#include "ggml-vulkan.h"
#include <vulkan/vulkan_core.h>
#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_CHECK_RESULTS)
#include <chrono>
#include "ggml-cpu.h"
#endif

// See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
#define VULKAN_HPP_DISPATCH_LOADER_DYNAMIC 1
// We use VULKAN_HPP_DEFAULT_DISPATCHER, but not VULKAN_HPP_DEFAULT_DISPATCH_LOADER_DYNAMIC_STORAGE
// to avoid conflicts with applications or other libraries who might use it.
#if VK_HEADER_VERSION >= 301
namespace vk::detail { class DispatchLoaderDynamic; }
using vk::detail::DispatchLoaderDynamic;
#else
namespace vk { class DispatchLoaderDynamic; }
using vk::DispatchLoaderDynamic;
#endif
DispatchLoaderDynamic & ggml_vk_default_dispatcher();
#define VULKAN_HPP_DEFAULT_DISPATCHER ggml_vk_default_dispatcher()

#include <vulkan/vulkan.hpp>

// Fallback definitions for VK_NV_cooperative_matrix_decode_vector in case the
// installed Vulkan headers predate the extension.
#ifndef VK_NV_cooperative_matrix_decode_vector
#define VK_NV_cooperative_matrix_decode_vector 1
#define VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME "VK_NV_cooperative_matrix_decode_vector"
#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_DECODE_VECTOR_FEATURES_NV ((VkStructureType)1000689000)
typedef struct VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV {
    VkStructureType    sType;
    void*              pNext;
    VkBool32           cooperativeMatrixDecodeVector;
} VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV;
#endif

// SPIR-V Headers: different SDK installations expose different include paths.
// LunarG Vulkan SDK on Windows typically provides <spirv-headers/spirv.hpp>.
// Linux packages, MSYS2 and MinGW often use the Khronos layout <spirv/unified1/spirv.hpp>.
#if __has_include(<spirv/unified1/spirv.hpp>)
#    include <spirv/unified1/spirv.hpp>
#elif __has_include(<spirv-headers/spirv.hpp>)
#    include <spirv-headers/spirv.hpp>
#elif __has_include(<spirv.hpp>)
#    include <spirv.hpp>
#else
     // Fallback to let the compiler throw a standard "file not found" error
#    include <spirv/unified1/spirv.hpp>
#endif

#include <algorithm>
#include <cmath>
#include <iomanip>
#include <iostream>
#include <tuple>
#include <vector>
#include <deque>
#include <sstream>
#include <utility>
#include <memory>
#include <limits>
#include <map>
#include <set>
#include <unordered_map>
#include <shared_mutex>
#include <mutex>
#include <future>
#include <condition_variable>
#include <thread>

#if defined(_MSC_VER)
# define NOMINMAX 1
# include <windows.h>
# define YIELD() YieldProcessor()
#elif defined(__clang__) || defined(__GNUC__)
# if defined(__x86_64__) ||defined(__i386__)
#  include <immintrin.h>
#  define YIELD() _mm_pause()
# elif defined(__arm__) || defined(__aarch64__)
#  if defined(__clang__)
#   include <arm_acle.h>
#   define YIELD() __yield()
#  else
#   define YIELD() asm volatile("yield")
#  endif
# endif
#endif

#if !defined(YIELD)
#define YIELD()
#endif

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

#include "ggml-vulkan-shaders.hpp"

// remove this once it's more widely available in the SDK
#if !defined(VK_KHR_shader_bfloat16)

#define VK_KHR_shader_bfloat16 1
#define VK_KHR_SHADER_BFLOAT16_SPEC_VERSION                          1
#define VK_KHR_SHADER_BFLOAT16_EXTENSION_NAME                        "VK_KHR_shader_bfloat16"
#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR ((VkStructureType)1000141000)
#define VK_COMPONENT_TYPE_BFLOAT16_KHR                               ((VkComponentTypeKHR)1000141000)

typedef struct VkPhysicalDeviceShaderBfloat16FeaturesKHR {
    VkStructureType                       sType;
    void*                                 pNext;
    VkBool32                              shaderBFloat16Type;
    VkBool32                              shaderBFloat16DotProduct;
    VkBool32                              shaderBFloat16CooperativeMatrix;
} VkPhysicalDeviceShaderBfloat16FeaturesKHR;
#endif

#if !defined(VK_VALVE_shader_mixed_float_dot_product)
#define VK_VALVE_shader_mixed_float_dot_product 1
#define VK_VALVE_SHADER_MIXED_FLOAT_DOT_PRODUCT_SPEC_VERSION 1
#define VK_VALVE_SHADER_MIXED_FLOAT_DOT_PRODUCT_EXTENSION_NAME "VK_VALVE_shader_mixed_float_dot_product"
#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_MIXED_FLOAT_DOT_PRODUCT_FEATURES_VALVE ((VkStructureType)1000673000)
typedef struct VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE {
    VkStructureType    sType;
    void*              pNext;
    VkBool32           shaderMixedFloatDotProductFloat16AccFloat32;
    VkBool32           shaderMixedFloatDotProductFloat16AccFloat16;
    VkBool32           shaderMixedFloatDotProductBFloat16Acc;
    VkBool32           shaderMixedFloatDotProductFloat8AccFloat32;
} VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE;
#endif

#define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1))
#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }

#define VK_VENDOR_ID_AMD 0x1002
#define VK_VENDOR_ID_APPLE 0x106b
#define VK_VENDOR_ID_INTEL 0x8086
#define VK_VENDOR_ID_NVIDIA 0x10de
#define VK_VENDOR_ID_QUALCOMM 0x5143

#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 256

#define VK_CHECK(err, msg)                                          \
    do {                                                            \
        vk::Result err_ = (err);                                    \
        if (err_ != vk::Result::eSuccess) {                         \
            fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n",  \
                #err, to_string(err_).c_str(), __FILE__, __LINE__); \
            exit(1);                                                \
        }                                                           \
    } while (0)

#ifdef GGML_VULKAN_DEBUG
#define VK_LOG_DEBUG(msg) std::cerr << msg << std::endl
#else
#define VK_LOG_DEBUG(msg) ((void) 0)
#endif // GGML_VULKAN_DEBUG

struct ggml_backend_vk_context;

#define MAX_PARAMETER_COUNT 12
// Max number of adds that can be fused without exceeding MAX_PARAMETER_COUNT.
#define MAX_FUSED_ADDS (MAX_PARAMETER_COUNT - 3)

typedef std::shared_ptr<struct vk_pipeline_struct> vk_pipeline;

struct vk_pipeline_struct {
    std::string name;
    vk::ShaderModule shader_module;
    vk::PipelineLayout layout;
    vk::Pipeline pipeline;
    uint32_t push_constant_size;
    uint32_t parameter_count;
    std::array<uint32_t, 3> wg_denoms;
    uint32_t align;
    // true if fields have been set by ggml_vk_create_pipeline
    bool initialized {};
    // true while a compile is in flight, used to dedupe concurrent claims.
    // Protected by device->compile_mutex.
    bool compile_pending {};
    // set to true when the shader has been compiled
    std::atomic<bool> compiled {};
    // number of registers used, extracted from pipeline executable properties
    uint32_t register_count {};

#if defined(VK_EXT_shader_64bit_indexing)
    bool is_64b_indexing {};
#endif
    // linked list of pipelines for multiple compilation variants.
    // currently only used to compile a 64-bit indexing variant.
    vk_pipeline next;
};

typedef std::weak_ptr<vk_pipeline_struct> vk_pipeline_ref;

static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);

struct vk_matmul_pipeline_struct {
    vk_pipeline l, m, s;
    vk_pipeline a_l, a_m, a_s;
    // Returns true when all unaligned pipelines are null.
    // We only check for unaligned variants since one of the unaligned pipelines must exist
    // while aligned pipelines are optional
    bool is_empty() const {
        return l == nullptr && m == nullptr && s == nullptr;
    }
};
typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;

struct vk_matmul_pipeline2 {
    vk_matmul_pipeline2() {
        f16acc = std::make_shared<vk_matmul_pipeline_struct>();
        f32acc = std::make_shared<vk_matmul_pipeline_struct>();
    }
    vk_matmul_pipeline f32acc;
    vk_matmul_pipeline f16acc;
};

struct vk_device_struct;
typedef std::shared_ptr<vk_device_struct> vk_device;
typedef std::weak_ptr<vk_device_struct> vk_device_ref;

struct vk_buffer_struct;
typedef std::shared_ptr<vk_buffer_struct> vk_buffer;
typedef std::weak_ptr<vk_buffer_struct> vk_buffer_ref;

struct ggml_backend_vk_buffer_type_context {
    std::string name;
    vk_device device;
};

struct vk_queue;

struct vk_command_buffer {
    vk::CommandBuffer buf;
    uint64_t use_counter = 0;
    bool in_use = false;
};

// Stores command pool/buffers. There's an instance of this
// for each (context,queue) pair and for each (device,queue) pair.
struct vk_command_pool {
    void init(vk_device& device, vk_queue *q_);
    void destroy(vk::Device& device);

    vk::CommandPool pool;
    // Using deque so the pointers to command buffers
    // remain valid even if we add more
    std::deque<vk_command_buffer> cmd_buffers;

    vk_queue *q;

    size_t buffers_in_use() const {
        return std::count_if(cmd_buffers.begin(), cmd_buffers.end(),
            [](const auto& cb) { return cb.in_use; });
    }
};

// Prevent simultaneous submissions to the same queue.
// This could be per vk_queue if we stopped having two vk_queue structures
// sharing the same vk::Queue.
static std::mutex queue_mutex;

struct vk_queue {
    uint32_t queue_family_index;
    vk::Queue queue;

    vk_command_pool cmd_pool;

    vk::PipelineStageFlags stage_flags;

    bool transfer_only;

    // copy everything except the cmd_pool
    void copyFrom(vk_queue &other) {
        queue_family_index = other.queue_family_index;
        queue = other.queue;
        stage_flags = other.stage_flags;
        transfer_only = other.transfer_only;
    }
};

static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft);
static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft);
static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft);
static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor);
static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
    /* .get_name         = */ ggml_backend_vk_buffer_type_name,
    /* .alloc_buffer     = */ ggml_backend_vk_buffer_type_alloc_buffer,
    /* .get_alignment    = */ ggml_backend_vk_buffer_type_get_alignment,
    /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
    /* .get_alloc_size   = */ ggml_backend_vk_buffer_type_get_alloc_size,
    /* .is_host          = */ NULL,
};

class vk_memory_logger;
class vk_perf_logger;
static void ggml_vk_destroy_buffer(vk_buffer& buf);
static void ggml_vk_synchronize(ggml_backend_vk_context * ctx);

static constexpr uint32_t mul_mat_vec_max_cols = 8;
static constexpr uint32_t p021_max_gqa_ratio = 8;

enum vk_device_architecture {
    OTHER,
    AMD_GCN,
    AMD_RDNA1,
    AMD_RDNA2,
    AMD_RDNA3,
    INTEL_XE2,
    NVIDIA_PRE_TURING,
    NVIDIA_TURING,
};

static vk_device_architecture get_device_architecture(const vk::PhysicalDevice& device) {
    vk::PhysicalDeviceProperties props = device.getProperties();

    if (props.vendorID == VK_VENDOR_ID_AMD) {
        const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();

        bool amd_shader_core_properties = false;
        bool integer_dot_product = false;
        bool subgroup_size_control = false;

        for (const auto& properties : ext_props) {
            if (strcmp("VK_AMD_shader_core_properties", properties.extensionName) == 0) {
                amd_shader_core_properties = true;
            } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0) {
                integer_dot_product = true;
            } else if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) {
                subgroup_size_control = true;
            }
        }

        if (!amd_shader_core_properties || !integer_dot_product || !subgroup_size_control) {
            return vk_device_architecture::OTHER;
        }

        vk::PhysicalDeviceProperties2 props2;
        vk::PhysicalDeviceShaderCorePropertiesAMD shader_core_props_amd;
        vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR integer_dot_props;
        vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props;

        props2.pNext = &shader_core_props_amd;
        shader_core_props_amd.pNext = &integer_dot_props;
        integer_dot_props.pNext = &subgroup_size_control_props;

        device.getProperties2(&props2);

        if (subgroup_size_control_props.maxSubgroupSize == 64 && subgroup_size_control_props.minSubgroupSize == 64) {
            return vk_device_architecture::AMD_GCN;
        }
        if (subgroup_size_control_props.maxSubgroupSize == 64 && subgroup_size_control_props.minSubgroupSize == 32) {
            // RDNA
            if (shader_core_props_amd.wavefrontsPerSimd == 20) {
                return vk_device_architecture::AMD_RDNA1;
            }
            if (integer_dot_props.integerDotProduct4x8BitPackedMixedSignednessAccelerated) {
                return vk_device_architecture::AMD_RDNA3;
            }
            return vk_device_architecture::AMD_RDNA2;
        }
    } else if (props.vendorID == VK_VENDOR_ID_INTEL) {
        const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();

        bool subgroup_size_control = false;

        for (const auto& properties : ext_props) {
            if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) {
                subgroup_size_control = true;
            }
        }

        if (!subgroup_size_control) {
            return vk_device_architecture::OTHER;
        }

        vk::PhysicalDeviceProperties2 props2;
        vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props;

        props2.pNext = &subgroup_size_control_props;
        device.getProperties2(&props2);

        if (subgroup_size_control_props.minSubgroupSize == 16) {
            // Xe2 architecture uses SIMD16 while previous Xe and Gen architecture uses SIMD8.
            // Minimum subgroup size matches the SIMD width so we distinguish architecture by checking this value.
            // https://www.intel.com/content/www/us/en/content-details/824434/2024-intel-tech-tour-xe2-and-lunar-lake-s-gpu.html
            // https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2025-0/intel-xe-gpu-architecture.html
            return vk_device_architecture::INTEL_XE2;
        }
    } else if (props.vendorID == VK_VENDOR_ID_NVIDIA) {
        const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();

        bool cooperative_matrix = false;
        bool sm_builtins = false;

        // Detect "pre-turing" based on lack of coopmat support.
        for (const auto& properties : ext_props) {
            if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0) {
                cooperative_matrix = true;
            } else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) {
                sm_builtins = true;
            }
        }

        if (!cooperative_matrix) {
            return vk_device_architecture::NVIDIA_PRE_TURING;
        }

        if (sm_builtins) {
            vk::PhysicalDeviceProperties2 props2;
            vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props;

            props2.pNext = &sm_props;

            device.getProperties2(&props2);

            // Turing has 32, following architectures have 48
            if (sm_props.shaderWarpsPerSM == 32) {
                return vk_device_architecture::NVIDIA_TURING;
            }
        }
    }
    return vk_device_architecture::OTHER;
}

enum vk_conv_shapes {
    CONV_SHAPE_128x128,
    CONV_SHAPE_64x32,
    CONV_SHAPE_32x256,
    CONV_SHAPE_64x128,
    CONV_SHAPE_COUNT,
};

struct vk_conv_block_size {
    uint32_t K;
    uint32_t NPQ;
    uint32_t CRS;
};

vk_conv_block_size vk_conv_block_sizes[CONV_SHAPE_COUNT] = {
    // K   NPQ  CRS
    { 128, 128, 16 }, // CONV_SHAPE_128x128
    {  64,  32, 32 }, // CONV_SHAPE_64x32
    {  32, 256, 16 }, // CONV_SHAPE_32x256
    {  64, 128, 16 }, // CONV_SHAPE_64x128
};

enum dmmv_wg_sizes {
    DMMV_WG_SIZE_SUBGROUP,
    DMMV_WG_SIZE_LARGE,
    DMMV_WG_SIZE_COUNT,
};

enum FaCodePath {
    FA_SCALAR,
    FA_COOPMAT1,
    FA_COOPMAT2,
};

struct vk_fa_pipeline_state {
    uint32_t HSK, HSV;
    uint32_t Br, Bc;
    uint32_t D_split, row_split;
    bool shmem_staging;
    FaCodePath path;
    uint32_t workgroup_size, subgroup_size;
    bool aligned;
    bool f32acc;
    uint32_t flags;
    uint32_t limit_occupancy_shmem;
    ggml_type k_type;
    ggml_type v_type;

    bool operator<(const vk_fa_pipeline_state &b) const {
        return std::tie(HSK, HSV, Br, Bc, D_split, row_split, shmem_staging, path, workgroup_size, subgroup_size, aligned, f32acc, flags, limit_occupancy_shmem, k_type, v_type) <
               std::tie(b.HSK, b.HSV, b.Br, b.Bc, b.D_split, b.row_split, b.shmem_staging, b.path, b.workgroup_size, b.subgroup_size, b.aligned, b.f32acc, b.flags, b.limit_occupancy_shmem, b.k_type, b.v_type);
    }
};

struct vk_conv2d_pipeline_state {
    vk_conv2d_pipeline_state(uint32_t s0, uint32_t s1, uint32_t p0, uint32_t p1, uint32_t d0, uint32_t d1, uint32_t KW, uint32_t KH, uint32_t aligned)
        : s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), KW(KW), KH(KH), aligned(aligned) {}

    uint32_t s0, s1, p0, p1, d0, d1, KW, KH;
    // when set, shader can skip K/CRS/NPQ bounds checks and address clamps
    uint32_t aligned;

    bool operator<(const vk_conv2d_pipeline_state &b) const {
        return std::tie(s0, s1, p0, p1, d0, d1, KW, KH, aligned) <
               std::tie(b.s0, b.s1, b.p0, b.p1, b.d0, b.d1, b.KW, b.KH, b.aligned);
    }
};

struct vk_solve_tri_pipeline_state {
    vk_solve_tri_pipeline_state(uint32_t N, uint32_t K)
        : N(N), K(K) {}

    uint32_t N, K;

    bool operator<(const vk_solve_tri_pipeline_state &b) const {
        return std::tie(N, K) <
               std::tie(b.N, b.K);
    }
};

enum shader_reduction_mode {
    SHADER_REDUCTION_MODE_SHMEM,
    SHADER_REDUCTION_MODE_HYBRID,
    SHADER_REDUCTION_MODE_SUBGROUP,
    SHADER_REDUCTION_MODE_COUNT,
};

// argsort pipelines for up to 1<<10 invocations per workgroup
static constexpr uint32_t num_argsort_pipelines = 11;
static constexpr uint32_t num_topk_moe_pipelines = 10;
static constexpr uint32_t num_topk_pipelines = 11;

static constexpr std::initializer_list<ggml_op> topk_moe_early_softmax_norm{ GGML_OP_SOFT_MAX, GGML_OP_RESHAPE,  GGML_OP_ARGSORT,
                                                                             GGML_OP_VIEW,     GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
                                                                             GGML_OP_SUM_ROWS, GGML_OP_CLAMP,    GGML_OP_DIV,
                                                                             GGML_OP_RESHAPE };

static constexpr std::initializer_list<ggml_op> topk_moe_sigmoid_norm_bias{ GGML_OP_UNARY,    GGML_OP_RESHAPE,  GGML_OP_ADD,
                                                                            GGML_OP_ARGSORT,  GGML_OP_VIEW,     GGML_OP_GET_ROWS,
                                                                            GGML_OP_RESHAPE,  GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
                                                                            GGML_OP_DIV,      GGML_OP_RESHAPE };

static constexpr std::initializer_list<ggml_op> topk_moe_early_softmax     { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE,  GGML_OP_ARGSORT,
                                                                             GGML_OP_VIEW,     GGML_OP_GET_ROWS };

static constexpr std::initializer_list<ggml_op> topk_moe_late_softmax      { GGML_OP_ARGSORT,  GGML_OP_VIEW,
                                                                             GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
                                                                             GGML_OP_SOFT_MAX, GGML_OP_RESHAPE };

// Snake activation: y = x + sin(a*x)^2 * inv_b. Used by the optimize_graph reorder
// pass so it keeps the chain contiguous and by the dispatcher to detect the fusion.
static constexpr std::initializer_list<ggml_op> snake_pattern              { GGML_OP_MUL,      GGML_OP_SIN,
                                                                             GGML_OP_SQR,      GGML_OP_MUL,
                                                                             GGML_OP_ADD };

//node #978 (  SOFT_MAX):     ffn_moe_probs-15 (   0K) [Vulka         ] use=2:    ffn_moe_logits-15 (   0K) [Vulka         ]
//node #979 (   RESHAPE): ffn_moe_probs-15 (re (   0K) [Vulka         ] use=1:     ffn_moe_probs-15 (   0K) [Vulka         ]
//node #980 (   ARGSORT):   ffn_moe_argsort-15 (   0K) [Vulka         ] use=1:     ffn_moe_probs-15 (   0K) [Vulka         ]
//node #981 (      VIEW):      ffn_moe_topk-15 (   0K) [Vulka         ] use=4:   ffn_moe_argsort-15 (   0K) [Vulka         ]
//node #982 (  GET_ROWS):   ffn_moe_weights-15 (   0K) [Vulka         ] use=1: ffn_moe_probs-15 (re (   0K) [Vulka         ]      ffn_moe_topk-15 (   0K) [Vulka         ]
//node #983 (   RESHAPE): ffn_moe_weights-15 ( (   0K) [Vulka         ] use=2:   ffn_moe_weights-15 (   0K) [Vulka         ]
//node #984 (  SUM_ROWS): ffn_moe_weights_sum- (   0K) [Vulka         ] use=1: ffn_moe_weights-15 ( (   0K) [Vulka         ]
//node #985 (     CLAMP): ffn_moe_weights_sum_ (   0K) [Vulka         ] use=1: ffn_moe_weights_sum- (   0K) [Vulka         ]
//node #986 (       DIV): ffn_moe_weights_norm (   0K) [Vulka         ] use=1: ffn_moe_weights-15 ( (   0K) [Vulka         ] ffn_moe_weights_sum_ (   0K) [Vulka         ]
//node #987 (   RESHAPE): ffn_moe_weights_norm (   0K) [Vulka         ] use=1: ffn_moe_weights_norm (   0K) [Vulka         ]
static constexpr std::initializer_list<std::array<int, 3>> topk_moe_early_softmax_norm_edges {
    { 1, 0, 0 }, // reshape->src[0]  == softmax
    { 2, 0, 0 }, // argsort->src[0]  == softmax
    { 3, 0, 2 }, // view->src[0]     == argsort
    { 4, 0, 1 }, // get_rows->src[0] == reshape
    { 4, 1, 3 }, // get_rows->src[1] == view
    { 5, 0, 4 }, // reshape->src[0]  == get_rows
    { 6, 0, 5 }, // sum_rows->src[0] == reshape
    { 7, 0, 6 }, // clamp->src[0]    == sum_rows
    { 8, 0, 5 }, // div->src[0]      == reshape
    { 8, 1, 7 }, // div->src[1]      == clamp
    { 9, 0, 8 }, // reshape->src[0]  == div
};

//node #436 (     UNARY):     ffn_moe_probs-10 ( 256K) [Vulka         ] use=2:    ffn_moe_logits-10 ( 256K) [Vulka         ]
//node #437 (   RESHAPE): ffn_moe_probs-10 (re ( 256K) [Vulka         ] use=1:     ffn_moe_probs-10 ( 256K) [Vulka         ]
//node #438 (       ADD): ffn_moe_probs_biased ( 256K) [Vulka         ] use=1:     ffn_moe_probs-10 ( 256K) [Vulka         ] blk.10.exp_probs_b.b (   0K) [Vulka         ]
//node #439 (   ARGSORT):   ffn_moe_argsort-10 ( 256K) [Vulka         ] use=1: ffn_moe_probs_biased ( 256K) [Vulka         ]
//node #440 (      VIEW):      ffn_moe_topk-10 ( 255K) [Vulka         ] use=3:   ffn_moe_argsort-10 ( 256K) [Vulka         ]
//node #441 (  GET_ROWS):   ffn_moe_weights-10 (  12K) [Vulka         ] use=1: ffn_moe_probs-10 (re ( 256K) [Vulka         ]      ffn_moe_topk-10 ( 255K) [Vulka         ]
//node #442 (   RESHAPE): ffn_moe_weights-10 ( (  12K) [Vulka         ] use=2:   ffn_moe_weights-10 (  12K) [Vulka         ]
//node #443 (  SUM_ROWS): ffn_moe_weights_sum- (   2K) [Vulka         ] use=1: ffn_moe_weights-10 ( (  12K) [Vulka         ]
//node #444 (     CLAMP): ffn_moe_weights_sum_ (   2K) [Vulka         ] use=1: ffn_moe_weights_sum- (   2K) [Vulka         ]
//node #445 (       DIV): ffn_moe_weights_norm (  12K) [Vulka         ] use=1: ffn_moe_weights-10 ( (  12K) [Vulka         ] ffn_moe_weights_sum_ (   2K) [Vulka         ]
//node #446 (   RESHAPE): ffn_moe_weights_norm (  12K) [Vulka         ] use=1: ffn_moe_weights_norm (  12K) [Vulka         ]
static constexpr std::initializer_list<std::array<int, 3>> topk_moe_sigmoid_norm_bias_edges {
    { 1, 0, 0 }, // reshape->src[0]  == sigmoid
    { 2, 0, 0 }, // add->src[0]      == sigmoid
    { 3, 0, 2 }, // argsort->src[0]  == add
    { 4, 0, 3 }, // view->src[0]     == argsort
    { 5, 0, 1 }, // get_rows->src[0] == reshape
    { 5, 1, 4 }, // get_rows->src[1] == view
    { 6, 0, 5 }, // reshape->src[0]  == get_rows
    { 7, 0, 6 }, // sum_rows->src[0] == reshape
    { 8, 0, 7 }, // clamp->src[0]    == sum_rows
    { 9, 0, 6 }, // div->src[0]      == reshape
    { 9, 1, 8 }, // div->src[1]      == clamp
    {10, 0, 9 }, // reshape->src[0]  == div
};

// same as early_softmax_norm but ending after the get_rows
static constexpr std::initializer_list<std::array<int, 3>> topk_moe_early_softmax_edges {
    { 1, 0, 0 }, // reshape->src[0]  == softmax
    { 2, 0, 0 }, // argsort->src[0]  == softmax
    { 3, 0, 2 }, // view->src[0]     == argsort
    { 4, 0, 1 }, // get_rows->src[0] == reshape
    { 4, 1, 3 }, // get_rows->src[1] == view
};

//node #652 (   ARGSORT):   ffn_moe_argsort-11 (   0K) [Vulka         ] use=1:     ffn_moe_probs-11 (   0K) [Vulka         ]
//node #653 (      VIEW):      ffn_moe_topk-11 (   0K) [Vulka         ] use=7:   ffn_moe_argsort-11 (   0K) [Vulka         ]
//node #654 (  GET_ROWS):   ffn_moe_weights-11 (   0K) [Vulka         ] use=1: ffn_moe_probs-11 (re (   0K) [Vulka         ]      ffn_moe_topk-11 (   0K) [Vulka         ]
//node #655 (   RESHAPE): ffn_moe_weights-11 ( (   0K) [Vulka         ] use=1:   ffn_moe_weights-11 (   0K) [Vulka         ]
//node #656 (  SOFT_MAX):             node_656 (   0K) [Vulka         ] use=1: ffn_moe_weights-11 ( (   0K) [Vulka         ]
//node #657 (   RESHAPE): ffn_moe_weights_soft (   0K) [Vulka         ] use=1:             node_656 (   0K) [Vulka         ]
static constexpr std::initializer_list<std::array<int, 3>> topk_moe_late_softmax_edges {
    { 1, 0, 0 }, // view->src[0]     == argsort
    { 2, 1, 1 }, // get_rows->src[1] == view
    { 3, 0, 2 }, // reshape->src[0]  == get_rows
    { 4, 0, 3 }, // soft_max->src[0] == reshape
    { 5, 0, 4 }, // reshape->src[0]  == soft_max
};

enum topk_moe_mode {
    TOPK_MOE_EARLY_SOFTMAX,
    TOPK_MOE_EARLY_SOFTMAX_NORM,
    TOPK_MOE_LATE_SOFTMAX,
    TOPK_MOE_SIGMOID_NORM_BIAS,
    TOPK_MOE_COUNT,
};

static constexpr std::initializer_list<std::array<int, 3>> rope_view_set_rows_edges {
    { 1, 0, 0 }, // view->src[0]     == rope
    { 2, 0, 1 }, // set_rows->src[0] == view
};

static constexpr std::initializer_list<std::array<int, 3>> rms_norm_mul_rope_view_set_rows_edges {
    { 1, 0, 0 }, // mul->src[0]      == rms
    { 2, 0, 1 }, // rope->src[0]     == mul
    { 3, 0, 2 }, // view->src[0]     == rope
    { 4, 0, 3 }, // set_rows->src[0] == view
};


struct vk_device_struct {
    std::recursive_mutex mutex;
    mutable std::shared_mutex pinned_memory_mutex;

    // Guards compile_pending, all_pipelines, and the dynamic pipeline maps
    // (flash_attn, fa_mask_opt, solve_tri, conv2d, etc). The actual compile
    // runs with no lock held, so different pipelines can compile in parallel.
    // Lock order is device->mutex -> compile_mutex, never the reverse.
    std::mutex compile_mutex;
    std::condition_variable compile_cv;

    vk::PhysicalDevice physical_device;
    vk::PhysicalDeviceProperties properties;
    std::string name;
    uint64_t max_memory_allocation_size;
    uint64_t max_buffer_size;
    uint64_t suballocation_block_size;
    uint64_t min_imported_host_pointer_alignment;
    bool external_memory_host {};
    bool fp16;
    bool bf16;
    bool pipeline_robustness;
    bool memory_priority;
    vk::Device device;
    uint32_t vendor_id;
    vk::DriverId driver_id;
    vk_device_architecture architecture;
    vk_queue compute_queue;
    vk_queue transfer_queue;
    bool single_queue;
    bool support_async;
    bool async_use_transfer_queue;
    uint32_t subgroup_size;
    uint32_t subgroup_size_log2;
    uint32_t shader_core_count;
    bool uma;
    bool prefer_host_memory;
    bool float_controls_rte_fp16;
    bool subgroup_basic;
    bool subgroup_arithmetic;
    bool subgroup_shuffle;
    bool subgroup_ballot;
    bool subgroup_clustered;
    bool subgroup_vote;
    bool multi_add;
    bool shader_int64;
    bool buffer_device_address;
    bool vulkan_memory_model;

    bool add_rms_fusion;
    uint32_t partials_binding_alignment;

    bool shader_64b_indexing;

    bool integer_dot_product;
    // 0: default, 1: force mmvq, -1: disable mmvq
    int32_t mmvq_mode;

    bool subgroup_size_control;
    uint32_t subgroup_min_size;
    uint32_t subgroup_max_size;
    bool subgroup_require_full_support;

    // floor(log2(maxComputeWorkGroupInvocations))
    uint32_t max_workgroup_size_log2 {};

    bool coopmat_support;
    bool coopmat_acc_f32_support {};
    bool coopmat_acc_f16_support {};
    bool coopmat_bf16_support {};
    bool coopmat_support_16x16x16_f16acc {};
    bool coopmat_support_16x16x16_f32acc {};
    bool coopmat1_fa_support {};
    uint32_t coopmat_m;
    uint32_t coopmat_n;
    uint32_t coopmat_k;

    bool coopmat_int_support;
    uint32_t coopmat_int_m;
    uint32_t coopmat_int_n;
    uint32_t coopmat_int_k;

    bool coopmat2;
    bool coopmat2_bf16_support {};
    bool coopmat2_decode_vector;

    bool dot2_f16 {};

    bool pipeline_executable_properties_support {};

    size_t idx;

    bool mul_mat_l[GGML_TYPE_COUNT];
    bool mul_mat_m[GGML_TYPE_COUNT];
    bool mul_mat_s[GGML_TYPE_COUNT];
    bool mul_mat_id_l[GGML_TYPE_COUNT];
    bool mul_mat_id_m[GGML_TYPE_COUNT];
    bool mul_mat_id_s[GGML_TYPE_COUNT];

    // Separate flags for the q8_1 (integer dot) mmq path, whose shader uses
    // a different shared-memory layout than the float matmul shaders.
    bool mul_mat_l_int[GGML_TYPE_COUNT];
    bool mul_mat_m_int[GGML_TYPE_COUNT];
    bool mul_mat_s_int[GGML_TYPE_COUNT];
    bool mul_mat_id_l_int[GGML_TYPE_COUNT];
    bool mul_mat_id_m_int[GGML_TYPE_COUNT];
    bool mul_mat_id_s_int[GGML_TYPE_COUNT];

    vk::DescriptorSetLayout dsl;

    vk_matmul_pipeline pipeline_matmul_f32 {};
    vk_matmul_pipeline pipeline_matmul_f32_f16 {};
    vk_matmul_pipeline pipeline_matmul_bf16 {};
    vk_matmul_pipeline2 pipeline_matmul_f16;
    vk_matmul_pipeline2 pipeline_matmul_f16_f32;

    vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT];
    vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_COUNT];
    vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_COUNT];

    vk_matmul_pipeline pipeline_matmul_id_f32 {};
    vk_matmul_pipeline pipeline_matmul_id_bf16 {};
    vk_matmul_pipeline2 pipeline_matmul_id_f16;
    vk_matmul_pipeline2 pipeline_matmul_id_f16_f32;

    vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT];
    vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_COUNT];

    vk_pipeline pipeline_matmul_split_k_reduce;
    vk_pipeline pipeline_quantize_q8_1_x4;

    vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT];

    vk_pipeline pipeline_dequant_mul_mat_vec_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
    vk_pipeline pipeline_dequant_mul_mat_vec_id_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT];

    vk_pipeline pipeline_mul_mat_vec_p021_f16_f32[p021_max_gqa_ratio];
    vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
    vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT];
    vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT];
    vk_pipeline pipeline_acc_f32;
    vk_pipeline pipeline_set_f32;

    // [src0 0=fp32,1=fp16][src1 0=fp32,1=fp16][dst 0=fp32,1=fp16]
    vk_pipeline pipeline_add[2][2][2];
    vk_pipeline pipeline_add_norepeat[2][2][2];
    vk_pipeline pipeline_sub[2][2][2];
    vk_pipeline pipeline_sub_norepeat[2][2][2];
    vk_pipeline pipeline_mul[2][2][2];
    vk_pipeline pipeline_mul_norepeat[2][2][2];
    vk_pipeline pipeline_div[2][2][2];
    vk_pipeline pipeline_div_norepeat[2][2][2];
    vk_pipeline pipeline_add_rms[2][2][2];
    vk_pipeline pipeline_add_rms_norepeat[2][2][2];

    // indexed by num_additional_fused_ops == num_adds - 1
    vk_pipeline pipeline_multi_add[MAX_FUSED_ADDS];
    vk_pipeline pipeline_multi_add_rms[MAX_FUSED_ADDS];

    vk_pipeline pipeline_add_id_f32;

    vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32;
    vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bicubic_f32, pipeline_upscale_bilinear_antialias_f32;
    vk_pipeline pipeline_scale_f32;
    vk_pipeline pipeline_sqr_f32;
    vk_pipeline pipeline_sqrt_f32;
    vk_pipeline pipeline_sin_f32;
    vk_pipeline pipeline_cos_f32;
    vk_pipeline pipeline_log[2];
    vk_pipeline pipeline_tri[2];
    vk_pipeline pipeline_diag[2];
    vk_pipeline pipeline_clamp_f32;
    vk_pipeline pipeline_pad_f32;
    vk_pipeline pipeline_roll_f32;
    vk_pipeline pipeline_repeat_i32, pipeline_repeat_back_f32;
    vk_pipeline pipeline_repeat_i16;
    vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16, pipeline_cpy_f16_f32, pipeline_cpy_f32_bf16, pipeline_cpy_bf16_f32, pipeline_cpy_f32_i32, pipeline_cpy_i32_f32;
    vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16, pipeline_contig_cpy_f16_f32, pipeline_contig_cpy_f32_bf16, pipeline_contig_cpy_bf16_f32, pipeline_contig_cpy_f32_i32, pipeline_contig_cpy_i32_f32;
    vk_pipeline pipeline_cpy_f32_quant[GGML_TYPE_COUNT];
    vk_pipeline pipeline_cpy_quant_f32[GGML_TYPE_COUNT];
    vk_pipeline pipeline_cpy_transpose_16, pipeline_cpy_transpose_32;
    vk_pipeline pipeline_set_rows_i32[GGML_TYPE_COUNT];
    vk_pipeline pipeline_set_rows_i64[GGML_TYPE_COUNT];
    vk_pipeline pipeline_norm_f32;
    vk_pipeline pipeline_group_norm_f32;
    vk_pipeline pipeline_rms_norm_f32;
    vk_pipeline pipeline_rms_norm_mul_f32;
    vk_pipeline pipeline_rms_norm_partials_f32;
    vk_pipeline pipeline_rms_norm_mul_partials_f32;
    vk_pipeline pipeline_rms_norm_mul_rope_f32_f32;
    vk_pipeline pipeline_rms_norm_mul_rope_f32_f16;
    vk_pipeline pipeline_rms_norm_back_f32;
    vk_pipeline pipeline_l2_norm_f32;

    // [src/dst 0=fp32,1=fp16]
    vk_pipeline pipeline_exp[2];
    vk_pipeline pipeline_elu[2];
    vk_pipeline pipeline_gelu[2];
    vk_pipeline pipeline_gelu_erf[2];
    vk_pipeline pipeline_gelu_quick[2];
    vk_pipeline pipeline_silu[2];
    vk_pipeline pipeline_relu[2];
    vk_pipeline pipeline_xielu[2];
    vk_pipeline pipeline_neg[2];
    vk_pipeline pipeline_tanh[2];
    vk_pipeline pipeline_sigmoid[2];
    vk_pipeline pipeline_hardsigmoid[2];
    vk_pipeline pipeline_hardswish[2];
    vk_pipeline pipeline_abs[2];
    vk_pipeline pipeline_softplus[2];
    vk_pipeline pipeline_step[2];
    vk_pipeline pipeline_round[2];
    vk_pipeline pipeline_ceil[2];
    vk_pipeline pipeline_floor[2];
    vk_pipeline pipeline_trunc[2];
    vk_pipeline pipeline_sgn[2];

    vk_pipeline pipeline_add1_f16_f16;
    vk_pipeline pipeline_add1_f16_f32;
    vk_pipeline pipeline_add1_f32_f32;

    vk_pipeline pipeline_arange_f32;

    vk_pipeline pipeline_fill_f32;
    vk_pipeline pipeline_fill_f16;

    vk_pipeline pipeline_geglu[2];
    vk_pipeline pipeline_reglu[2];
    vk_pipeline pipeline_swiglu[2];
    vk_pipeline pipeline_swiglu_oai[2];
    vk_pipeline pipeline_geglu_erf[2];
    vk_pipeline pipeline_geglu_quick[2];

    vk_pipeline pipeline_leaky_relu_f32;
    vk_pipeline pipeline_silu_back_f32;
    vk_pipeline pipeline_diag_mask_inf_f32;
    vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
    vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512;
    vk_pipeline pipeline_soft_max_back_f32;

    vk_pipeline pipeline_soft_max_large1_f32, pipeline_soft_max_large1_f32_f16;
    vk_pipeline pipeline_soft_max_large2_f32, pipeline_soft_max_large2_f32_f16;
    vk_pipeline pipeline_soft_max_large3_f32, pipeline_soft_max_large3_f32_f16;

    vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16;
    vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16;
    vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16, pipeline_rope_multi_f32_f16;
    vk_pipeline pipeline_rope_vision_f32, pipeline_rope_vision_f16;
    vk_pipeline pipeline_argsort_f32[num_argsort_pipelines];
    vk_pipeline pipeline_argsort_large_f32[num_argsort_pipelines];
    vk_pipeline pipeline_topk_f32[num_topk_pipelines];
    vk_pipeline pipeline_sum_rows_f32;
    vk_pipeline pipeline_fwht_f32[4];
    vk_pipeline pipeline_cumsum_f32;
    vk_pipeline pipeline_cumsum_small_f32;
    vk_pipeline pipeline_cumsum_multipass1_f32;
    vk_pipeline pipeline_cumsum_multipass2_f32;
    vk_pipeline pipeline_argmax_f32;
    vk_pipeline pipeline_count_equal_i32;
    std::map<vk_solve_tri_pipeline_state, vk_pipeline> pipeline_solve_tri_f32;
    vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
    vk_pipeline pipeline_im2col_3d_f32, pipeline_im2col_3d_f32_f16;
    vk_pipeline pipeline_timestep_embedding_f32;
    vk_pipeline pipeline_conv_transpose_1d_f32;
    vk_pipeline pipeline_snake_f32;
    vk_pipeline pipeline_snake_f16;
    vk_pipeline pipeline_snake_bf16;
    vk_pipeline pipeline_pool2d_f32;
    vk_pipeline pipeline_rwkv_wkv6_f32;
    vk_pipeline pipeline_rwkv_wkv7_f32;
    // [size_idx][kda] where size_idx: 0=d32, 1=d64, 2=d128
    vk_pipeline pipeline_gated_delta_net[3][2];
    vk_pipeline pipeline_ssm_scan_f32_d128;
    vk_pipeline pipeline_ssm_scan_f32_d256;
    vk_pipeline pipeline_ssm_conv_f32;
    vk_pipeline pipeline_ssm_conv_silu_f32;
    vk_pipeline pipeline_ssm_conv_bias_silu_f32;
    vk_pipeline pipeline_opt_step_adamw_f32;
    vk_pipeline pipeline_opt_step_sgd_f32;
    std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv2d_f32[CONV_SHAPE_COUNT];
    std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
    std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv_transpose_2d_f32[CONV_SHAPE_COUNT];
    std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv_transpose_2d_f16_f32[CONV_SHAPE_COUNT];
    vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32;
    vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32;

    std::map<vk_fa_pipeline_state, vk_pipeline> pipeline_flash_attn_f32_f16;

    std::map<std::pair<uint32_t, uint32_t>, vk_pipeline> pipeline_fa_mask_opt;

    vk_pipeline pipeline_flash_attn_split_k_reduce;
    vk_pipeline pipeline_count_experts;

    // [2] is for whether to take n_experts from spec constant (0) or push constant (1)
    vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][2];

    std::vector<vk_pipeline_ref> all_pipelines;

    std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;

    vk::Fence fence;
    vk_buffer sync_staging;

    ggml_backend_buffer_type buffer_type;

    bool disable_fusion;
    bool disable_host_visible_vidmem;
    bool allow_sysmem_fallback;
    bool disable_graph_optimize;

    std::unique_ptr<vk_memory_logger> memory_logger;

    ~vk_device_struct() {
        VK_LOG_DEBUG("destroy device " << name);

        device.destroyFence(fence);

        ggml_vk_destroy_buffer(sync_staging);

        compute_queue.cmd_pool.destroy(device);
        transfer_queue.cmd_pool.destroy(device);

        for (auto& pipeline : all_pipelines) {
            if (pipeline.expired()) {
                continue;
            }

            vk_pipeline pl = pipeline.lock();
            ggml_vk_destroy_pipeline(device, pl);
        }
        all_pipelines.clear();

        device.destroyDescriptorSetLayout(dsl);

        device.destroy();
    }
};

void vk_command_pool::init(vk_device& device, vk_queue *q_) {
    cmd_buffers.clear();
    q = q_;

    vk::CommandPoolCreateInfo command_pool_create_info(
        vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT | VK_COMMAND_POOL_CREATE_RESET_COMMAND_BUFFER_BIT),
        q->queue_family_index);
    pool = device->device.createCommandPool(command_pool_create_info);
}

void vk_command_pool::destroy(vk::Device& device) {
    device.destroyCommandPool(pool);
    pool = nullptr;
    cmd_buffers.clear();
}

struct vk_buffer_struct {
    vk::Buffer buffer = VK_NULL_HANDLE;
    vk::DeviceMemory device_memory = VK_NULL_HANDLE;
    vk::MemoryPropertyFlags memory_property_flags;
    void * ptr;
    size_t size = 0;
    vk::DeviceAddress bda_addr {};

    vk_device device;

    ~vk_buffer_struct() {
        if (size == 0) {
            return;
        }
        VK_LOG_DEBUG("~vk_buffer_struct(" << buffer << ", " << size << ")");

        device->device.freeMemory(device_memory);
        device->device.destroyBuffer(buffer);
    }
};

struct vk_subbuffer {
    vk_buffer buffer;
    uint64_t offset;
    uint64_t size;

    operator vk::DescriptorBufferInfo() const {
        return { buffer->buffer, offset, size };
    }
};

struct vk_semaphore {
    vk::Semaphore s;
    uint64_t value;
};

// vk_event is used for the event-related backend interfaces. It uses vk::Events for
// event_wait and a timeline semaphore for event_synchronize. Polling on an event for
// event_synchronize wouldn't be sufficient to wait for command buffers to complete,
// and would lead to validation errors.
struct vk_event {
    std::vector<vk::Event> events_free; // Events available for reuse
    std::vector<vk::Event> events_submitted; // Events that are fully submitted and can be reused on next synchronize
    vk::Event event;
    bool has_event;

    vk_semaphore tl_semaphore;
    vk_command_buffer* cmd_buffer = nullptr;
    uint64_t cmd_buffer_use_counter = 0;
};

struct vk_submission {
    vk_command_buffer* buffer = nullptr;
    std::vector<vk_semaphore> wait_semaphores;
    std::vector<vk_semaphore> signal_semaphores;
};

typedef std::vector<vk_submission> vk_sequence;

struct vk_mat_mat_push_constants {
    uint32_t M; uint32_t N; uint32_t K;
    uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
    uint32_t base_work_group_z; uint32_t num_batches;
    uint32_t k_split;
    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
    uint32_t padded_N;
};

#define MAT_VEC_FUSION_FLAGS_BIAS0 0x1
#define MAT_VEC_FUSION_FLAGS_BIAS1 0x2
#define MAT_VEC_FUSION_FLAGS_SCALE0 0x4
#define MAT_VEC_FUSION_FLAGS_SCALE1 0x8

struct vk_mat_vec_push_constants {
    uint32_t ncols;
    uint32_t stride_a;
    uint32_t stride_b;
    uint32_t stride_d;
    uint32_t batch_stride_a;
    uint32_t batch_stride_b;
    uint32_t batch_stride_d;
    uint32_t fusion_flags;
    uint32_t base_work_group_y;
    uint32_t ne02;
    uint32_t ne12;
    uint32_t broadcast2;
    uint32_t broadcast3;
};

struct vk_mat_vec_p021_push_constants {
    uint32_t ncols_x;
    uint32_t nrows_x;
    uint32_t nchannels_x;
    uint32_t nchannels_y;
    uint32_t b_offset;
    uint32_t d_offset;
    uint32_t fusion_flags;
};

struct vk_mat_vec_nc_push_constants {
    uint32_t ncols_x;
    uint32_t nrows_x;
    uint32_t row_stride_x;
    uint32_t channel_stride_x;
    uint32_t channel_stride_y;
    uint32_t channel_x_divisor;
    uint32_t ne12;
    uint32_t b_offset;
    uint32_t d_offset;
    uint32_t nb03;
    uint32_t nb13;
    uint32_t nb23;
    uint32_t fusion_flags;
};

struct vk_mat_mat_id_push_constants {
    uint32_t M; uint32_t N; uint32_t K;
    uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
    uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11;
    uint32_t padded_N;
};
struct vk_mat_vec_id_push_constants {
    uint32_t ncols;
    uint32_t stride_a;
    uint32_t stride_b;
    uint32_t stride_d;
    uint32_t batch_stride_a;
    uint32_t batch_stride_b;
    uint32_t batch_stride_d;
    uint32_t fusion_flags;
    uint32_t nei0;
    uint32_t ne11;
    uint32_t expert_i1;
    uint32_t nbi1;
};

struct vk_flash_attn_push_constants {
    uint32_t N;
    uint32_t KV;

    uint32_t ne1;
    uint32_t ne2;
    uint32_t ne3;

    uint32_t neq2;
    uint32_t neq3;
    uint32_t nek2;
    uint32_t nek3;
    uint32_t nev2;
    uint32_t nev3;
    uint32_t nem1;
    uint32_t nem2;
    uint32_t nem3;

    uint32_t nb01;
    uint32_t nb02;
    uint32_t nb03;
    uint32_t nb11;
    uint32_t nb12;
    uint32_t nb13;
    uint32_t nb21;
    uint32_t nb22;
    uint32_t nb23;

    float scale;
    float max_bias;
    float logit_softcap;

    uint32_t mask_n_head_log2;
    float m0;
    float m1;

    uint32_t gqa_ratio;
    uint32_t split_kv;
    uint32_t k_num;
};
static_assert(sizeof(vk_flash_attn_push_constants) <= 128, "sizeof(vk_flash_attn_push_constants) must be <= 128");

struct vk_op_push_constants {
    uint32_t KX;
    uint32_t KY;
    float param1;
    float param2;
    float param3;
    float param4;
};

struct vk_op_fwht_push_constants {
    uint32_t n_rows;
    uint32_t src_offset;
    uint32_t dst_offset;
    float scale;
};

struct vk_op_count_experts_push_constants {
    uint32_t ne00;
    uint32_t ne01;
    uint32_t nb00;
    uint32_t nb01;
    uint32_t a_offset;
};

struct vk_op_glu_push_constants {
    uint32_t N;
    uint32_t ne00;
    uint32_t ne20;
    uint32_t mode;  // 0: default, 1: swapped, 2: split
    float alpha; // for swiglu_oai
    float limit;
    uint32_t nb01;
    uint32_t nb02;
    uint32_t nb03;
    uint32_t ne01;
    uint32_t ne02;
    uint32_t nb11;
    uint32_t nb12;
    uint32_t nb13;
    uint32_t ne11;
    uint32_t ne12;
};

struct vk_op_unary_push_constants {
    uint32_t ne;
    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
    uint32_t misalign_offsets;
    float param1; float param2;
    uint32_t ne0_012mp; uint32_t ne0_012L;
    uint32_t ne0_01mp;  uint32_t ne0_01L;
    uint32_t ne0_0mp;   uint32_t ne0_0L;
    uint32_t ne1_012mp; uint32_t ne1_012L;
    uint32_t ne1_01mp;  uint32_t ne1_01L;
    uint32_t ne1_0mp;   uint32_t ne1_0L;
};
static_assert(sizeof(vk_op_unary_push_constants) <= 128, "sizeof(vk_op_unary_push_constants) must be <= 128");

static vk_op_unary_push_constants vk_op_unary_push_constants_init(const ggml_tensor * src0, const ggml_tensor * dst, int64_t ne = 0) {
    GGML_ASSERT(ne != 0 || (ggml_nelements(src0) == ggml_nelements(dst)));
    ne = ne != 0 ? ne : ggml_nelements(dst);
    GGML_ASSERT(ne <= (int64_t)std::numeric_limits<uint32_t>::max());

    vk_op_unary_push_constants p{};
    p.ne = (uint32_t)ne;

    size_t src0_tsize = ggml_type_size(src0->type);
    p.ne00 = (uint32_t)src0->ne[0];
    p.ne01 = (uint32_t)src0->ne[1];
    p.ne02 = (uint32_t)src0->ne[2];
    p.ne03 = (uint32_t)src0->ne[3];
    p.nb00 = (uint32_t)(src0->nb[0] / src0_tsize);
    p.nb01 = (uint32_t)(src0->nb[1] / src0_tsize);
    p.nb02 = (uint32_t)(src0->nb[2] / src0_tsize);
    p.nb03 = (uint32_t)(src0->nb[3] / src0_tsize);

    size_t dst_tsize = ggml_type_size(dst->type);
    p.ne10 = (uint32_t)dst->ne[0];
    p.ne11 = (uint32_t)dst->ne[1];
    p.ne12 = (uint32_t)dst->ne[2];
    p.ne13 = (uint32_t)dst->ne[3];
    p.nb10 = (uint32_t)(dst->nb[0] / dst_tsize);
    p.nb11 = (uint32_t)(dst->nb[1] / dst_tsize);
    p.nb12 = (uint32_t)(dst->nb[2] / dst_tsize);
    p.nb13 = (uint32_t)(dst->nb[3] / dst_tsize);

    return p; // offsets are initialized later in ggml_vk_op
}

struct vk_op_pad_push_constants {
    uint32_t ne;
    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
    uint32_t misalign_offsets;
    uint32_t circular;

    uint32_t lp0; uint32_t rp0;
    uint32_t lp1; uint32_t rp1;
    uint32_t lp2; uint32_t rp2;
    uint32_t lp3; uint32_t rp3;
};

static vk_op_pad_push_constants vk_op_pad_push_constants_init(const ggml_tensor * src0, const ggml_tensor * dst) {
    int64_t ne = ggml_nelements(dst);
    GGML_ASSERT(ne <= (int64_t)std::numeric_limits<uint32_t>::max());

    vk_op_pad_push_constants p{};
    p.ne = (uint32_t)ne;

    size_t src0_tsize = ggml_type_size(src0->type);
    p.ne00 = (uint32_t)src0->ne[0];
    p.ne01 = (uint32_t)src0->ne[1];
    p.ne02 = (uint32_t)src0->ne[2];
    p.ne03 = (uint32_t)src0->ne[3];
    p.nb00 = (uint32_t)(src0->nb[0] / src0_tsize);
    p.nb01 = (uint32_t)(src0->nb[1] / src0_tsize);
    p.nb02 = (uint32_t)(src0->nb[2] / src0_tsize);
    p.nb03 = (uint32_t)(src0->nb[3] / src0_tsize);

    size_t dst_tsize = ggml_type_size(dst->type);
    p.ne10 = (uint32_t)dst->ne[0];
    p.ne11 = (uint32_t)dst->ne[1];
    p.ne12 = (uint32_t)dst->ne[2];
    p.ne13 = (uint32_t)dst->ne[3];
    p.nb10 = (uint32_t)(dst->nb[0] / dst_tsize);
    p.nb11 = (uint32_t)(dst->nb[1] / dst_tsize);
    p.nb12 = (uint32_t)(dst->nb[2] / dst_tsize);
    p.nb13 = (uint32_t)(dst->nb[3] / dst_tsize);

    p.lp0 = dst->op_params[0];
    p.rp0 = dst->op_params[1];
    p.lp1 = dst->op_params[2];
    p.rp1 = dst->op_params[3];
    p.lp2 = dst->op_params[4];
    p.rp2 = dst->op_params[5];
    p.lp3 = dst->op_params[6];
    p.rp3 = dst->op_params[7];
    p.circular = dst->op_params[8];

    return p; // fastdiv values and offsets are initialized later in ggml_vk_op
}

// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
// Precompute mp (m' in the paper) and L such that division
// can be computed using a multiply (high 32b of 64b result)
// and a shift:
//
// n/d = (mulhi(n, mp) + n) >> L;
static void init_fastdiv_values(uint32_t d, uint32_t &mp, uint32_t &L)
{
    // compute L = ceil(log2(d));
    L = 0;
    while (L < 32 && (uint32_t{1} << L) < d) {
        L++;
    }

    mp = (uint32_t)((uint64_t{1} << 32) * ((uint64_t{1} << L) - d) / d + 1);
}

template <typename T> void init_pushconst_fastdiv(T &p) {
    GGML_UNUSED(p);
    static_assert(!std::is_const<T>::value, "unexpected type");
}

template <> void init_pushconst_fastdiv(vk_op_unary_push_constants &p) {
    // Compute magic values to divide by these six numbers.
    init_fastdiv_values(p.ne02*p.ne01*p.ne00,  p.ne0_012mp,    p.ne0_012L);
    init_fastdiv_values(p.ne01*p.ne00,         p.ne0_01mp,     p.ne0_01L);
    init_fastdiv_values(p.ne00,                p.ne0_0mp,      p.ne0_0L);
    init_fastdiv_values(p.ne12*p.ne11*p.ne10,  p.ne1_012mp,    p.ne1_012L);
    init_fastdiv_values(p.ne11*p.ne10,         p.ne1_01mp,     p.ne1_01L);
    init_fastdiv_values(p.ne10,                p.ne1_0mp,      p.ne1_0L);
}

struct vk_op_binary_push_constants {
    uint32_t ne;
    uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23;
    uint32_t misalign_offsets;
    float param1; float param2; int32_t param3;
};

struct vk_op_multi_add_push_constants {
    // shape for dst
    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23;

    // strides for srcs+dst
    uint32_t nb[MAX_PARAMETER_COUNT][4];

    uint32_t rms_partials;
};
// update multi_add.comp if this changes
static_assert(MAX_PARAMETER_COUNT == 12);
static_assert(sizeof(vk_op_multi_add_push_constants) <= 256);

struct vk_op_topk_moe_push_constants {
    uint32_t n_rows;
    uint32_t n_experts_push;
    uint32_t n_expert_used;
    float clamp_min;
    float clamp_max;
    uint32_t gating_func;
    uint32_t has_bias;
    uint32_t with_norm;
    float output_scale;
    float output_bias;
};

struct vk_op_add_id_push_constants {
    uint32_t ne0;
    uint32_t ne1;
    uint32_t s01;
    uint32_t s02;
    uint32_t s11;
    uint32_t s21;
};

struct vk_op_diag_mask_push_constants {
    uint32_t ncols;
    uint32_t rows_per_channel;
    int32_t n_past;
};

struct vk_op_rope_push_constants {
    uint32_t rope_mode;
    uint32_t nrows;
    uint32_t n_dims;
    float freq_scale;
    float freq_base;
    float ext_factor;
    float attn_factor;
    float corr_dims[2];
    float theta_scale;
    uint32_t has_ff;
    int32_t sections[4];
    uint32_t is_imrope;
    uint32_t is_back;
    uint32_t set_rows_stride;
    uint32_t ne00;
    uint32_t ne01;
    uint32_t ne02;
    uint32_t nb01;
    uint32_t nb02;
    uint32_t nb03;
    uint32_t nb11;
    uint32_t nb12;
    uint32_t nb13;
    uint32_t a_offset;
    uint32_t d_offset;
};
static_assert(sizeof(vk_op_rope_push_constants) <= 128, "sizeof(vk_op_rope_push_constants) must be <= 128");

// For fused rms_norm+mul+rope(+view+set_rows)
struct vk_op_rms_norm_mul_rope_push_constants {
    vk_op_binary_push_constants bin;
    vk_op_rope_push_constants rope;
};

struct vk_op_soft_max_push_constants {
    uint32_t KX;
    uint32_t KY;
    uint32_t ne00;
    uint32_t ne01;
    uint32_t ne02;
    uint32_t ne12;
    uint32_t ne13;
    uint32_t nb11;
    uint32_t nb12;
    uint32_t nb13;
    float scale;
    float max_bias;
    float m0;
    float m1;
    uint32_t n_head_log2;
    uint32_t nrows_x;
    uint32_t has_sinks;
};

struct vk_op_argsort_push_constants {
    uint32_t ncols;
    uint32_t ncols_padded;
    uint32_t ncols_padded_log2;
    uint32_t nrows;
    uint32_t order;
    uint32_t outer_start;
    uint32_t outer_end;
    uint32_t inner_start;
    uint32_t inner_end;
};

struct vk_op_topk_push_constants {
    uint32_t orig_ncols;
    uint32_t ncols_input;
    uint32_t ncols_output;
    uint32_t k;
    uint32_t nrows;
    uint32_t first_pass;
    uint32_t last_pass;
};

struct vk_op_im2col_push_constants {
    uint64_t dst_addr;
    uint32_t batch_offset; uint32_t offset_delta;
    uint32_t IC;
    uint32_t IW; uint32_t IH;
    uint32_t OW; uint32_t OH;
    uint32_t KW; uint32_t KH;
    uint32_t OH_batch;
    uint32_t CHW;
    int32_t s0; int32_t s1;
    int32_t p0; int32_t p1;
    int32_t d0; int32_t d1;
    uint32_t batch_IC;
};

struct vk_op_im2col_3d_push_constants {
    uint64_t dst_addr;
    uint32_t nb10;
    uint32_t nb11;
    uint32_t nb12;
    uint32_t nb13;
    uint32_t s0;
    uint32_t s1;
    uint32_t s2;
    uint32_t p0;
    uint32_t p1;
    uint32_t p2;
    uint32_t d0;
    uint32_t d1;
    uint32_t d2;
    uint32_t IW;
    uint32_t IH;
    uint32_t ID;
    uint32_t IC;
    uint32_t KW;
    uint32_t OH;
    uint32_t KD_KH_KW;
    uint32_t KH_KW;
    uint32_t IC_KD_KH_KW;
    uint32_t N_OD_OH;
    uint32_t OD_OH;
    uint32_t OD_OH_OW_IC_KD_KH_KW;
    uint32_t OH_OW_IC_KD_KH_KW;
    uint32_t OW_IC_KD_KH_KW;
    uint32_t misalign_offsets;
};

struct vk_op_timestep_embedding_push_constants {
    uint32_t nb1;
    uint32_t dim;
    uint32_t max_period;
};

struct vk_op_conv_transpose_1d_push_constants {
    uint32_t Cout;
    uint32_t Cin;
    uint32_t K;
    uint32_t L;
    uint32_t KL;

    uint32_t nb01;
    uint32_t nb02;
    uint32_t nb11;
    uint32_t nb1;

    int32_t s0;
};

struct vk_op_snake_push_constants {
    uint32_t ne0;
    uint32_t ne1;
};

struct vk_op_pool2d_push_constants {
    uint32_t IW; uint32_t IH;
    uint32_t OW; uint32_t OH;
    uint32_t OC;
    uint32_t pelements;
    uint32_t op;
    int32_t k0; int32_t k1;
    int32_t s0; int32_t s1;
    int32_t p0; int32_t p1;
};

struct vk_op_rwkv_wkv6_push_constants {
    uint32_t B;
    uint32_t T;
    uint32_t C;
    uint32_t H;
};

struct vk_op_rwkv_wkv7_push_constants {
    uint32_t B;
    uint32_t T;
    uint32_t C;
    uint32_t H;
};
struct vk_op_gated_delta_net_push_constants {
    uint32_t H;
    uint32_t n_tokens;
    uint32_t n_seqs;
    uint32_t s_off;
    uint32_t sq1, sq2, sq3;
    uint32_t sv1, sv2, sv3;
    uint32_t sb1, sb2, sb3;
    uint32_t neq1, rq3;
    float scale;
    uint32_t K;
};

struct vk_op_ssm_scan_push_constants {
    uint32_t nb02, nb03, nb12, nb13;
    uint32_t nb21, nb22, nb31;
    uint32_t nb42, nb43, nb52, nb53;
    uint32_t s_off;
    uint32_t n_head, d_head, n_group, n_tok;
};
struct vk_op_ssm_conv_push_constants {
    uint32_t nb01, nb02;
    uint32_t nb11;
    uint32_t dst_nb0, dst_nb1, dst_nb2;
    uint32_t nc, ncs, nr, n_t, n_s;
};

struct vk_op_conv2d_push_constants {
    uint32_t Cout;
    uint32_t Cin;
    uint32_t N;

    uint32_t W;
    uint32_t H;
    uint32_t OW;
    uint32_t OH;

    uint32_t nb01;
    uint32_t nb02;
    uint32_t nb03;

    uint32_t nb11;
    uint32_t nb12;
    uint32_t nb13;

    uint32_t nb1;
    uint32_t nb2;
    uint32_t nb3;

    // init_fastdiv_values constants for dividing by OW, OW*OH
    uint32_t OWmp;   uint32_t OWL;
    uint32_t OWOHmp; uint32_t OWOHL;
};

template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) {
    // Compute magic values to divide by OW, OW*OH
    init_fastdiv_values(p.OW,       p.OWmp,    p.OWL);
    init_fastdiv_values(p.OW*p.OH,  p.OWOHmp,  p.OWOHL);
}

struct vk_op_conv2d_dw_push_constants {
    uint32_t ne;
    uint32_t batches;
    uint32_t channels;
    uint32_t dst_w;
    uint32_t dst_h;
    uint32_t src_w;
    uint32_t src_h;
    uint32_t knl_w;
    uint32_t knl_h;
    int32_t stride_x;
    int32_t stride_y;
    int32_t pad_x;
    int32_t pad_y;
    int32_t dilation_x;
    int32_t dilation_y;
};

struct vk_op_upscale_push_constants {
    uint32_t ne; uint32_t a_offset; uint32_t d_offset;
    uint32_t ne00; uint32_t ne01;
    uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
    uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13;
    float sf0; float sf1; float sf2; float sf3;
    float pixel_offset;
};

struct vk_op_sum_rows_push_constants
{
    uint32_t n_cols;
    uint32_t ne01, ne02;
    uint32_t nb01, nb02, nb03;
    uint32_t nb11, nb12, nb13;
    float weight;
    uint32_t misalign_offsets;
    uint32_t ne0_12mp, ne0_12L;
    uint32_t ne0_1mp, ne0_1L;
};

static vk_op_sum_rows_push_constants vk_op_sum_rows_push_constants_init(const ggml_tensor * src, const ggml_tensor * dst, int64_t n_cols) {
    uint32_t type_size = (uint32_t)ggml_type_size(src->type);
    vk_op_sum_rows_push_constants p = {};
    p.n_cols = (uint32_t)n_cols;
    p.ne01 = (uint32_t)src->ne[1];
    p.ne02 = (uint32_t)src->ne[2];
    p.nb01 = (uint32_t)src->nb[1] / type_size;
    p.nb02 = (uint32_t)src->nb[2] / type_size;
    p.nb03 = (uint32_t)src->nb[3] / type_size;
    p.nb11 = (uint32_t)dst->nb[1] / type_size;
    p.nb12 = (uint32_t)dst->nb[2] / type_size;
    p.nb13 = (uint32_t)dst->nb[3] / type_size;
    p.weight = 1.0f;
    return p;
}

template <> void init_pushconst_fastdiv(vk_op_sum_rows_push_constants &p) {
    init_fastdiv_values(p.ne01*p.ne02, p.ne0_12mp, p.ne0_12L);
    init_fastdiv_values(p.ne01,        p.ne0_1mp,  p.ne0_1L);
}

struct vk_quantize_q8_1_push_constants {
    uint32_t ne;
    uint32_t num_blocks;
};

struct vk_op_flash_attn_split_k_reduce_push_constants {
    uint32_t D;
    uint32_t ne1;
    uint32_t ne2;
    uint32_t ne3;
    uint32_t k_num;
    uint32_t sinks;
};

struct vk_op_flash_attn_mask_opt_push_constants {
    uint32_t nem0;
    uint32_t nem1;
    uint32_t nem2;
    uint32_t nbm1;
    uint32_t nbm2;
    uint32_t nbm3;
    uint32_t nbd1;
    uint32_t nbd2;
    uint32_t nbd3;
};

// Allow pre-recording command buffers
struct vk_staging_memcpy {
    vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}

    void * dst;
    const void * src;
    size_t n;
};

struct vk_staging_memset {
    vk_staging_memset(void * _dst, uint32_t _val, size_t _n) : dst(_dst), val(_val), n(_n) {}

    void * dst;
    uint32_t val;
    size_t n;
};

struct vk_context_struct {
    vk_submission * s;
    std::vector<vk_sequence> seqs;

    int exit_tensor_idx;

    std::vector<vk_staging_memcpy> in_memcpys;
    std::vector<vk_staging_memcpy> out_memcpys;
    std::vector<vk_staging_memset> memsets;

    vk_command_pool * p {};
};
typedef std::shared_ptr<vk_context_struct> vk_context;
typedef std::weak_ptr<vk_context_struct> vk_context_ref;

struct ggml_vk_garbage_collector {
    std::vector<vk_semaphore> tl_semaphores;
    std::vector<vk_semaphore> semaphores;
    std::vector<vk::Event> events;
    std::vector<vk_context> contexts;
};

static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx);
static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested = nullptr);
static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx);

static bool vk_memory_logger_enabled = false;

#define VK_LOG_MEMORY(msg) if (vk_memory_logger_enabled) { std::cerr << "ggml_vulkan memory: " << msg << std::endl; }

static std::string format_size(size_t size) {
    const size_t kib = 1024;
    const size_t mib = kib * 1024;
    const size_t gib = mib * 1024;

    std::ostringstream oss;
    oss << std::fixed << std::setprecision(2);

    if (size >= gib) {
        oss << static_cast<double>(size) / gib << " GiB";
    } else if (size >= mib) {
        oss << static_cast<double>(size) / mib << " MiB";
    } else if (size >= kib) {
        oss << static_cast<double>(size) / kib << " KiB";
    } else {
        oss << size << " B";
    }

    return oss.str();
}

class vk_memory_logger {
public:
    vk_memory_logger(): total_device(0), total_host(0) {}
    void log_allocation(vk_buffer_ref buf_ref, size_t size);
    void log_deallocation(vk_buffer_ref buf_ref);

private:
    std::map<vk::Buffer, size_t> allocations; // Track allocations
    size_t total_device;
    size_t total_host;
    static std::mutex log_mutex;
};

std::mutex vk_memory_logger::log_mutex;

static bool vk_perf_logger_enabled = false;
static bool vk_perf_logger_concurrent = false;
static bool vk_enable_sync_logger = false;
// number of calls between perf logger prints
static uint32_t vk_perf_logger_frequency = 1;
static std::string vk_pipeline_stats_filter;

class vk_perf_logger {
  public:
    void print_timings(bool force = false) {
        if (timings.empty()) {
            return;
        }
        print_count++;
        if ((print_count % vk_perf_logger_frequency) != 0 && !force) {
            return;
        }
        print_count = 0;
        uint64_t total_all_op_times = 0;
        std::cerr << "----------------\nVulkan Timings:" << std::endl;
        for (const auto & t : timings) {
            uint64_t total_op_times = 0;
            for (const auto & time : t.second) {
                total_op_times += time;
            }
            std::cerr << t.first << ": " << t.second.size() << " x " << (total_op_times / t.second.size() / 1000.0)
                      << " us = " << (total_op_times / 1000.0) << " us";

            // If we have as many flops entries as timing entries for the op, then compute and log the flops/S.
            auto it = flops.find(t.first);
            if (it != flops.end() && (it->second).size() == t.second.size()) {
                uint64_t total_op_flops = 0;
                for (const auto & elem : it->second) {
                    total_op_flops += elem;
                }
                std::cerr << " ("
                          << (double(total_op_flops) / (1000.0 * 1000.0 * 1000.0)) /
                                 (double(total_op_times) / (1000.0 * 1000.0 * 1000.0))
                          << " GFLOPS/s)";
            }

            total_all_op_times += total_op_times;

            std::cerr << std::endl;
        }

        if (timings.size() > 0) {
            std::cerr << "Total time: " << total_all_op_times / 1000.0 << " us." << std::endl;
        }

        timings.clear();
        flops.clear();
    }

    std::string get_node_fusion_name(const ggml_tensor * node, const char *fusion_name, uint64_t *n_flops) {
        *n_flops = 0;
        std::string fusion_str;
        if (fusion_name) {
            fusion_str = fusion_name + std::string(" ");
        }
        if (node->op == GGML_OP_UNARY) {
            return fusion_str + ggml_unary_op_name(ggml_get_unary_op(node));
        }
        if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
            const uint64_t m     = node->ne[0];
            const uint64_t n     = node->ne[1];
            const uint64_t k     = node->src[1]->ne[0];
            const uint64_t batch = node->ne[2] * node->ne[3];
            std::string    name  = ggml_op_name(node->op);
            if ((node->op == GGML_OP_MUL_MAT && n <= mul_mat_vec_max_cols) ||
                (node->op == GGML_OP_MUL_MAT_ID && node->src[2]->ne[1] == 1)) {
                name += "_VEC";
            }
            name += " ";
            name += ggml_type_name(node->src[0]->type);
            name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k);
            if (node->op == GGML_OP_MUL_MAT_ID) {
                name += " n_expert=" + std::to_string(node->src[0]->ne[2]);
            }
            if (batch > 1) {
                name += " batch=" + std::to_string(batch);
            }
            name = fusion_str + name;
            *n_flops = m * n * (k + (k - 1)) * batch;
            return name;
        }
        if (node->op == GGML_OP_CONV_2D || node->op == GGML_OP_CONV_TRANSPOSE_2D) {
            std::string   name    = ggml_op_name(node->op);
            ggml_tensor * knl     = node->src[0];
            uint64_t      OW      = node->ne[0];
            uint64_t      OH      = node->ne[1];
            uint64_t      N       = node->ne[3];
            uint64_t      Cout    = node->ne[2];
            uint64_t      KW      = knl->ne[0];
            uint64_t      KH      = knl->ne[1];
            uint64_t      Cin     = node->src[1]->ne[2];
            // KxCRS @ CRSxNPQ = KxNPQ -> M=K, K=CRS, N=NPQ
            uint64_t      size_M  = Cout;
            uint64_t      size_K  = Cin * KW * KH;
            uint64_t      size_N  = N * OW * OH;
            *n_flops = size_M * size_N * (size_K + (size_K - 1));
            name += " M=Cout=" + std::to_string(size_M) + ", K=Cin*KW*KH=" + std::to_string(size_K) +
                    ", N=N*OW*OH=" + std::to_string(size_N);
            name = fusion_str + name;
            return name;
        }
        if (node->op == GGML_OP_RMS_NORM) {
            std::string   name    = ggml_op_name(node->op);
            name += "(" + std::to_string(node->ne[0]) + "," + std::to_string(node->ne[1]) + "," + std::to_string(node->ne[2]) + "," + std::to_string(node->ne[3]) + ")";
            name = fusion_str + name;
            return name;
        }
        if (node->op == GGML_OP_FLASH_ATTN_EXT) {
            const ggml_tensor * dst = node;
            const ggml_tensor * q = node->src[0];
            const ggml_tensor * k = node->src[1];
            const ggml_tensor * v = node->src[2];
            const ggml_tensor * m = node->src[3];
            std::stringstream name;
            name << fusion_str;
            name << ggml_op_name(node->op) <<
                " dst(" << dst->ne[0] << "," << dst->ne[1] << "," << dst->ne[2] << "," << dst->ne[3] << "), " <<
                " q(" << q->ne[0] << "," << q->ne[1] << "," << q->ne[2] << "," << q->ne[3] << "), " <<
                " k(" << k->ne[0] << "," << k->ne[1] << "," << k->ne[2] << "," << k->ne[3] << "), " <<
                " v(" << v->ne[0] << "," << v->ne[1] << "," << v->ne[2] << "," << v->ne[3] << "), " <<
                " m(" << (m?m->ne[0]:0) << "," << (m?m->ne[1]:0) << "," << (m?m->ne[2]:0) << "," << (m?m->ne[3]:0) << ")";
            *n_flops = 2ull * q->ne[1] * q->ne[2] * (k->ne[0] + v->ne[0]) * k->ne[1] * q->ne[3];
            return name.str();
        }
        if (node->op == GGML_OP_TOP_K) {
            std::stringstream name;
            name << fusion_str;
            name << ggml_op_name(node->op) <<
                " K=" << node->ne[0] <<
                " (" << node->src[0]->ne[0] << "," << node->src[0]->ne[1] << "," << node->src[0]->ne[2] << "," << node->src[0]->ne[3] << ")";
            return name.str();
        }
        return fusion_str + ggml_op_name(node->op);
    }

    void log_timing(const ggml_tensor * node, const char *fusion_name, uint64_t time) {
        uint64_t n_flops;
        std::string name = get_node_fusion_name(node, fusion_name, &n_flops);
        if (n_flops) {
            flops[name].push_back(n_flops);
        }
        timings[name].push_back(time);
    }

    void log_timing(const std::vector<ggml_tensor *> &nodes, const std::vector<const char *> &names, uint64_t time) {
        uint64_t total_flops = 0;
        std::string name;
        for (size_t n = 0; n < nodes.size(); ++n) {
            uint64_t n_flops = 0;
            name += get_node_fusion_name(nodes[n], names[n], &n_flops);
            total_flops += n_flops;

            if (n != nodes.size() - 1) {
                name += ", ";
            }
        }
        if (total_flops) {
            flops[name].push_back(total_flops);
        }
        timings[name].push_back(time);
    }

  private:
    std::map<std::string, std::vector<uint64_t>> timings;
    std::map<std::string, std::vector<uint64_t>> flops;
    uint32_t print_count {};
};

struct ggml_backend_vk_context {
    std::string name;

    vk_device device;

    size_t semaphore_idx, event_idx;
    ggml_vk_garbage_collector gc;
    size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k, prealloc_size_add_rms_partials, prealloc_size_add_rms_partials_offset;
    vk_buffer prealloc_x, prealloc_y, prealloc_split_k, prealloc_add_rms_partials, sync_staging;
    vk::Fence fence, almost_ready_fence;
    bool submit_pending {};
    bool almost_ready_fence_pending {};
    // Set before op_add and unset after op_rms_norm to indicate that the add should
    // write partial sums to accumulate the square of the vector components
    bool do_add_rms_partials_offset_calculation;
    bool do_add_rms_partials;

    uint64_t last_total_mul_mat_bytes {};

    // Cache most recent tensor that was converted into prealloc_y, and what pipeline it used to convert.
    vk_pipeline_struct * prealloc_y_last_pipeline_used {};
    const ggml_tensor * prealloc_y_last_tensor_used {};
    // True when prealloc_y holds the padded fp16 layout used by the coopmat2 B decode-vector callback.
    // If false, then it's contiguous.
    bool prealloc_y_last_decode_vector_staging {};

    // Track which nodes have been used since the last sync, and whether they were written to
    std::vector<const ggml_tensor *> unsynced_nodes_written;
    std::vector<const ggml_tensor *> unsynced_nodes_read;
    // Track which prealloc buffers have pending reads that need to be synchronized.
    // These are checked before writing to the buffer (and call ggml_vk_sync_buffers if set),
    // and set to true after the buffer contents are consumed.
    bool prealloc_x_need_sync, prealloc_y_need_sync, prealloc_split_k_need_sync;

    vk_context_ref compute_ctx;

    vk_context_ref transfer_ctx;
    vk_semaphore transfer_semaphore;
    uint64_t transfer_semaphore_last_submitted {};

    std::vector<vk_context_ref> tensor_ctxs;

    std::vector<vk::DescriptorPool> descriptor_pools;
    std::vector<vk::DescriptorSet> descriptor_sets;
    uint32_t descriptor_set_idx {};
    uint32_t pipeline_descriptor_set_requirements {};

    vk_command_pool compute_cmd_pool;
    vk_command_pool transfer_cmd_pool;

    // number of additional consecutive nodes that are being fused with the
    // node currently being processed
    int num_additional_fused_ops {};
    // Bitmask of which fused ops need to write an intermediate value to memory.
    // Bit 'i' means nodes[start_of_fusion + i] writes to memory.
    // If there's no fusion, bit 0 is still set.
    int fused_ops_write_mask {};
    topk_moe_mode fused_topk_moe_mode {};
    bool fused_topk_moe_scale {};

    // for GGML_VK_PERF_LOGGER
    std::unique_ptr<vk_perf_logger> perf_logger;
    vk::QueryPool query_pool;
    std::vector<const char *> query_fusion_names;
    std::vector<int> query_fusion_node_count;
    std::vector<ggml_tensor *> query_nodes;
    std::vector<int> query_node_idx;
    int32_t num_queries {};
    int32_t query_idx {};
};

static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000;  // NOLINT

static uint64_t vk_tensor_offset(const ggml_tensor * tensor) {
    if (tensor->view_src) {
        return (uint8_t *) tensor->view_src->data - (uint8_t *) vk_ptr_base;
    }
    return (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
}

static uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const ggml_tensor * t)
{
    return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));;
}

template <typename T> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    GGML_UNUSED(p);
    GGML_UNUSED(src0);
    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
    GGML_UNUSED(dst);
    static_assert(!std::is_const<T>::value, "unexpected type");
    GGML_ASSERT(!src0 || get_misalign_bytes(ctx, src0) == 0);
    GGML_ASSERT(!src1 || get_misalign_bytes(ctx, src1) == 0);
    GGML_ASSERT(!src2 || get_misalign_bytes(ctx, src2) == 0);
    GGML_ASSERT(!src3 || get_misalign_bytes(ctx, src3) == 0);
    GGML_ASSERT(!dst  || get_misalign_bytes(ctx, dst) == 0);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_mat_vec_p021_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.b_offset = b_offset;
    p.d_offset = d_offset;

    GGML_UNUSED(src0);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_mat_vec_nc_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.b_offset = b_offset;
    p.d_offset = d_offset;

    GGML_UNUSED(src0);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_fwht_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    p.src_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    p.dst_offset = get_misalign_bytes(ctx, dst)  / ggml_type_size(dst->type);

    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

struct ggml_backend_vk_buffer_context {
    vk_device_ref device;
    vk_buffer dev_buffer;
    std::string name;

    ggml_backend_vk_buffer_context(vk_device_ref device, vk_buffer&& dev_buffer, std::string& name) :
        device(device),
        dev_buffer(dev_buffer),
        name(name) {
    }

    ~ggml_backend_vk_buffer_context() {
        ggml_vk_destroy_buffer(dev_buffer);
    }
};

void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) {
    if (!vk_memory_logger_enabled) {
        return;
    }
    std::lock_guard<std::mutex> guard(log_mutex);
    vk_buffer buf = buf_ref.lock();
    const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal);
    const std::string type = device ? "device" : "host";
    allocations[buf->buffer] = size;
    total_device += device ? size : 0;
    total_host += device ? 0 : size;
    VK_LOG_MEMORY(buf->device->name << ": +" << format_size(size) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host));
}

void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) {
    if (buf_ref.expired() || buf_ref.lock()->size == 0 || !vk_memory_logger_enabled) {
        return;
    }

    std::lock_guard<std::mutex> guard(log_mutex);
    vk_buffer buf = buf_ref.lock();
    const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal);
    std::string type = device ? "device" : "host";
    auto it = allocations.find(buf->buffer);
    if (it != allocations.end()) {
        total_device -= device ? it->second : 0;
        total_host -= device ? 0 : it->second;
        VK_LOG_MEMORY(buf->device->name << ": -" << format_size(it->second) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host));
        allocations.erase(it);
    } else {
        VK_LOG_MEMORY("ERROR " << buf->device->name << ": Attempted to deallocate unknown " << type << " memory at " << buf->buffer);
    }
}

struct vk_instance_t {
    vk::Instance instance;

    bool debug_utils_support = false;  // VK_EXT_debug_utils enabled
    PFN_vkSetDebugUtilsObjectNameEXT pfn_vkSetDebugUtilsObjectNameEXT = {};
    PFN_vkQueueBeginDebugUtilsLabelEXT pfn_vkQueueBeginDebugUtilsLabelEXT = {};
    PFN_vkQueueEndDebugUtilsLabelEXT   pfn_vkQueueEndDebugUtilsLabelEXT   = {};
    PFN_vkCmdBeginDebugUtilsLabelEXT   pfn_vkCmdBeginDebugUtilsLabelEXT   = {};
    PFN_vkCmdEndDebugUtilsLabelEXT pfn_vkCmdEndDebugUtilsLabelEXT = {};
    PFN_vkCmdInsertDebugUtilsLabelEXT  pfn_vkCmdInsertDebugUtilsLabelEXT  = {};

    std::vector<size_t> device_indices;
    std::vector<bool>   device_supports_membudget;
    vk_device devices[GGML_VK_MAX_DEVICES];
};

static bool vk_instance_initialized = false;
static vk_instance_t vk_instance;

#ifdef GGML_VULKAN_CHECK_RESULTS
static size_t vk_skip_checks;
static size_t vk_output_tensor;

static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name);
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx);
static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx);
#endif

typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);

static void ggml_backend_vk_free(ggml_backend_t backend);

static VkDeviceSize ggml_vk_get_max_buffer_range(const ggml_backend_vk_context * ctx, const vk_buffer &buf, const VkDeviceSize offset) {
    const VkDeviceSize range = std::min(VkDeviceSize{buf->size - offset},
                                        VkDeviceSize{ctx->device->properties.limits.maxStorageBufferRange});
    return range;
}

// Wait for ctx->fence to be signaled.
static void ggml_vk_wait_for_fence(ggml_backend_vk_context * ctx) {
    // Use waitForFences while most of the graph executes. Hopefully the CPU can sleep
    // during this wait.
    if (ctx->almost_ready_fence_pending) {
        VK_CHECK(ctx->device->device.waitForFences({ ctx->almost_ready_fence }, true, UINT64_MAX), "almost_ready_fence");
        ctx->device->device.resetFences({ ctx->almost_ready_fence });
        ctx->almost_ready_fence_pending = false;
    }

    // Spin (w/pause) waiting for the graph to finish executing.
    vk::Result result;
    while ((result = ctx->device->device.getFenceStatus(ctx->fence)) != vk::Result::eSuccess) {
        if (result != vk::Result::eNotReady) {
            fprintf(stderr, "ggml_vulkan: error %s at %s:%d\n", to_string(result).c_str(), __FILE__, __LINE__);
            exit(1);
        }
        for (uint32_t i = 0; i < 100; ++i) {
            YIELD();
            YIELD();
            YIELD();
            YIELD();
            YIELD();
            YIELD();
            YIELD();
            YIELD();
            YIELD();
            YIELD();
        }
    }
    ctx->device->device.resetFences({ ctx->fence });
}

static constexpr uint32_t kSpvOpCooperativeMatrixLoadTensorNV = 5367;
static constexpr uint32_t kSpvCapabilityCooperativeMatrixDecodeVectorNV = 5447;
static constexpr uint32_t kSpvTensorAddressingDecodeVectorFuncBit = 0x4;

// Remove SPV_NV_cooperative_matrix_decode_vector usage from a SPIR-V module so it
// can be loaded on drivers that only support SPV_NV_cooperative_matrix2. Drops the
// OpExtension declaration, the CooperativeMatrixDecodeVectorNV OpCapability, and the
// DecodeVectorFunc operand from any OpCooperativeMatrixLoadTensorNV instruction.
// Returns true when the input used the extension (and `out` was populated with a
// stripped copy); returns false otherwise without touching `out`.
static bool ggml_vk_strip_decode_vector(const uint32_t * code, size_t word_count, std::vector<uint32_t> & out) {
    static const char kDecodeVectorExt[] = "SPV_NV_cooperative_matrix_decode_vector";

    if (word_count < 5) {
        return false;
    }

    bool uses_decode_vector = false;
    for (size_t pos = 5; pos < word_count; ) {
        uint32_t word = code[pos];
        uint32_t wc   = word >> spv::WordCountShift;
        uint32_t op   = word & spv::OpCodeMask;
        GGML_ASSERT(wc > 0 && pos + wc <= word_count);
        if (op == spv::OpExtension && wc >= 2) {
            const char * s = reinterpret_cast<const char *>(&code[pos + 1]);
            if (strcmp(s, kDecodeVectorExt) == 0) {
                uses_decode_vector = true;
                break;
            }
        }
        pos += wc;
    }

    if (!uses_decode_vector) {
        return false;
    }

    VK_LOG_DEBUG("ggml_vk_strip_decode_vector: stripping SPV_NV_cooperative_matrix_decode_vector");

    // Bulk-copy unchanged runs and only break the run when an instruction needs to
    // be dropped or patched. Use reserve + insert/push_back so the destination buffer
    // is touched exactly once (no zero-initialization pass from resize()).
    out.clear();
    out.reserve(word_count);

    size_t run_start = 0;
    auto flush_run = [&](size_t up_to) {
        if (up_to > run_start) {
            out.insert(out.end(), code + run_start, code + up_to);
        }
    };

    for (size_t pos = 5; pos < word_count; ) {
        uint32_t word = code[pos];
        uint32_t wc   = word >> spv::WordCountShift;
        uint32_t op   = word & spv::OpCodeMask;
        GGML_ASSERT(wc > 0 && pos + wc <= word_count);

        if (op == spv::OpExtension && wc >= 2) {
            const char * s = reinterpret_cast<const char *>(&code[pos + 1]);
            if (strcmp(s, kDecodeVectorExt) == 0) {
                flush_run(pos);
                pos += wc;
                run_start = pos;
                continue;
            }
        }

        if (op == spv::OpCapability && wc == 2 && code[pos + 1] == kSpvCapabilityCooperativeMatrixDecodeVectorNV) {
            flush_run(pos);
            pos += wc;
            run_start = pos;
            continue;
        }

        if (op == kSpvOpCooperativeMatrixLoadTensorNV) {
            // [opcode/wc][ResultType][Result][Pointer][Object][TensorLayout][MemOperand mask][mem extras...][TA mask][ta extras...]
            GGML_ASSERT(wc >= 8);

            uint32_t mem_mask = code[pos + 6];
            size_t   cur      = pos + 7;
            // Each of these MemoryAccess bits (when set) carries one trailing operand.
            cur += (mem_mask & 0x2)     ? 1 : 0; // Aligned
            cur += (mem_mask & 0x8)     ? 1 : 0; // MakePointerAvailable
            cur += (mem_mask & 0x10)    ? 1 : 0; // MakePointerVisible
            cur += (mem_mask & 0x10000) ? 1 : 0; // AliasScopeINTELMask
            cur += (mem_mask & 0x20000) ? 1 : 0; // NoAliasINTELMask
            GGML_ASSERT(cur < pos + wc);

            uint32_t ta_mask = code[cur];
            if ((ta_mask & kSpvTensorAddressingDecodeVectorFuncBit) == 0) {
                pos += wc;
                continue; // leave instruction inside the current unchanged run
            }

            flush_run(pos);

            // Append unchanged prefix of the instruction (header through the mem-extras).
            size_t inst_start = out.size();
            size_t pre_n      = cur - pos;
            out.insert(out.end(), code + pos, code + pos + pre_n);

            // Emit TA mask with the DecodeVectorFunc bit cleared.
            out.push_back(ta_mask & ~kSpvTensorAddressingDecodeVectorFuncBit);

            // TA extras: TensorView (0x1) and DecodeFunc (0x2) are kept verbatim;
            // DecodeVectorFunc (0x4) is dropped along with its trailing id operand.
            size_t keep_ta_extras = ((ta_mask & 0x1) ? 1 : 0) + ((ta_mask & 0x2) ? 1 : 0);
            if (keep_ta_extras) {
                out.insert(out.end(), code + cur + 1, code + cur + 1 + keep_ta_extras);
            }

            GGML_ASSERT(wc == pre_n + 1 + keep_ta_extras + 1);

            // Patch the instruction header with the new (one-shorter) word count.
            uint32_t new_wc = wc - 1;
            out[inst_start] = (new_wc << spv::WordCountShift) | op;

            pos += wc;
            run_start = pos;
            continue;
        }

        pos += wc;
    }

    flush_run(word_count);
    return true;
}

static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, size_t spv_size, const void* spv_data, const std::string entrypoint,
                                         uint32_t parameter_count, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants,
                                         bool disable_robustness, bool require_full_subgroups, uint32_t required_subgroup_size) {
    VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << pipeline->name << ", " << entrypoint << ", " << parameter_count <<
                 ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " <<
                 disable_robustness << ", " << require_full_subgroups << ", " << required_subgroup_size << ")");
    GGML_ASSERT(parameter_count > 0);
    GGML_ASSERT(parameter_count <= MAX_PARAMETER_COUNT);
    GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT

    vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast<const uint32_t *>(spv_data));

    // Patch SPIR-V to enable RTE rounding for FP16, avoiding the need for
    // separate shader variants compiled with -DRTE16.
    std::vector<uint32_t> spirv;
    if (device->float_controls_rte_fp16) {
        const uint32_t* spv_words = reinterpret_cast<const uint32_t *>(spv_data);
        size_t word_count = spv_size / sizeof(uint32_t);
        spirv.assign(spv_words, spv_words + word_count);

        // Find insertion points respecting SPIR-V layout order:
        //   Header(5) -> OpCapability -> OpExtension -> ... -> OpEntryPoint -> OpExecutionMode -> ...
        size_t pos = 5; // skip header
        size_t cap_insert_pos = pos;
        size_t ext_insert_pos = pos;
        size_t exec_insert_pos = pos;
        uint32_t entry_point_id = 0;

        while (pos < spirv.size()) {
            uint32_t opcode = spirv[pos] & spv::OpCodeMask;
            uint32_t len    = spirv[pos] >> spv::WordCountShift;
            if (len == 0) break;

            if (opcode == spv::OpCapability) {
                cap_insert_pos = pos + len;
                ext_insert_pos = pos + len;
            } else if (opcode == spv::OpExtension) {
                ext_insert_pos = pos + len;
            } else if (opcode == spv::OpEntryPoint) {
                entry_point_id = spirv[pos + 2];
                exec_insert_pos = pos + len;
            } else if (opcode == spv::OpExecutionMode || opcode == spv::OpExecutionModeId) {
                exec_insert_pos = pos + len;
            } else if (entry_point_id != 0) {
                break;
            }

            pos += len;
        }

        // Insert from latest position first so earlier indices stay valid.

