extern/whisper.cpp
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whisper.cpp/ggml-kompute.cpp

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src : relocate new backend sources
2024-02-10 08:50:24 +01:00
#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_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_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_orig_ctx,
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_orig_ctx;
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_orig_ctx,
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 true;
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_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 * dst = gf->nodes[i];
GGML_ASSERT(dst->data != nullptr);
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;
memcpy(&scale, dst->op_params, sizeof(float));
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);
// TODO: assert that dim2 and dim3 are contiguous
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_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:
{
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_orig_ctx = ((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_orig_ctx,
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 bool ggml_backend_kompute_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
GGML_UNUSED(buft);
return ggml_backend_is_kompute(backend);
}
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
/* .supports_backend = */ ggml_backend_kompute_buffer_type_supports_backend,
/* .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);
}
ggml : introduce ggml_status (ggml/750) * using enum as an exit code instead of macros * update return type from enum to unsigned int * indentation fix * compound update ggml_compute_exit_code -> ggml_status changed ggml_status from a bit-field type to simple codes ggml_status to string cast * ggml_status to string cast * GGML_CALL was removed Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
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auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
ggml_vk_graph_compute(ctx, cgraph);
ggml : introduce ggml_status (ggml/750) * using enum as an exit code instead of macros * update return type from enum to unsigned int * indentation fix * compound update ggml_compute_exit_code -> ggml_status changed ggml_status from a bit-field type to simple codes ggml_status to string cast * ggml_status to string cast * GGML_CALL was removed Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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return GGML_STATUS_SUCCESS;
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}
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 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_compute = */ NULL,
/* .graph_compute = */ ggml_backend_kompute_graph_compute,
/* .supports_op = */ ggml_backend_kompute_supports_op,
llama : add pipeline parallelism support (llama/6017) * llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs ggml-ci * server : add -ub, --ubatch-size parameter * fix server embedding test * llama : fix Mamba inference for pipeline parallelism Tested to work correctly with both `main` and `parallel` examples. * llama : limit max batch size to n_batch * add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism default increase to 4 (from 2) changing this value may improve performance for some systems, but increases memory usage * fix hip build * fix sycl build (disable cpy_tensor_async) * fix hip build * llama : limit n_batch and n_ubatch to n_ctx during context creation * llama : fix norm backend * batched-bench : sync after decode * swiftui : sync after decode * ggml : allow ggml_get_rows to use multiple threads if they are available * check n_ubatch >= n_tokens with non-casual attention * llama : do not limit n_batch to n_ctx with non-casual attn * server : construct batch with size of llama_n_batch * ggml_backend_cpu_graph_compute : fix return value when alloc fails * llama : better n_batch and n_ubatch comment * fix merge * small fix * reduce default n_batch to 2048 --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-13 18:54:21 +01:00
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .event_synchronize = */ NULL,
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};
Introduce backend GUIDs (ggml/743) * Introduce backend GUIDs Initial proposed implementation of backend GUIDs (Discussed in https://github.com/ggerganov/ggml/pull/741) Hardcoded CPU backend GUID (for now) Change ggml_backend_is_cpu logic to use GUID * Remove redundant functions Remove redundant functions `ggml_backend_i::get_name` and `ggml_backend_guid` which are not desired for future expansion * Add spaces to match style Co-authored-by: slaren <slarengh@gmail.com> * Fix brace style to match Co-authored-by: slaren <slarengh@gmail.com> * Add void to () in function signature Co-authored-by: slaren <slarengh@gmail.com> * Add back ggml_backend_guid and make CPU_GUID a local static in ggml_backend_cpu_guid * add guids to all backends ggml-ci --------- Co-authored-by: slaren <slarengh@gmail.com>
2024-02-24 17:27:36 +01:00
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;
}
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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 {
Introduce backend GUIDs (ggml/743) * Introduce backend GUIDs Initial proposed implementation of backend GUIDs (Discussed in https://github.com/ggerganov/ggml/pull/741) Hardcoded CPU backend GUID (for now) Change ggml_backend_is_cpu logic to use GUID * Remove redundant functions Remove redundant functions `ggml_backend_i::get_name` and `ggml_backend_guid` which are not desired for future expansion * Add spaces to match style Co-authored-by: slaren <slarengh@gmail.com> * Fix brace style to match Co-authored-by: slaren <slarengh@gmail.com> * Add void to () in function signature Co-authored-by: slaren <slarengh@gmail.com> * Add back ggml_backend_guid and make CPU_GUID a local static in ggml_backend_cpu_guid * add guids to all backends ggml-ci --------- Co-authored-by: slaren <slarengh@gmail.com>
2024-02-24 17:27:36 +01:00
/* .guid = */ ggml_backend_kompute_guid(),
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/* .interface = */ kompute_backend_i,
/* .context = */ s_kompute_context,
};
return kompute_backend;
}
bool ggml_backend_is_kompute(ggml_backend_t backend) {
Introduce backend GUIDs (ggml/743) * Introduce backend GUIDs Initial proposed implementation of backend GUIDs (Discussed in https://github.com/ggerganov/ggml/pull/741) Hardcoded CPU backend GUID (for now) Change ggml_backend_is_cpu logic to use GUID * Remove redundant functions Remove redundant functions `ggml_backend_i::get_name` and `ggml_backend_guid` which are not desired for future expansion * Add spaces to match style Co-authored-by: slaren <slarengh@gmail.com> * Fix brace style to match Co-authored-by: slaren <slarengh@gmail.com> * Add void to () in function signature Co-authored-by: slaren <slarengh@gmail.com> * Add back ggml_backend_guid and make CPU_GUID a local static in ggml_backend_cpu_guid * add guids to all backends ggml-ci --------- Co-authored-by: slaren <slarengh@gmail.com>
2024-02-24 17:27:36 +01:00
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_kompute_guid());
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}
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();
}
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