Merge mainline llama.cpp (#3)

* Merging mainline - WIP

* Merging mainline - WIP

AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.

* Merging mainline - fix Metal

* Remove check

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

1 files changed, 2038 insertions, 0 deletions

diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute.cpp
new file mode 100644
index 00000000..ed5f2e34
--- /dev/null
+++ b/ggml/src/ggml-kompute.cpp
@@ -0,0 +1,2038 @@
+#include "ggml.h"
+#include "ggml-backend.h"
+#include "ggml-backend-impl.h"
+#include "ggml-kompute.h"
+
+// These are generated at build time by cmake custom command
+#include "shaderop_scale.h"
+#include "shaderop_scale_8.h"
+#include "shaderop_add.h"
+#include "shaderop_addrow.h"
+#include "shaderop_mul.h"
+#include "shaderop_silu.h"
+#include "shaderop_relu.h"
+#include "shaderop_gelu.h"
+#include "shaderop_softmax.h"
+#include "shaderop_norm.h"
+#include "shaderop_rmsnorm.h"
+#include "shaderop_diagmask.h"
+#include "shaderop_mul_mat_f16.h"
+#include "shaderop_mul_mat_q8_0.h"
+#include "shaderop_mul_mat_q4_0.h"
+#include "shaderop_mul_mat_q4_1.h"
+#include "shaderop_mul_mat_q6_k.h"
+#include "shaderop_mul_mat_mat_f32.h"
+#include "shaderop_getrows_f32.h"
+#include "shaderop_getrows_f16.h"
+#include "shaderop_getrows_q4_0.h"
+#include "shaderop_getrows_q4_1.h"
+#include "shaderop_getrows_q6_k.h"
+#include "shaderop_rope_f16.h"
+#include "shaderop_rope_f32.h"
+#include "shaderop_cpy_f16_f16.h"
+#include "shaderop_cpy_f16_f32.h"
+#include "shaderop_cpy_f32_f16.h"
+#include "shaderop_cpy_f32_f32.h"
+
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cstdint>
+#include <cstdio>
+#include <cstring>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <kompute/Kompute.hpp>
+#include <vulkan/vulkan.hpp>
+
+#ifdef __linux__
+#include <cstdlib> // for setenv
+#endif
+
+#define QK4_0 32
+#define QR4_0 2
+#define QK4_1 32
+#define QK_NL 16
+
+typedef ggml_fp16_t half;
+
+static std::string ggml_kompute_format_name(int device) {
+    return "Kompute" + std::to_string(device);
+}
+
+struct ggml_kompute_context {
+    int device;
+    std::string name;
+    std::shared_ptr<vk::DescriptorPool> pool;
+
+    ggml_kompute_context(int device)
+        : device(device), name(ggml_kompute_format_name(device)) {}
+};
+
+// FIXME: It would be good to consolidate the kompute manager and the kompute context into one object
+// and consolidate the init functions and simplify object lifetime management. As it currently stands,
+// we *have* to have the kompute manager no matter what for device discovery, but the kompute context
+// is only created when a device is set and vulkan is explicitly turned on.
+static ggml_kompute_context *s_kompute_context = nullptr;
+
+class kompute_manager {
+    kp::Manager *s_mgr = nullptr;
+
+public:
+    kp::Manager *operator()() {
+        if (s_mgr && !s_mgr->hasInstance()) {
+            destroy();
+        }
+        if (!s_mgr) {
+            s_mgr = new kp::Manager;
+        }
+        return s_mgr;
+    }
+
+    void destroy() {
+        delete s_mgr;
+        s_mgr = nullptr;
+    }
+};
+
+static kompute_manager komputeManager;
+
+struct ggml_vk_memory {
+    void *data = nullptr;
+    size_t size = 0;
+    vk::DeviceMemory *primaryMemory = nullptr;
+    vk::Buffer *primaryBuffer = nullptr;
+    vk::DeviceMemory *stagingMemory = nullptr;
+    vk::Buffer *stagingBuffer = nullptr;
+};
+
+#ifdef __linux__
+__attribute__((constructor))
+static void enable_sam() {
+    setenv("RADV_PERFTEST", "sam", false);
+}
+#endif
+
+static bool ggml_vk_checkPhysicalDeviceFeatures(vk::PhysicalDevice physical_device) {
+    vk::PhysicalDeviceFeatures availableFeatures;
+    physical_device.getFeatures(&availableFeatures);
+
+    if (!availableFeatures.shaderInt16)
+        return false;
+
+    vk::PhysicalDeviceVulkan11Features availableFeatures11;
+    vk::PhysicalDeviceVulkan12Features availableFeatures12;
+
+    availableFeatures11.pNext = &availableFeatures12;
+    availableFeatures12.pNext = nullptr;
+
+    vk::PhysicalDeviceFeatures2 features2;
+    features2.pNext = &availableFeatures11;
+
+    physical_device.getFeatures2(&features2);
+
+    if (!availableFeatures11.uniformAndStorageBuffer16BitAccess ||
+        !availableFeatures11.storageBuffer16BitAccess) {
+        return false;
+    }
+
+    if (!availableFeatures12.storageBuffer8BitAccess ||
+        !availableFeatures12.uniformAndStorageBuffer8BitAccess ||
+        !availableFeatures12.shaderFloat16 ||
+        !availableFeatures12.shaderInt8) {
+        return false;
+    }
+
+    return true;
+}
+
+static const char * ggml_vk_getVendorName(uint32_t vendorID) {
+    switch (vendorID) {
+        case 0x10DE:
+            return "nvidia";
+        case 0x1002:
+            return "amd";
+        case 0x8086:
+            return "intel";
+        default:
+            return "unknown";
+    }
+}
+
+static std::vector<ggml_vk_device> ggml_vk_available_devices_internal(size_t memoryRequired) {
+    std::vector<ggml_vk_device> results;
+    if (!komputeManager()->hasVulkan() || !komputeManager()->hasInstance())
+        return results;
+
+    std::vector<vk::PhysicalDevice> physical_devices;
+    try {
+        physical_devices = komputeManager()->listDevices();
+    } catch (vk::SystemError & err) {
+        std::cerr << __func__ << ": ignoring Vulkan exception: " << err.what() << "\n";
+        return results;
+    }
+
+    uint32_t deviceCount = physical_devices.size();
+    if (deviceCount == 0)
+        return results;
+
+    std::unordered_map<std::string, size_t> count_by_name;
+
+    for (uint32_t i = 0; i < deviceCount; i++) {
+        const auto & physical_device = physical_devices[i];
+
+        VkPhysicalDeviceProperties dev_props = physical_device.getProperties();
+        VkPhysicalDeviceMemoryProperties memoryProperties = physical_device.getMemoryProperties();
+        const uint32_t major = VK_VERSION_MAJOR(dev_props.apiVersion);
+        const uint32_t minor = VK_VERSION_MINOR(dev_props.apiVersion);
+        if (major < 1 || minor < 2)
+            continue;
+
+        if (!ggml_vk_checkPhysicalDeviceFeatures(physical_device))
+            continue;
+
+        size_t heapSize = 0;
+        for (uint32_t j = 0; j < memoryProperties.memoryHeapCount; ++j) {
+            VkMemoryHeap heap = memoryProperties.memoryHeaps[j];
+            if (heap.flags & VK_MEMORY_HEAP_DEVICE_LOCAL_BIT) {
+                heapSize = heap.size;
+                break;
+            }
+        }
+
+        if (heapSize < memoryRequired)
+            continue;
+
+        auto ext_props = physical_device.enumerateDeviceExtensionProperties();
+        bool has_maintenance4 = false;
+
+        // Check if maintenance4 is supported
+        for (const auto & properties : ext_props) {
+            if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) {
+                has_maintenance4 = true;
+            }
+        }
+
+        vk::PhysicalDeviceSubgroupProperties subgroup_props;
+        vk::PhysicalDeviceProperties2 dev_props2;
+        vk::PhysicalDeviceMaintenance3Properties dev_props3;
+        vk::PhysicalDeviceMaintenance4Properties dev_props4;
+        dev_props2.pNext = &dev_props3;
+        dev_props3.pNext = &subgroup_props;
+        if (has_maintenance4) {
+            subgroup_props.pNext = &dev_props4;
+        }
+        physical_device.getProperties2(&dev_props2);
+
+        if (subgroup_props.subgroupSize < 32)
+            continue;
+
+        ggml_vk_device d;
+        d.index = i;
+        d.type = dev_props.deviceType;
+        d.heapSize = heapSize;
+        d.vendor = strdup(ggml_vk_getVendorName(dev_props.vendorID));
+        d.subgroupSize = subgroup_props.subgroupSize;
+        d.bufferAlignment = dev_props.limits.minStorageBufferOffsetAlignment;
+
+        if (has_maintenance4) {
+            d.maxAlloc = std::min(dev_props3.maxMemoryAllocationSize, dev_props4.maxBufferSize);
+        } else {
+            d.maxAlloc = dev_props3.maxMemoryAllocationSize;
+        }
+
+        std::string name(dev_props.deviceName);
+        size_t n_idx = ++count_by_name[name];
+        if (n_idx > 1) {
+            name += " (" + std::to_string(n_idx) + ")";
+        }
+        d.name = strdup(name.c_str());
+
+        results.push_back(d);
+    }
+
+    std::stable_sort(results.begin(), results.end(),
+        [](const ggml_vk_device& lhs, const ggml_vk_device& rhs) -> bool {
