml_tensor_extra_cl * extra;
        if (temp_tensor_extras.empty()) {
            extra = new ggml_tensor_extra_cl();
        } else {
            extra = temp_tensor_extras.back();
            temp_tensor_extras.pop_back();
        }

        temp_tensor_extras_in_use.push_back(extra);

        extra->reset();
        return extra;
    }

    ggml_tensor_extra_cl_q4_0 * ggml_opencl_alloc_temp_tensor_extra_q4_0() {
        ggml_tensor_extra_cl_q4_0 * extra;
        if (temp_tensor_extras_q4_0.empty()) {
            extra = new ggml_tensor_extra_cl_q4_0();
        } else {
            extra = temp_tensor_extras_q4_0.back();
            temp_tensor_extras_q4_0.pop_back();
        }

        temp_tensor_extras_q4_0_in_use.push_back(extra);

        extra->reset();
        return extra;
    }

    void reset() {
        for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) {
            temp_tensor_extras.push_back(e);
        }
        temp_tensor_extras_in_use.clear();

        for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
            temp_tensor_extras_q4_0.push_back(e);
        }
        temp_tensor_extras_q4_0_in_use.clear();
    }

    // Pools for extras. Available extras are in `temp_tensor_extras`. Extras
    // being used are in `temp_tensor_extras_in_use`. At the first run, new
    // extras get created and put in `in_use`. When the buffer is reset via
    // the `reset` callback, all extras in `in_use` get moved to available extras
    // for reuse.
    std::vector<ggml_tensor_extra_cl *> temp_tensor_extras;
    std::vector<ggml_tensor_extra_cl *> temp_tensor_extras_in_use;
    std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0;
    std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;

    // The buffer_context is initially created by ggml_backend_buft_alloc_buffer
    // before any tensor is initialized (at the beginning of alloc_tensor_range).
    // Hence, there is alway a buffer object in this vector. When each tensor is
    // being initialized, this original buffer object will be released if both
    // flattening and small allocation are enabled, and additional buffer
    // objects will be created in init_tensor to represent flattened quantized
    // weights.
    std::vector<cl_mem> buffer;
    // These are image1d_buffer_t objects that wrap around the quants and scales.
    // For Q4_0 quantization, there should be two of them - one for quants and
    // one for scales. They should be populated only when flattening and small
    // allocation are enabled.
    std::vector<cl_mem> img;
    std::string name;
};

static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000;

static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
    delete ctx;
}

static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
    return cl_ptr_base;

    GGML_UNUSED(buffer);
}

static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;

    ggml_cl2_init(buffer->buft->device);

    if (tensor->view_src != nullptr) {
        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);

        ggml_tensor_extra_cl * view_extra = (ggml_tensor_extra_cl *) tensor->view_src->extra;
        GGML_ASSERT(view_extra && "view_extra is nullptr?");

        // Reuse extra of the parent tensor. The offset of this view tensor
        // becomes `extra->offset + view_offs` and needs to be calculated when
        // it is used. This changes is needed because of the change to
        // ggml_alloc.c in https://github.com/ggerganov/llama.cpp/pull/7640.
        // `buffer` passed in here will always be `tensor->buffer`. It is OK
        // to allocate extras from the same buffer context for ordinary
        // intermediate tensors. But for views into kv cache tensors, doing so
        // would mess up the extras used by kv cache.
        // Before #7640, `buffer` is for intermediate tensors, which is always
        // different from that of kv cache tensors.
        //
        // NB: now extra->offset no longer accounts for view_offs.
        // NB: this should not apply to weight tensors (for end-to-end runs, but
        //     may apply for test-backend-ops).
        // FIXME: if any unexpected results are seen, double check the offset -
        // there could be other places that need fix.
        tensor->extra = view_extra;
    } else {
        {
            size_t offset = (char *)tensor->data - (char *)cl_ptr_base;

            ggml_tensor_extra_cl * extra = ctx->ggml_opencl_alloc_temp_tensor_extra();
            extra->offset = offset;
            extra->data_device = ctx->buffer[0];
            extra->actual_size = ggml_nbytes(tensor);

            tensor->extra = extra;
        }
    }
}

// The optimized gemm and gemv kernels are used for large matrices without batch.
// tensor is the quantized weights matrix.
inline bool use_adreno_kernels(const ggml_tensor *tensor) {
    return tensor->ne[0] >= 512 && tensor->ne[1] >= 512 &&
            tensor->ne[2] == 1 && tensor->ne[3] == 1;
}

static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device);

    cl_context context = backend_ctx->context;
    cl_command_queue queue = backend_ctx->queue;

#ifdef GGML_OPENCL_SOA_Q
    // We separate the quantized bits and scale from block_q4_0 by using an
    // additional kernel, where each thread handles a block. We first read the
    // original weights into a temporary buffer, then create two separate
    // buffers for quantized bits and scales, which are then populated by the
    // conversion kernel.
    if (tensor->type == GGML_TYPE_Q4_0) {
        // Tensors should have been preallocated, therefore they should
        // already have ggml_tensor_extra_cl as extra.
        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");

        // Allocate the new extra and create aliases from the original.
        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
        ggml_tensor_extra_cl_q4_0 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q4_0();

        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
        GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");

        cl_int err;
        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
            ggml_nbytes(tensor), NULL, &err);
        CL_CHECK(err);
        CL_CHECK(clEnqueueWriteBuffer(
            queue, data_device, CL_TRUE, 0,
            ggml_nbytes(tensor), data, 0, NULL, NULL));

        // We consider the specified offset arg as always, although For weights
        // the offset arg should be 0 (we do not assert this).
        //GGML_ASSERT(offset == 0);

        // We create subbuffers from the original tensor buffer for scales and
        // quants - i.e., scales and quants are aliases into the buffer obejct
        // that backs the original tensor. This is a cleaner way to adapt to the
        // new memory management.
        // In the old code, we allocate new buffers for scales and quants
        // respectively, which could still be done but would result in double
        // allocation; properly deallocating the preallocated buffer that backs
        // the tensors is tricky and would leak the backend specific information
        // into the general backend code.
        // Does this create misaligned subbuffers (alignment is 1024) in certain
        // cases ?
        cl_buffer_region region;

        // The original tensor memory is divided into scales and quants, i.e.,
        // we first store scales, then quants.
        // Create subbuffer for scales.
        region.origin = extra_orig->offset + tensor->view_offs + offset;
        region.size = size_d;
        extra->d = clCreateSubBuffer(
            extra_orig->data_device, CL_MEM_READ_WRITE,
            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
        CL_CHECK(err);

        // Create subbuffer for quants.
        region.origin = extra_orig->offset + tensor->view_offs + offset + size_d;
        region.size = size_q;
        extra->q = clCreateSubBuffer(
            extra_orig->data_device, CL_MEM_READ_WRITE,
            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
        CL_CHECK(err);

        //cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;

        // The optimized kernels need weights in natural order, so unshuffle.
        if (use_adreno_kernels(tensor)) {
            kernel = backend_ctx->kernel_convert_block_q4_0_noshuffle;
        }
    #else
        cl_kernel kernel = backend_ctx->kernel_convert_block_q4_0;
    #endif // GGML_OPENCL_USE_ADRENO_KERNELS
        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));

        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
        size_t local_work_size[] = {64, 1, 1};

        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
        CL_CHECK(clWaitForEvents(1, &evt));
        CL_CHECK(clReleaseMemObject(data_device));

        tensor->extra = extra;

        // transpose the weights and scales
    #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
        // Only do transpose for large, non batched matrix
        // TODO: use preallocated images instead of sub-buffer then image
        if (use_adreno_kernels(tensor)) {
        // <----------------------------------------------------------------------------------> //
        // start transpose
        // <----------------------------------------------------------------------------------> //
        int M = tensor->ne[1];   // ne01
        int K = tensor->ne[0];   // ne00

        // transpose is out of place, so we need to allocate transposed buffers
        // <----------------------------------------------------------------------------------> //
        // use sub_buffer of max buffer size instead

        size_t q_size_bytes = K * M / 8 * sizeof(float);
        cl_buffer_region region;
        region.origin = 0;
        region.size = q_size_bytes;
        cl_mem qT_d = clCreateSubBuffer(
            backend_ctx->A_q_d_max,
            0,
            CL_BUFFER_CREATE_TYPE_REGION,
            &region,
            &err);
        // cl_mem qT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, q_size_bytes, NULL, &err);
        CL_CHECK(err);

        // size_t d_size_bytes = M * (K / 32) / 2 * sizeof(float);
        size_t d_size_bytes = M * (K / 32) * 2;
        region.origin = 0;
        region.size = d_size_bytes;
        cl_mem dT_d = clCreateSubBuffer(
            backend_ctx->A_s_d_max,
            0,
            CL_BUFFER_CREATE_TYPE_REGION,
            &region,
            &err);
        // cl_mem dT_d = clCreateBuffer(context, CL_MEM_READ_WRITE, d_size_bytes, NULL, &err);
        CL_CHECK(err);

