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llama.cpp/ggml-vulkan.cpp
Adam Treat addac25293 Set the singleton to nullptr here.
2023-10-05 13:39:18 -04:00

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/**
* Copyright (c) 2023 Nomic, Inc. All rights reserved.
*
* This software is licensed under the terms of the Software for Open Models License (SOM),
* version 1.0, as detailed in the LICENSE_SOM.txt file. A copy of this license should accompany
* this software. Except as expressly granted in the SOM license, all rights are reserved by Nomic, Inc.
*/
#include "ggml-vulkan.h"
#include "ggml.h"
// These are generated at build time by cmake custom command
#include "shaderop_scale.h"
#include "shaderop_add.h"
#include "shaderop_addrow.h"
#include "shaderop_mul.h"
#include "shaderop_mulrow.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_q4_0.h"
#include "shaderop_mul_mat_q4_1.h"
#include "shaderop_getrows_f16.h"
#include "shaderop_getrows_q4_0.h"
#include "shaderop_getrows_q4_1.h"
#include "shaderop_rope.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 <iostream>
#include <vector>
#include <string>
#include <memory>
#include <vector>
#include <utility>
#include <fstream>
#include <exception>
#include <thread>
#include <mutex>
#include <atomic>
#include <cstring>
#include <immintrin.h>
#include <kompute/Kompute.hpp>
#define QK4_0 32
#define QR4_0 2
#define QK4_1 32
typedef ggml_fp16_t half;
struct ggml_kompute_context {
bool hasH2DAll = false;
std::vector<ggml_vk_memory> buffers;
std::shared_ptr<vk::DescriptorPool> pool;
};
// 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.
ggml_kompute_context *s_kompute_context = nullptr;
kp::Manager *komputeManager() {
static kp::Manager *s_mgr = nullptr;
if (s_mgr && !s_mgr->hasInstance()) {
delete s_mgr;
s_mgr = nullptr;
}
if (!s_mgr)
s_mgr = new kp::Manager;
return s_mgr;
}
#ifdef __linux__
__attribute__((constructor))
static void enable_sam() {
setenv("RADV_PERFTEST", "sam", false);
}
#endif
static bool ggml_vk_checkPhysicalDeviceFeatures(vk::PhysicalDevice physicalDevice) {
vk::PhysicalDeviceFeatures availableFeatures;
physicalDevice.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;
physicalDevice.getFeatures2(&features2);
if (!availableFeatures11.uniformAndStorageBuffer16BitAccess ||
!availableFeatures11.storageBuffer16BitAccess) {
return false;
}
if (!availableFeatures12.storageBuffer8BitAccess ||
!availableFeatures12.uniformAndStorageBuffer8BitAccess ||
!availableFeatures12.shaderFloat16 ||
!availableFeatures12.shaderInt8) {
return false;
}
return true;
}
static std::string ggml_vk_getVendorName(uint32_t vendorID) {
switch (vendorID) {
case 0x10DE:
return "nvidia";
case 0x1002:
return "amd";
case 0x8086:
return "intel";
default:
return "unknown";
}
}
std::vector<ggml_vk_device> ggml_vk_available_devices(size_t memoryRequired) {
std::vector<ggml_vk_device> results;
if (!komputeManager()->hasVulkan())
return results;
std::vector<vk::PhysicalDevice> physicalDevices = komputeManager()->listDevices();
uint32_t deviceCount = physicalDevices.size();
if (deviceCount == 0)
return results;
std::unordered_map<std::string, size_t> count_by_name;
for (uint32_t i = 0; i < deviceCount; i++) {
VkPhysicalDeviceProperties properties = physicalDevices.at(i).getProperties();
VkPhysicalDeviceMemoryProperties memoryProperties = physicalDevices.at(i).getMemoryProperties();
const uint32_t major = VK_VERSION_MAJOR(properties.apiVersion);
const uint32_t minor = VK_VERSION_MINOR(properties.apiVersion);
if (major < 1 || minor < 2)
continue;
if (!ggml_vk_checkPhysicalDeviceFeatures(physicalDevices.at(i)))
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;
ggml_vk_device d;
d.index = i;
d.type = properties.deviceType;
d.heapSize = heapSize;
d.name = properties.deviceName;
size_t n_idx = ++count_by_name[d.name];
if (n_idx > 1) {
d.name += " (" + std::to_string(n_idx) + ")";
}
d.vendor = ggml_vk_getVendorName(properties.vendorID);
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;
}
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()
);
}
bool ggml_vk_init_device(size_t memoryRequired, const std::string &device) {
if (device.empty())
return false;
std::vector<ggml_vk_device> devices = ggml_vk_available_devices(memoryRequired);
if (device == "gpu") {
if (devices.size() != 0)
return ggml_vk_init_device(devices.front());
} else if (device == "amd" || device == "nvidia" || device == "intel") {
ggml_vk_filterByVendor(devices, device);
if (devices.size() != 0)
return ggml_vk_init_device(devices.front());
} else {
ggml_vk_filterByName(devices, device);
if (devices.size() != 0)
return ggml_vk_init_device(devices.front());
