First Vulkan Relise
This commit is contained in:
+208
-172
@@ -2,56 +2,55 @@
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#include <cmath>
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#include <cstdlib>
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#include <omp.h>
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#include <vulkan/vulkan.h>
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#include <vulkan/vulkan.hpp>
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#include <iostream>
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#include <vector>
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#include <chrono>
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#include <fstream>
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#include <string.h>
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// --- КОНСТРУКТОР ---
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NeuralNetwork::NeuralNetwork(LayerStructure_t layers[], int count, bool useVulkanParam) {
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this->numLayers = count;
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this->useVulkan = useVulkanParam;
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this->vulkanResourcesInitialized = false;
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NeuralNetwork::NeuralNetwork(LayerStructure_t layers[], int count, bool useVulkan) : numLayers(count) {
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if (useVulkan) {
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vk::ApplicationInfo appInfo{"Xenith", 1, nullptr, 0, VK_API_VERSION_1_1};
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instance = vk::createInstance({{}, &appInfo});
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// 2. Выбор видеокарты
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auto physicalDevices = instance.enumeratePhysicalDevices();
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if (physicalDevices.empty()) throw std::runtime_error("GPU с поддержкой Vulkan не найдены!");
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physDev = physicalDevices[0];
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std::cout << "Используем GPU: " << physDev.getProperties().deviceName << std::endl;
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// 3. Поиск очереди для вычислений (Compute)
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auto queueProps = physDev.getQueueFamilyProperties();
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int computeFamily = -1;
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for (int i = 0; i < queueProps.size(); i++) {
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if (queueProps[i].queueFlags & vk::QueueFlagBits::eCompute) {
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computeFamily = i;
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break;
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if (this->useVulkan) {
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try {
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vk::ApplicationInfo appInfo{"Xenith", 1, nullptr, 0, VK_API_VERSION_1_1};
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instance = vk::createInstance({{}, &appInfo});
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auto physicalDevices = instance.enumeratePhysicalDevices();
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if (physicalDevices.empty()) throw std::runtime_error("GPU не найдены");
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physDev = physicalDevices[0];
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auto queueProps = physDev.getQueueFamilyProperties();
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int computeFamily = -1;
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for (int i = 0; i < (int)queueProps.size(); i++) {
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if (queueProps[i].queueFlags & vk::QueueFlagBits::eCompute) {
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computeFamily = i; break;
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}
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}
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if (computeFamily == -1) throw std::runtime_error("Compute не поддерживается");
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this->computeQueueFamilyIndex = (uint32_t)computeFamily;
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float priority = 1.0f;
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vk::DeviceQueueCreateInfo queueInfo({}, computeQueueFamilyIndex, 1, &priority);
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vk::DeviceCreateInfo deviceCreateInfo({}, 1, &queueInfo);
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device = physDev.createDevice(deviceCreateInfo);
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queue = device.getQueue(computeQueueFamilyIndex, 0);
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vk::CommandPoolCreateInfo poolInfo({}, computeQueueFamilyIndex);
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cmdPool = device.createCommandPool(poolInfo);
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std::cout << "Vulkan инициализирован на: " << physDev.getProperties().deviceName << std::endl;
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} catch (const std::exception& e) {
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std::cerr << "Ошибка Vulkan: " << e.what() << ". Переключение на CPU." << std::endl;
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this->useVulkan = false;
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}
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if (computeFamily == -1) throw std::runtime_error("GPU не поддерживает вычисления (Compute)");
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// ВАЖНО: Сохраняем индекс в переменную класса, чтобы использовать её везде
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this->computeQueueFamilyIndex = (uint32_t)computeFamily;
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// 4. Создание логического устройства
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float priority = 1.0f;
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vk::DeviceQueueCreateInfo queueInfo({}, computeQueueFamilyIndex, 1, &priority);
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vk::DeviceCreateInfo deviceCreateInfo({}, 1, &queueInfo);
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device = physDev.createDevice(deviceCreateInfo);
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// 5. Получаем саму очередь
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queue = device.getQueue(computeQueueFamilyIndex, 0);