        // OpExecutionMode %entrypoint RoundingModeRTE 16
        uint32_t exec_mode[] = { (4u << spv::WordCountShift) | spv::OpExecutionMode, entry_point_id, spv::ExecutionModeRoundingModeRTE, 16 };
        spirv.insert(spirv.begin() + exec_insert_pos, std::begin(exec_mode), std::end(exec_mode));

        // OpExtension "SPV_KHR_float_controls"
        const char ext_str[] = "SPV_KHR_float_controls";
        size_t ext_str_words = CEIL_DIV(sizeof(ext_str), sizeof(uint32_t));
        std::vector<uint32_t> extension(1 + ext_str_words, 0);
        extension[0] = (uint32_t)((1 + ext_str_words) << spv::WordCountShift) | spv::OpExtension;
        memcpy(&extension[1], ext_str, sizeof(ext_str));
        spirv.insert(spirv.begin() + ext_insert_pos, extension.begin(), extension.end());

        // OpCapability RoundingModeRTE
        uint32_t capability[] = { (2u << spv::WordCountShift) | spv::OpCapability, spv::CapabilityRoundingModeRTE };
        spirv.insert(spirv.begin() + cap_insert_pos, std::begin(capability), std::end(capability));

        shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data());
    }

#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT)
    if (device->coopmat2 && !device->coopmat2_decode_vector) {
        const uint32_t * src   = spirv.empty() ? reinterpret_cast<const uint32_t *>(spv_data) : spirv.data();
        size_t           src_n = spirv.empty() ? spv_size / sizeof(uint32_t) : spirv.size();
        std::vector<uint32_t> stripped;
        if (ggml_vk_strip_decode_vector(src, src_n, stripped)) {
            spirv = std::move(stripped);
            shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data());
        }
    }
#endif

    pipeline->shader_module = device->device.createShaderModule(shader_module_create_info);

    vk::PushConstantRange pcr(
        vk::ShaderStageFlagBits::eCompute,
        0,
        pipeline->push_constant_size
    );

    vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), device->dsl, pcr);
    pipeline->layout = device->device.createPipelineLayout(pipeline_layout_create_info);

    std::vector<vk::SpecializationMapEntry> specialization_entries(specialization_constants.size());

    for (size_t i = 0; i < specialization_constants.size(); i++) {
        specialization_entries[i].constantID = i;
        specialization_entries[i].offset = i * sizeof(uint32_t);
        specialization_entries[i].size = sizeof(uint32_t);
    }

    vk::SpecializationInfo specialization_info(
        specialization_entries.size(),
        specialization_entries.data(),
        specialization_constants.size() * sizeof(uint32_t),
        specialization_constants.data()
    );

    vk::PipelineShaderStageCreateFlags pipeline_shader_stage_create_flags{};

    if (device->subgroup_require_full_support && require_full_subgroups) {
        pipeline_shader_stage_create_flags |= vk::PipelineShaderStageCreateFlagBits::eRequireFullSubgroupsEXT;
    }

    vk::PipelineShaderStageCreateInfo pipeline_shader_create_info(
            pipeline_shader_stage_create_flags,
            vk::ShaderStageFlagBits::eCompute,
            pipeline->shader_module,
            entrypoint.c_str(),
            &specialization_info);

    vk::PipelineShaderStageRequiredSubgroupSizeCreateInfoEXT pipeline_shader_stage_required_subgroup_size_create_info;
    pipeline_shader_stage_required_subgroup_size_create_info.requiredSubgroupSize = required_subgroup_size;
    if (device->subgroup_size_control && required_subgroup_size > 0) {
        GGML_ASSERT(device->subgroup_min_size <= required_subgroup_size && required_subgroup_size <= device->subgroup_max_size);
        pipeline_shader_create_info.setPNext(&pipeline_shader_stage_required_subgroup_size_create_info);
    }

    vk::ComputePipelineCreateInfo compute_pipeline_create_info(
        device->pipeline_executable_properties_support ?
            vk::PipelineCreateFlagBits::eCaptureStatisticsKHR :
            vk::PipelineCreateFlags{},
        pipeline_shader_create_info,
        pipeline->layout);

    vk::PipelineRobustnessCreateInfoEXT rci;

    if (device->pipeline_robustness && disable_robustness) {
        rci.storageBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled;
        rci.uniformBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled;
        compute_pipeline_create_info.setPNext(&rci);
    }

#if defined(VK_EXT_shader_64bit_indexing)
    vk::PipelineCreateFlags2CreateInfo pipelineFlags2CreateInfo;
    if (pipeline->is_64b_indexing)
    {
        pipelineFlags2CreateInfo.flags = vk::PipelineCreateFlagBits2::e64BitIndexingEXT;
        if (device->pipeline_executable_properties_support) {
            pipelineFlags2CreateInfo.flags |= vk::PipelineCreateFlagBits2::eCaptureStatisticsKHR;
        }
        pipelineFlags2CreateInfo.setPNext(compute_pipeline_create_info.pNext);
        compute_pipeline_create_info.setPNext(&pipelineFlags2CreateInfo);
    }
#endif

    try {
        pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value;
    } catch (const vk::SystemError& e) {
        std::cerr << "ggml_vulkan: Compute pipeline creation failed for " << pipeline->name << std::endl;
        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
        throw e;
    }

    if (vk_instance.debug_utils_support) {
        vk::DebugUtilsObjectNameInfoEXT duoni;
        duoni.objectType = vk::ObjectType::ePipeline;
        duoni.pObjectName = pipeline->name.c_str();
        duoni.objectHandle = /*reinterpret_cast*/(uint64_t)(static_cast<VkPipeline>(pipeline->pipeline));
        vk_instance.pfn_vkSetDebugUtilsObjectNameEXT(device->device, &static_cast<VkDebugUtilsObjectNameInfoEXT &>(duoni));
    }

    if (device->pipeline_executable_properties_support) {
        vk::PipelineExecutableInfoKHR executableInfo;
        executableInfo.pipeline = pipeline->pipeline;

        auto statistics = device->device.getPipelineExecutableStatisticsKHR(executableInfo);

        bool print_stats = !vk_pipeline_stats_filter.empty() &&
                           pipeline->name.find(vk_pipeline_stats_filter) != std::string::npos;
        if (print_stats) {
            std::cerr << "ggml_vulkan: pipeline stats for " << pipeline->name << ":" << std::endl;
        }

        for (auto & s : statistics) {
            if (print_stats) {
                std::cerr << "ggml_vulkan:   " << s.name.data() << ": ";
                switch (s.format) {
                    case vk::PipelineExecutableStatisticFormatKHR::eBool32:
                        std::cerr << (s.value.b32 ? "true" : "false");
                        break;
                    case vk::PipelineExecutableStatisticFormatKHR::eInt64:
                        std::cerr << s.value.i64;
                        break;
                    case vk::PipelineExecutableStatisticFormatKHR::eUint64:
                        std::cerr << s.value.u64;
                        break;
                    case vk::PipelineExecutableStatisticFormatKHR::eFloat64:
                        std::cerr << s.value.f64;
                        break;
                }
                std::cerr << std::endl;
            }
            // "Register Count" is reported by NVIDIA drivers.
            if (strcmp(s.name, "Register Count") == 0) {
                VK_LOG_DEBUG(pipeline->name << " " << s.name << ": " << s.value.u64 << " registers");
                pipeline->register_count = (uint32_t)s.value.u64;
            }
        }
    }

    {
        std::lock_guard<std::mutex> guard(device->compile_mutex);
        device->all_pipelines.push_back(pipeline);
        pipeline->compiled = true;
        pipeline->compile_pending = false;
    }
    device->compile_cv.notify_all();
}

static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) {
    VK_LOG_DEBUG("ggml_pipeline_destroy_pipeline(" << pipeline->name << ")");
    device.destroyPipelineLayout(pipeline->layout);

    device.destroyShaderModule(pipeline->shader_module);

    device.destroyPipeline(pipeline->pipeline);
}

static void ggml_pipeline_request_descriptor_sets(ggml_backend_vk_context *ctx, vk_pipeline& pipeline, uint32_t n) {
    VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")");
    ctx->pipeline_descriptor_set_requirements += n;
    if (!pipeline->compiled) {
        ggml_vk_load_shaders(ctx->device, pipeline);
    }
    ggml_pipeline_allocate_descriptor_sets(ctx);
}

static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx) {

    if (ctx->descriptor_sets.size() >= ctx->pipeline_descriptor_set_requirements) {
        // Enough descriptors are available
        return;
    }

    vk_device& device = ctx->device;

    // Grow by 50% to avoid frequent allocations
    uint32_t needed = std::max(3 * ctx->descriptor_sets.size() / 2, size_t{ctx->pipeline_descriptor_set_requirements});
    uint32_t to_alloc = needed - ctx->descriptor_sets.size();
    uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - ctx->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE;
    uint32_t pool_idx = ctx->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE;

    while (to_alloc > 0) {
        const uint32_t alloc_count = std::min(pool_remaining, to_alloc);
        to_alloc -= alloc_count;
        pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE;

        if (pool_idx >= ctx->descriptor_pools.size()) {
            vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, MAX_PARAMETER_COUNT * VK_DEVICE_DESCRIPTOR_POOL_SIZE);
            vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size);
            ctx->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info));
        }

        std::vector<vk::DescriptorSetLayout> layouts(alloc_count);
        for (uint32_t i = 0; i < alloc_count; i++) {
            layouts[i] = device->dsl;
        }
        vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(ctx->descriptor_pools[pool_idx], alloc_count, layouts.data());
        std::vector<vk::DescriptorSet> sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info);
        ctx->descriptor_sets.insert(ctx->descriptor_sets.end(), sets.begin(), sets.end());

        pool_idx++;
    }
}

static vk_command_buffer* ggml_vk_create_cmd_buffer(vk_device& device, vk_command_pool& p) {
    VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()");
    vk::CommandBufferAllocateInfo command_buffer_alloc_info(
        p.pool,
        vk::CommandBufferLevel::ePrimary,
        1);
    const std::vector<vk::CommandBuffer> cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info);
    p.cmd_buffers.push_back({ cmd_buffers.front(), 0, true });
    return &p.cmd_buffers[p.cmd_buffers.size()-1];
}

static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
    if (ctx->seqs.empty()) {
        if (fence) {
            std::lock_guard<std::mutex> guard(queue_mutex);
            ctx->p->q->queue.submit({}, fence);
        }
        return;
    }
    VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")");

    std::vector<std::vector<uint64_t>> tl_wait_vals;
    std::vector<std::vector<uint64_t>> tl_signal_vals;
    std::vector<std::vector<vk::Semaphore>> tl_wait_semaphores;
    std::vector<std::vector<vk::Semaphore>> tl_signal_semaphores;
    std::vector<vk::TimelineSemaphoreSubmitInfo> tl_submit_infos;
    std::vector<vk::SubmitInfo> submit_infos;
    int idx = -1;
    std::vector<std::vector<vk::PipelineStageFlags>> stage_flags;

    size_t reserve = 0;

    for (const auto& sequence : ctx->seqs) {
        reserve += sequence.size();
    }

    // Pre-reserve vectors to prevent reallocation, which invalidates pointers
    tl_wait_semaphores.reserve(reserve);
    tl_wait_vals.reserve(reserve);
    tl_signal_semaphores.reserve(reserve);
    tl_signal_vals.reserve(reserve);
    tl_submit_infos.reserve(reserve);
    submit_infos.reserve(reserve);
    stage_flags.reserve(reserve);

    for (const auto& sequence : ctx->seqs) {
        for (const auto& submission : sequence) {
            stage_flags.push_back({});
            idx++;
            tl_wait_vals.push_back({});
            tl_wait_semaphores.push_back({});
            tl_signal_vals.push_back({});
            tl_signal_semaphores.push_back({});
            for (size_t i = 0; i < submission.wait_semaphores.size(); i++) {
                stage_flags[idx].push_back(ctx->p->q->stage_flags);
                tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value);
                tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s);
            }
            for (size_t i = 0; i < submission.signal_semaphores.size(); i++) {
                tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value);
                tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s);
            }
            tl_submit_infos.push_back({
                (uint32_t) submission.wait_semaphores.size(),
                tl_wait_vals[idx].data(),
                (uint32_t) submission.signal_semaphores.size(),
                tl_signal_vals[idx].data(),
            });
            tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo;
            tl_submit_infos[idx].pNext = nullptr;
            vk::SubmitInfo si{
                (uint32_t) submission.wait_semaphores.size(),
                tl_wait_semaphores[idx].data(),
                stage_flags[idx].data(),
                1,
                &submission.buffer->buf,
                (uint32_t) submission.signal_semaphores.size(),
                tl_signal_semaphores[idx].data(),
            };
            si.setPNext(&tl_submit_infos[idx]);
            submit_infos.push_back(si);
        }
    }

    std::lock_guard<std::mutex> guard(queue_mutex);
    ctx->p->q->queue.submit(submit_infos, fence);

    ctx->seqs.clear();
}

static uint32_t ggml_vk_find_queue_family_index(std::vector<vk::QueueFamilyProperties>& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) {
    VK_LOG_DEBUG("ggml_vk_find_queue_family_index()");
    const uint32_t qfsize = queue_family_props.size();

    // Try with avoid preferences first
    for (uint32_t i = 0; i < qfsize; i++) {
        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) {
            return i;
        }
    }

    // Fall back to only required
    for (size_t i = 0; i < qfsize; i++) {
        if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) {
            return i;
        }
    }

    // Fall back to reusing compute queue
    for (size_t i = 0; i < qfsize; i++) {
        if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) {
            return i;
        }
    }

    // Fall back to ignoring min_num_queries
    for (size_t i = 0; i < qfsize; i++) {
        if (queue_family_props[i].queueFlags & required) {
            return i;
        }
    }

    // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations.
    // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional.
    if (compute_index >= 0) {
        return compute_index;
    }

    std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl;

    for(auto &q_family : queue_family_props) {
        std::cerr << "Queue number: "  + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl;
    }
    abort();
}

static void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) {
    VK_LOG_DEBUG("ggml_vk_create_queue()");
    std::lock_guard<std::recursive_mutex> guard(device->mutex);

    q.queue_family_index = queue_family_index;
    q.transfer_only = transfer_only;

    q.cmd_pool.init(device, &q);

    q.queue = device->device.getQueue(queue_family_index, queue_index);

    q.stage_flags = stage_flags;
}

static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p) {
    vk_context result = std::make_shared<vk_context_struct>();
    VK_LOG_DEBUG("ggml_vk_create_context(" << result << ")");
    ctx->gc.contexts.emplace_back(result);
    result->p = &p;
    return result;
}

static vk_context ggml_vk_create_temporary_context(vk_command_pool& p) {
    vk_context result = std::make_shared<vk_context_struct>();
    VK_LOG_DEBUG("ggml_vk_create_temporary_context(" << result << ")");
    result->p = &p;
    return result;
}

static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) {
    VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()");
    vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 };
    vk::SemaphoreCreateInfo ci{};
    ci.setPNext(&tci);
    vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
    ctx->gc.semaphores.push_back({ semaphore, 0 });
    return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1];
}

static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) {
    VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()");
    if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) {
        vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
        vk::SemaphoreCreateInfo ci{};
        ci.setPNext(&tci);
        vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci);
        ctx->gc.tl_semaphores.push_back({ semaphore, 0 });
    }
    return &ctx->gc.tl_semaphores[ctx->semaphore_idx++];
}

static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) {
    if (ctx->event_idx >= ctx->gc.events.size()) {
        ctx->gc.events.push_back(ctx->device->device.createEvent({}));
    }
    return ctx->gc.events[ctx->event_idx++];
}

static void ggml_vk_command_pool_cleanup(vk_device& device, vk_command_pool& p) {
    VK_LOG_DEBUG("ggml_vk_command_pool_cleanup()");

    // Requires command buffers to be done
    device->device.resetCommandPool(p.pool);
    // Don't clear the command buffers and mark them as not in use.
    // This allows us to reuse them
    for (auto& cmd_buffer : p.cmd_buffers) {
        cmd_buffer.in_use = false;
    }
}

static void ggml_vk_queue_command_pools_cleanup(vk_device& device) {
    VK_LOG_DEBUG("ggml_vk_queue_command_pools_cleanup()");

    // Arbitrary frequency to cleanup/reuse command buffers
    static constexpr uint32_t cleanup_frequency = 10;

    if (device->compute_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) {
        ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool);
    }
    if (device->transfer_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) {
        ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool);
    }
}

static std::vector<uint32_t> ggml_vk_find_memory_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) {
    std::vector<uint32_t> indices;

    for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) {
        vk::MemoryType memory_type = mem_props->memoryTypes[i];
        if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) &&
            (flags & memory_type.propertyFlags) == flags &&
            mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) {
            indices.push_back(i);
        }
    }
    return indices;
}

static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std::initializer_list<vk::MemoryPropertyFlags> & req_flags_list,
                                       void *import_ptr = nullptr) {
    VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags_list.begin()[0]) << ", " << to_string(req_flags_list.begin()[req_flags_list.size()-1]) << ")");
    if (size > device->max_buffer_size) {
        throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device buffer size limit");
    }

    vk_buffer buf = std::make_shared<vk_buffer_struct>();

    if (size == 0) {
        buf->size = 0;
        return buf;
    }

    vk::BufferUsageFlags usage_flags = vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst;
    vk::MemoryAllocateFlags mem_flags {};
    if (device->buffer_device_address) {
        usage_flags |= vk::BufferUsageFlagBits::eShaderDeviceAddress;
        mem_flags |= vk::MemoryAllocateFlagBits::eDeviceAddress;
    }

    vk::BufferCreateInfo buffer_create_info{
        vk::BufferCreateFlags(),
        size,
        usage_flags,
        vk::SharingMode::eExclusive,
        0,
        nullptr,
    };

    vk::ExternalMemoryBufferCreateInfo external_memory_bci;
    if (import_ptr) {
        external_memory_bci.handleTypes = vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT;
        buffer_create_info.setPNext(&external_memory_bci);
    }

    buf->buffer = device->device.createBuffer(buffer_create_info);

    vk::MemoryRequirements mem_req = device->device.getBufferMemoryRequirements(buf->buffer);

    vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties();

    const vk::MemoryPriorityAllocateInfoEXT mem_priority_info { 1.0f };

    vk::MemoryAllocateFlagsInfo mem_flags_info { mem_flags };

    if (device->memory_priority) {
        mem_flags_info.setPNext(&mem_priority_info);
    }

    if (import_ptr) {
        vk::MemoryHostPointerPropertiesEXT host_pointer_props;
        try {
            host_pointer_props = device->device.getMemoryHostPointerPropertiesEXT(vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT, import_ptr);
        } catch (vk::SystemError& e) {
            GGML_LOG_WARN("ggml_vulkan: Failed getMemoryHostPointerPropertiesEXT (%s)\n", e.what());
            device->device.destroyBuffer(buf->buffer);
            return {};
        }
        vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties();

        uint32_t memory_type_idx;
        vk::MemoryPropertyFlags property_flags = *req_flags_list.begin();
        for (memory_type_idx = 0; memory_type_idx < 32; ++memory_type_idx) {
            if (!(host_pointer_props.memoryTypeBits & (1u << memory_type_idx))) {
                continue;
            }
            if (!(mem_req.memoryTypeBits & (1u << memory_type_idx))) {
                continue;
            }

            vk::MemoryType memory_type = mem_props.memoryTypes[memory_type_idx];
            // check for visible+coherent+cached. Other flags (e.g. devicelocal) are allowed
            if ((memory_type.propertyFlags & property_flags) == property_flags) {
                property_flags = memory_type.propertyFlags;
                break;
            }
        }
        if (memory_type_idx == 32) {
            GGML_LOG_WARN("ggml_vulkan: Memory type for host allocation not found\n");
            device->device.destroyBuffer(buf->buffer);
            return {};
        }

        buf->memory_property_flags = mem_props.memoryTypes[memory_type_idx].propertyFlags;
        try {
            vk::ImportMemoryHostPointerInfoEXT import_info;
            import_info.handleType = vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT;
            import_info.pHostPointer = import_ptr;
            import_info.setPNext(&mem_flags_info);
            buf->device_memory = device->device.allocateMemory({ size, memory_type_idx, &import_info });
        } catch (const vk::SystemError& e) {
        }
    } else {
        for (auto it = req_flags_list.begin(); it != req_flags_list.end(); it++) {
            const auto & req_flags = *it;

            const std::vector<uint32_t> memory_type_indices = ggml_vk_find_memory_properties(&mem_props, &mem_req, req_flags);

            if (memory_type_indices.empty()) {
                continue;
            }
            buf->memory_property_flags = req_flags;

            bool done = false;

            for (auto mtype_it = memory_type_indices.begin(); mtype_it != memory_type_indices.end(); mtype_it++) {
                try {
                    buf->device_memory = device->device.allocateMemory({ mem_req.size, *mtype_it, &mem_flags_info });
                    done = true;
                    break;
                } catch (const vk::SystemError& e) {
                    // loop and retry
                    // during last attempt throw the exception
                    if (it + 1 == req_flags_list.end() && mtype_it + 1 == memory_type_indices.end()) {
                        device->device.destroyBuffer(buf->buffer);
                        throw e;
                    }
                }
            }

            if (done) {
                break;
            }
        }
    }

    if (!buf->device_memory) {
        device->device.destroyBuffer(buf->buffer);
        throw vk::OutOfDeviceMemoryError("No suitable memory type found");
    }

    buf->ptr = nullptr;

    if (import_ptr) {
        buf->ptr = import_ptr;
    } else {
        if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
            buf->ptr = device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE);
        }
    }

    device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0);

    buf->device = device;
    buf->size = size;

    if (device->buffer_device_address) {
        const vk::BufferDeviceAddressInfo addressInfo(buf->buffer);
        buf->bda_addr = device->device.getBufferAddress(addressInfo);
    }

    device->memory_logger->log_allocation(buf, size);

    return buf;
}

static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) {
    try {
        return ggml_vk_create_buffer(device, size, {req_flags, fallback_flags});
    } catch (const vk::SystemError& e) {
        std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl;
        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
        throw e;
    }
}

static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) {
    vk_buffer buf;
    try {
        if (device->prefer_host_memory) {
            buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
                                                       vk::MemoryPropertyFlagBits::eDeviceLocal});
        } else if (device->uma) {
            // Fall back to host memory type
            buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal,
                                                       vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
        } else if (device->disable_host_visible_vidmem) {
            if (device->allow_sysmem_fallback) {
                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal,
                                                           vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
            } else {
                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal});
            }
        } else {
            // use rebar if available, otherwise fallback to device only visible memory
            if (device->allow_sysmem_fallback) {
                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
                                                           vk::MemoryPropertyFlagBits::eDeviceLocal,
                                                           vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});
            } else {
                buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent,
                                                           vk::MemoryPropertyFlagBits::eDeviceLocal});
            }
        }
    } catch (const vk::SystemError& e) {
        std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl;
        std::cerr << "ggml_vulkan: " << e.what() << std::endl;
        throw e;
    }

    return buf;
}

static void ggml_vk_destroy_buffer(vk_buffer& buf) {
    if (buf == nullptr) {
        return;
    }

    if (buf->device != nullptr) {
        buf->device->memory_logger->log_deallocation(buf);
    }

    buf.reset();
}

static vk_subbuffer ggml_vk_subbuffer(const ggml_backend_vk_context* ctx, const vk_buffer& buf, size_t offset = 0) {
    return { buf, offset, ggml_vk_get_max_buffer_range(ctx, buf, offset) };
}

static void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subctx) {
    VK_LOG_DEBUG("ggml_vk_sync_buffers()");

    const bool transfer_queue = subctx->p->q->transfer_only;

    if (ctx) {
        ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false;
    }

    subctx->s->buffer->buf.pipelineBarrier(
        subctx->p->q->stage_flags,
        subctx->p->q->stage_flags,
        {},
        { {
          { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) },
          { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }
        } },
        {},
        {}
    );
}

static void ggml_vk_reset_event(vk_context& ctx, vk::Event& event) {
    VK_LOG_DEBUG("ggml_vk_set_event()");

    ctx->s->buffer->buf.resetEvent(
        event,
        ctx->p->q->stage_flags
    );
}

static void ggml_vk_set_event(vk_context& ctx, vk::Event& event) {
    VK_LOG_DEBUG("ggml_vk_set_event()");

    ctx->s->buffer->buf.setEvent(
        event,
        ctx->p->q->stage_flags
    );
}

static void ggml_vk_wait_events(vk_context& ctx, std::vector<vk::Event>&& events) {
    VK_LOG_DEBUG("ggml_vk_wait_events()");
    if (events.empty()) {
        return;
    }

    ctx->s->buffer->buf.waitEvents(
        events,
        ctx->p->q->stage_flags,
        ctx->p->q->stage_flags,
        {},
        {},
        {}
    );
}

struct vk_fa_tuning_params {
    FaCodePath path;
    uint32_t workgroup_size;
    uint32_t subgroup_size;
    uint32_t block_rows;
    uint32_t block_cols;
    uint32_t d_split;
    uint32_t row_split;
    bool shmem_staging;
    bool disable_subgroups;
    uint32_t limit_occupancy_shmem;

    void print() const {
        std::cerr << "path=" << path << " workgroup_size=" << workgroup_size << " subgroup_size=" << subgroup_size <<
                     " block_rows=" << block_rows << " block_cols=" << block_cols << " d_split=" << d_split <<
                     " row_split=" << row_split << " shmem_staging=" << shmem_staging << " disable_subgroups=" << disable_subgroups <<
                     " limit_occupancy_shmem=" << limit_occupancy_shmem << std::endl;
    }
};

static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type);
static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16);

static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {

    vk_fa_tuning_params result{};
    result.path = FA_SCALAR;

    if (device->vendor_id == VK_VENDOR_ID_INTEL) {
        // Disable subgroup use due to performance issues when enforcing subgroup sizes
        result.subgroup_size = 32;
        result.disable_subgroups = true;
    } else if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != AMD_GCN) {
        result.subgroup_size = n_rows < 4 ? 32 : device->subgroup_size;
    } else {
        result.subgroup_size = device->subgroup_size;
    }

    // Row split splits the workgroup so that synchronization only has to happen within subgroups, which avoids barriers
    uint32_t row_split_max_hsk = 64;
    if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != AMD_GCN && !device->uma) {
        row_split_max_hsk = n_rows <= 8 ? 64 : 128;
    }
    result.row_split = (n_rows < 4 || hsk <= row_split_max_hsk) ? 1 : 4;

    if (result.subgroup_size > 32 && (n_rows < 4 || hsk < (result.row_split == 1 ? 128 : 64))) {
        result.workgroup_size = result.subgroup_size * 2;
    } else {
        result.workgroup_size = result.subgroup_size * 4;
    }

    const uint32_t D = hsk | hsv;

    const bool reduce_block_rows = D & 8 || n_kv < 1024 || device->vendor_id == VK_VENDOR_ID_INTEL;

    if (n_rows == 1) {
        result.block_rows = 1;
        result.block_cols = 64;
    } else {
        // row_split 1 means higher register use per row, so block size has to be adjusted
        if (result.row_split == 1) {
            result.block_rows = n_rows == 2 ? 2 : ((n_rows <= 4 || reduce_block_rows) ? 4 : 8);
        } else {
            result.block_rows = n_rows <= 4 ? 4 : ((n_rows <= 8 || reduce_block_rows) ? 8 : 16);
        }

        result.block_cols = (D & 8) ? 64 : 32;
    }

    const uint32_t D_lsb = D ^ (D & (D-1));  // extract lowest set bit

    result.d_split = std::min(std::min(result.subgroup_size, 8u), D_lsb / 4);

    result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0;

    if (!reduce_block_rows && !ggml_vk_flash_attn_scalar_shmem_support(device, result, hsk, hsv, f32acc, k_type, v_type)) {
        result.block_rows /= 2;
    }

    // On AMD RDNA, for small head sizes and big batch size the shader uses few registers, so too many subgroups get scheduled
    // at once and end up thrashing the cache. Fix this by setting a large (unused) shmem buffer that reduces occupancy.
    // This targets an occupancy of 4 subgroups per SIMD.
    if (device->vendor_id == VK_VENDOR_ID_AMD && device->properties.limits.maxComputeSharedMemorySize == 65536) {
        if (device->architecture != AMD_GCN && n_rows >= 64 && hsk <= 128) {
            // 30kb target for hsk > 64, 26kb for <= 64 due to smaller workgroup size
            // Values are guessed, tested on RDNA2
            result.limit_occupancy_shmem = (hsk <= 64 ? 26 : 30) * 1024 / 4 / 4;
        } else if (device->architecture == AMD_GCN && n_rows <= 8 && hsk >= 256) {
            // Same thing for GCN, with an occupancy target of 2 subgroups per SIMD.
            // Here low-batch FA with large head size is affected.
            // n_rows < 4 switch because workgroup size switches from 128 to 256 there.
            result.limit_occupancy_shmem = (n_rows < 4 ? 14 : 26) * 1024 / 4 / 4;
        }
    }

    return result;
}

static vk_fa_tuning_params get_fa_tuning_params_coopmat1(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
    GGML_UNUSED(n_rows);
    GGML_UNUSED(n_kv);
    GGML_UNUSED(k_type);
    GGML_UNUSED(v_type);
    GGML_UNUSED(f32acc);

    vk_fa_tuning_params result{};
    result.path = FA_COOPMAT1;

    const uint32_t D = hsk | hsv;

    const uint32_t coopmat_block_rows = 16;
    const uint32_t coopmat_block_cols = 16;

    const uint32_t num_subgroups = 4;

    result.block_rows = coopmat_block_rows;
    result.block_cols = coopmat_block_cols * num_subgroups;
    result.row_split = num_subgroups;
    result.subgroup_size = device->subgroup_size;
    result.workgroup_size = num_subgroups * result.subgroup_size;

    const uint32_t D_lsb = D ^ (D & (D-1));  // extract lowest set bit
    result.d_split = std::min(std::min(result.subgroup_size, 8u), D_lsb / 4);

    result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0;

    return result;
}

static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
    GGML_UNUSED(n_kv);
    GGML_UNUSED(f32acc);

    vk_fa_tuning_params result{};
    result.path = FA_COOPMAT2;

    const uint32_t D = hsk | hsv;

    const bool small_rows = n_rows < 32;

    if (small_rows) {
        result.block_rows = 32;
        result.block_cols = 32;
    } else if (ggml_is_quantized(k_type) || ggml_is_quantized(v_type) || hsk >= 256 || hsv >= 256) {
        result.block_rows = (hsk >= 512 || hsv >= 512) ? 32 : 64;
        result.block_cols = 32;
    } else {
        result.block_rows = 64;
        result.block_cols = 64;
    }

    result.subgroup_size = device->subgroup_size;
    result.workgroup_size = (small_rows && (D % 32) == 0) ? 256 : 128;

    return result;
}

static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
    FaCodePath path = device->coopmat2 ? FA_COOPMAT2 :
                      device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR;

    if (path == FA_COOPMAT2 && k_type == GGML_TYPE_BF16 && !device->coopmat2_bf16_support) {
        path = FA_COOPMAT1;
    }
    if (path == FA_COOPMAT1 && k_type == GGML_TYPE_BF16 && !device->coopmat_bf16_support) {
        path = FA_SCALAR;
    }

    if (path == FA_COOPMAT1 && device->architecture == vk_device_architecture::NVIDIA_TURING) {
        // Nvidia compiler bug, see https://github.com/ggml-org/llama.cpp/pull/19075#issuecomment-3820716090
        path = FA_SCALAR;
    }

    if (path == FA_COOPMAT1) {
        bool shape_ok = (f32acc && device->coopmat_support_16x16x16_f32acc) ||
                        (!f32acc && device->coopmat_support_16x16x16_f16acc);
        const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
        bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc, k_type);

        if (!shape_ok || !shmem_ok) {
            path = FA_SCALAR;
        }
    }

    // scalar is faster than coopmat when N==1
    if (n_rows == 1 && (path == FA_COOPMAT1 || path == FA_COOPMAT2)) {
        path = FA_SCALAR;
    }

    // Q1_0 K/V is only implemented on coopmat2 (flash_attn_cm2); there is no scalar FA shader for it.
    if ((k_type == GGML_TYPE_Q1_0 || v_type == GGML_TYPE_Q1_0) && device->coopmat2) {
        path = FA_COOPMAT2;
    }

    switch (path) {
    case FA_SCALAR:
        return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
    case FA_COOPMAT1:
        return get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
    case FA_COOPMAT2:
        return get_fa_tuning_params_coopmat2(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
    default:
        throw std::runtime_error("unsupported FaCodePath");
    }
}

static vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc,
                                                  bool use_mask, bool use_mask_opt, bool use_logit_softcap, ggml_type k_type, ggml_type v_type) {
    const bool old_amd_windows = device->vendor_id == VK_VENDOR_ID_AMD && device->driver_id == vk::DriverId::eAmdProprietary &&
                                 (device->architecture == AMD_GCN || device->architecture == AMD_RDNA1 || device->architecture == AMD_RDNA2);

    uint32_t flags = (use_mask_opt      ? 1 : 0) |
                     (use_mask          ? 2 : 0) |
                     (use_logit_softcap ? 4 : 0) |
                     (old_amd_windows   ? 8 : 0);

    const uint32_t subgroup_size = params.disable_subgroups ? 0 : params.subgroup_size;

    return vk_fa_pipeline_state{hsk, hsv, params.block_rows, params.block_cols, params.d_split, params.row_split, params.shmem_staging, params.path, params.workgroup_size, subgroup_size, aligned, f32acc, flags, params.limit_occupancy_shmem, k_type, v_type};
}

static std::vector<uint32_t> get_fa_spec_constants(const vk_fa_pipeline_state& state) {
    const auto fa_block_bytes = [](ggml_type t) -> uint32_t {
        if (t == GGML_TYPE_F32) return 16u;
        return (uint32_t) ggml_type_size(t);
    };
    return {
        /* 0 WorkGroupSize   */ state.workgroup_size,
        /* 1 Br              */ state.Br,
        /* 2 Bc              */ state.Bc,
        /* 3 HSK             */ state.HSK,
        /* 4 HSV             */ state.HSV,
        /* 5 Clamp           */ static_cast<uint32_t>(!state.aligned),
        /* 6 D_split         */ state.D_split,
        /* 7 row_split       */ state.row_split,
        /* 8 SubGroupSize    */ state.subgroup_size,
        /* 9 SHMEM_STAGING   */ state.shmem_staging ? 1u : 0u,
        /*10 Flags           */ state.flags,
        /*11 LIMIT_OCCUPANCY_SHMEM */ state.limit_occupancy_shmem,
        /*12 FaTypeK         */ static_cast<uint32_t>(state.k_type),
        /*13 FaTypeV         */ static_cast<uint32_t>(state.v_type),
        /*14 FaBlockBytesK   */ fa_block_bytes(state.k_type),
        /*15 FaBlockBytesV   */ fa_block_bytes(state.v_type),
    };
}

static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id, ggml_type src0_type) {

    uint32_t lut_size = 0;
    switch (src0_type) {
    case GGML_TYPE_IQ1_S:
    case GGML_TYPE_IQ1_M:
        // Regular matmul uses the compact uint16_t IQ1 grid; the expanded
        // uint32_t grid is only enabled for the q8_1/int-dot vector path.
        lut_size = 2*2048;
        break;
    case GGML_TYPE_IQ2_XXS:
        lut_size = 8*256;
        break;
    case GGML_TYPE_IQ2_XS:
        lut_size = 8*512;
        break;
    case GGML_TYPE_IQ2_S:
        lut_size = 8*1024;
        break;
    case GGML_TYPE_IQ3_XXS:
        lut_size = 4*256;
        break;
    case GGML_TYPE_IQ3_S:
        lut_size = 4*512;
        break;
    case GGML_TYPE_IQ4_NL:
    case GGML_TYPE_IQ4_XS:
    case GGML_TYPE_MXFP4:
        lut_size = 4*16;
        break;
    case GGML_TYPE_NVFP4:
        // Same kvalues budget as MXFP4 plus ue4m3_fp32_lut[128] (types.glsl, DATA_A_NVFP4).
        lut_size = 4*16 + 128u * (uint32_t)sizeof(float);
        break;
    default:
        break;
    }

    // Needs to be kept up to date on shader changes
    const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1;
    const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float);
    const uint32_t warps = warptile[0] / warptile[10];

    const uint32_t load_bufs = (warptile[1] + warptile[2]) * (warptile[3] + bank_conflict_offset) * type_size;
    const uint32_t mmid_row_ids = mul_mat_id ? (warptile[2] * 2 * sizeof(uint16_t)) : 0;
    const uint32_t coopmat_stage = device->coopmat_support ? warptile[7] * warptile[8] / warps * sizeof(float) : 0;
    const uint32_t ballots_sh = mul_mat_id ? (warps * 4 * sizeof(uint32_t)) : 0;

    const uint32_t total_size = load_bufs + mmid_row_ids + coopmat_stage + lut_size + ballots_sh;
    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;

    VK_LOG_DEBUG("ggml_vk_matmul_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), "
                 "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", supported=" << supported);

    return supported;
}

// Shmem usage for the q8_1 mmq shader (mul_mmq.comp), which uses
// block_a_cache / block_b_cache layouts (see mul_mmq_shmem_types.glsl) rather
// than the float load buffers checked by ggml_vk_matmul_shmem_support.
// Sizes follow std430 rules. Returns false for types without a q8_1 pipeline.
static bool ggml_vk_matmul_int_shmem_support(const vk_device& device, const std::vector<uint32_t>& warptile, bool mul_mat_id, ggml_type src0_type) {

    // FLOAT_TYPE in the shader is float16_t with fp16 support, otherwise float.
    const uint32_t fp_size   = device->fp16 ? 2u : 4u;
    const uint32_t fp_align  = fp_size;
    const uint32_t fp2_size  = 2u * fp_size;
    const uint32_t fp2_align = device->fp16 ? 4u : 8u;

    struct member { uint32_t size, align; };
    auto std430_size = [](std::initializer_list<member> members) {
        uint32_t off = 0, struct_align = 1;
        for (const auto &m : members) {
            off = (off + m.align - 1) & ~(m.align - 1);
            off += m.size;
            struct_align = std::max(struct_align, m.align);
        }
        return (off + struct_align - 1) & ~(struct_align - 1);
    };

    uint32_t block_a_size = 0;
    switch (src0_type) {
        case GGML_TYPE_Q4_0:    block_a_size = std430_size({{16, 4}, {fp_size,  fp_align}});                  break; // qs[16/4] + dm
        case GGML_TYPE_Q4_1:    block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}});                 break; // qs[16/4] + dm(vec2)
        case GGML_TYPE_Q5_0:    block_a_size = std430_size({{16, 4}, {4, 4}, {fp_size,  fp_align}});          break; // qs[16/4] + qh + dm
        case GGML_TYPE_Q5_1:    block_a_size = std430_size({{16, 4}, {4, 4}, {fp2_size, fp2_align}});         break; // qs[16/4] + qh + dm(vec2)
        case GGML_TYPE_Q8_0:    block_a_size = std430_size({{32, 4}, {fp_size,  fp_align}});                  break; // qs[8] + dm
        case GGML_TYPE_MXFP4:   block_a_size = std430_size({{32, 4}, {fp_size,  fp_align}});                  break; // qs[8] + d
        case GGML_TYPE_Q2_K:    block_a_size = std430_size({{ 8, 4}, {2, 2}, {fp2_size, fp2_align}});         break; // qs[2] + scales(u8vec2) + dm(vec2)
        case GGML_TYPE_Q3_K:    block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}});                 break; // qs[4] + d_scales(vec2)
        case GGML_TYPE_Q4_K:    block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}});                 break; // qs[4] + dm(vec2)
        case GGML_TYPE_Q5_K:    block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}});                 break; // qs[8] + dm(vec2)
        case GGML_TYPE_Q6_K:    block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}});                 break; // qs[8] + d_scales(vec2)
        default:
            return false;
    }

    // block_b_cache: { int32_t qs[8]; FLOAT_TYPEV2 ds; }
    const uint32_t block_b_size = std430_size({{32, 4}, {fp2_size, fp2_align}});

    const uint32_t BM = warptile[1];
    const uint32_t BN = warptile[2];
    // mul_mmq.comp: BK_STEP=1 for MUL_MAT_ID, 4 otherwise.
    const uint32_t BK_STEP = mul_mat_id ? 1u : 4u;

    const uint32_t buf_a_size = BM * BK_STEP * block_a_size;
    const uint32_t buf_b_size = BN * BK_STEP * block_b_size;
    const uint32_t mmid_row_ids = mul_mat_id ? (BN * 2u * (uint32_t)sizeof(uint16_t)) : 0u;

    const uint32_t warps = warptile[0] / warptile[10];
    const uint32_t ballots_sh = mul_mat_id ? (warps * 4u * (uint32_t)sizeof(uint32_t)) : 0u;

    const uint32_t total_size = buf_a_size + buf_b_size + mmid_row_ids + ballots_sh;
    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;

    VK_LOG_DEBUG("ggml_vk_matmul_int_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), "
                 "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", total=" << total_size << ", supported=" << supported);

    return supported;
}

struct GpuPipelineConfig {
    // GPU architecture identifier.
    // Example: vk_device_architecture::AMD_GCN
    vk_device_architecture arch;

    // Mapping of pipeline names to their specific subgroup sizes.
    // Example: {"soft_max_f32", 64}
    std::unordered_map<std::string, uint32_t> pipelines;

    // Default subgroup size for this GPU.
    // Defaults to 0 if not explicitly provided.
    uint32_t default_subgroup_size = 0;
};

// Pipeline configuration for RDNA1 GPUs.
static const std::unordered_map<std::string, uint32_t> rdna1_pipelines = {
    {"soft_max", 64}, {"im2col", 64},
    {"argmax", 64}, {"mul_mat_vec", 64},
    {"mul_mat_vec_f16", 32}, {"mul_mat_vec_f32_f16", 32}
};

// Pipeline configuration for RDNA2 GPUs.
static const std::unordered_map<std::string, uint32_t> rdna2_pipelines = {
    {"soft_max", 64}, {"im2col", 64},
};

static constexpr uint32_t RDNA_DEFAULT_SUBGROUP_SIZE = 32;

// Define configurations for different GPUs.
static std::vector<GpuPipelineConfig> gpu_pipeline_configs = {
    {
        vk_device_architecture::AMD_RDNA1,
        {
            rdna1_pipelines,
        },
        RDNA_DEFAULT_SUBGROUP_SIZE
    },
    {
        vk_device_architecture::AMD_RDNA2,
        {
            rdna2_pipelines,
        },
        RDNA_DEFAULT_SUBGROUP_SIZE
    },
};

static uint32_t get_subgroup_size(const std::string &pipeline_name, const vk_device_architecture &arch) {
    for (const auto &config : gpu_pipeline_configs) {
        if (config.arch == arch) {
            auto pipIt = config.pipelines.find(pipeline_name);
            if (pipIt != config.pipelines.end()) {
                return pipIt->second;
            }
            std::vector<std::pair<std::string, uint32_t>> sorted_pipelines(config.pipelines.begin(), config.pipelines.end());
            std::sort(sorted_pipelines.begin(), sorted_pipelines.end(),
                      [](const auto &a, const auto &b) { return a.first.size() > b.first.size(); });
            for (const auto &entry : sorted_pipelines) {
                if (pipeline_name.find(entry.first) != std::string::npos) {
                    return entry.second;
                }
            }
            return config.default_subgroup_size;
        }
    }
    return 0; // If no matching configuration is found
}

// Whether scalar flash attention will use the MMQ path for the given k_type.
static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type) {
#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
    return device->integer_dot_product && device->subgroup_clustered &&
           (k_type == GGML_TYPE_Q4_0 || k_type == GGML_TYPE_Q4_1 ||
            k_type == GGML_TYPE_Q5_0 || k_type == GGML_TYPE_Q5_1 ||
            k_type == GGML_TYPE_Q8_0);
#else
    GGML_UNUSED(device);
    GGML_UNUSED(k_type);
    return false;
#endif
}

// load_shaders walks the pipeline list under compile_mutex and either claims
// the requested pipeline for compilation or, if another thread is already
// compiling it, drops the lock and waits on compile_cv. Compiles themselves
// run unlocked.
struct CompileTask {
    vk_pipeline pipeline;
    size_t spv_size;
    const void * spv_data;
    std::string entrypoint;
    uint32_t parameter_count;
    std::array<uint32_t, 3> wg_denoms;
    std::vector<uint32_t> specialization_constants;
    bool disable_robustness;
    bool require_full_subgroups;
    uint32_t required_subgroup_size;
};

static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
    VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")");

    // some shaders have a minimum subgroup size
    const uint32_t subgroup_size_8 = std::max(device->subgroup_size, 8u);
    const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u);
    const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u);

    const uint32_t mul_mat_subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
    const uint32_t mul_mat_subgroup_size_8 = std::max(mul_mat_subgroup_size, 8u);
    const uint32_t mul_mat_subgroup_size_16 = std::max(mul_mat_subgroup_size, 16u);
    const uint32_t mul_mat_subgroup_size_32 = std::max(mul_mat_subgroup_size, 32u);

    const bool subgroup_min_size_16 = (!device->subgroup_size_control && device->subgroup_size >= 16) ||
                                      (device->subgroup_size_control && device->subgroup_max_size >= 16);

    // mulmat
    std::vector<uint32_t> l_warptile, m_warptile, s_warptile,
                          l_warptile_id, m_warptile_id, s_warptile_id,
                          l_warptile_mmq, m_warptile_mmq, s_warptile_mmq,
                          l_warptile_mmq_int, m_warptile_mmq_int, s_warptile_mmq_int,
                          l_warptile_mmq_int_k, m_warptile_mmq_int_k, s_warptile_mmq_int_k,
                          l_warptile_mmq_k, m_warptile_mmq_k, s_warptile_mmq_k,
                          l_warptile_mmqid, m_warptile_mmqid, s_warptile_mmqid,
                          l_warptile_mmqid_int, m_warptile_mmqid_int, s_warptile_mmqid_int,
                          l_warptile_mmqid_int_k, m_warptile_mmqid_int_k, s_warptile_mmqid_int_k;
    std::array<uint32_t, 3> l_wg_denoms, m_wg_denoms, s_wg_denoms,
                            l_mmq_wg_denoms, m_mmq_wg_denoms, s_mmq_wg_denoms,
                            l_mmq_wg_denoms_k, m_mmq_wg_denoms_k, s_mmq_wg_denoms_k,
                            l_mmqid_wg_denoms, m_mmqid_wg_denoms, s_mmqid_wg_denoms;

    uint32_t l_align, m_align, s_align;

    vk_pipeline wait_pipeline;
    CompileTask claimed_task {};
    bool has_claimed_task = false;

    // The rest of the walk reads and writes shared device state, so hold the
    // lock until we're done deciding what to compile.
    std::unique_lock<std::mutex> compile_lock(device->compile_mutex);

    if (device->coopmat2) {
        // spec constants and tile sizes for non-quant matmul/matmul_id
        l_warptile = { 256, 128, 256, 64, 1 };
        m_warptile = { 256, 128, 128, 64, 0 };
        s_warptile = { 128,  64,  64, 64, 0 };
        l_wg_denoms = {128, 256, 1 };
        m_wg_denoms = {128, 128, 1 };
        s_wg_denoms = { 64,  64, 1 };

        // spec constants and tile sizes for quant matmul (non-Qi_K)
        l_warptile_mmq = { 256, 128, 256, 64, 1 };
        m_warptile_mmq = { 256, 128, 128, 64, 1 };
        s_warptile_mmq = { 256, 32,  64, 128, 0 };
        l_mmq_wg_denoms = { 128, 256, 1 };
        m_mmq_wg_denoms = { 128, 128, 1 };
        s_mmq_wg_denoms = { 32,  64,  1 };

        // spec constants and tile sizes for quant matmul (Qi_K)
        l_warptile_mmq_k = { 256, 128, 256, 64, 1 };
        m_warptile_mmq_k = { 256, 128, 128, 64, 1 };
        s_warptile_mmq_k = { 256, 32,  64, 128, 0 };
        l_mmq_wg_denoms_k = { 128, 256, 1 };
        m_mmq_wg_denoms_k = { 128, 128, 1 };
        s_mmq_wg_denoms_k = { 32,  64,  1 };

        // spec constants and tile sizes for quant matmul_id
        const uint32_t mmqid_bk = device->coopmat2_decode_vector ? 64u : 32u;
        l_warptile_mmqid = { 256, 128, 128, mmqid_bk, 1, device->subgroup_size };
        m_warptile_mmqid = { 256, 128, 64,  mmqid_bk, 0, device->subgroup_size };
        s_warptile_mmqid = { 256, 128, 64,  mmqid_bk, 0, device->subgroup_size };
        l_mmqid_wg_denoms = { 128, 128, 1 };
        m_mmqid_wg_denoms = { 128, 64, 1 };
        s_mmqid_wg_denoms = { 128, 64, 1 };

        l_align = 128;
        m_align =  64;
        s_align =  32;
    } else {
        // Matrix cores require different warp group sizes
        const uint32_t tm_l = device->coopmat_support ? device->coopmat_m : 4;
        const uint32_t tm_m = device->coopmat_support ? device->coopmat_m : 4;
        const uint32_t tm_s = device->coopmat_support ? device->coopmat_m : 2;
        const uint32_t tn_l = device->coopmat_support ? device->coopmat_n : 4;
        const uint32_t tn_m = device->coopmat_support ? device->coopmat_n : 2;
        const uint32_t tn_s = device->coopmat_support ? device->coopmat_n : 2;
        const uint32_t tk_l = device->coopmat_support ? device->coopmat_k : 1;
        const uint32_t tk_m = device->coopmat_support ? device->coopmat_k : 1;
        const uint32_t tk_s = device->coopmat_support ? device->coopmat_k : 1;

        const uint32_t s_warptile_wm = device->subgroup_size == 8 ? 8 : 32;

        l_warptile = { 128,             128, 128, 16, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 };
        m_warptile = { 128,              64,  64, 16, subgroup_size_8,     32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
        s_warptile = { subgroup_size_32, 32,  32, 16, s_warptile_wm,       32, 2, tm_s, tn_s, tk_s, subgroup_size_8 };

        l_warptile_mmq = { 128,             128, 128, 32, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 };
        m_warptile_mmq = { 128,              64,  64, 32, subgroup_size_8,     32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
        s_warptile_mmq = { subgroup_size_32, 32,  32, 32, s_warptile_wm,       32, 2, tm_s, tn_s, tk_s, subgroup_size_8 };

        // Integer MMQ has a smaller shared memory profile, but heavier register use
        l_warptile_mmq_int = { 128,             128, 128, 32, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 };
        m_warptile_mmq_int = { 128,              64,  64, 32, subgroup_size_8,     32, 2, 2, 2, 1, subgroup_size_8 };
        s_warptile_mmq_int = { subgroup_size_32, 32,  32, 32, s_warptile_wm,       32, 2, 2, 1, 1, subgroup_size_8 };

        // K-quants use even more registers, mitigate by setting WMITER to 1
        l_warptile_mmq_int_k = { 128,               128, 128, 32, subgroup_size_8 * 2, 64, 1, 4, 4, 1, subgroup_size_8 };
        m_warptile_mmq_int_k = { 128,                64,  64, 32, subgroup_size_8,     32, 1, 2, 2, 1, subgroup_size_8 };
        s_warptile_mmq_int_k = { subgroup_size_32,   32,  32, 32, s_warptile_wm,       32, 1, 2, 1, 1, subgroup_size_8 };

        l_warptile_id = { 128,                      128, 128, 16, mul_mat_subgroup_size_16 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_16 };
        m_warptile_id = { 128,                       64,  64, 16, mul_mat_subgroup_size_16,     32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_16 };
        s_warptile_id = { mul_mat_subgroup_size_16,  32,  32, 16, s_warptile_wm,                32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_16 };

        l_warptile_mmqid = { 128,                       128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_8 };
        m_warptile_mmqid = { 128,                        64,  64, 32, mul_mat_subgroup_size_8,     32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_8 };
        s_warptile_mmqid = { mul_mat_subgroup_size_32,   32,  32, 32, s_warptile_wm,               32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_8 };

        l_warptile_mmqid_int = { 128,                       128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, 4, 4, 1, mul_mat_subgroup_size_8 };
        m_warptile_mmqid_int = { 128,                        64,  64, 32, mul_mat_subgroup_size_8,     32, 2, 2, 2, 1, mul_mat_subgroup_size_8 };
        s_warptile_mmqid_int = { mul_mat_subgroup_size_32,   32,  32, 32, s_warptile_wm,               32, 2, 2, 1, 1, mul_mat_subgroup_size_8 };

        l_warptile_mmqid_int_k = { 128,                     128, 128, 32, mul_mat_subgroup_size_16 * 2, 64, 1, 4, 4, 1, mul_mat_subgroup_size_16 };
        m_warptile_mmqid_int_k = { 128,                      64,  64, 32, mul_mat_subgroup_size_16,     32, 1, 2, 2, 1, mul_mat_subgroup_size_16 };
        s_warptile_mmqid_int_k = { mul_mat_subgroup_size_32, 32,  32, 32, s_warptile_wm,                32, 1, 2, 1, 1, mul_mat_subgroup_size_16 };

        // chip specific tuning
        if ((device->architecture == AMD_GCN) && (device->driver_id != vk::DriverId::eAmdProprietary)) {
            m_warptile_mmq = m_warptile_mmq_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 };
            m_warptile_mmqid = m_warptile_mmqid_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 };
        } else if (device->vendor_id == VK_VENDOR_ID_AMD && device->coopmat_support && device->driver_id != vk::DriverId::eAmdProprietary) {
            // This is intentionally using tx_m values, slight performance increase
            l_warptile = { 256, 128, 128, 16, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
            l_warptile_mmq = l_warptile_mmq_int = { 256, 128, 128, 32, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
            l_warptile_mmq_int_k = { 256, 128, 128, 32, subgroup_size_16, 64, 1, 4, 2, 1, subgroup_size_16 };
        } else if (device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support && device->architecture == INTEL_XE2) {
            // Xe2/Xe3 with coopmat enabled - warptile performance tuning
            l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
            l_warptile_mmq = { 512, 128, 128, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
        }

        l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
        m_mmq_wg_denoms = m_wg_denoms = { 64,  64, 1 };
        s_mmq_wg_denoms = s_wg_denoms = { 32,  32, 1 };
        l_align = 128;
        m_align =  64;
        s_align =  32;

        for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) {
            ggml_type t = (ggml_type)i;
            // Disable medium and large matrix multiplication if not enough shared memory is available
            // Check mmq warptiles as the largest configuration
            // Throw an error if not enough for any matrix multiplication is available
            if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, false, t)) {
                std::cerr << "ggml_vulkan: Error: Shared memory size too small for matrix multiplication." << std::endl;
                throw std::runtime_error("Shared memory size too small for matrix multiplication.");
            } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, false, t)) {
                device->mul_mat_m[i] = false;
                device->mul_mat_l[i] = false;
            } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, false, t)) {
                device->mul_mat_l[i] = false;
            }

            // Disable mul_mat_id if not enough shared memory is available
            if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmqid, true, t)) {
                device->mul_mat_id_s[i] = false;
                device->mul_mat_id_m[i] = false;
                device->mul_mat_id_l[i] = false;
            } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmqid, true, t)) {
                device->mul_mat_id_m[i] = false;
                device->mul_mat_id_l[i] = false;
            } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmqid, true, t)) {
                device->mul_mat_id_l[i] = false;
            }

            // The q8_1 mmq path has its own (larger) shmem layout, check it separately.
            // K-quants use the _int_k warptiles, others use _int.
            const bool is_k_quant = (t == GGML_TYPE_Q2_K || t == GGML_TYPE_Q3_K ||
                                     t == GGML_TYPE_Q4_K || t == GGML_TYPE_Q5_K ||
                                     t == GGML_TYPE_Q6_K);
            const auto & s_int   = is_k_quant ? s_warptile_mmq_int_k   : s_warptile_mmq_int;
            const auto & m_int   = is_k_quant ? m_warptile_mmq_int_k   : m_warptile_mmq_int;
            const auto & l_int   = is_k_quant ? l_warptile_mmq_int_k   : l_warptile_mmq_int;
            const auto & s_intid = is_k_quant ? s_warptile_mmqid_int_k : s_warptile_mmqid_int;
            const auto & m_intid = is_k_quant ? m_warptile_mmqid_int_k : m_warptile_mmqid_int;
            const auto & l_intid = is_k_quant ? l_warptile_mmqid_int_k : l_warptile_mmqid_int;

            if (!ggml_vk_matmul_int_shmem_support(device, s_int, false, t)) {
                device->mul_mat_s_int[i] = false;
                device->mul_mat_m_int[i] = false;
                device->mul_mat_l_int[i] = false;
            } else if (!ggml_vk_matmul_int_shmem_support(device, m_int, false, t)) {
                device->mul_mat_m_int[i] = false;
                device->mul_mat_l_int[i] = false;
            } else if (!ggml_vk_matmul_int_shmem_support(device, l_int, false, t)) {
                device->mul_mat_l_int[i] = false;
            }

            if (!ggml_vk_matmul_int_shmem_support(device, s_intid, true, t)) {
                device->mul_mat_id_s_int[i] = false;
                device->mul_mat_id_m_int[i] = false;
                device->mul_mat_id_l_int[i] = false;
            } else if (!ggml_vk_matmul_int_shmem_support(device, m_intid, true, t)) {
                device->mul_mat_id_m_int[i] = false;
                device->mul_mat_id_l_int[i] = false;
            } else if (!ggml_vk_matmul_int_shmem_support(device, l_intid, true, t)) {
                device->mul_mat_id_l_int[i] = false;
            }
        }
    }

    if (!device->pipeline_matmul_f32) {
        device->pipeline_matmul_f32 = std::make_shared<vk_matmul_pipeline_struct>();
    }
    if (!device->pipeline_matmul_f32_f16) {
        device->pipeline_matmul_f32_f16 = std::make_shared<vk_matmul_pipeline_struct>();
    }
    if (!device->pipeline_matmul_id_f32) {
        device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
    }
    if (!device->pipeline_matmul_bf16) {
        device->pipeline_matmul_bf16 = std::make_shared<vk_matmul_pipeline_struct>();
    }
    if (!device->pipeline_matmul_id_bf16) {
        device->pipeline_matmul_id_bf16 = std::make_shared<vk_matmul_pipeline_struct>();
    }

    auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& base_pipeline, const char *name, size_t spv_size, const void* spv_data, const char *entrypoint,
                                              uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
                                              uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {

        if (!require_full_subgroups && required_subgroup_size == 0) {
            required_subgroup_size = get_subgroup_size(name, device->architecture);
        }

        vk_pipeline *ptr = &base_pipeline;

        int num_pipelines = 1;
#if defined(VK_EXT_shader_64bit_indexing)
        if (device->shader_64b_indexing) {
            num_pipelines = 2;
        }
#endif
        for (int i = 0; i < num_pipelines; ++i, ptr = &(*ptr)->next) {
            vk_pipeline &pipeline = *ptr;
            if (!pipeline) {
                pipeline = std::make_shared<vk_pipeline_struct>();
            }
            if (!pipeline->initialized) {
                pipeline->name = name;
                pipeline->parameter_count = parameter_count;
                pipeline->push_constant_size = push_constant_size;
                pipeline->wg_denoms = wg_denoms;
                pipeline->align = align;
                pipeline->initialized = true;
#if defined(VK_EXT_shader_64bit_indexing)
                pipeline->is_64b_indexing = (i == 1);
#endif
            }

            // We only care about the pipeline this call asked for; the rest
            // (including the 64-bit indexing variant) are handled by their
            // own request_descriptor_sets / load_shaders calls.
            if (pipeline.get() != requested.get()) {
                continue;
            }

            if (pipeline->compiled) {
                continue;
            }

            wait_pipeline = pipeline;

            if (!pipeline->compile_pending) {
                pipeline->compile_pending = true;
                claimed_task.pipeline = pipeline;
                claimed_task.spv_size = spv_size;
                claimed_task.spv_data = spv_data;
                claimed_task.entrypoint = entrypoint;
                claimed_task.parameter_count = parameter_count;
                claimed_task.wg_denoms = wg_denoms;
                claimed_task.specialization_constants = specialization_constants;
                claimed_task.disable_robustness = disable_robustness;
                claimed_task.require_full_subgroups = require_full_subgroups;
                claimed_task.required_subgroup_size = required_subgroup_size;
                has_claimed_task = true;
            }
        }
    };

    auto const &ggml_vk_create_pipeline2 = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const char *entrypoint,
                                              uint32_t parameter_count, uint32_t push_constant_size, std::array<uint32_t, 3> wg_denoms, const std::vector<uint32_t>& specialization_constants,
                                              uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) {
        return ggml_vk_create_pipeline(device, pipeline, name.c_str(), spv_size, spv_data, entrypoint,
                                       parameter_count, push_constant_size, wg_denoms, specialization_constants,
                                       align, disable_robustness, require_full_subgroups, required_subgroup_size);
    };

    // FA scalar has two SPIR-V modules (MMQ vs non-MMQ); FA cm1 has one. K/V
    // quant type is selected at runtime via the FaTypeK / FaTypeV spec constants.

    for (auto &fa : device->pipeline_flash_attn_f32_f16) {
        if (fa.first.path != FA_SCALAR) continue;
        const uint32_t Br = fa.first.Br;
        const uint32_t Bc = fa.first.Bc;
        const bool aligned = fa.first.aligned;
        const bool f32acc = fa.first.f32acc;
        const uint32_t fa_sgs = fa.first.subgroup_size;
        const bool fa_ds = fa.first.subgroup_size == 0;

        const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16;
        const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type);
        const void * spv_data = nullptr;
        size_t spv_size = 0;
        const char *name = nullptr;
        if (bf16_kv) {
            spv_data = flash_attn_f32_f16_fp32_data;
            spv_size = flash_attn_f32_f16_fp32_len;
            name = aligned ? "flash_attn_f32_bf16_aligned" : "flash_attn_f32_bf16";
        } else if (use_mmq) {
#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
            if (device->fp16) {
                if (f32acc) { spv_data = flash_attn_f32_f16_int8_data;        spv_size = flash_attn_f32_f16_int8_len; }
                else        { spv_data = flash_attn_f32_f16_f16acc_int8_data; spv_size = flash_attn_f32_f16_f16acc_int8_len; }
            } else {
                spv_data = flash_attn_f32_f16_fp32_int8_data;
                spv_size = flash_attn_f32_f16_fp32_int8_len;
            }
#endif
            name = aligned ? "flash_attn_f32_f16_aligned" : "flash_attn_f32_f16";
        } else {
            if (device->fp16) {
                if (device->dot2_f16) {
                    if (f32acc) { spv_data = flash_attn_f32_f16_dot2_data;        spv_size = flash_attn_f32_f16_dot2_len; }
                    else        { spv_data = flash_attn_f32_f16_dot2_f16acc_data; spv_size = flash_attn_f32_f16_dot2_f16acc_len; }
                } else {
                    if (f32acc) { spv_data = flash_attn_f32_f16_data;        spv_size = flash_attn_f32_f16_len; }
                    else        { spv_data = flash_attn_f32_f16_f16acc_data; spv_size = flash_attn_f32_f16_f16acc_len; }
                }
            } else {
                spv_data = flash_attn_f32_f16_fp32_data;
                spv_size = flash_attn_f32_f16_fp32_len;
            }
            name = aligned ? "flash_attn_f32_f16_aligned" : "flash_attn_f32_f16";
        }
        ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7,
                                sizeof(vk_flash_attn_push_constants), {Br, 1, 1},
                                get_fa_spec_constants(fa.first), aligned ? Bc : 1, true,
                                !fa_ds, !fa_ds ? fa_sgs : 0);
    }

#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
    if (device->coopmat1_fa_support) {
        for (auto &fa : device->pipeline_flash_attn_f32_f16) {
            if (fa.first.path != FA_COOPMAT1) continue;
            const uint32_t Br = fa.first.Br;
            const uint32_t Bc = fa.first.Bc;
            const bool aligned = fa.first.aligned;
            const bool f32acc = fa.first.f32acc;
            const uint32_t fa_sgs = fa.first.subgroup_size;
            const bool fa_ds = fa.first.subgroup_size == 0;

            const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16;

            const void * spv_data;
            size_t spv_size;
            const char *name;
            if (bf16_kv) {
#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
                if (!device->coopmat_bf16_support) continue;
                spv_data = flash_attn_f32_f16_bf16_cm1_data;
                spv_size = flash_attn_f32_f16_bf16_cm1_len;
                name = aligned ? "flash_attn_f32_bf16_aligned_cm1" : "flash_attn_f32_bf16_cm1";
#else
                continue;
#endif
            } else {
                if (f32acc) { spv_data = flash_attn_f32_f16_cm1_data;        spv_size = flash_attn_f32_f16_cm1_len; }
                else        { spv_data = flash_attn_f32_f16_f16acc_cm1_data; spv_size = flash_attn_f32_f16_f16acc_cm1_len; }
                name = aligned ? "flash_attn_f32_f16_aligned_cm1" : "flash_attn_f32_f16_cm1";
            }
            ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7,
                                    sizeof(vk_flash_attn_push_constants), {Br, 1, 1},
                                    get_fa_spec_constants(fa.first), aligned ? Bc : 1, true,
                                    !fa_ds, !fa_ds ? fa_sgs : 0);
        }
    }
#endif

#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
    if (device->coopmat2) {
        for (auto &fa : device->pipeline_flash_attn_f32_f16) {
            if (fa.first.path != FA_COOPMAT2) continue;
            const uint32_t Br = fa.first.Br;
            const uint32_t Bc = fa.first.Bc;
            const bool aligned = fa.first.aligned;
            const bool f32acc = fa.first.f32acc;

            const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16;
            const void * spv_data;
            size_t spv_size;
            const char * name;
            if (bf16_kv) {
#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
                if (!device->coopmat2_bf16_support) continue;
                spv_data = flash_attn_f32_f16_bf16_cm2_data;
                spv_size = flash_attn_f32_f16_bf16_cm2_len;
                name = aligned ? "flash_attn_f32_bf16_aligned_cm2" : "flash_attn_f32_bf16_cm2";
#else
                continue;
#endif
            } else if (aligned) {
                if (f32acc) { spv_data = flash_attn_f32_f16_cm2_data;        spv_size = flash_attn_f32_f16_cm2_len;        name = "flash_attn_f32_f16_aligned_f32acc_cm2"; }
                else        { spv_data = flash_attn_f32_f16_f16acc_cm2_data; spv_size = flash_attn_f32_f16_f16acc_cm2_len; name = "flash_attn_f32_f16_aligned_f16acc_cm2"; }
            } else {
                if (f32acc) { spv_data = flash_attn_f32_f16_cm2_data;        spv_size = flash_attn_f32_f16_cm2_len;        name = "flash_attn_f32_f16_f32acc_cm2"; }
                else        { spv_data = flash_attn_f32_f16_f16acc_cm2_data; spv_size = flash_attn_f32_f16_f16acc_cm2_len; name = "flash_attn_f32_f16_f16acc_cm2"; }
            }
            ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7,
                                    sizeof(vk_flash_attn_push_constants), {Br, 1, 1},
                                    get_fa_spec_constants(fa.first), aligned ? Bc : 1, true, false, 0);
        }
    }
#endif

    const int mul_mat_id_param_count = 5;

#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
    if (device->coopmat2) {

        // Create 6 variants, {s,m,l}x{unaligned,aligned}
#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, true);   \
        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, true);   \
        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, true);   \
        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, true);   \
        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, true);   \
        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, true);   \

        // Create 2 variants, {f16,f32} accumulator
#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
        CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT)   \
        CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT)   \

        CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3)
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
        if (device->coopmat_bf16_support) {
            CREATE_MM(pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3)
        }
#endif
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q1_0], matmul_q1_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0], matmul_q4_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1], matmul_q4_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0], matmul_q5_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_1], matmul_q5_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q8_0], matmul_q8_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_K], matmul_q2_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q3_K], matmul_q3_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_K], matmul_q4_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_K], matmul_q5_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q6_K], matmul_q6_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ1_S],   matmul_iq1_s_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ1_M],   matmul_iq1_m_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_XS],  matmul_iq2_xs_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_S],   matmul_iq2_s_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_S],   matmul_iq3_s_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_MXFP4],   matmul_mxfp4_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_NVFP4],   matmul_nvfp4_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)

        GGML_ASSERT(device->subgroup_ballot);