+            if (lhs.type != rhs.type) {
+                if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return true;
+                if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return false;
+
+                if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return true;
+                if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return false;
+            }
+            return lhs.heapSize < rhs.heapSize;
+        }
+    );
+
+    return results;
+}
+
+// public API returns a C-style array
+ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count) {
+    auto devices = ggml_vk_available_devices_internal(memoryRequired);
+    *count = devices.size();
+    if (devices.empty()) {
+        return nullptr;
+    }
+
+    size_t nbytes = sizeof (ggml_vk_device) * (devices.size());
+    auto * arr = static_cast<ggml_vk_device *>(malloc(nbytes));
+    memcpy(arr, devices.data(), nbytes);
+    return arr;
+}
+
+static void ggml_vk_filterByVendor(std::vector<ggml_vk_device>& devices, const std::string& targetVendor) {
+    devices.erase(
+        std::remove_if(devices.begin(), devices.end(),
+            [&targetVendor](const ggml_vk_device& device) {
+                return device.vendor != targetVendor;
+            }),
+        devices.end()
+    );
+}
+
+static void ggml_vk_filterByName(std::vector<ggml_vk_device>& devices, const std::string& targetName) {
+    devices.erase(
+        std::remove_if(devices.begin(), devices.end(),
+            [&targetName](const ggml_vk_device& device) {
+                return device.name != targetName;
+            }),
+        devices.end()
+    );
+}
+
+static bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const std::string & name) {
+    if (name.empty())
+        return false;
+
+    auto devices = ggml_vk_available_devices_internal(memoryRequired);
+    if (name == "amd" || name == "nvidia" || name == "intel") {
+        ggml_vk_filterByVendor(devices, name);
+    } else if (name != "gpu") {
+        ggml_vk_filterByName(devices, name);
+    }
+
+    if (devices.empty())
+        return false;
+
+    *device = devices.front();
+    return true;
+}
+
+bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const char * name) {
+    return ggml_vk_get_device(device, memoryRequired, std::string(name));
+}
+
+bool ggml_vk_has_vulkan() {
+    return komputeManager()->hasVulkan();
+}
+
+bool ggml_vk_has_device() {
+    return komputeManager()->hasDevice();
+}
+
+ggml_vk_device ggml_vk_current_device() {
+    if (!komputeManager()->hasDevice())
+        return ggml_vk_device();
+
+    auto devices = ggml_vk_available_devices_internal(0);
+    ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data());
+    GGML_ASSERT(!devices.empty());
+    return devices.front();
+}
+
+static
+void ggml_vk_allocate_descriptor_pool(struct ggml_kompute_context * ctx, size_t size) {
+    std::vector<vk::DescriptorPoolSize> descriptorPoolSizes = {
+        vk::DescriptorPoolSize(
+          vk::DescriptorType::eStorageBuffer,
+          3 * size // Descriptor count is number of possible tensors to pass into an algorithm
+          )
+    };
+
+    vk::DescriptorPoolCreateInfo descriptorPoolInfo(
+      vk::DescriptorPoolCreateFlags(),
+      size, // Max sets
+      static_cast<uint32_t>(descriptorPoolSizes.size()),
+      descriptorPoolSizes.data());
+
+    ctx->pool = std::make_shared<vk::DescriptorPool>();
+    vk::Result r = komputeManager()->device()->createDescriptorPool(
+      &descriptorPoolInfo, nullptr, ctx->pool.get());
+    if (r != vk::Result::eSuccess)
+        std::cerr << "Error allocating descriptor pool" << vk::to_string(r);
+}
+
+static
+void ggml_vk_free_descriptor_pool(struct ggml_kompute_context * ctx) {
+    if (ctx->pool) {
+        komputeManager()->device()->destroy(
+          *ctx->pool,
+          (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        ctx->pool = nullptr;
+    }
+}
+
+static
+vk::Buffer *ggml_vk_allocate_buffer(size_t size) {
+    vk::BufferCreateInfo bufferCreateInfo;
+    bufferCreateInfo.size = size;
+    bufferCreateInfo.usage = vk::BufferUsageFlagBits::eStorageBuffer |
+                             vk::BufferUsageFlagBits::eTransferSrc |
+                             vk::BufferUsageFlagBits::eTransferDst;
+    bufferCreateInfo.sharingMode = vk::SharingMode::eExclusive;
+
+    vk::Buffer *vkBuffer = new vk::Buffer;
+    vk::Result r = komputeManager()->device()->createBuffer(&bufferCreateInfo, nullptr, vkBuffer);
+    if (r != vk::Result::eSuccess)
+        std::cerr << "Error allocating buffer " << vk::to_string(r) << std::endl;
+    return vkBuffer;
+}
+
+static
+vk::DeviceMemory *ggml_vk_allocate(size_t size, vk::MemoryPropertyFlags flags, vk::MemoryRequirements requirements, bool *isHostVisible) {
+
+    uint32_t memoryTypeIndex = -1;
+    bool memoryTypeIndexFound = false;
+    vk::PhysicalDeviceMemoryProperties memoryProperties = komputeManager()->physicalDevice()->getMemoryProperties();
+    for (uint32_t i = 0; i < memoryProperties.memoryTypeCount; i++) {
+        const vk::MemoryType &memoryType = memoryProperties.memoryTypes[i];
+        const vk::MemoryHeap &memoryHeap = memoryProperties.memoryHeaps[memoryType.heapIndex];
+        if (memoryHeap.size < size) {
+            continue;
+        }
+
+        if (requirements.memoryTypeBits & (1 << i)) {
+            if (((memoryProperties.memoryTypes[i]).propertyFlags &
+                 flags) == flags) {
+                memoryTypeIndex = i;
+                memoryTypeIndexFound = true;
+                if (isHostVisible && (memoryProperties.memoryTypes[i].propertyFlags & vk::MemoryPropertyFlagBits::eHostVisible)) {
+                    *isHostVisible = true;
+                }
+                break;
+            }
+        }
+    }
+    if (!memoryTypeIndexFound) {
+        throw std::runtime_error(
+          "Memory type index for buffer creation not found");
+    }
+
+    vk::MemoryAllocateInfo allocInfo;
+    allocInfo.allocationSize = size;
+    allocInfo.memoryTypeIndex = memoryTypeIndex;
+    vk::DeviceMemory *vkDeviceMemory =  new vk::DeviceMemory;
+    vk::Result r = komputeManager()->device()->allocateMemory(&allocInfo, nullptr, vkDeviceMemory);
+    if (r != vk::Result::eSuccess) {
+        std::cerr << "Error allocating memory " << vk::to_string(r) << std::endl;
+        throw std::runtime_error("Error allocating vulkan memory.");
+    }
+    return vkDeviceMemory;
+}
+
+static size_t ggml_vk_aligned_offset(ggml_backend_buffer_t buffer, size_t offset) {
+    size_t minStorageBufferOffsetAlignment = ggml_backend_buffer_get_alignment(buffer);
+
+    // If offset is already aligned, return it directly
+    if (offset % minStorageBufferOffsetAlignment == 0) {
+        return offset;
+    }
+
+    // Otherwise, return the largest multiple of minStorageBufferOffsetAlignment less than offset
+    return (offset / minStorageBufferOffsetAlignment) * minStorageBufferOffsetAlignment;
+}
+
+static ggml_vk_memory ggml_vk_allocate(size_t size) {
+    ggml_vk_memory memory;
+    bool isHostVisible = false;
+    {
+        memory.primaryBuffer = ggml_vk_allocate_buffer(size);
+        vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.primaryBuffer);
+        vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eDeviceLocal;
+        memory.primaryMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible);
+        komputeManager()->device()->bindBufferMemory(*memory.primaryBuffer, *memory.primaryMemory, 0);
+        if (isHostVisible) {
+            vk::Result r = komputeManager()->device()->mapMemory(*memory.primaryMemory, 0, size, vk::MemoryMapFlags(), &memory.data);
+            if (r != vk::Result::eSuccess)
+                std::cerr << "Error mapping memory" << vk::to_string(r);
+        }
+    }
+
+    if (!isHostVisible) {
+        memory.stagingBuffer = ggml_vk_allocate_buffer(size);
+        vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.stagingBuffer);
+        vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eHostVisible |
+                                                      vk::MemoryPropertyFlagBits::eHostCoherent |
+                                                      vk::MemoryPropertyFlagBits::eHostCached;
+        memory.stagingMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible);