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// 6. Создаем пул команд (теперь используем правильный индекс)
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vk::CommandPoolCreateInfo poolInfo({}, computeQueueFamilyIndex);
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cmdPool = device.createCommandPool(poolInfo);
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}
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// Инициализация CPU данных
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for (int i = 0; i < count; i++) sizes.push_back(layers[i].size);
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for (int i = 0; i < count - 1; i++) {
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std::vector<std::vector<double>> layerW;
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@@ -65,14 +64,173 @@ NeuralNetwork::NeuralNetwork(LayerStructure_t layers[], int count, bool useVulka
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weights.push_back(layerW);
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biases.push_back(std::vector<double>(sizes[i+1], 0.0));
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}
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if (this->useVulkan) {
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initVulkanResources();
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}
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}
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// --- ДЕСТРУКТОР ---
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NeuralNetwork::~NeuralNetwork() {
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if (useVulkan && vulkanResourcesInitialized) {
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device.waitIdle();
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device.destroyPipeline(pipeline);
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device.destroyPipelineLayout(pipeLayout);
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device.destroyShaderModule(shaderModule);
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device.destroyDescriptorPool(descriptorPool);
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device.destroyDescriptorSetLayout(dsLayout);
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device.destroyBuffer(gpuW); device.freeMemory(memW);
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device.destroyBuffer(gpuB); device.freeMemory(memB);
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device.destroyBuffer(gpuO); device.freeMemory(memO);
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device.destroyBuffer(gpuE); device.freeMemory(memE);
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device.destroyCommandPool(cmdPool);
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device.destroy();
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instance.destroy();
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}
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}
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// --- ИНИЦИАЛИЗАЦИЯ РЕСУРСОВ GPU ---
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void NeuralNetwork::initVulkanResources() {
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if (!useVulkan || vulkanResourcesInitialized) return;
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size_t wSize = 0, bSize = 0, oSize = 0;
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for (int i = 0; i < numLayers - 1; i++) {
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wSize += (size_t)sizes[i] * sizes[i+1];
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bSize += (size_t)sizes[i+1];
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}
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for (int s : sizes) oSize += s;
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auto createBuf = [&](size_t size, vk::Buffer& buf, vk::DeviceMemory& mem) {
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buf = device.createBuffer({{}, size * sizeof(float), vk::BufferUsageFlagBits::eStorageBuffer});
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vk::MemoryRequirements req = device.getBufferMemoryRequirements(buf);
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mem = device.allocateMemory({req.size, findMemoryType(req.memoryTypeBits, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent)});
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device.bindBufferMemory(buf, mem, 0);
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};
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createBuf(wSize, gpuW, memW);
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createBuf(bSize, gpuB, memB);
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createBuf(oSize, gpuO, memO);
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createBuf(oSize, gpuE, memE);
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std::vector<vk::DescriptorSetLayoutBinding> bindings = {
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{0, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute},
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{1, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute},
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{2, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute},
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{3, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute}
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};
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dsLayout = device.createDescriptorSetLayout({{}, (uint32_t)bindings.size(), bindings.data()});
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vk::DescriptorPoolSize poolSize(vk::DescriptorType::eStorageBuffer, 4);
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descriptorPool = device.createDescriptorPool({{}, 1, 1, &poolSize});
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descriptorSet = device.allocateDescriptorSets({descriptorPool, 1, &dsLayout})[0];
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vk::DescriptorBufferInfo bW(gpuW, 0, VK_WHOLE_SIZE), bB(gpuB, 0, VK_WHOLE_SIZE), bO(gpuO, 0, VK_WHOLE_SIZE), bE(gpuE, 0, VK_WHOLE_SIZE);
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device.updateDescriptorSets({{descriptorSet, 0, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &bW},
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{descriptorSet, 1, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &bB},
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{descriptorSet, 2, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &bO},
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{descriptorSet, 3, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &bE}}, {});
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auto shaderCode = readFile("Xenith/shader.comp.spv");