        // <----------------------------------------------------------------------------------> //


        // create images from the buffers
        // <----------------------------------------------------------------------------------> //
        cl_mem q_d_image1D;
        cl_mem d_d_image1D;
        cl_mem qT_d_image1D;
        cl_mem dT_d_image1D;

        cl_image_format img_fmt_1d = { CL_RGBA, CL_FLOAT };
        cl_image_desc img_desc_1d;

        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
        img_desc_1d.image_width = M * K / 8 / 4;
        img_desc_1d.buffer = extra->q;
        q_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
        CL_CHECK(err);

        img_fmt_1d = { CL_RGBA, CL_FLOAT };
        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
        img_desc_1d.image_width = M * K / 8 / 4;
        img_desc_1d.buffer = qT_d;
        qT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
        CL_CHECK(err);

        img_fmt_1d = { CL_RGBA, CL_FLOAT };
        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
        img_desc_1d.image_width = M * K / 32 / 4 / 2;
        img_desc_1d.buffer = extra->d;
        d_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
        CL_CHECK(err);

        img_fmt_1d = { CL_RGBA, CL_FLOAT };
        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
        img_desc_1d.image_width = M * K / 32 / 4 / 2;
        img_desc_1d.buffer = dT_d;
        dT_d_image1D = clCreateImage(context, 0, &img_fmt_1d, &img_desc_1d, NULL, &err);
        CL_CHECK(err);
        // <----------------------------------------------------------------------------------> //

        // set up and call the transpose kernels
        // <----------------------------------------------------------------------------------> //
        // weights
        int height_q = M / 8;
        int width_q = K / 8 / 4;
        kernel = backend_ctx->kernel_transpose_16;

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &q_d_image1D));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &qT_d_image1D));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_q));
        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_q));

        size_t local_size_q[3] = {4, 16, 1};
        size_t global_size_q[3] = {static_cast<size_t>(width_q), static_cast<size_t>(height_q), 1};
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_q, local_size_q, 0, NULL, &evt));
        CL_CHECK(clWaitForEvents(1, &evt));

        // scales
        int height_s = M / 8;
        int width_s = K / 32 / 8;

        kernel = backend_ctx->kernel_transpose_16;
        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &d_d_image1D));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &dT_d_image1D));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int), &height_s));
        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &width_s));

        size_t local_size_s[3] = {4, 16, 1};
        size_t global_size_s[3] = {static_cast<size_t>(width_s), static_cast<size_t>(height_s), 1};
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_size_s, local_size_s, 0, NULL, &evt));
        CL_CHECK(clWaitForEvents(1, &evt));
        // <----------------------------------------------------------------------------------> //

        // copy transposed buffer contents to original buffers
        // <----------------------------------------------------------------------------------> //
        // weights
        CL_CHECK(clEnqueueCopyBuffer(queue, qT_d, extra->q, 0, 0, q_size_bytes, 0, NULL, &evt));
        CL_CHECK(clWaitForEvents(1, &evt));

        // scales
        CL_CHECK(clEnqueueCopyBuffer(queue, dT_d, extra->d, 0, 0, d_size_bytes, 0, NULL, &evt));
        CL_CHECK(clWaitForEvents(1, &evt));
        // <----------------------------------------------------------------------------------> //

        // deallocate transpose buffers
        // <----------------------------------------------------------------------------------> //
        CL_CHECK(clReleaseMemObject(qT_d));
        CL_CHECK(clReleaseMemObject(dT_d));

        // deallocate temporary images
        CL_CHECK(clReleaseMemObject(q_d_image1D));
        CL_CHECK(clReleaseMemObject(d_d_image1D));
        CL_CHECK(clReleaseMemObject(qT_d_image1D));
        CL_CHECK(clReleaseMemObject(dT_d_image1D));
        // <----------------------------------------------------------------------------------> //
        // end transpose
        // <----------------------------------------------------------------------------------> //
        }
    #endif // GGML_OPENCL_USE_ADRENO_KERNELS

        return;
    }
#endif // GGML_OPENCL_SOA_Q

    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
    GGML_ASSERT(extra);

    CL_CHECK(clEnqueueWriteBuffer(
        queue, extra->data_device, CL_TRUE, extra->offset + offset,
        size, data, 0, NULL, NULL));

    GGML_UNUSED(buffer);
}

static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    GGML_ASSERT(tensor->extra);

    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device);

    cl_context context = backend_ctx->context;
    cl_command_queue queue = backend_ctx->queue;

    // Make sure all previously submitted commands are finished.
    CL_CHECK(clFinish(queue));

#ifdef GGML_OPENCL_SOA_Q
    // In end-to-end runs, get_tensor is usually used to get back the logits,
    // where we can simply do clEnqueueReadBuffer since they are f32.
    // However, in test-backend-ops, the GPU graph is copied to the CPU backend,
    // which requires reading back quantized weight tensors.
    // To properly support this, we need to restore block_q4_0 struct arrays
    // from the flattened buffers.
    if (tensor->type == GGML_TYPE_Q4_0) {
        ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *)tensor->extra;

        cl_int err;
        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
            ggml_nbytes(tensor), NULL, &err);
        CL_CHECK(err);

        cl_kernel kernel = backend_ctx->kernel_restore_block_q4_0;
        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));

        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
        size_t local_work_size[] = {1, 1, 1};

        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
            global_work_size, local_work_size, 0, NULL, &evt));
        CL_CHECK(clWaitForEvents(1, &evt));
        CL_CHECK(clEnqueueReadBuffer(
            queue, data_device, CL_TRUE, offset,
            size, data, 0, NULL, NULL));
        CL_CHECK(clReleaseMemObject(data_device));
        return;
    }
#endif // GGML_OPENCL_SOA_Q

    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;

    CL_CHECK(clEnqueueReadBuffer(
        queue, extra->data_device, CL_TRUE, extra->offset + tensor->view_offs + offset,
        size, data, 0, NULL, NULL));

    GGML_UNUSED(buffer);
}

static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    ggml_backend_dev_t dev = buffer->buft->device;
    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(dev);
    cl_command_queue queue = backend_ctx->queue;

    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
    for (cl_mem buf : ctx->buffer) {
        CL_CHECK(clEnqueueFillBuffer(queue, buf, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL));
    }
    CL_CHECK(clFinish(queue));
}

static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) {
    ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
    ctx->reset();
}

static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = {
    /* .free_buffer     = */ ggml_backend_opencl_buffer_free_buffer,
    /* .get_base        = */ ggml_backend_opencl_buffer_get_base,
    /* .init_tensor     = */ ggml_backend_opencl_buffer_init_tensor,
    /* .memset_tensor   = */ NULL,
    /* .set_tensor      = */ ggml_backend_opencl_buffer_set_tensor,
    /* .get_tensor      = */ ggml_backend_opencl_buffer_get_tensor,
    /* .cpy_tensor      = */ NULL,
    /* .clear           = */ ggml_backend_opencl_buffer_clear,
    /* .reset           = */ ggml_backend_opencl_buffer_reset,
};

//
// buffer type
//

static const char * ggml_backend_opencl_buffer_type_get_name(ggml_backend_buffer_type_t buffer_type) {
    return "OpenCL";

    GGML_UNUSED(buffer_type);
}

static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) {
    ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer_type->device);

    // clCreateBuffer returns -61 for size 0
    size = std::max(size, (size_t)1);

    cl_int err;
    cl_mem mem = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, size, NULL, &err);
    if (err != CL_SUCCESS) {
        GGML_LOG_INFO("%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0);
        return nullptr;
    }

    ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context(mem);

    return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size);
}

static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) {
    // FIXME: not thread safe, device may not be initialized yet
    static cl_uint alignment = -1;
    if (alignment == (cl_uint)-1) {
        ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(buffer_type->device);
        alignment = backend_ctx->alignment;
    }
    return alignment;
}

static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
    static size_t max_size = -1;
    if (max_size == (size_t)-1) {
        ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(buffer_type->device);
        max_size = backend_ctx->max_alloc_size;
    }
    return max_size;
}

static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
    return ggml_backend_is_opencl(backend);

    UNUSED(buft);
}

static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = {
    /* .get_name         = */ ggml_backend_opencl_buffer_type_get_name,
    /* .alloc_buffer     = */ ggml_backend_opencl_buffer_type_alloc_buffer,
    /* .get_alignment    = */ ggml_backend_opencl_buffer_type_get_alignment,
    /* .get_max_size     = */ ggml_backend_opencl_buffer_type_get_max_size,
    /* .get_alloc_size   = */ NULL,
    /* .is_host          = */ NULL,
};

ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() {
    static ggml_backend_buffer_type buffer_type = {
        /* .iface   = */ ggml_backend_opencl_buffer_type_interface,
        /* .device  = */ &g_ggml_backend_opencl_device,
        /* .context = */ nullptr,
    };

    return &buffer_type;
}

//
// backend device
//

static const char * ggml_backend_opencl_device_get_name(ggml_backend_dev_t dev) {
    return "GPUOpenCL";