+        komputeManager()->device()->bindBufferMemory(*memory.stagingBuffer, *memory.stagingMemory, 0);
+        vk::Result r = komputeManager()->device()->mapMemory(*memory.stagingMemory, 0, size, vk::MemoryMapFlags(), &memory.data);
+        if (r != vk::Result::eSuccess)
+            std::cerr << "Error mapping memory" << vk::to_string(r);
+    }
+
+    memory.size = size;
+    return memory;
+}
+
+static void ggml_vk_free_memory(ggml_vk_memory &memory)
+{
+    komputeManager()->device()->destroy(
+      *memory.primaryBuffer,
+      (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    if (memory.stagingBuffer) {
+        komputeManager()->device()->destroy(
+          *memory.stagingBuffer,
+          (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    }
+    komputeManager()->device()->freeMemory(
+      *memory.primaryMemory,
+      (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    if (memory.stagingMemory) {
+        komputeManager()->device()->freeMemory(
+          *memory.stagingMemory,
+          (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+    }
+}
+
+static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft);
+
+static
+ggml_vk_memory * ggml_vk_find_tensor(const struct ggml_tensor * t, uint64_t & offset) {
+    ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer;
+
+    // compatibility with ggml-backend
+    GGML_ASSERT(buffer && buffer->buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name);
+
+    ggml_vk_memory * buf_ctx = static_cast<ggml_vk_memory *>(buffer->context);
+
+    const intptr_t ioffs = intptr_t(t->data) - intptr_t(buf_ctx->data);
+
+    GGML_ASSERT(ioffs >= 0 && ioffs + int64_t(ggml_nbytes(t)) <= int64_t(buffer->size));
+
+    offset = uint64_t(ioffs);
+    return buf_ctx;
+}
+
+static
+const std::shared_ptr<kp::Tensor> ggml_vk_get_tensor(const struct ggml_tensor * t, uint32_t * alignedOffset = nullptr) {
+    uint64_t originalOffset = 0;
+    auto * res = ggml_vk_find_tensor(t, originalOffset);
+    if (!res) {
+        static std::shared_ptr<kp::Tensor> nullTensor = nullptr;
+        return nullTensor;
+    }
+
+    // Create a tensor whose memory will be composed of our buffers at the correct offset
+    const size_t nelements = ggml_nelements(t);
+    size_t nbytes = ggml_nbytes(t);
+
+    size_t vulkanOffset = ggml_vk_aligned_offset(t->buffer, originalOffset);
+    if (alignedOffset) {
+        *alignedOffset = originalOffset - vulkanOffset;
+        nbytes += *alignedOffset;
+    }
+
+    return komputeManager()->tensor(
+        t->data,
+        nelements,
+        nbytes, kp::Tensor::TensorDataTypes::eFloat,
+        res->primaryMemory, res->primaryBuffer,
+        res->stagingMemory, res->stagingBuffer,
+        vulkanOffset);
+}
+
+static std::vector<uint32_t> getSpirvShader(const unsigned char* rawData, size_t size) {
+    if (size % sizeof(uint32_t) != 0) {
+        throw std::runtime_error("Invalid size: must be divisible by sizeof(uint32_t)");
+    }
+
+    const uint32_t* data_ptr = reinterpret_cast<const uint32_t*>(rawData);
+    size_t count = size / sizeof(uint32_t);
+    return std::vector<uint32_t>(data_ptr, data_ptr + count);
+}
+
+inline static
+uint32_t safe_divide(uint32_t a, uint32_t b) {
+    if (b <= 1) {
+        return a;
+    }
+    if ((a % b) != 0) {
+        fprintf(stderr, "((%u %% %u) == %u) != 0\n", a, b, a % b);
+        GGML_ASSERT(!"safe_divide result would've had remainder");
+    }
+    return a / b;
+}
+
+static void ggml_vk_add(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03,
+    int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13,
+    int32_t ne0,
+    int32_t nb0,  int32_t nb1,  int32_t nb2,  int32_t nb3
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_add_comp_spv,
+        kp::shader_data::op_add_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00;
+        int32_t nb00, nb01, nb02, nb03;
+        int32_t ne10, ne11, ne12, ne13;
+        int32_t nb10, nb11, nb12, nb13;
+        int32_t ne0;
+        int32_t nb0, nb1, nb2, nb3;
+    } const pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00,
+        nb00, nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+        nb10, nb11, nb12, nb13,
+        ne0,
+        nb0, nb1, nb2, nb3
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_addrow(kp::Sequence& seq,
+                 const std::shared_ptr<kp::Tensor>& inA,
+                 const std::shared_ptr<kp::Tensor>& inB,
+                 const std::shared_ptr<kp::Tensor>& out,
+                 uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+                 uint32_t size, uint32_t row = 0) {
+
+    const static auto spirv = getSpirvShader(kp::shader_data::op_addrow_comp_spv,
+        kp::shader_data::op_addrow_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        uint32_t row;
+    } const pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        row
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__))
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts});
+    else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03,
+    int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13,
+    int32_t ne0,
+    int32_t nb0,  int32_t nb1,  int32_t nb2,  int32_t nb3
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_comp_spv,
+        kp::shader_data::op_mul_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00;
+        int32_t nb00, nb01, nb02, nb03;
+        int32_t ne10, ne11, ne12, ne13;
+        int32_t nb10, nb11, nb12, nb13;
+        int32_t ne0;
+        int32_t nb0, nb1, nb2, nb3;
+    } const pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00,
+        nb00, nb01, nb02, nb03,
+        ne10, ne11, ne12, ne13,
+        nb10, nb11, nb12, nb13,
+        ne0,
+        nb0, nb1, nb2, nb3
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_scale(kp::Sequence& seq,
+                   const std::shared_ptr<kp::Tensor>& in,
+                   const std::shared_ptr<kp::Tensor>& out,
+                   uint32_t inOff, uint32_t outOff,
+                   uint32_t size, float scale) {
+    const static auto spirv_1 = getSpirvShader(
+        kp::shader_data::op_scale_comp_spv, kp::shader_data::op_scale_comp_spv_len
+    );
+    const static auto spirv_8 = getSpirvShader(
+        kp::shader_data::op_scale_8_comp_spv, kp::shader_data::op_scale_8_comp_spv_len
+    );
+
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        float scale;
+    } const pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+        scale
+    };
+
+    const auto * spirv = &spirv_1;
+    std::string name(__func__);
+    if (size % 8 == 0) {
+        size /= 8;
+        name += "_8";
+        spirv = &spirv_8;
+    }
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, *spirv, {size}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_xxlu(
+    const std::vector<uint32_t>& spirv, const char * suffix, kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& in,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inOff, uint32_t outOff,
+    uint32_t size
+) {
+    struct PushConstants {
+        uint32_t inOff, outOff;
+    } const pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, spirv, {size}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_silu(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_silu_comp_spv,
+        kp::shader_data::op_silu_comp_spv_len);
+
+    ggml_vk_xxlu(spirv, "silu", std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_relu(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_relu_comp_spv,
+        kp::shader_data::op_relu_comp_spv_len);
+
+    ggml_vk_xxlu(spirv, "relu", std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_gelu(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_gelu_comp_spv,
+        kp::shader_data::op_gelu_comp_spv_len);
+
+    ggml_vk_xxlu(spirv, "gelu", std::forward<Args>(args)...);
+}
+
+static void ggml_vk_soft_max(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, uint32_t ne03,
+    float scale
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv,
+        kp::shader_data::op_softmax_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02;
+        float scale;
+        int32_t mask;
+    } pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02,
+        scale,
+        bool(inB)
+    };
+
+    auto & inB_ = inB ? inB : inA;
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        // FIXME: The softmax kernel needs to be fixed to use the subgroupsize which can vary by device