        CREATE_MM2(pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 5)
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
        if (device->coopmat_bf16_support) {
            CREATE_MM(pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 5)
        }
#endif
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_subgroup_iq1_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_subgroup_iq1_m_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_subgroup_iq2_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_subgroup_iq2_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_subgroup_iq3_s_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_subgroup_iq4_xs_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_subgroup_iq4_nl_f16,  mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_subgroup_mxfp4_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4],   matmul_id_subgroup_nvfp4_f16,   mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5)
#undef CREATE_MM
#undef CREATE_MM2
    } else
#endif  // defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
    if (device->coopmat_support) {
        // Create 6 variants, {s,m,l}x{unaligned,aligned}
#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, true);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, true);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, true);   \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, true);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, true);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, true);   \

        // Create 2 variants, {f16,f32} accumulator
#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
        if (device->coopmat_acc_f16_support) { \
            CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
        } \
        if (device->coopmat_acc_f32_support) { \
            CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
        } \

        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, );
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
        if (device->coopmat_bf16_support) {
            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, )
        }
#endif

        if (device->coopmat_acc_f16_support) {
            CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );

            CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K], matmul_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S],   matmul_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M],   matmul_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS],  matmul_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S],   matmul_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4],   matmul_nvfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        } else {
            CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0].f32acc, matmul_q1_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );

            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S].f32acc,   matmul_iq1_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M].f32acc,   matmul_iq1_m_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4].f32acc,   matmul_nvfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        }

        GGML_ASSERT(device->subgroup_ballot);

        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
        if (device->coopmat_bf16_support) {
            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        }
#endif

        CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_subgroup_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_subgroup_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_subgroup_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_subgroup_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_subgroup_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_subgroup_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_subgroup_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_subgroup_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
        CREATE_MM2(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4],   matmul_id_subgroup_nvfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id);
#undef CREATE_MM2
#undef CREATE_MM
    } else
#endif  // defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
    if (device->fp16) {
        // Create 6 variants, {s,m,l}x{unaligned,aligned}
        // Selects dot2 SPIR-V variant at runtime when device->dot2_f16 is true
#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _len : NAMELC ## _aligned ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _data : NAMELC ## _aligned ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _len : NAMELC ## _aligned ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _data : NAMELC ## _aligned ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _len : NAMELC ## _aligned ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _data : NAMELC ## _aligned ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \

        // bf16 scalar path promotes to f32, no dot2 variant
#define CREATE_MM_NODOT2(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \

#define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
        if (device->mul_mat ## ID ## _l_int[TYPE]) { \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->l, #NAMELC        "_l", NAMELC ## _len,        NAMELC ##  _data,        "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        } \
        if (device->mul_mat ## ID ## _m_int[TYPE]) { \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->m, #NAMELC        "_m", NAMELC ## _len,        NAMELC ##  _data,        "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        } \
        if (device->mul_mat ## ID ## _s_int[TYPE]) { \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->s, #NAMELC        "_s", NAMELC ## _len,        NAMELC ##  _data,        "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        } \

        // Create 2 variants, {f16,f32} accumulator
#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
        CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
        CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \

        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);

        CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);

        CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K], matmul_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S],   matmul_iq1_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M],   matmul_iq1_m_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS],  matmul_iq2_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S],   matmul_iq2_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM2(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4],   matmul_nvfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);

#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
        if (device->integer_dot_product) {
            CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0], matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1], matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0], matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_1], matmul_q5_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q8_0], matmul_q8_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0);

            CREATE_MMQ(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_MXFP4], matmul_mxfp4_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0);

            CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_K], matmul_q2_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q3_K], matmul_q3_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_K], matmul_q4_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_K], matmul_q5_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0);
            CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q6_K], matmul_q6_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0);
        }
#endif

        if (device->subgroup_ballot && device->subgroup_require_full_support && subgroup_min_size_16) {
            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_subgroup_iq1_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_subgroup_iq1_m_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_subgroup_iq2_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_subgroup_iq2_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_subgroup_iq3_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_subgroup_iq4_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_subgroup_iq4_nl_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_subgroup_mxfp4_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM2(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4],   matmul_id_subgroup_nvfp4_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);

#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
            if (device->integer_dot_product) {
                CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
                CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
                CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
                CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
                CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);

                CREATE_MMQ(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_MXFP4], matmul_id_subgroup_mxfp4_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);

                CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
                CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
                CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
                CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
                CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            }
#endif
        } else {
            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S],   matmul_id_iq1_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M],   matmul_id_iq1_m_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS],  matmul_id_iq2_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S],   matmul_id_iq2_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S],   matmul_id_iq3_s_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS],  matmul_id_iq4_xs_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL],  matmul_id_iq4_nl_f32,  mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4],   matmul_id_mxfp4_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM2(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4],   matmul_id_nvfp4_f32,   mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);

#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
            if (device->integer_dot_product) {
                CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_1], matmul_id_q5_1_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q8_0], matmul_id_q8_0_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);

                CREATE_MMQ(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_MXFP4], matmul_id_mxfp4_q8_1, mmq_wg_denoms, warptile_mmqid_int,   vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);

                CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_K], matmul_id_q2_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q3_K], matmul_id_q3_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_K], matmul_id_q4_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_K], matmul_id_q5_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
                CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q6_K], matmul_id_q6_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            }
#endif
        }
#undef CREATE_MM2
#undef CREATE_MMQ
#undef CREATE_MM
#undef CREATE_MM_NODOT2
    } else {
        // Create 6 variants, {s,m,l}x{unaligned,aligned}
#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _l[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _m[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
        if (device->mul_mat ## ID ## _s[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \

#define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
        if (device->mul_mat ## ID ## _l_int[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC "_l", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1);   \
        if (device->mul_mat ## ID ## _m_int[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC "_m", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1);   \
        if (device->mul_mat ## ID ## _s_int[TYPE]) \
            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC "_s", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1);   \

        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);

        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);

        CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0].f32acc, matmul_q1_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);

        CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S].f32acc,   matmul_iq1_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M].f32acc,   matmul_iq1_m_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc,  matmul_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc,   matmul_iq2_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4].f32acc,   matmul_nvfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0);

#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
        if (device->integer_dot_product) {
            CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, );

            CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, );
            CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, );
        }
#endif

        if (device->subgroup_ballot && device->subgroup_require_full_support && subgroup_min_size_16) {
            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_subgroup_f16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_subgroup_f16_f32, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);
            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16);

            CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_subgroup_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_subgroup_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_subgroup_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_subgroup_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_subgroup_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_subgroup_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_subgroup_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_subgroup_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_subgroup_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_subgroup_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_subgroup_q6_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc,   matmul_id_subgroup_iq1_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc,   matmul_id_subgroup_iq1_m_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_subgroup_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_subgroup_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_subgroup_iq2_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_subgroup_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_subgroup_iq3_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_subgroup_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_subgroup_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_subgroup_mxfp4_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
            CREATE_MM(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4].f32acc,   matmul_id_subgroup_nvfp4_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size);
        } else {
            CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);

            CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ1_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc,   matmul_id_iq1_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ1_M,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc,   matmul_id_iq1_m_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ2_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc,  matmul_id_iq2_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ2_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc,   matmul_id_iq2_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
            CREATE_MM(GGML_TYPE_NVFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4].f32acc,   matmul_id_nvfp4_f32,   , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
        }
    }
    // reusing CREATE_MM from the fp32 path
    if ((device->coopmat2 || device->coopmat_support)
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
        && !device->coopmat_bf16_support
#endif
        ) {
        const uint32_t s_warptile_wm = device->subgroup_size == 8 ? 8 : 32;

        // use scalar tile sizes
        l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 };
        m_warptile = { 128,  64,  64, 16, subgroup_size_8, 32, 2, 4, 2, 1, subgroup_size_8 };
        s_warptile = { subgroup_size_32, 32, 32, 16, s_warptile_wm, 32, 2, 2, 2, 1, subgroup_size_8 };

        l_wg_denoms = {128, 128, 1 };
        m_wg_denoms = { 64,  64, 1 };
        s_wg_denoms = { 32,  32, 1 };

        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0);
        CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0);
    }
#undef CREATE_MM

    // mul mat vec

    // the number of rows computed per shader depends on GPU model and quant
    uint32_t rm_stdq = 1;
    uint32_t rm_kq = 2;
    uint32_t rm_stdq_int = 1;
    uint32_t rm_kq_int = 1;
    auto const &rm_iq_int = [](uint32_t i) { return i == 0 ? 8u : 4u; };
    if (device->vendor_id == VK_VENDOR_ID_AMD) {
        if (device->architecture == AMD_GCN) {
            rm_stdq = 2;
            rm_kq = 4;
            rm_stdq_int = 4;
        }
    } else if (device->vendor_id == VK_VENDOR_ID_INTEL) {
        rm_stdq = 2;
        rm_stdq_int = 2;
    }
    uint32_t rm_iq = 2 * rm_kq;

    const bool use_subgroups = device->subgroup_arithmetic && device->architecture != vk_device_architecture::AMD_GCN;
    // Ensure a subgroup size >= 16 is available
    const bool use_subgroups16 = use_subgroups && subgroup_min_size_16;

    const uint32_t subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16) ? 16 : device->subgroup_size;
    const uint32_t subgroup_size16 = std::max(subgroup_size, 16u);

    const uint32_t force_subgroup_size = use_subgroups ? subgroup_size : 0;
    const uint32_t force_subgroup_size16 = use_subgroups16 ? subgroup_size16 : 0;
    static constexpr uint32_t mul_mat_vec_num_bindings = 5;
    static constexpr uint32_t mul_mat_vec_id_num_bindings = 6;

    for (uint32_t w = 0; w < DMMV_WG_SIZE_COUNT; ++w) {
        const uint32_t wg_size_subgroup   = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size : (subgroup_size * 4);
        const uint32_t wg_size_subgroup16 = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size16 : (subgroup_size16 * 4);

        const shader_reduction_mode reduc = (use_subgroups && w == DMMV_WG_SIZE_SUBGROUP) ? SHADER_REDUCTION_MODE_SUBGROUP :
                                            (use_subgroups && w == DMMV_WG_SIZE_LARGE) ? SHADER_REDUCTION_MODE_HYBRID :
                                            SHADER_REDUCTION_MODE_SHMEM;

        const shader_reduction_mode reduc16 = (use_subgroups16 && w == DMMV_WG_SIZE_SUBGROUP) ? SHADER_REDUCTION_MODE_SUBGROUP :
                                              (use_subgroups16 && w == DMMV_WG_SIZE_LARGE) ? SHADER_REDUCTION_MODE_HYBRID :
                                              SHADER_REDUCTION_MODE_SHMEM;

        for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32",  arr_dmmv_f32_f32_f32_len[reduc],  arr_dmmv_f32_f32_f32_data[reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32",  arr_dmmv_f16_f32_f32_len[reduc],  arr_dmmv_f16_f32_f32_data[reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q1_0][i], "mul_mat_vec_q1_0_f32_f32", arr_dmmv_q1_0_f32_f32_len[reduc], arr_dmmv_q1_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[reduc], arr_dmmv_q5_1_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[reduc], arr_dmmv_q8_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[reduc16], arr_dmmv_q2_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[reduc16], arr_dmmv_q3_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[reduc16], arr_dmmv_q4_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[reduc16], arr_dmmv_q5_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[reduc16], arr_dmmv_q6_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32",   arr_dmmv_iq1_s_f32_f32_len[reduc16],   arr_dmmv_iq1_s_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32",   arr_dmmv_iq1_m_f32_f32_len[reduc16],   arr_dmmv_iq1_m_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_iq2_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32",  arr_dmmv_iq2_xs_f32_f32_len[reduc16],  arr_dmmv_iq2_xs_f32_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32",   arr_dmmv_iq2_s_f32_f32_len[reduc16],   arr_dmmv_iq2_s_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_iq3_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32",   arr_dmmv_iq3_s_f32_f32_len[reduc16],   arr_dmmv_iq3_s_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32",  arr_dmmv_iq4_xs_f32_f32_len[reduc16],  arr_dmmv_iq4_xs_f32_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_iq4_nl_f32_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[reduc16],   arr_dmmv_mxfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_NVFP4][i],   "mul_mat_vec_nvfp4_f32_f32",   arr_dmmv_nvfp4_f32_f32_len[reduc16],   arr_dmmv_nvfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);

            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[reduc],  arr_dmmv_f32_f16_f32_data[reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[reduc],  arr_dmmv_f16_f16_f32_data[reduc],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q1_0][i], "mul_mat_vec_q1_0_f16_f32", arr_dmmv_q1_0_f16_f32_len[reduc], arr_dmmv_q1_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[reduc], arr_dmmv_q5_1_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[reduc], arr_dmmv_q8_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[reduc16], arr_dmmv_q2_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[reduc16], arr_dmmv_q3_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[reduc16], arr_dmmv_q4_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[reduc16], arr_dmmv_q5_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[reduc16], arr_dmmv_q6_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32",   arr_dmmv_iq1_s_f16_f32_len[reduc16],   arr_dmmv_iq1_s_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32",   arr_dmmv_iq1_m_f16_f32_len[reduc16],   arr_dmmv_iq1_m_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[reduc16], arr_dmmv_iq2_xxs_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32",  arr_dmmv_iq2_xs_f16_f32_len[reduc16],  arr_dmmv_iq2_xs_f16_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32",   arr_dmmv_iq2_s_f16_f32_len[reduc16],   arr_dmmv_iq2_s_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[reduc16], arr_dmmv_iq3_xxs_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32",   arr_dmmv_iq3_s_f16_f32_len[reduc16],   arr_dmmv_iq3_s_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32",  arr_dmmv_iq4_xs_f16_f32_len[reduc16],  arr_dmmv_iq4_xs_f16_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[reduc16],  arr_dmmv_iq4_nl_f16_f32_data[reduc16],  "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[reduc16],   arr_dmmv_mxfp4_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_NVFP4][i],   "mul_mat_vec_nvfp4_f16_f32",   arr_dmmv_nvfp4_f16_f32_len[reduc16],   arr_dmmv_nvfp4_f16_f32_data[reduc16],   "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);

#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
            if (device->integer_dot_product) {
                const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
                const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4);

                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);

                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_MXFP4][i], "mul_mat_vec_mxfp4_q8_1_f32", arr_dmmv_mxfp4_q8_1_f32_len[reduc], arr_dmmv_mxfp4_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);

                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_q8_1_f32", arr_dmmv_q2_k_q8_1_f32_len[reduc], arr_dmmv_q2_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_q8_1_f32", arr_dmmv_q3_k_q8_1_f32_len[reduc], arr_dmmv_q3_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_q8_1_f32", arr_dmmv_q4_k_q8_1_f32_len[reduc], arr_dmmv_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_q8_1_f32", arr_dmmv_q5_k_q8_1_f32_len[reduc], arr_dmmv_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_q8_1_f32", arr_dmmv_q6_k_q8_1_f32_len[reduc], arr_dmmv_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);

                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_IQ1_S][i], "mul_mat_vec_iq1_s_q8_1_f32", arr_dmmv_iq1_s_q8_1_f32_len[reduc], arr_dmmv_iq1_s_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(i), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(i), i+1}, 1, true, use_subgroups, subgroup_size_int);
                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_IQ1_M][i], "mul_mat_vec_iq1_m_q8_1_f32", arr_dmmv_iq1_m_q8_1_f32_len[reduc], arr_dmmv_iq1_m_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(i), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(i), i+1}, 1, true, use_subgroups, subgroup_size_int);

            }
#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
        }

        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",        arr_dmmv_id_f32_f32_f32_len[reduc],     arr_dmmv_id_f32_f32_f32_data[reduc],     "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {wg_size_subgroup, 1}, 1, false, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",        arr_dmmv_id_f16_f32_f32_len[reduc],     arr_dmmv_id_f16_f32_f32_data[reduc],     "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32",       arr_dmmv_id_bf16_f32_f32_len[reduc],    arr_dmmv_id_bf16_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q1_0], "mul_mat_vec_id_q1_0_f32",       arr_dmmv_id_q1_0_f32_f32_len[reduc],    arr_dmmv_id_q1_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32",       arr_dmmv_id_q4_0_f32_f32_len[reduc],    arr_dmmv_id_q4_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32",       arr_dmmv_id_q4_1_f32_f32_len[reduc],    arr_dmmv_id_q4_1_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32",       arr_dmmv_id_q5_0_f32_f32_len[reduc],    arr_dmmv_id_q5_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32",       arr_dmmv_id_q5_1_f32_f32_len[reduc],    arr_dmmv_id_q5_1_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32",       arr_dmmv_id_q8_0_f32_f32_len[reduc],    arr_dmmv_id_q8_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32",       arr_dmmv_id_q2_k_f32_f32_len[reduc16],    arr_dmmv_id_q2_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32",       arr_dmmv_id_q3_k_f32_f32_len[reduc16],    arr_dmmv_id_q3_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32",       arr_dmmv_id_q4_k_f32_f32_len[reduc16],    arr_dmmv_id_q4_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32",       arr_dmmv_id_q5_k_f32_f32_len[reduc16],    arr_dmmv_id_q5_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32",       arr_dmmv_id_q6_k_f32_f32_len[reduc16],    arr_dmmv_id_q6_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ1_S],   "mul_mat_vec_id_iq1_s_f32",   arr_dmmv_id_iq1_s_f32_f32_len[reduc16],   arr_dmmv_id_iq1_s_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ1_M],   "mul_mat_vec_id_iq1_m_f32",   arr_dmmv_id_iq1_m_f32_f32_len[reduc16],   arr_dmmv_id_iq1_m_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", arr_dmmv_id_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_id_iq2_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_XS],  "mul_mat_vec_id_iq2_xs_f32",  arr_dmmv_id_iq2_xs_f32_f32_len[reduc16],  arr_dmmv_id_iq2_xs_f32_f32_data[reduc16],  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   arr_dmmv_id_iq2_s_f32_f32_len[reduc16],   arr_dmmv_id_iq2_s_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", arr_dmmv_id_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_id_iq3_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   arr_dmmv_id_iq3_s_f32_f32_len[reduc16],   arr_dmmv_id_iq3_s_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  arr_dmmv_id_iq4_xs_f32_f32_len[reduc16],  arr_dmmv_id_iq4_xs_f32_f32_data[reduc16],  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  arr_dmmv_id_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_id_iq4_nl_f32_f32_data[reduc16],  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_MXFP4],   "mul_mat_vec_id_mxfp4_f32",   arr_dmmv_id_mxfp4_f32_f32_len[reduc16],   arr_dmmv_id_mxfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_NVFP4],   "mul_mat_vec_id_nvfp4_f32",   arr_dmmv_id_nvfp4_f32_f32_len[reduc16],   arr_dmmv_id_nvfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);

#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
        if (device->integer_dot_product) {
            const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
            const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4);

            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_q8_1_f32", arr_dmmv_id_q4_0_q8_1_f32_len[reduc], arr_dmmv_id_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_q8_1_f32", arr_dmmv_id_q4_1_q8_1_f32_len[reduc], arr_dmmv_id_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_q8_1_f32", arr_dmmv_id_q5_0_q8_1_f32_len[reduc], arr_dmmv_id_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_q8_1_f32", arr_dmmv_id_q5_1_q8_1_f32_len[reduc], arr_dmmv_id_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_q8_1_f32", arr_dmmv_id_q8_0_q8_1_f32_len[reduc], arr_dmmv_id_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);

            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_MXFP4], "mul_mat_vec_id_mxfp4_q8_1_f32", arr_dmmv_id_mxfp4_q8_1_f32_len[reduc], arr_dmmv_id_mxfp4_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);

            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_q8_1_f32", arr_dmmv_id_q2_k_q8_1_f32_len[reduc], arr_dmmv_id_q2_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_q8_1_f32", arr_dmmv_id_q3_k_q8_1_f32_len[reduc], arr_dmmv_id_q3_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_q8_1_f32", arr_dmmv_id_q4_k_q8_1_f32_len[reduc], arr_dmmv_id_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_q8_1_f32", arr_dmmv_id_q5_k_q8_1_f32_len[reduc], arr_dmmv_id_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_q8_1_f32", arr_dmmv_id_q6_k_q8_1_f32_len[reduc], arr_dmmv_id_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);

            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_IQ1_S], "mul_mat_vec_id_iq1_s_q8_1_f32", arr_dmmv_id_iq1_s_q8_1_f32_len[reduc], arr_dmmv_id_iq1_s_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_iq_int(0), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(0)}, 1, true, use_subgroups, subgroup_size_int);
            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_IQ1_M], "mul_mat_vec_id_iq1_m_q8_1_f32", arr_dmmv_id_iq1_m_q8_1_f32_len[reduc], arr_dmmv_id_iq1_m_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_iq_int(0), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(0)}, 1, true, use_subgroups, subgroup_size_int);
        }
#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
    }

#if !defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
    GGML_UNUSED(rm_stdq_int);
    GGML_UNUSED(rm_kq_int);
    GGML_UNUSED(rm_iq_int);
#endif

    // dequant shaders
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q1_0], "dequant_q1_0", dequant_q1_0_len, dequant_q1_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 8, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_k", dequant_q2_k_len, dequant_q2_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_k", dequant_q3_k_len, dequant_q3_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ1_S],   "dequant_iq1_s",   dequant_iq1_s_len,   dequant_iq1_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ1_M],   "dequant_iq1_m",   dequant_iq1_m_len,   dequant_iq1_m_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_XXS], "dequant_iq2_xxs", dequant_iq2_xxs_len, dequant_iq2_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_XS],  "dequant_iq2_xs",  dequant_iq2_xs_len,  dequant_iq2_xs_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_S],   "dequant_iq2_s",   dequant_iq2_s_len,   dequant_iq2_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_XXS], "dequant_iq3_xxs", dequant_iq3_xxs_len, dequant_iq3_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_S],   "dequant_iq3_s",   dequant_iq3_s_len,   dequant_iq3_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_XS],  "dequant_iq4_xs",  dequant_iq4_xs_len,  dequant_iq4_xs_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL],  "dequant_iq4_nl",  dequant_iq4_nl_len,  dequant_iq4_nl_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_MXFP4],   "dequant_mxfp4",   dequant_mxfp4_len,   dequant_mxfp4_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_NVFP4],   "dequant_nvfp4",   dequant_nvfp4_len,   dequant_nvfp4_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);

    // get_rows
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16",  get_rows_f16_len,  get_rows_f16_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_BF16], "get_rows_bf16", get_rows_bf16_len, get_rows_bf16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q1_0], "get_rows_q1_0", get_rows_q1_0_len, get_rows_q1_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q2_K], "get_rows_q2_k", get_rows_q2_k_len, get_rows_q2_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q3_K], "get_rows_q3_k", get_rows_q3_k_len, get_rows_q3_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_K], "get_rows_q4_k", get_rows_q4_k_len, get_rows_q4_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_K], "get_rows_q5_k", get_rows_q5_k_len, get_rows_q5_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q6_K], "get_rows_q6_k", get_rows_q6_k_len, get_rows_q6_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ1_S],   "get_rows_iq1_s",   get_rows_iq1_s_len,   get_rows_iq1_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ1_M],   "get_rows_iq1_m",   get_rows_iq1_m_len,   get_rows_iq1_m_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_XXS], "get_rows_iq2_xxs", get_rows_iq2_xxs_len, get_rows_iq2_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_XS],  "get_rows_iq2_xs",  get_rows_iq2_xs_len,  get_rows_iq2_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_S],   "get_rows_iq2_s",   get_rows_iq2_s_len,   get_rows_iq2_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs", get_rows_iq3_xxs_len, get_rows_iq3_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_S],   "get_rows_iq3_s",   get_rows_iq3_s_len,   get_rows_iq3_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs",  get_rows_iq4_xs_len,  get_rows_iq4_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4],   "get_rows_mxfp4",   get_rows_mxfp4_len,   get_rows_mxfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_NVFP4],   "get_rows_nvfp4",   get_rows_nvfp4_len,   get_rows_nvfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32],     "get_rows_i32",     get_rows_i32_len,     get_rows_i32_data,     "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_BF16], "get_rows_bf16_f32", get_rows_bf16_f32_len, get_rows_bf16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q1_0], "get_rows_q1_0_f32", get_rows_q1_0_f32_len, get_rows_q1_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q2_K], "get_rows_q2_k_f32", get_rows_q2_k_f32_len, get_rows_q2_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q3_K], "get_rows_q3_k_f32", get_rows_q3_k_f32_len, get_rows_q3_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_K], "get_rows_q4_k_f32", get_rows_q4_k_f32_len, get_rows_q4_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_K], "get_rows_q5_k_f32", get_rows_q5_k_f32_len, get_rows_q5_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q6_K], "get_rows_q6_k_f32", get_rows_q6_k_f32_len, get_rows_q6_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ1_S],   "get_rows_iq1_s_f32",   get_rows_iq1_s_f32_len,   get_rows_iq1_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ1_M],   "get_rows_iq1_m_f32",   get_rows_iq1_m_f32_len,   get_rows_iq1_m_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_XXS], "get_rows_iq2_xxs_f32", get_rows_iq2_xxs_f32_len, get_rows_iq2_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_XS],  "get_rows_iq2_xs_f32",  get_rows_iq2_xs_f32_len,  get_rows_iq2_xs_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_S],   "get_rows_iq2_s_f32",   get_rows_iq2_s_f32_len,   get_rows_iq2_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs_f32", get_rows_iq3_xxs_f32_len, get_rows_iq3_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_S],   "get_rows_iq3_s_f32",   get_rows_iq3_s_f32_len,   get_rows_iq3_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs_f32",  get_rows_iq4_xs_f32_len,  get_rows_iq4_xs_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4],   "get_rows_mxfp4_f32",   get_rows_mxfp4_f32_len,   get_rows_mxfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_NVFP4],   "get_rows_nvfp4_f32",   get_rows_nvfp4_f32_len,   get_rows_nvfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, sizeof(vk_op_flash_attn_split_k_reduce_push_constants), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);

    for (auto &it : device->pipeline_fa_mask_opt) {
        auto BrBc = it.first;
        ggml_vk_create_pipeline(device, it.second, "fa_mask_opt", fa_mask_opt_len, fa_mask_opt_data, "main", 2, sizeof(vk_op_flash_attn_mask_opt_push_constants), {1, 1, 1}, {128, 128 / device->subgroup_size, BrBc.first, BrBc.second}, 1, true, true, device->subgroup_size);
    }

    if (device->subgroup_clustered && device->subgroup_require_full_support) {
        ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_subgroup_len, quantize_q8_1_x4_subgroup_data, "main", 2, sizeof(vk_quantize_q8_1_push_constants), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1, true, true);
    } else {
        ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_len, quantize_q8_1_x4_data, "main", 2, sizeof(vk_quantize_q8_1_push_constants), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1);
    }

    for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) {
        if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_p021_push_constants), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true);
        } else {
            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_p021_push_constants), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
        }
    }
    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_nc_push_constants), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_f32, "rms_norm_mul_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_partials_f32, "rms_norm_partials_f32", rms_norm_partials_f32_len, rms_norm_partials_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_partials_f32, "rms_norm_mul_partials_f32", rms_norm_partials_f32_len, rms_norm_partials_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1, true);

    if (sizeof(vk_op_rms_norm_mul_rope_push_constants) <= device->properties.limits.maxPushConstantsSize) {
        ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_rope_f32_f32, "rms_norm_mul_rope_f32_f32", rms_norm_mul_rope_f32_f32_len, rms_norm_mul_rope_f32_f32_data, "main", 7, sizeof(vk_op_rms_norm_mul_rope_push_constants), {1, 1, 1}, {0, 1}, 1, true);
        ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_rope_f32_f16, "rms_norm_mul_rope_f32_f16", rms_norm_mul_rope_f32_f16_len, rms_norm_mul_rope_f32_f16_data, "main", 7, sizeof(vk_op_rms_norm_mul_rope_push_constants), {1, 1, 1}, {0, 1}, 1, true);
    }

    ggml_vk_create_pipeline(device, device->pipeline_rms_norm_back_f32, "rms_norm_back_f32", rms_norm_back_f32_len, rms_norm_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_l2_norm_f32, "l2_norm_f32", l2_norm_f32_len, l2_norm_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f32, "cpy_f16_f32", cpy_f16_f32_len, cpy_f16_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_bf16,"cpy_f32_bf16",cpy_f32_bf16_len,cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_bf16_f32,"cpy_bf16_f32",cpy_bf16_f32_len,cpy_bf16_f32_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_i32_f32, "cpy_i32_f32", cpy_i32_f32_len, cpy_i32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_i32, "cpy_f32_i32", cpy_f32_i32_len, cpy_f32_i32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f32, "contig_cpy_f16_f32", contig_cpy_f16_f32_len, contig_cpy_f16_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_bf16,"contig_cpy_f32_bf16",contig_cpy_f32_bf16_len,contig_cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_bf16_f32,"contig_cpy_bf16_f32",contig_cpy_bf16_f32_len,contig_cpy_bf16_f32_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_i32_f32, "contig_cpy_i32_f32", contig_cpy_i32_f32_len, contig_cpy_i32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_i32, "contig_cpy_f32_i32", contig_cpy_f32_i32_len, contig_cpy_f32_i32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_32, "cpy_transpose_32", cpy_transpose_32_len, cpy_transpose_32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_16, "cpy_transpose_16", cpy_transpose_16_len, cpy_transpose_16_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q1_0], "cpy_f32_q1_0", cpy_f32_q1_0_len, cpy_f32_q1_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_len, cpy_f32_q4_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_len, cpy_f32_q4_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0], "cpy_f32_q5_0", cpy_f32_q5_0_len, cpy_f32_q5_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_1], "cpy_f32_q5_1", cpy_f32_q5_1_len, cpy_f32_q5_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q8_0], "cpy_f32_q8_0", cpy_f32_q8_0_len, cpy_f32_q8_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_IQ4_NL], "cpy_f32_iq4_nl", cpy_f32_iq4_nl_len, cpy_f32_iq4_nl_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);

#define SET_ROWS(itype) \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F32],  "set_rows_f32" #itype,  set_rows_f32 ## itype ## _len,  set_rows_f32 ## itype ## _data,  "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F16],  "set_rows_f16" #itype,  set_rows_f16 ## itype ## _len,  set_rows_f16 ## itype ## _data,  "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_BF16], "set_rows_bf16" #itype, set_rows_bf16 ## itype ## _len, set_rows_bf16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q1_0], "set_rows_q1_0" #itype, set_rows_q1_0 ## itype ## _len, set_rows_q1_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_0], "set_rows_q4_0" #itype, set_rows_q4_0 ## itype ## _len, set_rows_q4_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_1], "set_rows_q4_1" #itype, set_rows_q4_1 ## itype ## _len, set_rows_q4_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_0], "set_rows_q5_0" #itype, set_rows_q5_0 ## itype ## _len, set_rows_q5_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_1], "set_rows_q5_1" #itype, set_rows_q5_1 ## itype ## _len, set_rows_q5_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q8_0], "set_rows_q8_0" #itype, set_rows_q8_0 ## itype ## _len, set_rows_q8_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
        ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_IQ4_NL], "set_rows_iq4_nl" #itype, set_rows_iq4_nl ## itype ## _len, set_rows_iq4_nl ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);

    SET_ROWS(_i32)
    SET_ROWS(_i64)
#undef SET_ROWS


    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q1_0], "cpy_q1_0_f32", cpy_q1_0_f32_len, cpy_q1_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q1_0), 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_0], "cpy_q4_0_f32", cpy_q4_0_f32_len, cpy_q4_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_1], "cpy_q4_1_f32", cpy_q4_1_f32_len, cpy_q4_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_0], "cpy_q5_0_f32", cpy_q5_0_f32_len, cpy_q5_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_0), 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_1], "cpy_q5_1_f32", cpy_q5_1_f32_len, cpy_q5_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_1), 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q8_0], "cpy_q8_0_f32", cpy_q8_0_f32_len, cpy_q8_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q8_0), 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_IQ4_NL], "cpy_iq4_nl_f32", cpy_iq4_nl_f32_len, cpy_iq4_nl_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_IQ4_NL), 1, 1}, {}, 1);

    auto get_suffix = [](bool src0_f16, bool src1_f16, bool dst_f16) {
        std::string s;
        s += std::string(src0_f16 ? "_f16" : "_f32");
        s += std::string(src1_f16 ? "_f16" : "_f32");
        s += std::string(dst_f16 ? "_f16" : "_f32");
        return s;
    };

#define CREATE_BINARY(name, namemod, spec, bindings) \
    for (int s0 : {0,1}) for (int s1 : {0,1}) for (int d : {0,1}) \
        ggml_vk_create_pipeline2(device, device->pipeline_ ## name ## namemod[s0][s1][d], \
                                #name + get_suffix(s0, s1, d) + #namemod, name ## _len[s0][s1][d], name ## _data[s0][s1][d], \
                                "main", (bindings), sizeof(vk_op_binary_push_constants), {512, 1, 1}, spec, 1);

    CREATE_BINARY(add, , {0}, 4)
    CREATE_BINARY(add, _norepeat, {1}, 4)
    CREATE_BINARY(sub, , {0}, 3)
    CREATE_BINARY(sub, _norepeat, {1}, 3)
    CREATE_BINARY(mul, , {0}, 3)
    CREATE_BINARY(mul, _norepeat, {1}, 3)
    CREATE_BINARY(div, , {0}, 3)
    CREATE_BINARY(div, _norepeat, {1}, 3)
    CREATE_BINARY(add_rms, , {0}, 4)
    CREATE_BINARY(add_rms, _norepeat, {1}, 4)
#undef CREATE_BINARY

    if (device->multi_add) {
        for (uint32_t i = 0; i < MAX_FUSED_ADDS; ++i) {
            ggml_vk_create_pipeline2(device, device->pipeline_multi_add[i],     "multi_add_f32_"     + std::to_string(i+1), multi_add_f32_len,     multi_add_f32_data,     "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
            ggml_vk_create_pipeline2(device, device->pipeline_multi_add_rms[i], "multi_add_rms_f32_" + std::to_string(i+1), multi_add_rms_f32_len, multi_add_rms_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1);
        }
    }

    ggml_vk_create_pipeline(device, device->pipeline_add_id_f32, "add_id_f32", add_id_f32_len, add_id_f32_data, "main", 4, sizeof(vk_op_add_id_push_constants), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0, 1}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_set_f32, "set_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0, 0}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_concat_f16, "concat_f16", concat_f16_len, concat_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_concat_i32, "concat_i32", concat_i32_len, concat_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_upscale_nearest_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_NEAREST}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_upscale_bilinear_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_BILINEAR}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_upscale_bicubic_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_BICUBIC}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_upscale_bilinear_antialias_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_sqrt_f32, "sqrt_f32", sqrt_f32_len, sqrt_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_sin_f32, "sin_f32", sin_f32_len, sin_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_cos_f32, "cos_f32", cos_f32_len, cos_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_log[0], "log_f32", log_f32_len, log_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_log[1], "log_f16", log_f16_len, log_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_roll_f32, "roll_f32", roll_f32_len, roll_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_repeat_i32, "repeat_i32", repeat_i32_len, repeat_i32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_repeat_back_f32, "repeat_back_f32", repeat_back_f32_len, repeat_back_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_repeat_i16, "repeat_i16", repeat_i16_len, repeat_i16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);

#define CREATE_UNARY(name)  \
    ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);  \
    ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    CREATE_UNARY(elu)
    CREATE_UNARY(gelu)
    CREATE_UNARY(gelu_erf)
    CREATE_UNARY(gelu_quick)
    CREATE_UNARY(silu)
    CREATE_UNARY(relu)
    CREATE_UNARY(xielu)
    CREATE_UNARY(neg)
    CREATE_UNARY(tanh)
    CREATE_UNARY(sigmoid)
    CREATE_UNARY(hardsigmoid)
    CREATE_UNARY(hardswish)
    CREATE_UNARY(abs)
    CREATE_UNARY(softplus)
    CREATE_UNARY(step)
    CREATE_UNARY(round)
    CREATE_UNARY(ceil)
    CREATE_UNARY(floor)
    CREATE_UNARY(trunc)
    CREATE_UNARY(sgn)
    CREATE_UNARY(exp)
#undef CREATE_UNARY

    ggml_vk_create_pipeline(device, device->pipeline_add1_f16_f16, "add1_f16_f16", add1_f16_f16_len, add1_f16_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_add1_f16_f32, "add1_f16_f32", add1_f16_f32_len, add1_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_add1_f32_f32, "add1_f32_f32", add1_f32_f32_len, add1_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_arange_f32, "arange_f32", arange_f32_len, arange_f32_data, "main", 1, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_fill_f32, "fill_f32", fill_f32_len, fill_f32_data, "main", 1, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_fill_f16, "fill_f16", fill_f16_len, fill_f16_data, "main", 1, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

#define CREATE_GLU(name)  \
    ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 3, sizeof(vk_op_glu_push_constants), {512, 1, 1}, {}, 1, true);   \
    ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 3, sizeof(vk_op_glu_push_constants), {512, 1, 1}, {}, 1, true);

    CREATE_GLU(geglu)
    CREATE_GLU(reglu)
    CREATE_GLU(swiglu)
    CREATE_GLU(swiglu_oai)
    CREATE_GLU(geglu_erf)
    CREATE_GLU(geglu_quick)
#undef CREATE_GLU

    ggml_vk_create_pipeline(device, device->pipeline_leaky_relu_f32, "leaky_relu_f32", leaky_relu_f32_len, leaky_relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_silu_back_f32, "silu_back_f32", silu_back_f32_len, silu_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true);

    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1, true);

    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32,     "soft_max_large1_f32",     soft_max_large1_f32_len,     soft_max_large1_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32,     "soft_max_large2_f32",     soft_max_large2_f32_len,     soft_max_large2_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32,     "soft_max_large3_f32",     soft_max_large3_f32_len,     soft_max_large3_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32_f16, "soft_max_large1_f32_f16", soft_max_large1_f32_f16_len, soft_max_large1_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32_f16, "soft_max_large2_f32_f16", soft_max_large2_f32_f16_len, soft_max_large2_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32_f16, "soft_max_large3_f32_f16", soft_max_large3_f32_f16_len, soft_max_large3_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);

    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32, "rope_multi_f32", rope_multi_f32_len, rope_multi_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_vision_f32, "rope_vision_f32", rope_vision_f32_len, rope_vision_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f16, "rope_multi_f16", rope_multi_f16_len, rope_multi_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_vision_f16, "rope_vision_f16", rope_vision_f16_len, rope_vision_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32_f16, "rope_norm_f32_f16", rope_norm_f32_f16_len, rope_norm_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32_f16, "rope_neox_f32_f16", rope_neox_f32_f16_len, rope_neox_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32_f16, "rope_multi_f32_f16", rope_multi_f32_f16_len, rope_multi_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);

    for (uint32_t i = 0; i < num_argsort_pipelines; ++i) {
        uint32_t BLOCK_SIZE = 1u << std::min(i, device->max_workgroup_size_log2);
        if (i <= device->max_workgroup_size_log2 &&
            2 * sizeof(int) * BLOCK_SIZE <= device->properties.limits.maxComputeSharedMemorySize) {
            const uint32_t NCOLS_PADDED_LOG2 = i;
            ggml_vk_create_pipeline2(device, device->pipeline_argsort_f32[i], "argsort_f32_"+std::to_string(i), argsort_f32_len, argsort_f32_data, "main", 3, sizeof(vk_op_argsort_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, NCOLS_PADDED_LOG2}, 1, true);
        }
        const uint32_t WG_UNROLL_FACTOR = BLOCK_SIZE > 1 ? 2 : 1;
        BLOCK_SIZE /= WG_UNROLL_FACTOR;
        ggml_vk_create_pipeline2(device, device->pipeline_argsort_large_f32[i], "argsort_large_f32_"+std::to_string(i), argsort_large_f32_len, argsort_large_f32_data, "main", 3, sizeof(vk_op_argsort_push_constants), {BLOCK_SIZE * WG_UNROLL_FACTOR, 1, 1}, {BLOCK_SIZE, WG_UNROLL_FACTOR}, 1, true);
    }

    for (uint32_t i = 0; i < num_topk_pipelines; ++i) {
        const uint32_t BLOCK_SIZE = 1u << i;
        const uint32_t NCOLS_PADDED_LOG2 = i;
        if (i <= device->max_workgroup_size_log2) {
            uint32_t nary_shmem = 2 * sizeof(int) * BLOCK_SIZE +
                                  sizeof(int) * device->subgroup_size +
                                  2 * sizeof(int) +
                                  2 * (BLOCK_SIZE / device->subgroup_size) * sizeof(int);
            if (device->subgroup_arithmetic && device->subgroup_require_full_support && device->subgroup_shuffle && device->subgroup_ballot &&
                nary_shmem <= device->properties.limits.maxComputeSharedMemorySize) {
                ggml_vk_create_pipeline2(device, device->pipeline_topk_f32[i], "topk_f32_"+std::to_string(i), topk_nary_search_f32_len, topk_nary_search_f32_data, "main", 2, sizeof(vk_op_topk_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, device->subgroup_size, device->subgroup_size_log2}, 1, true, true, device->subgroup_size);
            } else if (2 * sizeof(int) * BLOCK_SIZE <= device->properties.limits.maxComputeSharedMemorySize) {
                ggml_vk_create_pipeline2(device, device->pipeline_topk_f32[i], "topk_f32_"+std::to_string(i), topk_argsort_f32_len, topk_argsort_f32_data, "main", 2, sizeof(vk_op_topk_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, NCOLS_PADDED_LOG2}, 1, true);
            }
        }
    }

    ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);

    ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
    // Intel Arc B390 was observed segfaulting with this shader.
    if (device->subgroup_basic && device->subgroup_shuffle && device->vendor_id != VK_VENDOR_ID_INTEL) {
        int idx = 0;
        for (uint32_t n : {64, 128, 256, 512}) {
            if (device->subgroup_size <= n) {
                ggml_vk_create_pipeline(device, device->pipeline_fwht_f32[idx], "fwht_f32", fwht_f32_len, fwht_f32_data, "main", 2, sizeof(vk_op_fwht_push_constants), {1, 1, 1}, { device->subgroup_size, n }, 1, true, true, device->subgroup_size);
            }
            ++idx;
        }
    } else if (device->driver_id != vk::DriverId::eIntelProprietaryWindows) {
        // Disabled on Intel Windows due to a driver bug: https://github.com/ggml-org/llama.cpp/pull/23964#issuecomment-4598226147
        int idx = 0;
        for (uint32_t n : {64, 128, 256, 512}) {
            const uint32_t block_size = std::min(device->subgroup_size, n);
            ggml_vk_create_pipeline(device, device->pipeline_fwht_f32[idx], "fwht_shmem_f32", fwht_shmem_f32_len, fwht_shmem_f32_data, "main", 2, sizeof(vk_op_fwht_push_constants), {1, 1, 1}, { block_size, n }, 1);
            ++idx;
        }
    }

    const uint32_t cumsum_elem_per_thread = (device->vendor_id == VK_VENDOR_ID_AMD || device->vendor_id == VK_VENDOR_ID_INTEL) ? 2 : 4;
    ggml_vk_create_pipeline(device, device->pipeline_cumsum_f32,       "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 256, device->subgroup_size, cumsum_elem_per_thread }, 1, true, true, device->subgroup_size);
    ggml_vk_create_pipeline(device, device->pipeline_cumsum_small_f32, "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 128, device->subgroup_size, 1 }, 1, true, true, device->subgroup_size);
    ggml_vk_create_pipeline(device, device->pipeline_cumsum_multipass1_f32, "cumsum_multipass1_f32", cumsum_multipass1_f32_len, cumsum_multipass1_f32_data, "main", 3, sizeof(vk_op_sum_rows_push_constants), {256, 1, 1}, { 256, device->subgroup_size }, 1, true, true, device->subgroup_size);
    ggml_vk_create_pipeline(device, device->pipeline_cumsum_multipass2_f32, "cumsum_multipass2_f32", cumsum_multipass2_f32_len, cumsum_multipass2_f32_data, "main", 3, sizeof(vk_op_sum_rows_push_constants), {256, 1, 1}, { 256, device->subgroup_size }, 1, true, true, device->subgroup_size);

    ggml_vk_create_pipeline(device, device->pipeline_count_equal_i32, "count_equal_i32", count_equal_i32_len, count_equal_i32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, { device->subgroup_size }, 1);

    ggml_vk_create_pipeline(device, device->pipeline_count_experts, "count_experts", count_experts_len, count_experts_data, "main", 2, sizeof(vk_op_count_experts_push_constants), {1, 1, 1}, {}, 1, true);

    for (auto &s : device->pipeline_solve_tri_f32) {
        const vk_solve_tri_pipeline_state &state = s.first;

        // Max number of rows to load at a time, limited by shared memory
        const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((state.N + state.K) * sizeof(float));
        // Need at least K invocations, and prefer a minimum of 128 to spread out loading shared memory
        const uint32_t block_size = std::max(128u, 1u << (uint32_t)ceilf(log2f(float(state.K))));

        ggml_vk_create_pipeline(
            device, s.second, "solve_tri_f32",
            solve_tri_f32_len, solve_tri_f32_data, "main", 3,
            sizeof(vk_op_binary_push_constants), {1, 1, 1}, { 0, state.N, state.K, batch_N, block_size }, 1, true);
    }

#define IM2COL(bda) \
    ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32 ## bda ## _len, im2col_f32 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);   \
    ggml_vk_create_pipeline(device, device->pipeline_im2col_3d_f32, "im2col_3d_f32", im2col_3d_f32 ## bda ## _len, im2col_3d_f32 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_3d_push_constants), {512, 1, 1}, { 512 }, 1, true);      \
    ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16 ## bda ## _len, im2col_f32_f16 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);   \
    ggml_vk_create_pipeline(device, device->pipeline_im2col_3d_f32_f16, "im2col_3d_f32_f16", im2col_3d_f32_f16 ## bda ## _len, im2col_3d_f32_f16 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_3d_push_constants), {512, 1, 1}, { 512 }, 1, true);
    if (device->shader_int64 && device->buffer_device_address) {
        IM2COL(_bda)
    } else {
        IM2COL()
    }

    ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_conv_transpose_1d_f32, "conv_transpose_1d_f32", conv_transpose_1d_f32_len, conv_transpose_1d_f32_data, "main", 3, sizeof(vk_op_conv_transpose_1d_push_constants), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_snake_f32,  "snake_f32",  snake_f32_len,  snake_f32_data,  "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_snake_f16,  "snake_f16",  snake_f16_len,  snake_f16_data,  "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_snake_bf16, "snake_bf16", snake_bf16_len, snake_bf16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv7_f32, "rwkv_wkv7_f32", rwkv_wkv7_f32_len, rwkv_wkv7_f32_data, "main", 8, sizeof(vk_op_rwkv_wkv7_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);

    {
        const uint32_t gdn_sizes[] = {32, 64, 128};
        const char * gdn_names[][2] = {
            {"gated_delta_net_f32_d32",     "gated_delta_net_f32_d32_kda"},
            {"gated_delta_net_f32_d64",     "gated_delta_net_f32_d64_kda"},
            {"gated_delta_net_f32_d128",    "gated_delta_net_f32_d128_kda"},
        };
        const bool use_subgroup_reduce = device->subgroup_arithmetic;
        for (uint32_t si = 0; si < 3; si++) {
            const uint32_t S_V = gdn_sizes[si];
            GGML_ASSERT(is_pow2(S_V));

            uint32_t lanes_per_column;
            if (S_V >= 128u && device->subgroup_clustered) {
                lanes_per_column = 8u;
            } else {
                // Use largest power-of-two that divides both S_V and subgroup_size so that
                // (1) S_V % lanes_per_column == 0 and (2) S_V % (subgroup_size / lanes_per_column) == 0.
                // This means we don't need extra bounds checking logic in the shader.
                lanes_per_column = std::min(S_V, device->subgroup_size);
            }

            const bool need_clustered_shader = lanes_per_column != 1 && (lanes_per_column < device->subgroup_size);
            size_t gdn_len;
            const void * gdn_data;
            if (use_subgroup_reduce && need_clustered_shader) {
                gdn_len = gated_delta_net_f32_len;
                gdn_data = (const void *)gated_delta_net_f32_data;
            } else if (use_subgroup_reduce) {
                gdn_len = gated_delta_net_f32_nocluster_len;
                gdn_data = (const void *)gated_delta_net_f32_nocluster_data;
            } else {
                gdn_len = gated_delta_net_f32_shmem_len;
                gdn_data = (const void *)gated_delta_net_f32_shmem_data;
            }

            const uint32_t cols_per_wg = device->subgroup_size / lanes_per_column;
            const std::array<uint32_t, 3> wg_denoms = {1u, 1u, cols_per_wg};

            for (uint32_t kda = 0; kda < 2; kda++) {
                ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net[si][kda],
                    gdn_names[si][kda], gdn_len, gdn_data, "main", 7, sizeof(vk_op_gated_delta_net_push_constants),
                    wg_denoms, {S_V, kda, device->subgroup_size, lanes_per_column}, 1, true, use_subgroup_reduce, device->subgroup_size);
            }
        }
    }

    if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
        ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_128_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size}, 1, true, true);
        ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size}, 1, true, true);
    } else {
        ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_128_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size, 16}, 1, true, true);
        ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size, 16}, 1, true, true);
    }

    ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_f32,           "ssm_conv_f32",           ssm_conv_f32_len, ssm_conv_f32_data, "main", 4, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16, 0, 0}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_silu_f32,      "ssm_conv_silu_f32",      ssm_conv_f32_len, ssm_conv_f32_data, "main", 4, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16, 0, 1}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_bias_silu_f32, "ssm_conv_bias_silu_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 4, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16, 1, 1}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_opt_step_sgd_f32, "opt_step_sgd_f32", opt_step_sgd_f32_len, opt_step_sgd_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    // conv2d, conv_transpose_2d
    for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) {
        // smaller WG for the small-tile fallback gives more concurrent WGs per SM
        uint32_t conv2d_WG_SIZE  = (s == CONV_SHAPE_64x32) ? 128 : 256;
        uint32_t use_collectives = 0;  // Enables subgroup ops for preventing the re-calculation of indices.
        uint32_t conv2d_TS_K     = (s == CONV_SHAPE_64x32) ? 4 : 8;
        uint32_t conv2d_SHMEM_PAD = 4;
        vk_conv_block_size conv2d_BS = vk_conv_block_sizes[s];
        bool conv2d_UNROLL = true;

#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
        if (device->coopmat2) {
            conv2d_SHMEM_PAD = 8; // 8 float16_t
        }
#endif

        if (device->vendor_id == VK_VENDOR_ID_INTEL) {
            conv2d_SHMEM_PAD = 0;
            conv2d_UNROLL = false;
        } else if (device->vendor_id == VK_VENDOR_ID_AMD) {
            conv2d_SHMEM_PAD = device->architecture == vk_device_architecture::AMD_GCN ? 1 : 4;
            if (s == CONV_SHAPE_128x128 && device->architecture != vk_device_architecture::AMD_GCN) {
                conv2d_UNROLL = false;
            }
        }

        // Use collectives on pre-Turing NVIDIA GPUs and GCN AMD cards, which had slower integer math.
        bool allow_collectives_nv = device->vendor_id != VK_VENDOR_ID_NVIDIA ||
                                    device->architecture == vk_device_architecture::NVIDIA_PRE_TURING;
        bool allow_collectives_amd = device->vendor_id != VK_VENDOR_ID_AMD ||
                                     device->architecture == vk_device_architecture::AMD_GCN;

        if (device->subgroup_shuffle &&
            device->vendor_id != VK_VENDOR_ID_INTEL &&   // Do not enable collectives on Intel, see PR 14316.
            allow_collectives_nv &&
            allow_collectives_amd) {
            use_collectives = 1;
            conv2d_BS.CRS   = std::min(
                device->subgroup_size,
                conv2d_BS.CRS);  // CRS block size should be capped at subgroup size for correctness when shuffle is used.
        }

        // cm1 is used only when cm2 is unavailable; capped at 64x128 (due to shared memory size).
        // Requires 16x16x16 f16-acc since that's the fragment shape hard-coded in the shader.
        // Subgroup size must be 32 or 64 (to keep WG_SIZE sane) and we need
        // subgroup_size_control to force the driver to actually use it.
        bool conv2d_use_cm1 = false;
#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
        conv2d_use_cm1 = !device->coopmat2 &&
                         device->coopmat_support && device->coopmat_support_16x16x16_f16acc &&
                         device->subgroup_size_control &&
                         (device->subgroup_size == 32 || device->subgroup_size == 64) &&
                         s != CONV_SHAPE_128x128;
#endif

        const uint32_t conv2d_cm1_shmem_pad = 8;

        auto shmem_req = [&](uint32_t pad, bool csh_store, bool fp16_shmem) {
            const uint32_t elem_size = fp16_shmem ? (uint32_t)sizeof(uint16_t) : (uint32_t)sizeof(float);
            const uint32_t csh_elems = csh_store ? conv2d_BS.K * conv2d_BS.NPQ : 0u;
            return (conv2d_BS.K * (conv2d_BS.CRS + pad) + conv2d_BS.CRS * (conv2d_BS.NPQ + pad) + csh_elems) * elem_size;
        };

        // coopmat1 needs to store the output through shared memory, so check up front
        // whether it'll fit and disable it before applying coopmat1 parameters.
        if (conv2d_use_cm1 && device->properties.limits.maxComputeSharedMemorySize < shmem_req(conv2d_cm1_shmem_pad, true, true)) {
            conv2d_use_cm1 = false;
        }

        uint32_t conv2d_WM = 16, conv2d_WN = 16;  // cm1 subgroup tile, ignored otherwise
        if (conv2d_use_cm1) {
            conv2d_SHMEM_PAD = conv2d_cm1_shmem_pad;
            // 16x16x16 fragments; pick WM/WN to keep WG_SIZE at 256
            // (i.e. 8 subgroups for sg=32, 4 subgroups for sg=64).
            const bool sg64 = (device->subgroup_size == 64);
            switch (s) {
                case CONV_SHAPE_64x32:   conv2d_WM = sg64 ? 32 : 16; conv2d_WN = 16; break;
                case CONV_SHAPE_64x128:  conv2d_WM = 32; conv2d_WN = sg64 ? 64 : 32; break;
                case CONV_SHAPE_32x256:  conv2d_WM = sg64 ? 16 : 32; conv2d_WN = sg64 ? 128 : 32; break;
                default: break;
            }
            const uint32_t warps_M = conv2d_BS.K / conv2d_WM;
            const uint32_t warps_N = conv2d_BS.NPQ / conv2d_WN;
            conv2d_WG_SIZE         = warps_M * warps_N * device->subgroup_size;
        }

        // stage cm2 accumulator through shmem for coalesced global stores;
        // skipped on 128x128 where the extra Csh footprint hurts occupancy.
        // cm1 always uses the staged path.
        uint32_t conv2d_csh_store = (device->coopmat2 && s != CONV_SHAPE_128x128) ? 1u : 0u;
        if (conv2d_use_cm1) {
            conv2d_csh_store = 1;
        }

        // shmem is fp16 on cm2/cm1 (matches Csh), fp32 on scalar
        const bool conv2d_use_fp16_shmem = device->coopmat2 || conv2d_use_cm1;

        // shrink CRS if the non-cm1 config still doesn't fit
        if (device->properties.limits.maxComputeSharedMemorySize < shmem_req(conv2d_SHMEM_PAD, conv2d_csh_store, conv2d_use_fp16_shmem)) {
            GGML_ASSERT(!conv2d_use_cm1);
            conv2d_BS.CRS = 8;
            if (use_collectives) {
                conv2d_BS.CRS = std::min(device->subgroup_size, conv2d_BS.CRS);
            }
            conv2d_csh_store = 0;
        }

        std::array<uint32_t, 3> wg_denoms = { conv2d_BS.K, 1, 1 };
        std::vector<uint32_t> spec_constants = { conv2d_WG_SIZE, conv2d_BS.K, conv2d_BS.CRS, conv2d_BS.NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD };

        // cm1 needs a fixed subgroup width to match the WG_SIZE we computed
        const uint32_t conv2d_required_subgroup_size = conv2d_use_cm1 ? device->subgroup_size : 0;

#define CREATE_CONV(name, type_suffix, spv_suffix) \
        for (auto &c : device->pipeline_##name##type_suffix[s]) { \
            const vk_conv2d_pipeline_state &state = c.first;  \
            std::vector<uint32_t> spec_constants_cpy = spec_constants; \
            spec_constants_cpy.push_back(state.s0); \
            spec_constants_cpy.push_back(state.s1); \
            spec_constants_cpy.push_back(state.p0); \
            spec_constants_cpy.push_back(state.p1); \
            spec_constants_cpy.push_back(state.d0); \
            spec_constants_cpy.push_back(state.d1); \
            spec_constants_cpy.push_back(state.KW); \
            spec_constants_cpy.push_back(state.KH); \
            spec_constants_cpy.push_back(state.aligned); \
            spec_constants_cpy.push_back(conv2d_csh_store); \
            spec_constants_cpy.push_back(conv2d_WM); \
            spec_constants_cpy.push_back(conv2d_WN); \
            ggml_vk_create_pipeline( \
                device, c.second, #name #type_suffix, \
                name##type_suffix##spv_suffix##_len, name##type_suffix##spv_suffix##_data, "main", 3, \
                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants_cpy, 1, true, use_collectives || conv2d_required_subgroup_size, conv2d_required_subgroup_size);    \
        }
#define CREATE_CONVS(spv_suffix) \
        CREATE_CONV(conv2d, _f32, spv_suffix) \
        CREATE_CONV(conv2d, _f16_f32, spv_suffix) \
        CREATE_CONV(conv_transpose_2d, _f32, spv_suffix) \
        CREATE_CONV(conv_transpose_2d, _f16_f32, spv_suffix)
#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
        if (device->coopmat2) {
            CREATE_CONVS(_cm2)
        } else
#endif
#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
        if (conv2d_use_cm1) {
            CREATE_CONVS(_cm1)
        } else
#endif
        if (conv2d_UNROLL) {
            CREATE_CONVS(_unroll)
        } else {
            CREATE_CONVS( )
        }
#undef CREATE_CONV
#undef CREATE_CONVS
    }

    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f32, "conv2d_dw_cwhn_f32", conv2d_dw_cwhn_f32_len, conv2d_dw_cwhn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);

    for (uint32_t use_push = 0; use_push < 2; ++use_push) {
        for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][use_push], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 4, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, use_push}, 1, true, true, device->subgroup_size);
        }
    }

    // Drop compile_mutex so other threads can walk while we compile.
    compile_lock.unlock();

    // Compile what we claimed; create_pipeline_func reacquires compile_mutex
    // at the end to flip compile_pending/compiled and notify waiters.
    if (has_claimed_task) {
        auto & task = claimed_task;
        ggml_vk_create_pipeline_func(device, task.pipeline, task.spv_size, task.spv_data,
                                     task.entrypoint, task.parameter_count, task.wg_denoms,
                                     task.specialization_constants, task.disable_robustness,
                                     task.require_full_subgroups, task.required_subgroup_size);
    }

    // Another thread may be compiling the pipeline we need; block on it here.
    if (wait_pipeline) {
        std::unique_lock<std::mutex> wait_lock(device->compile_mutex);
        device->compile_cv.wait(wait_lock, [&] {
            return wait_pipeline->compiled.load();
        });
    }
}

static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch);
static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev);

static vk_device ggml_vk_get_device(size_t idx) {
    VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")");

    if (vk_instance.devices[idx] == nullptr) {
        VK_LOG_DEBUG("Initializing new vk_device");
        vk_device device = std::make_shared<vk_device_struct>();
        vk_instance.devices[idx] = device;

        device->memory_logger = std::unique_ptr<vk_memory_logger>(new vk_memory_logger());

        size_t dev_num = vk_instance.device_indices[idx];

        std::vector<vk::PhysicalDevice> physical_devices = vk_instance.instance.enumeratePhysicalDevices();

        if (dev_num >= physical_devices.size()) {
            std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
            throw std::runtime_error("Device not found");
        }

        device->physical_device = physical_devices[dev_num];
        const std::vector<vk::ExtensionProperties> ext_props = device->physical_device.enumerateDeviceExtensionProperties();

        device->architecture = get_device_architecture(device->physical_device);

        const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY");
        device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr;

        const char* GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM = getenv("GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM");
        device->disable_host_visible_vidmem = GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM != nullptr;

        const char* GGML_VK_ALLOW_SYSMEM_FALLBACK = getenv("GGML_VK_ALLOW_SYSMEM_FALLBACK");
        device->allow_sysmem_fallback = GGML_VK_ALLOW_SYSMEM_FALLBACK != nullptr;

        const char* GGML_VK_DISABLE_GRAPH_OPTIMIZE = getenv("GGML_VK_DISABLE_GRAPH_OPTIMIZE");
        device->disable_graph_optimize = GGML_VK_DISABLE_GRAPH_OPTIMIZE != nullptr;

        bool fp16_storage = false;
        bool fp16_compute = false;
        bool maintenance4_support = false;
        bool sm_builtins = false;
        bool amd_shader_core_properties2 = false;
        bool pipeline_robustness = false;
        bool coopmat2_support = false;
        bool coopmat2_decode_vector_support = false;
        bool pipeline_executable_properties_support = false;
        device->coopmat_support = false;
        device->integer_dot_product = false;
        device->shader_64b_indexing = false;
        bool bfloat16_support = false;
        bool dot2_f16_support = false;

        for (const auto& properties : ext_props) {
            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
                maintenance4_support = true;
            } else if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
                fp16_storage = true;
            } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
                fp16_compute = true;
            } else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) {
                sm_builtins = true;
            } else if (strcmp("VK_AMD_shader_core_properties2", properties.extensionName) == 0) {
                amd_shader_core_properties2 = true;
            } else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) {
                pipeline_robustness = true;
            } else if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) {
                device->subgroup_size_control = true;
#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
            } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 &&
                       !getenv("GGML_VK_DISABLE_COOPMAT")) {
                device->coopmat_support = true;
                device->coopmat_m = 0;
                device->coopmat_n = 0;
                device->coopmat_k = 0;
#endif
#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
            } else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 &&
                       !getenv("GGML_VK_DISABLE_COOPMAT2")) {
                coopmat2_support = true;
#endif
            } else if (strcmp(VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME, properties.extensionName) == 0 &&
                       !getenv("GGML_VK_DISABLE_COOPMAT2_DECODE_VECTOR")) {
                coopmat2_decode_vector_support = true;
#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
            } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0 &&
                       !getenv("GGML_VK_DISABLE_INTEGER_DOT_PRODUCT")) {
                device->integer_dot_product = true;
#endif
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
            } else if (strcmp("VK_KHR_shader_bfloat16", properties.extensionName) == 0 &&
                       !getenv("GGML_VK_DISABLE_BFLOAT16")) {
                bfloat16_support = true;
#endif
            } else if (strcmp("VK_VALVE_shader_mixed_float_dot_product", properties.extensionName) == 0 &&
                       !getenv("GGML_VK_DISABLE_DOT2")) {
                dot2_f16_support = true;
            } else if (strcmp("VK_KHR_pipeline_executable_properties", properties.extensionName) == 0) {
                pipeline_executable_properties_support = true;
            } else if (strcmp("VK_EXT_memory_priority", properties.extensionName) == 0 &&
                       getenv("GGML_VK_ENABLE_MEMORY_PRIORITY")) {
                device->memory_priority = true;
            } else if (strcmp("VK_EXT_external_memory_host", properties.extensionName) == 0) {
                device->external_memory_host = true;
#if defined(VK_EXT_shader_64bit_indexing)
            } else if (strcmp("VK_EXT_shader_64bit_indexing", properties.extensionName) == 0) {
                device->shader_64b_indexing = true;
#endif
            }
        }

        vk::PhysicalDeviceProperties2 props2;
        vk::PhysicalDeviceMaintenance3Properties props3;
        vk::PhysicalDeviceMaintenance4Properties props4;
        vk::PhysicalDeviceSubgroupProperties subgroup_props;
        vk::PhysicalDeviceDriverProperties driver_props;
        vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props;
        vk::PhysicalDeviceShaderCoreProperties2AMD amd_shader_core_properties2_props;
        vk::PhysicalDeviceVulkan11Properties vk11_props;
        vk::PhysicalDeviceVulkan12Properties vk12_props;
        vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props;
        vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR shader_integer_dot_product_props;
        vk::PhysicalDeviceExternalMemoryHostPropertiesEXT external_memory_host_props;

        props2.pNext = &props3;
        props3.pNext = &subgroup_props;
        subgroup_props.pNext = &driver_props;
        driver_props.pNext = &vk11_props;
        vk11_props.pNext = &vk12_props;

        VkBaseOutStructure * last_struct = (VkBaseOutStructure *)&vk12_props;

        if (maintenance4_support) {
            last_struct->pNext = (VkBaseOutStructure *)&props4;
            last_struct = (VkBaseOutStructure *)&props4;
        }
        if (sm_builtins) {
            last_struct->pNext = (VkBaseOutStructure *)&sm_props;
            last_struct = (VkBaseOutStructure *)&sm_props;
        }
        if (amd_shader_core_properties2) {
            last_struct->pNext = (VkBaseOutStructure *)&amd_shader_core_properties2_props;
            last_struct = (VkBaseOutStructure *)&amd_shader_core_properties2_props;
        }
        if (device->subgroup_size_control) {
            last_struct->pNext = (VkBaseOutStructure *)&subgroup_size_control_props;
            last_struct = (VkBaseOutStructure *)&subgroup_size_control_props;
        }

#if defined(VK_NV_cooperative_matrix2)
        vk::PhysicalDeviceCooperativeMatrix2PropertiesNV coopmat2_props;
        if (coopmat2_support) {
            last_struct->pNext = (VkBaseOutStructure *)&coopmat2_props;
            last_struct = (VkBaseOutStructure *)&coopmat2_props;
        }
#endif

        if (device->integer_dot_product) {
            last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_props;
            last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_props;
        }

        if (device->external_memory_host) {
            last_struct->pNext = (VkBaseOutStructure *)&external_memory_host_props;
            last_struct = (VkBaseOutStructure *)&external_memory_host_props;
        }

        device->physical_device.getProperties2(&props2);
        device->properties = props2.properties;
        device->vendor_id = device->properties.vendorID;
        device->driver_id = driver_props.driverID;

        if (device->driver_id == vk::DriverId::eMoltenvk) {
            // Disable external_memory_host until https://github.com/KhronosGroup/MoltenVK/pull/2622
            // is available in the Vulkan SDK.
            device->external_memory_host = false;
        }

        // Implementing the async backend interfaces seems broken on older Intel HW,
        // see https://github.com/ggml-org/llama.cpp/issues/17302.
        device->support_async = (device->vendor_id != VK_VENDOR_ID_INTEL ||
                                 std::string(device->properties.deviceName.data()).find("(DG1)") == std::string::npos) &&
                                getenv("GGML_VK_DISABLE_ASYNC") == nullptr;

        if (!device->support_async) {
            GGML_LOG_DEBUG("ggml_vulkan: WARNING: Async execution disabled on certain Intel devices.\n");
        }

        const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE");

        if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) {
            device->max_memory_allocation_size = std::stoull(GGML_VK_FORCE_MAX_ALLOCATION_SIZE);
        } else if (maintenance4_support) {
            device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize);
        } else {
            device->max_memory_allocation_size = props3.maxMemoryAllocationSize;
        }

        const char* GGML_VK_FORCE_MAX_BUFFER_SIZE = getenv("GGML_VK_FORCE_MAX_BUFFER_SIZE");

        if (GGML_VK_FORCE_MAX_BUFFER_SIZE != nullptr) {
            device->max_buffer_size = std::stoull(GGML_VK_FORCE_MAX_BUFFER_SIZE);
        } else if (maintenance4_support) {
            device->max_buffer_size = props4.maxBufferSize;
        } else {
            device->max_buffer_size = device->max_memory_allocation_size;
        }

        const char* GGML_VK_SUBALLOCATION_BLOCK_SIZE = getenv("GGML_VK_SUBALLOCATION_BLOCK_SIZE");

        if (GGML_VK_SUBALLOCATION_BLOCK_SIZE != nullptr) {
            device->suballocation_block_size = std::stoull(GGML_VK_SUBALLOCATION_BLOCK_SIZE);
        } else {
            // Limit batching of allocations to 1GB by default to avoid fragmentation issues
            device->suballocation_block_size = 1024*1024*1024;
        }
        device->suballocation_block_size = std::min(device->suballocation_block_size, device->max_memory_allocation_size);

        device->subgroup_size = subgroup_props.subgroupSize;
        device->subgroup_size_log2 = uint32_t(log2f(float(device->subgroup_size)));
        device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;
        if (sm_builtins) {
            device->shader_core_count = sm_props.shaderSMCount;
        } else if (amd_shader_core_properties2) {
            device->shader_core_count = amd_shader_core_properties2_props.activeComputeUnitCount;
        } else if (device->vendor_id == VK_VENDOR_ID_INTEL) {
            device->shader_core_count = ggml_vk_intel_shader_core_count(device->physical_device);
        } else {
            device->shader_core_count = 0;
        }
        device->float_controls_rte_fp16 = vk12_props.shaderRoundingModeRTEFloat16;

        device->subgroup_basic = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                 (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eBasic);
        device->subgroup_arithmetic = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                      (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eArithmetic);
#ifdef __APPLE__
        // Workaround for subgroup arithmetic failing on MoltenVK with AMD GPUs (issue 15846)
        if (device->vendor_id == VK_VENDOR_ID_AMD) {
            device->subgroup_arithmetic = false;
        }
#endif
        device->subgroup_shuffle = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                   (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eShuffle);
#ifdef __APPLE__
        if (device->vendor_id == VK_VENDOR_ID_AMD) {
            device->subgroup_shuffle = false;
        }
#endif
        device->subgroup_clustered = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                     (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eClustered);

        device->subgroup_ballot = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                  (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eBallot);

        device->subgroup_vote = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eVote);

        const bool force_disable_f16 = getenv("GGML_VK_DISABLE_F16") != nullptr;

        device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;

        if (!ggml_vk_khr_cooperative_matrix_support(device->properties, driver_props, device->architecture)) {
            device->coopmat_support = false;
        }

        device->integer_dot_product = device->integer_dot_product && shader_integer_dot_product_props.integerDotProduct4x8BitPackedSignedAccelerated;

        device->min_imported_host_pointer_alignment = external_memory_host_props.minImportedHostPointerAlignment;

        device->max_workgroup_size_log2 = uint32_t(log2f(float(device->properties.limits.maxComputeWorkGroupInvocations)));

        std::vector<vk::QueueFamilyProperties> queue_family_props = device->physical_device.getQueueFamilyProperties();

        // Try to find a non-graphics compute queue and transfer-focused queues
        // Allow overriding avoiding the graphics queue because it can increase performance on RADV
        const bool allow_graphics_queue = (getenv("GGML_VK_ALLOW_GRAPHICS_QUEUE") != nullptr);
        const vk::QueueFlagBits graphics_flag = allow_graphics_queue ? (vk::QueueFlagBits)0 : vk::QueueFlagBits::eGraphics;
        const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, graphics_flag, -1, 1);
        const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | graphics_flag, compute_queue_family_index, 1);

        const float priorities[] = { 1.0f, 1.0f };
        device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1;

        std::vector<vk::DeviceQueueCreateInfo> device_queue_create_infos;
        if (compute_queue_family_index != transfer_queue_family_index) {
            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1});
        } else if(!device->single_queue) {
            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities});
        } else {
            device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities});
        }
        vk::DeviceCreateInfo device_create_info{};
        std::vector<const char *> device_extensions;
        vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures();

        VkPhysicalDeviceFeatures2 device_features2;
        device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
        device_features2.pNext = nullptr;
        device_features2.features = (VkPhysicalDeviceFeatures)device_features;

        VkPhysicalDeviceVulkan11Features vk11_features;
        vk11_features.pNext = nullptr;
        vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
        device_features2.pNext = &vk11_features;

        VkPhysicalDeviceVulkan12Features vk12_features;
        vk12_features.pNext = nullptr;
        vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
        vk11_features.pNext = &vk12_features;

        last_struct = (VkBaseOutStructure *)&vk12_features;

        VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features;
        pl_robustness_features.pNext = nullptr;
        pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT;
        pl_robustness_features.pipelineRobustness = VK_FALSE;

        if (pipeline_robustness) {
            last_struct->pNext = (VkBaseOutStructure *)&pl_robustness_features;
            last_struct = (VkBaseOutStructure *)&pl_robustness_features;
            device_extensions.push_back("VK_EXT_pipeline_robustness");
        }

        VkPhysicalDeviceMemoryPriorityFeaturesEXT memory_priority_features;
        memory_priority_features.pNext = nullptr;
        memory_priority_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MEMORY_PRIORITY_FEATURES_EXT;
        memory_priority_features.memoryPriority = VK_FALSE;
        if (device->memory_priority) {
            last_struct->pNext = (VkBaseOutStructure *)&memory_priority_features;
            last_struct = (VkBaseOutStructure *)&memory_priority_features;
            device_extensions.push_back("VK_EXT_memory_priority");
        }

        VkPhysicalDeviceSubgroupSizeControlFeaturesEXT subgroup_size_control_features;
        subgroup_size_control_features.pNext = nullptr;
        subgroup_size_control_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_SIZE_CONTROL_FEATURES_EXT;
        subgroup_size_control_features.computeFullSubgroups = false;
        subgroup_size_control_features.subgroupSizeControl = false;

        if (device->subgroup_size_control) {
            last_struct->pNext = (VkBaseOutStructure *)&subgroup_size_control_features;
            last_struct = (VkBaseOutStructure *)&subgroup_size_control_features;
        }

#if defined(VK_KHR_cooperative_matrix)
        VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features;
        coopmat_features.pNext = nullptr;
        coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR;
        coopmat_features.cooperativeMatrix = VK_FALSE;

        if (device->coopmat_support) {
            last_struct->pNext = (VkBaseOutStructure *)&coopmat_features;
            last_struct = (VkBaseOutStructure *)&coopmat_features;
        }
#endif

#if defined(VK_NV_cooperative_matrix2)
        VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {};
        coopmat2_features.pNext = nullptr;
        coopmat2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_2_FEATURES_NV;
        if (coopmat2_support) {
            last_struct->pNext = (VkBaseOutStructure *)&coopmat2_features;
            last_struct = (VkBaseOutStructure *)&coopmat2_features;
            device_extensions.push_back("VK_NV_cooperative_matrix2");
        }
#endif

        VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV coopmat2_decode_vector_features {};
        coopmat2_decode_vector_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_DECODE_VECTOR_FEATURES_NV;
        if (coopmat2_decode_vector_support) {
            last_struct->pNext = (VkBaseOutStructure *)&coopmat2_decode_vector_features;
            last_struct = (VkBaseOutStructure *)&coopmat2_decode_vector_features;
            device_extensions.push_back(VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME);
        }

#if defined(VK_KHR_shader_bfloat16)
        VkPhysicalDeviceShaderBfloat16FeaturesKHR bfloat16_features {};
        bfloat16_features.pNext = nullptr;
        bfloat16_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR;
        if (bfloat16_support) {
            last_struct->pNext = (VkBaseOutStructure *)&bfloat16_features;
            last_struct = (VkBaseOutStructure *)&bfloat16_features;
            device_extensions.push_back("VK_KHR_shader_bfloat16");
        }
#endif

        VkPhysicalDeviceMaintenance4Features maint4_features {};
        maint4_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MAINTENANCE_4_FEATURES;
        if (maintenance4_support) {
            last_struct->pNext = (VkBaseOutStructure *)&maint4_features;
            last_struct = (VkBaseOutStructure *)&maint4_features;
            device_extensions.push_back("VK_KHR_maintenance4");
        }

        VkPhysicalDeviceShaderIntegerDotProductFeaturesKHR shader_integer_dot_product_features {};
        shader_integer_dot_product_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_INTEGER_DOT_PRODUCT_FEATURES_KHR;
        if (device->integer_dot_product) {
            last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_features;
            last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_features;
            device_extensions.push_back("VK_KHR_shader_integer_dot_product");
        }

        VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE dot2_features {};
        dot2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_MIXED_FLOAT_DOT_PRODUCT_FEATURES_VALVE;
        if (dot2_f16_support) {
            last_struct->pNext = (VkBaseOutStructure *)&dot2_features;
            last_struct = (VkBaseOutStructure *)&dot2_features;
            device_extensions.push_back("VK_VALVE_shader_mixed_float_dot_product");
        }

        VkPhysicalDevicePipelineExecutablePropertiesFeaturesKHR pep_features {};
        pep_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_EXECUTABLE_PROPERTIES_FEATURES_KHR;
        if (pipeline_executable_properties_support) {
            last_struct->pNext = (VkBaseOutStructure *)&pep_features;
            last_struct = (VkBaseOutStructure *)&pep_features;
            device_extensions.push_back("VK_KHR_pipeline_executable_properties");
        }

        if (device->external_memory_host) {
            device_extensions.push_back("VK_EXT_external_memory_host");
        }

#if defined(VK_EXT_shader_64bit_indexing)
        VkPhysicalDeviceShader64BitIndexingFeaturesEXT shader_64bit_indexing_features {};
        shader_64bit_indexing_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_64_BIT_INDEXING_FEATURES_EXT;
        if (device->shader_64b_indexing) {
            last_struct->pNext = (VkBaseOutStructure *)&shader_64bit_indexing_features;
            last_struct = (VkBaseOutStructure *)&shader_64bit_indexing_features;
            device_extensions.push_back("VK_EXT_shader_64bit_indexing");
        }
#endif

        vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);

        device->pipeline_executable_properties_support = pipeline_executable_properties_support;

        device->fp16 = device->fp16 && vk12_features.shaderFloat16;

#if defined(VK_KHR_shader_bfloat16)
        device->bf16 = bfloat16_support && bfloat16_features.shaderBFloat16Type;
#else
        device->bf16 = false;
#endif

        device->dot2_f16 = dot2_f16_support && dot2_features.shaderMixedFloatDotProductFloat16AccFloat32;

        device->pipeline_robustness = pl_robustness_features.pipelineRobustness;

        device->multi_add = vk12_props.shaderRoundingModeRTEFloat16 &&
                            device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_multi_add_push_constants) &&
                            getenv("GGML_VK_DISABLE_MULTI_ADD") == nullptr;

        device->shader_int64 = device_features2.features.shaderInt64;
        device->buffer_device_address = vk12_features.bufferDeviceAddress;
        device->vulkan_memory_model = vk12_features.vulkanMemoryModel;

        if (device->subgroup_size_control) {
            device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize;
            device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize;
            device_extensions.push_back("VK_EXT_subgroup_size_control");
        }

        device->subgroup_size_control = device->subgroup_size_control &&
                (subgroup_size_control_props.requiredSubgroupSizeStages & vk::ShaderStageFlagBits::eCompute) &&
                subgroup_size_control_features.subgroupSizeControl;

        device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups;

#if defined(VK_KHR_cooperative_matrix)
        device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix;
        device->coopmat1_fa_support = device->coopmat_support && device->subgroup_require_full_support;
#endif

        if (coopmat2_support) {
#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
            if (coopmat2_features.cooperativeMatrixWorkgroupScope &&
                coopmat2_features.cooperativeMatrixFlexibleDimensions &&
                coopmat2_features.cooperativeMatrixReductions &&
                coopmat2_features.cooperativeMatrixConversions &&
                coopmat2_features.cooperativeMatrixPerElementOperations &&
                coopmat2_features.cooperativeMatrixTensorAddressing &&
                coopmat2_features.cooperativeMatrixBlockLoads &&
                vk12_features.bufferDeviceAddress) {

                std::vector<VkCooperativeMatrixFlexibleDimensionsPropertiesNV> flexible_dimensions;
                uint32_t count = 0;

                PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV
                    _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV =
                        (PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV)
                        vk_instance.instance.getProcAddr("vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV");

                _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, nullptr);

                VkCooperativeMatrixFlexibleDimensionsPropertiesNV empty_prop {};
                empty_prop.sType = VK_STRUCTURE_TYPE_COOPERATIVE_MATRIX_FLEXIBLE_DIMENSIONS_PROPERTIES_NV;
                flexible_dimensions.resize(count, empty_prop);

                _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, flexible_dimensions.data());

                bool found_fp16_128 = false,
                     found_fp16_256 = false,
                     found_fp32_128 = false,
                     found_fp32_256 = false;
                bool found_bf16_128 = false,
                     found_bf16_256 = false;
                // need to support fp16*fp16 with fp16/fp32 accumulator, for workgroupsize 128
                // with 32x16x16 and 256 with 32x32x16.
                for (auto &prop : flexible_dimensions) {
                    if (prop.saturatingAccumulation == VK_FALSE &&
                        prop.scope == VK_SCOPE_WORKGROUP_KHR) {

                        if (prop.AType == VK_COMPONENT_TYPE_FLOAT16_KHR &&
                            prop.BType == VK_COMPONENT_TYPE_FLOAT16_KHR) {