    GGML_UNUSED(dev);
}

static const char * ggml_backend_opencl_device_get_description(ggml_backend_dev_t dev) {
    ggml_backend_opencl_device_context *dev_ctx = (ggml_backend_opencl_device_context *) dev->context;
    return dev_ctx->device_name.c_str();
}

static void ggml_backend_opencl_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
    *free = 1;
    *total = 1;

    GGML_UNUSED(dev);
}

static enum ggml_backend_dev_type ggml_backend_opencl_device_get_type(ggml_backend_dev_t dev) {
    return GGML_BACKEND_DEVICE_TYPE_GPU;

    GGML_UNUSED(dev);
}

static void ggml_backend_opencl_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
    props->name        = ggml_backend_opencl_device_get_name(dev);
    props->description = ggml_backend_opencl_device_get_description(dev);
    props->type        = ggml_backend_opencl_device_get_type(dev);
    ggml_backend_opencl_device_get_memory(dev, &props->memory_free, &props->memory_total);
    props->caps = ggml_backend_dev_caps {
        /* .async                 = */ false,
        /* .host_buffer           = */ false,
        /* .buffer_from_host_ptr  = */ false,
        /* .events                = */ false,
    };
}

static ggml_backend_t ggml_backend_opencl_device_init(ggml_backend_dev_t dev, const char * params) {
    ggml_backend_opencl_context * backend_ctx = ggml_cl2_init(dev);

    ggml_backend_t backend = new ggml_backend {
        /* .guid      = */ ggml_backend_opencl_guid(),
        /* .interface = */ ggml_backend_opencl_i,
        /* .device    = */ dev,
        /* .context   = */ backend_ctx,
    };

    return backend;

    GGML_UNUSED(params);
}

static ggml_backend_buffer_type_t ggml_backend_opencl_device_get_buffer_type(ggml_backend_dev_t dev) {
    return ggml_backend_opencl_buffer_type();

    GGML_UNUSED(dev);
}

static ggml_backend_buffer_t ggml_backend_opencl_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
    GGML_UNUSED(dev);
    GGML_UNUSED(ptr);
    GGML_UNUSED(size);
    GGML_UNUSED(max_tensor_size);
    return nullptr;
}

static bool ggml_backend_opencl_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
    return ggml_opencl_supports_op(dev, op);
}

static bool ggml_backend_opencl_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
    return buft->iface.get_name == ggml_backend_opencl_buffer_type_get_name;

    GGML_UNUSED(dev);
}

static struct ggml_backend_device_i ggml_backend_opencl_device_i = {
    /* .get_name             = */ ggml_backend_opencl_device_get_name,
    /* .get_description      = */ ggml_backend_opencl_device_get_description,
    /* .get_memory           = */ ggml_backend_opencl_device_get_memory,
    /* .get_type             = */ ggml_backend_opencl_device_get_type,
    /* .get_props            = */ ggml_backend_opencl_device_get_props,
    /* .init_backend         = */ ggml_backend_opencl_device_init,
    /* .get_buffer_type      = */ ggml_backend_opencl_device_get_buffer_type,
    /* .get_host_buffer_type = */ NULL,
    /* .buffer_from_host_ptr = */ ggml_backend_opencl_device_buffer_from_ptr,
    /* .supports_op          = */ ggml_backend_opencl_device_supports_op,
    /* .supports_buft        = */ ggml_backend_opencl_device_supports_buft,
    /* .offload_op           = */ NULL,
    /* .event_new            = */ NULL,
    /* .event_free           = */ NULL,
    /* .event_synchronize    = */ NULL,
};

// Backend registry

static const char * ggml_backend_opencl_reg_get_name(ggml_backend_reg_t reg) {
    return "OpenCL";

    GGML_UNUSED(reg);
}

static size_t ggml_backend_opencl_reg_device_count(ggml_backend_reg_t reg) {
    return ggml_backend_opencl_n_devices;

    GGML_UNUSED(reg);
}

static ggml_backend_dev_t ggml_backend_opencl_reg_device_get(ggml_backend_reg_t reg, size_t index) {
    GGML_ASSERT(index == 0);

    return &g_ggml_backend_opencl_device;

    GGML_UNUSED(reg);
    GGML_UNUSED(index);
}

static struct ggml_backend_reg_i ggml_backend_opencl_reg_i = {
    /* .get_name         = */ ggml_backend_opencl_reg_get_name,
    /* .device_count     = */ ggml_backend_opencl_reg_device_count,
    /* .device_get       = */ ggml_backend_opencl_reg_device_get,
    /* .get_proc_address = */ NULL,
};

ggml_backend_reg_t ggml_backend_opencl_reg(void) {
    // TODO: make this thread-safe somehow?
    static ggml_backend_reg reg;
    static bool initialized = false;

    if (!initialized) {
        reg = ggml_backend_reg {
            /* .api_version = */ GGML_BACKEND_API_VERSION,
            /* .iface   = */ ggml_backend_opencl_reg_i,
            /* .context = */ NULL,
        };

        g_ggml_backend_opencl_device = ggml_backend_device {
            /* .iface   = */ ggml_backend_opencl_device_i,
            /* .reg     = */ &reg,
            /* .context = */ &g_ggml_ctx_dev_main,
        };

        ggml_cl2_init(&g_ggml_backend_opencl_device);

        initialized = true;
    }

    return &reg;
}

GGML_BACKEND_DL_IMPL(ggml_backend_opencl_reg)

//------------------------------------------------------------------------------
// Debugging utils
//------------------------------------------------------------------------------
#if 0
#define QK4_0 32
typedef struct {
    ggml_fp16_t d;          // delta
    uint8_t qs[QK4_0 / 2];  // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2,
    "wrong q4_0 block size/padding");

#include <math.h>
#ifdef __cplusplus
#include "half.hpp"
#endif

static void dump_tensor(ggml_backend_t backend, const struct ggml_tensor * tensor) {
    void * buf = malloc(ggml_nbytes(tensor));

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;
#ifdef GGML_OPENCL_SOA_Q
    void * buf_q;
    void * buf_d;
#endif

#ifdef GGML_USE_OPENCL
    // Make sure everything is done.
    CL_CHECK(clFinish(queue));

#ifdef GGML_OPENCL_SOA_Q
    if (tensor->type == GGML_TYPE_Q4_0) {
        ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *) tensor->extra;
        GGML_ASSERT(extra);

        size_t size_q = ggml_nelements(tensor)/QK4_0 * QK4_0/2;
        size_t size_d = ggml_nelements(tensor)/QK4_0 * sizeof(ggml_fp16_t);
        GGML_ASSERT(size_q + size_d == ggml_nbytes(tensor));
        buf_q = malloc(size_q);
        buf_d = malloc(size_d);

        CL_CHECK(clEnqueueReadBuffer(queue, extra->q, CL_TRUE, 0, size_q, buf_q, 0, NULL, NULL));
        CL_CHECK(clEnqueueReadBuffer(queue, extra->d, CL_TRUE, 0, size_d, buf_d, 0, NULL, NULL));
        CL_CHECK(clFinish(queue));
    } else {
        // Read out the tensor from GPU memory.
        ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
        GGML_ASSERT(extra);

        CL_CHECK(clEnqueueReadBuffer(queue, extra->data_device, CL_TRUE,
        extra->offset, ggml_nbytes(tensor), buf, 0, NULL, NULL));
        CL_CHECK(clFinish(queue));
    }
#else
    // Read out the tensor from GPU memory.
    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
    GGML_ASSERT(extra);

    CL_CHECK(clEnqueueReadBuffer(queue, extra->data_device, CL_TRUE,
        extra->offset, ggml_nbytes(tensor), buf, 0, NULL, NULL));
    CL_CHECK(clFinish(queue));
#endif // GGML_OPENCL_SOA_Q
#endif // GGML_USE_OPENCL