}
return ggml_vk_has_device();
}
bool ggml_vk_init_device(const ggml_vk_device &device) {
return ggml_vk_init_device(device.index);
}
bool ggml_vk_init_device(int device) {
komputeManager()->initializeDevice(device, {},
{"VK_KHR_shader_float16_int8", "VK_KHR_8bit_storage",
"VK_KHR_16bit_storage", "VK_KHR_storage_buffer_storage_class"});
return ggml_vk_has_device();
}
bool ggml_vk_free_device() {
if (!ggml_vk_has_device())
return false;
komputeManager()->destroy();
// FIXME: The lifetime of these two needs to be tied together as we're relying upon the fact
// the llama_free(ctx) destroys this memory and we just set the singleton to nullptr here which
// is very brittle
s_kompute_context = nullptr;
return true;
}
bool ggml_vk_has_vulkan() {
return komputeManager()->hasVulkan();
}
bool ggml_vk_has_device() {
return komputeManager()->hasDevice();
}
bool ggml_vk_using_vulkan() {
return s_kompute_context != nullptr;
}
ggml_vk_device ggml_vk_current_device() {
if (!komputeManager()->hasDevice())
return ggml_vk_device();
std::vector<ggml_vk_device> devices = ggml_vk_available_devices(0);
ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName);
return devices.front();
}
ggml_kompute_context *ggml_vk_init() {
s_kompute_context = new ggml_kompute_context;
return s_kompute_context;
}
bool ggml_vk_has_h2d_all(struct ggml_kompute_context * ctx) {
return ctx->hasH2DAll;
}
void ggml_vk_free(struct ggml_kompute_context * ctx) {
assert(ctx == s_kompute_context);
s_kompute_context = nullptr;
delete ctx;
}
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++) {
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;
}
size_t ggml_vk_aligned_offset(size_t offset) {
static size_t minStorageBufferOffsetAlignment = 0;
if (minStorageBufferOffsetAlignment == 0) {
vk::PhysicalDeviceProperties deviceProperties;
deviceProperties = komputeManager()->physicalDevice()->getProperties();
vk::PhysicalDeviceLimits deviceLimits = deviceProperties.limits;
minStorageBufferOffsetAlignment = deviceLimits.minStorageBufferOffsetAlignment;
}
// 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 void ggml_vk_h2d_buffer(const ggml_vk_memory &memory) {
if (memory.stagingBuffer)
komputeManager()->sequence()->eval<kp::OpBufferSyncDevice>(memory.primaryBuffer, memory.stagingBuffer, memory.size);
}
static void ggml_vk_d2h_buffer(const ggml_vk_memory &memory) {
if (memory.stagingBuffer)
komputeManager()->sequence()->eval<kp::OpBufferSyncLocal>(memory.primaryBuffer, memory.stagingBuffer, memory.size);
}
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;
}
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
decltype(ggml_kompute_context::buffers)::iterator ggml_vk_find_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t, uint64_t & offset) {
for (auto it = ctx->buffers.begin(); ; it++) {
if (it == ctx->buffers.end()) {
fprintf(stderr, "%s: Failed to find tensor %p\n", __func__, t->data);
return it;
}
if (it->data <= t->data &&
reinterpret_cast<intptr_t>(it->data) + it->size >= (reinterpret_cast<intptr_t>(t->data) + ggml_nbytes(t))) {
offset = reinterpret_cast<intptr_t>(t->data) - reinterpret_cast<intptr_t>(it->data);
return it;
}
}
}
static
const std::shared_ptr<kp::Tensor> ggml_vk_get_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t, uint32_t *alignedOffset) {
uint64_t originalOffset = 0;
auto res = ggml_vk_find_tensor(ctx, t, originalOffset);
if (res == ctx->buffers.end()) {
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(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);
}
void ggml_vk_add_buffer(
struct ggml_kompute_context * ctx,
const char * /*name*/,
const ggml_vk_memory &memory) {
ctx->buffers.emplace_back(memory);
}
void ggml_vk_h2d_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) {
const auto res = ggml_vk_get_tensor(ctx, t, nullptr);
GGML_ASSERT(res);
komputeManager()->sequence()->eval<kp::OpTensorSyncDevice>({res});
}
void ggml_vk_h2d_all(struct ggml_kompute_context * ctx) {
for (auto& it : ctx->buffers) {
ggml_vk_h2d_buffer(it);
}
ctx->hasH2DAll = true;
}
void ggml_vk_d2h_all(struct ggml_kompute_context * ctx) {
for (auto& it : ctx->buffers) {
ggml_vk_d2h_buffer(it);
}
}
void ggml_vk_d2h_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) {
const auto res = ggml_vk_get_tensor(ctx, t, nullptr);
GGML_ASSERT(res);
komputeManager()->sequence()->eval<kp::OpTensorSyncLocal>({res});
}
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;
}
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,
uint32_t size) {
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;
} const pushConsts {
safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4)