                            if (prop.workgroupInvocations == 128 &&
                                prop.MGranularity <= 32 &&
                                prop.NGranularity <= 16 &&
                                prop.KGranularity <= 16) {
                                if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR &&
                                    prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) {
                                    found_fp16_128 = true;
                                }
                                if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
                                    prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) {
                                    found_fp32_128 = true;
                                }
                            }
                            if (prop.workgroupInvocations == 256 &&
                                prop.MGranularity <= 32 &&
                                prop.NGranularity <= 32 &&
                                prop.KGranularity <= 16) {
                                if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR &&
                                    prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) {
                                    found_fp16_256 = true;
                                }
                                if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
                                    prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) {
                                    found_fp32_256 = true;
                                }
                            }
                        }

#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
                        if (prop.AType == VK_COMPONENT_TYPE_BFLOAT16_KHR &&
                            prop.BType == VK_COMPONENT_TYPE_BFLOAT16_KHR &&
                            prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
                            prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) {

                            if (prop.workgroupInvocations == 128 &&
                                prop.MGranularity <= 32 &&
                                prop.NGranularity <= 16 &&
                                prop.KGranularity <= 16) {
                                found_bf16_128 = true;
                            }
                            if (prop.workgroupInvocations == 256 &&
                                prop.MGranularity <= 32 &&
                                prop.NGranularity <= 32 &&
                                prop.KGranularity <= 16) {
                                found_bf16_256 = true;
                            }
                        }
#endif
                    }
                }
                if (found_fp16_128 && found_fp16_256 &&
                    found_fp32_128 && found_fp32_256 &&
                    coopmat2_props.cooperativeMatrixFlexibleDimensionsMaxDimension >= 512) {
                    device->coopmat2 = true;
                    device->coopmat2_bf16_support = found_bf16_128 && found_bf16_256;
                    device->coopmat2_decode_vector = coopmat2_decode_vector_support && coopmat2_decode_vector_features.cooperativeMatrixDecodeVector;
                }
            }
#endif
        }

        if (!vk11_features.storageBuffer16BitAccess) {
            std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl;
            throw std::runtime_error("Unsupported device");
        }

        device_extensions.push_back("VK_KHR_16bit_storage");

#ifdef GGML_VULKAN_VALIDATE
        device_extensions.push_back("VK_KHR_shader_non_semantic_info");
#endif

        if (device->fp16) {
            device_extensions.push_back("VK_KHR_shader_float16_int8");
        }

#if defined(VK_KHR_cooperative_matrix)
        if (device->coopmat_support) {
            // Query supported shapes
            std::vector<VkCooperativeMatrixPropertiesKHR> cm_props;

            PFN_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR pfn_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR =
                (PFN_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR)vkGetInstanceProcAddr(vk_instance.instance, "vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR");

            uint32_t cm_props_num;

            pfn_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR(device->physical_device, &cm_props_num, nullptr);

            cm_props.resize(cm_props_num);

            for (auto& prop : cm_props) {
                prop.sType = VK_STRUCTURE_TYPE_COOPERATIVE_MATRIX_PROPERTIES_KHR;
            }

            pfn_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR(device->physical_device, &cm_props_num, cm_props.data());

            VK_LOG_DEBUG("ggml_vulkan: Cooperative Matrix Shapes: " << cm_props.size());

            for (auto& prop : cm_props) {
                VK_LOG_DEBUG("ggml_vulkan: M: " << prop.MSize << " N: " << prop.NSize << " K: " << prop.KSize << " A: " << vk::to_string((vk::ComponentTypeKHR)prop.AType) << " B: " << vk::to_string((vk::ComponentTypeKHR)prop.BType) << " C: " << vk::to_string((vk::ComponentTypeKHR)prop.CType) << " Result: " << vk::to_string((vk::ComponentTypeKHR)prop.ResultType) << " saturatingAccumulation: " << prop.saturatingAccumulation << " scope: " << vk::to_string((vk::ScopeKHR)prop.scope));

                if ((vk::ComponentTypeKHR)prop.AType == vk::ComponentTypeKHR::eFloat16 &&
                    (vk::ComponentTypeKHR)prop.BType == vk::ComponentTypeKHR::eFloat16 &&
                    (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup
                ) {
                    if ((vk::ComponentTypeKHR)prop.CType == vk::ComponentTypeKHR::eFloat32 &&
                        (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eFloat32) {
                        // coopmat sizes not set yet
                        if (device->coopmat_m == 0) {
                            device->coopmat_acc_f32_support = true;
                            device->coopmat_m = prop.MSize;
                            device->coopmat_n = prop.NSize;
                            device->coopmat_k = prop.KSize;
                        } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) {
                            // Only enable if shape is identical
                            device->coopmat_acc_f32_support = true;
                        }
                        if (prop.MSize == 16 && prop.NSize == 16 && prop.KSize == 16) {
                            device->coopmat_support_16x16x16_f32acc = true;
                        }
                    } else if ((vk::ComponentTypeKHR)prop.CType == vk::ComponentTypeKHR::eFloat16 &&
                               (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eFloat16) {
                        // coopmat sizes not set yet
                        if (device->coopmat_m == 0) {
                            device->coopmat_acc_f16_support = true;
                            device->coopmat_m = prop.MSize;
                            device->coopmat_n = prop.NSize;
                            device->coopmat_k = prop.KSize;
                        } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) {
                            // Only enable if shape is identical
                            device->coopmat_acc_f16_support = true;
                        }
                        if (prop.MSize == 16 && prop.NSize == 16 && prop.KSize == 16) {
                            device->coopmat_support_16x16x16_f16acc = true;
                        }
                    }
                } else if ((vk::ComponentTypeKHR)prop.AType      == vk::ComponentTypeKHR::eSint8 &&
                           (vk::ComponentTypeKHR)prop.BType      == vk::ComponentTypeKHR::eSint8 &&
                           (vk::ComponentTypeKHR)prop.CType      == vk::ComponentTypeKHR::eSint32 &&
                           (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eSint32 &&
                           (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup &&
                           device->coopmat_int_m == 0
                ) {
                    device->coopmat_int_support = true;
                    device->coopmat_int_m = prop.MSize;
                    device->coopmat_int_n = prop.NSize;
                    device->coopmat_int_k = prop.KSize;
                }
#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
                if (prop.AType == VK_COMPONENT_TYPE_BFLOAT16_KHR &&
                    prop.BType == VK_COMPONENT_TYPE_BFLOAT16_KHR &&
                    prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
                    prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR &&
                    (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup
                ) {
                    // coopmat sizes not set yet
                    if (device->coopmat_m == 0) {
                        device->coopmat_bf16_support = true;
                        device->coopmat_m = prop.MSize;
                        device->coopmat_n = prop.NSize;
                        device->coopmat_k = prop.KSize;
                    } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) {
                        // Only enable if shape is identical
                        device->coopmat_bf16_support = true;
                    }
                }
#endif
            }

            if (device->coopmat_m == 0 || !device->coopmat_acc_f32_support) {
                // No suitable matmul mode found
                GGML_LOG_DEBUG("ggml_vulkan: WARNING: No suitable matrix core mode found. Disabling matrix cores.\n");
                device->coopmat_support = false;
            }
            if (getenv("GGML_VK_DISABLE_BFLOAT16")) {
                device->coopmat_bf16_support = false;
            }
        }

        if (device->coopmat_support) {
            device_extensions.push_back("VK_KHR_cooperative_matrix");
        }
#if defined(VK_KHR_shader_bfloat16)
        if (device->coopmat_bf16_support) {
            device_extensions.push_back("VK_KHR_shader_bfloat16");
        }
#endif
#endif
        device->name = GGML_VK_NAME + std::to_string(idx);

        device_create_info
            .setFlags(vk::DeviceCreateFlags())
            .setQueueCreateInfos(device_queue_create_infos)
            .setPEnabledExtensionNames(device_extensions);
        device_create_info.setPNext(&device_features2);
        device->device = device->physical_device.createDevice(device_create_info);

        // Queues
        ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false);

        // Shaders
        // Disable matmul tile sizes early if performance low or not supported
        for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) {
            switch (device->vendor_id) {
#ifndef GGML_VULKAN_RUN_TESTS
            case VK_VENDOR_ID_AMD:
                device->mul_mat_l[i]    = device->coopmat_support && device->driver_id != vk::DriverId::eAmdProprietary;
                device->mul_mat_m[i]    = true;
                device->mul_mat_s[i]    = true;
                device->mul_mat_id_l[i] = false;
                device->mul_mat_id_m[i] = true;
                device->mul_mat_id_s[i] = true;
                break;
            case VK_VENDOR_ID_INTEL: {
                // Current Windows driver does not expose BF16 support.
                // We only want to use l_warptile if coopmat is available and is Xe2+
                const bool xe2_with_coopmat = device->coopmat_support && device->architecture == INTEL_XE2;
                const bool use_l_warptile = (i == GGML_TYPE_BF16) ? (device->coopmat_bf16_support && xe2_with_coopmat) : xe2_with_coopmat;
                device->mul_mat_l[i] = use_l_warptile;
                device->mul_mat_id_l[i] = use_l_warptile;
                device->mul_mat_m[i] = true;
                device->mul_mat_s[i] = true;
                device->mul_mat_id_m[i] = true;
                device->mul_mat_id_s[i] = true;
                break;
            }
            case VK_VENDOR_ID_APPLE:
                device->mul_mat_l[i] = false;
                device->mul_mat_m[i] = true;
                device->mul_mat_s[i] = false;
                device->mul_mat_id_l[i] = false;
                device->mul_mat_id_m[i] = true;
                device->mul_mat_id_s[i] = false;
                break;
#endif
            default:
                device->mul_mat_l[i] = true;
                device->mul_mat_m[i] = true;
                device->mul_mat_s[i] = true;
                device->mul_mat_id_l[i] = true;
                device->mul_mat_id_m[i] = true;
                device->mul_mat_id_s[i] = true;
                break;
            }

#if VK_HEADER_VERSION >= 287
            // Honeykrisp driver for Asahi Linux doesn't report VK_VENDOR_ID_APPLE.
            // Check for Honeykrisp driver and force same configuration as the VK_VENDOR_ID_APPLE case.
            if (device->driver_id == vk::DriverId::eMesaHoneykrisp) {
                device->mul_mat_l[i] = false;
                device->mul_mat_m[i] = true;
                device->mul_mat_s[i] = false;
                device->mul_mat_id_l[i] = false;
                device->mul_mat_id_m[i] = true;
                device->mul_mat_id_s[i] = false;
            }
#endif

            device->mul_mat_l_int[i]    = device->mul_mat_l[i];
            device->mul_mat_m_int[i]    = device->mul_mat_m[i];
            device->mul_mat_s_int[i]    = device->mul_mat_s[i];
            device->mul_mat_id_l_int[i] = device->mul_mat_id_l[i];
            device->mul_mat_id_m_int[i] = device->mul_mat_id_m[i];
            device->mul_mat_id_s_int[i] = device->mul_mat_id_s[i];
        }


        std::vector<vk::DescriptorSetLayoutBinding> dsl_binding;
        std::vector<vk::DescriptorBindingFlags> dsl_binding_flags;
        for (uint32_t i = 0; i < MAX_PARAMETER_COUNT; i++) {
            dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute});
            dsl_binding_flags.push_back({});
        }

        vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags };

        vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info(
            {},
            dsl_binding);
        descriptor_set_layout_create_info.setPNext(&dslbfci);
        device->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info);

        ggml_vk_load_shaders(device);

        // Prefer a dedicated transfer queue on AMD dGPUs (non-GCN) when graphics queue use is disabled.
        const bool prefers_transfer_queue =
            device->vendor_id == VK_VENDOR_ID_AMD &&
            device->architecture != AMD_GCN &&
            !device->uma &&
            !allow_graphics_queue;

        if (!device->single_queue) {
            const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0;
            ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true);

            device->async_use_transfer_queue = prefers_transfer_queue || (getenv("GGML_VK_ASYNC_USE_TRANSFER_QUEUE") != nullptr);
        } else {
            // TODO: Use pointer or reference to avoid copy
            device->transfer_queue.copyFrom(device->compute_queue);
            device->transfer_queue.cmd_pool.init(device, &device->transfer_queue);

            device->async_use_transfer_queue = false;
        }

        device->buffer_type = {
            /* .iface    = */ ggml_backend_vk_buffer_type_interface,
            /* .device   = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), idx),
            /* .context  = */ new ggml_backend_vk_buffer_type_context{ device->name, device },
        };

        device->fence = device->device.createFence({});

        device->idx = idx;

        device->disable_fusion = getenv("GGML_VK_DISABLE_FUSION") != nullptr;

        device->add_rms_fusion = !device->disable_fusion &&
                                 device->subgroup_arithmetic &&
                                 device->vendor_id != VK_VENDOR_ID_INTEL;
        device->partials_binding_alignment =
            std::max(4u, (uint32_t)device->properties.limits.minStorageBufferOffsetAlignment);

        device->mmvq_mode = 0;
        if (getenv("GGML_VK_DISABLE_MMVQ")) {
            device->mmvq_mode = -1;
        } else if (getenv("GGML_VK_FORCE_MMVQ")) {
            device->mmvq_mode = 1;
        }

        return device;
    }

    return vk_instance.devices[idx];
}

static void ggml_vk_print_gpu_info(size_t idx) {
    GGML_ASSERT(idx < vk_instance.device_indices.size());
    size_t dev_num = vk_instance.device_indices[idx];
    VK_LOG_DEBUG("ggml_vk_print_gpu_info(" << dev_num << ")");
    GGML_ASSERT(vk_instance_initialized);

    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();

    if (dev_num >= devices.size()) {
        std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl;
        throw std::runtime_error("Device not found");
    }

    vk::PhysicalDevice physical_device = devices[dev_num];
    std::vector<vk::ExtensionProperties> ext_props = physical_device.enumerateDeviceExtensionProperties();

    bool fp16_storage = false;
    bool fp16_compute = false;
    bool coopmat_support = false;
    bool coopmat2_support = false;
    bool coopmat2_decode_vector_support = false;
    bool integer_dot_product = false;
    bool bfloat16_support = false;
    bool dot2_f16_support = false;

    for (auto properties : ext_props) {
        if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) {
            fp16_storage = true;
        } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) {
            fp16_compute = true;
#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
       } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 &&
                   !getenv("GGML_VK_DISABLE_COOPMAT")) {
            coopmat_support = true;
#endif
#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
        } else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 &&
                   !getenv("GGML_VK_DISABLE_COOPMAT2")) {
            coopmat2_support = true;
#endif
        } else if (strcmp(VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME, properties.extensionName) == 0 &&
                   !getenv("GGML_VK_DISABLE_COOPMAT2_DECODE_VECTOR")) {
            coopmat2_decode_vector_support = true;
#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
        } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0 &&
                    !getenv("GGML_VK_DISABLE_INTEGER_DOT_PRODUCT")) {
            integer_dot_product = true;
#endif
#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
        } else if (strcmp("VK_KHR_shader_bfloat16", properties.extensionName) == 0 &&
                    !getenv("GGML_VK_DISABLE_BFLOAT16")) {
            bfloat16_support = true;
#endif
        } else if (strcmp("VK_VALVE_shader_mixed_float_dot_product", properties.extensionName) == 0 &&
                    !getenv("GGML_VK_DISABLE_DOT2")) {
            dot2_f16_support = true;
        }
    }

    const vk_device_architecture device_architecture = get_device_architecture(physical_device);

    const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16");
    bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr;

    bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute;

    vk::PhysicalDeviceProperties2 props2;
    vk::PhysicalDeviceMaintenance3Properties props3;
    vk::PhysicalDeviceSubgroupProperties subgroup_props;
    vk::PhysicalDeviceDriverProperties driver_props;
    vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR shader_integer_dot_product_props;
    props2.pNext = &props3;
    props3.pNext = &subgroup_props;
    subgroup_props.pNext = &driver_props;

    // Pointer to the last chain element
    VkBaseOutStructure * last_struct = (VkBaseOutStructure *)&driver_props;

    if (integer_dot_product) {
        last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_props;
        last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_props;
    }

    physical_device.getProperties2(&props2);

    VkPhysicalDeviceFeatures2 device_features2;
    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
    device_features2.pNext = nullptr;

    VkPhysicalDeviceVulkan11Features vk11_features;
    vk11_features.pNext = nullptr;
    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
    device_features2.pNext = &vk11_features;

    VkPhysicalDeviceVulkan12Features vk12_features;
    vk12_features.pNext = nullptr;
    vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES;
    vk11_features.pNext = &vk12_features;

    // Pointer to the last chain element
    last_struct = (VkBaseOutStructure *)&vk12_features;

#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
    VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features;
    coopmat_features.pNext = nullptr;
    coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR;
    coopmat_features.cooperativeMatrix = VK_FALSE;

    if (coopmat_support) {
        last_struct->pNext = (VkBaseOutStructure *)&coopmat_features;
        last_struct = (VkBaseOutStructure *)&coopmat_features;
    }
#endif

    VkPhysicalDeviceShaderIntegerDotProductFeaturesKHR shader_integer_dot_product_features {};
    shader_integer_dot_product_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_INTEGER_DOT_PRODUCT_FEATURES_KHR;
    if (integer_dot_product) {
        last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_features;
        last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_features;
    }

#if defined(VK_KHR_shader_bfloat16)
    VkPhysicalDeviceShaderBfloat16FeaturesKHR bfloat16_features {};
    bfloat16_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR;
    if (bfloat16_support) {
        last_struct->pNext = (VkBaseOutStructure *)&bfloat16_features;
        last_struct = (VkBaseOutStructure *)&bfloat16_features;
    }
#endif

#if defined(VK_NV_cooperative_matrix2)
    VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {};
    coopmat2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_2_FEATURES_NV;
    if (coopmat2_support) {
        last_struct->pNext = (VkBaseOutStructure *)&coopmat2_features;
        last_struct = (VkBaseOutStructure *)&coopmat2_features;
    }
#endif

    VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV coopmat2_decode_vector_features {};
    coopmat2_decode_vector_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_DECODE_VECTOR_FEATURES_NV;
    if (coopmat2_decode_vector_support) {
        last_struct->pNext = (VkBaseOutStructure *)&coopmat2_decode_vector_features;
        last_struct = (VkBaseOutStructure *)&coopmat2_decode_vector_features;
    }

    VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE dot2_features {};
    dot2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_MIXED_FLOAT_DOT_PRODUCT_FEATURES_VALVE;
    if (dot2_f16_support) {
        last_struct->pNext = (VkBaseOutStructure *)&dot2_features;
        last_struct = (VkBaseOutStructure *)&dot2_features;
    }

    vkGetPhysicalDeviceFeatures2(physical_device, &device_features2);

    fp16 = fp16 && vk12_features.shaderFloat16;

#if defined(VK_KHR_shader_bfloat16)
    bool bf16 = bfloat16_support && bfloat16_features.shaderBFloat16Type;
#else
    bool bf16 = false;
#endif

    uint32_t default_subgroup_size = get_subgroup_size("", device_architecture);
    const size_t subgroup_size = (default_subgroup_size != 0) ? default_subgroup_size : subgroup_props.subgroupSize;
    const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu;

    integer_dot_product = integer_dot_product
                       && shader_integer_dot_product_props.integerDotProduct4x8BitPackedSignedAccelerated
                       && shader_integer_dot_product_features.shaderIntegerDotProduct;

    coopmat_support = coopmat_support
#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
                   && coopmat_features.cooperativeMatrix
#endif
                   && ggml_vk_khr_cooperative_matrix_support(props2.properties, driver_props, device_architecture);

#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
    coopmat2_support = coopmat2_support &&
                       coopmat2_features.cooperativeMatrixWorkgroupScope &&
                       coopmat2_features.cooperativeMatrixFlexibleDimensions &&
                       coopmat2_features.cooperativeMatrixReductions &&
                       coopmat2_features.cooperativeMatrixConversions &&
                       coopmat2_features.cooperativeMatrixPerElementOperations &&
                       coopmat2_features.cooperativeMatrixTensorAddressing &&
                       coopmat2_features.cooperativeMatrixBlockLoads;
#else
    coopmat2_support = false;
#endif

    coopmat2_decode_vector_support = coopmat2_decode_vector_support && coopmat2_decode_vector_features.cooperativeMatrixDecodeVector;
#if !defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT)
    coopmat2_decode_vector_support = false;
#endif

    std::string matrix_cores = coopmat2_support ? (coopmat2_decode_vector_support ? "NV_coopmat2v" : "NV_coopmat2")
                             : coopmat_support  ? "KHR_coopmat"
                             : "none";

    bool dot2_f16 = dot2_f16_support && dot2_features.shaderMixedFloatDotProductFloat16AccFloat32;
    const char *fp16_str = fp16 ? (dot2_f16 ? "dot2" : "1") : "0";

    std::string device_name = props2.properties.deviceName.data();
    GGML_LOG_DEBUG("ggml_vulkan: %zu = %s (%s) | uma: %d | fp16: %s | bf16: %d | warp size: %zu | shared memory: %d | int dot: %d | matrix cores: %s\n",
              idx, device_name.c_str(), driver_props.driverName.data(), uma, fp16_str, bf16, subgroup_size,
              props2.properties.limits.maxComputeSharedMemorySize, integer_dot_product, matrix_cores.c_str());

    if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) {
        GGML_LOG_DEBUG("ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want.\n");
    }
}

static bool ggml_vk_instance_layer_settings_available();
static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
static bool ggml_vk_instance_debug_utils_ext_available(const std::vector<vk::ExtensionProperties> & instance_extensions);
static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev);

static DispatchLoaderDynamic ggml_vk_default_dispatcher_instance;
DispatchLoaderDynamic & ggml_vk_default_dispatcher() {
    return ggml_vk_default_dispatcher_instance;
}

static void ggml_vk_instance_init() {
    if (vk_instance_initialized) {
        return;
    }
    VK_LOG_DEBUG("ggml_vk_instance_init()");

    // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
    ggml_vk_default_dispatcher_instance.init(vkGetInstanceProcAddr);

    uint32_t api_version = vk::enumerateInstanceVersion();

    if (api_version < VK_API_VERSION_1_2) {
        std::cerr << "ggml_vulkan: Error: Vulkan 1.2 required." << std::endl;
        throw vk::SystemError(vk::Result::eErrorFeatureNotPresent, "Vulkan 1.2 required");
    }

    vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, api_version };

    const std::vector<vk::ExtensionProperties> instance_extensions = vk::enumerateInstanceExtensionProperties();
    const bool layer_settings = ggml_vk_instance_layer_settings_available();
#ifdef __APPLE__
    const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions);
#endif
    const bool debug_utils_ext = ggml_vk_instance_debug_utils_ext_available(instance_extensions) && getenv("GGML_VK_DEBUG_MARKERS") != nullptr;
    std::vector<const char*> layers;

    if (layer_settings) {
        layers.push_back("VK_LAYER_KHRONOS_validation");
    }
    std::vector<const char*> extensions;
    if (layer_settings) {
        extensions.push_back("VK_EXT_layer_settings");
    }
#ifdef __APPLE__
    if (portability_enumeration_ext) {
        extensions.push_back("VK_KHR_portability_enumeration");
    }
#endif
    if (debug_utils_ext) {
        extensions.push_back("VK_EXT_debug_utils");
    }
    VkBool32 enable_best_practice = layer_settings;
    std::vector<vk::LayerSettingEXT> settings = {
        {
            "VK_LAYER_KHRONOS_validation",
            "validate_best_practices",
            vk::LayerSettingTypeEXT::eBool32,
            1,
            &enable_best_practice
        },
    };
    vk::LayerSettingsCreateInfoEXT layer_setting_info(settings);
    vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions, &layer_setting_info);
#ifdef __APPLE__
    if (portability_enumeration_ext) {
        instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR;
    }
#endif

    vk_instance.instance = vk::createInstance(instance_create_info);
    vk_instance_initialized = true;

    if (debug_utils_ext) {
        vk_instance.debug_utils_support              = true;
        vk_instance.pfn_vkSetDebugUtilsObjectNameEXT = (PFN_vkSetDebugUtilsObjectNameEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkSetDebugUtilsObjectNameEXT");
        vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT = (PFN_vkQueueBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueBeginDebugUtilsLabelEXT");
        vk_instance.pfn_vkQueueEndDebugUtilsLabelEXT = (PFN_vkQueueEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueEndDebugUtilsLabelEXT");
        vk_instance.pfn_vkCmdBeginDebugUtilsLabelEXT = (PFN_vkCmdBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdBeginDebugUtilsLabelEXT");
        vk_instance.pfn_vkCmdEndDebugUtilsLabelEXT =   (PFN_vkCmdEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdEndDebugUtilsLabelEXT");
        vk_instance.pfn_vkCmdInsertDebugUtilsLabelEXT = (PFN_vkCmdInsertDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdInsertDebugUtilsLabelEXT");
    }

    vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
    vk_perf_logger_concurrent = getenv("GGML_VK_PERF_LOGGER_CONCURRENT") != nullptr;
    vk_enable_sync_logger = getenv("GGML_VK_SYNC_LOGGER") != nullptr;
    vk_memory_logger_enabled = getenv("GGML_VK_MEMORY_LOGGER") != nullptr;
    const char* GGML_VK_PIPELINE_STATS = getenv("GGML_VK_PIPELINE_STATS");
    if (GGML_VK_PIPELINE_STATS != nullptr) {
        vk_pipeline_stats_filter = GGML_VK_PIPELINE_STATS;
    }
    const char* GGML_VK_PERF_LOGGER_FREQUENCY = getenv("GGML_VK_PERF_LOGGER_FREQUENCY");

    if (GGML_VK_PERF_LOGGER_FREQUENCY != nullptr) {
        vk_perf_logger_frequency = std::stoul(GGML_VK_PERF_LOGGER_FREQUENCY);
    }

    // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
    VULKAN_HPP_DEFAULT_DISPATCHER.init(vk_instance.instance);

    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();

    // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
    char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES");
    if (devices_env != nullptr) {
        size_t num_available_devices = devices.size();

        std::string devices(devices_env);
        std::replace(devices.begin(), devices.end(), ',', ' ');

        std::stringstream ss(devices);
        size_t tmp;
        while (ss >> tmp) {
            if(tmp >= num_available_devices) {
                std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl;
                throw std::runtime_error("Invalid Vulkan device index");
            }
            vk_instance.device_indices.push_back(tmp);
        }
    } else {
        // If no vulkan devices are found, return early
        if (devices.empty()) {
            GGML_LOG_INFO("ggml_vulkan: No devices found.\n");
            return;
        }

        // Default to using all dedicated GPUs
        for (size_t i = 0; i < devices.size(); i++) {
            vk::PhysicalDeviceProperties2 new_props;
            vk::PhysicalDeviceDriverProperties new_driver;
            vk::PhysicalDeviceIDProperties new_id;
            new_props.pNext = &new_driver;
            new_driver.pNext = &new_id;
            devices[i].getProperties2(&new_props);

            if ((new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu || new_props.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu) && ggml_vk_device_is_supported(devices[i])) {
                // Check if there are two physical devices corresponding to the same GPU
                // This handles the case where the same GPU appears with different drivers (e.g., RADV + AMDVLK on Linux),
                // see https://github.com/ggml-org/llama.cpp/pull/7582 for original deduplication.
                // MoltenVK on macOS may report the same UUID for distinct GPUs on multi-GPU cards,
                // see https://github.com/KhronosGroup/MoltenVK/issues/2683. Skip when both old/new
                // driver is MoltenVK
                auto old_device = std::find_if(
                    vk_instance.device_indices.begin(),
                    vk_instance.device_indices.end(),
                    [&devices, &new_id, &new_driver](const size_t k){
                        vk::PhysicalDeviceProperties2 old_props;
                        vk::PhysicalDeviceDriverProperties old_driver;
                        vk::PhysicalDeviceIDProperties old_id;
                        old_props.pNext = &old_driver;
                        old_driver.pNext = &old_id;
                        devices[k].getProperties2(&old_props);

                        bool same_uuid = std::equal(std::begin(old_id.deviceUUID), std::end(old_id.deviceUUID), std::begin(new_id.deviceUUID));
                        same_uuid = same_uuid || (
                            old_id.deviceLUIDValid && new_id.deviceLUIDValid &&
                            std::equal(std::begin(old_id.deviceLUID), std::end(old_id.deviceLUID), std::begin(new_id.deviceLUID))
                        );
                        bool both_molten_vk = (new_driver.driverID == vk::DriverId::eMoltenvk && old_driver.driverID == vk::DriverId::eMoltenvk);

                        return same_uuid && !both_molten_vk;
                    }
                );
                if (old_device == vk_instance.device_indices.end()) {
                    vk_instance.device_indices.push_back(i);
                } else {
                    // There can be two physical devices corresponding to the same GPU if there are 2 different drivers
                    // This can cause error when splitting layers aross the devices, need to keep only 1
                    VK_LOG_DEBUG("Device " << i << " and device " << *old_device << " have the same deviceUUID");

                    vk::PhysicalDeviceProperties2 old_props;
                    vk::PhysicalDeviceDriverProperties old_driver;
                    old_props.pNext = &old_driver;
                    devices[*old_device].getProperties2(&old_props);

                    std::map<vk::DriverId, int> driver_priorities {};
                    int old_priority = std::numeric_limits<int>::max();
                    int new_priority = std::numeric_limits<int>::max();

                    // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id
                    // Smaller number -> higher priority
                    switch (old_props.properties.vendorID) {
                        case VK_VENDOR_ID_AMD:
                            driver_priorities[vk::DriverId::eMesaRadv] = 1;
                            driver_priorities[vk::DriverId::eAmdOpenSource] = 2;
                            driver_priorities[vk::DriverId::eAmdProprietary] = 3;
                            break;
                        case VK_VENDOR_ID_INTEL:
                            driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1;
                            driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2;
                            break;
                        case VK_VENDOR_ID_NVIDIA:
                            driver_priorities[vk::DriverId::eNvidiaProprietary] = 1;
#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235
                            driver_priorities[vk::DriverId::eMesaNvk] = 2;
#endif
                            break;
                        case VK_VENDOR_ID_QUALCOMM:
                            driver_priorities[vk::DriverId::eQualcommProprietary] = 1;
                            driver_priorities[vk::DriverId::eMesaTurnip] = 2;
                            break;
                    }
                    driver_priorities[vk::DriverId::eMesaDozen] = 100;

                    if (driver_priorities.count(old_driver.driverID)) {
                        old_priority = driver_priorities[old_driver.driverID];
                    }
                    if (driver_priorities.count(new_driver.driverID)) {
                        new_priority = driver_priorities[new_driver.driverID];
                    }

                    if (new_priority < old_priority) {
                        auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device);
                        vk_instance.device_indices.erase(r, vk_instance.device_indices.end());
                        vk_instance.device_indices.push_back(i);

                        VK_LOG_DEBUG("Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName);
                    }
                    else {
                        VK_LOG_DEBUG("Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl);
                    }
                }
            }
        }

        // If no GPUs found, fall back to the first non-CPU device.
        // If only CPU devices are available, return without devices.
        if (vk_instance.device_indices.empty()) {
            for (size_t i = 0; i < devices.size(); i++) {
                if (devices[i].getProperties().deviceType != vk::PhysicalDeviceType::eCpu) {
                    vk_instance.device_indices.push_back(i);
                    break;
                }
            }
        }

        if (vk_instance.device_indices.empty()) {
            GGML_LOG_INFO("ggml_vulkan: No devices found.\n");
            return;
        }
    }
    GGML_LOG_DEBUG("ggml_vulkan: Found %zu Vulkan devices:\n", vk_instance.device_indices.size());

    for (size_t i = 0; i < vk_instance.device_indices.size(); i++) {
        vk::PhysicalDevice vkdev = devices[vk_instance.device_indices[i]];
        std::vector<vk::ExtensionProperties> extensionprops = vkdev.enumerateDeviceExtensionProperties();

        bool membudget_supported = false;
        for (const auto & ext : extensionprops) {
            if (strcmp(VK_EXT_MEMORY_BUDGET_EXTENSION_NAME, ext.extensionName) == 0) {
                membudget_supported = true;
                break;
            }
        }

        vk_instance.device_supports_membudget.push_back(membudget_supported);

        ggml_vk_print_gpu_info(i);
    }
}

static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
    VK_LOG_DEBUG("ggml_vk_init(" << ctx->name << ", " << idx << ")");
    ggml_vk_instance_init();
    GGML_ASSERT(idx < vk_instance.device_indices.size());

    ctx->name = GGML_VK_NAME + std::to_string(idx);

    ctx->device = ggml_vk_get_device(idx);

    ctx->semaphore_idx = 0;
    ctx->event_idx = 0;

    ctx->prealloc_size_x = 0;
    ctx->prealloc_size_y = 0;
    ctx->prealloc_size_split_k = 0;
    // Fixed size of 1KB, for deterministic behavior
    ctx->prealloc_size_add_rms_partials = 1024;

    ctx->fence = ctx->device->device.createFence({});
    ctx->almost_ready_fence = ctx->device->device.createFence({});

    ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue);
    if (ctx->device->async_use_transfer_queue) {
        vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
        vk::SemaphoreCreateInfo ci{};
        ci.setPNext(&tci);
        ctx->transfer_semaphore.s = ctx->device->device.createSemaphore(ci);
        ctx->transfer_semaphore.value = 0;

        ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue);
    }

    if (vk_perf_logger_enabled) {
        ctx->perf_logger = std::unique_ptr<vk_perf_logger>(new vk_perf_logger());
    }

#ifdef GGML_VULKAN_CHECK_RESULTS
    const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
    vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
    const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR");
    vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor));
#endif
}

static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) {
    VK_LOG_DEBUG("ggml_vk_get_to_fp16()");
    switch (type) {
        case GGML_TYPE_F32:
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_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:
        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ1_M:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
            break;
        default:
            return nullptr;
    }

    return ctx->device->pipeline_dequant[type];
}

static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) {
    VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline(" << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ", " << prec << ")");
    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
        return ctx->device->pipeline_matmul_f32;
    }
    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
        return ctx->device->pipeline_matmul_f32_f16;
    }
    if (src0_type == GGML_TYPE_BF16 && src1_type == GGML_TYPE_BF16) {
        return ctx->device->pipeline_matmul_bf16;
    }
    if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) {
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
            return ctx->device->pipeline_matmul_f16_f32.f16acc;
        }
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
            return ctx->device->pipeline_matmul_f16.f16acc;
        }
    } else {
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
            return ctx->device->pipeline_matmul_f16_f32.f32acc;
        }
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
            return ctx->device->pipeline_matmul_f16.f32acc;
        }
    }

    // MMQ
    if (src1_type == GGML_TYPE_Q8_1) {
        vk_matmul_pipeline pipelines = ctx->device->pipeline_dequant_mul_mat_mat_q8_1[src0_type].f32acc;

        if (pipelines->is_empty()) {
            return nullptr;
        }

        return pipelines;
    }

    if (src1_type != GGML_TYPE_F32 && !ctx->device->coopmat2) {
        return nullptr;
    }

    switch (src0_type) {
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_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:
        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ1_M:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
            break;
        default:
            return nullptr;
    }

    if (ctx->device->coopmat2) {
        assert(src1_type == GGML_TYPE_F16);
        return prec == GGML_PREC_DEFAULT ? ctx->device->pipeline_dequant_mul_mat_mat_f16[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat_f16[src0_type].f32acc;
    }
    if (ctx->device->coopmat_support) {
        return (ctx->device->fp16 && ctx->device->coopmat_acc_f16_support && prec == GGML_PREC_DEFAULT) ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc;
    }
    return (ctx->device->fp16 && prec == GGML_PREC_DEFAULT) ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc;
}

static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols, uint32_t m, uint32_t k) {
    VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()");
    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16 || b_type == GGML_TYPE_Q8_1);
    GGML_ASSERT(num_cols >= 1 && num_cols <= mul_mat_vec_max_cols);

    if (b_type == GGML_TYPE_Q8_1) {
        switch (a_type) {
            case GGML_TYPE_Q4_0:
            case GGML_TYPE_Q4_1:
            case GGML_TYPE_Q5_0:
            case GGML_TYPE_Q5_1:
            case GGML_TYPE_Q8_0:
            case GGML_TYPE_MXFP4:
            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:
            case GGML_TYPE_IQ1_S:
            case GGML_TYPE_IQ1_M:
                break;
            default:
                return nullptr;
        }
    }

    switch (a_type) {
        case GGML_TYPE_F32:
        case GGML_TYPE_F16:
        case GGML_TYPE_BF16:
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_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:
        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ1_M:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
            break;
        default:
            return nullptr;
    }

    // heuristic to choose workgroup size
    uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
    if ((ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA && ctx->device->architecture != vk_device_architecture::NVIDIA_PRE_TURING) || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
        // Prefer larger workgroups when M is small, to spread the work out more
        // and keep more SMs busy.
        // q6_k seems to prefer small workgroup size even for "medium" values of M.
        if (a_type == GGML_TYPE_Q6_K) {
            if (m < 4096 && k >= 1024) {
                dmmv_wg = DMMV_WG_SIZE_LARGE;
            }
        } else {
            if (m <= 8192 && k >= 1024) {
                dmmv_wg = DMMV_WG_SIZE_LARGE;
            }
        }
    }

    if (b_type == GGML_TYPE_Q8_1) {
        if (ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
            dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
        }
        return ctx->device->pipeline_dequant_mul_mat_vec_q8_1_f32[dmmv_wg][a_type][num_cols-1];
    }

    return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[dmmv_wg][a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[dmmv_wg][a_type][num_cols-1];
}

static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) {
    VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_id_pipeline()");
    if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
        return ctx->device->pipeline_matmul_id_f32;
    }
    if (src0_type == GGML_TYPE_BF16 && src1_type == GGML_TYPE_BF16) {
        return ctx->device->pipeline_matmul_id_bf16;
    }
    if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) {
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
            return ctx->device->pipeline_matmul_id_f16_f32.f16acc;
        }
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
            return ctx->device->pipeline_matmul_id_f16.f16acc;
        }
    } else {
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) {
            return ctx->device->pipeline_matmul_id_f16_f32.f32acc;
        }
        if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
            return ctx->device->pipeline_matmul_id_f16.f32acc;
        }
    }

    // MMQ
    if (src1_type == GGML_TYPE_Q8_1) {
        vk_matmul_pipeline pipelines = ctx->device->pipeline_dequant_mul_mat_mat_id_q8_1[src0_type].f32acc;

        if (pipelines->is_empty()) {
            return nullptr;
        }

        return pipelines;
    }

    GGML_ASSERT(src1_type == GGML_TYPE_F32 || (ctx->device->coopmat2 && src1_type == GGML_TYPE_F16));

    switch (src0_type) {
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_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:
        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ1_M:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
            break;
        default:
            return nullptr;
    }

    vk_matmul_pipeline2& mmp = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type];
    // XXX TODO 'prec' is not actually allowed in mul_mat_id.
    bool prefer_fp16acc = ctx->device->fp16 /*&& prec == GGML_PREC_DEFAULT*/;
    bool support_fp16acc = !mmp.f16acc->is_empty();
    bool support_fp32acc = !mmp.f32acc->is_empty();

    if (support_fp16acc && (prefer_fp16acc || !support_fp32acc)) {
        return mmp.f16acc;
    } else {
        GGML_ASSERT(support_fp32acc);
        return mmp.f32acc;
    }
}

static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t m, uint32_t k) {
    VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec_id()");
    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_Q8_1);

    if (b_type == GGML_TYPE_Q8_1) {
        switch (a_type) {
            case GGML_TYPE_Q4_0:
            case GGML_TYPE_Q4_1:
            case GGML_TYPE_Q5_0:
            case GGML_TYPE_Q5_1:
            case GGML_TYPE_Q8_0:
            case GGML_TYPE_MXFP4:
            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:
            case GGML_TYPE_IQ1_S:
            case GGML_TYPE_IQ1_M:
                break;
            default:
                return nullptr;
        }
    }

    switch (a_type) {
        case GGML_TYPE_F32:
        case GGML_TYPE_F16:
        case GGML_TYPE_BF16:
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_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:
        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ1_M:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_NVFP4:
            break;
        default:
            return nullptr;
    }

    // heuristic to choose workgroup size
    uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
    if ((ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA && ctx->device->architecture != vk_device_architecture::NVIDIA_PRE_TURING) || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
        // Prefer larger workgroups when M is small, to spread the work out more
        // and keep more SMs busy.
        // q6_k seems to prefer small workgroup size even for "medium" values of M.
        if (a_type == GGML_TYPE_Q6_K) {
            if (m < 4096 && k >= 1024) {
                dmmv_wg = DMMV_WG_SIZE_LARGE;
            }
        } else {
            if (m <= 8192 && k >= 1024) {
                dmmv_wg = DMMV_WG_SIZE_LARGE;
            }
        }
    }

    if (b_type == GGML_TYPE_Q8_1) {
        if (ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
            dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
        }
        return ctx->device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[dmmv_wg][a_type];
    }

    return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[dmmv_wg][a_type];
}

static void * ggml_vk_host_malloc(vk_device& device, size_t size) {
    VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")");
    vk_buffer buf = ggml_vk_create_buffer(device, size,
        {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
         vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent});

    if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) {
        fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n",
            size/1024.0/1024.0);
        device->device.freeMemory(buf->device_memory);
        device->device.destroyBuffer(buf->buffer);
        return nullptr;
    }

    std::lock_guard<std::shared_mutex> guard(device->pinned_memory_mutex);
    device->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf));

    return buf->ptr;
}

static void ggml_vk_host_free(vk_device& device, void* ptr) {
    if (ptr == nullptr) {
        return;
    }
    VK_LOG_MEMORY("ggml_vk_host_free(" << ptr << ")");
    std::lock_guard<std::shared_mutex> guard(device->pinned_memory_mutex);

    vk_buffer buf;
    size_t index;
    for (size_t i = 0; i < device->pinned_memory.size(); i++) {
        const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]);
        const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]);
        if (ptr >= addr && ptr < endr) {
            buf = std::get<2>(device->pinned_memory[i]);
            index = i;
            break;
        }
    }
    if (buf == nullptr) {
        fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n");
        return;
    }

    ggml_vk_destroy_buffer(buf);

    device->pinned_memory.erase(device->pinned_memory.begin() + index);
}

static void ggml_vk_host_get(const vk_device& device, const void * ptr, vk_buffer& buf, size_t& buf_offset) {
    std::shared_lock<std::shared_mutex> guard(device->pinned_memory_mutex);
    buf = nullptr;
    buf_offset = 0;
    for (size_t i = 0; i < device->pinned_memory.size(); i++) {
        const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]);
        const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]);
        if (ptr >= addr && ptr < endr) {
            buf = std::get<2>(device->pinned_memory[i]);
            buf_offset = ((const uint8_t *)ptr) - addr;
            break;
        }
    }
}

static vk_subbuffer ggml_vk_tensor_subbuffer(
    const ggml_backend_vk_context * ctx, const ggml_tensor * tensor, bool allow_misalign = false) {

    vk_buffer buffer = nullptr;
    size_t offset = 0;
    if (ctx->device->uma) {
        ggml_vk_host_get(ctx->device, tensor->data, buffer, offset);
    }
    if (!buffer) {
        auto buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;
        buffer = buf_ctx->dev_buffer;
        offset = vk_tensor_offset(tensor) + tensor->view_offs;
    }
    GGML_ASSERT(buffer != nullptr);

    size_t size = ggml_nbytes(tensor);

    size_t misalign_bytes = offset & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1);
    // The shader must support misaligned offsets when indexing into the buffer
    GGML_ASSERT(allow_misalign || misalign_bytes == 0);
    offset &= ~misalign_bytes;
    size += misalign_bytes;

    return vk_subbuffer{buffer, offset, size};
}

// Get a command buffer from pool. Create a new one if no reusable buffer is available
static vk_command_buffer* ggml_vk_get_or_create_cmd_buffer(vk_device& device, vk_command_pool& pool) {
    for (auto& cmd_buffer : pool.cmd_buffers) {
        if (!cmd_buffer.in_use) {
            cmd_buffer.use_counter++;
            cmd_buffer.in_use = true;
            return &cmd_buffer;
        }
    }
    return ggml_vk_create_cmd_buffer(device, pool);
}

static vk_submission ggml_vk_begin_submission(vk_device& device, vk_command_pool& p, bool one_time = true) {
    vk_submission s;
    s.buffer = ggml_vk_get_or_create_cmd_buffer(device, p);
    if (one_time) {
        s.buffer->buf.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit });
    } else {
        s.buffer->buf.begin({ vk::CommandBufferUsageFlags{} });
    }

    return s;
}

template <typename T> size_t push_constant_size(const T &t) {
    static_assert(std::is_class<T>::value, "T must be a struct/class");
    GGML_UNUSED(t);
    return sizeof(T);
}
template <typename T> size_t push_constant_size(const std::vector<T> &t) {
    GGML_UNUSED(t);
    return sizeof(T) * t.size();
}
template <typename T, uint32_t N> size_t push_constant_size(const std::array<T, N> &t) {
    GGML_UNUSED(t);
    return sizeof(T) * N;
}

template <typename T> const T *push_constant_data(const T &t) {
    static_assert(std::is_class<T>::value, "T must be a struct/class");
    return &t;
}
template <typename T> const T *push_constant_data(const std::vector<T> &t) {
    return t.data();
}
template <typename T, uint32_t N> const T *push_constant_data(const std::array<T, N> &t) {
    return t.data();
}

template <typename T>
static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& subctx, vk_pipeline& pipeline, std::initializer_list<vk::DescriptorBufferInfo> const& descriptor_buffer_infos, const T &push_constants, std::array<uint32_t, 3> elements) {
    const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]);
    const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]);
    const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]);
    VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {";
    for (auto& buffer : descriptor_buffer_infos) {
        std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.range << "), ";
    }
    std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
    GGML_ASSERT(wg0 <= ctx->device->properties.limits.maxComputeWorkGroupCount[0] &&
                wg1 <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] &&
                wg2 <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
    GGML_ASSERT(ctx->descriptor_set_idx < ctx->descriptor_sets.size());
    GGML_ASSERT(descriptor_buffer_infos.size() <= MAX_PARAMETER_COUNT);
    GGML_ASSERT(pipeline->parameter_count == descriptor_buffer_infos.size());
    GGML_ASSERT(pipeline->push_constant_size == push_constant_size(push_constants));

    vk::DescriptorSet& descriptor_set = ctx->descriptor_sets[ctx->descriptor_set_idx++];
    vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() };
    ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {});

    subctx->s->buffer->buf.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size(push_constants), push_constant_data(push_constants));
    subctx->s->buffer->buf.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline);
    subctx->s->buffer->buf.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
                                pipeline->layout,
                                0,
                                { descriptor_set },
                                {});
    subctx->s->buffer->buf.dispatch(wg0, wg1, wg2);
}

static void ggml_vk_ctx_end(vk_context& ctx) {
    VK_LOG_DEBUG("ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")");
    if (ctx->s == nullptr) {
        return;
    }

    ctx->s->buffer->buf.end();
    ctx->s = nullptr;
}

static void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) {
    VK_LOG_DEBUG("ggml_vk_ctx_begin(" << device->name << ")");
    if (subctx->s != nullptr) {
        ggml_vk_ctx_end(subctx);
    }

    subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->p) });
    subctx->s = subctx->seqs[subctx->seqs.size() - 1].data();
}

static vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx) {
    vk_context result;
    if (!ctx->compute_ctx.expired()) {
        result = ctx->compute_ctx.lock();
    } else {
        result = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);

        ctx->compute_ctx = result;
        ggml_vk_ctx_begin(ctx->device, result);
    }

    if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) {
        result->s->wait_semaphores.push_back(ctx->transfer_semaphore);
        ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value;
    }

    return result;
}

// Submit any pending transfer queue work and signal the transfer semaphore.
// The next compute context created via ggml_vk_get_compute_ctx will wait on this semaphore.
// Returns true if work was submitted.
static bool ggml_vk_submit_transfer_ctx(ggml_backend_vk_context * ctx) {
    if (!ctx->device->async_use_transfer_queue || ctx->transfer_ctx.expired()) {
        return false;
    }

    vk_context cpy_ctx = ctx->transfer_ctx.lock();
    ggml_vk_ctx_end(cpy_ctx);

    for (auto& cpy : cpy_ctx->in_memcpys) {
        memcpy(cpy.dst, cpy.src, cpy.n);
    }

    ctx->transfer_semaphore.value++;
    cpy_ctx->seqs.back().back().signal_semaphores.push_back(ctx->transfer_semaphore);

    ggml_vk_submit(cpy_ctx, {});
    ctx->transfer_ctx.reset();
    return true;
}

static size_t ggml_vk_align_size(size_t width, size_t align) {
    VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")");
    return CEIL_DIV(width, align) * align;
}

static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector<vk_staging_memcpy>* memcpys = nullptr) {
    if (memcpys == nullptr) {
        memcpy(dst, src, size);
    } else {
        memcpys->emplace_back(dst, src, size);
    }
}

static void deferred_memset(void * dst, uint32_t val, size_t size, std::vector<vk_staging_memset>* memsets = nullptr) {
    if (memsets == nullptr) {
        memset(dst, val, size);
    } else {
        memsets->emplace_back(dst, val, size);
    }
}

static void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) {
    if (device->sync_staging == nullptr || device->sync_staging->size < size) {
        VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
        ggml_vk_destroy_buffer(device->sync_staging);
        device->sync_staging = ggml_vk_create_buffer_check(device, size,
            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
    }
}

static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
    if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
        VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
        ggml_vk_destroy_buffer(ctx->sync_staging);
        ctx->sync_staging = ggml_vk_create_buffer_check(ctx->device, size,
            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
    }
}

static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context& subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
    VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")");
    GGML_ASSERT(!ggml_is_contiguous(tensor));
    // Buffer is already mapped
    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
        std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl;
        GGML_ABORT("fatal error");
    }
    // Check if src is pinned memory
    vk_buffer buf = nullptr;
    size_t buf_offset = 0;
    ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset);

    const uint64_t ne0 = tensor->ne[0];
    const uint64_t ne1 = tensor->ne[1];
    const uint64_t ne2 = tensor->ne[2];
    const uint64_t ne3 = tensor->ne[3];
    const uint64_t nb0 = tensor->nb[0];
    const uint64_t nb1 = tensor->nb[1];
    const uint64_t nb2 = tensor->nb[2];
    const uint64_t nb3 = tensor->nb[3];
    const ggml_type type = tensor->type;
    const uint64_t ts = ggml_type_size(type);
    const uint64_t bs = ggml_blck_size(type);

    const uint64_t dstnb0 = ts;
    const uint64_t dstnb1 = dstnb0*(ne0/bs);
    const uint64_t dstnb2 = dstnb1*ne1;
    const uint64_t dstnb3 = dstnb2*ne2;

    const uint64_t ne = ggml_nelements(tensor);

    if (buf != nullptr) {
        // Memory is pinned, use as staging buffer
        std::vector<vk::BufferCopy> slices;

        for (uint64_t i3 = 0; i3 < ne3; i3++) {
            for (uint64_t i2 = 0; i2 < ne2; i2++) {
                // Find longest contiguous slice
                if (ne1*nb1 == dstnb2) {
                    slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 });
                } else {
                    for (uint64_t i1 = 0; i1 < ne1; i1++) {
                        if (ne0*nb0/bs == dstnb1) {
                            slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 });
                        } else {
                            const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
                            const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
                            for (uint64_t i0 = 0; i0 < ne0; i0++) {
                                slices.push_back({ s_off + i0*nb0, d_off + i0*dstnb0, dstnb0 });
                            }
                        }
                    }
                }
            }
        }

        ggml_vk_sync_buffers(ctx, subctx);
        subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices);
        return;
    }

    if (!sync_staging) {
        GGML_ABORT("Asynchronous write to non-pinned memory not supported");
    }

    // Staging buffer required
    vk_buffer& staging = ctx->device->sync_staging;
    const uint64_t copy_size = ts*ne/bs;
    ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size);
    VkBufferCopy buf_copy{ 0, offset, copy_size };

    ggml_vk_sync_buffers(ctx, subctx);
    vkCmdCopyBuffer(subctx->s->buffer->buf, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy);

    for (uint64_t i3 = 0; i3 < ne3; i3++) {
        for (uint64_t i2 = 0; i2 < ne2; i2++) {
            // Find longest contiguous slice
            if (ne1*nb1 == dstnb2) {
                deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys);
            } else {
                for (uint64_t i1 = 0; i1 < ne1; i1++) {
                    if (ne0*nb0/bs == dstnb1) {
                        deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys);
                    } else {
                        const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1;
                        const uint64_t d_off = i3*dstnb3 + i2*dstnb2 + i1*dstnb1;
                        for (uint64_t i0 = 0; i0 < ne0; i0++) {
                            deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys);
                        }
                    }
                }
            }
        }
    }
}

static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
    VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")");
    // Check if src is pinned memory
    vk_buffer buf = nullptr;
    size_t buf_offset = 0;
    ggml_vk_host_get(dst->device, src, buf, buf_offset);

    if (buf != nullptr) {
        // Memory is pinned, use as staging buffer
        std::vector<vk::BufferCopy> slices(1);
        if (width == spitch && width == dpitch) {
            // Only do single write if stride is equal
            slices[0].srcOffset = buf_offset;
            slices[0].dstOffset = offset;
            slices[0].size = width * height;
        } else {
            slices.resize(height);
            for (size_t i = 0; i < height; i++) {
                slices[i].srcOffset = buf_offset + i * spitch;
                slices[i].dstOffset = offset + i * dpitch;
                slices[i].size = width;
            }
        }

        ggml_vk_sync_buffers(nullptr, subctx);
        subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices);
        return true;
    }
    VK_LOG_DEBUG("STAGING");

    if (!sync_staging) {
        // copy was not handled caller needs to fall back
        return false;
    }

    // Staging buffer required
    const size_t staging_size = width * height;
    ggml_vk_ensure_sync_staging_buffer(dst->device, staging_size);

    vk_buffer& staging_buffer = dst->device->sync_staging;

    std::vector<vk::BufferCopy> slices(1);
    if (width == dpitch) {
        slices[0].srcOffset = 0;
        slices[0].dstOffset = offset;
        slices[0].size = staging_size;
    } else {
        slices.resize(height);
        for (size_t i = 0; i < height; i++) {
            slices[i].srcOffset = i * width;
            slices[i].dstOffset = offset + i * dpitch;
            slices[i].size = width;
        }
    }

    ggml_vk_sync_buffers(nullptr, subctx);
    subctx->s->buffer->buf.copyBuffer((VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, slices);

    if (width == spitch) {
        deferred_memcpy((uint8_t *)staging_buffer->ptr, src, staging_size, &subctx->in_memcpys);
    } else {
        for (size_t i = 0; i < height; i++) {
            deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys);
        }
    }
    return true;
}

static bool ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) {
    VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")");
    return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, size, 1, sync_staging);
}

static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height) {
    VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")");
    // Buffer is already mapped
    if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
        GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);

        if (width == spitch && width == dpitch) {
            memcpy((uint8_t *)dst->ptr + offset, src, width * height);
        } else {
            for (size_t i = 0; i < height; i++) {
                memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
            }
        }
    } else {
        std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);

        vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
        ggml_vk_ctx_begin(dst->device, subctx);
        bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, dpitch, width, height, true);
        GGML_ASSERT(ret);
        ggml_vk_ctx_end(subctx);

        for (auto& cpy : subctx->in_memcpys) {
            memcpy(cpy.dst, cpy.src, cpy.n);
        }

        for (auto& mset : subctx->memsets) {
            memset(mset.dst, mset.val, mset.n);
        }

        ggml_vk_submit(subctx, dst->device->fence);
        VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
        dst->device->device.resetFences({ dst->device->fence });
        ggml_vk_queue_command_pools_cleanup(dst->device);
    }
}

static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size) {
    VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")");
    ggml_vk_buffer_write_2d(dst, offset, src, size, size, size, 1);
}

static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
    VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")");
    GGML_ASSERT(width > 0);
    GGML_ASSERT(height > 0);
    GGML_ASSERT(src != nullptr);

    // TODO: staging_offset is not used

    // Check if dst is pinned memory
    vk_buffer buf = nullptr;
    size_t buf_offset = 0;
    ggml_vk_host_get(src->device, dst, buf, buf_offset);

    std::vector<vk::BufferCopy> slices(1);
    if (width == spitch && width == dpitch) {
        // Only do single write if stride is equal
        slices[0].srcOffset = offset;
        slices[0].dstOffset = buf_offset;
        slices[0].size = width * height;
    } else {
        slices.resize(height);
        for (size_t i = 0; i < height; i++) {
            slices[i].srcOffset = offset + i * spitch;
            slices[i].dstOffset = buf_offset + i * dpitch;
            slices[i].size = width;
        }
    }

    if (buf != nullptr) {
        // Memory is pinned, use as staging buffer
        ggml_vk_sync_buffers(nullptr, subctx);
        subctx->s->buffer->buf.copyBuffer(src->buffer, buf->buffer, slices);

        return true;
    }
    VK_LOG_DEBUG("STAGING");

    if (!sync_staging) {
        // copy was not handled caller needs to fall back
        return false;
    }

    // Fall back to staging buffer
    const size_t staging_size = width * height;
    ggml_vk_ensure_sync_staging_buffer(src->device, staging_size);

    vk_buffer& staging_buffer = src->device->sync_staging;

    std::vector<vk::BufferCopy> staging_slices(1);
    if (width == spitch) {
        staging_slices[0].srcOffset = offset;
        staging_slices[0].dstOffset = 0;
        staging_slices[0].size = staging_size;
    } else {
        staging_slices.resize(height);
        for (size_t i = 0; i < height; i++) {
            staging_slices[i].srcOffset = offset + i * spitch;
            staging_slices[i].dstOffset = i * width;
            staging_slices[i].size = width;
        }
    }

    ggml_vk_sync_buffers(nullptr, subctx);
    subctx->s->buffer->buf.copyBuffer(src->buffer, staging_buffer->buffer, staging_slices);

    if (width == dpitch) {
        deferred_memcpy(dst, staging_buffer->ptr, staging_size, &subctx->out_memcpys);
    } else {
        for (size_t i = 0; i < height; i++) {
            deferred_memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) staging_buffer->ptr + i * width, width, &subctx->out_memcpys);
        }
    }
    return true;
}

static bool ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
    return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging);
}

static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height) {
    VK_LOG_DEBUG("ggml_vk_buffer_read_2d(" << src->buffer << ", " << offset << ", " << width << ", " << height << ")");

    // If the device is not an UMA device the memory is host-accessible through rebar. While writing
    // through PCIe is sufficient fast reading back data from PCIe is slower than going through
    // the HW device to host copy path.
    if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) {
        GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);

        if (width == spitch && width == dpitch) {
            memcpy(dst, (const uint8_t *) src->ptr + offset, width * height);
        } else {
            for (size_t i = 0; i < height; i++) {
                memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
            }
        }
    } else {
        std::lock_guard<std::recursive_mutex> guard(src->device->mutex);

        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
        ggml_vk_ctx_begin(src->device, subctx);
        bool ret = ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, spitch, dpitch, width, height, true);
        GGML_ASSERT(ret);
        ggml_vk_ctx_end(subctx);

        ggml_vk_submit(subctx, src->device->fence);
        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read_2d waitForFences");
        src->device->device.resetFences({ src->device->fence });
        ggml_vk_queue_command_pools_cleanup(src->device);

        for (auto& cpy : subctx->out_memcpys) {
            memcpy(cpy.dst, cpy.src, cpy.n);
        }
    }
}

static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) {
    VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")");
    ggml_vk_buffer_read_2d(src, offset, dst, size, size, size, 1);
}

static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
    VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")");
    // Make sure both buffers are on same device
    GGML_ASSERT(src->device == dst->device);

    VkBufferCopy bc{ src_offset, dst_offset, size };

    vkCmdCopyBuffer(ctx->s->buffer->buf, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc);
}

static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) {
    if (src->device == dst->device) {
        std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
        VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")");
        // Copy within the device
        vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
        ggml_vk_ctx_begin(src->device, subctx);
        ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
        ggml_vk_ctx_end(subctx);
        ggml_vk_submit(subctx, src->device->fence);
        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
        src->device->device.resetFences({ src->device->fence });
        ggml_vk_queue_command_pools_cleanup(src->device);
    } else {
        VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")");
        // Copy device to device
        ggml_vk_ensure_sync_staging_buffer(src->device, size);

        // Copy to src staging buffer
        ggml_vk_buffer_copy(src->device->sync_staging, 0, src, src_offset, size);
        // Copy to dst buffer
        ggml_vk_buffer_write(dst, dst_offset, src->device->sync_staging->ptr, size);
    }
}

static void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
    VK_LOG_DEBUG("ggml_vk_buffer_memset_async(" << offset << ", " << c << ", " << size << ")");

    if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible &&
        dst->device->uma) {
        deferred_memset((uint8_t*)dst->ptr + offset, c, size, &ctx->memsets);
        return;
    }

    // Fall back to GPU fillBuffer for non-UMA or non-host-visible buffers
    ctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c);
}

static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
    VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")");

    if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible &&
        dst->device->uma) {
        memset((uint8_t*)dst->ptr + offset, c, size);
        return;
    }

    std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
    vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
    ggml_vk_ctx_begin(dst->device, subctx);
    subctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c);
    ggml_vk_ctx_end(subctx);

    ggml_vk_submit(subctx, dst->device->fence);
    VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences");
    dst->device->device.resetFences({ dst->device->fence });
    ggml_vk_queue_command_pools_cleanup(dst->device);
}

static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, uint32_t m, uint32_t n, uint32_t k, bool disable_split_k, const vk_pipeline& pipeline) {
    VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ", " << disable_split_k << ")");

    if (disable_split_k) {
        return 1;
    }

    uint32_t split_k = 1;
    if (ctx->device->shader_core_count != 0 && m >= pipeline->wg_denoms[0] && n >= pipeline->wg_denoms[1]) {
        // If k is 'large' and the SMs will fill less than halfway, use split_k.
        uint32_t m_tiles = CEIL_DIV(m, pipeline->wg_denoms[0]);
        uint32_t n_tiles = CEIL_DIV(n, pipeline->wg_denoms[1]);

        if (k >= 2048) {
            if (m_tiles * n_tiles <= ctx->device->shader_core_count / 2) {
                split_k = ctx->device->shader_core_count / (m_tiles * n_tiles);
            } else if (m_tiles * n_tiles <= ctx->device->shader_core_count * 2 / 3) {
                split_k = 3;
            }
            // Cap the split at 8x. Unless k is huge this is a lot of overhead.
            split_k = std::min(split_k, 8u);

            // ggml_vk_matmul will align the splits to be a multiple of 256.
            // If this rounded up size would cause the last split to be empty,
            // then reduce the split count.
            while (true) {
                if (split_k == 1) {
                    break;
                }
                uint32_t k_split = CEIL_DIV(k, split_k);
                k_split = ROUNDUP_POW2(k_split, 256);
                if (k_split * (split_k - 1) < k) {
                    break;
                }
                split_k--;
            }
        }
    }

    return split_k;
}

static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) {
    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");

    // The q8_1 (integer dot) mmq path uses a different shader with its own
    // shared-memory layout, so use the int-specific availability flags.
    const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1);
    const bool mm_l = is_q8_1 ? ctx->device->mul_mat_l_int[src0_type] : ctx->device->mul_mat_l[src0_type];
    const bool mm_m = is_q8_1 ? ctx->device->mul_mat_m_int[src0_type] : ctx->device->mul_mat_m[src0_type];
    const bool mm_s = is_q8_1 ? ctx->device->mul_mat_s_int[src0_type] : ctx->device->mul_mat_s[src0_type];

    if (ctx->device->coopmat2) {
        const uint32_t shader_core_count = ctx->device->shader_core_count;
        const uint32_t tiles_l = CEIL_DIV(m, mmp->a_l->wg_denoms[0]) * CEIL_DIV(n, mmp->a_l->wg_denoms[1]);
        const uint32_t tiles_m = CEIL_DIV(m, mmp->a_m->wg_denoms[0]) * CEIL_DIV(n, mmp->a_m->wg_denoms[1]);

        // Use large shader when the N dimension is greater than the medium shader's tile size
        uint32_t crossover_large = mmp->m->wg_denoms[1];

        // Prefer large over medium if either:
        // - medium or large tiles would overfill the GPU
        // - large tiles with a split_k==3 fits in the GPU and medium tiles with split_k==2 does not
        //   (medium with split_k==2 is probably better if it fits - more workgroups running and less split_k overhead)
        bool prefer_large = tiles_m > shader_core_count || tiles_l > shader_core_count ||
                            // split_k==3 with large tiles likely better than medium tiles with no split_k.
                            (tiles_l <= shader_core_count / 3 && tiles_m > shader_core_count / 2);

        if ((mm_l && (n > crossover_large && prefer_large)) || (!mm_m && !mm_s)) {
            return aligned ? mmp->a_l : mmp->l;
        }
        // Use medium shader when the N dimension is greater than the small shader's tile size
        uint32_t crossover_medium = mmp->s->wg_denoms[1];
        if ((mm_m && (n > crossover_medium)) || !mm_s) {
            return aligned ? mmp->a_m : mmp->m;
        }
        return aligned ? mmp->a_s : mmp->s;
    }

    if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) {
        return aligned ? mmp->a_s : mmp->s;
    }
    if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) {
        return aligned ? mmp->a_m : mmp->m;
    }
    return aligned ? mmp->a_l : mmp->l;
}

static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) {
    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
    return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true, src0_type, src1_type)->align;
}

static void ggml_vk_matmul(
        ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline,
        vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer,
        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
        uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3,
        uint32_t padded_n) {
        VK_LOG_DEBUG("ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", padded_n: " << padded_n << ")");
    if (split_k == 1) {
        ggml_pipeline_request_descriptor_sets(ctx, pipeline, CEIL_DIV(batch, ctx->device->properties.limits.maxComputeWorkGroupCount[2]));

        uint32_t base_work_group_z = 0;
        while (base_work_group_z < batch) {
            uint32_t groups_z = std::min(batch - base_work_group_z, ctx->device->properties.limits.maxComputeWorkGroupCount[2]);

            const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, base_work_group_z, batch, k, ne02, ne12, broadcast2, broadcast3, padded_n };
            ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc, { m, n, groups_z });
            base_work_group_z += groups_z;
        }
        return;
    }

    if (ctx->prealloc_split_k_need_sync) {
        ggml_vk_sync_buffers(ctx, subctx);
    }

    GGML_ASSERT(batch_stride_d == m * n);

    // Round the split size up to a multiple of 256 (k-quant alignment)
    uint32_t k_split = CEIL_DIV(k, split_k);
    k_split = ROUNDUP_POW2(k_split, 256);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, CEIL_DIV(batch, ctx->device->properties.limits.maxComputeWorkGroupCount[2]));

    uint32_t base_work_group_z = 0;
    while (base_work_group_z < batch) {
        uint32_t groups_z = std::min(batch - base_work_group_z, ctx->device->properties.limits.maxComputeWorkGroupCount[2]);

        const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, base_work_group_z, batch, k_split, ne02, ne12, broadcast2, broadcast3, padded_n };
        // Make sure enough workgroups get assigned for split k to work
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, groups_z });
        base_work_group_z += groups_z;
    }
    ggml_vk_sync_buffers(ctx, subctx);
    const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k };
    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2, { m * n * batch, 1, 1 });
    ctx->prealloc_split_k_need_sync = true;
}

static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) {
    VK_LOG_DEBUG("ggml_vk_guess_matmul_id_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");

    // The q8_1 (integer dot) mmq path uses a different shader with its own
    // shared-memory layout, so use the int-specific availability flags.
    const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1);
    const bool mm_l = is_q8_1 ? ctx->device->mul_mat_id_l_int[src0_type] : ctx->device->mul_mat_id_l[src0_type];
    const bool mm_m = is_q8_1 ? ctx->device->mul_mat_id_m_int[src0_type] : ctx->device->mul_mat_id_m[src0_type];
    const bool mm_s = is_q8_1 ? ctx->device->mul_mat_id_s_int[src0_type] : ctx->device->mul_mat_id_s[src0_type];

    if (ctx->device->coopmat2) {
        // Use large shader when the N dimension is greater than the medium shader's tile size
        uint32_t crossover_large = mmp->m->wg_denoms[1];
        if ((mm_l && (n > crossover_large)) || (!mm_m && !mm_s)) {
            return aligned ? mmp->a_l : mmp->l;
        }
        // Use medium shader when the N dimension is greater than the small shader's tile size
        uint32_t crossover_medium = mmp->s->wg_denoms[1];
        if ((mm_m && (n > crossover_medium)) || !mm_s) {
            return aligned ? mmp->a_m : mmp->m;
        }
        return aligned ? mmp->a_s : mmp->s;
    }

    if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) {
        return aligned ? mmp->a_s : mmp->s;
    }
    if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) {
        return aligned ? mmp->a_m : mmp->m;
    }
    return aligned ? mmp->a_l : mmp->l;
}

static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) {
    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");
    return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type, src1_type)->align;
}

static void ggml_vk_matmul_id(
        ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline,
        vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& ids, const vk_subbuffer & expert_count_buf,
        uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d,
        uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d,
        uint32_t n_as, uint32_t nei0, uint32_t nei1, uint32_t nbi1, uint32_t ne11,
        uint32_t padded_n) {
    VK_LOG_DEBUG("ggml_vk_matmul_id(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), ids: (" << ids.buffer->buffer << ", " << ids.offset << ", " << ids.size << "), expert_count: (" << expert_count_buf.buffer->buffer << ", " << expert_count_buf.offset << ", " << expert_count_buf.size << "), " <<
        "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " <<
        "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " <<
        "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")");
    const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d,
                                              nei0, nei1, nbi1, ne11, padded_n };
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids, expert_count_buf }, pc, { m, nei1, n_as });
}

static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) {
    return
        tensor->nb[0] == ggml_type_size(tensor->type) &&
        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
        (tensor->ne[3] == 1 || tensor->nb[3] == tensor->nb[2]*tensor->ne[2]);
}

static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src, const ggml_tensor * dst, ggml_type to) {

    // Choose "contiguous copy" shader if src/dst are contiguous
    bool contig = ggml_is_contiguous(src) && (!dst || ggml_is_contiguous(dst));

    // Use optimized "transpose" shader if src dim1 is the innermost dimension.
    bool transpose = dst && src->nb[1] == ggml_type_size(to) && ggml_are_same_shape(dst, src);

    if (transpose && src->type == to) {
        if (ggml_type_size(to) == 4) {
            return ctx->device->pipeline_cpy_transpose_32;
        } else if (ggml_type_size(to) == 2) {
            return ctx->device->pipeline_cpy_transpose_16;
        }
    }

    if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F32) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_f32_f32;
        } else {
            return ctx->device->pipeline_cpy_f32_f32;
        }
    }
    if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F16) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_f32_f16;
        } else {
            return ctx->device->pipeline_cpy_f32_f16;
        }
    }
    if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F16) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_f16_f16;
        } else {
            return ctx->device->pipeline_cpy_f16_f16;
        }
    }
    if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F32) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_f16_f32;
        } else {
            return ctx->device->pipeline_cpy_f16_f32;
        }
    }
    if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_BF16) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_f32_bf16;
        } else {
            return ctx->device->pipeline_cpy_f32_bf16;
        }
    }
    if (src->type == GGML_TYPE_BF16 && to == GGML_TYPE_F32) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_bf16_f32;
        } else {
            return ctx->device->pipeline_cpy_bf16_f32;
        }
    }
    if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_I32) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_f32_i32;
        } else {
            return ctx->device->pipeline_cpy_f32_i32;
        }
    }
    if (src->type == GGML_TYPE_I32 && to == GGML_TYPE_F32) {
        if (contig) {
            return ctx->device->pipeline_contig_cpy_i32_f32;
        } else {
            return ctx->device->pipeline_cpy_i32_f32;
        }
    }
    if (src->type == GGML_TYPE_F32) {
        switch (to) {
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_IQ4_NL:
            return ctx->device->pipeline_cpy_f32_quant[to];
        default:
            break;
        }
    }

    if (to == GGML_TYPE_F32) {
        switch (src->type) {
        case GGML_TYPE_Q1_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_IQ4_NL:
            return ctx->device->pipeline_cpy_quant_f32[src->type];
        default:
            break;
        }
    }

    if (src->type == to) {
        // Copy two or four bytes at a time, depending on block size.
        // For quantized types, we scale by block size/type size. But
        // this path is also used for bf16->bf16 for example, where the
        // type size must be exactly 2 or 4.
        GGML_ASSERT(ggml_is_quantized(to) || ggml_type_size(src->type) == 2 || ggml_type_size(src->type) == 4);
        if ((ggml_type_size(src->type) % 4) == 0) {
            if (contig) {
                return ctx->device->pipeline_contig_cpy_f32_f32;
            } else {
                return ctx->device->pipeline_cpy_f32_f32;
            }
        } else {
            if (contig) {
                return ctx->device->pipeline_contig_cpy_f16_f16;
            } else {
                return ctx->device->pipeline_cpy_f16_f16;
            }
        }
    }

    std::cerr << "Missing CPY op for types: " << ggml_type_name(src->type) << " " << ggml_type_name(to) << std::endl;
    GGML_ABORT("fatal error");
}

static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, const vk_subbuffer & in, const vk_subbuffer & out) {
    VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
    std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
    const int tensor_type_size = ggml_type_size(tensor->type);

    const uint32_t ne = ggml_nelements(tensor);
    std::array<uint32_t, 3> elements;

    if (ne > 262144) {
        elements = { 512, 512, CEIL_DIV(ne, 262144) };
    } else if (ne > 512) {
        elements = { 512, CEIL_DIV(ne, 512), 1 };
    } else {
        elements = { ne, 1, 1 };
    }

    vk_op_unary_push_constants pc = {
        (uint32_t)ne,
        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3],                       1                   , (uint32_t)tensor->ne[0]                   , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]),
        0,
        0.0f, 0.0f,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    };
    init_pushconst_fastdiv(pc);
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
    ggml_vk_sync_buffers(ctx, subctx);
}

// Copy/convert tensor into a caller-defined dense layout. Destination strides
// are in output elements, not bytes.
static void ggml_vk_cpy_to_strided(
        ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor,
        const vk_subbuffer & in, const vk_subbuffer & out,
        uint32_t nb10, uint32_t nb11, uint32_t nb12, uint32_t nb13) {
    VK_LOG_DEBUG("ggml_vk_cpy_to_strided((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), ";
    std::cerr << "dst_nb=(" << nb10 << ", " << nb11 << ", " << nb12 << ", " << nb13 << "), buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")");
    const int tensor_type_size = ggml_type_size(tensor->type);

    const uint32_t ne = ggml_nelements(tensor);
    std::array<uint32_t, 3> elements;

    if (ne > 262144) {
        elements = { 512, 512, CEIL_DIV(ne, 262144) };
    } else if (ne > 512) {
        elements = { 512, CEIL_DIV(ne, 512), 1 };
    } else {
        elements = { ne, 1, 1 };
    }

    vk_op_unary_push_constants pc = {
        (uint32_t)ne,
        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size,
        (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], nb10, nb11, nb12, nb13,
        0,
        0.0f, 0.0f,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    };
    init_pushconst_fastdiv(pc);
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements);
    ggml_vk_sync_buffers(ctx, subctx);
}

static vk_pipeline ggml_vk_get_quantize_pipeline(ggml_backend_vk_context * ctx, ggml_type type) {
    switch(type) {
        case GGML_TYPE_Q8_1:
            return ctx->device->pipeline_quantize_q8_1_x4;
        default:
            std::cerr << "Missing quantize pipeline for type: " << ggml_type_name(type) << std::endl;
            GGML_ABORT("fatal error");
    }
}

static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& subctx, const vk_subbuffer & in, const vk_subbuffer & out, uint32_t ne) {
    VK_LOG_DEBUG("ggml_vk_quantize_q8_1(" << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ", " << ne << ")");

    vk_pipeline pipeline = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);

    const uint32_t num_blocks = CEIL_DIV(ne, pipeline->wg_denoms[0]);
    // clamp the number of elements to the max workgroup count. The shader will iterate over the total number of blocks.
    const uint64_t max_elements = std::min<uint64_t>(uint64_t{ctx->device->properties.limits.maxComputeWorkGroupCount[0]} * pipeline->wg_denoms[0], std::numeric_limits<uint32_t>::max());
    const uint32_t elements = std::min(ne, static_cast<uint32_t>(max_elements));

    const vk_quantize_q8_1_push_constants pc = {
        ne,
        num_blocks,
    };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, { elements, 1, 1 });
    ggml_vk_sync_buffers(ctx, subctx);
}

static vk_pipeline ggml_vk_get_64b_indexing_pipeline(ggml_backend_vk_context * ctx, vk_pipeline &pipeline) {
    GGML_UNUSED(ctx);
#if defined(VK_EXT_shader_64bit_indexing)
    vk_pipeline *ptr = &pipeline;
    while (*ptr) {
        if ((*ptr)->is_64b_indexing) {
            return *ptr;
        }
        ptr = &(*ptr)->next;
    }
#endif
    return pipeline;
}

static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool disable_split_k) {
    VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << ggml_type_name(src0->type) << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << ggml_type_name(src1->type) << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << ggml_type_name(dst->type) << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << "))");
    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16);  // NOLINT
    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT

    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
    const uint64_t ne03 = src0->ne[3];

    const uint64_t ne10 = src1->ne[0];
    const uint64_t ne11 = src1->ne[1];
    const uint64_t ne12 = src1->ne[2];
    const uint64_t ne13 = src1->ne[3];

    const uint64_t ne21 = dst->ne[1];
    const uint32_t stride_d = dst->nb[1] / ggml_type_size(dst->type);
    const uint32_t stride_batch_d = stride_d*ne21;

    const uint64_t r2 = ne12 / ne02;
    const uint64_t r3 = ne13 / ne03;

    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
    ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;

    vk_buffer d_Qx = nullptr;
    size_t qx_buf_offset = 0;
    vk_buffer d_Qy = nullptr;
    size_t qy_buf_offset = 0;

    bool src0_uma = false;
    bool src1_uma = false;

    if (ctx->device->uma) {
        ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset);
        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
        src0_uma = d_Qx != nullptr;
        src1_uma = d_Qy != nullptr;
    }

    // Reformat and convert to fp16 if non-contiguous, or for coopmat2 for better perf
    const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) ||
                              !ggml_vk_dim01_contiguous(src0);
    const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) ||
                              (src0->type == GGML_TYPE_BF16 && src1->type != GGML_TYPE_BF16) ||
                              !ggml_vk_dim01_contiguous(src1);

    // If src0 is BF16, try to use a BF16 x BF16 multiply
    ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16;

    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;

    bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0;

    // Check for mmq first
    vk_matmul_pipeline mmp = quantize_y ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, GGML_TYPE_Q8_1, (ggml_prec)dst->op_params[0]) : nullptr;

    if (mmp == nullptr) {
        // Fall back to f16 dequant mul mat
        mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? f16_type : src1->type, (ggml_prec)dst->op_params[0]);
        quantize_y = false;
    }

    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
    const bool qy_needs_dequant = !quantize_y && ((src1->type != f16_type && !y_f32_kernel) || y_non_contig);

    if (qx_needs_dequant) {
        // Fall back to dequant + f16 mulmat
        mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, f16_type, y_f32_kernel ? GGML_TYPE_F32 : f16_type, (ggml_prec)dst->op_params[0]);
    }

    // Not implemented
    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT

    const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type);

    const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, effective_src1_type));
    const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8;

    vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type);

    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
    }

    // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking
    uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) : ne11;
    const uint64_t x_ne = ggml_nelements(src0);
    // 128 elements per Q8_1 x4 block
    const uint64_t y_ne = padded_n * ne10 * ne12 * ne13;
    const uint64_t d_ne = ggml_nelements(dst);

    const uint32_t split_k = ggml_vk_guess_split_k(ctx, ne01, ne11, ne10, disable_split_k, pipeline);

    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
    const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type);
    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
    const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : (y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne);
    const uint64_t d_sz = sizeof(float) * d_ne;

    vk_pipeline to_fp16_vk_0 = nullptr;
    vk_pipeline to_fp16_vk_1 = nullptr;
    vk_pipeline to_q8_1 = nullptr;

    if (x_non_contig) {
        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, f16_type);
    } else {
        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
    }
    if (y_non_contig) {
        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, f16_type);
    } else {
        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
    }
    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT

    if (quantize_y) {
        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
    }