    // Open file and dump.
    char fname[512];
    sprintf(fname, "./tensor-dumps/%s.txt", tensor->name);
    FILE * f = fopen(fname, "w");
    if (!f) {
        printf("Failed to open %s\n", fname);
        return;
    }

    if (tensor->type == GGML_TYPE_F32) {
        float * data = (float *) buf;
        for (int i = 0; i < ggml_nelements(tensor); ++i) {
            if (isnan(data[i])) {
                printf("NaN found: %s\n", tensor->name);
                break;
            }
            fprintf(f, "%f\n", data[i]);
        }
    } else if (tensor->type == GGML_TYPE_I32) {
        int * data = (int *) buf;
        for (int i = 0; i < ggml_nelements(tensor); ++i) {
            if (isnan(data[i])) {
                printf("NaN found: %s\n", tensor->name);
                break;
            }
            fprintf(f, "%d\n", data[i]);
        }
    } else if (tensor->type == GGML_TYPE_F16) {
#ifdef __cplusplus
        half_float::half * data = (half_float::half *) buf;
        for (int i = 0; i < ggml_nelements(tensor); ++i) {
            if (std::isnan(data[i])) {
                printf("NaN found: %s\n", tensor->name);
                break;
            }
            fprintf(f, "%f\n", float(data[i]));
        }
#endif
    } else if (tensor->type == GGML_TYPE_Q4_0) {
#ifdef GGML_OPENCL_SOA_Q
        ggml_fp16_t * data_d = (ggml_fp16_t *)buf_d;
        unsigned char * data_q = (unsigned char *)buf_q;

        for (int i = 0; i < ggml_nelements(tensor)/QK4_0; ++i) {
            fprintf(f, "%04x, ", data_d[i]);
            for (int k = 0; k < QK4_0/2; ++k) {
                fprintf(f, "%02x, ", data_q[k]);
            }
            fprintf(f, "\n");
            data_q += QK4_0/2;
        }
        free(buf_d);
        free(buf_q);
#else
        block_q4_0 * data = (block_q4_0 *) buf;
        for (int i = 0; i < ggml_nelements(tensor)/QK4_0; ++i) {
            fprintf(f, "%04x, ", data[i].d);
            for (int k = 0; k < QK4_0/2; ++k) {
                fprintf(f, "%02x, ", data[i].qs[k]);
            }
            fprintf(f, "\n");
        }
#endif // GGML_OPENCL_SOA_Q
    }
    free(buf);
    fflush(f);
    fclose(f);
}
#else
#define dump_tensor(tensor)
#endif

//------------------------------------------------------------------------------
// Profiling utility
//------------------------------------------------------------------------------
#ifdef GGML_OPENCL_PROFILING
void populateProfilingInfo(
        ProfilingInfo& info, cl_event evt, cl_kernel kernel,
        size_t global_size[3], size_t local_size[3],
        const ggml_tensor * tensor) {
    cl_ulong start;
    cl_ulong end;
    CL_CHECK(clWaitForEvents(1, &evt));
    CL_CHECK(clGetEventProfilingInfo(
        evt, CL_PROFILING_COMMAND_START, sizeof(cl_ulong), &start, NULL));
    CL_CHECK(clGetEventProfilingInfo(
        evt, CL_PROFILING_COMMAND_END, sizeof(cl_ulong), &end, NULL));

    char kernel_name[512];
    CL_CHECK(clGetKernelInfo(kernel, CL_KERNEL_FUNCTION_NAME,
        sizeof(kernel_name), kernel_name, NULL));

    info.duration_ns = end - start;
    info.op_name = tensor->name;
    info.kernel_name = kernel_name;
    info.local_size[0]  = local_size[0];
    info.local_size[1]  = local_size[1];
    info.local_size[2]  = local_size[2];
    info.global_size[0] = global_size[0];
    info.global_size[1] = global_size[1];
    info.global_size[2] = global_size[2];
    info.output_size[0] = tensor->ne[0];
    info.output_size[1] = tensor->ne[1];
    info.output_size[2] = tensor->ne[2];
    info.output_size[3] = tensor->ne[3];
}
#endif

//------------------------------------------------------------------------------
// Ops
//------------------------------------------------------------------------------

static bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
    const int64_t ne10 = src1->ne[0];

    const int64_t ne0 = dst->ne[0];
    const int64_t ne1 = dst->ne[1];

    // TODO: find the optimal values for these
    return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
            src1->type == GGML_TYPE_F32 &&
             dst->type == GGML_TYPE_F32 &&
            (ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
}

static void ggml_cl_nop(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    UNUSED(backend);
    UNUSED(src0);
    UNUSED(src1);
    UNUSED(dst);
}

static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(src1);
    GGML_ASSERT(src1->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    const int      ne00 = src0 ? src0->ne[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
    const int      ne10 = src1 ? src1->ne[0] : 0;
    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
    const int      ne11 = src1 ? src1->ne[1] : 0;
    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
    const cl_ulong nb1  = dst  ?  dst->nb[1] : 0;
    const cl_ulong nb2  = dst  ?  dst->nb[2] : 0;

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offset1 = extra1->offset + src1->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_kernel kernel;

    switch (src0->type) {
        case GGML_TYPE_F32:
            kernel = backend_ctx->kernel_get_rows_f32;
            break;
        case GGML_TYPE_F16:
            kernel = backend_ctx->kernel_get_rows_f16;
            break;
        case GGML_TYPE_Q4_0:
            kernel = backend_ctx->kernel_get_rows_q4_0;
            break;
        default:
            GGML_ASSERT(false && "not implemented");
    }

    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb10));
    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb11));
    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb1));
    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb2));

    size_t global_work_size[] = {(size_t)ne10, (size_t)ne11, 1};
    size_t local_work_size[] = {1, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(src1);
    GGML_ASSERT(src1->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    const int  ne00 = src0 ? src0->ne[0] : 0;
    const int  ne01 = src0 ? src0->ne[1] : 0;
    const int  ne02 = src0 ? src0->ne[2] : 0;
    const int  ne03 = src0 ? src0->ne[3] : 0;

    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;

    const int  ne10 = src1 ? src1->ne[0] : 0;
    const int  ne11 = src1 ? src1->ne[1] : 0;
    const int  ne12 = src1 ? src1->ne[2] : 0;
    const int  ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);

    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
    const cl_ulong nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);

    const int  ne0  = dst ? dst->ne[0] : 0;
    const int  ne1  = dst ? dst->ne[1] : 0;
    const int  ne2  = dst ? dst->ne[2] : 0;
    const int  ne3  = dst ? dst->ne[3] : 0;

    const cl_ulong nb0  = dst ? dst->nb[0] : 0;
    const cl_ulong nb1  = dst ? dst->nb[1] : 0;
    const cl_ulong nb2  = dst ? dst->nb[2] : 0;
    const cl_ulong nb3  = dst ? dst->nb[3] : 0;

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offset1 = extra1->offset + src1->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    bool bcast_row = false;
    cl_kernel kernel;

    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
        GGML_ASSERT(ggml_is_contiguous(src0));

        // src1 is a row
        GGML_ASSERT(ne11 == 1);

        bcast_row = true;
        int ne = ne00 / 4;
        kernel = backend_ctx->kernel_add_row;

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
    } else {
        kernel = backend_ctx->kernel_add;

        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
        CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
        CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
        CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
        CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
        CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
        CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
        CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
    }

    if (bcast_row) {
        int n = ggml_nelements(dst)/4;
        size_t global_work_size[] = {(size_t)n, 1, 1};
        size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    } else {
        unsigned int nth = MIN(64, ne0);
        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
        size_t local_work_size[] = {nth, 1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    }
}

static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(src1);
    GGML_ASSERT(src1->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    const int ne00 = src0 ? src0->ne[0] : 0;
    const int ne01 = src0 ? src0->ne[1] : 0;
    const int ne02 = src0 ? src0->ne[2] : 0;
    const int ne03 = src0 ? src0->ne[3] : 0;

    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;

    const int ne10 = src1 ? src1->ne[0] : 0;
    const int ne11 = src1 ? src1->ne[1] : 0;
    const int ne12 = src1 ? src1->ne[2] : 0;
    const int ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);

    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
    const cl_ulong nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13);

    const int ne0  = dst ? dst->ne[0] : 0;
    const int ne1  = dst ? dst->ne[1] : 0;
    const int ne2  = dst ? dst->ne[2] : 0;
    const int ne3  = dst ? dst->ne[3] : 0;

    const cl_ulong nb0  = dst ? dst->nb[0] : 0;
    const cl_ulong nb1  = dst ? dst->nb[1] : 0;
    const cl_ulong nb2  = dst ? dst->nb[2] : 0;
    const cl_ulong nb3  = dst ? dst->nb[3] : 0;

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offset1 = extra1->offset + src1->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    bool bcast_row = false;
    cl_kernel kernel;

    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
        GGML_ASSERT(ggml_is_contiguous(src0));

        // src1 is a row
        GGML_ASSERT(ne11 == 1);

        bcast_row = true;
        int ne = ne00 / 4;
        kernel = backend_ctx->kernel_mul_row;

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
    } else {
        kernel = backend_ctx->kernel_mul;

        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne03));
        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb03));
        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne10));
        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne11));
        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne12));
        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne13));
        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb10));
        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb11));
        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb12));
        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb13));
        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &ne0));
        CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &ne1));
        CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &ne2));
        CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &ne3));
        CL_CHECK(clSetKernelArg(kernel, 26, sizeof(cl_ulong), &nb0));
        CL_CHECK(clSetKernelArg(kernel, 27, sizeof(cl_ulong), &nb1));
        CL_CHECK(clSetKernelArg(kernel, 28, sizeof(cl_ulong), &nb2));
        CL_CHECK(clSetKernelArg(kernel, 29, sizeof(cl_ulong), &nb3));
    }

    if (bcast_row) {
        int n = ggml_nelements(dst)/4;
        size_t global_work_size[] = {(size_t)n, 1, 1};
        size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    } else {
        unsigned int nth = MIN(64, ne0);
        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
        size_t local_work_size[] = {nth, 1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    }
}

static void ggml_cl_gelu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_kernel kernel;

    int n = ggml_nelements(dst);

    if (n % 4 == 0) {
        kernel = backend_ctx->kernel_gelu_4;
        n /= 4;
    } else {
        kernel = backend_ctx->kernel_gelu;
    }