+        const uint32_t local_x = 32;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB_, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB_, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_norm_(
+    const std::vector<uint32_t>& spirv, const char * suffix, kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& in,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inOff, uint32_t outOff,
+    int32_t ne00, int32_t nb01,
+    int32_t nrows, float epsilon
+) {
+    GGML_ASSERT(nb01%sizeof(float) == 0);
+    GGML_ASSERT(ne00%sizeof(float) == 0);
+
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        uint32_t ne00, nb01;
+        float eps;
+    } pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+        (uint32_t)ne00, (uint32_t)nb01, epsilon
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, spirv, {(uint32_t)nrows}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({(uint32_t)nrows});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_norm(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_norm_comp_spv,
+        kp::shader_data::op_norm_comp_spv_len);
+
+    ggml_vk_norm_(spirv, "norm", std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_rms_norm(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_rmsnorm_comp_spv,
+        kp::shader_data::op_rmsnorm_comp_spv_len);
+
+    ggml_vk_norm_(spirv, "rms", std::forward<Args>(args)...);
+}
+
+static void ggml_vk_diag_mask_inf(kp::Sequence& seq,
+                           const std::shared_ptr<kp::Tensor>& in,
+                           const std::shared_ptr<kp::Tensor>& out,
+                           uint32_t inOff, uint32_t outOff,
+                           uint32_t n_past,
+                           int32_t ne00, int32_t ne01, int32_t ne02) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_diagmask_comp_spv,
+        kp::shader_data::op_diagmask_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        uint32_t n_past;
+        int32_t ne00, ne01;
+    } pushConsts {
+        safe_divide(inOff, 4), safe_divide(outOff, 4),
+        n_past,
+        ne00, ne01
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__))
+        s_algo = komputeManager()->algorithm<float, PushConstants>(__func__, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne00), unsigned(ne01), unsigned(ne02)}, {}, {pushConsts});
+    else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({unsigned(ne00), unsigned(ne01), unsigned(ne02)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul_mat_f16(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02,
+    uint32_t nb00, uint32_t nb01, uint32_t nb02,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    uint32_t nb10, uint32_t nb11, uint32_t nb12,
+    int32_t ne0, int32_t ne1,
+    uint32_t r2, uint32_t r3
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_f16_comp_spv,
+        kp::shader_data::op_mul_mat_f16_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02;
+        uint32_t nb00, nb01, nb02;
+        int32_t ne10, ne11, ne12;
+        uint32_t nb10, nb11, nb12;
+        int32_t ne0, ne1;
+        uint32_t r2, r3;
+    } pushConsts {
+        safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02,
+        nb00, nb01, nb02,
+        ne10, ne11, ne12,
+        nb10, nb11, nb12,
+        ne0, ne1,
+        r2, r3
+    };
+
+    const unsigned ny = unsigned((ne11 + 4 - 1)/4);
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), ny, unsigned(ne12*ne13)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), ny, unsigned(ne12*ne13)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul_mat_mat_f32(kp::Sequence& seq,
+                         const std::shared_ptr<kp::Tensor>& inA,
+                         const std::shared_ptr<kp::Tensor>& inB,
+                         const std::shared_ptr<kp::Tensor>& out,
+                         uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+                         int32_t ne00, int32_t ne01, int32_t ne02,
+                         uint32_t nb01, uint32_t nb02,
+                         int32_t ne11, int32_t ne12,
+                         uint32_t nb11, uint32_t nb12,
+                         uint32_t nb1, uint32_t nb2) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_mat_f32_comp_spv,
+        kp::shader_data::op_mul_mat_mat_f32_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02, ne11, ne12;
+        uint32_t nb01, nb02;
+        uint32_t nb11, nb12;
+        uint32_t nb1, nb2;
+    } pushConsts {
+        safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02, ne11, ne12,
+        nb01, nb02, nb11, nb12,
+        nb1, nb2
+    };
+
+    const uint32_t local_x = ggml_vk_current_device().subgroupSize;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(),
+        {inA, inB, out}, spirv,
+        {unsigned(ne01),
+         unsigned(ne11),
+         unsigned(std::max(ne12, ne02))
+         },
+        {local_x},
+        {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01),
+                              unsigned(ne11),
+                              unsigned(std::max(ne12, ne02)),
+                              });
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_mul_mat_impl(
+    const std::vector<uint32_t>& spirv, const char * suffix, uint32_t block_size, kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02,
+    int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13,
+    int32_t ne0, int32_t ne1,
+    uint32_t r2, uint32_t r3
+) {
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne01, ne02;
+        int32_t ne10, ne12;
+        int32_t ne0, ne1;
+        uint32_t r2, r3;
+    } pushConsts {
+        safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne01, ne02,
+        ne10, ne12,
+        ne0, ne1,
+        r2, r3
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_mul_mat_q4_0(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_0_comp_spv,
+        kp::shader_data::op_mul_mat_q4_0_comp_spv_len);
+
+    ggml_vk_mul_mat_impl(spirv, "q4_0", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_mul_mat_q4_1(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_1_comp_spv,
+        kp::shader_data::op_mul_mat_q4_1_comp_spv_len);
+
+    ggml_vk_mul_mat_impl(spirv, "q4_1", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_mul_mat_q8_0(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q8_0_comp_spv,
+        kp::shader_data::op_mul_mat_q8_0_comp_spv_len);
+
+    ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
+}
+
+static void ggml_vk_mul_mat_q6_k(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t ne10, int32_t ne0, int32_t ne1,
+    int32_t ne01, int32_t ne11, int32_t ne12, int32_t ne02
+) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q6_k_comp_spv,
+        kp::shader_data::op_mul_mat_q6_k_comp_spv_len);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, ne10, ne0, ne1, ne01, gqa;
+    } pushConsts {
+        inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, ne10, ne0, ne1, ne01, ne12/ne02
+    };
+
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(__func__)) {
+        const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2;
+        s_algo = komputeManager()->algorithm<uint32_t, PushConstants>(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}, {local_x}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(__func__);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_get_rows(
+    const std::vector<uint32_t>& spirv,
+    const char * suffix,
+    unsigned element_size, unsigned qk,
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    int32_t ne00, int32_t nb01, int32_t nb1,
+    uint32_t size
+) {
+    GGML_ASSERT(nb01%element_size == 0);
+    GGML_ASSERT(nb1%sizeof(float) == 0);
+    if (qk) GGML_ASSERT(ne00%qk == 0);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t ne00, nb01, nb1;
+    } pushConsts {
+        safe_divide(inAOff, element_size), safe_divide(inBOff, 4), safe_divide(outOff, 4),
+        ne00, nb01, nb1
+    };
+
+    auto name = std::string(__func__) + "_" + suffix;
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts});
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({size});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_f32(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f32_comp_spv,
+        kp::shader_data::op_getrows_f32_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "f32", sizeof(float), 0, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_f16(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv,