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shaderModule = device.createShaderModule({{}, shaderCode.size(), (uint32_t*)shaderCode.data()});
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vk::PushConstantRange pushRange(vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams));
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pipeLayout = device.createPipelineLayout({{}, 1, &dsLayout, 1, &pushRange});
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vk::PipelineShaderStageCreateInfo stageInfo({}, vk::ShaderStageFlagBits::eCompute, shaderModule, "main");
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pipeline = device.createComputePipeline(nullptr, {{}, stageInfo, pipeLayout}).value;
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vulkanResourcesInitialized = true;
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}
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// --- ОБУЧЕНИЕ VULKAN ---
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double NeuralNetwork::trainVulkan(const std::vector<double>& input, const std::vector<double>& target, double lr) {
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if (!useVulkan) return train(input, target, lr);
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if (!vulkanResourcesInitialized) initVulkanResources();
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std::vector<double> pred = feedForward(input);
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std::vector<std::vector<double>> errors(numLayers);
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errors[numLayers - 1].resize(sizes[numLayers - 1]);
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double totalErr = 0;
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for (int i = 0; i < sizes[numLayers - 1]; i++) {
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double e = target[i] - pred[i];
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errors[numLayers - 1][i] = e * pred[i] * (1.0 - pred[i]);
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totalErr += e * e;
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}
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for (int i = numLayers - 2; i > 0; i--) {
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errors[i].resize(sizes[i]);
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for (int j = 0; j < sizes[i]; j++) {
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double e = 0;
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for (int k = 0; k < sizes[i + 1]; k++) e += errors[i + 1][k] * weights[i][k][j];
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errors[i][j] = e * outputs[i][j] * (1.0 - outputs[i][j]);
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}
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}
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std::vector<float> fW, fB, fO, fE;
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std::vector<uint32_t> wOff, bOff, oOff;
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for (int i = 0; i < numLayers - 1; i++) {
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wOff.push_back(fW.size());
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for (auto& row : weights[i]) for (double v : row) fW.push_back((float)v);
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bOff.push_back(fB.size());
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for (double v : biases[i]) fB.push_back((float)v);
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}
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for (int i = 0; i < numLayers; i++) {
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oOff.push_back(fO.size());
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for (double v : outputs[i]) fO.push_back((float)v);
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for (double v : errors[i]) fE.push_back((float)v);
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}
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auto upload = [&](vk::DeviceMemory mem, void* data, size_t size) {
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if (size == 0) return;
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void* mapped = device.mapMemory(mem, 0, size);
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memcpy(mapped, data, size);
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device.unmapMemory(mem);
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};
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upload(memW, fW.data(), fW.size() * sizeof(float));
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upload(memB, fB.data(), fB.size() * sizeof(float));
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upload(memO, fO.data(), fO.size() * sizeof(float));
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upload(memE, fE.data(), fE.size() * sizeof(float));
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vk::CommandBufferAllocateInfo allocInfo(cmdPool, vk::CommandBufferLevel::ePrimary, 1);
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vk::CommandBuffer cmd = device.allocateCommandBuffers(allocInfo)[0];
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cmd.begin({vk::CommandBufferUsageFlagBits::eOneTimeSubmit});
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cmd.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline);
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cmd.bindDescriptorSets(vk::PipelineBindPoint::eCompute, pipeLayout, 0, {descriptorSet}, {});
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for (int i = 0; i < numLayers - 1; i++) {
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TrainParams p = {(uint32_t)sizes[i], (uint32_t)sizes[i+1], wOff[i], bOff[i], oOff[i], oOff[i+1], (float)lr};
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cmd.pushConstants(pipeLayout, vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams), &p);
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cmd.dispatch((sizes[i+1] + 255) / 256, 1, 1);
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}
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cmd.end();
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queue.submit(vk::SubmitInfo(0, nullptr, nullptr, 1, &cmd), nullptr);
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queue.waitIdle();
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device.freeCommandBuffers(cmdPool, cmd);
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void* wPtr = device.mapMemory(memW, 0, fW.size() * sizeof(float));
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memcpy(fW.data(), wPtr, fW.size() * sizeof(float));
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device.unmapMemory(memW);