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));

    size_t global_work_size[] = {(size_t)n, 1, 1};
    size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt);

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL);
#endif
}

static void ggml_cl_silu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_kernel kernel;

    int n = ggml_nelements(dst);

    if (n % 4 == 0) {
        kernel = backend_ctx->kernel_silu_4;
        n /= 4;
    } else {
        kernel = backend_ctx->kernel_silu;
    }

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));

    size_t global_work_size[] = {(size_t)n, 1, 1};
    size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_relu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_kernel kernel = backend_ctx->kernel_relu;

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));

    const int64_t n = ggml_nelements(dst);

    size_t global_work_size[] = {(size_t)n, 1, 1};
    size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_clamp(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    float min;
    float max;
    memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float));
    memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float));

    cl_kernel kernel = backend_ctx->kernel_clamp;

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(float),    &min));
    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(float),    &max));

    const int64_t n = ggml_nelements(dst);

    size_t global_work_size[] = {(size_t)n, 1, 1};
    size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));

    const int ne00 = src0 ? src0->ne[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;

    GGML_ASSERT(ggml_is_contiguous_1(src0));

    const int nth = MIN(64, ne00);

    cl_kernel kernel = backend_ctx->kernel_norm;

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),    &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong),  &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),    &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong),  &offsetd));
    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),       &ne00));
    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong),  &nb01));
    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float),     &eps));
    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(float)*nth, NULL));

    const int64_t nrows = ggml_nrows(src0);

    size_t global_work_size[] = {(size_t)nrows*nth, 1, 1};
    size_t local_work_size[] = {(size_t)nth, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_rms_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_backend_opencl_device_context * dev_ctx =
        (ggml_backend_opencl_device_context *)backend->device->context;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));

    const int ne00 = src0 ? src0->ne[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;

    GGML_ASSERT(ne00 % 4 == 0);
    GGML_ASSERT(ggml_is_contiguous_1(src0));

    const int nth = MIN(64, ne00);

    const int64_t nrows = ggml_nrows(src0);

    size_t global_work_size[] = {(size_t)nrows*nth, 1, 1};
    size_t local_work_size[] = {(size_t)nth, 1, 1};

    cl_kernel kernel = backend_ctx->kernel_rms_norm;

    // Note, this kernel declares local memory in kernel args and the size
    // depends on subgroup size.
    // Retrieve subgroup size.
    // Note, this requires OpenCL 2.1 and above
    size_t sgs;
    CL_CHECK(clGetKernelSubGroupInfo(kernel, dev_ctx->device,
        CL_KERNEL_MAX_SUB_GROUP_SIZE_FOR_NDRANGE,
        sizeof(local_work_size), local_work_size,
        sizeof(size_t), &sgs, NULL));

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),    &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong),  &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),    &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong),  &offsetd));
    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),       &ne00));
    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong),  &nb01));
    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float),     &eps));
    // This is local memory - the size depends on subgroup size.
    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(float)*nth/sgs,  NULL));

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(src1);
    GGML_ASSERT(src1->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offset1 = extra1->offset + src1->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

#ifdef GGML_OPENCL_SOA_Q
    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
#endif

    const int  ne00 = src0 ? src0->ne[0] : 0;
    const int  ne01 = src0 ? src0->ne[1] : 0;
    const int  ne02 = src0 ? src0->ne[2] : 0;
    const int  ne03 = src0 ? src0->ne[3] : 0;

    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;

    const int  ne10 = src1 ? src1->ne[0] : 0;
    const int  ne11 = src1 ? src1->ne[1] : 0;
    const int  ne12 = src1 ? src1->ne[2] : 0;
    const int  ne13 = src1 ? src1->ne[3] : 0;

    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
    const cl_ulong nb13 = src1 ? src1->nb[3] : 0;

    const int  ne0 = dst ? dst->ne[0] : 0;
    const int  ne1 = dst ? dst->ne[1] : 0;

    int r2 = ne12/ne02;
    int r3 = ne13/ne03;

    GGML_ASSERT(ne00 == ne10);

    int nth0 = 32;
    int nth1 = 1;
    int nrows = 1;
    // The number of values produced by each subgroup
    int ndst = 4;

    cl_kernel kernel;

#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
    cl_context context = backend_ctx->context;

    if (ne01 && ne1 && use_adreno_kernels(src0)) {

    // init CL objects
    // <--------------------------------------------> //
    cl_int              status;
    cl_image_format     img_fmt_1d;
    cl_image_desc       img_desc_1d;
    cl_buffer_region    region;
    cl_mem              A_image1d = nullptr;
    cl_mem              B_image1d = nullptr;
    cl_mem              B_sub_buffer = nullptr;
    cl_mem              C_d = nullptr;
    // for B transpose
    cl_mem B_d = nullptr;
    cl_mem B_d_input_image = nullptr;
    // <--------------------------------------------> //

    // define matrix dimensions
    // <--------------------------------------------> //
    int M = ne01;
    int N = ne1;
    int K = ne00;
    int padding;
    // <--------------------------------------------> //

    // q4_0 x fp32
    if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) {
        // TODO: remove duplicate definitions of image description + format -- move to top

        // create an image for A
        // <--------------------------------------------> //
        if (N == 1) {
            img_fmt_1d = { CL_R, CL_UNSIGNED_INT32};
        } else {
            img_fmt_1d = { CL_R, CL_FLOAT};
        }
        memset(&img_desc_1d, 0, sizeof(img_desc_1d));
        img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
        img_desc_1d.image_width = M * K / 2 / 4;    // Divide by 4 for char -> float
        img_desc_1d.buffer = extra0_q4_0->q;
        A_image1d = clCreateImage(
            context,
            CL_MEM_READ_ONLY,
            &img_fmt_1d,
            &img_desc_1d,
            NULL,
            &status);
        CL_CHECK(status);
        // <--------------------------------------------> //


        // create a sub_buffer for B
        // <--------------------------------------------> //
        region.origin = (extra1->offset);
        region.size = K * N * sizeof(float);
        B_sub_buffer = clCreateSubBuffer(
            extra1->data_device,
            0,
            CL_BUFFER_CREATE_TYPE_REGION,
            &region,
            &status);
        CL_CHECK(status);
        // <--------------------------------------------> //

        // transpose activation for Skyler's gemm
        if (N != 1) {
            //how many extra elements beyond multiple of 8
            int extra_elements = N % 8;

            //how much padding to add
            padding = 0;
            if (extra_elements > 0){
                padding = 8 - extra_elements;
            }

            // Specify the starting offset (in bytes)
            region.origin = 0;
            // Specify the size of the sub-buffer (divide by 2 for FP16)
            region.size = K * (N + padding) * sizeof(float)/2;
            B_d = clCreateSubBuffer(
                backend_ctx->B_d_max,
                0,
                CL_BUFFER_CREATE_TYPE_REGION,
                &region,
                &status);
            CL_CHECK(status);

            cl_image_format image_format_B_d_input = { CL_RGBA, CL_FLOAT };
            cl_image_desc image_desc_B_d_input = {
                CL_MEM_OBJECT_IMAGE1D_BUFFER,
                static_cast<size_t>(K * N / 4),
                0, 0, 0, 0, 0, 0, 0, { B_sub_buffer }
            };
            B_d_input_image = clCreateImage(
                context,
                0,
                &image_format_B_d_input,
                &image_desc_B_d_input,
                NULL,
                &status);
            CL_CHECK(status);

            cl_image_format image_format_B_d_output = { CL_RGBA, CL_HALF_FLOAT }; //(CL_HALF_FLOAT for FP16)
            cl_image_desc image_desc_B_d_output = {
                CL_MEM_OBJECT_IMAGE1D_BUFFER,
                static_cast<size_t>(K * (N + padding)/4),
                0, 0, 0, 0, 0, 0, 0, { B_d }
            };
            B_image1d = clCreateImage(
                context,
                0,
                &image_format_B_d_output,
                &image_desc_B_d_output,
                NULL,
                &status);
            CL_CHECK(status);

            int height_B = N/4;
            int width_B = K/4;
            int padded_height_B = (N + padding)/4;

            kernel = backend_ctx->kernel_transpose_32_16;
            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &B_d_input_image));
            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &B_image1d));
            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));

            size_t local_size_t[2] = { 1, 16 };
            //WGS tuning
            if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
                local_size_t[0]=4;
                local_size_t[1]=8;
            } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
                local_size_t[0]=2;
                local_size_t[1]=8;
            } else if(ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
                local_size_t[0]=1;
                local_size_t[1]=8;
            } else if(ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
                local_size_t[0]=2;
                local_size_t[1]=8;
            }

            size_t global_size_t[2] = {
                static_cast<size_t>(width_B),
                static_cast<size_t>(padded_height_B)
            };