+        kp::shader_data::op_getrows_f16_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "f16", sizeof(half), 0, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_q4_0(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_0_comp_spv,
+        kp::shader_data::op_getrows_q4_0_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "q4_0", 1/*We access blocks unaligned*/, QK4_0, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_q4_1(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_1_comp_spv,
+        kp::shader_data::op_getrows_q4_1_comp_spv_len);
+
+    ggml_vk_get_rows(spirv, "q4_1", 1/*We access blocks unaligned*/, QK4_1, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_get_rows_q6_k(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q6_k_comp_spv,
+        kp::shader_data::op_getrows_q6_k_comp_spv_len);
+    ggml_vk_get_rows(spirv, "q6_k", 1/*We access blocks unaligned*/, QK_NL, std::forward<Args>(args)...);
+}
+
+static void ggml_vk_rope(
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& inA,
+    const std::shared_ptr<kp::Tensor>& inB,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inAOff, uint32_t inBOff, uint32_t outOff,
+    ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_ctx_orig,
+    float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow,
+    int32_t ne01, int32_t ne02, int32_t ne03,
+    uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03,
+    int32_t ne0,
+    uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3
+) {
+    GGML_ASSERT(src0t == GGML_TYPE_F16 || src0t == GGML_TYPE_F32);
+
+    static const auto spirv_f16 = getSpirvShader(
+        kp::shader_data::op_rope_f16_comp_spv, kp::shader_data::op_rope_f16_comp_spv_len
+    );
+    static const auto spirv_f32 = getSpirvShader(
+        kp::shader_data::op_rope_f32_comp_spv, kp::shader_data::op_rope_f32_comp_spv_len
+    );
+
+    int type_size = src0t == GGML_TYPE_F16 ? 2 : 4;
+
+    GGML_ASSERT(nb03 % type_size == 0);
+    GGML_ASSERT(nb02 % type_size == 0);
+    GGML_ASSERT(nb01 % type_size == 0);
+    GGML_ASSERT(nb00 % type_size == 0);
+    GGML_ASSERT(nb3  % type_size == 0);
+    GGML_ASSERT(nb2  % type_size == 0);
+    GGML_ASSERT(nb1  % type_size == 0);
+    GGML_ASSERT(nb0  % type_size == 0);
+
+    struct PushConstants {
+        uint32_t inAOff, inBOff, outOff;
+        int32_t n_dims, mode, n_ctx_orig;
+        float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+        uint32_t nb00, nb01, nb02, nb03;
+        int32_t ne0;
+        uint32_t nb0, nb1, nb2, nb3;
+    } pushConsts {
+        safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size),
+        n_dims, mode, n_ctx_orig,
+        freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow,
+        nb00, nb01, nb02, nb03,
+        ne0,
+        nb0, nb1, nb2, nb3
+    };
+
+    auto name = std::string(__func__) + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32");
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name)) {
+        s_algo = komputeManager()->algorithm<float, PushConstants>(
+            name, s_kompute_context->pool.get(), {inA, inB, out},
+            src0t == GGML_TYPE_F16 ? spirv_f16 : spirv_f32,
+            {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}
+        );
+    } else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({inA, inB, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+static void ggml_vk_cpy(
+    const std::vector<uint32_t>& spirv,
+    uint32_t in_element_size, uint32_t out_element_size,
+    kp::Sequence& seq,
+    const std::shared_ptr<kp::Tensor>& in,
+    const std::shared_ptr<kp::Tensor>& out,
+    uint32_t inOff, uint32_t outOff,
+    int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03,
+    uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03,
+    int32_t ne0, int32_t ne1, int32_t ne2,
+    uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3
+) {
+    struct PushConstants {
+        uint32_t inOff, outOff;
+        int32_t ne00, ne01, ne02;
+        uint32_t nb00, nb01, nb02, nb03;
+        int32_t ne0, ne1, ne2;
+        uint32_t nb0, nb1, nb2, nb3;
+    } pushConsts {
+        safe_divide(inOff, in_element_size), safe_divide(outOff, out_element_size),
+        ne00, ne01, ne02,
+        nb00, nb01, nb02, nb03,
+        ne0, ne1, ne2,
+        nb0, nb1, nb2, nb3
+    };
+
+    std::string name = std::string(__func__)
+                       + "_i_" + std::to_string(in_element_size)
+                       + "_o_" + std::to_string(out_element_size);
+    std::shared_ptr<kp::Algorithm> s_algo = nullptr;
+    if (!komputeManager()->hasAlgorithm(name))
+        s_algo = komputeManager()->algorithm<float, PushConstants>(name, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
+    else {
+        s_algo = komputeManager()->getAlgorithm(name);
+        s_algo->setTensors({in, out});
+        s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)});
+        s_algo->setPushConstants<PushConstants>({pushConsts});
+        s_algo->updateDescriptors(s_kompute_context->pool.get());
+    }
+    seq.record<kp::OpAlgoDispatch>(s_algo);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f32_f16(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f16_comp_spv,
+        kp::shader_data::op_cpy_f32_f16_comp_spv_len);
+    ggml_vk_cpy(spirv, 4, 2, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f32_f32(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f32_comp_spv,
+        kp::shader_data::op_cpy_f32_f32_comp_spv_len);
+    ggml_vk_cpy(spirv, 4, 4, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f16_f16(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f16_comp_spv,
+        kp::shader_data::op_cpy_f16_f16_comp_spv_len);
+    ggml_vk_cpy(spirv, 2, 2, std::forward<Args>(args)...);
+}
+
+template <typename... Args>
+static void ggml_vk_cpy_f16_f32(Args&&... args) {
+    const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f32_comp_spv,
+        kp::shader_data::op_cpy_f16_f32_comp_spv_len);
+    ggml_vk_cpy(spirv, 2, 4, std::forward<Args>(args)...);
+}
+
+static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
+    switch (op->type) {
+        case GGML_TYPE_F16:
+        case GGML_TYPE_F32:
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            break;
+        default:
+            return false;
+    }
+
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                    return ggml_is_contiguous(op->src[0]);
+                default:
+                    ;
+            }
+            break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_SCALE:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_NORM:
+        case GGML_OP_ROPE:
+            return true;
+        case GGML_OP_DUP:
+        case GGML_OP_CPY:
+        case GGML_OP_CONT:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                    break;
+                default:
+                    return false;
+            }
+            switch (op->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                    break;
+                default:
+                    return false;
+            }
+            return true;
+        case GGML_OP_DIAG_MASK_INF:
+            return op->ne[3] == 1;
+        case GGML_OP_GET_ROWS:
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                case GGML_TYPE_Q6_K:
+                    return op->ne[2] == 1 && op->ne[3] == 1;
+                default:
+                    ;
+            }
+            return false;
+        case GGML_OP_MUL_MAT:
+            if (op->src[1]->type != GGML_TYPE_F32 || ggml_is_transposed(op->src[0]) || ggml_is_transposed(op->src[1]))
+                return false;
+
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_Q6_K:
+                    return op->ne[3] == 1;
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q8_0:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                    return true;
+                default:
+                    ;
+            }
+        default:
+            ;
+    }
+    return false;
+}
+
+static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) {
+    const int n_seq = 8;
+
+    // FIXME: Figure out if we can somehow optimize the size of the pool... right now we're setting
+    // it to the size of the graph, but I think it can be made smaller?