};
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);
}
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);
}
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,
uint32_t size) {
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;
} const pushConsts {
safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4)
};
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);
}
void ggml_vk_mulrow(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_mulrow_comp_spv,
kp::shader_data::op_mulrow_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);
}
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 = getSpirvShader(kp::shader_data::op_scale_comp_spv,
kp::shader_data::op_scale_comp_spv_len);
struct PushConstants {
uint32_t inOff, outOff;
float scale;
} const pushConsts {
safe_divide(inOff, 4), safe_divide(outOff, 4),
scale
};
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, {size}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
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);
}
void ggml_vk_xxlu(const std::vector<uint32_t>& spirv, 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),
};
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, {size}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
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>
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, std::forward<Args>(args)...);
}
template <typename... Args>
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, std::forward<Args>(args)...);
}
template <typename... Args>
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, std::forward<Args>(args)...);
}
void ggml_vk_soft_max(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, uint32_t ne03) {
const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv,
kp::shader_data::op_softmax_comp_spv_len);
struct PushConstants {
uint32_t inOff, outOff;
int32_t ne00, ne01, ne02;
} pushConsts {
safe_divide(inOff, 4), safe_divide(outOff, 4),
ne00, ne01, ne02
};
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(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
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);
}
void ggml_vk_norm_(const std::vector<uint32_t>& spirv, 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) {
GGML_ASSERT(nb01%sizeof(float) == 0);
GGML_ASSERT(ne00%sizeof(float) == 0);
const float epsilon = 1e-6f; // this is what ggml.c uses for rms norm
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
};
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, {(uint32_t)nrows}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
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>
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, std::forward<Args>(args)...);
}
template <typename... Args>
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, std::forward<Args>(args)...);
}
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);
}
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,
uint32_t nb01, uint32_t nb02,
int32_t ne11, int32_t ne12,
uint32_t nb11, uint32_t nb12,
int32_t ne0, int32_t ne1) {
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;
uint32_t nb01, nb02;
uint32_t nb11, nb12;
int32_t ne0, ne1;
} pushConsts {
safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4),
ne00, nb01, nb02, nb11, nb12, ne0, ne1,
};
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(ne11), unsigned(ne12)}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
s_algo->setTensors({inA, inB, out});
s_algo->setWorkgroup({unsigned(ne01), unsigned(ne11), unsigned(ne12)});
s_algo->setPushConstants<PushConstants>({pushConsts});
s_algo->updateDescriptors(s_kompute_context->pool.get());
}
seq.record<kp::OpAlgoDispatch>(s_algo);
}
void ggml_vk_mul_mat_q4_x(const std::vector<uint32_t>& spirv, 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 ne10, int32_t ne0,
int32_t ne01, int32_t ne11) {
struct PushConstants {
uint32_t inAOff, inBOff, outOff;
int32_t ne00, ne10, ne0;
} pushConsts {
safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4),
ne00, ne10, ne0,
};
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(ne11)}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
s_algo->setTensors({inA, inB, out});
s_algo->setWorkgroup({unsigned(ne01), unsigned(ne11)});
s_algo->setPushConstants<PushConstants>({pushConsts});
s_algo->updateDescriptors(s_kompute_context->pool.get());
}
seq.record<kp::OpAlgoDispatch>(s_algo);
}
template <typename... Args>
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_q4_x(spirv, 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
}
// FIXME: This could be improved like was done in q4_0 version but needs testing...