            #ifdef GGML_OPENCL_PROFILING
                cl_event evt;
                CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_size_t, local_size_t, 0, NULL, &evt));

                g_profiling_info.emplace_back();
                populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_size_t, local_size_t, dst);
            #else
                CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 2, NULL, global_size_t, local_size_t, 0, NULL, NULL));
            #endif
        } else {
            // no need to transpose B in other cases
            // create an image for B from sub_buffer
            // <--------------------------------------------> //
            img_fmt_1d = {CL_RGBA, CL_FLOAT};

            memset(&img_desc_1d, 0, sizeof(img_desc_1d));
            img_desc_1d.image_width = K * N / 4;
            img_desc_1d.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
            img_desc_1d.buffer = B_sub_buffer;
            B_image1d = clCreateImage(
                context,
                CL_MEM_READ_ONLY,
                &img_fmt_1d,
                &img_desc_1d,
                NULL,
                &status);
            CL_CHECK(status);
            // <--------------------------------------------> //
        }

        // choose gemm or gemv kernel
        // <--------------------------------------------> //
        if (N == 1) {
            kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general;
            if (M == 4096 && K == 4096) {
                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096;
            } else if (M == 4096 && K == 11008) {
                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008;
            } else if (M == 11008 && K == 4096) {
                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096;
            } else if (M == 32000 && K == 4096) {
                kernel = backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_32000_1_4096;
            }
        } else {
            kernel = backend_ctx->CL_mul_mat_Ab_Bi_8x4;
        }
        // <--------------------------------------------> //

        // set kernel args
        // <--------------------------------------------> //
        cl_uint k_arg = 0;

        if (N == 1) {
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &A_image1d));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &extra0_q4_0->d));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &B_image1d));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_ulong), &extra1->offset));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_mem),   &extrad->data_device));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(cl_ulong), &extrad->offset));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne00));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne01));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne02));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne10));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne12));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne0));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &ne1));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &r2));
            CL_CHECK(clSetKernelArg(kernel,  k_arg++, sizeof(int),      &r3));
        } else {
            region.origin = extrad->offset; // Specify the starting offset (in bytes)
            region.size = M * N * sizeof(float); // Specify the size of the sub-buffer
            C_d = clCreateSubBuffer(extrad->data_device, CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &status);
            CL_CHECK(status);

            int padded_N = ne1 + padding;

            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q4_0->q)); //A_q_dextra0_q4_0->q
            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q4_0->d)); //A_s_d
            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &B_image1d)); //B_d
            CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_mem), &C_d)); //C_d
            CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &ne01)); //M
            CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),    &padded_N)); //N with padding
            CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),    &ne00)); //K
            CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int),    &ne1)); //N without padding
        }
        // <--------------------------------------------> //

        // choose workgroup size
        // <--------------------------------------------> //
        size_t global_work_size[3] = {
            64, static_cast<size_t>((M+63)/64), static_cast<size_t>((N+31)/32)};
        size_t local_work_size[3] = {64, 2, 4};

        global_work_size[0] = (size_t)(ceil((float)ne1/8));
        global_work_size[1] = (size_t)(ne01/4);
        global_work_size[2] = (size_t)(1);

        local_work_size[0]  = (size_t)(1); //4x32 for FP32
        local_work_size[1]  = (size_t)(128);
        local_work_size[2]  = (size_t)(1);

        //WGS tuning
        if (ne0 == 4096 && ne1 == 128 && ne10 == 4096) {
            local_work_size[0] = 1;
            local_work_size[1] = 128;
        } else if (ne0 == 11008 && ne1 == 128 && ne10 == 4096) {
            local_work_size[0] = 2;
            local_work_size[1] = 64;
        } else if (ne0 == 4096 && ne1 == 128 && ne10 == 11008) {
            local_work_size[0] = 2;
            local_work_size[1] = 64;
        } else if (ne0 == 32000 && ne1 == 128 && ne10 == 4096) {
            local_work_size[0] = 2;
            local_work_size[1] = 64;
        }

        if (N == 1) {
            local_work_size[0] = backend_ctx->adreno_wave_size; // localsize
            local_work_size[1] = 4; // reduce factor
            local_work_size[2] = 1;

            global_work_size[0] = M / 2;
            global_work_size[1] = 4; // reduce factor
            global_work_size[2] = 1;
        }
        // <--------------------------------------------> //

        // enqueue kernel with profiling
        // <--------------------------------------------> //
    #ifdef GGML_OPENCL_PROFILING
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
        // enqueue kernel without profiling
    #else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
    #endif
        // <--------------------------------------------> //

        // deallocate sub buffers and images
        // <--------------------------------------------> //
        CL_CHECK(clReleaseMemObject(A_image1d));
        CL_CHECK(clReleaseMemObject(B_sub_buffer));
        CL_CHECK(clReleaseMemObject(B_image1d));

        if (N != 1) {
            CL_CHECK(clReleaseMemObject(B_d));
            CL_CHECK(clReleaseMemObject(B_d_input_image));
            CL_CHECK(clReleaseMemObject(C_d));
        }
        // <--------------------------------------------> //

        return;
    }
    } // if (ne01 && ne1)
#endif // GGML_OPENCL_USE_ADRENO_KERNELS

    if (!ggml_is_transposed(src0) &&
        !ggml_is_transposed(src1) &&
        src1t == GGML_TYPE_F32 &&
        ne00%32 == 0 &&
        ne11 > 2) {
#ifdef GGML_OPENCL_SOA_Q
        // Set up kernel.
        switch(src0t) {
            case GGML_TYPE_Q4_0:
                // This should have been satisfied.
                GGML_ASSERT(ne11 == ne1);
                GGML_ASSERT(ne01 == ne0);

                if (backend_ctx->gpu_family == INTEL) {
                    nth0 = 16;
                    nth1 = 1;

                    kernel = backend_ctx->kernel_mul_mat_q4_0_f32_1d_16x_flat;
                } else if (backend_ctx->gpu_family == ADRENO) {
                    nth0 = 64;
                    nth1 = 1;

                    kernel = backend_ctx->kernel_mul_mat_q4_0_f32_1d_8x_flat;
                } else {
                    GGML_ASSERT(false && "TODO: Unknown GPU");
                }

                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
                break;
            default:
                break;
        }

        // Launch kernel.
        if (src0t == GGML_TYPE_Q4_0) {
            size_t global_work_size[] = {(size_t)(ne01 + 7)/8*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
            size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};

            if (backend_ctx->gpu_family == INTEL) {
                // Set global size for Intel. It uses 16x output values.
                global_work_size[0] = (size_t)(ne01 + 15)/16*nth0;
                global_work_size[1] = (size_t)ne11*nth1;
                global_work_size[2] = (size_t)ne12*ne13;
            }

#ifdef GGML_OPENCL_PROFILING
            cl_event evt;
            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

            g_profiling_info.emplace_back();
            populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
            return;
        }
#else // GGML_OPENCL_SOA_Q
        // TODO: add block_q4_0 variant.
#endif // GGML_OPENCL_SOA_Q
    }

    // use custom matrix x vector kernel
    switch (src0t) {
        case GGML_TYPE_F32:
            //GGML_ASSERT(ne02 == ne12);
            GGML_ASSERT(src1t == GGML_TYPE_F32);
            kernel = backend_ctx->kernel_mul_mat_f32_f32;
            nrows = 4;

            if (backend_ctx->gpu_family == INTEL) {
                nth0 = 32;
                nth1 = 1;
            } else if (backend_ctx->gpu_family == ADRENO) {
                nth0 = 64;
                nth1 = 1;
            } else {
                GGML_ASSERT(false && "TODO: Unknown GPU");
            }

            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb00));
            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb03));
            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10));
            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne11));
            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne12));
            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb10));
            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb11));
            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb12));
            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb13));
            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne0));
            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne1));
            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
            break;
        case GGML_TYPE_F16:
            //GGML_ASSERT(ne02 == ne12);
            if (backend_ctx->gpu_family == INTEL) {
                nth0 = 32;
                nth1 = 1;
            } else if (backend_ctx->gpu_family == ADRENO) {
                nth0 = 64;
                nth1 = 1;
            } else {
                GGML_ASSERT(false && "TODO: Unknown GPU");
            }

            if (src1t == GGML_TYPE_F32) {
                if (ne11 * ne12 < 4) {
                    kernel = backend_ctx->kernel_mul_mat_f16_f32_1row;
                } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
                    kernel = backend_ctx->kernel_mul_mat_f16_f32_l4;
                    nrows = ne11;
                } else {
                    kernel = backend_ctx->kernel_mul_mat_f16_f32;
                    nrows = 4;
                }
            } else {
                kernel = backend_ctx->kernel_mul_mat_f16_f16;
                nrows = 4;
            }