+    ggml_vk_allocate_descriptor_pool(ctx, gf->n_nodes);
+
+    std::vector<std::shared_ptr<kp::Sequence>> sequences(n_seq);
+
+    for (auto& sequence : sequences) {
+        sequence = komputeManager()->sequence();
+    }
+    for (int seq_idx = 0; seq_idx < n_seq; ++seq_idx) {
+        const int n_nodes_per_seq = (gf->n_nodes + n_seq - 1) / n_seq;
+
+        auto& seq = *sequences[seq_idx];
+
+        const int node_start = (seq_idx + 0) * n_nodes_per_seq;
+        const int node_end   = std::min((seq_idx == n_seq - 1) ? gf->n_nodes : (seq_idx + 1) * n_nodes_per_seq, gf->n_nodes);
+
+        bool any_commands_recorded = false;
+
+        for (int i = node_start; i < node_end; ++i) {
+            struct ggml_tensor * src0 = gf->nodes[i]->src[0];
+            struct ggml_tensor * src1 = gf->nodes[i]->src[1];
+            struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2);
+            struct ggml_tensor * dst = gf->nodes[i];
+            GGML_ASSERT(dst->data != nullptr);
+
+            if (ggml_is_empty(dst)) {
+                continue;
+            }
+
+            switch (dst->op) {
+                case GGML_OP_NONE:
+                case GGML_OP_RESHAPE:
+                case GGML_OP_VIEW:
+                case GGML_OP_TRANSPOSE:
+                case GGML_OP_PERMUTE:
+                    continue; // noop -> next node
+                default:
+                    break;
+            }
+
+            any_commands_recorded = true;
+
+            if (!ggml_vk_supports_op(dst)) {
+                 fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst));
+                 GGML_ASSERT(!"unsupported op");
+            }
+
+            const int32_t ne00 = src0 ? src0->ne[0] : 0;
+            const int32_t ne01 = src0 ? src0->ne[1] : 0;
+            const int32_t ne02 = src0 ? src0->ne[2] : 0;
+            const int32_t ne03 = src0 ? src0->ne[3] : 0;
+
+            const uint32_t nb00 = src0 ? src0->nb[0] : 0;
+            const uint32_t nb01 = src0 ? src0->nb[1] : 0;
+            const uint32_t nb02 = src0 ? src0->nb[2] : 0;
+            const uint32_t nb03 = src0 ? src0->nb[3] : 0;
+
+            const int32_t ne10 = src1 ? src1->ne[0] : 0;
+            const int32_t ne11 = src1 ? src1->ne[1] : 0;
+            const int32_t ne12 = src1 ? src1->ne[2] : 0;
+            const int32_t ne13 = src1 ? src1->ne[3] : 0;
+
+            const uint32_t nb10 = src1 ? src1->nb[0] : 0;
+            const uint32_t nb11 = src1 ? src1->nb[1] : 0;
+            const uint32_t nb12 = src1 ? src1->nb[2] : 0;
+            const uint32_t nb13 = src1 ? src1->nb[3] : 0;
+
+            const int32_t ne0 = dst ? dst->ne[0] : 0;
+            const int32_t ne1 = dst ? dst->ne[1] : 0;
+            const int32_t ne2 = dst ? dst->ne[2] : 0;
+//            const int32_t ne3 = dst ? dst->ne[3] : 0;
+
+            const uint32_t nb0 = dst ? dst->nb[0] : 0;
+            const uint32_t nb1 = dst ? dst->nb[1] : 0;
+            const uint32_t nb2 = dst ? dst->nb[2] : 0;
+            const uint32_t nb3 = dst ? dst->nb[3] : 0;
+
+            const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
+            const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+            const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT;
+
+            const static std::shared_ptr<kp::Tensor> nullTensor = nullptr;
+            uint32_t off_src0 = 0;
+            uint32_t off_src1 = 0;
+            uint32_t off_dst  = 0;
+            const std::shared_ptr<kp::Tensor>& id_src0 = src0 ? ggml_vk_get_tensor(src0, &off_src0) : nullTensor;
+            const std::shared_ptr<kp::Tensor>& id_src1 = src1 ? ggml_vk_get_tensor(src1, &off_src1) : nullTensor;
+            const std::shared_ptr<kp::Tensor>& id_dst  = dst  ? ggml_vk_get_tensor(dst,  &off_dst)  : nullTensor;
+
+            switch (dst->op) {
+                case GGML_OP_ADD:
+                    {
+                        if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+                            // src1 is a row
+                            ggml_vk_addrow(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst)/4, ne00);
+                        } else {
+                            ggml_vk_add(
+                                seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                ne00, ne01, ne02, ne03,
+                                nb00, nb01, nb02, nb03,
+                                ne10, ne11, ne12, ne13,
+                                nb10, nb11, nb12, nb13,
+                                ne0,
+                                nb0, nb1, nb2, nb3
+                            );
+                        }
+                    } break;
+                case GGML_OP_MUL:
+                    {
+                        ggml_vk_mul(
+                            seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                            ne00, ne01, ne02, ne03,
+                            nb00, nb01, nb02, nb03,
+                            ne10, ne11, ne12, ne13,
+                            nb10, nb11, nb12, nb13,
+                            ne0,
+                            nb0, nb1, nb2, nb3
+                        );
+                    } break;
+                case GGML_OP_SCALE:
+                    {
+                        float scale; memcpy(&scale, dst->op_params, sizeof(float));
+
+                        ggml_vk_scale(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst), scale);
+                    } break;
+                case GGML_OP_UNARY:
+                    {
+                        int64_t n = ggml_nelements(dst);
+                        GGML_ASSERT(n % 4 == 0);
+                        switch (ggml_get_unary_op(gf->nodes[i])) {
+                            case GGML_UNARY_OP_SILU:
+                                {
+                                    ggml_vk_silu(seq, id_src0, id_dst, off_src0, off_dst, n/4);
+                                } break;
+                            case GGML_UNARY_OP_RELU:
+                                {
+                                    ggml_vk_relu(seq, id_src0, id_dst, off_src0, off_dst, n/4);
+                                } break;
+                            case GGML_UNARY_OP_GELU:
+                                {
+                                    GGML_ASSERT(n % 8 == 0);
+                                    ggml_vk_gelu(seq, id_src0, id_dst, off_src0, off_dst, n/8);
+                                } break;
+                            default:
+                                {
+                                    fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                                    GGML_ASSERT(false);
+                                }
+                        }
+                    } break;
+                case GGML_OP_SOFT_MAX:
+                    {
+                        float scale;
+                        float max_bias;
+
+                        memcpy(&scale,    (float *)dst->op_params + 0, sizeof(float));
+                        memcpy(&max_bias, (float *)dst->op_params + 1, sizeof(float));
+
+#pragma message("TODO: add ggml_vk_soft_max() F16 src1 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
+                        GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32);
+
+#pragma message("TODO: add ALiBi support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/7192")
+                        GGML_ASSERT(max_bias == 0.0f);
+
+                        ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale);
+                    } break;
+                case GGML_OP_DIAG_MASK_INF:
+                    {
+                        const int n_past = ((int32_t *)(dst->op_params))[0];
+                        ggml_vk_diag_mask_inf(seq, id_src0, id_dst, off_src0, off_dst, n_past, ne00, ne01, ne02);
+                    } break;
+                case GGML_OP_NORM:
+                    {
+                        float eps;
+                        memcpy(&eps, dst->op_params, sizeof(float));
+                        ggml_vk_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps);
+                    } break;
+                case GGML_OP_RMS_NORM:
+                    {