    {
        const uint64_t split_k_size = split_k > 1 ? d_sz * split_k : 0;
        if (
                (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) ||
                (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange) ||
                (split_k > 1 && split_k_size > ctx->device->properties.limits.maxStorageBufferRange)) {
            GGML_ABORT("Requested preallocation size is too large");
        }
        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) {
            ctx->prealloc_size_x = x_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) {
            ctx->prealloc_size_y = y_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if (split_k > 1 && ctx->prealloc_size_split_k < split_k_size) {
            ctx->prealloc_size_split_k = split_k_size;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }

        // Request descriptor sets
        if (qx_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
        }
        if (qy_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
        }
        if (quantize_y) {
            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
        }
        if (split_k > 1) {
            ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1);
        }
    }

    vk_buffer d_D = dst_buf_ctx->dev_buffer;
    const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
    GGML_ASSERT(d_D != nullptr);
    GGML_ASSERT(d_D->size >= d_buf_offset + d_sz);
    vk_buffer d_X;
    uint64_t x_buf_offset = 0;
    vk_buffer d_Y;
    uint64_t y_buf_offset = 0;
    if (!src0_uma) {
        d_Qx = src0_buf_ctx->dev_buffer;
        qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs;
        GGML_ASSERT(d_Qx != nullptr);
    }
    if (!src1_uma) {
        d_Qy = src1_buf_ctx->dev_buffer;
        qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs;
        GGML_ASSERT(d_Qy != nullptr);
    }
    if (qx_needs_dequant) {
        d_X = ctx->prealloc_x;
        GGML_ASSERT(d_X->size >= x_sz);
    } else {
        d_X = d_Qx;
        x_buf_offset = qx_buf_offset;
        GGML_ASSERT(qx_sz == x_sz);
    }
    if (qy_needs_dequant) {
        d_Y = ctx->prealloc_y;
        GGML_ASSERT(d_Y->size >= y_sz);
    } else if (quantize_y) {
        d_Y = ctx->prealloc_y;
        GGML_ASSERT(d_Y->size >= CEIL_DIV(y_sz, 144) * 144);
    } else {
        d_Y = d_Qy;
        y_buf_offset = qy_buf_offset;
        GGML_ASSERT(qy_sz == y_sz);
    }

    if (x_non_contig || qx_needs_dequant) {
        if (ctx->prealloc_x_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }
    }

    if (x_non_contig) {
        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, ggml_vk_subbuffer(ctx, d_Qx, qx_buf_offset), ggml_vk_subbuffer(ctx, d_X, 0));
    } else if (qx_needs_dequant) {
        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_X, 0, x_sz } }, pc, { (uint32_t)(x_ne), 1, 1});
        ggml_vk_sync_buffers(ctx, subctx);
    }
    if (y_non_contig) {
        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0));
            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }
    if (quantize_y) {
        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0), y_ne);
            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }

    uint32_t stride_batch_x = ne00*ne01;
    uint32_t stride_batch_y = ne10*ne11;

    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
    }

    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant && !quantize_y) {
        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
    }

    // compute
    ggml_vk_matmul(
        ctx, subctx, pipeline,
        { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz },
        ggml_vk_subbuffer(ctx, d_D, d_buf_offset), { ctx->prealloc_split_k, 0, d_sz * split_k },
        ne01, ne11, ne10,
        ne10, ne10, stride_d, stride_batch_x, stride_batch_y, stride_batch_d,
        split_k, ne12*ne13, ne02, ne12, r2, r3, padded_n
    );  // NOLINT

    if (x_non_contig || qx_needs_dequant) {
        ctx->prealloc_x_need_sync = true;
    }
    if (y_non_contig || quantize_y) {
        ctx->prealloc_y_need_sync = true;
    }
}

// Device tuning
static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_t n, uint32_t k, ggml_type src0_type) {
    if (device->mmvq_mode == 1) {
        return true;
    } else if (device->mmvq_mode == -1) {
        return false;
    }

    // q6_k only has 2-byte alignment which makes it somewhat problematic,
    // using MMVQ is only a win on Intel.
    bool mmvq_q6 = device->vendor_id == VK_VENDOR_ID_INTEL;
    if (src0_type == GGML_TYPE_Q6_K && !mmvq_q6) {
        return false;
    }

    // MMVQ is generally good for batches
    if (n > 1) {
        return true;
    }

    // Quantization overhead is not worth it for small k
    switch (device->vendor_id) {
    case VK_VENDOR_ID_NVIDIA:
        if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) {
            return true;
        }

        if (k <= 4096) {
            return false;
        }

        switch (src0_type) {
        case GGML_TYPE_MXFP4:
        case GGML_TYPE_Q8_0:
            return device->architecture == vk_device_architecture::NVIDIA_PRE_TURING;
        default:
            return true;
        }
    case VK_VENDOR_ID_AMD:
        if (k < 2048) {
            return false;
        }

        switch (src0_type) {
        case GGML_TYPE_Q8_0:
            return device->architecture == vk_device_architecture::AMD_GCN;
        default:
            return true;
        }
    case VK_VENDOR_ID_INTEL:
        if (device->architecture == vk_device_architecture::INTEL_XE2) {
            if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) {
                return true;
            }
        }

        if (device->driver_id == vk::DriverId::eIntelProprietaryWindows) {
            // Intel Windows proprietary driver MMVQ performance for !Q2/Q3/Q6 is worse than fp16,
            // see https://github.com/ggml-org/llama.cpp/issues/17628 and
            // https://github.com/ggml-org/llama.cpp/pull/23056
            return false;
        }

        if (k < 2048) {
            return false;
        }

        switch (src0_type) {
        // From tests on A770 Linux, may need more tuning
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q5_1:
            return false;
        default:
            return true;
        }
    default:
        return true;
    }

    GGML_UNUSED(m);
}

static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];

    VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << ")),)");
    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16);  // NOLINT
    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT

    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
    const uint64_t ne03 = src0->ne[3];

    const uint64_t ne10 = src1->ne[0];
    const uint64_t ne11 = src1->ne[1];
    const uint64_t ne12 = src1->ne[2];
    const uint64_t ne13 = src1->ne[3];

    const uint64_t ne20 = dst->ne[0];
    const uint64_t ne21 = dst->ne[1];
    // const uint64_t ne22 = dst->ne[2];
    // const uint64_t ne23 = dst->ne[3];

    const uint64_t r2 = ne12 / ne02;
    const uint64_t r3 = ne13 / ne03;

    // batch_n indicates that we need to compute a few vector results, and this assumes
    // ne12 and ne13 are 1. It overloads the batch_strides to hold the row strides.
    GGML_ASSERT(ne11 == 1 || ne12 * ne13 == 1);
    bool batch_n = ne11 > 1;

    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);

    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
    bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0 && ggml_vk_should_use_mmvq(ctx->device, ne01, ne11, ne10, src0->type);

    vk_pipeline to_fp16_vk_0 = nullptr;
    vk_pipeline to_fp16_vk_1 = nullptr;
    if (x_non_contig) {
        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type);
    }
    if (y_non_contig) {
        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type);
    } else {
        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
    }

    // Check for mmq first
    vk_pipeline dmmv = quantize_y ? ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, GGML_TYPE_Q8_1, ne11, ne20, ne00) : nullptr;
    vk_pipeline to_q8_1 = nullptr;

    if (dmmv == nullptr) {
        // Fall back to f16 dequant mul mat
        dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11, ne20, ne00);
        quantize_y = false;
    }

    if (quantize_y) {
        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
    }

    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
        dmmv = ggml_vk_get_64b_indexing_pipeline(ctx, dmmv);
    }

    const bool qx_needs_dequant = x_non_contig;
    const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig);

    // Not implemented
    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT

    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
    GGML_ASSERT(dmmv != nullptr);

    const uint64_t x_ne = ggml_nelements(src0);
    const uint64_t y_ne = ggml_nelements(src1);

    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
    const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) :
                         (f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne);

    {
        if (
                (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) ||
                (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange)) {
            GGML_ABORT("Requested preallocation size is too large");
        }
        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) {
            ctx->prealloc_size_x = x_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) {
            ctx->prealloc_size_y = y_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }

        // Request descriptor sets
        if (qx_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
        }
        if (qy_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
        }
        if (quantize_y) {
            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
        }
    }

    vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]);
    vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0);
    vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1);
    vk_subbuffer d_X, d_Y;

    if (qx_needs_dequant) {
        d_X = { ctx->prealloc_x, 0, ctx->prealloc_x->size };
    } else {
        d_X = d_Qx;
        GGML_ASSERT(qx_sz == x_sz);
    }
    if (qy_needs_dequant || quantize_y) {
        d_Y = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
    } else {
        d_Y = d_Qy;
    }

    if (x_non_contig) {
        if (ctx->prealloc_x_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }

        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, d_Qx, d_X);
    }
    if (y_non_contig) {
        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, d_Qy, d_Y);
            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }
    if (quantize_y) {
        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_quantize_q8_1(ctx, subctx, d_Qy, d_Y, y_ne);
            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }

    // For batch_n, the A matrix is the same for each batch, and B/D use the row stride as the batch stride
    uint32_t stride_batch_x = batch_n ? 0 : ne00*ne01;
    uint32_t stride_batch_y = batch_n ? ne10 : (ne10*ne11);
    uint32_t stride_batch_d = batch_n ? ne20 : (ne20*ne21);

    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
    }

    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
    }

    const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0];

    uint32_t groups_x = ne01;
    uint32_t groups_z = 1;

    if (ne01 > max_groups_x) {
        groups_z = 64;
        groups_x = CEIL_DIV(groups_x, groups_z);
    }

    uint32_t fusion_flags = 0;

    vk_subbuffer d_F0 = d_D;
    if (ctx->num_additional_fused_ops > 0) {
        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
        const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0];

        d_F0 = ggml_vk_tensor_subbuffer(ctx, bias);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0;
    }

    vk_subbuffer d_F1 = d_D;
    if (ctx->num_additional_fused_ops == 2) {
        const ggml_tensor * add = cgraph->nodes[node_idx + 2];
        const ggml_tensor * bias = add->src[0] == cgraph->nodes[node_idx + 1] ? add->src[1] : add->src[0];

        d_F1 = ggml_vk_tensor_subbuffer(ctx, bias);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS1;
    }

    ggml_pipeline_request_descriptor_sets(ctx, dmmv, CEIL_DIV(ne12 * ne13, ctx->device->properties.limits.maxComputeWorkGroupCount[1]));

    uint32_t base_work_group_y = 0;
    while (base_work_group_y < ne12 * ne13) {

        uint32_t groups_y = std::min((uint32_t)(ne12 * ne13) - base_work_group_y, ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
        const vk_mat_vec_push_constants pc = {
            (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
            stride_batch_x, stride_batch_y, stride_batch_d,
            fusion_flags, base_work_group_y,
            (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
        };
        ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
                                  {
                                    d_X,
                                    d_Y,
                                    d_D,
                                    d_F0,
                                    d_F1,
                                  },
                                  pc, { groups_x, groups_y, groups_z });
        base_work_group_y += groups_y;
    }

    if (x_non_contig) {
        ctx->prealloc_x_need_sync = true;
    }
    if (y_non_contig || quantize_y) {
        ctx->prealloc_y_need_sync = true;
    }
}

static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
    VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32(" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << "))");
    GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
    GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]);  // NOLINT
    GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]);  // NOLINT
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
    // const uint64_t ne03 = src0->ne[3];

    //const uint64_t ne10 = src1->ne[0];
    const uint64_t ne11 = src1->ne[1];
    const uint64_t ne12 = src1->ne[2];
    // const uint64_t ne13 = src1->ne[3];

    GGML_ASSERT(ne11 == 1);

    // With grouped query attention there are > 1 Q matrices per K, V matrix.
    uint32_t gqa_ratio = (uint32_t)ne12 / (uint32_t)ne02;
    if (gqa_ratio > 8 || gqa_ratio == 0 || ne12 != ne02 * gqa_ratio) {
        gqa_ratio = 1;
    }

    vk_pipeline pipeline = ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1];

    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
    }

    {
        // Request descriptor sets
        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
    }

    vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops], true);
    vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0);
    vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1, true);

    vk_subbuffer d_F0 = d_D;

    uint32_t fusion_flags = 0;

    if (ctx->num_additional_fused_ops > 0) {
        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
        const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0];

        d_F0 = ggml_vk_tensor_subbuffer(ctx, bias);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0;
    }

    vk_subbuffer d_F1 = d_D;
    if (ctx->num_additional_fused_ops > 1) {
        const ggml_tensor * bias = cgraph->nodes[node_idx + 2]->src[1];

        d_F1 = ggml_vk_tensor_subbuffer(ctx, bias);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS1;
    }

    // compute

    vk_mat_vec_p021_push_constants pc = {
        (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12,
        0, 0, fusion_flags
    };

    init_pushconst_tensor_offsets(ctx, pc, src0, src1, nullptr, nullptr, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]);

    uint32_t workgroups_z = (uint32_t)ne12;
    // When gqa_ratio > 1, each invocation does multiple rows and we can launch fewer workgroups
    if (gqa_ratio > 1) {
        workgroups_z /= gqa_ratio;
    }

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
        {
            d_Qx,
            d_Qy,
            d_D,
            d_F0,
            d_F1,
        }, pc, { 1, (uint32_t)ne01, workgroups_z });
}

static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
    VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << "))");
    GGML_ASSERT(!ggml_is_transposed(src0));
    GGML_ASSERT(!ggml_is_transposed(src1));
    GGML_ASSERT(!ggml_is_permuted(src0));
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);

    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
    const uint64_t ne03 = src0->ne[3];

    const uint64_t nb01 = src0->nb[1];
    const uint64_t nb02 = src0->nb[2];

    const uint64_t nb12 = src1->nb[2];

    // const uint64_t ne10 = src1->ne[0];
    const uint64_t ne11 = src1->ne[1];
    const uint64_t ne12 = src1->ne[2];
    // const uint64_t ne13 = src1->ne[3];

    const uint32_t nb03 = (uint32_t)(src0->nb[3] / sizeof(ggml_fp16_t));
    const uint32_t nb13 = (uint32_t)(src1->nb[3] / sizeof(float));
    const uint32_t nb23 = (uint32_t)(dst->nb[3] / sizeof(float));

    GGML_ASSERT(ne11 == 1);
    GGML_ASSERT(src0->ne[3] == src1->ne[3]); // checked in supports_op

    const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
    const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
    const uint32_t channel_stride_y = nb12 / sizeof(float);

    vk_pipeline pipeline = ctx->device->pipeline_mul_mat_vec_nc_f16_f32;
    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
    }

    {
        // Request descriptor sets
        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
    }

    vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops], true);
    vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0);
    vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1, true);
    vk_subbuffer d_F0 = d_D;

    uint32_t fusion_flags = 0;

    if (ctx->num_additional_fused_ops > 0) {
        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
        const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0];

        d_F0 = ggml_vk_tensor_subbuffer(ctx, bias);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0;
    }

    vk_subbuffer d_F1 = d_D;
    if (ctx->num_additional_fused_ops > 1) {
        const ggml_tensor * bias = cgraph->nodes[node_idx + 2]->src[1];

        d_F1 = ggml_vk_tensor_subbuffer(ctx, bias);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS1;
    }

    // compute
    vk_mat_vec_nc_push_constants pc = {
        (uint32_t)ne00, (uint32_t)ne01,
        row_stride_x, channel_stride_x, channel_stride_y,
        (uint32_t)(ne12 / ne02), (uint32_t)ne12,
        0, 0,
        nb03, nb13, nb23, fusion_flags
    };

    init_pushconst_tensor_offsets(ctx, pc, src0, src1, nullptr, nullptr, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]);

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
        {
            d_Qx,
            d_Qy,
            d_D,
            d_F0,
            d_F1,
        }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 });
}

static int ggml_vk_fwht_pipeline_idx(int64_t n) {
    switch (n) {
        case 64:  return 0;
        case 128: return 1;
        case 256: return 2;
        case 512: return 3;
        default:  return -1;
    }
}

static bool ggml_vk_can_use_fwht(const ggml_backend_vk_context * ctx, const ggml_tensor * src1, const ggml_tensor * dst) {
    if (ctx->num_additional_fused_ops != 0) {
        return false;
    }

    if (ggml_get_op_params_i32(dst, 1) != GGML_HINT_SRC0_IS_HADAMARD) {
        return false;
    }

    const int idx = ggml_vk_fwht_pipeline_idx(src1->ne[0]);
    if (idx < 0 || ctx->device->pipeline_fwht_f32[idx] == nullptr) {
        return false;
    }

    if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
        return false;
    }

    if (!ggml_is_contiguous(src1)) {
        return false;
    }
    GGML_ASSERT(ggml_is_contiguous(dst));

    return true;
}

static void ggml_vk_fwht(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src, ggml_tensor * dst) {
    const int idx = ggml_vk_fwht_pipeline_idx(src->ne[0]);
    vk_pipeline pipeline = ctx->device->pipeline_fwht_f32[idx];

    const uint32_t rows_per_workgroup = 4;
    const uint32_t n_rows = (uint32_t)ggml_nrows(src);
    const uint32_t max_workgroups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0];

    const uint32_t total_workgroups = CEIL_DIV(n_rows, rows_per_workgroup);
    const uint32_t workgroups_x = std::min(total_workgroups, max_workgroups_x);
    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    const vk_subbuffer src_buf = ggml_vk_tensor_subbuffer(ctx, src, true);
    const vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, true);

    vk_op_fwht_push_constants pc = {
        n_rows,
        0,
        0,
        1.0f / std::sqrt((float)src->ne[0]),
    };
    init_pushconst_tensor_offsets(ctx, pc, src, nullptr, nullptr, nullptr, dst);

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src_buf, dst_buf }, pc, { workgroups_x, 1, 1 });
}

static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    ggml_tensor * src0 = dst->src[0];
    ggml_tensor * src1 = dst->src[1];
    VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")");

    // Handle huge A matrix by splitting the M dimensions. This works well for convolution use cases
    // where the M dimension is very large.
    // Split_k doesn't work with M splitting.
    // This only supports batchsize == 1.
    const size_t nbytes = ggml_nbytes(src0);
    const bool needs_split = dst->ne[2] == 1 && dst->ne[3] == 1 && nbytes > ctx->device->properties.limits.maxStorageBufferRange;
    if (needs_split) {
        // Choose the number of rows that can fit (and divide by two, to allow for any additional offsets)
        const uint32_t M_split = ctx->device->properties.limits.maxStorageBufferRange / (2 * src0->nb[1]);
        uint32_t m_offset = 0;
        while (m_offset < dst->ne[0]) {
            const uint32_t cur_M_size = std::min(M_split, (uint32_t)(dst->ne[0] - m_offset));
            ggml_tensor dst2 = *dst;
            ggml_tensor src02 = *src0;

            dst2.view_src = dst->view_src ? dst->view_src : dst;
            src02.view_src = src0->view_src ? src0->view_src : src0;

            dst2.view_offs += m_offset * dst->nb[0];
            src02.view_offs += m_offset * src0->nb[1];
            dst2.ne[0] = cur_M_size;
            src02.ne[1] = cur_M_size;

            ggml_vk_mul_mat_q_f16(ctx, subctx, &src02, src1, &dst2, true);

            m_offset += cur_M_size;
        }
    } else if (ggml_vk_can_use_fwht(ctx, src1, dst)) {
        ggml_vk_fwht(ctx, subctx, src1, dst);
    } else if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1 &&
        // detect 0213 permutation, and batch size of 1
        src0->nb[0] <= src0->nb[2] &&
        src0->nb[2] <= src0->nb[1] &&
        src0->nb[1] <= src0->nb[3] &&
        src1->nb[0] <= src1->nb[2] &&
        src1->nb[2] <= src1->nb[1] &&
        src1->nb[1] <= src1->nb[3] &&
        src0->ne[3] == 1 &&
        src1->ne[3] == 1 &&
        src0->ne[1] <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] &&
        src1->ne[2] <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]) {
        ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, cgraph, node_idx);
    } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 &&
               !ggml_is_permuted(src0) && !ggml_is_permuted(src1) &&
               src0->ne[3] <= ctx->device->properties.limits.maxComputeWorkGroupCount[0] &&
               src0->ne[1] <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] &&
               src1->ne[2] <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]) {
        ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, cgraph, node_idx);
    // mul_mat_vec supports batching ne12*ne13 when ne11==1, or treating ne11 as the batch size (up to four)
    // when ne12 and ne13 are one.
    } else if ((dst->ne[1] == 1 || (dst->ne[1] <= mul_mat_vec_max_cols && src1->ne[2] * src1->ne[3] == 1)) &&
               (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16 || ggml_is_quantized(src0->type))) {
        ggml_vk_mul_mat_vec_q_f16(ctx, subctx, cgraph, node_idx);
    } else {
        ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, false);
    }
}

static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
    VK_LOG_DEBUG("ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)");
    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
    GGML_ASSERT(ids->type == GGML_TYPE_I32);

    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
    // const uint64_t ne03 = src0->ne[3];

    const uint64_t ne10 = src1->ne[0];
    const uint64_t ne11 = src1->ne[1];
    const uint64_t ne12 = src1->ne[2];
    const uint64_t ne13 = src1->ne[3];

    const uint64_t nei0 = ids->ne[0];
    const uint64_t nei1 = ids->ne[1];

    const uint32_t nbi0 = ids->nb[0];
    const uint32_t nbi1 = ids->nb[1];
    const uint32_t nbi2 = ids->nb[2];

    const uint64_t ne20 = dst->ne[0];
    const uint64_t ne21 = dst->ne[1];
    // const uint64_t ne22 = dst->ne[2];
    // const uint64_t ne23 = dst->ne[3];

    const uint64_t n_as = ne02;

    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
    ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
    ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context;

    vk_buffer d_Qx = nullptr;
    size_t qx_buf_offset = 0;
    vk_buffer d_Qy = nullptr;
    size_t qy_buf_offset = 0;
    vk_buffer d_ids = nullptr;
    size_t ids_buf_offset = 0;

    bool src0_uma = false;
    bool src1_uma = false;
    bool ids_uma = false;

    if (ctx->device->uma) {
        ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset);
        ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset);
        ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset);
        src0_uma = d_Qx != nullptr;
        src1_uma = d_Qy != nullptr;
        ids_uma = d_ids != nullptr;
    }

    // Reformat and convert to fp16 if non-contiguous, or for coopmat2 for better perf
    const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) ||
                              !ggml_vk_dim01_contiguous(src0);
    // If src0 is BF16, try to use a BF16 x BF16 multiply
    ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16;
#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT)
    // B must already be, or be convertible to, the matmul B type used by this path.
    const bool y_decode_vector_supported = ctx->device->coopmat2_decode_vector &&
                                           (f16_type != GGML_TYPE_BF16 || ctx->device->coopmat2_bf16_support) &&
                                           (src1->type == GGML_TYPE_F32 || src1->type == f16_type);
    // If B is copied to prealloc_y, we can choose a 4-element-aligned row stride.
    const bool y_decode_vector_uses_prealloc = !ggml_vk_dim01_contiguous(src1) || src1->type != f16_type;
    // Direct B reads are safe only if row starts and the original buffer offset are 4-element aligned.
    const bool y_decode_vector_aligned =
        (ne10 % 4 == 0) &&
        (y_decode_vector_uses_prealloc || get_misalign_bytes(ctx, src1) % (4 * ggml_type_size(src1->type)) == 0);
    // Stage B only when decode-vector is available and direct B reads would be misaligned.
    const bool y_decode_vector_staging = y_decode_vector_supported && !y_decode_vector_aligned;
#else
    const bool y_decode_vector_staging = false;
#endif
    const bool y_non_contig = y_decode_vector_staging ||
                              (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) ||
                              (src0->type == GGML_TYPE_BF16 && src1->type != GGML_TYPE_BF16) ||
                              !ggml_vk_dim01_contiguous(src1);

    const uint32_t y_staged_row_stride = y_decode_vector_staging ? (uint32_t)ggml_vk_align_size(ne10, 4) : (uint32_t)ne10;

    const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig;

    bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0;

    // Check for mmq first
    vk_matmul_pipeline mmp = quantize_y ? ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, GGML_TYPE_Q8_1, (ggml_prec)dst->op_params[0]) : nullptr;

    if (mmp == nullptr) {
        // Fall back to f16 dequant mul mat
        mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? f16_type : src1->type, (ggml_prec)dst->op_params[0]);
        quantize_y = false;
    }

    const bool qx_needs_dequant = mmp == nullptr || x_non_contig;
    const bool qy_needs_dequant = !quantize_y && ((src1->type != f16_type && !y_f32_kernel) || y_non_contig);

    if (qx_needs_dequant) {
        // Fall back to dequant + f16 mulmat
        mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, f16_type, y_f32_kernel ? GGML_TYPE_F32 : f16_type, (ggml_prec)dst->op_params[0]);
    }

    // Not implemented
    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT

    const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type);

    const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type, effective_src1_type));
    const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && nei1 > 8;

    vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type);

    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
        pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline);
    }
    // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking
    uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) :ne11;
    const uint64_t x_ne = ggml_nelements(src0);
    const uint64_t y_ne = (uint64_t)y_staged_row_stride * padded_n * ne12 * ne13;
    const uint64_t d_ne = ggml_nelements(dst);

    const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type);
    const uint64_t qy_sz = ggml_type_size(src1->type) * ggml_nelements(src1) / ggml_blck_size(src1->type);
    const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne;
    const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : (y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne);
    const uint64_t ids_sz = nbi2;
    const uint64_t d_sz = sizeof(float) * d_ne;

    vk_pipeline to_fp16_vk_0 = nullptr;
    vk_pipeline to_fp16_vk_1 = nullptr;
    vk_pipeline to_q8_1 = nullptr;

    auto make_y_staged_dst = [&]() {
        ggml_tensor y_staged_dst = *src1;
        y_staged_dst.type = f16_type;
        y_staged_dst.nb[0] = ggml_type_size(f16_type);
        y_staged_dst.nb[1] = y_staged_dst.nb[0] * y_staged_row_stride;
        y_staged_dst.nb[2] = y_staged_dst.nb[1] * padded_n;
        y_staged_dst.nb[3] = y_staged_dst.nb[2] * y_staged_dst.ne[2];
        return y_staged_dst;
    };

    if (x_non_contig) {
        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, f16_type);
    } else {
        to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type);
    }
    if (y_non_contig) {
        ggml_tensor y_staged_dst;
        const ggml_tensor * y_staged_dst_ptr = nullptr;
        if (y_decode_vector_staging) {
            y_staged_dst = make_y_staged_dst();
            y_staged_dst_ptr = &y_staged_dst;
        }

        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, y_staged_dst_ptr, f16_type);
    } else {
        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
    }
    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT

    if (quantize_y) {
        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
    }
    vk_pipeline count_experts = ctx->device->pipeline_count_experts;

    uint32_t expert_count_size = sizeof(uint32_t) * n_as;

    {
        if (
                (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) ||
                (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange)) {
            GGML_ABORT("Requested preallocation size is too large");
        }
        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) {
            ctx->prealloc_size_x = x_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) {
            ctx->prealloc_size_y = y_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if (ctx->prealloc_size_split_k < expert_count_size) {
            ctx->prealloc_size_split_k = expert_count_size;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }

        // Request descriptor sets
        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
        if (qx_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
        }
        if (qy_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
        }
        if (quantize_y) {
            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
        }
        ggml_pipeline_request_descriptor_sets(ctx, count_experts, 1);
    }

    vk_buffer d_D = dst_buf_ctx->dev_buffer;
    const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
    GGML_ASSERT(d_D != nullptr);
    vk_buffer d_X;
    uint64_t x_buf_offset = 0;
    vk_buffer d_Y;
    uint64_t y_buf_offset = 0;
    if (!src0_uma) {
        d_Qx = src0_buf_ctx->dev_buffer;
        qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs;
        GGML_ASSERT(d_Qx != nullptr);
    }
    if (!src1_uma) {
        d_Qy = src1_buf_ctx->dev_buffer;
        qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs;
        GGML_ASSERT(d_Qy != nullptr);
    }
    if (!ids_uma) {
        d_ids = ids_buf_ctx->dev_buffer;
        ids_buf_offset = vk_tensor_offset(ids) + ids->view_offs;
        GGML_ASSERT(d_ids != nullptr);
    }
    if (qx_needs_dequant) {
        d_X = ctx->prealloc_x;
        GGML_ASSERT(d_X->size >= x_sz);
    } else {
        d_X = d_Qx;
        x_buf_offset = qx_buf_offset;
        GGML_ASSERT(qx_sz == x_sz);
    }
    if (qy_needs_dequant) {
        d_Y = ctx->prealloc_y;
        GGML_ASSERT(d_Y->size >= y_sz);
    } else if (quantize_y) {
        d_Y = ctx->prealloc_y;
        GGML_ASSERT(d_Y->size >= CEIL_DIV(y_sz, 144) * 144);
    } else {
        d_Y = d_Qy;
        y_buf_offset = qy_buf_offset;
        GGML_ASSERT(qy_sz == y_sz);
    }

    if (x_non_contig || qx_needs_dequant) {
        if (ctx->prealloc_x_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }
    }
    // Count how many times each expert is used
    vk_subbuffer expert_count_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);
    if (ctx->prealloc_split_k_need_sync) {
        ggml_vk_sync_buffers(ctx, subctx);
    }
    {
        const std::vector<uint32_t> pc = { (uint32_t)nei0,
                                           (uint32_t)nei1,
                                           (uint32_t)(nbi0 / ggml_type_size(ids->type)),
                                           (uint32_t)(nbi1 / ggml_type_size(ids->type)),
                                           (uint32_t)(get_misalign_bytes(ctx, ids) / ggml_type_size(ids->type)) };
        ggml_vk_dispatch_pipeline(ctx, subctx, count_experts,
            { vk_subbuffer{ d_ids, ids_buf_offset, ids_sz }, expert_count_buf }, pc, { (uint32_t)n_as, 1, 1});
    }

    if (x_non_contig) {
        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, ggml_vk_subbuffer(ctx, d_Qx, qx_buf_offset), ggml_vk_subbuffer(ctx, d_X, 0));
    } else if (qx_needs_dequant) {
        const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) };
        ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0,
            { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_X, 0, x_sz } }, pc, { (uint32_t)x_ne, 1, 1});
    }
    if (y_non_contig) {
        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging != y_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            if (y_decode_vector_staging) {
                const ggml_tensor y_staged_dst = make_y_staged_dst();
                const uint32_t y_staged_dst_type_size = ggml_type_size(y_staged_dst.type);
                ggml_vk_cpy_to_strided(
                    ctx, subctx, to_fp16_vk_1, src1,
                    ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0),
                    (uint32_t)(y_staged_dst.nb[0] / y_staged_dst_type_size),
                    (uint32_t)(y_staged_dst.nb[1] / y_staged_dst_type_size),
                    (uint32_t)(y_staged_dst.nb[2] / y_staged_dst_type_size),
                    (uint32_t)(y_staged_dst.nb[3] / y_staged_dst_type_size));
            } else {
                ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0));
            }
            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = y_decode_vector_staging;
        }
    }
    if (quantize_y) {
        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0), y_ne);
            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }
    ggml_vk_sync_buffers(ctx, subctx);

    uint32_t stride_batch_x = ne00*ne01;
    uint32_t stride_b_y = y_decode_vector_staging ? y_staged_row_stride : ne10;
    uint32_t stride_batch_y = y_decode_vector_staging ? y_staged_row_stride * padded_n : ne10*ne11;

    if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) {
        stride_batch_x = src0->nb[0] / ggml_type_size(src0->type);
    }

    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant && !quantize_y) {
        stride_batch_y = src1->nb[0] / ggml_type_size(src1->type);
    }

    // compute
    ggml_vk_matmul_id(
        ctx, subctx, pipeline,
        { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz },
        { d_D, d_buf_offset, d_sz }, { d_ids, ids_buf_offset, ids_sz }, expert_count_buf,
        ne01, ne21, ne10, ne10, stride_b_y, ne01,
        stride_batch_x, stride_batch_y, ne20*ne21,
        n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11, padded_n
    );  // NOLINT

    if (x_non_contig || qx_needs_dequant) {
        ctx->prealloc_x_need_sync = true;
    }
    if (y_non_contig || quantize_y) {
        ctx->prealloc_y_need_sync = true;
    }
    ctx->prealloc_split_k_need_sync = true;
}

static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    ggml_tensor * src0 = dst->src[0];
    ggml_tensor * src1 = dst->src[1];
    ggml_tensor * ids = dst->src[2];
    VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << "))");
    GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16);  // NOLINT
    GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);  // NOLINT
    GGML_ASSERT(ids->type == GGML_TYPE_I32);

    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    // const uint64_t ne02 = src0->ne[2];
    // const uint64_t ne03 = src0->ne[3];

    const uint64_t ne10 = src1->ne[0];
    const uint64_t ne11 = src1->ne[1];
    const uint64_t ne12 = src1->ne[2];
    // const uint64_t ne13 = src1->ne[3];

    const uint64_t nei0 = ids->ne[0];
    const uint64_t nei1 = ids->ne[1];
    const uint32_t nbi1 = (uint32_t)(ids->nb[1] / sizeof(int));

    const uint64_t ne20 = dst->ne[0];
    const uint64_t ne21 = dst->ne[1];
    // const uint64_t ne22 = dst->ne[2];
    // const uint64_t ne23 = dst->ne[3];

    const bool x_non_contig = !ggml_vk_dim01_contiguous(src0);
    const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);

    const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
    bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0 && ggml_vk_should_use_mmvq(ctx->device, ne01, ne12, ne10, src0->type);

    vk_pipeline to_fp16_vk_0 = nullptr;
    vk_pipeline to_fp16_vk_1 = nullptr;
    if (x_non_contig) {
        to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type);
    }
    if (y_non_contig) {
        to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type);
    } else {
        to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
    }

    // Check for mmq first
    vk_pipeline dmmv = quantize_y ? ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, GGML_TYPE_Q8_1, ne20, ne00) : nullptr;
    vk_pipeline to_q8_1 = nullptr;

    if (dmmv == nullptr) {
        // Fall back to f16 dequant mul mat
        dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type, ne20, ne00);
        quantize_y = false;
    }

    if (quantize_y) {
        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
    }

    const bool qx_needs_dequant = x_non_contig;
    const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig);

    if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) {
        dmmv = ggml_vk_get_64b_indexing_pipeline(ctx, dmmv);
    }

    // Not implemented
    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
    GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
    GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
    GGML_ASSERT(dmmv != nullptr);

    const uint64_t x_ne = ggml_nelements(src0);
    const uint64_t y_ne = ggml_nelements(src1);

    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
    const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) :
                                       (f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne);

    {
        if (
                (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) ||
                (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange)) {
            GGML_ABORT("Requested preallocation size is too large");
        }
        if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) {
            ctx->prealloc_size_x = x_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) {
            ctx->prealloc_size_y = y_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }

        // Request descriptor sets
        if (qx_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1);
        }
        if (qy_needs_dequant) {
            ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
        }
        if (quantize_y) {
            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
        }
        ggml_pipeline_request_descriptor_sets(ctx, dmmv, nei1);
    }

    vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]);
    vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0);
    vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1);
    vk_subbuffer d_ids = ggml_vk_tensor_subbuffer(ctx, ids);
    vk_subbuffer d_F0 = d_D;
    vk_subbuffer d_X, d_Y;

    if (qx_needs_dequant) {
        d_X = { ctx->prealloc_x, 0, ctx->prealloc_x->size };
    } else {
        d_X = d_Qx;
    }
    if (qy_needs_dequant || quantize_y) {
        d_Y = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
    } else {
        d_Y = d_Qy;
    }

    if (x_non_contig) {
        if (ctx->prealloc_x_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }
    }

    if (x_non_contig) {
        GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
        ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, d_Qx, d_X);
    }
    if (y_non_contig) {
        GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
        if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, d_Qy, d_Y);
            ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }
    if (quantize_y) {
        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
            ctx->prealloc_y_last_tensor_used != src1 ||
            ctx->prealloc_y_last_decode_vector_staging) {
            if (ctx->prealloc_y_need_sync) {
                ggml_vk_sync_buffers(ctx, subctx);
            }
            ggml_vk_quantize_q8_1(ctx, subctx, d_Qy, d_Y, y_ne);
            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
            ctx->prealloc_y_last_tensor_used = src1;
            ctx->prealloc_y_last_decode_vector_staging = false;
        }
    }

    uint32_t stride_batch_y = ne10*ne11;

    if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) {
        stride_batch_y = src1->nb[2] / ggml_type_size(src1->type);
    }

    const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0];

    uint32_t groups_x = ne01;
    uint32_t groups_z = 1;

    if (ne01 > max_groups_x) {
        groups_z = 64;
        groups_x = CEIL_DIV(groups_x, groups_z);
    }

    uint32_t fusion_flags = 0;

    if (ctx->num_additional_fused_ops > 0) {
        const ggml_tensor * bias = cgraph->nodes[node_idx + 1]->src[1];

        d_F0 = ggml_vk_tensor_subbuffer(ctx, bias);

        if (cgraph->nodes[node_idx + 1]->op == GGML_OP_MUL) {
            fusion_flags |= MAT_VEC_FUSION_FLAGS_SCALE0;
        } else {
            GGML_ASSERT(cgraph->nodes[node_idx + 1]->op == GGML_OP_ADD_ID);
            fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0;
        }
    }

    vk_subbuffer d_F1 = d_D;
    if (ctx->num_additional_fused_ops > 1) {
        const ggml_tensor * scale = cgraph->nodes[node_idx + 2]->src[1];

        d_F1 = ggml_vk_tensor_subbuffer(ctx, scale);
        fusion_flags |= MAT_VEC_FUSION_FLAGS_SCALE1;
    }

    // Loop over the batch dimension
    for (uint32_t expert_i1 = 0; expert_i1 < nei1; ++expert_i1) {
        const vk_mat_vec_id_push_constants pc = {
            (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
            (uint32_t)(ne00 * ne01), stride_batch_y, (uint32_t)(ne20 * ne21),
            fusion_flags,
            (uint32_t)nei0, (uint32_t)ne11, expert_i1, nbi1
        };
        ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
            {
                d_X,
                d_Y,
                d_D,
                d_F0,
                d_F1,
                d_ids,
            },
            pc, { groups_x, (uint32_t)nei0, groups_z });
    }

    if (x_non_contig) {
        ctx->prealloc_x_need_sync = true;
    }
    if (y_non_contig || quantize_y) {
        ctx->prealloc_y_need_sync = true;
    }
}

static bool ggml_vk_use_mul_mat_vec_id(const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    ggml_tensor * src0 = dst->src[0];
    ggml_tensor * src2 = dst->src[2];
    return (src2->ne[1] <= 8) && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type));
}

static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    ggml_tensor * src0 = dst->src[0];
    ggml_tensor * src1 = dst->src[1];
    ggml_tensor * src2 = dst->src[2];
    VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")");
    if (ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) {
        ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, cgraph, node_idx);
    } else {
        ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst);
    }
}

static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) {
    GGML_UNUSED(f32acc);
    GGML_UNUSED(v_type);
    // Needs to be kept up to date on shader changes
    const uint32_t wg_size = params.workgroup_size;
    const uint32_t Br = params.block_rows;
    const uint32_t Bc = params.block_cols;

    // BF16 uses the fp32 shader (FLOAT_TYPE=float)
    const uint32_t float_type_size = (device->fp16 && k_type != GGML_TYPE_BF16) ? sizeof(ggml_fp16_t) : sizeof(float);

    const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type);

    // tmpsh is overestimated slightly
    const uint32_t tmpsh = wg_size * sizeof(float);
    const uint32_t tmpshv4 = wg_size * 4 * float_type_size;

    const uint32_t masksh = Bc * (Br + 1) * float_type_size;

    uint32_t Qf, kvsh, kblocksh_size;
    if (mmq) {
        // block_b_cache: int32_t qs[8] + FLOAT_TYPEV2 ds
        const uint32_t block_b_size = 8 * sizeof(int32_t) + 2 * float_type_size;
        Qf = Br * (hsk / 32) * block_b_size;

        // kvsh uses D = HSV (K goes through kblocksh instead)
        kvsh = params.shmem_staging ? Bc * (hsv / 4 + 1) * 4 * float_type_size : 4 * float_type_size;

        // The mixed MMQ shader uses a superset block_a_cache that fits every
        // FA-supported quant: int32_t qs[8] + uint32_t qh + FLOAT_TYPEV2 dm.
        // Single-scale types leave dm.y unused; non-Q5_* leave qh unused.
        const uint32_t block_a_size = 8 * sizeof(int32_t) + sizeof(uint32_t) + 2 * float_type_size;
        kblocksh_size = params.shmem_staging ? Bc * (hsk / 32) * block_a_size : block_a_size;
    } else {
        Qf = Br * (hsk / 4 + 1) * 4 * float_type_size;

        const uint32_t D = std::max(hsk, hsv);
        kvsh = params.shmem_staging ? Bc * (D / 4 + 1) * 4 * float_type_size : 4 * float_type_size;

        kblocksh_size = 0;
    }

    const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf + kvsh + kblocksh_size;
    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;

    VK_LOG_DEBUG("ggml_vk_flash_attn_scalar_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", mmq=" << mmq << ", total_size=" << total_size << ", supported=" << supported);

    return supported;
}

static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type) {
    // Needs to be kept up to date on shader changes
    const uint32_t Br = params.block_rows;
    const uint32_t Bc = params.block_cols;

    const uint32_t MatBr = 16, MatBc = 16;

    const uint32_t row_split = Bc / MatBc;

    const uint32_t hsk_pad = ROUNDUP_POW2(hsk, 16);
    const uint32_t hsv_pad = ROUNDUP_POW2(hsv, 16);

    const uint32_t acctype = f32acc ? 4 : 2;
    const uint32_t f16vec4 = 8;

    const uint32_t tmpsh = (Bc / MatBc) * sizeof(float);

    const uint32_t qstride = hsk_pad / 4 + 2;
    const uint32_t Qf = Br * qstride * f16vec4;

    const uint32_t psh_stride = Br / 4 + 2;
    const uint32_t Psh = Bc * psh_stride * f16vec4;

    const uint32_t sfshstride = (hsk <= 128) ? (Br + 8) : Br;
    const uint32_t sfsh = Bc * sfshstride * acctype;

    const uint32_t kvshstride = (params.shmem_staging ? std::max(hsk_pad, hsv_pad) : MatBr) / 4 + 2;
    const uint32_t vsh_stride = MatBc / 4 * row_split;
    const uint32_t ksh = ((kvshstride >= vsh_stride) ? (Bc * kvshstride) : (Bc * vsh_stride)) * f16vec4;

    // BF16 PVMat accumulator is f32 (no bf16 accumulator support), so pvsh is vec4 (16 bytes)
    const uint32_t pvsh_elem_size = (k_type == GGML_TYPE_BF16) ? 16u : f16vec4;
    const uint32_t osh_stride = params.row_split * MatBr / 4;
    const uint32_t pvsh = MatBc * osh_stride * pvsh_elem_size;

    const uint32_t slope = Br * acctype;

    const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + pvsh + slope;
    const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;

    VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported);

    return supported;
}

static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst) {
    VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3];
    std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3];
    std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3];
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    if (sinks) {
        std::cerr << "), (" << sinks << ", name=" << sinks->name << ", type=" << sinks->type << ", ne0=" << sinks->ne[0] << ", ne1=" << sinks->ne[1] << ", ne2=" << sinks->ne[2] << ", ne3=" << sinks->ne[3] << ", nb0=" << sinks->nb[0] << ", nb1=" << sinks->nb[1] << ", nb2=" << sinks->nb[2] << ", nb3=" << sinks->nb[3];
    }
    std::cerr << "))");

    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)

    const uint32_t nem0 = mask ? mask->ne[0] : 0;
    const uint32_t nem1 = mask ? mask->ne[1] : 0;
    const uint32_t nem2 = mask ? mask->ne[2] : 0;
    const uint32_t nem3 = mask ? mask->ne[3] : 0;

    const uint32_t HSK = nek0;
    const uint32_t HSV = nev0;
    uint32_t N = neq1;
    const uint32_t KV = nek1;

    GGML_ASSERT(ne0 == HSV);
    GGML_ASSERT(ne2 == N);

    // input tensor rows must be contiguous
    GGML_ASSERT(nbq0 == ggml_type_size(q->type));
    GGML_ASSERT(nbk0 == ggml_type_size(k->type));
    GGML_ASSERT(nbv0 == ggml_type_size(v->type));

    GGML_ASSERT(neq0 == HSK);

    GGML_ASSERT(neq1 == N);

    GGML_ASSERT(nev1 == nek1);

    // dst cannot be transposed or permuted
    GGML_ASSERT(nb0 == sizeof(float));
    GGML_ASSERT(nb0 <= nb1);
    GGML_ASSERT(nb1 <= nb2);
    GGML_ASSERT(nb2 <= nb3);

    assert(dst->type == GGML_TYPE_F32);
    assert(q->type == GGML_TYPE_F32);
    uint32_t gqa_ratio = 1;
    uint32_t qk_ratio = neq2 / nek2;
    uint32_t workgroups_x = (uint32_t)neq1;
    uint32_t workgroups_y = (uint32_t)neq2;
    uint32_t workgroups_z = (uint32_t)neq3;

    const bool f32acc = !ctx->device->fp16 || dst->op_params[3] == GGML_PREC_F32 || k->type == GGML_TYPE_BF16;

    // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga.
    // For coopmat2 FA, we always use the small size (which is still pretty large for gqa).
    vk_fa_tuning_params tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, 512, KV, k->type, v->type, f32acc);
    const uint32_t max_gqa = std::min(tuning_params.block_rows, 32u);

    if (N <= 8 && qk_ratio > 1 && qk_ratio <= max_gqa &&
        qk_ratio * nek2 == neq2 && nek2 == nev2 && nem2 <= 1) {
        // grouped query attention - make the N dimension equal to gqa_ratio, reduce
        // workgroups proportionally in y dimension. The shader will detect gqa_ratio > 1
        // and change addressing calculations to index Q's dimension 2.
        gqa_ratio = qk_ratio;
        N = gqa_ratio;
        workgroups_y /= gqa_ratio;
    }

    tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, N, KV, k->type, v->type, f32acc);

    const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type));
    uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type));
    uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type));

    // For F32, the shader treats it as a block of size 4 (for vec4 loads)
    if (k->type == GGML_TYPE_F32) {
        k_stride /= 4;
    }
    if (v->type == GGML_TYPE_F32) {
        v_stride /= 4;
    }

    const uint32_t alignment = tuning_params.block_cols;
    bool aligned = (KV % alignment) == 0 &&
                   // the "aligned" shader variant will forcibly align strides, for performance
                   (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0;

    // Need to use the coopmat2 variant that clamps loads when HSK/HSV aren't sufficiently aligned.
    if (((HSK | HSV) % 16) != 0 && tuning_params.path == FA_COOPMAT2) {
        aligned = false;
    }

    float scale         = 1.0f;
    float max_bias      = 0.0f;
    float logit_softcap = 0.0f;

    memcpy(&scale,         (const float *) dst->op_params + 0, sizeof(float));
    memcpy(&max_bias,      (const float *) dst->op_params + 1, sizeof(float));
    memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float));

    if (logit_softcap != 0) {
        scale /= logit_softcap;
    }

    // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively.
    bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768 && nem0 >= tuning_params.block_cols * 16;
    vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(ctx->device, tuning_params, HSK, HSV, aligned, f32acc,
                                                                   mask != nullptr, use_mask_opt, logit_softcap != 0, k->type, v->type);

    vk_pipeline pipeline = nullptr;

    {
        std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
        auto &pipelines = ctx->device->pipeline_flash_attn_f32_f16;
        auto it = pipelines.find(fa_pipeline_state);
        if (it != pipelines.end()) {
            pipeline = it->second;
        } else {
            pipelines[fa_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
        }
    }

    assert(pipeline);
    // Compile early to initialize wg_denoms.
    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    uint32_t split_kv = KV;
    uint32_t split_k = 1;

    // Intel Alchemist prefers more workgroups
    const uint32_t shader_core_count_multiplier = (ctx->device->vendor_id == VK_VENDOR_ID_INTEL && ctx->device->architecture != INTEL_XE2) ? 2 : 1;

    // Use a placeholder core count if one isn't available. split_k is a big help for perf.
    const uint32_t shader_core_count = ctx->device->shader_core_count ? ctx->device->shader_core_count * shader_core_count_multiplier : 16;

    const uint32_t Br = fa_pipeline_state.Br;
    const uint32_t Bc = fa_pipeline_state.Bc;

    GGML_ASSERT(Br == pipeline->wg_denoms[0]);
    const uint32_t Tr = CEIL_DIV(N, Br);

    // Try to use split_k when KV is large enough to be worth the overhead.
    if (gqa_ratio > 1 && workgroups_x <= Br) {
        split_k = shader_core_count * 2 / (workgroups_x * workgroups_y * workgroups_z);
    } else if (gqa_ratio <= 1) {
        uint32_t total_wgs_no_split = Tr * workgroups_y * workgroups_z;
        if (total_wgs_no_split < shader_core_count * 2) {
            split_k = shader_core_count * 2 / total_wgs_no_split;
        }
    }

    if (split_k > 1) {
        // Try to evenly split KV into split_k chunks, but it needs to be a multiple
        // of "align", so recompute split_k based on that.
        split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), alignment);
        split_k = CEIL_DIV(KV, split_kv);
    }

    // Reserve space for split_k temporaries. For each split x batch, we need to store the O matrix (D x ne1)
    // and the per-row m and L values (ne1 rows). We store all the matrices first, followed by the rows.
    // For matrices, the order is (inner to outer) [HSV, ne1, k, ne2, ne3].
    // For L/M, the order is (inner to outer) [ne1, k, ne2, ne3].
    const uint64_t split_k_size = split_k > 1 ? (HSV * ne1 * sizeof(float) + ne1 * sizeof(float) * 2) * split_k * ne2 * ne3 : 0;
    if (split_k_size > ctx->device->properties.limits.maxStorageBufferRange) {
        GGML_ABORT("Requested preallocation size is too large");
    }
    if (ctx->prealloc_size_split_k < split_k_size) {
        ctx->prealloc_size_split_k = split_k_size;
        ggml_vk_preallocate_buffers(ctx, subctx);
    }

    const uint32_t mask_opt_num_dwords = CEIL_DIV(nem0, 16 * Bc);
    const uint64_t mask_opt_size = sizeof(uint32_t) * mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2 * nem3;

    vk_pipeline pipeline_fa_mask_opt = nullptr;
    if (use_mask_opt) {
        {
            std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
            auto &pipelines = ctx->device->pipeline_fa_mask_opt;
            auto it = pipelines.find({Br, Bc});
            if (it != pipelines.end()) {
                pipeline_fa_mask_opt = it->second;
            } else {
                pipelines[{Br, Bc}] = pipeline_fa_mask_opt = std::make_shared<vk_pipeline_struct>();
            }
        }
        assert(pipeline_fa_mask_opt);
        ggml_pipeline_request_descriptor_sets(ctx, pipeline_fa_mask_opt, 1);

        if (ctx->prealloc_size_y < mask_opt_size) {
            ctx->prealloc_size_y = mask_opt_size;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if (ctx->prealloc_y_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }
    }

    const uint32_t n_head_kv   = neq2;
    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

    vk_subbuffer q_buf = ggml_vk_tensor_subbuffer(ctx, q);
    vk_subbuffer k_buf = ggml_vk_tensor_subbuffer(ctx, k);
    vk_subbuffer v_buf = ggml_vk_tensor_subbuffer(ctx, v);
    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
    vk_subbuffer mask_buf = mask ? ggml_vk_tensor_subbuffer(ctx, mask) : q_buf;
    vk_subbuffer sinks_buf = sinks ? ggml_vk_tensor_subbuffer(ctx, sinks) : q_buf;
    vk_subbuffer mask_opt_buf = use_mask_opt ? ggml_vk_subbuffer(ctx, ctx->prealloc_y, 0) : q_buf;

    uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | n_head_log2;

    if (use_mask_opt)
    {
        const vk_op_flash_attn_mask_opt_push_constants opt_pc = {
            nem0,
            nem1,
            nem2,
            (uint32_t)(mask->nb[1] / sizeof(ggml_fp16_t)),
            (uint32_t)(mask->nb[2] / sizeof(ggml_fp16_t)),
            (uint32_t)(mask->nb[3] / sizeof(ggml_fp16_t)),
            mask_opt_num_dwords,
            mask_opt_num_dwords * CEIL_DIV(nem1, Br),
            mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2,
        };

        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline_fa_mask_opt,
                                  { mask_buf, mask_opt_buf }, opt_pc,
                                  { mask_opt_num_dwords, CEIL_DIV(nem1, Br), nem2 * nem3 });
        ggml_vk_sync_buffers(ctx, subctx);
    }

    const vk_flash_attn_push_constants pc = { N, KV,
                                              (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
                                              (uint32_t)neq2, (uint32_t)neq3,
                                              (uint32_t)nek2, (uint32_t)nek3,
                                              (uint32_t)nev2, (uint32_t)nev3,
                                              nem1, nem2, nem3,
                                              q_stride, (uint32_t)nbq2, (uint32_t)nbq3,
                                              k_stride, (uint32_t)nbk2, (uint32_t)nbk3,
                                              v_stride, (uint32_t)nbv2, (uint32_t)nbv3,
                                              scale, max_bias, logit_softcap,
                                              mask_n_head_log2, m0, m1,
                                              gqa_ratio, split_kv, split_k };

    if (split_k > 1) {
        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1);

        if (ctx->prealloc_split_k_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }

        // We reuse workgroups_x to mean the number of splits, so we need to
        // cancel out the divide by wg_denoms[0].
        uint32_t dispatch_x;
        if (gqa_ratio > 1) {
            workgroups_x *= pipeline->wg_denoms[0];
            dispatch_x = split_k * workgroups_x;
        } else {
            dispatch_x = Tr * split_k * pipeline->wg_denoms[0];
        }

        vk_subbuffer split_k_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
                                    {q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf, mask_opt_buf},
                                    pc, { dispatch_x, workgroups_y, workgroups_z });

        ggml_vk_sync_buffers(ctx, subctx);
        const vk_op_flash_attn_split_k_reduce_push_constants pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, split_k, (sinks != nullptr) };
        ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce,
                                    {split_k_buf, sinks_buf, dst_buf},
                                    pc2, { (uint32_t)ne1, HSV, (uint32_t)(ne2 * ne3) });
        ctx->prealloc_split_k_need_sync = true;
    } else {
        if (gqa_ratio > 1) {
            // When using gqa, we want one actual workgroup per batch, so cancel out wg_denoms
            workgroups_x *= pipeline->wg_denoms[0];
        }
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
                                    {q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf, mask_opt_buf},
                                    pc, { workgroups_x, workgroups_y, workgroups_z });
    }
}

static vk_conv_shapes ggml_vk_conv_select_shape(ggml_backend_vk_context * ctx, uint32_t K, uint32_t NPQ) {
    auto n_tiles = [&](vk_conv_shapes s) {
        return CEIL_DIV(K, vk_conv_block_sizes[s].K)
            * CEIL_DIV(NPQ, vk_conv_block_sizes[s].NPQ);
    };

    // We can't query number of shader cores on Intel, use 32 as a placeholder
    // so small convolutions will still choose a smaller tile.
    const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32;

    // 128x128 isn't used with cm1 due to shared memory size; fall through to a smaller tile.
    bool allow_128x128 = true;
#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
    if (!ctx->device->coopmat2 && ctx->device->coopmat_support && ctx->device->coopmat_support_16x16x16_f16acc) {
        allow_128x128 = false;
    }
#endif

    if (allow_128x128 && K > 64 && n_tiles(CONV_SHAPE_128x128) >= shader_core_count * 2) {
        return CONV_SHAPE_128x128;
    } else if (K <= 32 && n_tiles(CONV_SHAPE_32x256) >= shader_core_count * 2) {
        return CONV_SHAPE_32x256;
    } else if (K <= 64 && n_tiles(CONV_SHAPE_64x128) >= shader_core_count * 2) {
        return CONV_SHAPE_64x128;
    } else if (!allow_128x128 && K > 64 && n_tiles(CONV_SHAPE_64x128) >= shader_core_count * 2) {
        // cm1 fallback for large K when 128x128 isn't available
        return CONV_SHAPE_64x128;
    } else {
        return CONV_SHAPE_64x32;
    }
}

static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * dst, ggml_op op) {
    switch (op) {
    case GGML_OP_GET_ROWS:
        GGML_ASSERT(src1->type == GGML_TYPE_I32);
        if (src0->type == GGML_TYPE_I32) {
            // i32 src only supports i32 result
            GGML_ASSERT(dst->type == GGML_TYPE_I32);
            return ctx->device->pipeline_get_rows[src0->type];
        }
        if (dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_get_rows[src0->type];
        }
        if (dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_get_rows_f32[src0->type];
        }
        return nullptr;
    case GGML_OP_ACC:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_acc_f32;
        }
        return nullptr;
    case GGML_OP_SET:
        if (src0->type == src1->type && src0->type == dst->type &&
            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32)) {
            return ctx->device->pipeline_set_f32;
        }
        return nullptr;
    case GGML_OP_ADD:
    case GGML_OP_SUB:
    case GGML_OP_MUL:
    case GGML_OP_DIV:
        if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) ||
            (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16) ||
            (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16)) {
            return nullptr;
        }
        switch (op) {
        case GGML_OP_ADD:
        {
            if (ctx->num_additional_fused_ops > 0) {
                if (ctx->do_add_rms_partials) {
                    return ctx->device->pipeline_multi_add_rms[ctx->num_additional_fused_ops];
                } else {
                    return ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops];
                }
            }
            if (ctx->do_add_rms_partials) {
                auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_rms_norepeat : ctx->device->pipeline_add_rms;
                return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
            } else {
                auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_norepeat : ctx->device->pipeline_add;
                return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
            }
        }
        case GGML_OP_SUB:
        {
            auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_sub_norepeat : ctx->device->pipeline_sub;
            return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
        }
        case GGML_OP_MUL:
        {
            auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_mul_norepeat : ctx->device->pipeline_mul;
            return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
        }
        case GGML_OP_DIV:
        {
            auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_div_norepeat : ctx->device->pipeline_div;
            return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16];
        }
        default:
            break;
        }
        return nullptr;
    case GGML_OP_ADD_ID:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && src2->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_add_id_f32;
        }
        return nullptr;
    case GGML_OP_CONCAT:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_concat_f32;
        }
        if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_concat_f16;
        }
        if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
            return ctx->device->pipeline_concat_i32;
        }
        return nullptr;
    case GGML_OP_UPSCALE:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            uint32_t mode = (ggml_get_op_params_i32(dst, 0) & (0xFF | GGML_SCALE_FLAG_ANTIALIAS));
            switch (mode) {
                case GGML_SCALE_MODE_NEAREST:
                    return ctx->device->pipeline_upscale_nearest_f32;
                case GGML_SCALE_MODE_BILINEAR:
                    return ctx->device->pipeline_upscale_bilinear_f32;
                case GGML_SCALE_MODE_BICUBIC:
                    return ctx->device->pipeline_upscale_bicubic_f32;
                case GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS:
                    return ctx->device->pipeline_upscale_bilinear_antialias_f32;
                default:
                    return nullptr;
            }
        }
        return nullptr;
    case GGML_OP_SCALE:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_scale_f32;
        }
        return nullptr;
    case GGML_OP_SQR:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_sqr_f32;
        }
        return nullptr;
    case GGML_OP_SQRT:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_sqrt_f32;
        }
        return nullptr;
    case GGML_OP_SIN:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_sin_f32;
        }
        return nullptr;
    case GGML_OP_COS:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_cos_f32;
        }
        return nullptr;
    case GGML_OP_LOG:
        if (src0->type == dst->type &&
            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
            return ctx->device->pipeline_log[dst->type == GGML_TYPE_F16];
        }
        return nullptr;
    case GGML_OP_TRI:
        if (src0->type == dst->type &&
            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
            return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16];
        }
        return nullptr;
    case GGML_OP_DIAG:
        if (src0->type == dst->type &&
            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
            return ctx->device->pipeline_diag[dst->type == GGML_TYPE_F16];
        }
        return nullptr;
    case GGML_OP_CLAMP:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_clamp_f32;
        }
        return nullptr;
    case GGML_OP_PAD:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_pad_f32;
        }
        return nullptr;
    case GGML_OP_ROLL:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_roll_f32;
        }
        return nullptr;
    case GGML_OP_REPEAT:
        if (ggml_type_size(src0->type) == sizeof(float) && ggml_type_size(dst->type) == sizeof(float)) {
            return ctx->device->pipeline_repeat_i32;
        }
        if (ggml_type_size(src0->type) == 2 && ggml_type_size(dst->type) == 2) {
            return ctx->device->pipeline_repeat_i16;
        }
        return nullptr;
    case GGML_OP_REPEAT_BACK:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_repeat_back_f32;
        }
        return nullptr;
    case GGML_OP_CPY:
    case GGML_OP_CONT:
    case GGML_OP_DUP:
        return ggml_vk_get_cpy_pipeline(ctx, src0, dst, dst->type);
    case GGML_OP_SET_ROWS:
        if (src1->type == GGML_TYPE_I64) {
            return ctx->device->pipeline_set_rows_i64[dst->type];
        } else {
            return ctx->device->pipeline_set_rows_i32[dst->type];
        }
    case GGML_OP_SILU_BACK:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_silu_back_f32;
        }
        return nullptr;
    case GGML_OP_NORM:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_norm_f32;
        }
        return nullptr;
    case GGML_OP_GROUP_NORM:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_group_norm_f32;
        }
        return nullptr;
    case GGML_OP_RMS_NORM:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            if (ctx->do_add_rms_partials) {
                return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_partials_f32 : ctx->device->pipeline_rms_norm_partials_f32;
            } else {
                return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_f32 : ctx->device->pipeline_rms_norm_f32;
            }
        }
        return nullptr;
    case GGML_OP_RMS_NORM_BACK:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_rms_norm_back_f32;
        }
        return nullptr;
    case GGML_OP_L2_NORM:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_l2_norm_f32;
        }
        return nullptr;
    case GGML_OP_UNARY:
        if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) ||
            (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) ||
            (src0->type != dst->type)) {
            return nullptr;
        }

        switch (ggml_get_unary_op(dst)) {
            case GGML_UNARY_OP_EXP:
                return ctx->device->pipeline_exp[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_ELU:
                return ctx->device->pipeline_elu[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_SILU:
                return ctx->device->pipeline_silu[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_GELU:
                return ctx->device->pipeline_gelu[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_GELU_ERF:
                return ctx->device->pipeline_gelu_erf[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_GELU_QUICK:
                return ctx->device->pipeline_gelu_quick[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_RELU:
                return ctx->device->pipeline_relu[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_XIELU:
                return ctx->device->pipeline_xielu[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_NEG:
                return ctx->device->pipeline_neg[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_TANH:
                return ctx->device->pipeline_tanh[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_SIGMOID:
                return ctx->device->pipeline_sigmoid[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_HARDSIGMOID:
                return ctx->device->pipeline_hardsigmoid[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_HARDSWISH:
                return ctx->device->pipeline_hardswish[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_ABS:
                return ctx->device->pipeline_abs[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_SOFTPLUS:
                return ctx->device->pipeline_softplus[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_STEP:
                return ctx->device->pipeline_step[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_ROUND:
                return ctx->device->pipeline_round[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_CEIL:
                return ctx->device->pipeline_ceil[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_FLOOR:
                return ctx->device->pipeline_floor[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_TRUNC:
                return ctx->device->pipeline_trunc[dst->type == GGML_TYPE_F16];
            case GGML_UNARY_OP_SGN:
                return ctx->device->pipeline_sgn[dst->type == GGML_TYPE_F16];
            default:
                break;
        }
        return nullptr;
    case GGML_OP_GLU:
        if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) ||
            (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) ||
            (src0->type != dst->type)) {
            return nullptr;
        }

        switch (ggml_get_glu_op(dst)) {
            case GGML_GLU_OP_GEGLU:
                return ctx->device->pipeline_geglu[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_REGLU:
                return ctx->device->pipeline_reglu[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_SWIGLU:
                return ctx->device->pipeline_swiglu[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_SWIGLU_OAI:
                return ctx->device->pipeline_swiglu_oai[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_GEGLU_ERF:
                return ctx->device->pipeline_geglu_erf[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_GEGLU_QUICK:
                return ctx->device->pipeline_geglu_quick[dst->type == GGML_TYPE_F16];
            default:
                break;
        }
        return nullptr;
    case GGML_OP_DIAG_MASK_INF:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_diag_mask_inf_f32;
        }
        return nullptr;
    case GGML_OP_SOFT_MAX:
        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
        GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32);

        if (ctx->num_additional_fused_ops) {
            uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
            GGML_ASSERT(idx < num_topk_moe_pipelines);
            // use n_experts from push constant if it's not equal to the power of two spec constant
            bool use_push = dst->ne[0] != (1u << idx);
            return ctx->device->pipeline_topk_moe[idx][use_push];
        }

        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
            return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32;
        }
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
            return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_f16_wg512 : ctx->device->pipeline_soft_max_f32_f16;
        }
        return nullptr;
    case GGML_OP_SOFT_MAX_BACK:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_soft_max_back_f32;
        }
        return nullptr;
    case GGML_OP_ROPE:
    case GGML_OP_ROPE_BACK:
        {
            const ggml_tensor *rope = ctx->num_additional_fused_ops == 2 ? dst->src[0]->src[0] : dst;
            const int mode = ((const int32_t *) rope->op_params)[2];
            const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
            const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
            const bool is_vision = mode == GGML_ROPE_TYPE_VISION;

            if (is_neox) {
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
                    return ctx->device->pipeline_rope_neox_f32;
                }
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_neox_f32_f16;
                }
                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_neox_f16;
                }
            } else if (is_mrope && !is_vision) {
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
                    return ctx->device->pipeline_rope_multi_f32;
                }
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_multi_f32_f16;
                }
                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_multi_f16;
                }
            } else if (is_vision) {
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
                    return ctx->device->pipeline_rope_vision_f32;
                }
                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_vision_f16;
                }
            } else {
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
                    return ctx->device->pipeline_rope_norm_f32;
                }
                if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_norm_f32_f16;
                }
                if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
                    return ctx->device->pipeline_rope_norm_f16;
                }
            }
            return nullptr;
        }
    case GGML_OP_SUM:
    case GGML_OP_SUM_ROWS:
    case GGML_OP_MEAN:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_sum_rows_f32;
        }
        return nullptr;
    case GGML_OP_CUMSUM:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            if (src0->ne[0] <= 512) {
                return ctx->device->pipeline_cumsum_small_f32;
            } else {
                return ctx->device->pipeline_cumsum_f32;
            }
        }
        return nullptr;
    case GGML_OP_SOLVE_TRI:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {

            vk_solve_tri_pipeline_state solve_tri_pipeline_state(src0->ne[0], src1->ne[0]);

            vk_pipeline pipeline = nullptr;

            {
                std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
                auto it = ctx->device->pipeline_solve_tri_f32.find(solve_tri_pipeline_state);
                if (it != ctx->device->pipeline_solve_tri_f32.end()) {
                    pipeline = it->second;
                } else {
                    ctx->device->pipeline_solve_tri_f32[solve_tri_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
                }
            }

            return pipeline;
        }
        return nullptr;
    case GGML_OP_ARGMAX:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
            return ctx->device->pipeline_argmax_f32;
        }
        return nullptr;
    case GGML_OP_COUNT_EQUAL:
        if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I64) {
            return ctx->device->pipeline_count_equal_i32;
        }
        return nullptr;
    case GGML_OP_IM2COL:
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_im2col_f32;
        }
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_im2col_f32_f16;
        }
        return nullptr;
    case GGML_OP_IM2COL_3D:
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_im2col_3d_f32;
        }
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_im2col_3d_f32_f16;
        }
        return nullptr;
    case GGML_OP_TIMESTEP_EMBEDDING:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_timestep_embedding_f32;
        }
        return nullptr;
    case GGML_OP_CONV_TRANSPOSE_1D:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_conv_transpose_1d_f32;
        }
        return nullptr;
    case GGML_OP_POOL_2D:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_pool2d_f32;
        }
        return nullptr;
    case GGML_OP_RWKV_WKV6:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_rwkv_wkv6_f32;
        }
        return nullptr;
    case GGML_OP_RWKV_WKV7:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_rwkv_wkv7_f32;
        }
        return nullptr;
    case GGML_OP_GATED_DELTA_NET:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            const uint32_t S_v = dst->src[2]->ne[0];
            const uint32_t kda = (dst->src[3]->ne[0] == (int64_t)S_v) ? 1 : 0;
            uint32_t si;
            switch (S_v) {
                case 32:  si = 0; break;
                case 64:  si = 1; break;
                case 128: si = 2; break;
                default: return nullptr;
            }
            return ctx->device->pipeline_gated_delta_net[si][kda];
        }
        return nullptr;
    case GGML_OP_SSM_SCAN:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            const uint32_t d_state = src0->ne[0];
            if (d_state == 128) {
                return ctx->device->pipeline_ssm_scan_f32_d128;
            } else if (d_state == 256) {
                return ctx->device->pipeline_ssm_scan_f32_d256;
            }
        }
        return nullptr;
    case GGML_OP_SSM_CONV:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            switch (ctx->num_additional_fused_ops) {
                case 0:  return ctx->device->pipeline_ssm_conv_f32;
                case 1:  return ctx->device->pipeline_ssm_conv_silu_f32;
                case 2:  return ctx->device->pipeline_ssm_conv_bias_silu_f32;
                default: return nullptr;
            }
        }
        return nullptr;
    case GGML_OP_OPT_STEP_ADAMW:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_opt_step_adamw_f32;
        }
        return nullptr;
    case GGML_OP_OPT_STEP_SGD:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_opt_step_sgd_f32;
        }
        return nullptr;
    case GGML_OP_LEAKY_RELU:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_leaky_relu_f32;
        }
        return nullptr;
    case GGML_OP_CONV_2D:
    case GGML_OP_CONV_TRANSPOSE_2D:
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            uint32_t K = dst->ne[2]; // Cout
            uint32_t NPQ = dst->ne[3] * dst->ne[1] * dst->ne[0]; // N * OH * OW
            vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, K, NPQ);

            bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D;
            uint32_t KW = (uint32_t)src0->ne[0];
            uint32_t KH = (uint32_t)src0->ne[1];
            uint32_t s0 = (uint32_t)(ggml_get_op_params_i32(dst, 0));
            uint32_t s1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 1) : s0;
            uint32_t p0 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 2) : 0;
            uint32_t p1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 3) : 0;
            uint32_t d0 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 4) : 1;
            uint32_t d1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 5) : 1;

            // tile-aligned shapes let the shader skip bounds checks
            const uint32_t Cin = (uint32_t)src1->ne[2];
            const uint32_t CRS = Cin * KW * KH;
            const uint32_t BS_K   = vk_conv_block_sizes[shape].K;
            const uint32_t BS_CRS = vk_conv_block_sizes[shape].CRS;
            const uint32_t BS_NPQ = vk_conv_block_sizes[shape].NPQ;
            const uint32_t aligned = ((K   % BS_K   == 0) &&
                                      (CRS % BS_CRS == 0) &&
                                      (NPQ % BS_NPQ == 0)) ? 1u : 0u;

            vk_conv2d_pipeline_state conv2d_pipeline_state(s0, s1, p0, p1, d0, d1, KW, KH, aligned);

            std::map<vk_conv2d_pipeline_state, vk_pipeline> *pipelines = nullptr;
            if (op == GGML_OP_CONV_2D) {
                if (src0->type == GGML_TYPE_F32) {
                    pipelines = &ctx->device->pipeline_conv2d_f32[shape];
                } else if (src0->type == GGML_TYPE_F16) {
                    pipelines = &ctx->device->pipeline_conv2d_f16_f32[shape];
                }
            } else if (op == GGML_OP_CONV_TRANSPOSE_2D) {
                if (src0->type == GGML_TYPE_F32) {
                    pipelines = &ctx->device->pipeline_conv_transpose_2d_f32[shape];
                } else if (src0->type == GGML_TYPE_F16) {
                    pipelines = &ctx->device->pipeline_conv_transpose_2d_f16_f32[shape];
                }
            }

            vk_pipeline pipeline = nullptr;

            {
                std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
                auto it = pipelines->find(conv2d_pipeline_state);
                if (it != pipelines->end()) {
                    pipeline = it->second;
                } else {
                    (*pipelines)[conv2d_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
                }
            }

            return pipeline;
        }
        return nullptr;
    case GGML_OP_CONV_2D_DW:
        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            if (ggml_is_contiguous(src1)) {
                return ctx->device->pipeline_conv2d_dw_whcn_f32;
            } else if (ggml_is_contiguous_channels(src1)) {
                return ctx->device->pipeline_conv2d_dw_cwhn_f32;
            }
        } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
            if (ggml_is_contiguous(src1)) {
                return ctx->device->pipeline_conv2d_dw_whcn_f16_f32;
            } else if (ggml_is_contiguous_channels(src1)) {
                return ctx->device->pipeline_conv2d_dw_cwhn_f16_f32;
            }
        }
        return nullptr;
    case GGML_OP_ADD1:
        if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_add1_f16_f16;
        }
        if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_add1_f16_f32;
        }
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_add1_f32_f32;
        }
        return nullptr;
    case GGML_OP_ARANGE:
        if (dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_arange_f32;
        }
        return nullptr;
    case GGML_OP_FILL:
        if (dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_fill_f32;
        }
        if (dst->type == GGML_TYPE_F16) {
            return ctx->device->pipeline_fill_f16;
        }
        return nullptr;
    default:
        return nullptr;
    }

    GGML_UNUSED(src2);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_unary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.misalign_offsets = (a_offset << 16) | d_offset;

    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_sum_rows_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.misalign_offsets = (a_offset << 16) | d_offset;

    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_pad_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.misalign_offsets = (a_offset << 16) | d_offset;

    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_im2col_3d_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.misalign_offsets = (a_offset << 16) | d_offset;

    GGML_UNUSED(src0);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_binary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && b_offset == 0 && d_offset == 0));

    p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;

    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);

    p.a_offset = a_offset;
    p.d_offset = d_offset;

    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_rope_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    p.a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    p.d_offset = get_misalign_bytes(ctx, dst)  / ggml_type_size(dst->type);

    GGML_UNUSED(src1);
    GGML_UNUSED(src2);
    GGML_UNUSED(src3);
}

template<typename PC>
static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst, ggml_op op, PC&& pc) {
    VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
    if (src1 != nullptr) {
        std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
    }
    if (src2 != nullptr) {
        std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3];
    }
    if (src3 != nullptr) {
        std::cerr << "), (" << src3 << ", name=" << src3->name << ", type=" << src3->type << ", ne0=" << src3->ne[0] << ", ne1=" << src3->ne[1] << ", ne2=" << src3->ne[2] << ", ne3=" << src3->ne[3] << ", nb0=" << src3->nb[0] << ", nb1=" << src3->nb[1] << ", nb2=" << src3->nb[2] << ", nb3=" << src3->nb[3];
    }
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << "), " << ggml_op_name(op) << ")");
    GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
    GGML_ASSERT(dst->buffer != nullptr);
    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
    const uint64_t ne03 = src0->ne[3];

    const bool use_src1 = src1 != nullptr;
    const uint64_t ne10 = use_src1 ? src1->ne[0] : 0;
    const uint64_t ne11 = use_src1 ? src1->ne[1] : 0;
    const uint64_t ne12 = use_src1 ? src1->ne[2] : 0;
    const uint64_t ne13 = use_src1 ? src1->ne[3] : 0;

    const bool use_src2 = src2 != nullptr;
    const bool use_src3 = src3 != nullptr;

    init_pushconst_fastdiv(pc);

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op);

    if (pipeline == nullptr) {
        std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type);
        if (src1 != nullptr) {
            std::cerr << " and " << ggml_type_name(src1->type);
        }
        std::cerr << " to " << ggml_type_name(dst->type) << std::endl;
        GGML_ABORT("fatal error");
    }

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    vk_subbuffer src0_buf = ggml_vk_tensor_subbuffer(ctx, src0, true);
    vk_subbuffer src1_buf = use_src1 ? ggml_vk_tensor_subbuffer(ctx, src1, true) : vk_subbuffer{};
    vk_subbuffer src2_buf = use_src2 ? ggml_vk_tensor_subbuffer(ctx, src2, true) : vk_subbuffer{};
    vk_subbuffer src3_buf = use_src3 ? ggml_vk_tensor_subbuffer(ctx, src3, true) : vk_subbuffer{};
    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, true);

    // Compute misalignment offset for descriptors and store it in in push constants.
    init_pushconst_tensor_offsets(ctx, pc, src0, src1, src2, src3, dst);

    std::array<uint32_t, 3> elements;

    switch (op) {
    case GGML_OP_NORM:
    case GGML_OP_RMS_NORM_BACK:
    case GGML_OP_L2_NORM:
    case GGML_OP_SOFT_MAX:
    case GGML_OP_SOFT_MAX_BACK:
    case GGML_OP_SUM_ROWS:
    case GGML_OP_CUMSUM:
    case GGML_OP_MEAN:
    case GGML_OP_ARGMAX:
        {
            const uint32_t nr = ggml_nrows(src0);
            if (nr > 262144) {
                elements = { 512, 512, CEIL_DIV(nr, 262144) };
            } else if (nr > 512) {
                elements = { 512, CEIL_DIV(nr, 512), 1 };
            } else {
                elements = { nr, 1, 1 };
            }
        } break;
    case GGML_OP_SOLVE_TRI:
        {
            uint32_t nr = (uint32_t)(ne02 * ne03);
            if (nr > 262144) {
                elements = { 512, 512, CEIL_DIV(nr, 262144) };
            } else if (nr > 512) {
                elements = { 512, CEIL_DIV(nr, 512), 1 };
            } else {
                elements = { nr, 1, 1 };
            }
        }
        break;
    case GGML_OP_RMS_NORM:
        if (ctx->do_add_rms_partials) {
            // Run one element per thread, 128 threads per workgroup
            elements = { (uint32_t)CEIL_DIV(ne00, 128), 1, 1 };
        } else {
            elements = { (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne03 };
        }
        break;

    case GGML_OP_SUM:
        // We use GGML_OP_SUM_ROWS with 1 row.
        elements = { 1, 1, 1 };
        break;
    case GGML_OP_GROUP_NORM:
        {
            const uint32_t num_groups = dst->op_params[0];
            elements = { num_groups * (uint32_t)src0->ne[3], 1, 1 };
        } break;
    case GGML_OP_DIAG_MASK_INF:
        elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 };
        break;
    case GGML_OP_ROPE:
    case GGML_OP_ROPE_BACK:
        {
            uint32_t nrows = (uint32_t)ggml_nrows(src0);
            uint32_t z = 1;
            if (nrows > ctx->device->properties.limits.maxComputeWorkGroupCount[0]) {
                z = CEIL_DIV(nrows, 32768);
                nrows = 32768;
            }
            elements = { nrows, (uint32_t)ne00, z };