            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb00));
            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb03));
            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne10));
            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne11));
            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne12));
            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb10));
            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb11));
            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb12));
            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb13));
            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne0));
            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne1));
            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
            break;
        case GGML_TYPE_Q4_0:
            // This should have been satisfied.
            GGML_ASSERT(ne11 == ne1);
            GGML_ASSERT(ne01 == ne0);

#ifdef GGML_OPENCL_SOA_Q
            if (backend_ctx->gpu_family == INTEL) {
                nth0 = 16;
                nth1 = 1;

                kernel = backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat;
                ndst = 8;
            } else if (backend_ctx->gpu_family == ADRENO) {
                nth0 = 64;
                nth1 = 1;

                kernel = backend_ctx->kernel_mul_mat_q4_0_f32_8x_flat;
                ndst =8;
            } else {
                GGML_ASSERT(false && "TODO: Unknown GPU");
            }

            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q4_0->q));
            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q4_0->d));
            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
#else // GGML_OPENCL_SOA_Q
            if (backend_ctx->gpu_family == INTEL) {
                // Use 1D local size. Each workgroup is a SIMD group. Each SIMD
                // group produces N_DST (4 for Q4_0 kernel) values in the result.
                // The number of workgroups on dim 0 (the leading dimension) is
                // the nearest multiple of 4 that covers ne0 (equals ne01).
                nth0 = 16;
                nth1 = 1;

                kernel = backend_ctx->kernel_mul_mat_q4_0_f32;
                ndst = 4;
            } else if (backend_ctx->gpu_family == ADRENO) {
                nth0 = 64;
                nth1 = 1;

                kernel = backend_ctx->kernel_mul_mat_q4_0_f32_v;
                ndst = 4;
            } else {
                GGML_ASSERT(false && "TODO: Unknown GPU");
            }

            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
#endif // GGML_OPENCL_SOA_Q
            break;
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
        case GGML_TYPE_Q5_K:
        case GGML_TYPE_Q6_K:
            kernel = backend_ctx->kernel_mul_mv_q6_K_f32;

            if (backend_ctx->gpu_family == INTEL) {
                nth0 = 2;
                nth1 = 16;
            } else if (backend_ctx->gpu_family == ADRENO) {
                nth0 = 2;
                nth1 = 64;
            } else {
                GGML_ASSERT(false && "TODO: Unknown GPU");
            }

            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
            break;
        default:
            GGML_ASSERT(false && "not implemented");
    }

    if (src0t == GGML_TYPE_Q4_0 ||
        src0t == GGML_TYPE_Q4_1 ||
        src0t == GGML_TYPE_Q8_0 ||
        src0t == GGML_TYPE_Q2_K) {
        // Each SIMD group produces N_DST values in the result. Assuming each
        // workgroup has N_SIMDGROUP SIMD groups, then each workgroup will
        // produce N_DST*N_SIMDGROUP values in the result. Hence, the grid size
        // (number of workgroups) will be a nearest multiple of
        // N_DST*N_SIMDGROUP to cover the size of the dimension. Below, 4 is
        // N_DST*N_SIMDGROUP (see the kernel for Q4_0 matmul).
        size_t global_work_size[] = {(size_t)(ne01 + ndst-1)/ndst*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    } else if (src0t == GGML_TYPE_Q4_K) {
        GGML_ASSERT(false && "not implemented");
    } else if (src0t == GGML_TYPE_Q3_K) {
        GGML_ASSERT(false && "not implemented");
    } else if (src0t == GGML_TYPE_Q5_K) {
        GGML_ASSERT(false && "not implemented");
    } else if (src0t == GGML_TYPE_Q6_K) {
        size_t global_work_size[] = {(size_t)(ne01+1)/2*nth0, (size_t)ne11*nth1, (size_t)ne12*ne13};
        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    } else {
        int64_t ny = (ne11 + nrows - 1)/nrows;

        size_t global_work_size[] = {(size_t)ne01*nth0, (size_t)ny*nth1, (size_t)ne12*ne13};
        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    }
}

static void ggml_cl_scale(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);
    GGML_UNUSED(src1);

    GGML_ASSERT(ggml_is_contiguous(src0));

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    float scale;
    memcpy(&scale, dst->op_params, sizeof(scale));

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_kernel kernel = backend_ctx->kernel_scale;

    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(float),    &scale));

    int n = ggml_nelements(dst)/4;

    size_t global_work_size[] = {(size_t)n, 1, 1};
    size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_cpy(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(src1);
    GGML_ASSERT(src1->extra);

    // GGML_OP_CPY happens between src0 and src1.
    // GGML_OP_DUP and GGML_OP_CONT happen between src0 and dst.
    UNUSED(dst);

    const int ne00 = src0 ? src0->ne[0] : 0;
    const int ne01 = src0 ? src0->ne[1] : 0;
    const int ne02 = src0 ? src0->ne[2] : 0;
    const int ne03 = src0 ? src0->ne[3] : 0;

    const cl_ulong nb00 = src0 ? src0->nb[0] : 0;
    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;

    const int ne10 = src1 ? src1->ne[0] : 0;
    const int ne11 = src1 ? src1->ne[1] : 0;
    const int ne12 = src1 ? src1->ne[2] : 0;
    const int ne13 = src1 ? src1->ne[3] : 0;

    const cl_ulong nb10 = src1 ? src1->nb[0] : 0;
    const cl_ulong nb11 = src1 ? src1->nb[1] : 0;
    const cl_ulong nb12 = src1 ? src1->nb[2] : 0;
    const cl_ulong nb13 = src1 ? src1->nb[3] : 0;

    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offset1 = extra1->offset + src1->view_offs;

    cl_kernel kernel;

    switch (src0t) {
        case GGML_TYPE_F32:
            switch (src1t) {
                case GGML_TYPE_F16:
                    kernel = backend_ctx->kernel_cpy_f32_f16;
                    break;
                case GGML_TYPE_F32:
                    kernel = backend_ctx->kernel_cpy_f32_f32;
                    break;
                default:
                    GGML_ASSERT(false && "not implemented");
            }
            break;
        case GGML_TYPE_F16:
            switch (src1t) {
                case GGML_TYPE_F16:
                    kernel = backend_ctx->kernel_cpy_f16_f16;
                    break;
                case GGML_TYPE_F32:
                    kernel = backend_ctx->kernel_cpy_f16_f32;
                    break;
                default:
                    GGML_ASSERT(false && "not implemented");
            }
            break;
        default:
            GGML_ASSERT(false && "not implemented");
    }

    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(int),      &ne00));
    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),      &ne01));
    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne02));
    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne03));
    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb00));
    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10));
    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne11));
    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne12));
    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne13));
    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb10));
    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb11));
    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb12));
    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb13));

    const int nth = MIN(64, ne00);

    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
    size_t local_work_size[] = {(size_t)nth, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, src1);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_dup(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    ggml_cl_cpy(backend, src0, dst, nullptr);
    UNUSED(src1);
}

static void ggml_cl_diag_mask_inf(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    UNUSED(src1);

    int n_past = ((int32_t *)(dst->op_params))[0];

    const int  ne00 = src0 ? src0->ne[0] : 0;
    const int  ne01 = src0 ? src0->ne[1] : 0;
    const int  ne02 = src0 ? src0->ne[2] : 0;

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_kernel kernel;

    if (ne00%8 == 0) {
        kernel = backend_ctx->kernel_diag_mask_inf_8;

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne00));
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &ne01));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &n_past));

        size_t global_work_size[] = {(size_t)ne00*ne01*ne02/8, 1, 1};
        size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    } else {
        kernel = backend_ctx->kernel_diag_mask_inf;

        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne00));
        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &ne01));
        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &n_past));

        size_t global_work_size[] = {(size_t)ne00, (size_t)ne01, (size_t)ne02};
        size_t local_work_size[] = {64, 1, 1};

#ifdef GGML_OPENCL_PROFILING
        cl_event evt;
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

        g_profiling_info.emplace_back();
        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
    }
}

static void ggml_cl_soft_max(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    // Softmax can now fuse KQ mask and KQ scale, which used to be two additional
    // ops before softmax. It now also fuses alibi if `max_bias > 0`. For llama,
    // alibi is not used; however, for some other models, it is used.
    // KQ_mask
    if (src1) {
        GGML_ASSERT(src1);
        GGML_ASSERT(src1->extra);
    }

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    ggml_tensor_extra_cl * extra1 = src1 ? (ggml_tensor_extra_cl *)src1->extra : nullptr;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    cl_ulong offset1 = extra1 ? extra1->offset + src1->view_offs : offset0;

    const int  ne00 = src0 ? src0->ne[0] : 0;
    const int  ne01 = src0 ? src0->ne[1] : 0;
    const int  ne02 = src0 ? src0->ne[2] : 0;
    const int  ne03 = src0 ? src0->ne[3] : 0;

    float scale, max_bias;
    memcpy(&scale,    dst->op_params + 0, sizeof(float));
    memcpy(&max_bias, dst->op_params + 1, sizeof(float));

    const int nrows_x = ggml_nrows(src0);
    const int nrows_y = src0->ne[1];

    const int n_head      = nrows_x/nrows_y;
    const int n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));