+                        GGML_ASSERT(ne00 % 4 == 0);
+
+                        float eps;
+                        memcpy(&eps, dst->op_params, sizeof(float));
+                        ggml_vk_rms_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps);
+                    } break;
+                case GGML_OP_MUL_MAT:
+                    {
+                        GGML_ASSERT(ne00 == ne10);
+
+                        GGML_ASSERT(ne12 % ne02 == 0);
+                        GGML_ASSERT(ne13 % ne03 == 0);
+
+                        const uint32_t r2 = ne12/ne02;
+                        const uint32_t r3 = ne13/ne03;
+
+                        if (src1t != GGML_TYPE_F32) {
+                            fprintf(stderr, "%s: %s: Unsupported src1 type: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t);
+                            goto not_implemented;
+                        }
+
+                        if (ggml_is_transposed(src0) ||
+                            ggml_is_transposed(src1)) {
+                            fprintf(stderr, "%s: %s: matmul on tranposed tensor not supported: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t);
+                            goto not_implemented;
+                        }
+
+                        switch (src0t) {
+                            case GGML_TYPE_F32:
+                                ggml_vk_mul_mat_mat_f32(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, nb01, nb02, ne11, ne12, nb11, nb12, nb1, nb2
+                                );
+                                break;
+                            case GGML_TYPE_F16:
+                                ggml_vk_mul_mat_f16(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, ne13, nb10, nb11, nb12,
+                                    ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q8_0:
+                                ggml_vk_mul_mat_q8_0(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q4_0:
+                                ggml_vk_mul_mat_q4_0(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q4_1:
+                                ggml_vk_mul_mat_q4_1(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3
+                                );
+                                break;
+                            case GGML_TYPE_Q6_K:
+                                ggml_vk_mul_mat_q6_k(
+                                    seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst,
+                                    ne00, ne10, ne0, ne1, ne01, ne11, ne12, ne02
+                                );
+                                break;
+                            default: {
+                                fprintf(stderr, "%s: %s: Unsupported quantization: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t);
+                                goto not_implemented;
+                            }
+                        }
+
+                    } break;
+                case GGML_OP_GET_ROWS:
+                    {
+                        if (src0t == GGML_TYPE_F32) {
+                            ggml_vk_get_rows_f32(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_F16) {
+                            ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_Q4_0) {
+                            ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_Q4_1) {
+                            ggml_vk_get_rows_q4_1(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else if (src0t == GGML_TYPE_Q6_K) {
+                            ggml_vk_get_rows_q6_k(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1));
+                        } else {
+                            fprintf(stderr, "%s: %s: Unsupported quantization: %u\n", __func__, ggml_op_name(dst->op), src0t);
+                            goto not_implemented;
+                        }
+                    } break;
+                case GGML_OP_ROPE:
+                    {
+#pragma message("TODO: implement phi3 frequency factors support")
+#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7225")
+                        GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet");
+
+#pragma message("TODO: update rope NORM mode to match NEOX mode")
+#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7634")
+
+                        GGML_ASSERT(ne10 == ne02);
+                        GGML_ASSERT(src0t == dstt);
+                        // const int n_past = ((int32_t *) dst->op_params)[0];
+                        const int n_dims     = ((int32_t *) dst->op_params)[1];
+                        const int mode       = ((int32_t *) dst->op_params)[2];
+                        // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan
+                        const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
+
+                        float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+                        memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+                        memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+                        memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+                        memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+                        memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+                        memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+                        ggml_vk_rope(
+                            seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_ctx_orig,
+                            freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow,
+                            ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3
+                        );
+                    } break;
+                case GGML_OP_DUP:
+                case GGML_OP_CPY:
+                case GGML_OP_CONT:
+                    {
+                        switch (src0t) {
+                            case GGML_TYPE_F32:
+                                {
+                                    switch (dstt) {
+                                        case GGML_TYPE_F16: ggml_vk_cpy_f32_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                        case GGML_TYPE_F32: ggml_vk_cpy_f32_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                        default: goto not_implemented;
+                                    }
+                                } break;
+                            case GGML_TYPE_F16:
+                                {
+                                    switch (dstt) {
+                                        case GGML_TYPE_F16: ggml_vk_cpy_f16_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                        case GGML_TYPE_F32: ggml_vk_cpy_f16_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break;
+                                    default: goto not_implemented;
+                                } break;
+                            default: goto not_implemented;
+                            }
+                        }
+                    } break;
+                default: goto not_implemented;
+            }
+            continue;
+            not_implemented: {}
+            fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+            //GGML_ASSERT(false);
+        }
+
+        // Evaluate sequence
+        if (any_commands_recorded) {
+            seq.evalAsync();
+        }
+    }
+
+    // Wait for all sequences to finish
+    for (auto& sequence : sequences) {
+        if (sequence->isRunning())
+            sequence->evalAwait();
+    }
+
+    ggml_vk_free_descriptor_pool(ctx);
+}
+
+template<>
+kp::Tensor::TensorDataTypes
+kp::TensorT<half>::dataType()
+{
+    return TensorDataTypes::eFloat;
+}
+
+template<>
+kp::Tensor::TensorDataTypes
+kp::TensorT<uint8_t>::dataType()
+{
+    return TensorDataTypes::eUnsignedInt;
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
+// backend interface
+
+struct ggml_backend_kompute_buffer_type_context {
+    int         device;
+    int         device_ref = 0;
+    uint64_t    buffer_alignment;
+    uint64_t    max_alloc;
+    std::string name;
+
+    ggml_backend_kompute_buffer_type_context(int device, uint64_t buffer_alignment, uint64_t max_alloc)