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void* bPtr = device.mapMemory(memB, 0, fB.size() * sizeof(float));
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memcpy(fB.data(), bPtr, fB.size() * sizeof(float));
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device.unmapMemory(memB);
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int wi = 0, bi = 0;
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for (int i = 0; i < numLayers - 1; i++) {
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for (int j = 0; j < sizes[i+1]; j++) {
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for (int k = 0; k < sizes[i]; k++) weights[i][j][k] = fW[wi++];
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biases[i][j] = fB[bi++];
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}
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}
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return totalErr;
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}
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// --- ВСПОМОГАТЕЛЬНЫЕ ФУНКЦИИ ---
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std::vector<double> NeuralNetwork::feedForward(const std::vector<double>& input) {
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outputs.clear();
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outputs.push_back(input);
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std::vector<double> curr = input;
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for (int i = 0; i < numLayers - 1; i++) {
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std::vector<double> next;
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for (int j = 0; j < sizes[i+1]; j++) {
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@@ -86,170 +244,48 @@ std::vector<double> NeuralNetwork::feedForward(const std::vector<double>& input)
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return curr;
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}
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double NeuralNetwork::train(const std::vector<double>& input, const std::vector<double>& target, double lr) {
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omp_set_num_threads(cpu_count);
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std::vector<double> pred = feedForward(input);
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std::vector<std::vector<double>> errors(numLayers);
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errors[numLayers - 1].resize(sizes[numLayers - 1]);
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double totalErr = 0;
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for (int i = 0; i < sizes[numLayers - 1]; i++) {
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double e = target[i] - pred[i];
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errors[numLayers - 1][i] = e * pred[i] * (1.0 - pred[i]);
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totalErr += e * e;
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}
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for (int i = numLayers - 2; i > 0; i--) {
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errors[i].resize(sizes[i]);
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#pragma omp parallel for
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for (int j = 0; j < sizes[i]; j++) {
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double e = 0;
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for (int k = 0; k < sizes[i + 1]; k++) {
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e += errors[i + 1][k] * weights[i][k][j];
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}
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for (int k = 0; k < sizes[i + 1]; k++) e += errors[i + 1][k] * weights[i][k][j];
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errors[i][j] = e * outputs[i][j] * (1.0 - outputs[i][j]);
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}
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}
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for (int i = 0; i < numLayers - 1; i++) {
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#pragma omp parallel for
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for (int j = 0; j < sizes[i + 1]; j++) {
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double errorTerm = lr * errors[i + 1][j];
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// Вложенный цикл обновления весов
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for (int k = 0; k < sizes[i]; k++) {
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weights[i][j][k] += errorTerm * outputs[i][k];
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}
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biases[i][j] += errorTerm;
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double errT = lr * errors[i + 1][j];
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for (int k = 0; k < sizes[i]; k++) weights[i][j][k] += errT * outputs[i][k];
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biases[i][j] += errT;
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}
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}
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return totalErr;
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}
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uint32_t NeuralNetwork::findMemoryType(uint32_t typeFilter, vk::MemoryPropertyFlags properties) {
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vk::PhysicalDeviceMemoryProperties memProperties = physDev.getMemoryProperties();
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for (uint32_t i = 0; i < memProperties.memoryTypeCount; i++) {
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if ((typeFilter & (1 << i)) && (memProperties.memoryTypes[i].propertyFlags & properties) == properties) {
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return i;
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}
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if ((typeFilter & (1 << i)) && (memProperties.memoryTypes[i].propertyFlags & properties) == properties) return i;
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}
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throw std::runtime_error("Не удалось найти подходящий тип памяти!");
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throw std::runtime_error("Память не найдена");
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}
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// Внутри класса NeuralNetwork в секции private:
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std::vector<char> NeuralNetwork::readFile(const std::string& filename) {
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std::ifstream file(filename, std::ios::ate | std::ios::binary);