        } break;
    case GGML_OP_GET_ROWS:
        elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) };
        elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
        elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
        break;
    case GGML_OP_ARGSORT:
        GGML_ASSERT(0);
        break;
    case GGML_OP_IM2COL:
        {
            const bool is_2D = dst->op_params[6] == 1;

            const uint32_t IC = src1->ne[is_2D ? 2 : 1];

            const uint32_t KH = is_2D ? src0->ne[1] : 1;
            const uint32_t KW =         src0->ne[0];

            const uint32_t OH = is_2D ? dst->ne[2] : 1;
            const uint32_t OW =         dst->ne[1];

            const uint32_t batch = src1->ne[is_2D ? 3 : 2];

            const uint32_t CHW = IC * KH * KW;
            // Cap X workgroups to limit concurrent IC channel reads.
            // The shader loops over X to cover the full CHW dimension.
            // AMD prefers a lower limit
            const uint32_t min_cap = ctx->device->vendor_id == VK_VENDOR_ID_AMD ? 512u : 4096u;
            const uint32_t x_elements = std::min(CHW, std::max(min_cap, OW * KH * KW));
            elements = { x_elements, OW, OH * batch };
            elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
            elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
        } break;
    case GGML_OP_IM2COL_3D:
        {
            const uint32_t IC = ((const uint32_t *)(dst->op_params))[9];

            const uint32_t N  = ne13 / IC;

            const uint32_t KD = ne02;
            const uint32_t KH = ne01;
            const uint32_t KW = ne00;

            const uint32_t OD = dst->ne[3] / N;
            const uint32_t OH = dst->ne[2];
            const uint32_t OW = dst->ne[1];

            const uint32_t IC_KD_KH_KW = IC*KD*KH*KW;
            const uint32_t N_OD_OH = N*OD*OH;

            elements = { IC_KD_KH_KW, OW, N_OD_OH };
            elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
        } break;
    case GGML_OP_TIMESTEP_EMBEDDING:
        {
            const uint32_t dim = dst->op_params[0];
            uint32_t half_ceil = (dim + 1) / 2;
            elements = { half_ceil, (uint32_t)src0->ne[0], 1 };
        } break;
    case GGML_OP_CONV_TRANSPOSE_1D:
        {
            elements = {uint32_t(src0->ne[1]), 1, 1}; // parallelize in {Cout, 1, 1}
        } break;
    case GGML_OP_POOL_2D:
        {
            const uint32_t N = dst->ne[3];
            const uint32_t OC = dst->ne[2];
            const uint32_t OH = dst->ne[1];
            const uint32_t OW = dst->ne[0];
            elements = { N * OC * OH * OW, 1, 1};
        } break;
    case GGML_OP_CONV_2D:
    case GGML_OP_CONV_TRANSPOSE_2D:
        if constexpr (std::is_same_v<PC, vk_op_conv2d_push_constants>) {
            const uint32_t NPQ = pc.N * pc.OH * pc.OW;
            const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, pc.Cout, NPQ);
            const uint32_t NPQ_blocks = CEIL_DIV(NPQ, vk_conv_block_sizes[shape].NPQ);

            elements = { pc.Cout, NPQ_blocks, 1 };
            if (elements[1] > 512) {
                elements[2] = CEIL_DIV(elements[1], 512);
                elements[1] = 512;
            }
        } else {
            GGML_ABORT("invalid push constant type for CONV_2D");
        }
        break;
    case GGML_OP_ADD:
    case GGML_OP_SUB:
    case GGML_OP_DIV:
    case GGML_OP_MUL:
    case GGML_OP_ADD1:
    case GGML_OP_ARANGE:
    case GGML_OP_FILL:
    case GGML_OP_SCALE:
    case GGML_OP_SQR:
    case GGML_OP_SQRT:
    case GGML_OP_SIN:
    case GGML_OP_COS:
    case GGML_OP_LOG:
    case GGML_OP_TRI:
    case GGML_OP_DIAG:
    case GGML_OP_CLAMP:
    case GGML_OP_PAD:
    case GGML_OP_ROLL:
    case GGML_OP_REPEAT:
    case GGML_OP_REPEAT_BACK:
    case GGML_OP_CPY:
    case GGML_OP_CONCAT:
    case GGML_OP_UPSCALE:
    case GGML_OP_UNARY:
    case GGML_OP_GLU:
    case GGML_OP_CONV_2D_DW:
        {
            uint32_t ne = ggml_nelements(dst);
            if (op == GGML_OP_CPY && ggml_is_quantized(src0->type) && ggml_is_quantized(dst->type)) {
                // Convert from number of logical elements to 2- or 4-byte units.
                ne /= ggml_blck_size(src0->type);
                if ((ggml_type_size(src0->type) % 4) == 0) {
                    ne *= ggml_type_size(src0->type) / 4;
                } else {
                    ne *= ggml_type_size(src0->type) / 2;
                }
            }
            // copy_to_quant has block size of 32, and each thread does QUANT_K elements.
            // Splitting into 512x512xZ wouldn't work well since each workgroup does 1024 elements.
            // So divide by block size here before splitting into 512x512 groups.
            if (op == GGML_OP_CPY && !ggml_is_quantized(src0->type) && ggml_is_quantized(dst->type)) {
                ne = CEIL_DIV(ne, ggml_blck_size(dst->type));
            }
            if (ne > 262144) {
                elements = { 512, 512, CEIL_DIV(ne, 262144) };
            } else if (ne > 512) {
                elements = { 512, CEIL_DIV(ne, 512), 1 };
            } else {
                elements = { ne, 1, 1 };
            }

            if (pipeline == ctx->device->pipeline_cpy_transpose_32 ||
                pipeline == ctx->device->pipeline_cpy_transpose_16) {
                // 32x32 tiles
                elements[0] = (uint32_t)CEIL_DIV(dst->ne[0], 32);
                elements[1] = (uint32_t)CEIL_DIV(dst->ne[1], 32);
                elements[2] = (uint32_t)(dst->ne[2]*dst->ne[3]);
                elements[0] = std::min(elements[0], ctx->device->properties.limits.maxComputeWorkGroupCount[0]);
                elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
                elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
            }
        } break;
    case GGML_OP_ADD_ID:
        {
            elements = { (uint32_t)ne01, (uint32_t)ne02, 1 };
        } break;
    case GGML_OP_SET_ROWS:
        {
            uint32_t ne = ggml_nelements(src0);
            if (ggml_is_quantized(dst->type)) {
                // quants run 32 threads each doing QUANT_K elements
                ne = CEIL_DIV(ne, 32 * ggml_blck_size(dst->type));
            } else {
                // scalar types do one element per thread, running 512 threads
                ne = CEIL_DIV(ne, 512);
            }
            if (ne > 262144) {
                elements = { 512, 512, CEIL_DIV(ne, 262144) };
            } else if (ne > 512) {
                elements = { 512, CEIL_DIV(ne, 512), 1 };
            } else {
                elements = { ne, 1, 1 };
            }
        }
        break;
    case GGML_OP_SSM_CONV:
        {
            const uint32_t nr  = src0->ne[1];
            const uint32_t n_t = dst->ne[1];
            const uint32_t n_s = dst->ne[2];
            elements = { nr, n_t, n_s };
        }
        break;
    default:
        elements = { (uint32_t)ggml_nelements(src0), 1, 1 };
        break;
    }

    if (op == GGML_OP_ADD || op == GGML_OP_RMS_NORM) {
        vk_subbuffer a_buf = src0_buf;
        if (ctx->do_add_rms_partials) {
            a_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_add_rms_partials, ctx->prealloc_size_add_rms_partials_offset);
        }
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
            { src0_buf, src1_buf, dst_buf, a_buf }, pc, elements);
    } else if (op == GGML_OP_GLU) {
        // Empty src1 is possible in glu, but the shader needs a buffer
        vk_subbuffer subbuf1 = use_src1 ? src1_buf : src0_buf;
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, dst_buf }, pc, elements);
    } else if (op == GGML_OP_SOFT_MAX) {
        // Empty src1 and src2 is possible in soft_max, but the shader needs a buffer
        vk_subbuffer subbuf1 = use_src1 ? src1_buf : src0_buf;
        vk_subbuffer subbuf2 = use_src2 ? src2_buf : src0_buf;
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, subbuf2, dst_buf }, pc, elements);
    } else if (op == GGML_OP_ROPE || op == GGML_OP_ROPE_BACK) {
        // Empty src2 and src3 is possible in rope, but the shader needs a buffer
        vk_subbuffer subbuf2 = use_src2 ? src2_buf : src0_buf;
        vk_subbuffer subbuf3 = use_src3 ? src3_buf : src0_buf;
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, subbuf2, dst_buf, subbuf3 }, pc, elements);
    } else if (op == GGML_OP_IM2COL || op == GGML_OP_IM2COL_3D) {
        if (ctx->device->shader_int64 && ctx->device->buffer_device_address) {
            // buffer device address path doesn't use dst buffer
            dst_buf.size = 1;
        }
        // im2col uses only src1 and dst buffers
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src1_buf, dst_buf }, pc, elements);
    } else if (op == GGML_OP_COUNT_EQUAL) {
        // count_equal assumes that destination buffer is initialized with zeroes
        ggml_vk_buffer_memset_async(subctx, dst_buf.buffer, dst_buf.offset, 0, dst_buf.size);
        ggml_vk_sync_buffers(ctx, subctx);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, dst_buf }, pc, elements);
    } else if (op == GGML_OP_OPT_STEP_SGD) {
        // OPT_STEP_SGD works on src0, it does not need dst
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, src2_buf }, pc, elements);
    } else if (use_src3) {
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, src2_buf, src3_buf, dst_buf }, pc, elements);
    } else if (use_src2) {
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, src2_buf, dst_buf }, pc, elements);
    } else if (use_src1) {
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, dst_buf }, pc, elements);
    } else {
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, dst_buf }, pc, elements);
    }
}

static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GET_ROWS, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    int nb1 = dst->op_params[0] / src0_type_size; // 4 bytes of float32
    int nb2 = dst->op_params[1] / src0_type_size; // 4 bytes of float32
    int nb3 = dst->op_params[2] / src0_type_size; // 4 bytes of float32
    int offset = dst->op_params[3] / src0_type_size; // offset in bytes

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)nb3,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)nb3,
        0,
        0.0f, 0.0f, offset,
    });
}

static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) {
    const ggml_tensor *first_node = cgraph->nodes[node_idx];
    const ggml_tensor *dst = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];

    // Make a list of all the tensors used by the op.
    // Last element of the list is the dest tensor.
    const ggml_tensor *tensors[MAX_PARAMETER_COUNT];
    uint32_t num_srcs = ctx->num_additional_fused_ops + 2;
    uint32_t num_tensors = num_srcs + 1;
    GGML_ASSERT(num_tensors + ctx->do_add_rms_partials <= MAX_PARAMETER_COUNT);

    tensors[0] = first_node->src[0];
    tensors[1] = first_node->src[1];
    for (int32_t i = 0; i < ctx->num_additional_fused_ops; ++i) {
        // check whether the previous result is src[0] or src[1]
        if (cgraph->nodes[node_idx + i] == cgraph->nodes[node_idx + i + 1]->src[0]) {
            tensors[i+2] = cgraph->nodes[node_idx + i + 1]->src[1];
        } else {
            tensors[i+2] = cgraph->nodes[node_idx + i + 1]->src[0];
        }
    }
    tensors[num_srcs] = dst;

    vk_op_multi_add_push_constants pc;
    pc.ne20 = (uint32_t)dst->ne[0];
    pc.ne21 = (uint32_t)dst->ne[1];
    pc.ne22 = (uint32_t)dst->ne[2];
    pc.ne23 = (uint32_t)dst->ne[3];

    for (uint32_t i = 0; i < num_tensors; ++i) {
        const ggml_tensor *t = tensors[i];
        pc.nb[i][0] = (uint32_t)t->nb[0] / sizeof(float);
        pc.nb[i][1] = (uint32_t)t->nb[1] / sizeof(float);
        pc.nb[i][2] = (uint32_t)t->nb[2] / sizeof(float);
        pc.nb[i][3] = (uint32_t)t->nb[3] / sizeof(float);
    }
    pc.rms_partials = ctx->do_add_rms_partials;

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, tensors[0], tensors[1], nullptr, dst, dst->op);

    if (pipeline == nullptr) {
        std::cerr << "ggml_vulkan: Error: Missing multi_add";
        GGML_ABORT("fatal error");
    }

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    ggml_backend_vk_buffer_context * buf_ctx[MAX_PARAMETER_COUNT];
    vk_buffer buf[MAX_PARAMETER_COUNT];
    size_t offset[MAX_PARAMETER_COUNT];
    bool uma[MAX_PARAMETER_COUNT];

    for (uint32_t i = 0; i < num_tensors; ++i) {
        buf_ctx[i] = (ggml_backend_vk_buffer_context *)tensors[i]->buffer->context;
        buf[i] = nullptr;
        offset[i] = 0;
        uma[i] = false;

        if (ctx->device->uma) {
            ggml_vk_host_get(ctx->device, tensors[i]->data, buf[i], offset[i]);
            uma[i] = buf[i] != nullptr;
        }
        if (!uma[i]) {
            buf[i] = buf_ctx[i]->dev_buffer;
            offset[i] = vk_tensor_offset(tensors[i]) + tensors[i]->view_offs;
        }
        GGML_ASSERT(buf[i] != nullptr);
    }
    // If any remaining descriptors are unused, just point them at src[0]
    for (uint32_t i = num_tensors; i < MAX_PARAMETER_COUNT; ++i) {
        buf[i] = buf[0];
        offset[i] = 0;
    }
    if (ctx->do_add_rms_partials) {
        buf[num_tensors] = ctx->prealloc_add_rms_partials;
        offset[num_tensors] = ctx->prealloc_size_add_rms_partials_offset;
    }

    std::array<uint32_t, 3> elements;

    uint32_t ne = ggml_nelements(dst);
    if (ne > 262144) {
        elements = { 512, 512, CEIL_DIV(ne, 262144) };
    } else if (ne > 512) {
        elements = { 512, CEIL_DIV(ne, 512), 1 };
    } else {
        elements = { ne, 1, 1 };
    }

    static_assert(MAX_PARAMETER_COUNT == 12);
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
        {
            ggml_vk_subbuffer(ctx, buf[0], offset[0]),
            ggml_vk_subbuffer(ctx, buf[1], offset[1]),
            ggml_vk_subbuffer(ctx, buf[2], offset[2]),
            ggml_vk_subbuffer(ctx, buf[3], offset[3]),
            ggml_vk_subbuffer(ctx, buf[4], offset[4]),
            ggml_vk_subbuffer(ctx, buf[5], offset[5]),
            ggml_vk_subbuffer(ctx, buf[6], offset[6]),
            ggml_vk_subbuffer(ctx, buf[7], offset[7]),
            ggml_vk_subbuffer(ctx, buf[8], offset[8]),
            ggml_vk_subbuffer(ctx, buf[9], offset[9]),
            ggml_vk_subbuffer(ctx, buf[10], offset[10]),
            ggml_vk_subbuffer(ctx, buf[11], offset[11]),
        }, pc, elements);
}

static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_ADD, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, ctx->do_add_rms_partials,
    });
}

static void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SUB, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_MUL, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_DIV, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_add_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t src2_type_size = ggml_type_size(src2->type);

    ggml_vk_op_f32<vk_op_add_id_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_ADD_ID, {
        (uint32_t)dst->ne[0],
        (uint32_t)dst->ne[1],
        (uint32_t)src0->nb[1] / src0_type_size,
        (uint32_t)src0->nb[2] / src0_type_size,
        (uint32_t)src1->nb[1] / src1_type_size,
        (uint32_t)src2->nb[1] / src2_type_size,
    });
}

static void ggml_vk_op_f32_wkv(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, int version) {
    GGML_ASSERT(version == 6 || version == 7);
    int num_srcs = version == 6 ? 6 : 7;

    for (int i = 0; i < num_srcs; i++) {
        GGML_ASSERT(!ggml_is_quantized(dst->src[i]->type));
    }

    GGML_ASSERT(dst->buffer != nullptr);

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
    GGML_ASSERT(pipeline != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
    vk_subbuffer src_buf[7] = {};
    for (int i = 0; i < num_srcs; i++) {
        src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]);
    }

    std::array<uint32_t, 3> elements = {
        (uint32_t)(pc.B * pc.H),
        1,
        1
    };

    if (version == 6) {
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
            {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf},
            pc, elements);
    } else if (version == 7) {
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
            {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], src_buf[6], dst_buf},
            pc, elements);
    } else {
        // shouldn't happen
        GGML_ASSERT(false);
    }
}

static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    const size_t seq_length = dst->src[0]->ne[2];
    const size_t n_embed = dst->ne[0];
    const size_t n_heads = dst->src[0]->ne[1];
    const size_t n_seqs = dst->src[5]->ne[1];

    ggml_vk_op_f32_wkv(
        ctx, subctx, dst,
        {
            (uint32_t)n_seqs,
            (uint32_t)seq_length,
            (uint32_t)n_embed,
            (uint32_t)n_heads,
        },
        6
    );
}

static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    const size_t seq_length = dst->src[0]->ne[2];
    const size_t n_embed = dst->ne[0];
    const size_t n_heads = dst->src[0]->ne[1];
    const size_t n_seqs = dst->src[6]->ne[1];

    ggml_vk_op_f32_wkv(
        ctx, subctx, dst,
        {
            (uint32_t)n_seqs,
            (uint32_t)seq_length,
            (uint32_t)n_embed,
            (uint32_t)n_heads,
        },
        7
    );
}

static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    const ggml_tensor * src_q     = dst->src[0];
    const ggml_tensor * src_v     = dst->src[2];
    const ggml_tensor * src_beta  = dst->src[4];

    GGML_ASSERT(dst->buffer != nullptr);

    const uint32_t S_v      = (uint32_t)src_v->ne[0];
    const uint32_t H        = (uint32_t)src_v->ne[1];
    const uint32_t n_tokens = (uint32_t)src_v->ne[2];
    const uint32_t n_seqs   = (uint32_t)src_v->ne[3];

    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
    const uint32_t K = (uint32_t)ggml_get_op_params_i32(dst, 0);

    const uint32_t s_off = S_v * H * n_tokens * n_seqs;

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
    GGML_ASSERT(pipeline != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
    vk_subbuffer src_buf[6] = {};
    for (int i = 0; i < 6; i++) {
        src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]);
    }

    const uint32_t sq1 = (uint32_t)(src_q->nb[1] / sizeof(float));
    const uint32_t sq2 = (uint32_t)(src_q->nb[2] / sizeof(float));
    const uint32_t sq3 = (uint32_t)(src_q->nb[3] / sizeof(float));
    const uint32_t sv1 = (uint32_t)(src_v->nb[1] / sizeof(float));
    const uint32_t sv2 = (uint32_t)(src_v->nb[2] / sizeof(float));
    const uint32_t sv3 = (uint32_t)(src_v->nb[3] / sizeof(float));
    const uint32_t sb1 = (uint32_t)(src_beta->nb[1] / sizeof(float));
    const uint32_t sb2 = (uint32_t)(src_beta->nb[2] / sizeof(float));
    const uint32_t sb3 = (uint32_t)(src_beta->nb[3] / sizeof(float));

    const uint32_t neq1 = (uint32_t)src_q->ne[1];
    const uint32_t rq3  = (uint32_t)(src_v->ne[3] / src_q->ne[3]);

    const float scale = 1.0f / sqrtf((float)S_v);
    const vk_op_gated_delta_net_push_constants pc = {
        H, n_tokens, n_seqs, s_off,
        sq1, sq2, sq3,
        sv1, sv2, sv3,
        sb1, sb2, sb3,
        neq1, rq3,
        scale,
        K
    };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
        {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf},
        pc, { H, n_seqs, S_v });
}

static void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
    const ggml_tensor * src2 = dst->src[2];
    const ggml_tensor * src3 = dst->src[3];
    const ggml_tensor * src4 = dst->src[4];
    const ggml_tensor * src5 = dst->src[5];

    GGML_ASSERT(dst->buffer != nullptr);

    const uint32_t head_dim = src0->ne[1];
    const uint32_t n_head = src1->ne[1];
    const uint32_t n_group = src4->ne[1];
    const uint32_t n_tok = src1->ne[2];
    const uint32_t n_seq = src1->ne[3];

    bool is_mamba2 = (src3->nb[1] == sizeof(float));
    GGML_ASSERT(is_mamba2);

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, dst->op);
    GGML_ASSERT(pipeline != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    const int64_t s_off = ggml_nelements(src1) * sizeof(float);

    const vk_op_ssm_scan_push_constants pc = {
        (uint32_t)src0->nb[2], (uint32_t)src0->nb[3],
        (uint32_t)src1->nb[2], (uint32_t)src1->nb[3],
        (uint32_t)src2->nb[1], (uint32_t)src2->nb[2],
        (uint32_t)src3->nb[1],
        (uint32_t)src4->nb[2], (uint32_t)src4->nb[3],
        (uint32_t)src5->nb[2], (uint32_t)src5->nb[3],
        (uint32_t)s_off,
        n_head, head_dim, n_group, n_tok
    };

    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
    vk_subbuffer src_buf[7] = {};
    for (int i = 0; i < 7 && dst->src[i] != nullptr; i++) {
        src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]);
    }

    std::array<uint32_t, 3> elements;

    const uint32_t d_state = src0->ne[0];
    uint32_t num_subgroups = d_state / ctx->device->subgroup_size;
    const uint32_t num_workgroups_x = CEIL_DIV(n_head * head_dim, num_subgroups);
    const uint32_t num_workgroups_y = n_seq;
    elements = { num_workgroups_x, num_workgroups_y, 1 };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
        {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], src_buf[6], dst_buf},
        pc, elements);
}

static void ggml_vk_ssm_conv(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
    ggml_tensor * conv = cgraph->nodes[node_idx];
    const ggml_tensor * src0 = conv->src[0];
    const ggml_tensor * src1 = conv->src[1];

    // Pick the destination tensor (last node in the fused chain) and the optional bias.
    // Fusion modes: 0 = ssm_conv, 1 = ssm_conv+silu, 2 = ssm_conv+add(bias)+silu.
    ggml_tensor * dst = conv;
    const ggml_tensor * bias = nullptr;

    if (ctx->num_additional_fused_ops == 1) {
        dst = cgraph->nodes[node_idx + 1]; // silu
    } else if (ctx->num_additional_fused_ops == 2) {
        ggml_tensor * add = cgraph->nodes[node_idx + 1];
        bias = (add->src[0] == conv) ? add->src[1] : add->src[0];
        dst = cgraph->nodes[node_idx + 2]; // silu
    }

    // The shader always declares 4 bindings; bind src0 as a dummy when bias isn't fused.
    const ggml_tensor * src2 = bias ? bias : src0;

    ggml_vk_op_f32<vk_op_ssm_conv_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SSM_CONV, {
        (uint32_t)src0->nb[1], (uint32_t)src0->nb[2],
        (uint32_t)src1->nb[1],
        (uint32_t)dst->nb[0], (uint32_t)dst->nb[1], (uint32_t)dst->nb[2],
        (uint32_t)src1->ne[0],
        (uint32_t)src0->ne[0],
        (uint32_t)src0->ne[1],
        (uint32_t)dst->ne[1],
        (uint32_t)dst->ne[2],
    });
}

static void ggml_vk_op_f32_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_push_constants&& pc) {
    const ggml_tensor * x = dst->src[0];
    const ggml_tensor * g = dst->src[1];
    const ggml_tensor * gm = dst->src[2];
    const ggml_tensor * gv = dst->src[3];
    const ggml_tensor * p = dst->src[4];

    GGML_ASSERT(x->type == GGML_TYPE_F32);
    GGML_ASSERT(g->type == GGML_TYPE_F32);
    GGML_ASSERT(gm->type == GGML_TYPE_F32);
    GGML_ASSERT(gv->type == GGML_TYPE_F32);
    GGML_ASSERT(p->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->buffer != nullptr);
    GGML_ASSERT(ggml_is_contiguous(x));
    GGML_ASSERT(ggml_is_contiguous(g));
    GGML_ASSERT(ggml_is_contiguous(gm));
    GGML_ASSERT(ggml_is_contiguous(gv));
    GGML_ASSERT(ggml_is_contiguous(p));
    GGML_ASSERT(ggml_are_same_shape(x, g));
    GGML_ASSERT(ggml_are_same_shape(x, gm));
    GGML_ASSERT(ggml_are_same_shape(x, gv));
    GGML_ASSERT(ggml_nelements(p) == 7);

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, g, gm, gv, dst, GGML_OP_OPT_STEP_ADAMW);
    GGML_ASSERT(pipeline != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    vk_subbuffer x_buf = ggml_vk_tensor_subbuffer(ctx, x);
    vk_subbuffer g_buf = ggml_vk_tensor_subbuffer(ctx, g);
    vk_subbuffer gm_buf = ggml_vk_tensor_subbuffer(ctx, gm);
    vk_subbuffer gv_buf = ggml_vk_tensor_subbuffer(ctx, gv);
    vk_subbuffer p_buf = ggml_vk_tensor_subbuffer(ctx, p);

    std::array<uint32_t, 3> elements = { (uint32_t)ggml_nelements(x), 1, 1 };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
        {x_buf, g_buf, gm_buf, gv_buf, p_buf},
        pc, elements);
}

static void ggml_vk_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    const size_t n = ggml_nelements(dst->src[0]);

    ggml_vk_op_f32_opt_step_adamw(
        ctx, subctx, dst,
        { (uint32_t)n, 0, 0.0f, 0.0f, 0.0f, 0.0f }
    );
}

static void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
    const size_t n = ggml_nelements(dst->src[0]);

    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_OPT_STEP_SGD, { (uint32_t)n, 0, 0.0f, 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    int * op_params = (int *)dst->op_params;

    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, {
        (uint32_t)ggml_nelements(dst),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, op_params[0],
    });
}

static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t mode = (uint32_t)ggml_get_op_params_i32(dst, 0);

    GGML_TENSOR_UNARY_OP_LOCALS

    float sf0 = (float)ne0 / ne00;
    float sf1 = (float)ne1 / ne01;
    float sf2 = (float)ne2 / ne02;
    float sf3 = (float)ne3 / ne03;
    float pixel_offset = 0.5f;

    if (mode & GGML_SCALE_FLAG_ALIGN_CORNERS) {
        sf0 = ne0 > 1 && ne00 > 1 ? (float)(ne0 - 1) / (ne00 - 1) : sf0;
        sf1 = ne1 > 1 && ne01 > 1 ? (float)(ne1 - 1) / (ne01 - 1) : sf1;
        pixel_offset = 0.0f;
    }

    ggml_vk_op_f32<vk_op_upscale_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UPSCALE, {
        (uint32_t)ggml_nelements(dst), 0, 0,
        (uint32_t)ne00, (uint32_t)ne01,
        (uint32_t)nb00 / src0_type_size, (uint32_t)nb01 / src0_type_size, (uint32_t)nb02 / src0_type_size, (uint32_t)nb03 / src0_type_size,
        (uint32_t)ne0, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3,
        sf0, sf1, sf2, sf3, pixel_offset
    });
}

static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
    p.param1 = ggml_get_op_params_f32(dst, 0);
    p.param2 = ggml_get_op_params_f32(dst, 1);

    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SCALE, std::move(p));
}

static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SQR, vk_op_unary_push_constants_init(src0, dst));
}

static void ggml_vk_sqrt(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SQRT, vk_op_unary_push_constants_init(src0, dst));
}

static void ggml_vk_add1(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_ADD1, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_arange(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    VK_LOG_DEBUG("ggml_vk_arange(dst=" << dst << ", ne=" << ggml_nelements(dst) << ")");

    vk_op_push_constants pc = {
        (uint32_t)ggml_nelements(dst),
        1,
        ggml_get_op_params_f32(dst, 0),
        ggml_get_op_params_f32(dst, 2),
        0.0f, 0.0f,
    };

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, dst, GGML_OP_ARANGE);
    GGML_ASSERT(pipeline != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, false);

    std::array<uint32_t, 3> elements = { (uint32_t)ggml_nelements(dst), 1, 1 };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { dst_buf }, pc, elements);
}

static void ggml_vk_fill(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
    VK_LOG_DEBUG("ggml_vk_fill(dst=" << dst << ", ne=" << ggml_nelements(dst) << ")");

    vk_op_push_constants pc = {
        (uint32_t)ggml_nelements(dst),
        1,
        ggml_get_op_params_f32(dst, 0),
        0.0f,
        0.0f, 0.0f,
    };

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, dst, GGML_OP_FILL);
    GGML_ASSERT(pipeline != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, false);

    std::array<uint32_t, 3> elements = { (uint32_t)ggml_nelements(dst), 1, 1 };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { dst_buf }, pc, elements);
}

static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SIN, vk_op_unary_push_constants_init(src0, dst));
}

static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_COS, vk_op_unary_push_constants_init(src0, dst));
}

static void ggml_vk_log(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LOG, vk_op_unary_push_constants_init(src0, dst));
}

static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
    p.param1 = ggml_get_op_params_f32(dst, 0);

    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p));
}

static void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));

    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG, std::move(p));
}

static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
    p.param1 = ggml_get_op_params_f32(dst, 0);
    p.param2 = ggml_get_op_params_f32(dst, 1);

    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CLAMP, std::move(p));
}

static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_pad_push_constants p = vk_op_pad_push_constants_init(src0, dst);
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_PAD, std::move(p));
}

static void ggml_vk_roll(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const int32_t s0 = ggml_get_op_params_i32(dst, 0);
    const int32_t s1 = ggml_get_op_params_i32(dst, 1);
    const int32_t s2 = ggml_get_op_params_i32(dst, 2);
    const int32_t s3 = ggml_get_op_params_i32(dst, 3);
    const uint32_t s01_packed = ((s0 + 0x8000) << 16) | (s1 + 0x8000);
    const uint32_t s23_packed = ((s2 + 0x8000) << 16) | (s3 + 0x8000);

    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
    memcpy(&p.param1, &s01_packed, sizeof(float));
    memcpy(&p.param2, &s23_packed, sizeof(float));

    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_ROLL, std::move(p));
}

static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_REPEAT, std::move(p));
}

static void ggml_vk_repeat_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_REPEAT_BACK, std::move(p));
}

static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    uint32_t ne = (uint32_t)ggml_nelements(src0);
    if (ggml_is_quantized(src0->type) && ggml_is_quantized(dst->type)) {
        // Convert from number of logical elements to 2- or 4-byte units.
        ne /= ggml_blck_size(src0->type);
        if ((ggml_type_size(src0->type) % 4) == 0) {
            ne *= ggml_type_size(src0->type) / 4;
        } else {
            ne *= ggml_type_size(src0->type) / 2;
        }
    }

    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ne);
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CPY, std::move(p));
}

static void ggml_vk_set_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    // Skip empty skip_rows operations. For most ops the empty check at the start
    // of ggml_vk_build_graph is sufficient, but set_rows can have a nonempty dst
    // with empty srcs.
    if (ggml_is_empty(src0) || ggml_is_empty(src1)) {
        return;
    }

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SET_ROWS, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_silu_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SILU_BACK, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    float * op_params = (float *)dst->op_params;

    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const int * int_op_params = (const int *)dst->op_params;
    const float * float_op_params = (const float *)dst->op_params;

    const uint32_t num_groups = int_op_params[0];
    const float eps = float_op_params[1];
    const uint32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);

    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f, 0.0f, 0.0f });
}

static uint32_t ggml_vk_rms_num_partials(ggml_backend_vk_context * ctx, const ggml_tensor *node) {
    const uint32_t ne = (uint32_t)node->ne[0];
    const uint32_t denom = ctx->device->pipeline_add_rms[0][0][0]->wg_denoms[0];
    const uint32_t num_partials = CEIL_DIV(ne, denom);
    return num_partials;
}

static uint32_t ggml_vk_rms_partials_size(ggml_backend_vk_context * ctx, const ggml_tensor *node) {
    const uint32_t num_partials = ggml_vk_rms_num_partials(ctx, node);
    const uint32_t num_bytes = ROUNDUP_POW2(num_partials * sizeof(uint32_t), ctx->device->partials_binding_alignment);
    return num_bytes;
}

static vk_op_rope_push_constants ggml_vk_make_rope_constants(const ggml_tensor *dst, const ggml_tensor *src0, const bool has_ff, bool backprop, const uint32_t set_rows_stride) {
    const int n_dims        = ((const int32_t *) dst->op_params)[1];
    const int mode          = ((const int32_t *) dst->op_params)[2];
    // const int n_ctx         = ((const int32_t *) dst->op_params)[3];
    const int n_ctx_orig    = ((const int32_t *) dst->op_params)[4];
    const float freq_base   = ((const float *)   dst->op_params)[5];
    const float freq_scale  = ((const float *)   dst->op_params)[6];
    const float ext_factor  = ((const float *)   dst->op_params)[7];
    const float attn_factor = ((const float *)   dst->op_params)[8];
    const float beta_fast   = ((const float *)   dst->op_params)[9];
    const float beta_slow   = ((const float *)   dst->op_params)[10];
    int sections[4] {};
    if (mode & GGML_ROPE_TYPE_MROPE) {
        memcpy(sections, (const int32_t *) dst->op_params + 11, sizeof(int)*4);
    }

    const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE;

    float corr_dims[2];
    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);

    const float theta_scale = powf(freq_base, -2.0f/n_dims);

    uint32_t nb01 = src0->nb[1] / ggml_type_size(src0->type);
    uint32_t nb02 = src0->nb[2] / ggml_type_size(src0->type);
    uint32_t nb03 = src0->nb[3] / ggml_type_size(src0->type);

    uint32_t nb11 = dst->nb[1] / ggml_type_size(dst->type);
    uint32_t nb12 = dst->nb[2] / ggml_type_size(dst->type);
    uint32_t nb13 = dst->nb[3] / ggml_type_size(dst->type);

    vk_op_rope_push_constants rope {
        (uint32_t)mode, (uint32_t)ggml_nrows(src0), (uint32_t)n_dims, freq_scale,
        freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale, has_ff,
        { sections[0], sections[1], sections[2], sections[3] }, is_imrope, backprop, set_rows_stride,

        (uint32_t)src0->ne[0],
        (uint32_t)src0->ne[1],
        (uint32_t)src0->ne[2],
        nb01, nb02, nb03,
        nb11, nb12, nb13,
        0, 0, // a_offset, d_offset filled in by init_pushconst_tensor_offsets
    };

    return rope;
}

static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, float * op_params) {
    ggml_tensor * dst;
    const ggml_tensor * src0;
    const ggml_tensor * src1;

    if (ctx->num_additional_fused_ops > 0) {
        // fused rms_norm + mul
        ggml_tensor *mul = cgraph->nodes[node_idx + 1];
        ggml_tensor *other_src = mul->src[0] == cgraph->nodes[node_idx + 0] ? mul->src[1] : mul->src[0];
        dst = mul;
        src0 = cgraph->nodes[node_idx]->src[0];
        src1 = other_src;
    } else {
        dst = cgraph->nodes[node_idx];
        src0 = src1 = dst->src[0];
    }

    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    uint32_t param3 = ctx->do_add_rms_partials ? ggml_vk_rms_num_partials(ctx, dst) : 0;

    vk_op_binary_push_constants bin {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        op_params[0], 0.0f, (int32_t)param3,
    };

    // more than one fused op means rms_norm+mul+rope
    if (ctx->num_additional_fused_ops > 1) {
        static constexpr uint32_t max_tensors = 7;
        const ggml_tensor *tensors[max_tensors] {};

        ggml_tensor *rms = cgraph->nodes[node_idx + 0];
        ggml_tensor *mul = cgraph->nodes[node_idx + 1];
        ggml_tensor *rope = cgraph->nodes[node_idx + 2];

        ggml_tensor *other_src = mul->src[0] == rms ? mul->src[1] : mul->src[0];

        bool do_set_rows = ctx->num_additional_fused_ops == 4;

        tensors[0] = rms->src[0];
        tensors[1] = other_src;
        tensors[2] = mul;
        tensors[3] = rope->src[1]; // pos
        tensors[4] = rope->src[2]; // ff
        tensors[5] = cgraph->nodes[node_idx + ctx->num_additional_fused_ops]; // dst
        tensors[6] = do_set_rows ? tensors[5]->src[1] : nullptr;
        const uint32_t set_rows_stride = do_set_rows ? tensors[5]->nb[1] / ggml_type_size(tensors[5]->type) : 0;

        vk_op_rms_norm_mul_rope_push_constants pc;
        pc.bin = bin;
        pc.rope = ggml_vk_make_rope_constants(rope, rope->src[0], tensors[4] != nullptr, false, set_rows_stride);

        vk_pipeline pipeline = tensors[5]->type == GGML_TYPE_F16 ? ctx->device->pipeline_rms_norm_mul_rope_f32_f16 : ctx->device->pipeline_rms_norm_mul_rope_f32_f32;

        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

        ggml_backend_vk_buffer_context * buf_ctx[max_tensors];
        vk_buffer buf[max_tensors];
        size_t offset[max_tensors];
        bool uma[max_tensors];

        for (uint32_t i = 0; i < max_tensors; ++i) {
            if (!tensors[i]) {
                // If any remaining descriptors are unused, just point them at src[0]
                buf[i] = buf[0];
                offset[i] = 0;
                continue;
            }
            buf_ctx[i] = (ggml_backend_vk_buffer_context *)tensors[i]->buffer->context;
            buf[i] = nullptr;
            offset[i] = 0;
            uma[i] = false;

            if (ctx->device->uma) {
                ggml_vk_host_get(ctx->device, tensors[i]->data, buf[i], offset[i]);
                uma[i] = buf[i] != nullptr;
            }
            if (!uma[i]) {
                buf[i] = buf_ctx[i]->dev_buffer;
                offset[i] = vk_tensor_offset(tensors[i]) + tensors[i]->view_offs;
            }
            GGML_ASSERT(buf[i] != nullptr);
        }

        // a_offset is unused (the fused path reads from shared memory), but the rope/set_rows dst can be misaligned.
        // Round the binding offset down to the storage buffer alignment; the in-element shift goes in pc.rope.d_offset.
        pc.rope.d_offset = get_misalign_bytes(ctx, tensors[5]) / ggml_type_size(tensors[5]->type);
        offset[5] &= ~(size_t(ctx->device->properties.limits.minStorageBufferOffsetAlignment) - 1);

        std::array<uint32_t, 3> elements;
        elements = { (uint32_t)rms->src[0]->ne[1], (uint32_t)rms->src[0]->ne[2], (uint32_t)rms->src[0]->ne[3] };

        static_assert(max_tensors == 7);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
            {
                ggml_vk_subbuffer(ctx, buf[0], offset[0]),
                ggml_vk_subbuffer(ctx, buf[1], offset[1]),
                ggml_vk_subbuffer(ctx, buf[2], offset[2]),
                ggml_vk_subbuffer(ctx, buf[3], offset[3]),
                ggml_vk_subbuffer(ctx, buf[4], offset[4]),
                ggml_vk_subbuffer(ctx, buf[5], offset[5]),
                ggml_vk_subbuffer(ctx, buf[6], offset[6]),
            }, pc, elements);
    } else {
        ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_RMS_NORM, std::move(bin));
    }

    if (ctx->do_add_rms_partials_offset_calculation) {
        ctx->prealloc_size_add_rms_partials_offset += ggml_vk_rms_partials_size(ctx, src0);
        ctx->do_add_rms_partials = false;
        ctx->do_add_rms_partials_offset_calculation = false;
    }
}

static void ggml_vk_rms_norm_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    float * op_params = (float *)dst->op_params;
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_RMS_NORM_BACK, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_l2_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const float * op_params = (const float *)dst->op_params;
    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
    p.param1 = op_params[0];
    ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_L2_NORM, std::move(p));
}

static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_xielu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    float * op_params = (float *)dst->op_params;
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY,
        {
            (uint32_t)ggml_nelements(src0), 0,
            op_params[1], op_params[2], op_params[3], op_params[4]
        }
    );
}

static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const float * op_params_f = (const float *)dst->op_params;

    const bool swapped = (bool)dst->op_params[1];
    const bool split = src1 != nullptr;
    const float alpha = op_params_f[2];
    const float limit = op_params_f[3];

    if (!split) {
        GGML_ASSERT(src0->ne[0] / 2 == dst->ne[0]);
    } else {
        GGML_ASSERT(src0->ne[0] == src1->ne[0]);
        GGML_ASSERT(src0->ne[0] == dst->ne[0]);
        GGML_ASSERT(src0->type == src1->type);
    }

    const uint32_t mode = split ? 2 : (swapped ? 1 : 0);

    ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GLU,
        {
            (uint32_t)ggml_nelements(dst),
            (uint32_t)src0->ne[0],
            (uint32_t)dst->ne[0],
            mode,
            alpha,
            limit,
            (uint32_t)(src0->nb[1] / src0->nb[0]),
            (uint32_t)(src0->nb[2] / src0->nb[0]),
            (uint32_t)(src0->nb[3] / src0->nb[0]),
            (uint32_t)src0->ne[1],
            (uint32_t)src0->ne[2],
            (uint32_t)(dst->nb[1] / dst->nb[0]),
            (uint32_t)(dst->nb[2] / dst->nb[0]),
            (uint32_t)(dst->nb[3] / dst->nb[0]),
            (uint32_t)dst->ne[1],
            (uint32_t)dst->ne[2]
        });
}

static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    int32_t * op_params = (int32_t *)dst->op_params;
    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] });
}

static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) {
    float * op_params = (float *)dst->op_params;

    float scale = op_params[0];
    float max_bias = op_params[1];

    const uint32_t ncols =   (uint32_t)src0->ne[0];
    const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
    const uint32_t nrows_y = (uint32_t)src0->ne[1];

    const uint32_t ne12 = src1 ? (uint32_t)(src1->ne[2]) : 0u;
    const uint32_t ne13 = src1 ? (uint32_t)(src1->ne[3]) : 0u;
    const uint32_t nb11 = src1 ? (uint32_t)(src1->nb[1] / src1->nb[0]) : 0u;
    const uint32_t nb12 = src1 ? (uint32_t)(src1->nb[2] / src1->nb[0]) : 0u;
    const uint32_t nb13 = src1 ? (uint32_t)(src1->nb[3] / src1->nb[0]) : 0u;

    const uint32_t n_head_kv   = src0->ne[2];
    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));

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

    vk_op_soft_max_push_constants pc {
        ncols,
        src1 != nullptr ? nrows_y : (uint32_t)0,
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],
        ne12, ne13,
        nb11, nb12, nb13,
        scale, max_bias,
        m0, m1,
        n_head_log2,
        nrows_x,
        src2 != nullptr
    };

    if (ncols <= 16384) {
        ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, std::move(pc));
    } else {

        vk_subbuffer buf_a = ggml_vk_tensor_subbuffer(ctx, src0);
        vk_subbuffer buf_b = src1 ? ggml_vk_tensor_subbuffer(ctx, src1) : buf_a;
        vk_subbuffer buf_c = src2 ? ggml_vk_tensor_subbuffer(ctx, src2) : buf_a;
        vk_subbuffer buf_d = ggml_vk_tensor_subbuffer(ctx, dst);

        uint32_t elems_per_wg = 128 * 4;
        uint32_t num_wgs = CEIL_DIV(ncols, elems_per_wg);
        size_t tmp_size = num_wgs * nrows_x * sizeof(float);

        if (ctx->prealloc_size_x < tmp_size) {
            ctx->prealloc_size_x = tmp_size;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if (ctx->prealloc_size_y < tmp_size) {
            ctx->prealloc_size_y = tmp_size;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if (ctx->prealloc_x_need_sync || ctx->prealloc_y_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }

        vk_subbuffer buf_x = { ctx->prealloc_x, 0, tmp_size };
        vk_subbuffer buf_y = { ctx->prealloc_y, 0, tmp_size };

        std::array<uint32_t, 3> elements = { num_wgs, nrows_x, 1 };

        vk_pipeline pipeline1 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large1_f32_f16 : ctx->device->pipeline_soft_max_large1_f32;
        vk_pipeline pipeline2 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large2_f32_f16 : ctx->device->pipeline_soft_max_large2_f32;
        vk_pipeline pipeline3 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large3_f32_f16 : ctx->device->pipeline_soft_max_large3_f32;

        ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1);
        ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1);
        ggml_pipeline_request_descriptor_sets(ctx, pipeline3, 1);

        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
        ggml_vk_sync_buffers(ctx, subctx);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
        ggml_vk_sync_buffers(ctx, subctx);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline3, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);

        ctx->prealloc_x_need_sync = true;
        ctx->prealloc_y_need_sync = true;
    }
}

static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    float * op_params = (float *)dst->op_params;
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SOFT_MAX_BACK, { (uint32_t)src0->ne[0], (uint32_t)ggml_nrows(src0), op_params[0], op_params[1], 0.0f, 0.0f });
}

static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) {
    topk_moe_mode mode = ctx->fused_topk_moe_mode;
    ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0];
    ggml_tensor * bias = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 2]->src[1] : logits;
    ggml_tensor * weights = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
    ggml_tensor * ids = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 4] :
                        (mode == TOPK_MOE_LATE_SOFTMAX) ?      cgraph->nodes[node_idx + 1] :
                                                               cgraph->nodes[node_idx + 3];

    GGML_ASSERT(logits->type == GGML_TYPE_F32);
    GGML_ASSERT(bias->type == GGML_TYPE_F32);
    GGML_ASSERT(weights->type == GGML_TYPE_F32);
    GGML_ASSERT(ids->type == GGML_TYPE_I32);

    const int n_experts = logits->ne[0];
    const int n_rows    = logits->ne[1];
    const int n_expert_used = weights->ne[1];

    GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts);

    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, cgraph->nodes[node_idx], GGML_OP_SOFT_MAX);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    vk_subbuffer logits_buf = ggml_vk_tensor_subbuffer(ctx, logits);
    vk_subbuffer bias_buf = ggml_vk_tensor_subbuffer(ctx, bias);
    vk_subbuffer weights_buf = ggml_vk_tensor_subbuffer(ctx, weights);
    vk_subbuffer ids_buf = ggml_vk_tensor_subbuffer(ctx, ids);

    vk_op_topk_moe_push_constants pc {};
    pc.n_rows = n_rows;
    pc.n_experts_push = n_experts;
    pc.n_expert_used = n_expert_used;
    pc.clamp_min = -std::numeric_limits<float>::infinity();
    pc.clamp_max = std::numeric_limits<float>::infinity();
    if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) {
        ggml_tensor * clamp = cgraph->nodes[node_idx + 7];
        GGML_ASSERT(clamp->op == GGML_OP_CLAMP);
        pc.clamp_min = ggml_get_op_params_f32(clamp, 0);
        pc.clamp_max = ggml_get_op_params_f32(clamp, 1);
    }
    if (mode == TOPK_MOE_SIGMOID_NORM_BIAS) {
        ggml_tensor * clamp = cgraph->nodes[node_idx + 8];
        GGML_ASSERT(clamp->op == GGML_OP_CLAMP);
        pc.clamp_min = ggml_get_op_params_f32(clamp, 0);
        pc.clamp_max = ggml_get_op_params_f32(clamp, 1);
    }

#define GATING_FUNC_SOFTMAX 0
#define GATING_FUNC_SIGMOID 1
#define GATING_FUNC_SOFTMAX_WEIGHT 2

    pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? GATING_FUNC_SIGMOID :
                     mode == TOPK_MOE_LATE_SOFTMAX ?      GATING_FUNC_SOFTMAX_WEIGHT :
                                                          GATING_FUNC_SOFTMAX;
    pc.has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS;
    pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || mode == TOPK_MOE_SIGMOID_NORM_BIAS;
    if (ctx->fused_topk_moe_scale) {
        GGML_ASSERT(weights->op == GGML_OP_SCALE);
        pc.output_scale = ggml_get_op_params_f32(weights, 0);
        pc.output_bias = ggml_get_op_params_f32(weights, 1);
    } else {
        pc.output_scale = 1.0f;
        pc.output_bias = 0.0f;
    }

    GGML_ASSERT(n_expert_used <= n_experts);

    const uint32_t rows_per_block = 4;
    std::array<uint32_t, 3> elements = { CEIL_DIV(n_rows, rows_per_block), 1, 1 };

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {logits_buf, bias_buf, weights_buf, ids_buf}, pc, elements);
}

static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_cgraph * cgraph, int node_idx, bool backprop) {
    ggml_tensor * dst = cgraph->nodes[node_idx];
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
    const ggml_tensor * src2 = dst->src[2];
    const ggml_tensor * src3 = nullptr;
    const int n_dims        = ((int32_t *) dst->op_params)[1];
    const int mode          = ((int32_t *) dst->op_params)[2];
    // const int n_ctx         = ((int32_t *) dst->op_params)[3];
    const int n_ctx_orig    = ((int32_t *) dst->op_params)[4];
    const float freq_base   = ((float *)   dst->op_params)[5];
    const float beta_fast   = ((float *)   dst->op_params)[9];
    const float beta_slow   = ((float *)   dst->op_params)[10];
    int sections[4] {};
    if (mode & GGML_ROPE_TYPE_MROPE) {
        memcpy(sections, (int32_t *) dst->op_params + 11, sizeof(int)*4);
    }

    float corr_dims[2];
    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);

    uint32_t set_rows_stride = 0;
    // Fused rope + view + set_rows passes the set_rows destination stride in set_rows_stride
    // and overrides the dst and sets src3=row_indices
    if (ctx->num_additional_fused_ops > 0) {
        set_rows_stride = cgraph->nodes[node_idx + 2]->nb[1] / ggml_type_size(cgraph->nodes[node_idx + 2]->type);
        src3 = cgraph->nodes[node_idx + 2]->src[1];
        dst = cgraph->nodes[node_idx + 2];
    }

    ggml_vk_op_f32<vk_op_rope_push_constants>(ctx, subctx, src0, src1, src2, src3, dst, GGML_OP_ROPE,
        ggml_vk_make_rope_constants(cgraph->nodes[node_idx], src0, src2 != nullptr, backprop, set_rows_stride));
}

static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const uint32_t * op_params = (const uint32_t *)dst->op_params;

    uint32_t ncols = src0->ne[0];
    uint32_t nrows = ggml_nrows(src0);

    uint32_t ncols_pad_log2 = (uint32_t)ceilf(log2f(float(ncols)));
    uint32_t ncolsp2 = 1 << ncols_pad_log2;

    vk_op_argsort_push_constants pc { ncols, ncolsp2, ncols_pad_log2, nrows, op_params[0], 0, 0, 0, 0, };

    // Pick the largest workgroup size <= ncolsp2
    uint32_t pipeline_idx = std::min(ncols_pad_log2, num_argsort_pipelines - 1);

    // Use the "small" argsort shader if the whole sort can be done by a single workgroup.
    bool use_small = ncols_pad_log2 <= ctx->device->max_workgroup_size_log2 &&
                     ctx->device->pipeline_argsort_f32[pipeline_idx] != nullptr;

    vk_pipeline pipeline = use_small ? ctx->device->pipeline_argsort_f32[pipeline_idx]
                                     : ctx->device->pipeline_argsort_large_f32[pipeline_idx];

    vk_subbuffer src0_buf = ggml_vk_tensor_subbuffer(ctx, src0);
    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
    vk_subbuffer subbuf1 = dst_buf;

    // Reserve space for ivec2 per element, with rows padded to a power of two
    if (!use_small) {
        const size_t x_sz = size_t{ncolsp2} * nrows * 2 * sizeof(int);

        if (ctx->prealloc_size_x < x_sz) {
            ctx->prealloc_size_x = x_sz;
            ggml_vk_preallocate_buffers(ctx, subctx);
        }
        if (ctx->prealloc_x_need_sync) {
            ggml_vk_sync_buffers(ctx, subctx);
        }
        subbuf1 = { ctx->prealloc_x, 0, ctx->prealloc_x->size };
    }

    std::array<uint32_t, 3> elements;

    elements[0] = ncolsp2;
    elements[1] = std::min((uint32_t)ggml_nrows(src0), ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
    elements[2] = 1;

    // First dispatch initializes tmp_idx and does the first N passes where
    // there is only communication between threads in the same workgroup.
    {
        vk_op_argsort_push_constants pc2 = pc;
        pc2.outer_start = 0;
        pc2.outer_end = std::min(ncols_pad_log2, ctx->device->max_workgroup_size_log2);
        pc2.inner_start = 0;
        pc2.inner_end = 100;
        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, dst_buf }, pc2, elements);
    }
    if (!use_small) {
        ggml_vk_sync_buffers(ctx, subctx);
        // Loop over outer/inner passes, synchronizing between each pass.
        for (uint32_t outer = ctx->device->max_workgroup_size_log2; outer < ncols_pad_log2; ++outer) {
            for (uint32_t inner = 0; inner < outer + 1; ++inner) {
                vk_op_argsort_push_constants pc2 = pc;
                pc2.outer_start = outer;
                pc2.outer_end = outer + 1;
                pc2.inner_start = inner;
                pc2.inner_end = inner + 1;
                // When the inner idx is large enough, there's only communication
                // within a workgroup. So the remaining inner iterations can all
                // run in the same dispatch.
                if (outer - inner < pipeline_idx) {
                    pc2.inner_end = 100;
                    inner = outer;
                    pipeline = ctx->device->pipeline_argsort_large_f32[pipeline_idx];
                } else {
                    // Smaller workgroup empirically seems to perform better
                    pipeline = ctx->device->pipeline_argsort_large_f32[pipeline_idx - 2];
                }
                ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
                ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, dst_buf }, pc2, elements);
                ggml_vk_sync_buffers(ctx, subctx);
            }
        }
        ctx->prealloc_x_need_sync = true;
    }
}

static void ggml_vk_topk(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    uint32_t ncols = src0->ne[0];
    uint32_t nrows = ggml_nrows(src0);
    uint32_t k = dst->ne[0];

    vk_op_topk_push_constants pc { ncols, ncols, ncols, k, nrows, 0, 0 };

    if (ctx->prealloc_x_need_sync) {
        ggml_vk_sync_buffers(ctx, subctx);
    }

    std::array<uint32_t, 3> elements;
    elements[1] = std::min(nrows, ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
    elements[2] = 1;

    uint32_t num_elements = ncols;

    // Each iteration reduces a workgroup's worth of elements down to the K
    // largest elements. Repeat until we have the top K elements.
    // Need to do at least one iteration to write out the results.
    bool done_one_iter = false;
    uint32_t dbl_buf_index = 0;
    size_t dbl_buf_size;
    while (num_elements > k || !done_one_iter) {

        // Prefer going as small as num_topk_pipelines - 3 for perf reasons.
        // But if K is larger, then we need a larger workgroup
        uint32_t max_pipeline = num_topk_pipelines - 1;
        uint32_t preferred_pipeline = std::max(num_topk_pipelines - 3, (uint32_t)log2f(float(k)) + 2);
        max_pipeline = std::min(preferred_pipeline, max_pipeline);
        uint32_t min_pipeline = (uint32_t)log2f(float(k)) + 1;
        // require full subgroup
        min_pipeline = std::max(min_pipeline, ctx->device->subgroup_size_log2);

        uint32_t pipeline_idx = (uint32_t)ceilf(log2f(float(num_elements)));
        pipeline_idx = std::min(pipeline_idx, max_pipeline);
        pipeline_idx = std::max(pipeline_idx, min_pipeline);

        if (num_elements > (1u << pipeline_idx)) {
            // If we could finish on this loop iteration (i.e. a single workgroup)
            // then do so. It's better than the overhead of another pass.
            for (uint32_t i = pipeline_idx; i < num_topk_pipelines; ++i) {
                if (num_elements <= (1u << i)) {
                    pipeline_idx = i;
                    break;
                }
            }
        }

        vk_pipeline pipeline = ctx->device->pipeline_topk_f32[pipeline_idx];
        // If the device doesn't support a pipeline this large, use smaller
        while (!pipeline) {
            pipeline_idx--;
            GGML_ASSERT(pipeline_idx >= min_pipeline);
            pipeline = ctx->device->pipeline_topk_f32[pipeline_idx];
        }

        vk_op_topk_push_constants pc2 = pc;
        pc2.ncols_input = num_elements;

        // Number of elements remaining after this pass
        uint32_t num_dst_elements = (num_elements / pipeline->wg_denoms[0]) * k + std::min(k, num_elements % pipeline->wg_denoms[0]);

        pc2.ncols_output = num_dst_elements;

        if (!done_one_iter) {
            // Reserve space for ivec2 per element, double buffered
            // K per workgroup per row
            dbl_buf_size = num_dst_elements * nrows * 2 * sizeof(int);
            dbl_buf_size = ROUNDUP_POW2(dbl_buf_size, ctx->device->properties.limits.minStorageBufferOffsetAlignment);
            const size_t x_sz = dbl_buf_size * 2;

            if (ctx->prealloc_size_x < x_sz) {
                ctx->prealloc_size_x = x_sz;
                ggml_vk_preallocate_buffers(ctx, subctx);
            }
        }

        vk_subbuffer src_buf;
        vk_subbuffer dst_buf;

        if (num_elements == ncols) {
            pc2.first_pass = 1;
            src_buf = ggml_vk_tensor_subbuffer(ctx, src0);
        } else {
            src_buf = { ctx->prealloc_x, dbl_buf_index * dbl_buf_size, dbl_buf_size };
        }
        if (num_dst_elements == k) {
            pc2.last_pass = 1;
            dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
        } else {
            dst_buf = { ctx->prealloc_x, (dbl_buf_index ^ 1) * dbl_buf_size, dbl_buf_size };
        }

        elements[0] = num_elements;

        ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src_buf, dst_buf }, pc2, elements);
        num_elements = num_dst_elements;
        dbl_buf_index ^= 1;
        if (num_elements > k) {
            ggml_vk_sync_buffers(ctx, subctx);
        }
        done_one_iter = true;
    }
    ctx->prealloc_x_need_sync = true;
}

static void ggml_vk_sum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, ggml_nelements(src0));
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SUM, p);
}

static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, p);
}

static void ggml_vk_mean(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
    p.weight = 1.0f / (float)src0->ne[0];
    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_MEAN, p);
}

static void ggml_vk_cumsum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    vk_op_sum_rows_push_constants pc = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
    // Use the single pass shader when the rows are small or there are enough rows to fill the GPU.
    // For fewer, larger rows, use the multipass shader to spread each row across SMs.
    if (dst->ne[0] <= 4096 || ggml_nrows(dst) >= ctx->device->shader_core_count) {
        ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CUMSUM, pc);
        return;
    }

    // First pass computes partial sums within a block, and stores the last partial
    // to the temp buffer. Second pass sums the block partials from the temp buffer
    // and adds that to the result of the first pass.
    vk_pipeline pipeline1 = ctx->device->pipeline_cumsum_multipass1_f32;
    vk_pipeline pipeline2 = ctx->device->pipeline_cumsum_multipass2_f32;
    GGML_ASSERT(pipeline1 != nullptr && pipeline2 != nullptr);

    ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1);
    ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1);

    std::array<uint32_t, 3> elements;

    elements[0] = dst->ne[0];
    elements[1] = (uint32_t)ggml_nrows(dst);
    elements[2] = 1;

    size_t temp_size = sizeof(float) * elements[0] * ggml_nrows(dst);

    if (ctx->prealloc_size_split_k < temp_size) {
        ctx->prealloc_size_split_k = temp_size;
        ggml_vk_preallocate_buffers(ctx, subctx);
    }

    vk_subbuffer src_buf = ggml_vk_tensor_subbuffer(ctx, src0);
    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
    vk_subbuffer temp_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);

    if (ctx->prealloc_split_k_need_sync) {
        ggml_vk_sync_buffers(ctx, subctx);
    }

    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, {src_buf, dst_buf, temp_buf}, pc, elements);
    ggml_vk_sync_buffers(ctx, subctx);
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, {src_buf, dst_buf, temp_buf}, pc, elements);

    ctx->prealloc_split_k_need_sync = true;
}

static void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_ARGMAX, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_count_equal(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_COUNT_EQUAL, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f });
}

static void ggml_vk_solve_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const uint32_t src0_type_size = ggml_type_size(src0->type);
    const uint32_t src1_type_size = ggml_type_size(src1->type);
    const uint32_t dst_type_size = ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SOLVE_TRI, {
        (uint32_t)ggml_nelements(src0),
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
        0,
        0.0f, 0.0f, 0,
    });
}

static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    const int32_t s0 = dst->op_params[0];
    const int32_t s1 = dst->op_params[1];
    const int32_t p0 = dst->op_params[2];
    const int32_t p1 = dst->op_params[3];
    const int32_t d0 = dst->op_params[4];
    const int32_t d1 = dst->op_params[5];

    const bool is_2D = dst->op_params[6] == 1;

    const uint32_t IC = src1->ne[is_2D ? 2 : 1];
    const uint32_t IH = is_2D ? src1->ne[1] : 1;
    const uint32_t IW =         src1->ne[0];

    const uint32_t KH = is_2D ? src0->ne[1] : 1;
    const uint32_t KW =         src0->ne[0];

    const uint32_t OH = is_2D ? dst->ne[2] : 1;
    const uint32_t OW =         dst->ne[1];

    const uint32_t offset_delta = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
    const uint32_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32

    const uint32_t batch = src1->ne[is_2D ? 3 : 2];

    const ggml_backend_vk_buffer_context * d_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    const vk_buffer d_buf = d_buf_ctx->dev_buffer;

    const vk::DeviceAddress dst_addr = d_buf->bda_addr + vk_tensor_offset(dst) + dst->view_offs;

    ggml_vk_op_f32<vk_op_im2col_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_IM2COL, {
        dst_addr,
        batch_offset, offset_delta,
        IC, IW, IH, OW, OH, KW, KH,
        OH * batch,
        IC * KH * KW,
        s0, s1, p0, p1, d0, d1, batch * IC
    });
}

static void ggml_vk_im2col_3d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_TENSOR_BINARY_OP_LOCALS

    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
    const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
    const int32_t s2 = ((const int32_t *)(dst->op_params))[2];
    const int32_t p0 = ((const int32_t *)(dst->op_params))[3];
    const int32_t p1 = ((const int32_t *)(dst->op_params))[4];
    const int32_t p2 = ((const int32_t *)(dst->op_params))[5];
    const int32_t d0 = ((const int32_t *)(dst->op_params))[6];
    const int32_t d1 = ((const int32_t *)(dst->op_params))[7];
    const int32_t d2 = ((const int32_t *)(dst->op_params))[8];
    const int32_t IC = ((const int32_t *)(dst->op_params))[9];

    const int64_t N  = ne13 / IC;
    const int64_t ID = ne12;
    const int64_t IH = ne11;
    const int64_t IW = ne10;

    const int64_t KD = ne02;
    const int64_t KH = ne01;
    const int64_t KW = ne00;

    const int64_t OD = ne3 / N;
    const int64_t OH = ne2;
    const int64_t OW = ne1;

    const ggml_backend_vk_buffer_context * d_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    const vk_buffer d_buf = d_buf_ctx->dev_buffer;

    const vk::DeviceAddress dst_addr = d_buf->bda_addr + vk_tensor_offset(dst) + dst->view_offs;

    vk_op_im2col_3d_push_constants pc {};

    pc.dst_addr = dst_addr;
    pc.nb10 = nb10 / ggml_type_size(src1->type);
    pc.nb11 = nb11 / ggml_type_size(src1->type);
    pc.nb12 = nb12 / ggml_type_size(src1->type);
    pc.nb13 = nb13 / ggml_type_size(src1->type);
    pc.s0 = s0;
    pc.s1 = s1;
    pc.s2 = s2;
    pc.p0 = p0;
    pc.p1 = p1;
    pc.p2 = p2;
    pc.d0 = d0;
    pc.d1 = d1;
    pc.d2 = d2;
    pc.IW = IW;
    pc.IH = IH;
    pc.ID = ID;
    pc.IC = IC;
    pc.KW = KW;
    pc.OH = OH;
    pc.KD_KH_KW = KD*KH*KW;
    pc.KH_KW = KH*KW;
    pc.IC_KD_KH_KW = IC*KD*KH*KW;
    pc.N_OD_OH = N*OD*OH;
    pc.OD_OH = OD*OH;
    pc.OD_OH_OW_IC_KD_KH_KW = OD*OH*OW*IC*KD*KH*KW;
    pc.OH_OW_IC_KD_KH_KW = OH*OW*IC*KD*KH*KW;
    pc.OW_IC_KD_KH_KW = OW*IC*KD*KH*KW;

    ggml_vk_op_f32<vk_op_im2col_3d_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_IM2COL_3D, std::move(pc));
}

static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const uint32_t dim = dst->op_params[0];
    const uint32_t max_period = dst->op_params[1];
    const uint32_t nb1 = dst->nb[1] / ggml_type_size(dst->type);

    ggml_vk_op_f32<vk_op_timestep_embedding_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TIMESTEP_EMBEDDING, {
        nb1, dim, max_period,
    });
}

static void ggml_vk_conv_transpose_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    // src0: (K, Cout, Cin, 1) -- kernel
    // src1: (L, Cin, 1, 1) -- input
    // dst: (*, Cout, 1, 1)

    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);

    GGML_TENSOR_BINARY_OP_LOCALS

    GGML_ASSERT(nb00 == sizeof(float));
    GGML_ASSERT(nb10 == sizeof(float));

    const int32_t s0 = dst->op_params[0];

    vk_op_conv_transpose_1d_push_constants p{};
    p.Cout = static_cast<uint32_t>(ne01);
    p.Cin = static_cast<uint32_t>(ne02);
    p.K = static_cast<uint32_t>(ne00);
    p.L = static_cast<uint32_t>(ne10);
    p.KL = static_cast<uint32_t>(ne0);
    p.nb01 = static_cast<uint32_t>(nb01 / nb00);
    p.nb02 = static_cast<uint32_t>(nb02 / nb00);
    p.nb11 = static_cast<uint32_t>(nb11 / nb10);
    p.nb1 = static_cast<uint32_t>(nb1 / nb0);
    p.s0 = static_cast<uint32_t>(s0);

    ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_TRANSPOSE_1D, std::move(p));
}

// Dispatch the fused snake activation: y = x + sin^2(a * x) * inv_b.
// Match the naive mul -> sin -> sqr -> mul -> add chain and run the
// dedicated kernel directly. The pattern is validated by
// ggml_vk_can_fuse_snake before this call.
static void ggml_vk_snake_dispatch_fused(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) {
    const ggml_tensor * mul0 = cgraph->nodes[node_idx + 0];
    const ggml_tensor * sqr  = cgraph->nodes[node_idx + 2];
    const ggml_tensor * mul1 = cgraph->nodes[node_idx + 3];
    ggml_tensor *       add  = cgraph->nodes[node_idx + 4];

    // x carries the full activation shape, a is the broadcast operand
    const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1];
    const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0];

    // mul1 reads sqr and inv_b in either operand order
    const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0];

    vk_pipeline pipeline = nullptr;
    switch (x->type) {
        case GGML_TYPE_F32:  pipeline = ctx->device->pipeline_snake_f32;  break;
        case GGML_TYPE_F16:  pipeline = ctx->device->pipeline_snake_f16;  break;
        case GGML_TYPE_BF16: pipeline = ctx->device->pipeline_snake_bf16; break;
        default:             GGML_ABORT("unsupported type");
    }
    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);

    vk_subbuffer x_buf     = ggml_vk_tensor_subbuffer(ctx, x);
    vk_subbuffer a_buf     = ggml_vk_tensor_subbuffer(ctx, a);
    vk_subbuffer inv_b_buf = ggml_vk_tensor_subbuffer(ctx, inv_b);
    vk_subbuffer dst_buf   = ggml_vk_tensor_subbuffer(ctx, add);

    vk_op_snake_push_constants pc{};
    pc.ne0 = static_cast<uint32_t>(x->ne[0]);
    pc.ne1 = static_cast<uint32_t>(x->ne[1]);

    std::array<uint32_t, 3> elements = { pc.ne0, pc.ne1, 1 };
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { x_buf, a_buf, inv_b_buf, dst_buf }, pc, elements);
}

static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    uint32_t op = static_cast<uint32_t>(dst->op_params[0]);
    const int32_t k1 = dst->op_params[1];
    const int32_t k0 = dst->op_params[2];
    const int32_t s1 = dst->op_params[3];
    const int32_t s0 = dst->op_params[4];
    const int32_t p1 = dst->op_params[5];
    const int32_t p0 = dst->op_params[6];

    const uint32_t IH = src0->ne[1];
    const uint32_t IW = src0->ne[0];

    const uint32_t N = dst->ne[3];

    const uint32_t OC = dst->ne[2];
    const uint32_t OH = dst->ne[1];
    const uint32_t OW = dst->ne[0];

    const uint32_t parallel_elements = N * OC * OH * OW;

    ggml_vk_op_f32<vk_op_pool2d_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_POOL_2D, {
        IW, IH, OW, OH, OC,
        parallel_elements,
        op,
        k0, k1, s0, s1, p0, p1,
    });
}

static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx, const ggml_tensor * src0,
                            const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

    GGML_TENSOR_BINARY_OP_LOCALS
    GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t));
    GGML_ASSERT(nb10 == sizeof(float));
    GGML_ASSERT(nb0 == sizeof(float));

    bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D;

    vk_op_conv2d_push_constants p{};
    p.Cout = static_cast<uint32_t>(!transpose ? ne03 : ne02);
    p.Cin  = static_cast<uint32_t>(!transpose ? ne02 : ne03);
    p.N    = static_cast<uint32_t>(ne13);
    GGML_ASSERT(p.Cout == ne2);
    GGML_ASSERT(p.Cin == ne12);

    p.W  = static_cast<uint32_t>(ne10);
    p.H  = static_cast<uint32_t>(ne11);
    p.OW = static_cast<uint32_t>(ne0);
    p.OH = static_cast<uint32_t>(ne1);

    p.nb01 = static_cast<uint32_t>(nb01 / nb00);
    p.nb02 = static_cast<uint32_t>(nb02 / nb00);
    p.nb03 = static_cast<uint32_t>(nb03 / nb00);

    p.nb11 = static_cast<uint32_t>(nb11 / nb10);
    p.nb12 = static_cast<uint32_t>(nb12 / nb10);
    p.nb13 = static_cast<uint32_t>(nb13 / nb10);

    p.nb1 = static_cast<uint32_t>(nb1 / nb0);
    p.nb2 = static_cast<uint32_t>(nb2 / nb0);
    p.nb3 = static_cast<uint32_t>(nb3 / nb0);

    ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, std::move(p));
}

static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    vk_op_conv2d_dw_push_constants p{};
    p.ne = ggml_nelements(dst);
    p.channels = dst->ne[2];
    p.batches = dst->ne[3];
    p.dst_w = dst->ne[0];
    p.dst_h = dst->ne[1];
    p.src_w = src1->ne[0];
    p.src_h = src1->ne[1];
    p.knl_w = src0->ne[0];
    p.knl_h = src0->ne[1];
    p.stride_x = dst->op_params[0];
    p.stride_y = dst->op_params[1];
    p.pad_x = dst->op_params[2];
    p.pad_y = dst->op_params[3];
    p.dilation_x = dst->op_params[4];
    p.dilation_y = dst->op_params[5];

    GGML_ASSERT(src0->ne[3] == p.channels);
    GGML_ASSERT(src1->ne[3] == p.batches);

    ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_2D_DW, std::move(p));
}

static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
    const float * op_params = (const float *)dst->op_params;
    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f, 0.0f, 0.0f });
}

#ifdef GGML_VULKAN_RUN_TESTS
static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) {
    if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) {
        return;
    }
    i0 = std::max(i0, 5);
    i1 = std::max(i1, 5);
    i2 = std::max(i2, 0);
    fprintf(stderr, "         ");
    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
        fprintf(stderr, "%7d ", idx1);
    }
    fprintf(stderr, "\n");
    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
        fprintf(stderr, "%7d: ", idx0);
        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
            if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) {
                float val;
                if (type == GGML_TYPE_F32) {
                    val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0);
                } else if (type == GGML_TYPE_F16) {
                    val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0));
                } else {
                    GGML_ABORT("fatal error");
                }
                fprintf(stderr, "% 7.2f ", val);
            } else {
                fprintf(stderr, "        ");
            }
        }
        fprintf(stderr, "\n");
    }
}

template <typename X_TYPE, typename Y_TYPE>
static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) {
    VK_LOG_DEBUG("ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")");
    const size_t x_ne = m * k * batch;
    const size_t y_ne = k * n * batch;
    const size_t d_ne = m * n * batch;

    vk_pipeline p;
    std::string shname;
    if (shader_size == 0) {
        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f32->a_s;
            shname = "F32_ALIGNED_S";
        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f32_f16->a_s;
            shname = "F32_F16_ALIGNED_S";
        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_s;
            shname = "F16_F32_ALIGNED_S";
        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f16.f32acc->a_s;
            shname = "F16_ALIGNED_S";
        } else {
            GGML_ABORT("fatal error");
        }
    } else if (shader_size == 1) {
        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f32->a_m;
            shname = "F32_ALIGNED_M";
        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f32_f16->a_m;
            shname = "F32_F16_ALIGNED_M";
        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_m;
            shname = "F16_F32_ALIGNED_M";
        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f16.f32acc->a_m;
            shname = "F16_ALIGNED_M";
        } else {
            GGML_ABORT("fatal error");
        }
    } else if (shader_size == 2) {
        if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f32->a_l;
            shname = "F32_ALIGNED_L";
        } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f32_f16->a_l;
            shname = "F32_F16_ALIGNED_L";
        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_l;
            shname = "F16_F32_ALIGNED_L";
        } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
            p = ctx->device->pipeline_matmul_f16.f32acc->a_l;
            shname = "F16_ALIGNED_L";
        } else {
            GGML_ABORT("fatal error");
        }
    } else {
        GGML_ASSERT(0);
    }

    const size_t kpad = ggml_vk_align_size(k, p->align);

    if (k != kpad) {
        if (shader_size == 0) {
            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f32->s;
                shname = "F32_S";
            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f32_f16->s;
                shname = "F32_F16_S";
            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f16_f32.f32acc->s;
                shname = "F16_F32_S";
            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f16.f32acc->s;
                shname = "F16_S";
            }
        } else if (shader_size == 1) {
            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f32->m;
                shname = "F32_M";
            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f32_f16->m;
                shname = "F32_F16_M";
            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f16_f32.f32acc->m;
                shname = "F16_F32_M";
            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f16.f32acc->m;
                shname = "F16_M";
            }
        } else if (shader_size == 2) {
            if (std::is_same<float, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f32->l;
                shname = "F32_L";
            } else if (std::is_same<float, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f32_f16->l;
                shname = "F32_F16_L";
            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<float, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f16_f32.f32acc->l;
                shname = "F16_F32_L";
            } else if (std::is_same<ggml_fp16_t, X_TYPE>() && std::is_same<ggml_fp16_t, Y_TYPE>()) {
                p = ctx->device->pipeline_matmul_f16.f32acc->l;
                shname = "F16_L";
            }
        }
    }

    if (split_k > 1) {
        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);

        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
            // Resize buffer
            if (ctx->prealloc_split_k != nullptr) {
                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
            }
            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, {vk::MemoryPropertyFlagBits::eDeviceLocal});
        }
    }

    ggml_pipeline_allocate_descriptor_sets(ctx);

    vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal});

    X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
    Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
    float* d = (float *) malloc(sizeof(float) * d_ne);

    for (size_t i = 0; i < x_ne; i++) {
        if (std::is_same<float, X_TYPE>()) {
            x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
            // x[i] = 1.0f;
            // x[i] = i + 1;
            // x[i] = (i % k == i / k) ? 1.0f : 0.0f;
        } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
            x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
            // x[i] = ggml_fp32_to_fp16(1.0f);
            // x[i] = ggml_fp32_to_fp16(i + 1);
            // x[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
        } else {
            GGML_ABORT("fatal error");
        }
    }
    for (size_t i = 0; i < y_ne; i++) {
        if (std::is_same<float, Y_TYPE>()) {
            y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
            // y[i] = (i % k == i / k) ? 1.0f : 0.0f;
            // y[i] = i + 1;
        } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
            y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f);
            // y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f);
            // y[i] = ggml_fp32_to_fp16(i + 1);
        } else {
            GGML_ABORT("fatal error");
        }
    }

    ggml_vk_buffer_write(d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
    ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);

    vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
    ggml_vk_ctx_begin(ctx->device, subctx);
    for (size_t i = 0; i < num_it; i++) {
        ggml_vk_matmul(
            ctx, subctx, p, ggml_vk_subbuffer(ctx, d_X), ggml_vk_subbuffer(ctx, d_Y), ggml_vk_subbuffer(ctx, d_D), ggml_vk_subbuffer(ctx, ctx->prealloc_split_k),
            m, n, k,
            k, k, m, k*m, k*n, m*n,
            split_k, batch, batch, batch, 1, 1, n
        );
    }
    ggml_vk_ctx_end(subctx);

    auto begin = std::chrono::high_resolution_clock::now();
    ggml_vk_submit(subctx, ctx->fence);
    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
    ctx->device->device.resetFences({ ctx->fence });
    ggml_vk_queue_command_pools_cleanup(ctx->device);

    auto end = std::chrono::high_resolution_clock::now();
    double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;

    // copy dst to host
    ggml_vk_buffer_read(d_D, 0, d, sizeof(float) * d_ne);

    float * d_chk = (float *) malloc(sizeof(float) * d_ne);

    ggml_init_params iparams = {
        /*.mem_size   =*/ 1024*1024*1024,
        /*.mem_buffer =*/ NULL,
        /*.no_alloc   =*/ true,
    };

    ggml_context * ggml_ctx = ggml_init(iparams);

    ggml_type src0_type;
    ggml_type src1_type;

    if (std::is_same<float, X_TYPE>()) {
        src0_type = GGML_TYPE_F32;
    } else if (std::is_same<ggml_fp16_t, X_TYPE>()) {
        src0_type = GGML_TYPE_F16;
    } else {
        GGML_ABORT("fatal error");
    }
    if (std::is_same<float, Y_TYPE>()) {
        src1_type = GGML_TYPE_F32;
    } else if (std::is_same<ggml_fp16_t, Y_TYPE>()) {
        src1_type = GGML_TYPE_F16;
    } else {
        GGML_ABORT("fatal error");
    }

    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch);
    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch);
    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);

    src0_ggml->data = x;
    src1_ggml->data = y;
    tensor_ggml->data = d_chk;

    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
    ggml_build_forward_expand(cgraph, tensor_ggml);

    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);

    ggml_free(ggml_ctx);

    double avg_err = 0.0;
    int first_err_n = -1;
    int first_err_m = -1;
    int first_err_b = -1;

    for (size_t i = 0; i < m*n*batch; i++) {
        double err = std::fabs(d[i] - d_chk[i]);
        avg_err += err;

        if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
            first_err_b = i / (m * n);
            first_err_n = (i % (m * n)) / m;
            first_err_m = (i % (m * n)) % m;
        }
    }

    avg_err /= m * n;

    double tflops = 2.0*m*n*k*batch*num_it / (time / 1000.0) / (1000.0*1000.0*1000.0*1000.0);

    std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl;

    if (avg_err > 0.1 || std::isnan(avg_err)) {
        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
        std::cerr << "Actual result: " << std::endl << std::endl;
        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
        std::cerr << "Expected result: " << std::endl << std::endl;
        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

        if (split_k > 1) {
            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
            ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);

            std::cerr << "d_buf0: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            std::cerr << "d_buf1: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            std::cerr << "d_buf2: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            std::cerr << "d_buf3: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            free(split_k_buf);
        }
    }

    free(d_chk);

    ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool);

    ggml_vk_destroy_buffer(d_X);
    ggml_vk_destroy_buffer(d_Y);
    ggml_vk_destroy_buffer(d_D);

    free(x);
    free(y);
    free(d);
}

static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
        return;
    }
    i0 = std::max(i0, 5);
    i1 = std::max(i1, 5);
    i2 = std::max(i2, 0);
    i3 = std::max(i3, 0);
    fprintf(stderr, "         ");
    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
        fprintf(stderr, "%7d ", idx1);
    }
    fprintf(stderr, "\n");
    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
        fprintf(stderr, "%7d: ", idx0);
        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
                float val;
                if (tensor->type == GGML_TYPE_F32) {
                    val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
                } else if (tensor->type == GGML_TYPE_F16) {
                    val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
                } else {
                    GGML_ABORT("fatal error");
                }
                fprintf(stderr, "% 7.2f ", val);
            } else {
                fprintf(stderr, "        ");
            }
        }
        fprintf(stderr, "\n");
    }
}

static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
}

static void ggml_vk_dequantize_data(const void * from, float * to, size_t ne, ggml_type quant) {
    if (quant == GGML_TYPE_F32) {
        memcpy(to, from, sizeof(float) * ne);
        return;
    }

    const auto * tt = ggml_get_type_traits(quant);

    ggml_to_float_t dequant_fn = tt->to_float;

    dequant_fn(from, to, ne);
}

static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
    VK_LOG_DEBUG("ggml_vk_test_dequant(" << ne << ")");
    const size_t x_sz = sizeof(float) * ne;
    const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne;
    const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
    float * x = (float *) malloc(x_sz);
    void * qx = malloc(qx_sz);
    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    float * x_ref = (float *) malloc(x_sz);
    ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);

    for (size_t i = 0; i < ne; i++) {
        x[i] = rand() / (float)RAND_MAX;
    }

    vk_pipeline p = ggml_vk_get_to_fp16(ctx, quant);

    ggml_vk_quantize_data(x, qx, ne, quant);
    ggml_vk_dequantize_data(qx, x_ref, ne, quant);

    ggml_pipeline_request_descriptor_sets(ctx, p, 1);

    ggml_pipeline_allocate_descriptor_sets(ctx);

    ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);

    vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
    ggml_vk_ctx_begin(ctx->device, subctx);
    const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
    ggml_vk_dispatch_pipeline(ctx, subctx, p, { vk_subbuffer{ qx_buf, 0, qx_sz }, vk_subbuffer{ x_buf, 0, x_sz_f16 } }, pc, { (uint32_t)ne, 1, 1});
    ggml_vk_ctx_end(subctx);

    auto begin = std::chrono::high_resolution_clock::now();

    ggml_vk_submit(subctx, ctx->fence);
    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
    ctx->device->device.resetFences({ ctx->fence });
    ggml_vk_queue_command_pools_cleanup(ctx->device);

    auto end = std::chrono::high_resolution_clock::now();

    double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
    ggml_vk_buffer_read(x_buf, 0, x_chk, x_sz_f16);

    int first_err = -1;

    double avg_err = 0.0;
    for (size_t i = 0; i < ne; i++) {
        double error = std::fabs(x_ref[i] - ggml_fp16_to_fp32(x_chk[i]));
        avg_err += error;

        if (first_err < 0 && error > 0.05) {
            first_err = i;
        }
    }

    avg_err /= ne;

    std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl;

    if (avg_err > 0.1) {
        std::cerr << "first_error = " << first_err << std::endl;
        std::cerr << "Actual result: " << std::endl << std::endl;
        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
            std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", ";
        }
        std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
        for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
            std::cerr << x_ref[i] << ", ";
        }
        std::cerr << std::endl;
    }

    ggml_vk_destroy_buffer(x_buf);
    ggml_vk_destroy_buffer(qx_buf);

    free(x);
    free(qx);
    free(x_ref);
    free(x_chk);
}

// This does not work without ggml q8_1 quantization support
//
// typedef uint16_t ggml_half;
// typedef uint32_t ggml_half2;
//
// #define QK8_1 32
// typedef struct {
//     union {
//         struct {
//             ggml_half d; // delta
//             ggml_half s; // d * sum(qs[i])
//         } GGML_COMMON_AGGR_S;
//         ggml_half2 ds;
//     } GGML_COMMON_AGGR_U;
//     int8_t qs[QK8_1]; // quants
// } block_q8_1;
//
// static void ggml_vk_test_quantize(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
//     VK_LOG_DEBUG("ggml_vk_test_quantize(" << ne << ")");
//     GGML_ASSERT(quant == GGML_TYPE_Q8_1);
//
//     const size_t x_sz = sizeof(float) * ne;
//     const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
//     float * x = (float *) malloc(x_sz);
//     block_q8_1 * qx     = (block_q8_1 *)malloc(qx_sz);
//     block_q8_1 * qx_res = (block_q8_1 *)malloc(qx_sz);
//     vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
//     vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
//
//     for (size_t i = 0; i < ne; i++) {
//         x[i] = rand() / (float)RAND_MAX;
//     }
//
//     vk_pipeline p = ggml_vk_get_quantize_pipeline(ctx, quant);
//
//     ggml_pipeline_request_descriptor_sets(ctx, p, 1);
//
//     ggml_pipeline_allocate_descriptor_sets(ctx);
//
//     ggml_vk_buffer_write(x_buf, 0, x, x_sz);
//
//     vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
//     ggml_vk_ctx_begin(ctx->device, subctx);
//     ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(ctx, x_buf), ggml_vk_subbuffer(ctx, qx_buf), ne);
//     ggml_vk_ctx_end(subctx);
//
//     auto begin = std::chrono::high_resolution_clock::now();
//
//     ggml_vk_submit(subctx, ctx->fence);
//     VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_quantize waitForFences");
//     ctx->device->device.resetFences({ ctx->fence });
//     ggml_vk_queue_command_pools_cleanup(ctx->device);
//
//     auto end = std::chrono::high_resolution_clock::now();
//
//     double ms_quant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
//     ggml_vk_buffer_read(qx_buf, 0, qx, qx_sz);
//
//     ggml_vk_quantize_data(x, qx_res, ne, quant);
//
//     int first_err = -1;
//
//     for (size_t i = 0; i < ne / 32; i++) {
//         double error = std::fabs(ggml_fp16_to_fp32(qx_res[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d) - ggml_fp16_to_fp32(qx[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d));
//
//         if (first_err < 0 && error > 0.1) {
//             first_err = i;
//         }
//
//         error = std::fabs(ggml_fp16_to_fp32(qx_res[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s) - ggml_fp16_to_fp32(qx[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s));
//
//         if (first_err < 0 && error > 0.1) {
//             first_err = i;
//         }
//
//         for (size_t j = 0; j < 32; j++) {
//             uint64_t error = std::abs(qx_res[i].qs[j] - qx[i].qs[j]);
//
//             if (first_err < 0 && error > 1) {
//                 first_err = i;
//             }
//         }
//     }
//
//     std::cerr << "TEST QUANTIZE " << ggml_type_name(quant) << " time=" << ms_quant << "ms " << (first_err == -1 ? "CORRECT" : "INCORRECT") << std::endl;
//
//     if (first_err != -1) {
//         std::cerr << "first_error = " << first_err << std::endl;
//         std::cerr << "Actual result: " << std::endl << std::endl;
//         std::cout << "d=" << ggml_fp16_to_fp32(qx[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d) << " s=" << ggml_fp16_to_fp32(qx[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s) << " ";
//         for (size_t j = 0; j < 32; j++) {
//             std::cout << " qs" << j << "=" << (uint32_t)qx[first_err].qs[j] << " ";
//         }
//         std::cerr << std::endl << std::endl << "Expected result: " << std::endl << std::endl;
//         std::cout << "d=" << ggml_fp16_to_fp32(qx_res[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d) << " s=" << ggml_fp16_to_fp32(qx_res[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s) << " ";
//         for (size_t j = 0; j < 32; j++) {
//             std::cout << " qs" << j << "=" << (uint32_t)qx_res[first_err].qs[j] << " ";
//         }
//         std::cerr << std::endl;
//     }
//
//     ggml_vk_destroy_buffer(x_buf);
//     ggml_vk_destroy_buffer(qx_buf);
//
//     free(x);
//     free(qx);
//     free(qx_res);
// }

static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant, bool mmq = false) {
    VK_LOG_DEBUG("ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")");
    const size_t x_ne = m * k * batch;
    const size_t y_ne = k * n * batch;
    const size_t d_ne = m * n * batch;

    vk_matmul_pipeline2 * pipelines;

    if (mmq) {
        pipelines = ctx->device->pipeline_dequant_mul_mat_mat_q8_1;
    } else {
        pipelines = ctx->device->pipeline_dequant_mul_mat_mat;
    }

    const bool fp16acc = ctx->device->fp16;

    vk_pipeline p;
    std::string shname;
    if (shader_size == 0) {
        p = fp16acc ? pipelines[quant].f16acc->a_s : pipelines[quant].f32acc->a_s;
        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S";
    } else if (shader_size == 1) {
        p = fp16acc ? pipelines[quant].f16acc->a_m : pipelines[quant].f32acc->a_m;
        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M";
    } else if (shader_size == 2) {
        p = fp16acc ? pipelines[quant].f16acc->a_l : pipelines[quant].f32acc->a_l;
        shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L";
    } else {
        GGML_ASSERT(0);
    }

    const size_t kpad = mmq ? 0 : ggml_vk_align_size(k, p->align);

    if (mmq || k != kpad) {
        if (shader_size == 0) {
            p = fp16acc ? pipelines[quant].f16acc->s : pipelines[quant].f32acc->s;
            shname = std::string(ggml_type_name(quant)) + "_S";
        } else if (shader_size == 1) {
            p = fp16acc ? pipelines[quant].f16acc->m : pipelines[quant].f32acc->m;
            shname = std::string(ggml_type_name(quant)) + "_M";
        } else if (shader_size == 2) {
            p = fp16acc ? pipelines[quant].f16acc->l : pipelines[quant].f32acc->l;
            shname = std::string(ggml_type_name(quant)) + "_L";
        } else {
            GGML_ASSERT(0);
        }
    }

    if (p == nullptr) {
        std::cerr << "error: no pipeline for ggml_vk_test_dequant_matmul " << ggml_type_name(quant) << std::endl;
        return;
    }

    const size_t x_sz = sizeof(float) * x_ne;
    const size_t y_sz = sizeof(float) * y_ne;
    const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant);
    const size_t qy_sz = mmq ? y_ne * ggml_type_size(GGML_TYPE_Q8_1)/ggml_blck_size(GGML_TYPE_Q8_1) : y_sz;
    const size_t d_sz = sizeof(float) * d_ne;
    float * x = (float *) malloc(x_sz);
    float * y = (float *) malloc(y_sz);
    void * qx = malloc(qx_sz);
    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    vk_buffer qy_buf = ggml_vk_create_buffer_check(ctx->device, qy_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal});
    float * d = (float *) malloc(d_sz);
    float * d_chk = (float *) malloc(d_sz);

    for (size_t i = 0; i < x_ne; i++) {
        x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
        // x[i] = (i % k == i / k) ? 1.0f : 0.0f;
        // x[i] = i % k;
    }

    ggml_vk_quantize_data(x, qx, x_ne, quant);

    for (size_t i = 0; i < y_ne; i++) {
        y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f;
        // y[i] = (i % k == i / k) ? 1.0f : 0.0f;
        // y[i] = i % k;
    }

    if (split_k > 1) {
        ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);

        if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
            // Resize buffer
            if (ctx->prealloc_split_k != nullptr) {
                ggml_vk_destroy_buffer(ctx->prealloc_split_k);
            }
            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, {vk::MemoryPropertyFlagBits::eDeviceLocal});
        }
    }
    if (mmq) {
        vk_pipeline pipeline_quantize_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
        ggml_pipeline_request_descriptor_sets(ctx, pipeline_quantize_q8_1, num_it);
    }

    ggml_pipeline_allocate_descriptor_sets(ctx);

    ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
    ggml_vk_buffer_write(y_buf, 0, y, y_sz);

    vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
    ggml_vk_ctx_begin(ctx->device, subctx);
    if (mmq) {
        for (size_t i = 0; i < num_it; i++) {
            ggml_vk_quantize_q8_1(ctx, subctx, { y_buf, 0, y_sz }, { qy_buf, 0, qy_sz }, y_ne);
            ggml_vk_matmul(
                ctx, subctx, p, { qx_buf, 0, qx_sz }, { qy_buf, 0, qy_sz }, { d_buf, 0, d_sz }, { ctx->prealloc_split_k, 0, ctx->prealloc_size_split_k },
                m, n, k,
                k, k, m, k*m, k*n, m*n,
                split_k, batch, batch, batch, 1, 1, n
            );
        }
    } else {
        for (size_t i = 0; i < num_it; i++) {
            ggml_vk_matmul(
                ctx, subctx, p, { qx_buf, 0, qx_sz }, { y_buf, 0, y_sz }, { d_buf, 0, d_sz }, { ctx->prealloc_split_k, 0, ctx->prealloc_size_split_k },
                m, n, k,
                k, k, m, k*m, k*n, m*n,
                split_k, batch, batch, batch, 1, 1, n
            );
        }
    }
    ggml_vk_ctx_end(subctx);

    auto begin = std::chrono::high_resolution_clock::now();

    ggml_vk_submit(subctx, ctx->fence);
    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
    ctx->device->device.resetFences({ ctx->fence });
    ggml_vk_queue_command_pools_cleanup(ctx->device);

    auto end = std::chrono::high_resolution_clock::now();

    double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
    ggml_vk_buffer_read(d_buf, 0, d, d_sz);

    ggml_init_params iparams = {
        /*.mem_size   =*/ 1024*1024*1024,
        /*.mem_buffer =*/ NULL,
        /*.no_alloc   =*/ true,
    };

    ggml_context * ggml_ctx = ggml_init(iparams);

    ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch);
    ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch);
    ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml);

    src0_ggml->data = qx;
    src1_ggml->data = y;
    tensor_ggml->data = d_chk;

    ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx);
    ggml_build_forward_expand(cgraph, tensor_ggml);

    ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1);

    ggml_free(ggml_ctx);

    double avg_err = 0.0;
    int first_err_n = -1;
    int first_err_m = -1;
    int first_err_b = -1;

    for (size_t i = 0; i < m*n*batch; i++) {
        double err = std::fabs(d[i] - d_chk[i]);
        avg_err += err;

        if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) {
            first_err_b = i / (m * n);
            first_err_n = (i % (m * n)) / m;
            first_err_m = (i % (m * n)) % m;
        }
    }

    avg_err /= m * n;

    double tflops = 2.0*m*n*k*batch*num_it / (time_ms / 1000.0) / (1000.0*1000.0*1000.0*1000.0);

    std::cerr << "TEST dequant matmul " << shname;
    if (mmq) {
        std::cerr << " mmq";
    }
    std::cerr << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl;

    if (avg_err > 0.01 || std::isnan(avg_err)) {
        std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl;
        std::cerr << "Actual result: " << std::endl << std::endl;
        ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
        std::cerr << std::endl;
        std::cerr << "Expected result: " << std::endl << std::endl;
        ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

        std::cerr << "src0: " << std::endl << std::endl;
        ggml_vk_print_matrix_area(x, GGML_TYPE_F32, k, m, first_err_m, first_err_n, first_err_b);
        std::cerr << std::endl;
        std::cerr << "src1: " << std::endl << std::endl;
        ggml_vk_print_matrix_area(y, GGML_TYPE_F32, k, n, first_err_m, first_err_n, first_err_b);

        if (split_k > 1) {
            float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
            ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);

            std::cerr << "d_buf0: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            std::cerr << "d_buf1: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            std::cerr << "d_buf2: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            std::cerr << "d_buf3: " << std::endl << std::endl;
            ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);

            free(split_k_buf);
        }
    }

    ggml_vk_destroy_buffer(qx_buf);
    ggml_vk_destroy_buffer(y_buf);
    ggml_vk_destroy_buffer(qy_buf);
    ggml_vk_destroy_buffer(d_buf);

    free(x);
    free(qx);
    free(y);
    free(d);
    free(d_chk);
}
#endif

static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx) {
#if defined(GGML_VULKAN_RUN_TESTS)
    const std::vector<size_t> vals {
        512, 512, 128,
        128, 512, 512,
        4096, 512, 4096,
        11008, 512, 4096,
        4096, 512, 11008,
        32000, 512, 4096,
        8, 8, 8,
        100, 46, 576,
        623, 111, 128,
        100, 46, 558,
        512, 1, 256,
        128, 110, 622,
        511, 511, 127,
        511, 511, 7,
        511, 511, 17,
        49, 49, 128,
        128, 49, 49,
        4096, 49, 4096,
    };
    const size_t num_it = 100;

    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q4_0);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q4_0);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q4_0);

    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q4_0, true);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q4_0, true);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q4_0, true);

    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q8_0);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q8_0);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q8_0);

    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q8_0, true);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q8_0, true);
    ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q8_0, true);

    abort();

    for (size_t i = 0; i < vals.size(); i += 3) {
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2);
        std::cerr << '\n';
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 0);
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 1);
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 2);
        std::cerr << '\n';
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
        std::cerr << '\n' << std::endl;

        if (vals[i + 2] % 32 == 0) {
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0, GGML_TYPE_Q4_0);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1, GGML_TYPE_Q4_0);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2, GGML_TYPE_Q4_0);
            std::cerr << '\n';
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 0, GGML_TYPE_Q4_0);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 1, GGML_TYPE_Q4_0);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 2, GGML_TYPE_Q4_0);
            std::cerr << '\n';
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0, GGML_TYPE_Q4_0);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1, GGML_TYPE_Q4_0);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2, GGML_TYPE_Q4_0);
            std::cerr << '\n' << std::endl;
        }

        if (vals[i + 2] % 256 == 0) {
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0, GGML_TYPE_Q4_K);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1, GGML_TYPE_Q4_K);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2, GGML_TYPE_Q4_K);
            std::cerr << '\n';
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 0, GGML_TYPE_Q4_K);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 1, GGML_TYPE_Q4_K);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 2, GGML_TYPE_Q4_K);
            std::cerr << '\n';
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0, GGML_TYPE_Q4_K);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1, GGML_TYPE_Q4_K);
            ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2, GGML_TYPE_Q4_K);
            std::cerr << '\n' << std::endl;
        }
    }

    GGML_ABORT("fatal error");
#endif

    if (subctx) {
        // Submit and wait for any pending work before reallocating the buffers
        ggml_vk_ctx_end(subctx);
        ggml_vk_submit(subctx, {});
        ctx->submit_pending = true;
        ggml_vk_synchronize(ctx);
        GGML_ASSERT(ctx->compute_ctx.expired());
        ggml_vk_ctx_begin(ctx->device, subctx);
        ctx->compute_ctx = subctx;
    }

    if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) {
        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << ")");
        // Resize buffer
        if (ctx->prealloc_x != nullptr) {
            ggml_vk_destroy_buffer(ctx->prealloc_x);
        }
        ctx->prealloc_x = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_x);
    }
    if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) {
        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(y_size: " << ctx->prealloc_size_y << ")");
        // Resize buffer
        if (ctx->prealloc_y != nullptr) {
            ggml_vk_destroy_buffer(ctx->prealloc_y);
        }
        ctx->prealloc_y = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_y);
        ctx->prealloc_y_last_pipeline_used = nullptr;
        ctx->prealloc_y_last_tensor_used = nullptr;
        ctx->prealloc_y_last_decode_vector_staging = false;
    }
    if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) {
        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(split_k_size: " << ctx->prealloc_size_split_k << ")");
        // Resize buffer
        if (ctx->prealloc_split_k != nullptr) {
            ggml_vk_destroy_buffer(ctx->prealloc_split_k);
        }
        ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_split_k);
    }
    if (ctx->prealloc_add_rms_partials == nullptr || (ctx->prealloc_size_add_rms_partials > 0 && ctx->prealloc_add_rms_partials->size < ctx->prealloc_size_add_rms_partials)) {
        VK_LOG_MEMORY("ggml_vk_preallocate_buffers(add_partials_size: " << ctx->prealloc_add_rms_partials << ")");
        // Resize buffer
        if (ctx->prealloc_add_rms_partials != nullptr) {
            ggml_vk_destroy_buffer(ctx->prealloc_add_rms_partials);
        }
        ctx->prealloc_add_rms_partials = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_add_rms_partials);
    }
}

static void ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool almost_ready);

// Returns true if node has enqueued work into the queue, false otherwise
// If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution.
static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool last_node, bool almost_ready, bool submit){
    ggml_tensor * node = cgraph->nodes[node_idx];
    if (ggml_is_empty(node) || ggml_op_is_empty(node->op) || !node->buffer) {
        return false;
    }
    if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) {
        return false;
    }

    VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")");
    ctx->semaphore_idx = 0;

    ggml_tensor * src0 = node->src[0];
    ggml_tensor * src1 = node->src[1];
    ggml_tensor * src2 = node->src[2];
    ggml_tensor * src3 = node->src[3];

    if (node->op == GGML_OP_ADD) {
        int next_node_idx = node_idx + 1 + ctx->num_additional_fused_ops;
        if (next_node_idx < cgraph->n_nodes &&
            cgraph->nodes[next_node_idx]->op == GGML_OP_RMS_NORM &&
            cgraph->nodes[next_node_idx]->src[0] == cgraph->nodes[next_node_idx - 1] &&
            ggml_nrows(cgraph->nodes[next_node_idx]) == 1 &&
            ctx->device->add_rms_fusion) {
            uint32_t size = ggml_vk_rms_partials_size(ctx, cgraph->nodes[node_idx]);
            ctx->do_add_rms_partials_offset_calculation = true;
            if (ctx->prealloc_size_add_rms_partials_offset + size <= ctx->prealloc_size_add_rms_partials) {
                ctx->do_add_rms_partials = true;
            }
        }
    }

    vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);

    {
        // This logic detects dependencies between modes in the graph and calls ggml_vk_sync_buffers
        // to synchronize them. This handles most "normal" synchronization when computing the graph, and when
        // there is no auxiliary memory use, it shouldn't be necessary to call ggml_vk_sync_buffers
        // outside of this logic. When a node uses one of the prealloc buffers for something like
        // dequantization or split_k, additional synchronization is needed between those passes.
        bool need_sync = false;

        // Check whether "node" requires synchronization. The node requires synchronization if it
        // overlaps in memory with another unsynchronized node and at least one of them is a write.
        // Destination nodes are checked against both the written/read lists. Source nodes are only
        // checked against the written list. Two nodes overlap in memory if they come from the same
        // buffer and the tensor or view ranges overlap.
        auto const &overlaps_unsynced = [&](const ggml_tensor *node, const std::vector<const ggml_tensor *> &unsynced_nodes) -> bool {
            if (unsynced_nodes.size() == 0) {
                return false;
            }
            auto n_base = vk_tensor_offset(node) + node->view_offs;
            auto n_size = ggml_nbytes(node);
            ggml_backend_vk_buffer_context * a_buf_ctx = (ggml_backend_vk_buffer_context *)node->buffer->context;
            vk_buffer a_buf = a_buf_ctx->dev_buffer;
            for (auto &other : unsynced_nodes) {
                ggml_backend_vk_buffer_context * o_buf_ctx = (ggml_backend_vk_buffer_context *)other->buffer->context;
                vk_buffer o_buf = o_buf_ctx->dev_buffer;
                if (a_buf == o_buf) {
                    auto o_base = vk_tensor_offset(other) + other->view_offs;
                    auto o_size = ggml_nbytes(other);

                    if ((o_base <= n_base && n_base < o_base + o_size) ||
                        (n_base <= o_base && o_base < n_base + n_size)) {
                        return true;
                    }
                }
            }
            return false;
        };

        // For all fused ops, check if the destination node or any of the source
        // nodes require synchronization.
        for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1 && !need_sync; ++i) {
            const ggml_tensor *cur_node = cgraph->nodes[node_idx + i];
            // If the node actually writes to memory, then check if it needs to sync
            if (ctx->fused_ops_write_mask & (1 << i)) {
                if (overlaps_unsynced(cur_node, ctx->unsynced_nodes_read) || overlaps_unsynced(cur_node, ctx->unsynced_nodes_written)) {
                    need_sync = true;
                    break;
                }
            }
            for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) {
                if (!cur_node->src[j]) {
                    continue;
                }
                if (overlaps_unsynced(cur_node->src[j], ctx->unsynced_nodes_written)) {
                    need_sync = true;
                    break;
                }
            }
        }

        if (need_sync) {
            if (vk_enable_sync_logger) {
                std::cerr <<  "sync" << std::endl;
            }
            ctx->unsynced_nodes_written.clear();
            ctx->unsynced_nodes_read.clear();
            ggml_vk_sync_buffers(ctx, compute_ctx);

            if (vk_perf_logger_enabled && vk_perf_logger_concurrent) {
                ctx->query_node_idx[ctx->query_idx] = node_idx;
                compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
                ggml_vk_sync_buffers(ctx, compute_ctx);
            }
        }
        // Add all fused nodes to the unsynchronized lists.
        for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1; ++i) {
            const ggml_tensor *cur_node = cgraph->nodes[node_idx + i];
            // Multiple outputs could be written, e.g. in topk_moe. Add them all to the list.
            if (ctx->fused_ops_write_mask & (1 << i)) {
                ctx->unsynced_nodes_written.push_back(cur_node);
            }
            for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) {
                if (!cur_node->src[j]) {
                    continue;
                }
                ctx->unsynced_nodes_read.push_back(cur_node->src[j]);
            }
        }
    }
    if (vk_enable_sync_logger) {
        for (int i = 0; i < ctx->num_additional_fused_ops + 1; ++i) {
            auto *n = cgraph->nodes[node_idx + i];
            std::cerr << node_idx + i << " " << ggml_op_name(n->op) << " " <<  n->name;
            if (n->op == GGML_OP_GLU) {
                std::cerr << " " << ggml_glu_op_name(ggml_get_glu_op(n)) << " " << (n->src[1] ? "split" : "single") << " ";
            }
            if (n->op == GGML_OP_ROPE) {
                const int mode = ((const int32_t *) n->op_params)[2];
                std::cerr << " rope mode: " << mode;
            }
            std::cerr << std::endl;
        }
    }

    switch (node->op) {
    case GGML_OP_REPEAT:
        ggml_vk_repeat(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_REPEAT_BACK:
        ggml_vk_repeat_back(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_ACC:
    case GGML_OP_SET:
        ggml_vk_acc(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_GET_ROWS:
        ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_ADD:
        if (ctx->num_additional_fused_ops) {
            ggml_vk_multi_add(ctx, compute_ctx, cgraph, node_idx);
        } else {
            ggml_vk_add(ctx, compute_ctx, src0, src1, node);
        }
        break;
    case GGML_OP_SUB:
        ggml_vk_sub(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_MUL:
        if (ctx->num_additional_fused_ops) {
            ggml_vk_snake_dispatch_fused(ctx, compute_ctx, cgraph, node_idx);
        } else {
            ggml_vk_mul(ctx, compute_ctx, src0, src1, node);
        }

        break;
    case GGML_OP_DIV:
        ggml_vk_div(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_ADD_ID:
        ggml_vk_add_id(ctx, compute_ctx, src0, src1, src2, node);

        break;
    case GGML_OP_CONCAT:
        ggml_vk_concat(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_UPSCALE:
        ggml_vk_upscale(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_ADD1:
        ggml_vk_add1(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_ARANGE:
        ggml_vk_arange(ctx, compute_ctx, node);

        break;
    case GGML_OP_FILL:
        ggml_vk_fill(ctx, compute_ctx, node);

        break;
    case GGML_OP_SCALE:
        ggml_vk_scale(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SQR:
        ggml_vk_sqr(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SQRT:
        ggml_vk_sqrt(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SIN:
        ggml_vk_sin(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_COS:
        ggml_vk_cos(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_LOG:
        ggml_vk_log(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_TRI:
        ggml_vk_tri(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_DIAG:
        ggml_vk_diag(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_CLAMP:
        ggml_vk_clamp(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_PAD:
        ggml_vk_pad(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_ROLL:
        ggml_vk_roll(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_CPY:
    case GGML_OP_CONT:
    case GGML_OP_DUP:
        ggml_vk_cpy(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SET_ROWS:
        ggml_vk_set_rows(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_SILU_BACK:
        ggml_vk_silu_back(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_NORM:
        ggml_vk_norm(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_GROUP_NORM:
        ggml_vk_group_norm(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_RMS_NORM:
        ggml_vk_rms_norm(ctx, compute_ctx, cgraph, node_idx, (float *)node->op_params);
        break;
    case GGML_OP_RMS_NORM_BACK:
        ggml_vk_rms_norm_back(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_L2_NORM:
        ggml_vk_l2_norm(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_UNARY:
        if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
            ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx);
            break;
        }

        switch (ggml_get_unary_op(node)) {
        case GGML_UNARY_OP_ELU:
        case GGML_UNARY_OP_EXP:
        case GGML_UNARY_OP_SILU:
        case GGML_UNARY_OP_GELU:
        case GGML_UNARY_OP_GELU_ERF:
        case GGML_UNARY_OP_GELU_QUICK:
        case GGML_UNARY_OP_RELU:
        case GGML_UNARY_OP_NEG:
        case GGML_UNARY_OP_TANH:
        case GGML_UNARY_OP_SIGMOID:
        case GGML_UNARY_OP_HARDSIGMOID:
        case GGML_UNARY_OP_HARDSWISH:
        case GGML_UNARY_OP_ABS:
        case GGML_UNARY_OP_SOFTPLUS:
        case GGML_UNARY_OP_STEP:
        case GGML_UNARY_OP_ROUND:
        case GGML_UNARY_OP_CEIL:
        case GGML_UNARY_OP_FLOOR:
        case GGML_UNARY_OP_TRUNC:
        case GGML_UNARY_OP_SGN:
            ggml_vk_unary(ctx, compute_ctx, src0, node);
            break;
        case GGML_UNARY_OP_XIELU:
            ggml_vk_xielu(ctx, compute_ctx, src0, node);
            break;
        default:
            return false;
        }
        break;
    case GGML_OP_GLU:
        switch (ggml_get_glu_op(node)) {
        case GGML_GLU_OP_GEGLU:
        case GGML_GLU_OP_REGLU:
        case GGML_GLU_OP_SWIGLU:
        case GGML_GLU_OP_SWIGLU_OAI:
        case GGML_GLU_OP_GEGLU_ERF:
        case GGML_GLU_OP_GEGLU_QUICK:
            ggml_vk_glu(ctx, compute_ctx, src0, src1, node);
            break;
        default:
            return false;
        }
        break;
    case GGML_OP_DIAG_MASK_INF:
        ggml_vk_diag_mask_inf(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SOFT_MAX:
        if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
            ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx);
        } else {
            ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node);
        }

        break;
    case GGML_OP_SOFT_MAX_BACK:
        ggml_vk_soft_max_back(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_ROPE:
        ggml_vk_rope(ctx, compute_ctx, cgraph, node_idx, false);

        break;
    case GGML_OP_ROPE_BACK:
        ggml_vk_rope(ctx, compute_ctx, cgraph, node_idx, true);

        break;
    case GGML_OP_ARGSORT:
        if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
            ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx);
        } else {
            ggml_vk_argsort(ctx, compute_ctx, src0, node);
        }

        break;
    case GGML_OP_TOP_K:
        ggml_vk_topk(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SUM:
        ggml_vk_sum(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_SUM_ROWS:
        ggml_vk_sum_rows(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_CUMSUM:
        ggml_vk_cumsum(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_MEAN:
        ggml_vk_mean(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_ARGMAX:
        ggml_vk_argmax(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_COUNT_EQUAL:
        ggml_vk_count_equal(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_SOLVE_TRI:
        ggml_vk_solve_tri(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_IM2COL:
        ggml_vk_im2col(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_IM2COL_3D:
        ggml_vk_im2col_3d(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_TIMESTEP_EMBEDDING:
        ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_CONV_TRANSPOSE_1D:
        ggml_vk_conv_transpose_1d(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_POOL_2D:
        ggml_vk_pool_2d(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_CONV_2D:
    case GGML_OP_CONV_TRANSPOSE_2D:
        ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_CONV_2D_DW:
        ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node);

        break;
    case GGML_OP_LEAKY_RELU:
        ggml_vk_leaky_relu(ctx, compute_ctx, src0, node);

        break;
    case GGML_OP_MUL_MAT:
        ggml_vk_mul_mat(ctx, compute_ctx, cgraph, node_idx);

        break;
    case GGML_OP_MUL_MAT_ID:
        ggml_vk_mul_mat_id(ctx, compute_ctx, cgraph, node_idx);

        break;

    case GGML_OP_FLASH_ATTN_EXT:
        ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node->src[4], node);

        break;

    case GGML_OP_RWKV_WKV6:
        ggml_vk_rwkv_wkv6(ctx, compute_ctx, node);

        break;

    case GGML_OP_RWKV_WKV7:
        ggml_vk_rwkv_wkv7(ctx, compute_ctx, node);

        break;

    case GGML_OP_GATED_DELTA_NET:
        ggml_vk_gated_delta_net(ctx, compute_ctx, node);

        break;

    case GGML_OP_SSM_SCAN:
        ggml_vk_ssm_scan(ctx, compute_ctx, node);

        break;

    case GGML_OP_SSM_CONV:
        ggml_vk_ssm_conv(ctx, compute_ctx, cgraph, node_idx);

        break;

    case GGML_OP_OPT_STEP_ADAMW:
        ggml_vk_opt_step_adamw(ctx, compute_ctx, node);

        break;

    case GGML_OP_OPT_STEP_SGD:
        ggml_vk_opt_step_sgd(ctx, compute_ctx, src0, src1, src2, node);

        break;
    default:
        return false;
    }

    ctx->tensor_ctxs[node_idx] = compute_ctx;

#if defined(GGML_VULKAN_CHECK_RESULTS)
    // Force context reset on each node so that each tensor ends up in its own context
    // and can be run and compared to its CPU equivalent separately
    last_node = true;
#endif

    if (submit || last_node) {
        ggml_vk_ctx_end(compute_ctx);

        // TODO probably it'd be better to pass a exit_node flag to ggml_vk_compute_forward
        if (last_node) {
            compute_ctx->exit_tensor_idx = node_idx_begin;
        }
        else {
            compute_ctx->exit_tensor_idx = -1;
        }

        ctx->compute_ctx.reset();

        ggml_vk_compute_forward(ctx, cgraph, node_begin, node_idx_begin, almost_ready);
    }
    return true;
}

static void ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, ggml_tensor * tensor, int tensor_idx, bool almost_ready = false) {
    GGML_UNUSED(cgraph);
    GGML_UNUSED(tensor);

    VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")");

    vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock();

    // Only run if ctx hasn't been submitted yet
    if (!subctx->seqs.empty()) {
#ifdef GGML_VULKAN_CHECK_RESULTS
        ggml_vk_check_results_0(ctx, cgraph, tensor_idx);
#endif

        // Do staging buffer copies
        for (auto& cpy : subctx->in_memcpys) {
            memcpy(cpy.dst, cpy.src, cpy.n);
        }

        for (auto& mset : subctx->memsets) {
            memset(mset.dst, mset.val, mset.n);
        }

        if (almost_ready && !ctx->almost_ready_fence_pending) {
            ggml_vk_submit(subctx, ctx->almost_ready_fence);
            ctx->almost_ready_fence_pending = true;
        } else {
            ggml_vk_submit(subctx, {});
        }
        ctx->submit_pending = true;

#ifdef GGML_VULKAN_CHECK_RESULTS
        ggml_vk_synchronize(ctx);
        ggml_vk_check_results_1(ctx, cgraph, tensor_idx);
#endif
    }

    if (tensor_idx == subctx->exit_tensor_idx) {
        // Do staging buffer copies
        for (auto& cpy : subctx->out_memcpys) {
            memcpy(cpy.dst, cpy.src, cpy.n);
        }
        subctx->in_memcpys.clear();
        subctx->out_memcpys.clear();
        subctx->memsets.clear();
    }
}

// Clean up after graph processing is done
static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
    VK_LOG_DEBUG("ggml_vk_graph_cleanup()");
    ctx->prealloc_y_last_pipeline_used = {};
    ctx->prealloc_y_last_tensor_used = nullptr;
    ctx->prealloc_y_last_decode_vector_staging = false;

    ctx->unsynced_nodes_written.clear();
    ctx->unsynced_nodes_read.clear();
    ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false;

    ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool);
    if (ctx->device->async_use_transfer_queue) {
        ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool);
    }

    for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) {
        ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s });
    }
    ctx->gc.semaphores.clear();

    for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) {
        ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s });
    }
    ctx->gc.tl_semaphores.clear();
    ctx->semaphore_idx = 0;

    ctx->event_idx = 0;

    for (auto& event : ctx->gc.events) {
        ctx->device->device.resetEvent(event);
    }

    ctx->tensor_ctxs.clear();
    ctx->gc.contexts.clear();
    ctx->pipeline_descriptor_set_requirements = 0;
    ctx->descriptor_set_idx = 0;
}

// Clean up on backend free
static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
    VK_LOG_DEBUG("ggml_vk_cleanup(" << ctx->name << ")");
    // discard any unsubmitted command buffers
    ctx->compute_ctx.reset();
    // wait for any pending command buffers to finish
    ggml_vk_synchronize(ctx);

    ggml_vk_graph_cleanup(ctx);

    ggml_vk_destroy_buffer(ctx->prealloc_x);
    ggml_vk_destroy_buffer(ctx->prealloc_y);
    ggml_vk_destroy_buffer(ctx->prealloc_split_k);
    ggml_vk_destroy_buffer(ctx->prealloc_add_rms_partials);
    ggml_vk_destroy_buffer(ctx->sync_staging);

    ctx->prealloc_y_last_pipeline_used = nullptr;
    ctx->prealloc_y_last_tensor_used = nullptr;
    ctx->prealloc_y_last_decode_vector_staging = false;

    ctx->prealloc_size_x = 0;
    ctx->prealloc_size_y = 0;
    ctx->prealloc_size_split_k = 0;

    for (auto& event : ctx->gc.events) {
        ctx->device->device.destroyEvent(event);
    }
    ctx->gc.events.clear();

    ctx->device->device.destroyFence(ctx->fence);
    ctx->device->device.destroyFence(ctx->almost_ready_fence);

    for (auto& pool : ctx->descriptor_pools) {
        ctx->device->device.destroyDescriptorPool(pool);
    }
    ctx->descriptor_pools.clear();
    ctx->descriptor_sets.clear();

    ctx->compute_cmd_pool.destroy(ctx->device->device);
    if (ctx->device->async_use_transfer_queue) {
        ctx->device->device.destroySemaphore(ctx->transfer_semaphore.s);

        ctx->transfer_cmd_pool.destroy(ctx->device->device);
    }
    if (vk_perf_logger_enabled) {
        ctx->perf_logger->print_timings(true);
    }
}

static int ggml_vk_get_device_count() {
    ggml_vk_instance_init();

    return vk_instance.device_indices.size();
}

static void ggml_vk_get_device_description(int device, char * description, size_t description_size) {
    ggml_vk_instance_init();

    std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();

    vk::PhysicalDeviceProperties props;
    devices[device].getProperties(&props);

    snprintf(description, description_size, "%s", props.deviceName.data());
}

// backend interface

#define UNUSED GGML_UNUSED

// device backend

static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) {
    return buffer->buft->iface.get_name == ggml_backend_vk_buffer_type_name;
}

static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    VK_LOG_MEMORY("ggml_backend_vk_buffer_free_buffer()");
    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
    ggml_vk_destroy_buffer(ctx->dev_buffer);
    delete ctx;
}

static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
    return vk_ptr_base;

    UNUSED(buffer);
}

static enum ggml_status ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
    VK_LOG_DEBUG("ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")");
    if (tensor->view_src != nullptr) {
        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
    }
    return GGML_STATUS_SUCCESS;
}

static void ggml_backend_vk_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
    VK_LOG_DEBUG("ggml_backend_vk_buffer_memset_tensor(" << buffer << ", " << tensor << ", " << value << ", " << offset << ", " << size << ")");
    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
    vk_buffer buf = buf_ctx->dev_buffer;

    if (size == 0) {
        return;
    }

    uint32_t val32 = (uint32_t)value * 0x01010101;
    ggml_vk_buffer_memset(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, val32, size);
}

static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
    vk_buffer buf = buf_ctx->dev_buffer;

    if (size == 0) {
        return;
    }

    ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
}

static void ggml_backend_vk_buffer_set_tensor_2d(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset,
                                                 size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
    VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor_2d(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ", " <<
                 n_copies << ", " << stride_tensor << ", " << stride_data << ")");
    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;
    vk_buffer buf = buf_ctx->dev_buffer;

    if (size == 0) {
        return;
    }

    ggml_vk_buffer_write_2d(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, stride_data, stride_tensor, size, n_copies);
}

static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;

    if (size == 0) {
        return;
    }

    vk_buffer buf = buf_ctx->dev_buffer;

    ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
}

static void ggml_backend_vk_buffer_get_tensor_2d(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset,
                                                 size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
    VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor_2d(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ", " <<
                 n_copies << ", " << stride_tensor << ", " << stride_data << ")");
    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context;

    if (size == 0) {
        return;
    }

    vk_buffer buf = buf_ctx->dev_buffer;

    ggml_vk_buffer_read_2d(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, stride_tensor, stride_data, size, n_copies);
}

static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
    if (ggml_nbytes(src) == 0) {
        return true;
    }

    if (ggml_backend_buffer_is_vk(src->buffer)) {
        ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context;
        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;

        vk_buffer src_buf = src_buf_ctx->dev_buffer;
        vk_buffer dst_buf = dst_buf_ctx->dev_buffer;

        ggml_vk_buffer_copy(dst_buf, vk_tensor_offset(dst) + dst->view_offs, src_buf, vk_tensor_offset(src) + src->view_offs, ggml_nbytes(src));

        return true;
    }
    return false;

    UNUSED(buffer);
}

static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
    ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;

    ggml_vk_buffer_memset(ctx->dev_buffer, 0, value, buffer->size);
}

static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
    /* .free_buffer     = */ ggml_backend_vk_buffer_free_buffer,
    /* .get_base        = */ ggml_backend_vk_buffer_get_base,
    /* .init_tensor     = */ ggml_backend_vk_buffer_init_tensor,
    /* .memset_tensor   = */ ggml_backend_vk_buffer_memset_tensor,
    /* .set_tensor      = */ ggml_backend_vk_buffer_set_tensor,
    /* .get_tensor      = */ ggml_backend_vk_buffer_get_tensor,
    /* .set_tensor_2d   = */ ggml_backend_vk_buffer_set_tensor_2d,
    /* .get_tensor_2d   = */ ggml_backend_vk_buffer_get_tensor_2d,
    /* .cpy_tensor      = */ ggml_backend_vk_buffer_cpy_tensor,
    /* .clear           = */ ggml_backend_vk_buffer_clear,
    /* .reset           = */ NULL,
};

// vk buffer type
static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) {
    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context;

    return ctx->name.c_str();
}

static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    VK_LOG_MEMORY("ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")");
    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;

    vk_buffer dev_buffer = nullptr;
    try {
        dev_buffer = ggml_vk_create_buffer_device(ctx->device, size);
    } catch (const vk::SystemError& e) {
        return nullptr;
    }

    ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->device, std::move(dev_buffer), ctx->name);

    return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size);
}

static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
    return ctx->device->properties.limits.minStorageBufferOffsetAlignment;
}

static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
    ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context;
    return ctx->device->suballocation_block_size;
}

static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
    return ggml_nbytes(tensor);

    UNUSED(buft);
}

ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) {
    ggml_vk_instance_init();

    VK_LOG_DEBUG("ggml_backend_vk_buffer_type(" << dev_num << ")");

    vk_device dev = ggml_vk_get_device(dev_num);

    return &dev->buffer_type;
}

// host buffer type

static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
    return GGML_VK_NAME "_Host";

    UNUSED(buft);
}

static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
    VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()");
    ggml_vk_host_free(vk_instance.devices[0], buffer->context);
}

static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
    VK_LOG_MEMORY("ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")");

    size += 32;  // Behave like the CPU buffer type
    void * ptr = nullptr;
    try {
        ptr = ggml_vk_host_malloc(vk_instance.devices[0], size);
    } catch (vk::SystemError& e) {
        GGML_LOG_WARN("ggml_vulkan: Failed to allocate pinned memory (%s)\n", e.what());
        // fallback to cpu buffer
        return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size);
    }

    ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
    buffer->buft = buft;
    buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer;

    return buffer;

    UNUSED(buft);
}

static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
    return vk_instance.devices[0]->properties.limits.minMemoryMapAlignment;

    UNUSED(buft);
}

static size_t ggml_backend_vk_host_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
    return vk_instance.devices[0]->suballocation_block_size;

    UNUSED(buft);
}

// Should be changed to return device-specific host buffer type
// but that probably requires changes in llama.cpp
ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
    static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = {
        /* .iface    = */ {
            /* .get_name         = */ ggml_backend_vk_host_buffer_type_name,
            /* .alloc_buffer     = */ ggml_backend_vk_host_buffer_type_alloc_buffer,
            /* .get_alignment    = */ ggml_backend_vk_host_buffer_type_get_alignment,
            /* .get_max_size     = */ ggml_backend_vk_host_buffer_type_get_max_size,
            /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
            /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
        },
        /* .device   = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), 0),
        /* .context  = */ nullptr,
    };

    // Make sure device 0 is initialized
    ggml_vk_instance_init();
    ggml_vk_get_device(0);

    return &ggml_backend_vk_buffer_type_host;
}


// backend

static const char * ggml_backend_vk_name(ggml_backend_t backend) {
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;

    return ctx->name.c_str();
}

static void ggml_backend_vk_free(ggml_backend_t backend) {
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
    VK_LOG_DEBUG("ggml_backend_vk_free(" << ctx->name << ")");

    ggml_vk_cleanup(ctx);

    delete ctx;
    delete backend;
}

static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) {
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;

    return &ctx->device->buffer_type;
}

static void ggml_backend_vk_set_tensor_2d_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset,
                                                size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
    VK_LOG_DEBUG("ggml_backend_vk_set_tensor_2d_async(" << size << ", " << n_copies << ")");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");

    if (size == 0) {
        return;
    }

    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;

    vk_context cpy_ctx;

    if (ctx->device->async_use_transfer_queue) {
        if (ctx->transfer_ctx.expired()) {
            cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
            ctx->transfer_ctx = cpy_ctx;
            ggml_vk_ctx_begin(ctx->device, cpy_ctx);
        } else {
            cpy_ctx = ctx->transfer_ctx.lock();
        }
    } else {
        cpy_ctx = ggml_vk_get_compute_ctx(ctx);
    }

    vk_buffer buf = buf_ctx->dev_buffer;

    auto dst_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset;

    bool ret = ggml_vk_buffer_write_2d_async(cpy_ctx, buf, dst_offset, data, stride_data, stride_tensor, size, n_copies);

    if (!ret) {
        const size_t staging_size = size * n_copies;
        ggml_vk_ensure_sync_staging_buffer(ctx, staging_size);
        ggml_vk_sync_buffers(nullptr, cpy_ctx);

        std::vector<vk::BufferCopy> slices(1);
        if (size == stride_tensor) {
            slices[0].srcOffset = 0;
            slices[0].dstOffset = dst_offset;
            slices[0].size = staging_size;
        } else {
            slices.resize(n_copies);
            for (size_t i = 0; i < n_copies; i++) {
                slices[i].srcOffset = i * size;
                slices[i].dstOffset = dst_offset + i * stride_tensor;
                slices[i].size = size;
            }
        }

        cpy_ctx->s->buffer->buf.copyBuffer(ctx->sync_staging->buffer, buf->buffer, slices);

        if (size == stride_data) {
            deferred_memcpy(ctx->sync_staging->ptr, data, staging_size, &cpy_ctx->in_memcpys);
        } else {
            for (size_t i = 0; i < n_copies; i++) {
                deferred_memcpy((uint8_t *)ctx->sync_staging->ptr + i * size, (const uint8_t *)data + i * stride_data, size, &cpy_ctx->in_memcpys);
            }
        }
        ggml_vk_synchronize(ctx);
    }
}

static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
    VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")");
    ggml_backend_vk_set_tensor_2d_async(backend, tensor, data, offset, size, 1, size, size);
}

static void ggml_backend_vk_get_tensor_2d_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset,
                                                size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
    VK_LOG_DEBUG("ggml_backend_vk_get_tensor_2d_async(" << size << ", " << n_copies << ")");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
    GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");

    if (size == 0) {
        return;
    }

    ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;

    vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);

    vk_buffer buf = buf_ctx->dev_buffer;

    auto src_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset;
    bool ret = ggml_vk_buffer_read_2d_async(compute_ctx, buf, src_offset, data, stride_tensor, stride_data, size, n_copies);

    if (!ret) {
        const size_t staging_size = size * n_copies;
        ggml_vk_ensure_sync_staging_buffer(ctx, staging_size);
        ggml_vk_sync_buffers(nullptr, compute_ctx);

        std::vector<vk::BufferCopy> slices(1);
        if (size == stride_tensor) {
            slices[0].srcOffset = src_offset;
            slices[0].dstOffset = 0;
            slices[0].size = staging_size;
        } else {
            slices.resize(n_copies);
            for (size_t i = 0; i < n_copies; i++) {
                slices[i].srcOffset = src_offset + i * stride_tensor;
                slices[i].dstOffset = i * size;
                slices[i].size = size;
            }
        }

        compute_ctx->s->buffer->buf.copyBuffer(buf->buffer, ctx->sync_staging->buffer, slices);

        if (size == stride_data) {
            deferred_memcpy(data, ctx->sync_staging->ptr, staging_size, &compute_ctx->out_memcpys);
        } else {
            for (size_t i = 0; i < n_copies; i++) {
                deferred_memcpy((uint8_t *)data + i * stride_data, (const uint8_t *)ctx->sync_staging->ptr + i * size, size, &compute_ctx->out_memcpys);
            }
        }
        ggml_vk_synchronize(ctx);
    }
}

static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
    VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")");
    ggml_backend_vk_get_tensor_2d_async(backend, tensor, data, offset, size, 1, size, size);
}

static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
    VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async(" << src << " -> " << dst << ", size=" << ggml_nbytes(src) << ")");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend_dst->context;

    // Skip zero-size tensors
    if (ggml_nbytes(src) == 0) {
        return true;
    }

    if (dst->buffer->buft != ggml_backend_vk_get_default_buffer_type(backend_dst)) {
        return false;
    }

    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    vk_buffer dst_buf = dst_buf_ctx->dev_buffer;

    if (ggml_backend_buffer_is_vk(src->buffer)) {
        ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context;

        // Async copy only works within the same device
        if (src_buf_ctx->dev_buffer->device != dst_buf->device) {
            return false;
        }

        vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);

        ggml_vk_buffer_copy_async(compute_ctx, dst_buf, vk_tensor_offset(dst) + dst->view_offs,
                                   src_buf_ctx->dev_buffer, vk_tensor_offset(src) + src->view_offs,
                                   ggml_nbytes(src));
        return true;
    }

    if (ggml_backend_buffer_is_host(src->buffer)) {
        vk_buffer pinned_buf = nullptr;
        size_t pinned_offset = 0;
        ggml_vk_host_get(ctx->device, src->data, pinned_buf, pinned_offset);
        if (pinned_buf == nullptr) {
            return false;
        }

        vk_context cpy_ctx;
        if (ctx->device->async_use_transfer_queue) {
            if (ctx->transfer_ctx.expired()) {
                cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
                ctx->transfer_ctx = cpy_ctx;
                ggml_vk_ctx_begin(ctx->device, cpy_ctx);
            } else {
                cpy_ctx = ctx->transfer_ctx.lock();
            }
        } else {
            cpy_ctx = ggml_vk_get_compute_ctx(ctx);
        }

        return ggml_vk_buffer_write_async(cpy_ctx, dst_buf,
                                          vk_tensor_offset(dst) + dst->view_offs,
                                          src->data, ggml_nbytes(src));
    }

    GGML_UNUSED(backend_src);
    return false;
}

static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) {
    VK_LOG_DEBUG("ggml_vk_synchronize()");

    bool do_transfer = !ctx->compute_ctx.expired();

    if (ggml_vk_submit_transfer_ctx(ctx)) {
        ctx->submit_pending = true;
    }

    vk_context compute_ctx;
    vk_command_buffer* cmd_buf = nullptr;
    if (do_transfer) {
        compute_ctx = ctx->compute_ctx.lock();
        if (compute_ctx->s) {
            cmd_buf = compute_ctx->s->buffer;
        }

        ggml_vk_ctx_end(compute_ctx);

        for (auto& cpy : compute_ctx->in_memcpys) {
            memcpy(cpy.dst, cpy.src, cpy.n);
        }

        ggml_vk_submit(compute_ctx, {});
        ctx->submit_pending = true;
    }

    if (ctx->submit_pending) {
        if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) {
            vk::TimelineSemaphoreSubmitInfo tl_info{
                1, &ctx->transfer_semaphore.value,
                0, nullptr,
            };
            vk::PipelineStageFlags stage = ctx->device->transfer_queue.stage_flags;
            vk::SubmitInfo si{
                1, &ctx->transfer_semaphore.s, &stage,
                0, nullptr,
                0, nullptr,
            };
            si.setPNext(&tl_info);
            std::lock_guard<std::mutex> guard(queue_mutex);
            ctx->device->compute_queue.queue.submit({ si }, ctx->fence);
            ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value;
        } else {
            std::lock_guard<std::mutex> guard(queue_mutex);
            ctx->device->compute_queue.queue.submit({}, ctx->fence);
        }
        ggml_vk_wait_for_fence(ctx);
        ctx->submit_pending = false;
        if (cmd_buf) {
            cmd_buf->in_use = false;
            cmd_buf->buf.reset();
        }
    }

    if (do_transfer) {
        for (auto& cpy : compute_ctx->out_memcpys) {
            memcpy(cpy.dst, cpy.src, cpy.n);
        }
        ctx->compute_ctx.reset();
    }
}

static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
    VK_LOG_DEBUG("ggml_backend_vk_synchronize()");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;

    ggml_vk_synchronize(ctx);

    ggml_vk_graph_cleanup(ctx);
}

static bool ggml_vk_is_empty(ggml_tensor * node) {
    return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
}

static bool ggml_vk_can_fuse(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
    if (!ggml_can_fuse(cgraph, node_idx, ops)) {
        return false;
    }

    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_RMS_NORM && ops.begin()[1] == GGML_OP_MUL) {
        // additional constraints specific to this fusion
        const ggml_tensor *rms_norm = cgraph->nodes[node_idx];
        const ggml_tensor *mul = cgraph->nodes[node_idx + 1];

        GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32);
        GGML_ASSERT(rms_norm->type == GGML_TYPE_F32);
        // rms_norm only supports f32
        if (mul->src[0]->type != GGML_TYPE_F32 ||
            mul->src[1]->type != GGML_TYPE_F32 ||
            mul->type != GGML_TYPE_F32) {
            return false;
        }
        // if rms_norm is the B operand, then we don't handle broadcast
        if (rms_norm == mul->src[1] &&
            !ggml_are_same_shape(mul->src[0], rms_norm)) {
            return false;
        }
        // rms_norm shader assumes contiguous rows
        if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) {
            return false;
        }
    }
    auto const &mm_add_ok = [&](const ggml_tensor *mul, const ggml_tensor *add) {
        const ggml_tensor *bias = add->src[0] == mul ? add->src[1] : add->src[0];

        // mat-vec only
        if (ggml_nrows(mul) != 1) {
            return false;
        }
        // shaders assume the types match
        if (mul->type != bias->type) {
            return false;
        }
        // shaders reuse the D shape for bias
        if (!ggml_are_same_shape(mul, bias) ||
            !ggml_are_same_stride(mul, bias)) {
            return false;
        }
        // unaligned bias isn't handled
        if (get_misalign_bytes(ctx, bias) != 0) {
            return false;
        }
        return true;
    };

    if ((ops.size() == 2 || ops.size() == 3) && ops.begin()[0] == GGML_OP_MUL_MAT && ops.begin()[1] == GGML_OP_ADD) {
        // additional constraints specific to this fusion
        const ggml_tensor *mul = cgraph->nodes[node_idx];
        const ggml_tensor *add = cgraph->nodes[node_idx + 1];

        if (!mm_add_ok(mul, add)) {
            return false;
        }
        if (ops.size() == 3) {
            if (ops.begin()[2] != GGML_OP_ADD) {
                return false;
            }
            if (!mm_add_ok(add, cgraph->nodes[node_idx + 2])) {
                return false;
            }
        }
    }

    auto const &mmid_mul_ok = [&](const ggml_tensor *mmid, const ggml_tensor *mul) {
        const ggml_tensor *scale = mul->src[1];

        if (mmid != mul->src[0]) {
            return false;
        }
        // mat-vec only
        if (!ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) {
            return false;
        }
        // shaders assume the types match
        if (mmid->type != scale->type) {
            return false;
        }
        // shaders assume the bias is contiguous
        if (!ggml_is_contiguous(scale)) {
            return false;
        }
        // unaligned bias isn't handled
        if (get_misalign_bytes(ctx, scale) != 0) {
            return false;
        }
        // shader only indexes by expert index
        if (scale->ne[0] != 1 ||
            scale->ne[1] != mul->ne[1] ||
            scale->ne[2] != 1 ||
            scale->ne[3] != 1) {
            return false;
        }
        return true;
    };

    if ((ops.size() == 2 || ops.size() == 3) && ops.begin()[0] == GGML_OP_MUL_MAT_ID && ops.begin()[1] == GGML_OP_ADD_ID) {
        // additional constraints specific to this fusion
        const ggml_tensor *mul = cgraph->nodes[node_idx];
        const ggml_tensor *add = cgraph->nodes[node_idx + 1];
        const ggml_tensor *bias = add->src[1];

        if (mul != add->src[0]) {
            return false;
        }
        // mat-vec only
        if (!ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) {
            return false;
        }
        // shaders assume the types match
        if (mul->type != bias->type) {
            return false;
        }
        // shaders assume the bias is contiguous
        if (!ggml_is_contiguous(bias)) {
            return false;
        }
        // the ID tensor must be the same for mul_mat_id and add_id
        if (mul->src[2] != add->src[2]) {
            return false;
        }
        // unaligned bias isn't handled
        if (get_misalign_bytes(ctx, bias) != 0) {
            return false;
        }

        if (ops.size() == 3) {
            if (ops.begin()[2] != GGML_OP_MUL) {
                return false;
            }
            const ggml_tensor *mul = cgraph->nodes[node_idx + 2];
            return mmid_mul_ok(add, mul);
        }
    }

    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_MUL_MAT_ID && ops.begin()[1] == GGML_OP_MUL) {
        // additional constraints specific to this fusion
        const ggml_tensor *mmid = cgraph->nodes[node_idx];
        const ggml_tensor *mul = cgraph->nodes[node_idx + 1];

        if (!mmid_mul_ok(mmid, mul)) {
            return false;
        }
    }

    return true;
}

// Match SSM_CONV + UNARY(SILU) or SSM_CONV + ADD + UNARY(SILU). num_extra is 1 or 2.
static bool ggml_vk_can_fuse_ssm_conv(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph,
                                      int node_idx, int num_extra) {
    const ggml_tensor * conv = cgraph->nodes[node_idx];
    if (conv->op != GGML_OP_SSM_CONV) {
        return false;
    }

    const ggml_tensor * silu = nullptr;
    const ggml_tensor * bias = nullptr;

    if (num_extra == 1) {
        if (!ggml_can_fuse(cgraph, node_idx, { GGML_OP_SSM_CONV, GGML_OP_UNARY })) {
            return false;
        }
        silu = cgraph->nodes[node_idx + 1];
    } else if (num_extra == 2) {
        if (!ggml_can_fuse(cgraph, node_idx, { GGML_OP_SSM_CONV, GGML_OP_ADD, GGML_OP_UNARY })) {
            return false;
        }
        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
        silu = cgraph->nodes[node_idx + 2];
        bias = (add->src[0] == conv) ? add->src[1] : add->src[0];

        if (bias->type != GGML_TYPE_F32 || !ggml_is_contiguous(bias)) {
            return false;
        }
        // bias must be channel-wise (one element per channel of the conv output)
        if (ggml_nelements(bias) != conv->ne[0] || bias->ne[0] != conv->ne[0]) {
            return false;
        }
        if (add->type != GGML_TYPE_F32) {
            return false;
        }
        // The shader doesn't apply per-tensor offsets, so reject misaligned bias.
        if (get_misalign_bytes(ctx, bias) != 0) {
            return false;
        }
    } else {
        return false;
    }

    if (ggml_get_unary_op(silu) != GGML_UNARY_OP_SILU) {
        return false;
    }
    if (conv->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) {
        return false;
    }
    // The shader writes to the fused dst using its own strides, but the push constants don't
    // carry a per-tensor offset, so the binding must be naturally aligned.
    if (get_misalign_bytes(ctx, silu) != 0) {
        return false;
    }
    return true;
}

static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph,
                                      int node_idx, topk_moe_mode mode) {

    const ggml_tensor * softmax;
    const ggml_tensor * weights;
    const ggml_tensor * get_rows;
    const ggml_tensor * argsort;

    switch (mode) {
    case TOPK_MOE_EARLY_SOFTMAX_NORM:
        softmax = cgraph->nodes[node_idx + 0];
        weights = cgraph->nodes[node_idx + 9];
        get_rows = cgraph->nodes[node_idx + 4];
        argsort = cgraph->nodes[node_idx + 2];
        break;
    case TOPK_MOE_SIGMOID_NORM_BIAS:
        softmax = cgraph->nodes[node_idx + 0]; // really sigmoid
        weights = cgraph->nodes[node_idx + 10];
        get_rows = cgraph->nodes[node_idx + 5];
        argsort = cgraph->nodes[node_idx + 3];
        if (ggml_get_unary_op(softmax) != GGML_UNARY_OP_SIGMOID) {
            return false;
        }
        // bias is expected to be 1D
        if (ggml_nrows(cgraph->nodes[node_idx + 2]->src[1]) != 1 ||
            !ggml_is_contiguous(cgraph->nodes[node_idx + 2]->src[1])) {
            return false;
        }
        // sigmoid fusion seems to generate infinities on moltenvk
        if (ctx->device->driver_id == vk::DriverId::eMoltenvk) {
            return false;
        }
        break;
    case TOPK_MOE_EARLY_SOFTMAX:
        softmax = cgraph->nodes[node_idx + 0];
        weights = cgraph->nodes[node_idx + 4];
        get_rows = cgraph->nodes[node_idx + 4];
        argsort = cgraph->nodes[node_idx + 2];
        break;
    case TOPK_MOE_LATE_SOFTMAX:
        softmax = cgraph->nodes[node_idx + 4];
        weights = cgraph->nodes[node_idx + 5];
        get_rows = cgraph->nodes[node_idx + 2];
        argsort = cgraph->nodes[node_idx + 0];
        break;
    default:
        return false;
    }

    ggml_tensor * probs = get_rows->src[0];
    if (probs->op != GGML_OP_RESHAPE) {
        return false;
    }
    probs = probs->src[0];
    ggml_tensor * selection_probs = argsort->src[0];

    if (probs != selection_probs && mode != TOPK_MOE_SIGMOID_NORM_BIAS) {
        return false;
    }

    if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) {
        return false;
    }

    if (softmax->op == GGML_OP_SOFT_MAX) {
        const float * op_params = (const float *)softmax->op_params;

        float scale = op_params[0];
        float max_bias = op_params[1];

        if (scale != 1.0f || max_bias != 0.0f) {
            return false;
        }

        // don't fuse when masks or sinks are present
        if (softmax->src[1] || softmax->src[2]) {
            return false;
        }
    }

    const int n_expert = softmax->ne[0];
    if (n_expert > (1 << (num_topk_moe_pipelines-1))) {
        return false;
    }

    if (!ctx->device->subgroup_arithmetic ||
        !ctx->device->subgroup_shuffle ||
        !ctx->device->subgroup_require_full_support ||
        ctx->device->disable_fusion) {
        return false;
    }

    return true;
}

static bool ggml_vk_can_fuse_rope_set_rows(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph,
                                           int node_idx) {
    GGML_UNUSED(ctx);
    const ggml_tensor *rope = cgraph->nodes[node_idx + 0];
    const ggml_tensor *view = cgraph->nodes[node_idx + 1];
    const ggml_tensor *set_rows = cgraph->nodes[node_idx + 2];

    // ne3 not tested
    if (rope->src[0]->ne[3] != 1) {
        return false;
    }

    if (set_rows->type != GGML_TYPE_F32 && set_rows->type != GGML_TYPE_F16) {
        return false;
    }

    if (set_rows->src[1]->type != GGML_TYPE_I64) {
        return false;
    }

    // The view should flatten two dims of rope into one dim
    if (!ggml_is_contiguous(view) ||
        view->ne[0] != rope->ne[0] * rope->ne[1]) {
        return false;
    }

    // Only norm/neox/mrope shaders have the fusion code
    const int mode = ((const int32_t *) rope->op_params)[2];
    if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_MROPE) {
        return false;
    }

    return true;
}

// Pattern check for the 5-op Snake fusion: mul -> sin -> sqr -> mul -> add.
// Verifies the chain shape, the closure x_in_add == x_in_mul0, and that
// the broadcast operands a and inv_b share a [1, C] layout.
static bool ggml_vk_can_fuse_snake(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) {
    GGML_UNUSED(ctx);
    if (!ggml_can_fuse(cgraph, node_idx, snake_pattern)) {
        return false;
    }

    const ggml_tensor * mul0     = cgraph->nodes[node_idx + 0];
    const ggml_tensor * sin_node = cgraph->nodes[node_idx + 1];
    const ggml_tensor * sqr      = cgraph->nodes[node_idx + 2];
    const ggml_tensor * mul1     = cgraph->nodes[node_idx + 3];
    const ggml_tensor * add      = cgraph->nodes[node_idx + 4];

    const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1];
    const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0];

    const ggml_tensor * inv_b    = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0];
    const ggml_tensor * x_in_add = (add->src[0] == mul1) ? add->src[1] : add->src[0];

    if (x_in_add != x) {
        return false;
    }
    if (x->type != GGML_TYPE_F32 && x->type != GGML_TYPE_F16 && x->type != GGML_TYPE_BF16) {
        return false;
    }
    // Shader bindings: data_a is A_TYPE so it follows x's precision, while
    // data_b and data_c are hardcoded float, so the broadcast operands must
    // be F32 regardless of x's type.
    if (a->type     != GGML_TYPE_F32) return false;
    if (inv_b->type != GGML_TYPE_F32) return false;
    // Chain intermediates and output share x's precision (single A_TYPE / D_TYPE pipeline).
    if (mul0->type     != x->type) return false;
    if (sin_node->type != x->type) return false;
    if (sqr->type      != x->type) return false;
    if (mul1->type     != x->type) return false;
    if (add->type      != x->type) return false;
    if (!ggml_are_same_shape(a, inv_b)) {
        return false;
    }
    if (a->ne[0] != 1 || a->ne[1] != x->ne[1]) {
        return false;
    }
    // Dispatch is 2D over (ne0, ne1), so x and add must be 2D and a / inv_b
    // must collapse to [1, C, 1, 1]. Higher dims are not handled by the shader.
    if (x->ne[2]     != 1 || x->ne[3]     != 1) return false;
    if (add->ne[2]   != 1 || add->ne[3]   != 1) return false;
    if (a->ne[2]     != 1 || a->ne[3]     != 1) return false;
    if (inv_b->ne[2] != 1 || inv_b->ne[3] != 1) return false;
    // Shader uses idx = i0 + i1 * ne0 and reads data_b[i1] / data_c[i1],
    // so every operand must be contiguous.
    if (!ggml_is_contiguous(x) || !ggml_is_contiguous(add) ||
        !ggml_is_contiguous(a) || !ggml_is_contiguous(inv_b)) {
        return false;
    }
    return true;
}

// Check whether the tensors overlap in memory.
// Fusions can potentially overwrite src tensors in ways that are not prevented
// by ggml-alloc. If the fusion src is being applied in a way that's elementwise
// with the destination, then it's OK for them to overlap if they are exactly equal.
static bool ggml_vk_tensors_overlap(const ggml_tensor * a, const ggml_tensor * b, bool elementwise) {
    ggml_backend_vk_buffer_context * a_buf_ctx = (ggml_backend_vk_buffer_context *)a->buffer->context;
    vk_buffer a_buf = a_buf_ctx->dev_buffer;
    ggml_backend_vk_buffer_context * b_buf_ctx = (ggml_backend_vk_buffer_context *)b->buffer->context;
    vk_buffer b_buf = b_buf_ctx->dev_buffer;
    if (a_buf == b_buf) {
        auto a_base = vk_tensor_offset(a) + a->view_offs;
        auto a_size = ggml_nbytes(a);
        auto b_base = vk_tensor_offset(b) + b->view_offs;
        auto b_size = ggml_nbytes(b);

        if (elementwise && a_base == b_base && a_size == b_size) {
            return false;
        }

        if ((b_base <= a_base && a_base < b_base + b_size) ||
            (a_base <= b_base && b_base < a_base + a_size)) {
            return true;
        }
    }
    return false;
}

static bool ggml_vk_can_fuse_rms_norm_mul_rope(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph,
                                               int node_idx) {
    GGML_UNUSED(ctx);
    const ggml_tensor *rms = cgraph->nodes[node_idx + 0];
    const ggml_tensor *mul = cgraph->nodes[node_idx + 1];
    const ggml_tensor *rope = cgraph->nodes[node_idx + 2];

    const int mode = ((const int32_t *) rope->op_params)[2];

    // noncontig tensors aren't tested, and don't seem common in practice
    if (!ggml_is_contiguous(rms) ||
        !ggml_is_contiguous(mul) ||
        !ggml_is_contiguous(rope)) {
        return false;
    }

    // only norm/neox are handled in the shader
    if (mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_NORMAL) {
        return false;
    }

    // shared memory size for passing data from mul->rope
    if (mul->ne[0] > 1024) {
        return false;
    }

    // conditions for pipeline creation
    if (sizeof(vk_op_rms_norm_mul_rope_push_constants) > ctx->device->properties.limits.maxPushConstantsSize) {
        return false;
    }

    return true;
}

static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) {

    const ggml_tensor *first_node = cgraph->nodes[node_idx];
    if (first_node->op != GGML_OP_ADD) {
        return 0;
    }

    if (!ctx->device->multi_add) {
        return 0;
    }

    int32_t num_adds = 1;
    while (node_idx + num_adds < cgraph->n_nodes &&
           cgraph->nodes[node_idx + num_adds]->op == GGML_OP_ADD &&
           num_adds < MAX_FUSED_ADDS) {
        num_adds++;
    }

    // The shader currently requires same shapes (but different strides are allowed),
    // everything f32, and no misalignment
    for (int32_t i = 0; i < num_adds; ++i) {
        const ggml_tensor *next_node = cgraph->nodes[node_idx + i];
        if (!ggml_are_same_shape(first_node, next_node->src[0]) ||
            !ggml_are_same_shape(first_node, next_node->src[1]) ||
            next_node->type != GGML_TYPE_F32 ||
            next_node->src[0]->type != GGML_TYPE_F32 ||
            next_node->src[1]->type != GGML_TYPE_F32 ||
            get_misalign_bytes(ctx, next_node) ||
            get_misalign_bytes(ctx, next_node->src[0]) ||
            get_misalign_bytes(ctx, next_node->src[1])) {
            num_adds = i;
        }
    }

    // Verify we can fuse these
    ggml_op adds[MAX_FUSED_ADDS];
    for (int32_t i = 0; i < num_adds; ++i) {
        adds[i] = GGML_OP_ADD;
    }

    // decrease num_adds if they can't all be fused
    while (num_adds > 1 && !ggml_can_fuse(cgraph, node_idx, adds, num_adds)) {
        num_adds--;
    }

    // a single add is not "fused", so just return zero
    if (num_adds == 1) {
        return 0;
    }
    return num_adds;
}

static int32_t find_first_set(uint32_t x) {
    int32_t ret = 0;
    if (!x) {
        return -1;
    }
    while (!(x & 1)) {
        x >>= 1;
        ret++;
    }
    return ret;
}

static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
    VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;

    if (vk_instance.debug_utils_support) {
        vk::DebugUtilsLabelEXT dul = {};
        dul.pLabelName = "ggml_backend_vk_graph_compute";
        dul.color = std::array<float,4>{1.0f, 1.0f, 1.0f, 1.0f};
        vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast<VkDebugUtilsLabelEXT*>(&dul));
    }

    ctx->prealloc_size_add_rms_partials_offset = 0;
    ctx->do_add_rms_partials = false;
    ctx->do_add_rms_partials_offset_calculation = false;

    int last_node = cgraph->n_nodes - 1;

    // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
    while (last_node > 0 && (ggml_vk_is_empty(cgraph->nodes[last_node]) || ((cgraph->nodes[last_node]->flags & GGML_TENSOR_FLAG_COMPUTE) == 0))) {
        last_node -= 1;
    }

    // Reserve tensor context space for all nodes
    ctx->tensor_ctxs.resize(cgraph->n_nodes);

    bool first_node_in_batch = true; // true if next node will be first node in a batch
    int submit_node_idx = 0; // index to first node in a batch

    ggml_vk_submit_transfer_ctx(ctx);

    vk_context compute_ctx;
    if (vk_perf_logger_enabled) {
        // allocate/resize the query pool
        if (ctx->num_queries < cgraph->n_nodes + 1) {
            if (ctx->query_pool) {
                ctx->device->device.destroyQueryPool(ctx->query_pool);
            }
            vk::QueryPoolCreateInfo query_create_info;
            query_create_info.queryType = vk::QueryType::eTimestamp;
            query_create_info.queryCount = cgraph->n_nodes + 100;
            ctx->query_pool = ctx->device->device.createQueryPool(query_create_info);
            ctx->num_queries = query_create_info.queryCount;
            ctx->query_fusion_names.resize(ctx->num_queries);
            ctx->query_fusion_node_count.resize(ctx->num_queries);
            ctx->query_nodes.resize(ctx->num_queries);
            ctx->query_node_idx.resize(ctx->num_queries);
        }

        ctx->device->device.resetQueryPool(ctx->query_pool, 0, cgraph->n_nodes+1);
        std::fill(ctx->query_fusion_names.begin(), ctx->query_fusion_names.end(), nullptr);
        std::fill(ctx->query_fusion_node_count.begin(), ctx->query_fusion_node_count.end(), 0);
        std::fill(ctx->query_nodes.begin(), ctx->query_nodes.end(), nullptr);
        std::fill(ctx->query_node_idx.begin(), ctx->query_node_idx.end(), 0);

        GGML_ASSERT(ctx->compute_ctx.expired());
        compute_ctx = ggml_vk_get_compute_ctx(ctx);
        ctx->query_idx = 0;
        compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
        ggml_vk_sync_buffers(ctx, compute_ctx);
    }

    ctx->prealloc_y_last_pipeline_used = nullptr;
    ctx->prealloc_y_last_tensor_used = nullptr;
    ctx->prealloc_y_last_decode_vector_staging = false;

    if (ctx->prealloc_size_add_rms_partials) {
        ggml_vk_preallocate_buffers(ctx, nullptr);
        compute_ctx = ggml_vk_get_compute_ctx(ctx);
        // initialize partial sums to zero.
        ggml_vk_buffer_memset_async(compute_ctx, ctx->prealloc_add_rms_partials, 0, 0, ctx->prealloc_size_add_rms_partials);
        ggml_vk_sync_buffers(ctx, compute_ctx);
    }

    // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution.
    // Estimate the amount of matmul work by looking at the weight matrix size, and submit every 100MB
    // (and scaled down based on model size, so smaller models submit earlier).
    // Also submit at least every 100 nodes, in case there are workloads without as much matmul.
    int nodes_per_submit = 100;
    int submitted_nodes = 0;
    int submit_count = 0;
    uint64_t mul_mat_bytes = 0;
    uint64_t total_mul_mat_bytes = 0;
    uint64_t mul_mat_bytes_per_submit = std::min(uint64_t(100*1000*1000), ctx->last_total_mul_mat_bytes / 40u);
    for (int i = 0; i < cgraph->n_nodes; i++) {
        if (first_node_in_batch) {
            submit_node_idx = i;
        }

        if (cgraph->nodes[i]->op == GGML_OP_MUL_MAT || cgraph->nodes[i]->op == GGML_OP_MUL_MAT_ID) {
            auto bytes = ggml_nbytes(cgraph->nodes[i]->src[0]);
            mul_mat_bytes += bytes;
            total_mul_mat_bytes += bytes;
        }

        // op_srcs_fused_elementwise indicates whether an op's srcs all contribute to
        // the fused result in an elementwise-way. This affects whether the memory for
        // the src is allowed to overlap the memory for the destination.
        // The array is sized to handle the largest fusion (asserted later).
        bool op_srcs_fused_elementwise[12];

        ctx->fused_topk_moe_mode = TOPK_MOE_COUNT;
        ctx->fused_topk_moe_scale = false;
        const char *fusion_string {};
        if (!ctx->device->disable_fusion) {
            uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
            if (num_adds) {
                ctx->num_additional_fused_ops = num_adds - 1;
                fusion_string = "MULTI_ADD";
                std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, true);
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_ADD })) {
                ctx->num_additional_fused_ops = 2;
                fusion_string = "MUL_MAT_ADD_ADD";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
                op_srcs_fused_elementwise[2] = true;
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) {
                ctx->num_additional_fused_ops = 1;
                fusion_string = "MUL_MAT_ADD";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_MUL })) {
                ctx->num_additional_fused_ops = 2;
                fusion_string = "MUL_MAT_ID_ADD_ID_MUL";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
                op_srcs_fused_elementwise[2] = true;
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID })) {
                ctx->num_additional_fused_ops = 1;
                fusion_string = "MUL_MAT_ID_ADD_ID";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_MUL })) {
                ctx->num_additional_fused_ops = 1;
                fusion_string = "MUL_MAT_ID_MUL";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
            } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 4 }) &&
                       ggml_check_edges(cgraph, i, rms_norm_mul_rope_view_set_rows_edges) &&
                       ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i) &&
                       ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i + 2)) {
                ctx->num_additional_fused_ops = 4;
                fusion_string = "RMS_NORM_MUL_ROPE_VIEW_SET_ROWS";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = false;
                op_srcs_fused_elementwise[2] = false;
                op_srcs_fused_elementwise[3] = false;
                op_srcs_fused_elementwise[4] = false;
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE })&&
                       ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i)) {
                ctx->num_additional_fused_ops = 2;
                fusion_string = "RMS_NORM_MUL_ROPE";
                // rope is approximately elementwise - whole rows are done by a single workgroup and it's row-wise
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
                op_srcs_fused_elementwise[2] = true;
            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
                ctx->num_additional_fused_ops = 1;
                fusion_string = "RMS_NORM_MUL";
                // rms_norm is not elementwise, but whole rows must be consumed and the scale factor computed before
                // they are overwritten, and one workgroup per row. So close enough.
                op_srcs_fused_elementwise[0] = true;
                op_srcs_fused_elementwise[1] = true;
            } else if (ggml_vk_can_fuse_ssm_conv(ctx, cgraph, i, 2)) {
                ctx->num_additional_fused_ops = 2;
                fusion_string = "SSM_CONV_BIAS_SILU";
                // ssm_conv reads multiple input tokens per output, so it's not elementwise w.r.t. its srcs.
                // The downstream add and silu are elementwise on the conv output.
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
                op_srcs_fused_elementwise[2] = true;
            } else if (ggml_vk_can_fuse_ssm_conv(ctx, cgraph, i, 1)) {
                ctx->num_additional_fused_ops = 1;
                fusion_string = "SSM_CONV_SILU";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = true;
            } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 2 }) &&
                       ggml_check_edges(cgraph, i, rope_view_set_rows_edges) &&
                       ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i)) {
                ctx->num_additional_fused_ops = 2;
                fusion_string = "ROPE_VIEW_SET_ROWS";
                op_srcs_fused_elementwise[0] = false;
                op_srcs_fused_elementwise[1] = false;
                op_srcs_fused_elementwise[2] = false;
            } else if (ggml_vk_can_fuse_snake(ctx, cgraph, i)) {
                ctx->num_additional_fused_ops = 4;
                fusion_string = "SNAKE";
                // elementwise=true: snake.comp is safe under exact aliasing because each
                // thread reads data_x[idx] into a register before writing data_d[idx]
                // with a data dependency on that register. The overlap check still
                // rejects partial overlaps (different base or size).
                std::fill_n(op_srcs_fused_elementwise, 5, true);
            } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax_norm, { i + 3, i + 9 }) &&
                       ggml_check_edges(cgraph, i, topk_moe_early_softmax_norm_edges) &&
                       ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX_NORM)) {
                ctx->num_additional_fused_ops = topk_moe_early_softmax_norm.size() - 1;
                // view of argsort writes to memory
                ctx->fused_ops_write_mask |= 1 << 3;
                ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX_NORM;
                fusion_string = "TOPK_MOE_EARLY_SOFTMAX_NORM";
                std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false);
            } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_sigmoid_norm_bias, { i + 4, i + 10 }) &&
                       ggml_check_edges(cgraph, i, topk_moe_sigmoid_norm_bias_edges) &&
                       ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_SIGMOID_NORM_BIAS)) {
                ctx->num_additional_fused_ops = topk_moe_sigmoid_norm_bias.size() - 1;
                // view of argsort writes to memory
                ctx->fused_ops_write_mask |= 1 << 4;
                ctx->fused_topk_moe_mode = TOPK_MOE_SIGMOID_NORM_BIAS;
                fusion_string = "TOPK_MOE_SIGMOID_NORM_BIAS";
                std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false);
            } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) &&
                       ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) &&
                       ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) {
                ctx->num_additional_fused_ops = topk_moe_early_softmax.size() - 1;
                // view of argsort writes to memory
                ctx->fused_ops_write_mask |= 1 << 3;
                ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX;
                fusion_string = "TOPK_MOE_EARLY_SOFTMAX";
                std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false);
            } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_late_softmax, { i + 1, i + 5 }) &&
                       ggml_check_edges(cgraph, i, topk_moe_late_softmax_edges) &&
                       ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_LATE_SOFTMAX)) {
                ctx->num_additional_fused_ops = topk_moe_late_softmax.size() - 1;
                // view of argsort writes to memory
                ctx->fused_ops_write_mask |= 1 << 1;
                ctx->fused_topk_moe_mode = TOPK_MOE_LATE_SOFTMAX;
                fusion_string = "TOPK_MOE_LATE_SOFTMAX";
                std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false);
            }
            if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
                // Look for an additional scale op to fuse - occurs in deepseek2 and nemotron3 nano.
                if (ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops - 1, { GGML_OP_DIV, GGML_OP_RESHAPE, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 }) ||
                    ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops, { GGML_OP_GET_ROWS, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 })) {
                    ctx->fused_topk_moe_scale = true;
                    ctx->num_additional_fused_ops++;
                    op_srcs_fused_elementwise[ctx->num_additional_fused_ops] = false;
                }
            }
        }
        GGML_ASSERT(ctx->num_additional_fused_ops < (int)(sizeof(op_srcs_fused_elementwise) / sizeof(op_srcs_fused_elementwise[0])));
        ctx->fused_ops_write_mask |= 1 << ctx->num_additional_fused_ops;