+        : device(device), buffer_alignment(buffer_alignment), max_alloc(max_alloc), name(ggml_kompute_format_name(device)) {}
+};
+
+static void ggml_backend_kompute_device_ref(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+
+    if (!ctx->device_ref) {
+        komputeManager()->initializeDevice(
+            ctx->device, {}, {
+                "VK_KHR_shader_float16_int8", "VK_KHR_8bit_storage",
+                "VK_KHR_16bit_storage", "VK_KHR_shader_non_semantic_info"
+            }
+        );
+    }
+
+    assert(ggml_vk_has_device());
+    ctx->device_ref++;
+}
+
+static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+
+    assert(ctx->device_ref > 0);
+
+    ctx->device_ref--;
+
+    if (!ctx->device_ref) {
+        komputeManager.destroy();
+    }
+}
+
+static const char * ggml_backend_kompute_buffer_get_name(ggml_backend_buffer_t buffer) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buffer->buft->context);
+    return ctx->name.c_str();
+}
+
+static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+    auto * memory = (ggml_vk_memory *)buffer->context;
+    if (ggml_vk_has_device()) {
+        ggml_vk_free_memory(*memory);
+    }
+    delete memory;
+}
+
+static void * ggml_backend_kompute_buffer_get_base(ggml_backend_buffer_t buffer) {
+    return ((ggml_vk_memory *)buffer->context)->data;
+}
+
+static void ggml_backend_kompute_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    GGML_UNUSED(buffer);
+
+    const auto res = ggml_vk_get_tensor(tensor);
+    GGML_ASSERT(res);
+
+    memcpy((char *)tensor->data + offset, data, size);
+
+    komputeManager()->sequence()->eval<kp::OpTensorSyncDevice>({res});
+}
+
+static void ggml_backend_kompute_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    GGML_UNUSED(buffer);
+
+    const auto res = ggml_vk_get_tensor(tensor);
+    GGML_ASSERT(res);
+
+    komputeManager()->sequence()->eval<kp::OpTensorSyncLocal>({res});
+
+    memcpy(data, (const char *)tensor->data + offset, size);
+}
+
+static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    auto * memory = (ggml_vk_memory *)buffer->context;
+    memset(memory->data, value, buffer->size);
+
+    if (memory->stagingBuffer)
+        komputeManager()->sequence()->eval<kp::OpBufferSyncDevice>(memory->primaryBuffer, memory->stagingBuffer, memory->size);
+}
+
+static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = {
+    /* .get_name        = */ ggml_backend_kompute_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_kompute_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_kompute_buffer_get_base,
+    /* .init_tensor     = */ NULL,
+    /* .set_tensor      = */ ggml_backend_kompute_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_kompute_buffer_get_tensor,
+    /* .cpy_tensor      = */ NULL,
+    /* .clear           = */ ggml_backend_kompute_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// default buffer type
+
+static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+    return ctx->name.c_str();
+}
+
+static ggml_backend_buffer_t ggml_backend_kompute_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    ggml_backend_kompute_device_ref(buft);
+    auto * ctx = new ggml_vk_memory(ggml_vk_allocate(size));
+    return ggml_backend_buffer_init(buft, ggml_backend_kompute_buffer_i, ctx, size);
+}
+
+static size_t ggml_backend_kompute_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+    return ctx->buffer_alignment;
+}
+
+static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+    auto * ctx = static_cast<ggml_backend_kompute_buffer_type_context *>(buft->context);
+    return ctx->max_alloc;
+}
+
+static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_kompute_buffer_type_get_name,
+    /* .alloc_buffer     = */ ggml_backend_kompute_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_kompute_buffer_type_get_alignment,
+    /* .get_max_size     = */ ggml_backend_vk_buffer_type_get_max_size,
+    /* .get_alloc_size   = */ NULL, // defaults to ggml_nbytes
+    /* .is_host          = */ NULL,
+};
+
+ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) {
+    static std::vector<ggml_backend_buffer_type> bufts = []() {
+        std::vector<ggml_backend_buffer_type> vec;
+        auto devices = ggml_vk_available_devices_internal(0);
+        vec.reserve(devices.size());
+
+        for (const auto & dev : devices) {
+            vec.push_back({
+                /* .iface   = */ ggml_backend_kompute_buffer_type_interface,
+                /* .context = */ new ggml_backend_kompute_buffer_type_context(dev.index, dev.bufferAlignment, dev.maxAlloc)
+            });
+        }
+        return vec;
+    }();
+
+    auto it = std::find_if(bufts.begin(), bufts.end(), [device](const ggml_backend_buffer_type & t) {
+        return device == static_cast<ggml_backend_kompute_buffer_type_context *>(t.context)->device;
+    });
+    return it < bufts.end() ? &*it : nullptr;
+}
+
+// backend
+
+static const char * ggml_backend_kompute_name(ggml_backend_t backend) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+    return ctx->name.c_str();
+}
+
+static void ggml_backend_kompute_free(ggml_backend_t backend) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+
+    assert(ctx == s_kompute_context);
+    s_kompute_context = nullptr;
+    if (ctx != nullptr) {
+        delete ctx;
+    }
+
+    delete backend;
+}
+
+static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(ggml_backend_t backend) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+    return ggml_backend_kompute_buffer_type(ctx->device);
+}
+
+static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+    auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
+    ggml_vk_graph_compute(ctx, cgraph);
+    return GGML_STATUS_SUCCESS;
+}
+
+static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
+    GGML_UNUSED(backend);
+    return ggml_vk_supports_op(op);
+}
+
+static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    GGML_UNUSED(backend);
+    return buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name;
+}
+
+static struct ggml_backend_i kompute_backend_i = {
+    /* .get_name                = */ ggml_backend_kompute_name,
+    /* .free                    = */ ggml_backend_kompute_free,
+    /* .get_default_buffer_type = */ ggml_backend_kompute_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_async        = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
+    /* .synchronize             = */ NULL,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_kompute_graph_compute,
+    /* .supports_op             = */ ggml_backend_kompute_supports_op,
+    /* .supports_buft           = */ ggml_backend_kompute_supports_buft,
+    /* .offload_op              = */ NULL,
+    /* .event_new               = */ NULL,
+    /* .event_free              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .event_synchronize       = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_kompute_guid() {
+    static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49, 0xfb, 0x35, 0xfa, 0x9b, 0x18, 0x31, 0x1d, 0xca };
+    return &guid;
+}
+
+ggml_backend_t ggml_backend_kompute_init(int device) {
+    GGML_ASSERT(s_kompute_context == nullptr);
+    s_kompute_context = new ggml_kompute_context(device);
+
+    ggml_backend_t kompute_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_kompute_guid(),
+        /* .interface = */ kompute_backend_i,
+        /* .context   = */ s_kompute_context,
+    };
+
+    return kompute_backend;
+}
+
+bool ggml_backend_is_kompute(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid());
+}
+
+static ggml_backend_t ggml_backend_reg_kompute_init(const char * params, void * user_data) {
+    GGML_UNUSED(params);
+    return ggml_backend_kompute_init(intptr_t(user_data));
+}
+
+extern "C" int ggml_backend_kompute_reg_devices();
+
+int ggml_backend_kompute_reg_devices() {
+    auto devices = ggml_vk_available_devices_internal(0);
+    for (const auto & device : devices) {
+        ggml_backend_register(
+            ggml_kompute_format_name(device.index).c_str(),
+            ggml_backend_reg_kompute_init,
+            ggml_backend_kompute_buffer_type(device.index),
+            reinterpret_cast<void *>(intptr_t(device.index))
+        );
+    }
+    return devices.size();
+}