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if (!file.is_open()) {
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throw std::runtime_error("Не удалось открыть файл шейдера: " + filename);
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}
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if (!file.is_open()) throw std::runtime_error("Файл не найден: " + filename);
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size_t fileSize = (size_t)file.tellg();
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std::vector<char> buffer(fileSize);
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file.seekg(0);
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file.read(buffer.data(), fileSize);
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file.close();
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return buffer;
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}
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double NeuralNetwork::trainVulkan() {
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// 1. Создание буферов
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vk::Buffer inputBuffer = device.createBuffer({{}, sizeof(float) * 2, vk::BufferUsageFlagBits::eStorageBuffer});
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vk::Buffer outputBuffer = device.createBuffer({{}, sizeof(float), vk::BufferUsageFlagBits::eStorageBuffer});
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// 2. Выделение и привязка памяти для ВХОДА
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vk::MemoryRequirements inReq = device.getBufferMemoryRequirements(inputBuffer);
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vk::DeviceMemory inputMemory = device.allocateMemory({
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inReq.size,
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findMemoryType(inReq.memoryTypeBits, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent)
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});
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device.bindBufferMemory(inputBuffer, inputMemory, 0); // КРИТИЧНО: привязываем память к буферу
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// 3. Копирование данных во входной буфер
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float inputData[2] = {2.51f, 2.32f};
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void* pIn = device.mapMemory(inputMemory, 0, sizeof(float) * 2);
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memcpy(pIn, inputData, sizeof(float) * 2);
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device.unmapMemory(inputMemory);
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// 4. Выделение и привязка памяти для ВЫХОДА
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vk::MemoryRequirements outReq = device.getBufferMemoryRequirements(outputBuffer);
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vk::DeviceMemory outputMemory = device.allocateMemory({
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outReq.size,
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findMemoryType(outReq.memoryTypeBits, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent)
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});
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device.bindBufferMemory(outputBuffer, outputMemory, 0);
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|
||||
// 5. ДЕСКРИПТОРЫ (Связь C++ -> Шейдер)
|
||||
// Описываем, что у нас есть 2 слота (binding 0 и 1)
|
||||
std::vector<vk::DescriptorSetLayoutBinding> bindings = {
|
||||
{0, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute},
|
||||
{1, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute}
|
||||
};
|
||||
vk::DescriptorSetLayout dsLayout = device.createDescriptorSetLayout({{}, (uint32_t)bindings.size(), bindings.data()});
|
||||
|
||||
// Создаем пул и выделяем сет дескрипторов
|
||||
vk::DescriptorPoolSize poolSize{vk::DescriptorType::eStorageBuffer, 2};
|
||||
vk::DescriptorPool pool = device.createDescriptorPool({{}, 1, 1, &poolSize});
|
||||
vk::DescriptorSet ds = device.allocateDescriptorSets({pool, 1, &dsLayout})[0];
|
||||
|
||||
// Указываем, какие именно буферы в какие слоты вставить
|
||||
vk::DescriptorBufferInfo bInInfo{inputBuffer, 0, VK_WHOLE_SIZE};
|
||||
vk::DescriptorBufferInfo bOutInfo{outputBuffer, 0, VK_WHOLE_SIZE};
|
||||
device.updateDescriptorSets({
|
||||
{ds, 0, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &bInInfo},
|
||||
{ds, 1, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &bOutInfo}
|
||||
}, {});
|
||||
|
||||
// 6. ПАЙПЛАЙН (Загрузка шейдера)
|
||||
auto shaderCode = readFile("Xenith/shader.comp.spv"); // Твоя функция чтения файла
|
||||
vk::ShaderModule shaderModule = device.createShaderModule({{}, shaderCode.size(), (uint32_t*)shaderCode.data()});
|
||||
vk::PipelineLayout pipeLayout = device.createPipelineLayout({{}, 1, &dsLayout});
|
||||
|
||||
vk::ComputePipelineCreateInfo pipeInfo{{}, {{}, vk::ShaderStageFlagBits::eCompute, shaderModule, "main"}, pipeLayout};
|
||||
vk::Pipeline pipeline = device.createComputePipeline(nullptr, pipeInfo).value;
|
||||
|
||||
// 7. КОМАНДЫ И ЗАПУСК (Command Buffer)
|
||||
// (Предполагаем, что cmdPool и queue уже созданы в классе)
|
||||
vk::CommandBufferAllocateInfo cmdAllocInfo(cmdPool, vk::CommandBufferLevel::ePrimary, 1);
|
||||
vk::CommandBuffer cmd = device.allocateCommandBuffers(cmdAllocInfo)[0];
|
||||
|
||||
cmd.begin({vk::CommandBufferUsageFlagBits::eOneTimeSubmit});
|
||||
cmd.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline);
|
||||
cmd.bindDescriptorSets(vk::PipelineBindPoint::eCompute, pipeLayout, 0, {ds}, {});
|
||||
cmd.dispatch(1, 1, 1); // Запускаем 1 поток
|
||||
cmd.end();
|
||||
|
||||
queue.submit(vk::SubmitInfo(0, nullptr, nullptr, 1, &cmd), nullptr);
|
||||
queue.waitIdle();
|
||||
|
||||
// 8. ЗАБИРАЕМ РЕЗУЛЬТАТ
|
||||
float result = 0;
|
||||
void* pOut = device.mapMemory(outputMemory, 0, sizeof(float));
|
||||
memcpy(&result, pOut, sizeof(float));
|
||||
device.unmapMemory(outputMemory);
|
||||
|
||||
// Очистка (в реальном коде лучше делать в деструкторе)
|
||||
device.destroyPipeline(pipeline);
|
||||
device.destroyPipelineLayout(pipeLayout);
|
||||
device.destroyShaderModule(shaderModule);
|
||||
device.destroyDescriptorPool(pool);
|
||||
device.destroyDescriptorSetLayout(dsLayout);
|
||||
device.destroyBuffer(inputBuffer); device.freeMemory(inputMemory);
|
||||
device.destroyBuffer(outputBuffer); device.freeMemory(outputMemory);
|
||||
|
||||
return (double)result;
|
||||
}
|
||||
|
||||
NeuralNetwork::~NeuralNetwork() {
|
||||
// Здесь позже мы добавим удаление vkInstance, vkDevice и прочего,
|
||||
// чтобы не было утечек памяти на видеокарте.