        // Check whether fusion would overwrite src operands while they're still in use.
        // If so, disable fusion.
        if (ctx->num_additional_fused_ops) {
            // There are up to two output nodes - topk_moe has two.
            uint32_t bits = ctx->fused_ops_write_mask & ~(1 << ctx->num_additional_fused_ops);
            ggml_tensor *output_nodes[2] {};
            output_nodes[0] = cgraph->nodes[i + ctx->num_additional_fused_ops];
            if (bits) {
                int output_idx = find_first_set(bits);
                GGML_ASSERT(bits == (1u << output_idx));
                output_nodes[1] = cgraph->nodes[i + output_idx];
            }

            bool need_disable = false;

            // topk_moe often overwrites the source, but for a given row all the src values are
            // loaded before anything is stored. If there's only one row, this is safe, so treat
            // this as a special case.
            bool is_topk_moe_single_row = ctx->fused_topk_moe_mode != TOPK_MOE_COUNT &&
                                          ggml_nrows(cgraph->nodes[i]->src[0]) == 1;

            if (!is_topk_moe_single_row) {
                for (int j = 0; j < 2; ++j) {
                    ggml_tensor *dst = output_nodes[j];
                    if (!dst) {
                        continue;
                    }
                    // Loop over all srcs of all nodes in the fusion. If the src overlaps
                    // the destination and the src is not an intermediate node that's being
                    // elided, then disable fusion.
                    for (int k = 0; k <= ctx->num_additional_fused_ops; ++k) {
                        for (uint32_t s = 0; s < GGML_MAX_SRC; ++s) {
                            ggml_tensor *src = cgraph->nodes[i + k]->src[s];
                            if (!src || src->op == GGML_OP_NONE) {
                                continue;
                            }
                            if (ggml_vk_tensors_overlap(src, dst, op_srcs_fused_elementwise[k])) {
                                bool found = false;
                                for (int n = 0; n < k; ++n) {
                                    if (cgraph->nodes[i + n] == src) {
                                        found = true;
                                        break;
                                    }
                                }
                                if (!found) {
                                    need_disable = true;
                                }
                            }
                        }
                    }
                }
            }
            if (need_disable) {
                ctx->num_additional_fused_ops = 0;
                ctx->fused_ops_write_mask = 1;
                ctx->fused_topk_moe_mode = TOPK_MOE_COUNT;
                ctx->fused_topk_moe_scale = false;
            }
        }

        // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining)
        bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5;
        bool submit = (submitted_nodes >= nodes_per_submit) ||
                      (mul_mat_bytes_per_submit != 0 && mul_mat_bytes >= mul_mat_bytes_per_submit) ||
                      (i + ctx->num_additional_fused_ops >= last_node) ||
                      (almost_ready && !ctx->almost_ready_fence_pending);

        bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, i + ctx->num_additional_fused_ops >= last_node, almost_ready, submit);

        if (vk_perf_logger_enabled && enqueued) {
            compute_ctx = ggml_vk_get_compute_ctx(ctx);
            if (!vk_perf_logger_concurrent) {
                // track a single node/fusion for the current query
                ctx->query_nodes[ctx->query_idx] = cgraph->nodes[i];
                ctx->query_fusion_names[ctx->query_idx] = fusion_string;
                compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
                ggml_vk_sync_buffers(ctx, compute_ctx);
            } else {
                // track a fusion string and number of fused ops for the current node_idx
                ctx->query_fusion_names[i] = fusion_string;
                ctx->query_fusion_node_count[i] = ctx->num_additional_fused_ops;
            }
        }

        if (enqueued) {
            ++submitted_nodes;

#ifndef GGML_VULKAN_CHECK_RESULTS
            if (first_node_in_batch) {
                first_node_in_batch = false;
            }
#endif
        }

        if (submit && enqueued) {
            first_node_in_batch = true;
            submitted_nodes = 0;
            mul_mat_bytes = 0;
            if (submit_count < 3) {
                mul_mat_bytes_per_submit *= 2;
            }
            submit_count++;
        }
        i += ctx->num_additional_fused_ops;
        ctx->num_additional_fused_ops = 0;
        ctx->fused_ops_write_mask = 0;
    }

    ctx->last_total_mul_mat_bytes = total_mul_mat_bytes;

    if (vk_perf_logger_enabled) {
        // End the command buffer and submit/wait
        GGML_ASSERT(!ctx->compute_ctx.expired());
        compute_ctx = ctx->compute_ctx.lock();
        ggml_vk_ctx_end(compute_ctx);

        ggml_vk_submit(compute_ctx, ctx->device->fence);
        VK_CHECK(ctx->device->device.waitForFences({ ctx->device->fence }, true, UINT64_MAX), "GGML_VULKAN_PERF waitForFences");
        ctx->device->device.resetFences({ ctx->device->fence });
        ctx->compute_ctx.reset();

        // Get the results and pass them to the logger
        std::vector<uint64_t> timestamps(cgraph->n_nodes + 1);
        VK_CHECK(ctx->device->device.getQueryPoolResults(ctx->query_pool, 0, ctx->query_idx, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait), "get timestamp results");
        if (!vk_perf_logger_concurrent) {
            // Log each op separately
            for (int i = 1; i < ctx->query_idx; i++) {
                auto node = ctx->query_nodes[i];
                auto name = ctx->query_fusion_names[i];
                ctx->perf_logger->log_timing(node, name, uint64_t((timestamps[i] - timestamps[i-1]) * ctx->device->properties.limits.timestampPeriod));
            }
        } else {
            // Log each group of nodes
            int prev_node_idx = 0;
            for (int i = 1; i < ctx->query_idx; i++) {
                auto cur_node_idx = ctx->query_node_idx[i];
                std::vector<ggml_tensor *> nodes;
                std::vector<const char *> names;
                for (int node_idx = prev_node_idx; node_idx < cur_node_idx; ++node_idx) {
                    if (ggml_op_is_empty(cgraph->nodes[node_idx]->op)) {
                        continue;
                    }
                    nodes.push_back(cgraph->nodes[node_idx]);
                    names.push_back(ctx->query_fusion_names[node_idx]);
                    node_idx += ctx->query_fusion_node_count[node_idx];
                }
                prev_node_idx = cur_node_idx;
                ctx->perf_logger->log_timing(nodes, names, uint64_t((timestamps[i] - timestamps[i-1]) * ctx->device->properties.limits.timestampPeriod));
            }
        }
        ctx->perf_logger->print_timings();
    }

    if (!ctx->device->support_async) {
        ggml_vk_synchronize(ctx);
    }

    return GGML_STATUS_SUCCESS;

    UNUSED(backend);
}

// Sort the graph for improved parallelism.
static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * graph)
{
    VK_LOG_DEBUG("ggml_vk_graph_optimize(" << graph->n_nodes << " nodes)");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;

    if (ctx->device->disable_graph_optimize) {
        return;
    }

    auto const &is_empty = [](ggml_tensor * node) -> bool {
        return node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
    };

    auto const &is_src_of = [](const ggml_tensor *dst, const ggml_tensor *src) -> bool {
        for (uint32_t s = 0; s < GGML_MAX_SRC; ++s) {
            if (dst->src[s] == src) {
                return true;
            }
        }
        // implicit dependency if they view the same tensor
        const ggml_tensor *dst2 = dst->view_src ? dst->view_src : dst;
        const ggml_tensor *src2 = src->view_src ? src->view_src : src;
        if (dst2 == src2) {
            return true;
        }
        return false;
    };

    std::vector<ggml_tensor *> new_order;
    std::vector<bool> used(graph->n_nodes, false);
    std::set<ggml_tensor *> used_node_set;

    int first_unused = 0;
    while (first_unused < graph->n_nodes) {
        std::vector<int> current_set;

        // Check for fusion patterns and avoid reordering them
        auto const &match_pattern = [&](const std::initializer_list<ggml_op> &pattern, int start) -> bool {
            if (start + (int)pattern.size() <= graph->n_nodes) {
                bool is_pattern = true;
                for (size_t j = 0; j < pattern.size(); ++j) {
                    if (graph->nodes[start + j]->op != pattern.begin()[j] || used[start + j]) {
                        is_pattern = false;
                    }
                }
                return is_pattern;
            }
            return false;
        };

        auto const &keep_pattern = [&](const std::initializer_list<ggml_op> &pattern) -> bool {
            if (match_pattern(pattern, first_unused)) {
                for (size_t j = 0; j < pattern.size(); ++j) {
                    new_order.push_back(graph->nodes[first_unused + j]);
                    used_node_set.insert(graph->nodes[first_unused + j]);
                    used[first_unused + j] = true;
                }
                while (first_unused < graph->n_nodes && used[first_unused]) {
                    first_unused++;
                }
                return true;
            }
            return false;
        };

        if (keep_pattern(topk_moe_early_softmax_norm)) {
            continue;
        }
        if (keep_pattern(topk_moe_sigmoid_norm_bias)) {
            continue;
        }
        if (keep_pattern(topk_moe_early_softmax)) {
            continue;
        }
        if (keep_pattern(topk_moe_late_softmax)) {
            continue;
        }
        if (keep_pattern(snake_pattern)) {
            continue;
        }

        // First, grab the next unused node.
        current_set.push_back(first_unused);

        // Loop through the next N nodes. Grab any that don't depend on other nodes that
        // haven't already been run. Nodes that have already been run have used[i] set
        // to true. Allow nodes that depend on the previous node if it's a fusion pattern
        // that we support (e.g. RMS_NORM + MUL).
        // This first pass only grabs "real" (non-view nodes). Second pass grabs view nodes.
        // The goal is to not interleave real and view nodes in a way that breaks fusion.
        const int NUM_TO_CHECK = 20;
        for (int j = first_unused+1; j < std::min(first_unused + NUM_TO_CHECK, graph->n_nodes); ++j) {
            if (used[j]) {
                continue;
            }
            if (is_empty(graph->nodes[j])) {
                continue;
            }
            // Don't pull forward nodes from fusion patterns
            if (match_pattern(topk_moe_early_softmax_norm, j) ||
                match_pattern(topk_moe_sigmoid_norm_bias, j) ||
                match_pattern(topk_moe_early_softmax, j) ||
                match_pattern(topk_moe_late_softmax, j) ||
                match_pattern(snake_pattern, j)) {
                continue;
            }
            bool ok = true;
            for (int c = first_unused; c < j; ++c) {
                if (!used[c] &&
                    is_src_of(graph->nodes[j], graph->nodes[c]) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_RMS_NORM && graph->nodes[j]->op == GGML_OP_MUL) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT && graph->nodes[j]->op == GGML_OP_ADD) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT_ID && graph->nodes[j]->op == GGML_OP_ADD_ID) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT_ID && graph->nodes[j]->op == GGML_OP_MUL) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_ADD && graph->nodes[j]->op == GGML_OP_ADD) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_SSM_CONV && graph->nodes[j]->op == GGML_OP_ADD) &&
                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_SSM_CONV && graph->nodes[j]->op == GGML_OP_UNARY)) {
                    ok = false;
                    break;
                }
            }
            if (ok) {
                current_set.push_back(j);

                int rope_idx = j;

                // When we've found RMS_NORM + MUL, try to find a ROPE that uses it
                if (j > 0 &&
                    graph->nodes[j]->op == GGML_OP_MUL &&
                    graph->nodes[j-1]->op == GGML_OP_RMS_NORM) {
                    for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) {
                        if (graph->nodes[k]->op == GGML_OP_ROPE &&
                            graph->nodes[k]->src[0] == graph->nodes[j] &&
                            // Check that other srcs are already valid
                            graph->nodes[k]->src[1]->op == GGML_OP_NONE &&
                            (graph->nodes[k]->src[2] == nullptr || graph->nodes[k]->src[2]->op == GGML_OP_NONE)) {
                            rope_idx = k;
                            current_set.push_back(rope_idx);
                            used[rope_idx] = true;
                            break;
                        }
                    }
                }
                // Look for ROPE + VIEW + SET_ROWS and make them consecutive
                if (graph->nodes[rope_idx]->op == GGML_OP_ROPE) {
                    int view_idx = -1;
                    int set_rows_idx = -1;
                    for (int k = rope_idx+1; k < std::min(rope_idx + 10, graph->n_nodes); ++k) {
                        if (view_idx == -1 &&
                            graph->nodes[k]->op == GGML_OP_VIEW &&
                            graph->nodes[k]->src[0] == graph->nodes[rope_idx]) {
                            view_idx = k;
                            continue;
                        }
                        if (view_idx != -1 &&
                            set_rows_idx == -1 &&
                            graph->nodes[k]->op == GGML_OP_SET_ROWS &&
                            graph->nodes[k]->src[0] == graph->nodes[view_idx]) {
                            set_rows_idx = k;
                            break;
                        }
                    }
                    if (set_rows_idx != -1) {
                        current_set.push_back(view_idx);
                        current_set.push_back(set_rows_idx);
                        used[view_idx] = true;
                        used[set_rows_idx] = true;
                    }
                }
                // Look for MUL_MAT_ID + ADD_ID + MUL
                if (j > 0 &&
                    graph->nodes[j]->op == GGML_OP_ADD_ID &&
                    graph->nodes[j-1]->op == GGML_OP_MUL_MAT_ID) {
                    for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) {
                        if (graph->nodes[k]->op == GGML_OP_MUL &&
                            graph->nodes[k]->src[0] == graph->nodes[j] &&
                            // src1 must either be weights or already processed
                            (graph->nodes[k]->src[1]->op == GGML_OP_NONE || used_node_set.find(graph->nodes[k]->src[1]) != used_node_set.end())) {
                            current_set.push_back(k);
                            used[k] = true;
                            break;
                        }
                    }
                }
                // Look for MUL_MAT + ADD + ADD
                if (j > 0 &&
                    graph->nodes[j]->op == GGML_OP_ADD &&
                    graph->nodes[j-1]->op == GGML_OP_MUL_MAT) {
                    for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) {
                        if (graph->nodes[k]->op == GGML_OP_ADD &&
                            graph->nodes[k]->src[0] == graph->nodes[j] &&
                            // src1 must either be weights or already processed
                            (graph->nodes[k]->src[1]->op == GGML_OP_NONE || used_node_set.find(graph->nodes[k]->src[1]) != used_node_set.end())) {
                            current_set.push_back(k);
                            used[k] = true;
                            break;
                        }
                    }
                }
                // SSM_CONV + ADD + UNARY: pull the consuming UNARY forward
                if (j > 0 &&
                    graph->nodes[j]->op == GGML_OP_ADD &&
                    graph->nodes[j-1]->op == GGML_OP_SSM_CONV) {
                    for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) {
                        if (graph->nodes[k]->op == GGML_OP_UNARY &&
                            graph->nodes[k]->src[0] == graph->nodes[j]) {
                            current_set.push_back(k);
                            used[k] = true;
                            break;
                        }
                    }
                }
            }
        }
        // Second pass grabs view nodes.
        // Skip this if it would break a fusion optimization (don't split up add->rms_norm or add->add).
        if (graph->nodes[current_set.back()]->op != GGML_OP_ADD) {
            for (int j = first_unused+1; j < std::min(first_unused + NUM_TO_CHECK, graph->n_nodes); ++j) {
                if (used[j]) {
                    continue;
                }
                if (!is_empty(graph->nodes[j])) {
                    continue;
                }
                bool ok = true;
                for (int c = first_unused; c < j; ++c) {
                    bool c_in_current_set = std::find(current_set.begin(), current_set.end(), c) != current_set.end();
                    // skip views whose srcs haven't been processed.
                    if (!used[c] &&
                        is_src_of(graph->nodes[j], graph->nodes[c]) &&
                        !c_in_current_set) {
                        ok = false;
                        break;
                    }
                }
                if (ok) {
                    current_set.push_back(j);
                }
            }
        }

        // Push the current set into new_order
        for (auto c : current_set) {
            new_order.push_back(graph->nodes[c]);
            used_node_set.insert(graph->nodes[c]);
            used[c] = true;
        }
        while (first_unused < graph->n_nodes && used[first_unused]) {
            first_unused++;
        }
    }
    // Replace the graph with the new order.
    for (int i = 0; i < graph->n_nodes; ++i) {
        graph->nodes[i] = new_order[i];
    }
}

static void ggml_backend_vk_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
    VK_LOG_DEBUG("ggml_backend_vk_event_record(backend=" << backend << ", event=" << event << ")");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
    vk_event *vkev = (vk_event *)event->context;

    ggml_vk_submit_transfer_ctx(ctx);

    vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);
    auto* cmd_buf = compute_ctx->s->buffer; // retrieve pointer before it gets reset

    if (vkev->has_event) {
        // Move existing event into submitted
        vkev->events_submitted.push_back(vkev->event);
    }

    // Grab the next event and record it, create one if necessary
    if (vkev->events_free.empty()) {
        vkev->event = ctx->device->device.createEvent({});
    } else {
        vkev->event = vkev->events_free.back();
        vkev->events_free.pop_back();
    }

    vkev->has_event = true;

    ggml_vk_set_event(compute_ctx, vkev->event);

    vkev->tl_semaphore.value++;
    compute_ctx->s->signal_semaphores.push_back(vkev->tl_semaphore);
    ggml_vk_ctx_end(compute_ctx);

    ggml_vk_submit(compute_ctx, {});
    ctx->submit_pending = true;
    vkev->cmd_buffer = cmd_buf;
    vkev->cmd_buffer_use_counter = cmd_buf->use_counter;
    ctx->compute_ctx.reset();
}

static void ggml_backend_vk_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
    VK_LOG_DEBUG("ggml_backend_vk_event_wait(backend=" << backend << ", event=" << event << ")");
    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
    vk_event *vkev = (vk_event *)event->context;

    vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx);

    if (vkev->has_event) {
        // Wait for latest event
        ggml_vk_wait_events(compute_ctx, { vkev->event });
    }
}

// TODO: enable async and synchronize
static ggml_backend_i ggml_backend_vk_interface = {
    /* .get_name                = */ ggml_backend_vk_name,
    /* .free                    = */ ggml_backend_vk_free,
    /* .set_tensor_async        = */ ggml_backend_vk_set_tensor_async,
    /* .get_tensor_async        = */ ggml_backend_vk_get_tensor_async,
    /* .set_tensor_2d_async     = */ ggml_backend_vk_set_tensor_2d_async,
    /* .get_tensor_2d_async     = */ ggml_backend_vk_get_tensor_2d_async,
    /* .cpy_tensor_async        = */ ggml_backend_vk_cpy_tensor_async,
    /* .synchronize             = */ ggml_backend_vk_synchronize,
    /* .graph_plan_create       = */ NULL,
    /* .graph_plan_free         = */ NULL,
    /* .graph_plan_update       = */ NULL,
    /* .graph_plan_compute      = */ NULL,
    /* .graph_compute           = */ ggml_backend_vk_graph_compute,
    /* .event_record            = */ ggml_backend_vk_event_record,
    /* .event_wait              = */ ggml_backend_vk_event_wait,
    /* .graph_optimize          = */ ggml_vk_graph_optimize,
};

static ggml_guid_t ggml_backend_vk_guid() {
    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
    return &guid;
}

ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
    VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")");

    ggml_backend_vk_context * ctx = new ggml_backend_vk_context;
    ggml_vk_init(ctx, dev_num);

    ggml_backend_t vk_backend = new ggml_backend {
        /* .guid    = */ ggml_backend_vk_guid(),
        /* .iface   = */ ggml_backend_vk_interface,
        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num),
        /* .context = */ ctx,
    };

    if (!ctx->device->support_async) {
        vk_backend->iface.get_tensor_async = nullptr;
    }

    return vk_backend;
}

bool ggml_backend_is_vk(ggml_backend_t backend) {
    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
}

int ggml_backend_vk_get_device_count() {
    return ggml_vk_get_device_count();
}

void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
    int dev_idx = vk_instance.device_indices[device];
    ggml_vk_get_device_description(dev_idx, description, description_size);
}

void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
    GGML_ASSERT(device < (int) vk_instance.device_supports_membudget.size());

    vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
    vk::PhysicalDeviceMemoryBudgetPropertiesEXT budgetprops;
    vk::PhysicalDeviceMemoryProperties2 memprops = {};
    const bool membudget_supported = vk_instance.device_supports_membudget[device];
    const bool is_integrated_gpu = vkdev.getProperties().deviceType == vk::PhysicalDeviceType::eIntegratedGpu;

    if (membudget_supported) {
        memprops.pNext = &budgetprops;
    }
    vkdev.getMemoryProperties2(&memprops);

    *total = 0;
    *free = 0;

    for (uint32_t i = 0; i < memprops.memoryProperties.memoryHeapCount; ++i) {
        const vk::MemoryHeap & heap = memprops.memoryProperties.memoryHeaps[i];

        if (is_integrated_gpu || (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal)) {
            *total += heap.size;

            if (membudget_supported && i < budgetprops.heapUsage.size()) {
                *free += budgetprops.heapBudget[i] - budgetprops.heapUsage[i];
            } else {
                *free += heap.size;
            }
        }
    }
}

static vk::PhysicalDeviceType ggml_backend_vk_get_device_type(int device_idx) {
    GGML_ASSERT(device_idx >= 0 && device_idx < (int) vk_instance.device_indices.size());

    vk::PhysicalDevice device = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device_idx]];

    vk::PhysicalDeviceProperties2 props = {};
    device.getProperties2(&props);

    return props.properties.deviceType;
}

static std::string ggml_backend_vk_get_device_pci_id(int device_idx) {
    GGML_ASSERT(device_idx >= 0 && device_idx < (int) vk_instance.device_indices.size());

    vk::PhysicalDevice device = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device_idx]];

    const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();

    bool ext_support = false;

    for (const auto& properties : ext_props) {
        if (strcmp("VK_EXT_pci_bus_info", properties.extensionName) == 0) {
            ext_support = true;
            break;
        }
    }

    if (!ext_support) {
        return "";
    }

    vk::PhysicalDeviceProperties2 props = {};
    vk::PhysicalDevicePCIBusInfoPropertiesEXT pci_bus_info = {};

    props.pNext = &pci_bus_info;

    device.getProperties2(&props);

    const uint32_t pci_domain = pci_bus_info.pciDomain;
    const uint32_t pci_bus = pci_bus_info.pciBus;
    const uint32_t pci_device = pci_bus_info.pciDevice;
    const uint8_t pci_function = (uint8_t) pci_bus_info.pciFunction; // pci function is between 0 and 7, prevent printf overflow warning

    char pci_bus_id[16] = {};
    snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.%x", pci_domain, pci_bus, pci_device, pci_function);

    return std::string(pci_bus_id);
}

//////////////////////////

struct ggml_backend_vk_device_context {
    size_t device;
    std::string name;
    std::string description;
    bool is_integrated_gpu;
    std::string pci_bus_id;
    int op_offload_min_batch_size;
};

static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    return ctx->name.c_str();
}

static const char * ggml_backend_vk_device_get_description(ggml_backend_dev_t dev) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    return ctx->description.c_str();
}

static void ggml_backend_vk_device_get_memory(ggml_backend_dev_t device, size_t * free, size_t * total) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)device->context;
    ggml_backend_vk_get_device_memory(ctx->device, free, total);
}

static ggml_backend_buffer_type_t ggml_backend_vk_device_get_buffer_type(ggml_backend_dev_t dev) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    return ggml_backend_vk_buffer_type(ctx->device);
}

static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(ggml_backend_dev_t dev) {
    UNUSED(dev);
    return ggml_backend_vk_host_buffer_type();
}

static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;

    return ctx->is_integrated_gpu ? GGML_BACKEND_DEVICE_TYPE_IGPU : GGML_BACKEND_DEVICE_TYPE_GPU;
}

static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;

    props->name        = ggml_backend_vk_device_get_name(dev);
    props->description = ggml_backend_vk_device_get_description(dev);
    props->type        = ggml_backend_vk_device_get_type(dev);
    props->device_id   = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str();
    ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total);
    props->caps = {
        /* .async                 = */ true,
        /* .host_buffer           = */ true,
        /* .buffer_from_host_ptr  = */ false,
        /* .events                = */ true,
    };
}

static ggml_backend_t ggml_backend_vk_device_init(ggml_backend_dev_t dev, const char * params) {
    UNUSED(params);
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    return ggml_backend_vk_init(ctx->device);
}

static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    const vk_device& device = ggml_vk_get_device(ctx->device);

    const bool uses_bda = (op->op == GGML_OP_IM2COL || op->op == GGML_OP_IM2COL_3D) &&
                          device->shader_int64 && device->buffer_device_address;

    auto const & tensor_size_supported = [&](size_t tensor_size) {
        if (tensor_size > device->max_buffer_size) {
            return false;
        }
        // For im2col shaders using BDA, maxStorageBufferRange limit doesn't apply.
        // If shader64BitIndexing is enabled, maxStorageBufferRange limit doesn't apply.
        if (!uses_bda && !device->shader_64b_indexing) {
            if (tensor_size > device->properties.limits.maxStorageBufferRange) {
                return false;
            }
        }
        return true;
    };
    // reject any tensors larger than the max buffer size
    for (int i = 0; i < GGML_MAX_SRC; i++) {
        if (op->src[i] && !tensor_size_supported(ggml_nbytes(op->src[i]))) {
            return false;
        }
    }
    if (!tensor_size_supported(ggml_nbytes(op))) {
        return false;
    }

    switch (op->op) {
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
                case GGML_UNARY_OP_EXP:
                case GGML_UNARY_OP_ELU:
                case GGML_UNARY_OP_GELU:
                case GGML_UNARY_OP_GELU_ERF:
                case GGML_UNARY_OP_GELU_QUICK:
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_RELU:
                case GGML_UNARY_OP_XIELU:
                case GGML_UNARY_OP_NEG:
                case GGML_UNARY_OP_TANH:
                case GGML_UNARY_OP_SIGMOID:
                case GGML_UNARY_OP_HARDSIGMOID:
                case GGML_UNARY_OP_HARDSWISH:
                case GGML_UNARY_OP_ABS:
                case GGML_UNARY_OP_SOFTPLUS:
                case GGML_UNARY_OP_STEP:
                case GGML_UNARY_OP_ROUND:
                case GGML_UNARY_OP_CEIL:
                case GGML_UNARY_OP_FLOOR:
                case GGML_UNARY_OP_TRUNC:
                case GGML_UNARY_OP_SGN:
                    return ggml_is_contiguous(op->src[0]) &&
                           (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                           (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
                           (op->src[0]->type == op->type);
                default:
                    return false;
            }
        case GGML_OP_GLU:
            switch (ggml_get_glu_op(op)) {
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_SWIGLU:
                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                           (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
                           (op->src[0]->type == op->type);
                default:
                    return false;
            }
        case GGML_OP_MUL_MAT:
        case GGML_OP_MUL_MAT_ID:
            {
                ggml_type src0_type = op->src[0]->type;
                if (op->op == GGML_OP_MUL_MAT_ID) {
                    if (!device->mul_mat_id_s[src0_type] && !device->mul_mat_id_m[src0_type] && !device->mul_mat_id_l[src0_type]) {
                        // If there's not enough shared memory for row_ids and the result tile, fallback to CPU
                        return false;
                    }
                }
                switch (src0_type) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q1_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_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:
                    case GGML_TYPE_IQ1_S:
                    case GGML_TYPE_IQ1_M:
                    case GGML_TYPE_IQ2_XXS:
                    case GGML_TYPE_IQ2_XS:
                    case GGML_TYPE_IQ2_S:
                    case GGML_TYPE_IQ3_XXS:
                    case GGML_TYPE_IQ3_S:
                    case GGML_TYPE_IQ4_XS:
                    case GGML_TYPE_IQ4_NL:
                    case GGML_TYPE_MXFP4:
                    case GGML_TYPE_NVFP4:
                        break;
                    default:
                        return false;
                }
                struct ggml_tensor * a;
                struct ggml_tensor * b;
                if (op->op == GGML_OP_MUL_MAT) {
                    a = op->src[0];
                    b = op->src[1];
                } else {
                    a = op->src[2];
                    b = op->src[1];
                }
                if (a->ne[3] != b->ne[3]) {
                    return false;
                }
                if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_BF16) ||
                    !(ggml_vk_dim01_contiguous(op->src[1]) || op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)) {
                    return false;
                }
                if (op->src[0]->type == GGML_TYPE_BF16 && op->src[1]->type == GGML_TYPE_F16) {
                    // We currently don't have a bf16 x f16 shader, or an fp16->bf16 copy shader.
                    // So don't support this combination for now.
                    return false;
                }

                return true;
            }
        case GGML_OP_FLASH_ATTN_EXT:
            {
                bool coopmat2 = device->coopmat2;
                uint32_t HSK = op->src[1]->ne[0];
                uint32_t HSV = op->src[2]->ne[0];
                if ((HSK % 8) != 0 || (HSV % 8) != 0) {
                    return false;
                }
                if (op->src[4] && op->src[4]->type != GGML_TYPE_F32) {
                    return false;
                }
                if (op->src[0]->type != GGML_TYPE_F32) {
                    return false;
                }
                if (op->type != GGML_TYPE_F32) {
                    return false;
                }
                if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) {
                    return false;
                }
                auto fa_kv_ok = [coopmat2](ggml_type t) {
                    switch (t) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q8_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q4_0:
                        return true;
                    case GGML_TYPE_Q1_0:
                        return coopmat2;
                    default:
                        return false;
                    }
                };
                if (!fa_kv_ok(op->src[1]->type) || !fa_kv_ok(op->src[2]->type)) {
                    return false;
                }
                if ((op->src[1]->type == GGML_TYPE_BF16) != (op->src[2]->type == GGML_TYPE_BF16)) {
                    return false;
                }
                if (!coopmat2 && !(device->subgroup_shuffle && device->subgroup_vote)) {
                    // scalar/coopmat1 FA uses subgroupShuffle/subgroupAll
                    return false;
                }
                return true;
            }
        case GGML_OP_GET_ROWS:
            {
                switch (op->src[0]->type) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q1_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_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:
                    case GGML_TYPE_IQ1_S:
                    case GGML_TYPE_IQ1_M:
                    case GGML_TYPE_IQ2_XXS:
                    case GGML_TYPE_IQ2_XS:
                    case GGML_TYPE_IQ2_S:
                    case GGML_TYPE_IQ3_XXS:
                    case GGML_TYPE_IQ3_S:
                    case GGML_TYPE_IQ4_XS:
                    case GGML_TYPE_IQ4_NL:
                    case GGML_TYPE_MXFP4:
                    case GGML_TYPE_NVFP4:
                    case GGML_TYPE_I32:
                        return true;
                    default:
                        return false;
                }
            }
        case GGML_OP_SET_ROWS:
            {
                switch (op->type) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q1_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q8_0:
                    case GGML_TYPE_IQ4_NL:
                        return true;
                    default:
                        return false;
                }
            }
        case GGML_OP_CONT:
        case GGML_OP_CPY:
        case GGML_OP_DUP:
            {
                ggml_type src0_type = op->src[0]->type;
                ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type;

                if (src0_type == GGML_TYPE_F32) {
                    switch (src1_type) {
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q1_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q8_0:
                    case GGML_TYPE_IQ4_NL:
                        return true;
                    default:
                        break;
                    }
                }
                if (src1_type == GGML_TYPE_F32) {
                    switch (src0_type) {
                    case GGML_TYPE_F16:
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_Q1_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q8_0:
                    case GGML_TYPE_IQ4_NL:
                        return true;
                    default:
                        break;
                    }
                }

                if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
                    return true;
                }

                if (
                    (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_I32) ||
                    (src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_F32)
                ) {
                    return true;
                }

                // We can handle copying from a type to the same type if it's
                // either not quantized or is quantized and contiguous.
                // We use f16 or f32 shaders to do the copy,
                // so the type/block size must be a multiple of 4.
                if (src0_type == src1_type &&
                    (!ggml_is_quantized(src0_type) || (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op))) &&
                    (ggml_type_size(src0_type) % 2) == 0) {
                    return true;
                }
                return false;
            }
        case GGML_OP_REPEAT:
            return ggml_type_size(op->type) == ggml_type_size(op->src[0]->type) &&
                  (ggml_type_size(op->type) == sizeof(float) || ggml_type_size(op->type) == 2);
        case GGML_OP_REPEAT_BACK:
            return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_ROPE:
            return ggml_is_contiguous_rows(op) && ggml_is_contiguous_rows(op->src[0]);
        case GGML_OP_ROPE_BACK:
        case GGML_OP_NONE:
        case GGML_OP_RESHAPE:
        case GGML_OP_VIEW:
        case GGML_OP_PERMUTE:
        case GGML_OP_TRANSPOSE:
        case GGML_OP_RMS_NORM:
            return true;
        case GGML_OP_NORM:
        case GGML_OP_GROUP_NORM:
            return ggml_is_contiguous(op->src[0]);
        case GGML_OP_L2_NORM:
            return ggml_is_contiguous_rows(op->src[0]) &&
                   op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
        case GGML_OP_ADD:
        case GGML_OP_SUB:
        case GGML_OP_MUL:
        case GGML_OP_DIV:
            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                   (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) &&
                   (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
        case GGML_OP_ADD_ID:
            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->src[2]->type == GGML_TYPE_I32 &&
                   op->type == GGML_TYPE_F32;
        case GGML_OP_SILU_BACK:
        case GGML_OP_RMS_NORM_BACK:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_SQR:
        case GGML_OP_SQRT:
        case GGML_OP_SIN:
        case GGML_OP_COS:
        case GGML_OP_CLAMP:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_LEAKY_RELU:
        case GGML_OP_OPT_STEP_ADAMW:
        case GGML_OP_OPT_STEP_SGD:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_LOG:
        case GGML_OP_TRI:
        case GGML_OP_DIAG:
            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                   op->type == op->src[0]->type;
        case GGML_OP_ARGSORT:
            {
                if (!ggml_is_contiguous(op) || !ggml_is_contiguous(op->src[0])) {
                    return false;
                }
                // pipeline_argsort_large_f32 requires vulkan memory model.
                if (device->vulkan_memory_model) {
                    return true;
                } else {
                    return op->ne[0] <= (1 << device->max_workgroup_size_log2);
                }
            }
        case GGML_OP_TOP_K:
            {
                if (!ggml_is_contiguous(op) || !ggml_is_contiguous(op->src[0])) {
                    return false;
                }
                // We could potentially support larger, using argsort to sort the
                // whole thing. Not clear if this is needed.
                uint32_t min_pipeline = (uint32_t)log2f(float(op->ne[0])) + 1;
                if (min_pipeline >= num_topk_pipelines ||
                    !device->pipeline_topk_f32[min_pipeline]) {
                    return false;
                }
            }
            return true;
        case GGML_OP_UPSCALE:
            if (op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS) {
                if ((op->op_params[0] & 0xFF) != GGML_SCALE_MODE_BILINEAR) {
                    return false;
                }
            }
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_ACC:
            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
        case GGML_OP_SET:
            return op->src[0]->type == op->src[1]->type && op->src[0]->type == op->type &&
                   (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_I32);
        case GGML_OP_CONCAT:
            return ggml_type_size(op->src[0]->type) == ggml_type_size(GGML_TYPE_F32);
        case GGML_OP_ADD1:
            return (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32)
                || (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F32)
                || (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16);
        case GGML_OP_ARANGE:
            return op->type == GGML_TYPE_F32;
        case GGML_OP_FILL:
            return op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16;
        case GGML_OP_SCALE:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_PAD:
        case GGML_OP_ROLL:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_DIAG_MASK_INF:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_SOFT_MAX:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32
                && (!op->src[1] || (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16));
        case GGML_OP_SOFT_MAX_BACK:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32
                && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32;
        case GGML_OP_SUM:
        case GGML_OP_SUM_ROWS:
        case GGML_OP_MEAN:
            return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]);
        case GGML_OP_CUMSUM:
            {
                if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
                    return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]);
                }
                return false;
            }
        case GGML_OP_SOLVE_TRI:
            {
                if (op->type != GGML_TYPE_F32 || op->src[0]->type != GGML_TYPE_F32) {
                    return false;
                }
                const uint32_t N = op->src[0]->ne[0];
                const uint32_t K = op->src[1]->ne[0];
                // K dimension limited to workgroup size
                if (K > 1u << device->max_workgroup_size_log2) {
                    return false;
                }
                const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((N + K) * sizeof(float));

                if (batch_N == 0) {
                    return false;
                }
                return true;
            }
        case GGML_OP_ARGMAX:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_COUNT_EQUAL:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_I32
                && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_I32;
        case GGML_OP_IM2COL:
            return ggml_is_contiguous(op->src[1])
                && op->src[1]->type == GGML_TYPE_F32
                && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
        case GGML_OP_IM2COL_3D:
            return op->src[1]->type == GGML_TYPE_F32
                && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
        case GGML_OP_TIMESTEP_EMBEDDING:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_CONV_2D_DW:
            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16)
                && op->src[1]->type == GGML_TYPE_F32;
        case GGML_OP_POOL_2D:
            return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_RWKV_WKV6:
        case GGML_OP_RWKV_WKV7:
            return true; // all inputs are contiguous, see ggml.c
        case GGML_OP_GATED_DELTA_NET:
            {
                const uint32_t S_v = op->src[2]->ne[0];
                if (S_v != 32 && S_v != 64 && S_v != 128) {
                    return false;
                }
                for (int i = 0; i < 6; i++) {
                    if (op->src[i] == nullptr || op->src[i]->type != GGML_TYPE_F32) {
                        return false;
                    }
                }
                return op->type == GGML_TYPE_F32;
            }
        case GGML_OP_SSM_SCAN:
            {
                for (int i = 0; i < 6; i++) {
                    if (op->src[i] && ggml_is_quantized(op->src[i]->type)) {
                        return false;
                    }
                }
                if (op->src[6] && op->src[6]->type != GGML_TYPE_I32) {
                    return false;
                }
                if (op->src[0]->type != GGML_TYPE_F32 || op->type != GGML_TYPE_F32) {
                    return false;
                }

                const uint32_t d_state = op->src[0]->ne[0];
                const uint32_t head_dim = op->src[0]->ne[1];

                bool is_mamba2 = (op->src[3] && op->src[3]->nb[1] == sizeof(float));
                if (!is_mamba2) {
                    return false;
                }

                if ((d_state != 128 && d_state != 256) || head_dim % 16 != 0) {
                    return false;
                }

                size_t shmem_size = d_state * sizeof(float);

                if (shmem_size > device->properties.limits.maxComputeSharedMemorySize) {
                    return false;
                }

                if (!device->subgroup_basic) {
                    return false;
                }

                return true;
            }
        case GGML_OP_SSM_CONV:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_CONV_TRANSPOSE_1D:
            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
        case GGML_OP_CONV_2D:
        case GGML_OP_CONV_TRANSPOSE_2D:
            {
                // Channel-contiguous format is not supported yet.
                return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                    op->src[1]->type == GGML_TYPE_F32 &&
                    op->type == GGML_TYPE_F32 &&
                    ggml_is_contiguous(op->src[0]) &&
                    ggml_is_contiguous(op->src[1]) &&
                    ggml_is_contiguous(op));
            }
        default:
            return false;
    }

    UNUSED(dev);
}

static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
    if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) {
        return false;
    }

    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;

    return buft_ctx->device->idx == ctx->device;
}

static int64_t ggml_vk_get_op_batch_size(const ggml_tensor * op) {
    switch (op->op) {
        case GGML_OP_GET_ROWS:
            return 0;
        case GGML_OP_MUL_MAT:
            return op->ne[1];
        case GGML_OP_MUL_MAT_ID:
        case GGML_OP_ROPE:
        case GGML_OP_ROPE_BACK:
            return op->ne[2];
        default:
            return ggml_nrows(op);
    }
}

static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
    ggml_backend_vk_device_context * dev_ctx = (ggml_backend_vk_device_context *)dev->context;

    return ggml_vk_get_op_batch_size(op) >= dev_ctx->op_offload_min_batch_size;
}

static ggml_backend_event_t ggml_backend_vk_device_event_new(ggml_backend_dev_t dev) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    auto device = ggml_vk_get_device(ctx->device);

    vk_event *vkev = new vk_event;
    if (!vkev) {
        return nullptr;
    }

    // No events initially, they get created on demand
    vkev->has_event = false;

    vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 };
    vk::SemaphoreCreateInfo ci{};
    ci.setPNext(&tci);
    vkev->tl_semaphore = { device->device.createSemaphore(ci), 0 };

    return new ggml_backend_event {
        /* .device  = */ dev,
        /* .context = */ vkev,
    };
}

static void ggml_backend_vk_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) {
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    auto device = ggml_vk_get_device(ctx->device);

    vk_event *vkev = (vk_event *)event->context;

    device->device.destroySemaphore(vkev->tl_semaphore.s);
    for (auto& event : vkev->events_free) {
        device->device.destroyEvent(event);
    }
    for (auto& event : vkev->events_submitted) {
        device->device.destroyEvent(event);
    }
    if (vkev->has_event) {
        device->device.destroyEvent(vkev->event);
    }
    delete vkev;
    delete event;
}

static void ggml_backend_vk_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) {
    VK_LOG_DEBUG("ggml_backend_vk_device_event_synchronize(backend=" << dev << ", event=" << event << ")");
    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    auto device = ggml_vk_get_device(ctx->device);
    vk_event *vkev = (vk_event *)event->context;

    // Only do something if the event has actually been used
    if (vkev->has_event) {
        vk::Semaphore sem = vkev->tl_semaphore.s;
        uint64_t val = vkev->tl_semaphore.value;
        vk::SemaphoreWaitInfo swi{vk::SemaphoreWaitFlags{}, sem, val};
        VK_CHECK(device->device.waitSemaphores(swi, UINT64_MAX), "event_synchronize");

        // Reset and move submitted events
        for (auto& event : vkev->events_submitted) {
            device->device.resetEvent(event);
        }
        vkev->events_free.insert(vkev->events_free.end(), vkev->events_submitted.begin(), vkev->events_submitted.end());
        vkev->events_submitted.clear();

        // Finished using current command buffer so we flag for reuse
        if (vkev->cmd_buffer) {
            // Only flag for reuse if it hasn't been reused already
            if (vkev->cmd_buffer_use_counter == vkev->cmd_buffer->use_counter) {
                vkev->cmd_buffer->in_use = false;
                vkev->cmd_buffer->buf.reset();
            }
            vkev->cmd_buffer = nullptr;
        }
    }
}

static vk_buffer ggml_vk_buffer_from_host_ptr(vk_device & device, void * ptr, size_t size) {
    if (!device->external_memory_host) {
        return {};
    }

    uintptr_t uptr = reinterpret_cast<uintptr_t>(ptr);
    if (uptr & (device->min_imported_host_pointer_alignment - 1)) {
        return {};
    }
    if (size & (device->min_imported_host_pointer_alignment - 1)) {
        return {};
    }

    const vk::MemoryPropertyFlags property_flags = vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached;

    vk_buffer buf {};
    try {
        buf = ggml_vk_create_buffer(device, size, { property_flags }, ptr);
    } catch (vk::SystemError& e) {
        GGML_LOG_WARN("ggml_vulkan: Failed ggml_vk_create_buffer (%s)\n", e.what());
    }

    return buf;
}

static ggml_backend_buffer_t ggml_backend_vk_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
    VK_LOG_DEBUG("ggml_backend_vk_device_buffer_from_host_ptr(backend=" << dev << ", ptr=" << ptr << ", size=" << size << ")");
    GGML_UNUSED(max_tensor_size);

    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
    auto device = ggml_vk_get_device(ctx->device);

    vk_buffer buf = ggml_vk_buffer_from_host_ptr(device, ptr, size);

    if (!buf) {
        return {};
    }

    ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(device, std::move(buf), device->name);

    ggml_backend_buffer_t ret = ggml_backend_buffer_init(ggml_backend_vk_device_get_buffer_type(dev), ggml_backend_vk_buffer_interface, bufctx, size);

    return ret;
}

static const struct ggml_backend_device_i ggml_backend_vk_device_i = {
    /* .get_name             = */ ggml_backend_vk_device_get_name,
    /* .get_description      = */ ggml_backend_vk_device_get_description,
    /* .get_memory           = */ ggml_backend_vk_device_get_memory,
    /* .get_type             = */ ggml_backend_vk_device_get_type,
    /* .get_props            = */ ggml_backend_vk_device_get_props,
    /* .init_backend         = */ ggml_backend_vk_device_init,
    /* .get_buffer_type      = */ ggml_backend_vk_device_get_buffer_type,
    /* .get_host_buffer_type = */ ggml_backend_vk_device_get_host_buffer_type,
    /* .buffer_from_host_ptr = */ ggml_backend_vk_device_buffer_from_host_ptr,
    /* .supports_op          = */ ggml_backend_vk_device_supports_op,
    /* .supports_buft        = */ ggml_backend_vk_device_supports_buft,
    /* .offload_op           = */ ggml_backend_vk_device_offload_op,
    /* .event_new            = */ ggml_backend_vk_device_event_new,
    /* .event_free           = */ ggml_backend_vk_device_event_free,
    /* .event_synchronize    = */ ggml_backend_vk_device_event_synchronize,
};

static const char * ggml_backend_vk_reg_get_name(ggml_backend_reg_t reg) {
    UNUSED(reg);
    return GGML_VK_NAME;
}

static size_t ggml_backend_vk_reg_get_device_count(ggml_backend_reg_t reg) {
    UNUSED(reg);
    return ggml_backend_vk_get_device_count();
}

static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, size_t device) {
    static std::vector<ggml_backend_dev_t> devices;

    static bool initialized = false;

    {
        static std::mutex mutex;
        std::lock_guard<std::mutex> lock(mutex);
        if (!initialized) {
            const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32;
            for (int i = 0; i < ggml_backend_vk_get_device_count(); i++) {
                ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context;
                char desc[256];
                ggml_backend_vk_get_device_description(i, desc, sizeof(desc));
                ctx->device = i;
                ctx->name = GGML_VK_NAME + std::to_string(i);
                ctx->description = desc;
                ctx->is_integrated_gpu = ggml_backend_vk_get_device_type(i) == vk::PhysicalDeviceType::eIntegratedGpu;
                ctx->pci_bus_id = ggml_backend_vk_get_device_pci_id(i);
                ctx->op_offload_min_batch_size = min_batch_size;
                devices.push_back(new ggml_backend_device {
                    /* .iface   = */ ggml_backend_vk_device_i,
                    /* .reg     = */ reg,
                    /* .context = */ ctx,
                });
            }
            initialized = true;
        }
    }

    GGML_ASSERT(device < devices.size());
    return devices[device];
}

static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = {
    /* .get_name         = */ ggml_backend_vk_reg_get_name,
    /* .get_device_count = */ ggml_backend_vk_reg_get_device_count,
    /* .get_device       = */ ggml_backend_vk_reg_get_device,
    /* .get_proc_address = */ NULL,
};

ggml_backend_reg_t ggml_backend_vk_reg() {
    static ggml_backend_reg reg = {
        /* .api_version = */ GGML_BACKEND_API_VERSION,
        /* .iface       = */ ggml_backend_vk_reg_i,
        /* .context     = */ nullptr,
    };
    try {
        ggml_vk_instance_init();
        return &reg;
    } catch (const vk::SystemError& e) {
        VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: System error: " << e.what());
        return nullptr;
    } catch (const std::exception &e) {
        VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: " << e.what());
        return nullptr;
    } catch (...) {
        VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: unknown exception during Vulkan init");
        return nullptr;
    }
}

// Extension availability
static bool ggml_vk_instance_layer_settings_available() {
#ifdef GGML_VULKAN_VALIDATE
    // Check if validation layer provides the extension
    const std::string layer_name = "VK_LAYER_KHRONOS_validation";
    for (const auto& layer : vk::enumerateInstanceLayerProperties()) {
        if (layer_name == layer.layerName.data()) {
            for (const auto& ext : vk::enumerateInstanceExtensionProperties(layer_name)) {
                if (strcmp("VK_EXT_layer_settings", ext.extensionName.data()) == 0) {
                    return true;
                }
            }
        }
    }

    std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_layer_settings not found." << std::endl;
#endif
    return false;
}
static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions) {
#ifdef __APPLE__
    // Check for portability enumeration extension for MoltenVK support
    for (const auto& properties : instance_extensions) {
        if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) {
            return true;
        }
    }
    std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl;
#endif
    return false;

    UNUSED(instance_extensions);
}

// Extension availability
static bool ggml_vk_instance_debug_utils_ext_available(
    const std::vector<vk::ExtensionProperties> & instance_extensions) {
    // Check for portability enumeration extension for MoltenVK support
    for (const auto & properties : instance_extensions) {
        if (strcmp("VK_EXT_debug_utils", properties.extensionName) == 0) {
            return true;
        }
    }

    std::cerr << "ggml_vulkan: WARNING: Instance extension VK_EXT_debug_utils not found." << std::endl;
    return false;

    UNUSED(instance_extensions);
}

static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev) {
    VkPhysicalDeviceFeatures2 device_features2;
    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;

    VkPhysicalDeviceVulkan11Features vk11_features;
    vk11_features.pNext = nullptr;
    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
    device_features2.pNext = &vk11_features;

    vkGetPhysicalDeviceFeatures2(vkdev, &device_features2);

    return vk11_features.storageBuffer16BitAccess;
}

static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) {
    switch (props.vendorID) {
    case VK_VENDOR_ID_INTEL:
        // Only allowing Xe2 GPU at the moment since Xe2 GPU can gain significant performance boost,
        // while some older hardware (ex. Arc A770) has performance regressions
        return arch == vk_device_architecture::INTEL_XE2;
    case VK_VENDOR_ID_AMD:
        if (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource) {
            // Workaround for AMD proprietary driver reporting support on all GPUs
            return arch == vk_device_architecture::AMD_RDNA3;
        }
        return true;
    default:
        return true;
    }
}

static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev) {
    VkPhysicalDeviceProperties2 props = vkdev.getProperties2();

    if (props.properties.vendorID != VK_VENDOR_ID_INTEL) {
        return 0;
    }

    const uint32_t device_id = props.properties.deviceID;

    switch (device_id) {
    case 0x56A6:  // A310
        return 6;
    case 0x5693:  // A370M
    case 0x56A5:  // A380
    case 0x56B1:  // Pro A40/A50
        return 8;
    case 0x5697:  // A530M
        return 12;
    case 0x5692:  // A550M
    case 0x56B3:  // Pro A60
        return 16;
    case 0x56A2:  // A580
        return 24;
    case 0x5691:  // A730M
    case 0x56A1:  // A750
        return 28;
    case 0x56A0:  // A770
    case 0x5690:  // A770M
        return 32;
    case 0xE212:  // Pro B50
        return 16;
    case 0xE20C:  // B570
        return 18;
    case 0xE20B:  // B580
    case 0xE211:  // Pro B60
        return 20;
    default:
        return 0;
    }
}

// checks

#ifdef GGML_VULKAN_CHECK_RESULTS
static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector<const ggml_tensor *>& done, int level = 0) {
    if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) {
        return;
    }
    for (int j = 0; j < level; j++) {
        std::cerr << " ";
    }
    std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl;

    done.push_back(tensor);

    for (int i = 0; i < GGML_MAX_SRC; i++) {
        if (tensor->src[i] != nullptr) {
            ggml_vk_print_graph_origin(tensor->src[i], done, level + 1);
        }
    }
}

static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) {
    if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) {
        return;
    }
    i0 = std::max(i0, 5);
    i1 = std::max(i1, 5);
    i2 = std::max(i2, 0);
    i3 = std::max(i3, 0);
    fprintf(stderr, "         ");
    for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
        fprintf(stderr, "%7d ", idx1);
    }
    fprintf(stderr, "\n");
    for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) {
        fprintf(stderr, "%7d: ", idx0);
        for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) {
            if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) {
                float val;
                if (tensor->type == GGML_TYPE_F32) {
                    val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
                } else if (tensor->type == GGML_TYPE_F16) {
                    val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]));
                } else if (tensor->type == GGML_TYPE_I32) {
                    val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]);
                } else {
                    GGML_ABORT("fatal error");
                }
                fprintf(stderr, "% 7.2f ", val);
            } else {
                fprintf(stderr, "        ");
            }
        }
        fprintf(stderr, "\n");
    }
}

static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) {
    void * tensor_data = tensor->data;

    const bool is_gpu = tensor->buffer != nullptr && ggml_backend_buffer_is_vk(tensor->buffer);

    if (is_gpu) {
        const size_t tensor_size = ggml_nbytes(tensor);
        tensor_data = malloc(tensor_size);

        ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;

        vk_buffer buffer_gpu = buf_ctx->dev_buffer;
        ggml_vk_buffer_read(buffer_gpu, vk_tensor_offset(tensor) + tensor->view_offs, tensor_data, tensor_size);
    }

    std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
    std::cerr << "tensor=" << tensor << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl;
    if (tensor->src[0] != nullptr) {
        std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl;
    }
    if (tensor->src[1] != nullptr) {
        std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl;
    }
    std::cerr << std::endl << "Result:" << std::endl;
    ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
    std::cerr << std::endl;
    std::vector<const ggml_tensor *> done;
    ggml_vk_print_graph_origin(tensor, done);

    if (is_gpu) {
        free(tensor_data);
    }
}

void * comp_result;
size_t comp_size;
size_t comp_nb[GGML_MAX_DIMS];
size_t check_counter = 0;
static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx) {
    ggml_tensor * tensor = cgraph->nodes[tensor_idx + ctx->num_additional_fused_ops];
    if (tensor->op == GGML_OP_TRANSPOSE || tensor->op == GGML_OP_SET_ROWS) {
        return;
    }

    check_counter++;
    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
        return;
    }

    VK_LOG_DEBUG("ggml_vk_check_results_0(" << tensor->name << ")");

    struct ggml_init_params iparams = {
        /*.mem_size   =*/ 2ul*1024ul*1024ul*1024ul,
        /*.mem_buffer =*/ NULL,
        /*.no_alloc   =*/ false,
    };

    struct ggml_context * ggml_ctx = ggml_init(iparams);

    std::array<struct ggml_tensor *, GGML_MAX_SRC> src_clone = {nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr};
    const char * srci_name[GGML_MAX_SRC] = {"src0", "src1", "src2", "src3", "src4", "src5", "src6", "src7", "src8", "src9"};

    std::map<ggml_tensor *, ggml_tensor *> cloned_tensors;
    std::vector<void *> cloned_mallocs;

    struct ggml_tensor * tensor_clone = nullptr;

    for (int f = 0; f < ctx->num_additional_fused_ops + 1; ++f) {
        tensor = cgraph->nodes[tensor_idx + f];
        for (int i = 0; i < GGML_MAX_SRC; i++) {
            ggml_tensor * srci = tensor->src[i];
            if (srci == nullptr) {
                continue;
            }
            // If a src tensor has been cloned, use that one
            auto it = cloned_tensors.find(srci);
            if (it != cloned_tensors.end()) {
                src_clone[i] = it->second;
                continue;
            }
            ggml_tensor * srci_clone = ggml_dup_tensor(ggml_ctx, srci);
            size_t srci_size = ggml_nbytes(srci);

            src_clone[i] = srci_clone;
            void *src_buffer = malloc(srci_size);
            cloned_mallocs.push_back(src_buffer);

            srci_clone->data = src_buffer;
            if (ggml_backend_buffer_is_host(srci->buffer)) {
                memcpy(srci_clone->data, srci->data, srci_size);
                memcpy(srci_clone->nb, srci->nb, sizeof(size_t) * GGML_MAX_DIMS);
            } else if (ggml_backend_buffer_is_vk(srci->buffer)) {
                ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)srci->buffer->context;
                vk_buffer& buffer_gpu = buf_ctx->dev_buffer;
                uint64_t offset = vk_tensor_offset(srci) + srci->view_offs;
                if (!ggml_is_contiguous(srci) && ggml_vk_dim01_contiguous(srci)) {
                    for (int i3 = 0; i3 < srci->ne[3]; i3++) {
                        for (int i2 = 0; i2 < srci->ne[2]; i2++) {
                            const int idx = i3*srci->ne[2] + i2;
                            ggml_vk_buffer_read(buffer_gpu, offset + idx * srci->nb[2], ((char *)srci_clone->data + idx * srci_clone->nb[2]), srci->ne[1] * srci->nb[1]);
                        }
                    }

                    srci_clone->nb[0] = srci->nb[0];
                    srci_clone->nb[1] = srci->nb[1];
                    for (int i = 2; i < GGML_MAX_DIMS; i++) {
                        srci_clone->nb[i] = srci_clone->nb[i - 1]*srci_clone->ne[i - 1];
                    }
                } else {
                    if (offset + srci_size >= buffer_gpu->size) {
                        srci_size = buffer_gpu->size - offset;
                    }
                    ggml_vk_buffer_read(buffer_gpu, offset, srci_clone->data, srci_size);
                    memcpy(srci_clone->nb, srci->nb, sizeof(size_t) * GGML_MAX_DIMS);
                }
            } else {
                GGML_ABORT("fatal error");
            }

            if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
                ggml_vk_print_tensor(srci, srci_name[i]);
            }
        }

        if (tensor->op == GGML_OP_FLASH_ATTN_EXT) {
            const float * params = (const float *)tensor->op_params;
            tensor_clone = ggml_flash_attn_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], params[0], params[1], params[2]);
            if (src_clone[4]) {
                ggml_flash_attn_ext_add_sinks(tensor_clone, src_clone[4]);
            }
        } else if (tensor->op == GGML_OP_MUL_MAT) {
            tensor_clone = ggml_mul_mat(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_MUL_MAT_ID) {
            tensor_clone = ggml_mul_mat_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]);
        } else if (tensor->op == GGML_OP_SUB) {
            tensor_clone = ggml_sub(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_MUL) {
            tensor_clone = ggml_mul(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_DIV) {
            tensor_clone = ggml_div(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_CONCAT) {
            tensor_clone = ggml_concat(ggml_ctx, src_clone[0], src_clone[1], *(int *)tensor->op_params);
        } else if (tensor->op == GGML_OP_UPSCALE) {
            tensor_clone = ggml_interpolate(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], (ggml_scale_mode) tensor->op_params[0]);
        } else if (tensor->op == GGML_OP_SCALE) {
            const float * params = (const float *)tensor->op_params;
            tensor_clone = ggml_scale_bias(ggml_ctx, src_clone[0], params[0], params[1]);
        } else if (tensor->op == GGML_OP_ADD1) {
            tensor_clone = ggml_add1(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_ARANGE) {
            const float start = ggml_get_op_params_f32(tensor, 0);
            const float stop = ggml_get_op_params_f32(tensor, 1);
            const float step = ggml_get_op_params_f32(tensor, 2);
            tensor_clone = ggml_arange(ggml_ctx, start, stop, step);
        } else if (tensor->op == GGML_OP_FILL) {
            const float value = ggml_get_op_params_f32(tensor, 0);
            tensor_clone = ggml_fill(ggml_ctx, src_clone[0], value);
        } else if (tensor->op == GGML_OP_SQR) {
            tensor_clone = ggml_sqr(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_SQRT) {
            tensor_clone = ggml_sqrt(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_SIN) {
            tensor_clone = ggml_sin(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_COS) {
            tensor_clone = ggml_cos(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_LOG) {
            tensor_clone = ggml_log(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_TRI) {
            tensor_clone = ggml_tri(ggml_ctx, src_clone[0], (ggml_tri_type)ggml_get_op_params_i32(tensor, 0));
        } else if (tensor->op == GGML_OP_DIAG) {
            tensor_clone = ggml_diag(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_CLAMP) {
            const float * params = (const float *)tensor->op_params;
            tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]);
        } else if (tensor->op == GGML_OP_PAD) {
            tensor_clone = ggml_pad_ext(ggml_ctx, src_clone[0], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3],
                                                                tensor->op_params[4], tensor->op_params[5], tensor->op_params[6], tensor->op_params[7]);
        } else if (tensor->op == GGML_OP_REPEAT) {
            tensor_clone = ggml_repeat(ggml_ctx, src_clone[0], tensor);
        } else if (tensor->op == GGML_OP_REPEAT_BACK) {
            tensor_clone = ggml_repeat_back(ggml_ctx, src_clone[0], tensor);
        } else if (tensor->op == GGML_OP_ADD) {
            tensor_clone = ggml_add(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_ACC) {
            tensor_clone = ggml_acc(ggml_ctx, src_clone[0], src_clone[1], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3]);
        } else if (tensor->op == GGML_OP_SET) {
            tensor_clone = ggml_set(ggml_ctx, src_clone[0], src_clone[1], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3]);
        } else if (tensor->op == GGML_OP_NORM) {
            tensor_clone = ggml_norm(ggml_ctx, src_clone[0], *(float *)tensor->op_params);
        } else if (tensor->op == GGML_OP_GROUP_NORM) {
            const float * float_params = (const float *)tensor->op_params;
            tensor_clone = ggml_group_norm(ggml_ctx, src_clone[0], tensor->op_params[0], float_params[1]);
        } else if (tensor->op == GGML_OP_RMS_NORM) {
            tensor_clone = ggml_rms_norm(ggml_ctx, src_clone[0], *(float *)tensor->op_params);
        } else if (tensor->op == GGML_OP_RMS_NORM_BACK) {
            const float eps = ((float *) tensor->op_params)[0];
            tensor_clone = ggml_rms_norm_back(ggml_ctx, src_clone[0], src_clone[1], eps);
        } else if (tensor->op == GGML_OP_SILU_BACK) {
            tensor_clone = ggml_silu_back(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_L2_NORM) {
            const float eps = ((float *) tensor->op_params)[0];
            tensor_clone = ggml_l2_norm(ggml_ctx, src_clone[0], eps);
        } else if (tensor->op == GGML_OP_SOFT_MAX) {
            if (tensor->src[1] != nullptr) {
                const float * params = (const float *)tensor->op_params;
                tensor_clone = ggml_soft_max_ext(ggml_ctx, src_clone[0], src_clone[1], params[0], params[1]);
            } else {
                tensor_clone = ggml_soft_max(ggml_ctx, src_clone[0]);
            }
        } else if (tensor->op == GGML_OP_SOFT_MAX_BACK) {
            tensor_clone = ggml_soft_max_ext_back(ggml_ctx, src_clone[0], src_clone[1], ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]);
        } else if (tensor->op == GGML_OP_DIAG_MASK_INF) {
            tensor_clone = ggml_diag_mask_inf(ggml_ctx, src_clone[0], tensor->op_params[0]);
        } else if (tensor->op == GGML_OP_ROPE || tensor->op == GGML_OP_ROPE_BACK) {
            const int n_dims      = ((int32_t *) tensor->op_params)[1];
            const int mode        = ((int32_t *) tensor->op_params)[2];
            //const int n_ctx_ggml       = ((int32_t *) tensor->op_params)[3];
            const int n_ctx_orig_ggml  = ((int32_t *) tensor->op_params)[4];
            const float freq_base       = ((float *) tensor->op_params)[5];
            const float freq_scale      = ((float *) tensor->op_params)[6];
            const float ext_factor      = ((float *) tensor->op_params)[7];
            const float attn_factor     = ((float *) tensor->op_params)[8];
            const float beta_fast       = ((float *) tensor->op_params)[9];
            const float beta_slow       = ((float *) tensor->op_params)[10];
            if (mode & GGML_ROPE_TYPE_MROPE) {
                int32_t *sections = ((int32_t *) tensor->op_params) + 11;
                if (tensor->op == GGML_OP_ROPE) {
                    tensor_clone = ggml_rope_multi(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, sections, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
                } else {
                    tensor_clone = ggml_rope_multi_back(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, sections, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
                }
            } else {
                if (tensor->op == GGML_OP_ROPE) {
                    tensor_clone = ggml_rope_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
                } else {
                    tensor_clone = ggml_rope_ext_back(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
                }
            }
        } else if (tensor->op == GGML_OP_UNARY) {
            switch (ggml_get_unary_op(tensor)) {
            case GGML_UNARY_OP_EXP:
                tensor_clone = ggml_exp(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_ELU:
                tensor_clone = ggml_elu(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_SILU:
                tensor_clone = ggml_silu(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_GELU:
                tensor_clone = ggml_gelu(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_GELU_ERF:
                tensor_clone = ggml_gelu_erf(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_GELU_QUICK:
                tensor_clone = ggml_gelu_quick(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_RELU:
                tensor_clone = ggml_relu(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_XIELU:
                tensor_clone = ggml_xielu(ggml_ctx, src_clone[0], 0, 0, 0, 0);
                ggml_set_op_params_f32(tensor_clone, 1, ggml_get_op_params_f32(tensor, 1));
                ggml_set_op_params_f32(tensor_clone, 2, ggml_get_op_params_f32(tensor, 2));
                ggml_set_op_params_f32(tensor_clone, 3, ggml_get_op_params_f32(tensor, 3));
                ggml_set_op_params_f32(tensor_clone, 4, ggml_get_op_params_f32(tensor, 4));
                break;
            case GGML_UNARY_OP_NEG:
                tensor_clone = ggml_neg(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_TANH:
                tensor_clone = ggml_tanh(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_SIGMOID:
                tensor_clone = ggml_sigmoid(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_HARDSIGMOID:
                tensor_clone = ggml_hardsigmoid(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_HARDSWISH:
                tensor_clone = ggml_hardswish(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_ABS:
                tensor_clone = ggml_abs(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_SOFTPLUS:
                tensor_clone = ggml_softplus(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_STEP:
                tensor_clone = ggml_step(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_ROUND:
                tensor_clone = ggml_round(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_CEIL:
                tensor_clone = ggml_ceil(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_FLOOR:
                tensor_clone = ggml_floor(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_TRUNC:
                tensor_clone = ggml_trunc(ggml_ctx, src_clone[0]);
                break;
            case GGML_UNARY_OP_SGN:
                tensor_clone = ggml_sgn(ggml_ctx, src_clone[0]);
                break;
            default:
                std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
                GGML_ABORT("fatal error");
            }
        } else if (tensor->op == GGML_OP_GLU) {
            if (src_clone[1] == nullptr) {
                tensor_clone = ggml_glu(ggml_ctx, src_clone[0], (ggml_glu_op) tensor->op_params[0], tensor->op_params[1]);
            } else {
                tensor_clone = ggml_glu_split(ggml_ctx, src_clone[0], src_clone[1], (ggml_glu_op) tensor->op_params[0]);
            }
            ggml_set_op_params_i32(tensor_clone, 2, ggml_get_op_params_i32(tensor, 2));
            ggml_set_op_params_i32(tensor_clone, 3, ggml_get_op_params_i32(tensor, 3));
        } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) {
            if (tensor->src[1] == nullptr) {
                tensor_clone = ggml_dup(ggml_ctx, src_clone[0]);
                tensor_clone->type = tensor->type;
            } else {
                tensor_clone = ggml_cpy(ggml_ctx, src_clone[0], src_clone[1]);
            }
        } else if (tensor->op == GGML_OP_CONT) {
            tensor_clone = ggml_cont_4d(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
        } else if (tensor->op == GGML_OP_RESHAPE) {
            tensor_clone = ggml_reshape_4d(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
        } else if (tensor->op == GGML_OP_VIEW) {
            tensor_clone = ggml_view_4d(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]);
        } else if (tensor->op == GGML_OP_PERMUTE) {
            int32_t * params = (int32_t *)tensor->op_params;
            tensor_clone = ggml_permute(ggml_ctx, src_clone[0], params[0], params[1], params[2], params[3]);
        } else if (tensor->op == GGML_OP_TRANSPOSE) {
            tensor_clone = ggml_transpose(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_GET_ROWS) {
            tensor_clone = ggml_get_rows(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_ARGSORT) {
            tensor_clone = ggml_argsort(ggml_ctx, src_clone[0], (ggml_sort_order) *(int *)tensor->op_params);
        } else if (tensor->op == GGML_OP_TOP_K) {
            tensor_clone = ggml_top_k(ggml_ctx, src_clone[0], tensor->ne[0]);
        } else if (tensor->op == GGML_OP_SUM) {
            tensor_clone = ggml_sum(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_SUM_ROWS) {
            tensor_clone = ggml_sum_rows(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_CUMSUM) {
            tensor_clone = ggml_cumsum(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_MEAN) {
            tensor_clone = ggml_mean(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_ARGMAX) {
            tensor_clone = ggml_argmax(ggml_ctx, src_clone[0]);
        } else if (tensor->op == GGML_OP_COUNT_EQUAL) {
            tensor_clone = ggml_count_equal(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_SOLVE_TRI) {
            tensor_clone = ggml_solve_tri(ggml_ctx, src_clone[0], src_clone[1], true, true, false);
        } else if (tensor->op == GGML_OP_IM2COL) {
            const int32_t s0 = tensor->op_params[0];
            const int32_t s1 = tensor->op_params[1];
            const int32_t p0 = tensor->op_params[2];
            const int32_t p1 = tensor->op_params[3];
            const int32_t d0 = tensor->op_params[4];
            const int32_t d1 = tensor->op_params[5];

            const bool is_2D = tensor->op_params[6] == 1;
            tensor_clone = ggml_im2col(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1, is_2D, tensor->type);
        } else if (tensor->op == GGML_OP_IM2COL_3D) {
            const int32_t s0 = tensor->op_params[0];
            const int32_t s1 = tensor->op_params[1];
            const int32_t s2 = tensor->op_params[2];
            const int32_t p0 = tensor->op_params[3];
            const int32_t p1 = tensor->op_params[4];
            const int32_t p2 = tensor->op_params[5];
            const int32_t d0 = tensor->op_params[6];
            const int32_t d1 = tensor->op_params[7];
            const int32_t d2 = tensor->op_params[8];
            const int32_t IC = tensor->op_params[9];

            tensor_clone = ggml_im2col_3d(ggml_ctx, src_clone[0], src_clone[1], IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, tensor->type);
        } else if (tensor->op == GGML_OP_TIMESTEP_EMBEDDING) {
            const int32_t dim = tensor->op_params[0];
            const int32_t max_period = tensor->op_params[1];
            tensor_clone = ggml_timestep_embedding(ggml_ctx, src_clone[0], dim, max_period);
        } else if (tensor->op == GGML_OP_CONV_TRANSPOSE_1D){
            const int32_t s0 = tensor->op_params[0];
            const int32_t p0 = tensor->op_params[1];
            const int32_t d0 = tensor->op_params[2];
            tensor_clone = ggml_conv_transpose_1d(ggml_ctx, src_clone[0], src_clone[1], s0, p0, d0);
        } else if (tensor->op == GGML_OP_POOL_2D) {
            enum ggml_op_pool op = static_cast<ggml_op_pool>(tensor->op_params[0]);
            const int32_t k0 = tensor->op_params[1];
            const int32_t k1 = tensor->op_params[2];
            const int32_t s0 = tensor->op_params[3];
            const int32_t s1 = tensor->op_params[4];
            const int32_t p0 = tensor->op_params[5];
            const int32_t p1 = tensor->op_params[6];

            tensor_clone = ggml_pool_2d(ggml_ctx, src_clone[0], op, k0, k1, s0, s1, p0, p1);
        } else if (tensor->op == GGML_OP_CONV_2D) {
            const int32_t s0 = tensor->op_params[0];
            const int32_t s1 = tensor->op_params[1];
            const int32_t p0 = tensor->op_params[2];
            const int32_t p1 = tensor->op_params[3];
            const int32_t d0 = tensor->op_params[4];
            const int32_t d1 = tensor->op_params[5];
            tensor_clone = ggml_conv_2d(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1);
        } else if (tensor->op == GGML_OP_CONV_2D_DW) {
            const int32_t s0 = tensor->op_params[0];
            const int32_t s1 = tensor->op_params[1];
            const int32_t p0 = tensor->op_params[2];
            const int32_t p1 = tensor->op_params[3];
            const int32_t d0 = tensor->op_params[4];
            const int32_t d1 = tensor->op_params[5];
            tensor_clone = ggml_conv_2d_dw_direct(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1);
        } else if (tensor->op == GGML_OP_CONV_TRANSPOSE_2D) {
            const int32_t s = tensor->op_params[0];
            tensor_clone = ggml_conv_transpose_2d_p0(ggml_ctx, src_clone[0], src_clone[1], s);
        } else if (tensor->op == GGML_OP_LEAKY_RELU) {
            const float * op_params = (const float *)tensor->op_params;
            tensor_clone = ggml_leaky_relu(ggml_ctx, src_clone[0], op_params[0], false);
        } else if (tensor->op == GGML_OP_RWKV_WKV6) {
            tensor_clone = ggml_rwkv_wkv6(ggml_ctx, src_clone[0], src_clone[1],
            src_clone[2], src_clone[3], src_clone[4], src_clone[5]);
        } else if (tensor->op == GGML_OP_RWKV_WKV7) {
            tensor_clone = ggml_rwkv_wkv7(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3],
            src_clone[4], src_clone[5], src_clone[6]);
        } else if (tensor->op == GGML_OP_GATED_DELTA_NET) {
            tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1],
            src_clone[2], src_clone[3], src_clone[4], src_clone[5],
            ggml_get_op_params_i32(tensor, 0));
        } else if (tensor->op == GGML_OP_OPT_STEP_ADAMW) {
            src_clone[0]->flags = tensor->src[0]->flags;
            tensor_clone = ggml_opt_step_adamw(ggml_ctx, src_clone[0], src_clone[1],
            src_clone[2], src_clone[3], src_clone[4]);
        } else if (tensor->op == GGML_OP_OPT_STEP_SGD) {
            src_clone[0]->flags = tensor->src[0]->flags;
            tensor_clone = ggml_opt_step_sgd(ggml_ctx, src_clone[0], src_clone[1],
            src_clone[2]);
        } else if (tensor->op == GGML_OP_ADD_ID) {
            tensor_clone = ggml_add_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]);
        } else if (tensor->op == GGML_OP_SSM_SCAN) {
            tensor_clone = ggml_ssm_scan(ggml_ctx, src_clone[0], src_clone[1], src_clone[2],
                                         src_clone[3], src_clone[4], src_clone[5], src_clone[6]);
        } else if (tensor->op == GGML_OP_SSM_CONV) {
            tensor_clone = ggml_ssm_conv(ggml_ctx, src_clone[0], src_clone[1]);
        } else if (tensor->op == GGML_OP_ROLL) {
            const int32_t s0 = tensor->op_params[0];
            const int32_t s1 = tensor->op_params[1];
            const int32_t s2 = tensor->op_params[2];
            const int32_t s3 = tensor->op_params[3];
            tensor_clone = ggml_roll(ggml_ctx, src_clone[0], s0, s1, s2, s3);
        }
        else {
            std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
            GGML_ABORT("fatal error");
        }
        cloned_tensors[tensor] = tensor_clone;
    }

    ggml_cgraph * cgraph_cpu = ggml_new_graph(ggml_ctx);
    ggml_build_forward_expand(cgraph_cpu, tensor_clone);

    ggml_graph_compute_with_ctx(ggml_ctx, cgraph_cpu, 8);

    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
        ggml_vk_print_tensor(tensor_clone, "tensor_clone");
    }

    comp_size = ggml_nbytes(tensor_clone);

    comp_result = malloc(comp_size);
    memcpy(comp_result, tensor_clone->data, comp_size);
    memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS);

    for (auto m : cloned_mallocs) {
        free(m);
    }

    ggml_free(ggml_ctx);

    VK_LOG_DEBUG("END ggml_vk_check_results_0(" << tensor->name << ")");
}

static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx) {
    ggml_tensor * tensor = cgraph->nodes[tensor_idx + ctx->num_additional_fused_ops];
    if (tensor->op == GGML_OP_TRANSPOSE || tensor->op == GGML_OP_SET_ROWS) {
        return;
    }

    if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
        return;
    }

    VK_LOG_DEBUG("ggml_vk_check_results_1(" << tensor->name << ")");

    ggml_tensor * src0 = tensor->src[0];
    ggml_tensor * src1 = tensor->src[1];
    ggml_tensor * src2 = tensor->src[2];
    ggml_tensor * src3 = tensor->src[3];

    void * tensor_data = tensor->data;

    if (ggml_backend_buffer_is_vk(tensor->buffer)) {
        size_t tensor_size = ggml_nbytes(tensor);
        tensor_data = malloc(tensor_size);

        ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context;

        vk_buffer& buffer_gpu = buf_ctx->dev_buffer;
        uint64_t offset = vk_tensor_offset(tensor) + tensor->view_offs;
        if (offset + tensor_size >= buffer_gpu->size) {
            tensor_size = buffer_gpu->size - offset;
        }

        ggml_vk_buffer_read(buffer_gpu, offset, tensor_data, tensor_size);
    }

    float first_error_result = -1.0f;
    float first_error_correct = -1.0f;
    std::array<int, 4> first_error = { -1, -1, -1, -1 };
    double avg_err = 0.0;
    size_t counter = 0;

    for (int i3 = 0; i3 < tensor->ne[3]; i3++) {
        for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
            for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                    const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size;
                    float correct = 0.0f;
                    float result = 0.0f;

                    if (buffer_size_fit) {
                        if (tensor->type == GGML_TYPE_F32) {
                            correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
                            result  = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
                        } else if (tensor->type == GGML_TYPE_F16) {
                            correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]));
                            result  = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
                        } else if (tensor->type == GGML_TYPE_BF16) {
                            correct = ggml_bf16_to_fp32(*(ggml_bf16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]));
                            result  = ggml_bf16_to_fp32(*(ggml_bf16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]));
                        } else if (tensor->type == GGML_TYPE_I32) {
                            correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
                            result  = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
                        } else if (tensor->type == GGML_TYPE_I64) {
                            correct = *(int64_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]);
                            result  = *(int64_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]);
                        } else {
                            std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl;
                        }
                    } else {
                        std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl;
                        GGML_ABORT("fatal error");
                    }

                    if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) {
                        std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl;
                        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
                        if (src0 != nullptr) {
                            std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
                        }
                        if (src1 != nullptr) {
                            std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
                        }
                        if (src2 != nullptr) {
                            std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
                        }
                        if (src3 != nullptr) {
                            std::cerr << "src3=" << src3 << " src3->name=" << src3->name << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl;
                        }
                        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
                        std::cerr << std::endl << "Result:" << std::endl;
                        ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3);
                        std::cerr << std::endl << "Correct:" << std::endl;
                        ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3);
                        std::cerr << std::endl;
                        std::vector<const ggml_tensor *> done;
                        ggml_vk_print_graph_origin(tensor, done);
                        GGML_ABORT("fatal error");
                    }
                    const double denom = std::fabs(correct) > 1.0f ? (std::fabs(correct) > 1e-8 ? std::fabs(correct) : 1e-8) : 1.0f;
                    if (first_error[0] == -1 && std::fabs(correct - result) / denom > 0.5) {
                        first_error[0] = i0;
                        first_error[1] = i1;
                        first_error[2] = i2;
                        first_error[3] = i3;
                        first_error_result = result;
                        first_error_correct = correct;
                    }

                    // Special case, value is infinite, avoid NaN result in avg_err
                    // NaN also appears in results, if both are nan error is 0
                    if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) {
                        avg_err += std::fabs(correct - result) / denom;
                    }
                    counter++;
                }
            }
        }
    }

    avg_err /= counter;

    if (vk_output_tensor > 0 && vk_output_tensor == check_counter) {
        std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
        if (src0 != nullptr) {
            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
        }
        if (src1 != nullptr) {
            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
        }
        if (src2 != nullptr) {
            std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
        }
        if (src3 != nullptr) {
            std::cerr << "src3=" << src3 << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl;
        }
        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
        std::cerr << std::endl << "Result:" << std::endl;
        ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0);
        std::cerr << std::endl << "Correct:" << std::endl;
        ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0);
        std::cerr << std::endl;
        std::vector<const ggml_tensor *> done;
        ggml_vk_print_graph_origin(tensor, done);
    }

    if (avg_err > 0.01 || std::isnan(avg_err)) {
        std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl;
        std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl;
        if (src0 != nullptr) {
            std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl;
        }
        if (src1 != nullptr) {
            std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl;
        }
        if (src2 != nullptr) {
            std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl;
        }
        if (src3 != nullptr) {
            std::cerr << "src3=" << src3 << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl;
        }
        std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct  << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl;
        std::cerr << std::endl << "Result:" << std::endl;
        ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]);
        std::cerr << std::endl << "Correct:" << std::endl;
        ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]);
        std::cerr << std::endl;
        std::vector<const ggml_tensor *> done;
        ggml_vk_print_graph_origin(tensor, done);
        GGML_ABORT("fatal error");
    } else {
        std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl;
    }

    free(comp_result);
    comp_result = nullptr;
    comp_size = 0;

    if (ggml_backend_buffer_is_vk(tensor->buffer)) {
        free(tensor_data);
    }

    VK_LOG_DEBUG("END ggml_vk_check_results_1(" << tensor->name << ")");
}
#endif

GGML_BACKEND_DL_IMPL(ggml_backend_vk_reg)