    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

    // Local size must be wave size. Each workgroup is a wave, working on a row,
    // where a row corresponds to leading dimension.
    int nth = MIN(32, ne00);

    if (backend_ctx->gpu_family == INTEL) {
        // This is the same as the initial value.
        nth = MIN(32, ne00);
    }
    else if (backend_ctx->gpu_family == ADRENO) {
        nth = 64;
    } else {
        GGML_ASSERT(false && "TODO: Unknown GPU");
    }

    cl_kernel kernel;

    if (ne00%4 == 0) {
        kernel = backend_ctx->kernel_soft_max_4;
    } else {
        kernel = backend_ctx->kernel_soft_max;
    }

    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   extra1 ? &extra1->data_device : &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(float),    &scale));
    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(float),    &max_bias));
    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float),    &m0));
    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float),    &m1));
    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &n_head_log2));

    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
    size_t local_work_size[] = {(size_t)nth, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
    GGML_ASSERT(src1);
    GGML_ASSERT(src1->extra);
    GGML_ASSERT(dst);
    GGML_ASSERT(dst->extra);

    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
    cl_command_queue queue = backend_ctx->queue;

    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;

    cl_ulong offset0 = extra0->offset + src0->view_offs;
    cl_ulong offset1 = extra1->offset + src1->view_offs;
    cl_ulong offsetd = extrad->offset + dst->view_offs;

    ggml_tensor * src2 = dst->src[2];
    ggml_tensor_extra_cl * extra2 = src2 ? (ggml_tensor_extra_cl *)src2->extra : nullptr;

    cl_ulong offset2 = extra2 ? extra2->offset + src2->view_offs : offset0;

    const int  ne00 = src0 ? src0->ne[0] : 0;
    const int  ne01 = src0 ? src0->ne[1] : 0;
    const int  ne02 = src0 ? src0->ne[2] : 0;
    const int  ne03 = src0 ? src0->ne[3] : 0;

    const int  nb00 = src0 ? src0->nb[0] : 0;
    const int  nb01 = src0 ? src0->nb[1] : 0;
    const int  nb02 = src0 ? src0->nb[2] : 0;
    const int  nb03 = src0 ? src0->nb[3] : 0;

    const int ne10 = src1 ? src1->ne[0] : 0;
    const int ne11 = src1 ? src1->ne[1] : 0; UNUSED(ne11);
    const int ne12 = src1 ? src1->ne[2] : 0; UNUSED(ne12);
    const int ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13);

    const int  ne0 = dst ? dst->ne[0] : 0;
    const int  ne1 = dst ? dst->ne[1] : 0;
    const int  ne2 = dst ? dst->ne[2] : 0;
    const int  ne3 = dst ? dst->ne[3] : 0;

    const int  nb0 = dst ? dst->nb[0] : 0;
    const int  nb1 = dst ? dst->nb[1] : 0;
    const int  nb2 = dst ? dst->nb[2] : 0;
    const int  nb3 = dst ? dst->nb[3] : 0;

    GGML_ASSERT(ne10 == ne02);

    int nth = MIN(64, ne00);

    const int n_past     = ((int *) dst->op_params)[0];
    const int n_dims     = ((int *) dst->op_params)[1];
    const int mode       = ((int *) dst->op_params)[2];
    const int n_ctx_orig = ((int32_t *) dst->op_params)[4];

    float freq_base;
    float freq_scale;
    float ext_factor;
    float attn_factor;
    float beta_fast;
    float beta_slow;

    memcpy(&freq_base,   (int32_t *) dst->op_params + 5, sizeof(float));
    memcpy(&freq_scale,  (int32_t *) dst->op_params + 6, sizeof(float));
    memcpy(&ext_factor,  (int32_t *) dst->op_params + 7, sizeof(float));
    memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
    memcpy(&beta_fast,   (int32_t *) dst->op_params + 9, sizeof(float));
    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));

    const bool is_neox = mode & 2;

    cl_kernel kernel;

    if (!is_neox) {
        switch (src0->type) {
            case GGML_TYPE_F32:
                kernel = backend_ctx->kernel_rope_norm_f32;
                break;
            case GGML_TYPE_F16:
                kernel = backend_ctx->kernel_rope_norm_f16;
                break;
            default:
                GGML_ASSERT(false);
        };
    } else {
        switch (src0->type) {
            case GGML_TYPE_F32:
                kernel = backend_ctx->kernel_rope_neox_f32;
                break;
            case GGML_TYPE_F16:
                kernel = backend_ctx->kernel_rope_neox_f16;
                break;
            default:
                GGML_ASSERT(false);
        };
    }

    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   extra2 ? &extra2->data_device : &extra0->data_device));
    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne01));
    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne02));
    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne03));
    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb00));
    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb01));
    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb02));
    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb03));
    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne0));
    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne1));
    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne2));
    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &ne3));
    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb0));
    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb1));
    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &nb2));
    CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &nb3));
    CL_CHECK(clSetKernelArg(kernel, 24, sizeof(int),      &n_past));
    CL_CHECK(clSetKernelArg(kernel, 25, sizeof(int),      &n_dims));
    CL_CHECK(clSetKernelArg(kernel, 26, sizeof(int),      &n_ctx_orig));
    CL_CHECK(clSetKernelArg(kernel, 27, sizeof(float),    &freq_base));
    CL_CHECK(clSetKernelArg(kernel, 28, sizeof(float),    &freq_scale));
    CL_CHECK(clSetKernelArg(kernel, 29, sizeof(float),    &ext_factor));
    CL_CHECK(clSetKernelArg(kernel, 30, sizeof(float),    &attn_factor));
    CL_CHECK(clSetKernelArg(kernel, 31, sizeof(float),    &beta_fast));
    CL_CHECK(clSetKernelArg(kernel, 32, sizeof(float),    &beta_slow));

    size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
    size_t local_work_size[] = {(size_t)nth, 1, 1};

#ifdef GGML_OPENCL_PROFILING
    cl_event evt;
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));

    g_profiling_info.emplace_back();
    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}

//------------------------------------------------------------------------------
// Op offloading
//------------------------------------------------------------------------------

typedef void (*ggml_cl_func_t)(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);

bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor) {
    ggml_cl_func_t func = nullptr;

    ggml_tensor * src0 = tensor->src[0];
    ggml_tensor * src1 = tensor->src[1];

    const bool any_on_device = tensor->extra
        || (src0 != nullptr && src0->extra)
        || (src1 != nullptr && src1->extra);

    switch (tensor->op) {
        case GGML_OP_GET_ROWS:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_get_rows;
            break;
        case GGML_OP_CPY:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_cpy;
            break;
        case GGML_OP_DUP:
        case GGML_OP_CONT:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_dup;
            break;
        case GGML_OP_ADD:
            if (!any_on_device) {
                return false;
            }
            GGML_ASSERT(ggml_is_contiguous(src0));
            GGML_ASSERT(ggml_is_contiguous(src1));
            func = ggml_cl_add;
            break;
        case GGML_OP_MUL:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_mul;
            break;
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(tensor)) {
                case GGML_UNARY_OP_GELU:
                    if (!any_on_device) {
                        return false;
                    }
                    func = ggml_cl_gelu;
                    break;
                case GGML_UNARY_OP_SILU:
                    if (!any_on_device) {
                        return false;
                    }
                    func = ggml_cl_silu;
                    break;
                case GGML_UNARY_OP_RELU:
                    if (!any_on_device) {
                        return false;
                    }
                    func = ggml_cl_relu;
                    break;
                default:
                    return false;
            } break;
        case GGML_OP_CLAMP:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_clamp;
            break;
        case GGML_OP_NORM:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_norm;
            break;
        case GGML_OP_RMS_NORM:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_rms_norm;
            break;
        case GGML_OP_MUL_MAT:
            if (!any_on_device && !ggml_cl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
                return false;
            }
            func = ggml_cl_mul_mat;
            break;
        case GGML_OP_SCALE:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_scale;
            break;
        case GGML_OP_RESHAPE:
        case GGML_OP_VIEW:
        case GGML_OP_PERMUTE:
        case GGML_OP_TRANSPOSE:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_nop;
            break;
        case GGML_OP_DIAG_MASK_INF:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_diag_mask_inf;
            break;
        case GGML_OP_SOFT_MAX:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_soft_max;
            break;
        case GGML_OP_ROPE:
            if (!any_on_device) {
                return false;
            }
            func = ggml_cl_rope;
            break;
        default:
            return false;
    }

    func(backend, tensor->src[0], tensor->src[1], tensor);
    return true;
}