|
||||
}
|
||||
}
|
||||
+25
-2
@@ -36,6 +36,30 @@ private:
|
||||
double sigmoid(double x) { return 1.0 / (1.0 + exp(-x)); }
|
||||
double sigmoidDeriv(double x) { return x * (1.0 - x); }
|
||||
|
||||
struct TrainParams {
|
||||
uint32_t prevLayerSize;
|
||||
uint32_t nextLayerSize;
|
||||
uint32_t weightOffset;
|
||||
uint32_t biasOffset;
|
||||
uint32_t outOffset;
|
||||
uint32_t errOffset;
|
||||
float lr;
|
||||
};
|
||||
|
||||
vk::Buffer gpuW, gpuB, gpuO, gpuE;
|
||||
vk::DeviceMemory memW, memB, memO, memE;
|
||||
vk::DescriptorPool descriptorPool;
|
||||
vk::DescriptorSet descriptorSet;
|
||||
vk::DescriptorSetLayout dsLayout;
|
||||
vk::PipelineLayout pipeLayout;
|
||||
vk::Pipeline pipeline;
|
||||
vk::ShaderModule shaderModule;
|
||||
|
||||
bool vulkanResourcesInitialized = false;
|
||||
|
||||
void initVulkanResources(); // Метод для разовой инициализации
|
||||
void cleanupVulkanResources();
|
||||
|
||||
public:
|
||||
int cpu_count = 1;
|
||||
|
||||
@@ -49,8 +73,7 @@ public:
|
||||
std::vector<double> feedForward(const std::vector<double>& input);
|
||||
double train(const std::vector<double>& input, const std::vector<double>& target, double lr);
|
||||
|
||||
// Наш тест Vulkan
|
||||
double trainVulkan();
|
||||
double trainVulkan(const std::vector<double>& input, const std::vector<double>& target, double lr);
|
||||
};
|
||||
|
||||
#endif
|
||||
+30
-10
@@ -1,16 +1,36 @@
|
||||
#version 450
|
||||
|
||||
layout(local_size_x = 1) in; // Запускаем 1 поток
|
||||
layout(local_size_x = 256) in;
|
||||
|
||||
layout(std430, binding = 0) buffer InputBuffer {
|
||||
float a;
|
||||
float b;
|
||||
} inputs;
|
||||
layout(std430, binding = 0) buffer WeightBuffer { float weights[]; };
|
||||
layout(std430, binding = 1) buffer BiasBuffer { float biases[]; };
|
||||
layout(std430, binding = 2) buffer OutputBuffer { float outputs[]; };
|
||||
layout(std430, binding = 3) buffer ErrorBuffer { float errors[]; };
|
||||
|
||||
layout(std430, binding = 1) buffer OutputBuffer {
|
||||
float result;
|
||||
} outputs;
|
||||
layout(push_constant) uniform Params {
|
||||
uint prevLayerSize;
|
||||
uint nextLayerSize;
|
||||
uint weightOffset;
|
||||
uint biasOffset;
|
||||
uint outOffset;
|
||||
uint errOffset;
|
||||
float lr;
|
||||
} p;
|
||||
|
||||
void main() {
|
||||
outputs.result = inputs.a * inputs.b;
|
||||
}
|
||||
uint j = gl_GlobalInvocationID.x; // Индекс нейрона следующего слоя
|
||||
|
||||
if (j < p.nextLayerSize) {
|
||||
float errorTerm = p.lr * errors[p.errOffset + j];
|
||||
|
||||
// Обновляем веса, входящие в этот нейрон
|
||||
for (uint k = 0; k < p.prevLayerSize; k++) {
|
||||
uint wIdx = p.weightOffset + (j * p.prevLayerSize + k);
|
||||
uint outIdx = p.outOffset + k;
|
||||
weights[wIdx] += errorTerm * outputs[outIdx];
|
||||
}
|
||||
|
||||
// Обновляем биас этого нейрона
|
||||
biases[p.biasOffset + j] += errorTerm;
|
||||
}
|
||||
}
|
||||
Binary file not shown.
Reference in New Issue
Block a user