template <typename... Args>
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_q4_x(spirv, 1/*We access blocks unaligned*/, std::forward<Args>(args)...);
}
void ggml_vk_get_rows(const std::vector<uint32_t>& spirv,
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
};
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);
}
template <typename... Args>
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, sizeof(half), 0, std::forward<Args>(args)...);
}
template <typename... Args>
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, 1/*We access blocks unaligned*/, QK4_0, std::forward<Args>(args)...);
}
template <typename... Args>
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, 1/*We access blocks unaligned*/, QK4_1, std::forward<Args>(args)...);
}
void ggml_vk_rope(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 n_dims, int32_t mode,
float freq_base, float freq_scale,
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) {
const static auto spirv = getSpirvShader(kp::shader_data::op_rope_comp_spv,
kp::shader_data::op_rope_comp_spv_len);
GGML_ASSERT(nb03%sizeof(float) == 0);
GGML_ASSERT(nb02%sizeof(float) == 0);
GGML_ASSERT(nb01%sizeof(float) == 0);
GGML_ASSERT(nb00%sizeof(float) == 0);
GGML_ASSERT(nb3%sizeof(float) == 0);
GGML_ASSERT(nb2%sizeof(float) == 0);
GGML_ASSERT(nb1%sizeof(float) == 0);
GGML_ASSERT(nb0%sizeof(float) == 0);
struct PushConstants {
uint32_t inOff, outOff;
uint32_t n_past;
int32_t n_dims, mode;
float freq_base, freq_scale;
uint32_t nb00, nb01, nb02, nb03;
int32_t ne0;
uint32_t nb0, nb1, nb2, nb3;
} pushConsts {
safe_divide(inOff, 4), safe_divide(outOff, 4),
n_past, n_dims, mode,
freq_base, freq_scale,
nb00, nb01, nb02, nb03,
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(), {in, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(__func__);
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<uint32_t in_element_size, uint32_t out_element_size>
void ggml_vk_cpy(const std::vector<uint32_t>& spirv,
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
};
static std::string unique_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(unique_name))
s_algo = komputeManager()->algorithm<float, PushConstants>(unique_name, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts});
else {
s_algo = komputeManager()->getAlgorithm(unique_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>
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<4, 2>(spirv, std::forward<Args>(args)...);
}
template <typename... Args>
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<4, 4>(spirv, std::forward<Args>(args)...);
}
template <typename... Args>
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<2, 2>(spirv, std::forward<Args>(args)...);
}
template <typename... Args>
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<2, 4>(spirv, std::forward<Args>(args)...);
}
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 = (seq_idx == n_seq - 1) ? gf->n_nodes : (seq_idx + 1) * n_nodes_per_seq;
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);
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(ctx, src0, &off_src0) : nullTensor;
const std::shared_ptr<kp::Tensor>& id_src1 = src1 ? ggml_vk_get_tensor(ctx, src1, &off_src1) : nullTensor;
const std::shared_ptr<kp::Tensor>& id_dst = dst ? ggml_vk_get_tensor(ctx, dst, &off_dst) : nullTensor;
switch (dst->op) {
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:
case GGML_OP_PERMUTE:
{
// noop
} break;
case GGML_OP_ADD:
{
if (ggml_nelements(src1) == ne10) {
// src1 is a row
ggml_vk_addrow(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst), ne00);
} else {
ggml_vk_add(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst));
}
} break;
case GGML_OP_MUL:
{
if (ggml_nelements(src1) == ne10) {
// src1 is a row
ggml_vk_mulrow(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst), ne00);
} else {
ggml_vk_mul(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst));
}
} break;
case GGML_OP_SCALE:
{
const float scale = *(const float *) src1->data;
ggml_vk_scale(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst), scale);
} break;
case GGML_OP_UNARY:
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, ggml_nelements(dst));
} break;
case GGML_UNARY_OP_RELU:
{
ggml_vk_relu(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst));
} break;
case GGML_UNARY_OP_GELU:
{
ggml_vk_gelu(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst));
} 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:
{
ggml_vk_soft_max(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03);
} 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:
{
ggml_vk_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0));
} break;
case GGML_OP_RMS_NORM:
{
ggml_vk_rms_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0));
} break;
case GGML_OP_MUL_MAT:
{
if ((src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32)
&& src1->type == GGML_TYPE_F32) {
ggml_vk_mul_mat_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, nb01, nb02, ne11, ne12, nb11, nb12, ne0, ne1);
} else if (src0->type == GGML_TYPE_Q4_0
&& src1->type == GGML_TYPE_F32) {
ggml_vk_mul_mat_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne10, ne0, ne01, ne11);
} else if (src0->type == GGML_TYPE_Q4_1
&& src1->type == GGML_TYPE_F32) {
ggml_vk_mul_mat_q4_1(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne10, ne0, ne01, ne11);
} else {
fprintf(stderr, "%s: %s: Unsupported quantization: %u/%u\n", __func__, ggml_op_name(dst->op), src0->type, src1->type);
goto not_implemented;
}
} break;
case GGML_OP_GET_ROWS:
{
if (src0->type == 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 (src0->type == 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 (src0->type == 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 {
fprintf(stderr, "%s: %s: Unsupported quantization: %u\n", __func__, ggml_op_name(dst->op), src0->type);
goto not_implemented;
}
} break;
case GGML_OP_ROPE:
{
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];
float freq_base;
float freq_scale;
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
ggml_vk_rope(seq, id_src0, id_dst, off_src0, off_dst, n_past, n_dims, mode, freq_base, freq_scale, 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
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;
}
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