339 lines
13 KiB
C++
339 lines
13 KiB
C++
#include "core.hpp"
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#include <cmath>
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#include <iostream>
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#include <fstream>
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#include <cstring>
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int NeuralNetwork::calculateTotalInputSize(int layerIdx) const {
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int total = 0;
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for (size_t i = 0; i < layerSources[layerIdx].size(); i++) {
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int srcIdx = layerSources[layerIdx][i];
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total += sizes[srcIdx];
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}
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return total;
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}
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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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uint32_t curW = 0, curB = 0, curO = 0;
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// 1. Индексация нейронов и веток
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for (int i = 0; i < count; i++) {
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sizes.push_back(layers[i].size);
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layerSources.push_back(layers[i].sources);
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layerSourceBranches.push_back(layers[i].sourceBranches);
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branches.push_back(layers[i].branch);
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splits.push_back(layers[i].isSplit);
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oOff.push_back(curO);
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curO += layers[i].size;
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}
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// 2. Инициализация весов
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for (int i = 0; i < count; i++) {
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if (layerSources[i].empty()) {
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wOff.push_back(0);
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bOff.push_back(0);
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continue;
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}
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wOff.push_back(curW);
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bOff.push_back(curB);
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int totalInSize = calculateTotalInputSize(i);
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int wCount = totalInSize * sizes[i];
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float scale = sqrt(2.0f / totalInSize);
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for (int j = 0; j < wCount; j++) {
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h_weights.push_back(((float)rand() / RAND_MAX * 2.0f - 1.0f) * scale);
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}
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for (int j = 0; j < sizes[i]; j++) h_biases.push_back(0.0f);
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curW += wCount;
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curB += sizes[i];
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}
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h_outputs.resize(curO, 0.0f);
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h_errors.resize(curO, 0.0f);
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if (this->useVulkan) {
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initVulkan();
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initVulkanResources();
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syncToGPU();
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}
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}
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double NeuralNetwork::train(const std::map<int, std::vector<double>>& inputs,
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const std::vector<double>& target, double lr) {
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if (!useVulkan) return 0.0;
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// Подготовка входных данных
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for (auto const& [layerIdx, data] : inputs) {
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if (layerIdx >= 0 && layerIdx < numLayers) {
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float* ptr = (float*)pO + oOff[layerIdx];
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size_t copySize = std::min(data.size(), (size_t)sizes[layerIdx]);
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for (size_t i = 0; i < copySize; i++) ptr[i] = (float)data[i];
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}
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}
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// Подготовка target
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float* fTar = (float*)pT;
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for (size_t i = 0; i < target.size(); i++) fTar[i] = (float)target[i];
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// Получаем command buffer из пула
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if (cmdBuffers.empty()) {
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vk::CommandBufferAllocateInfo ai(cmdPool, vk::CommandBufferLevel::ePrimary, 4);
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cmdBuffers = device.allocateCommandBuffers(ai);
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}
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vk::CommandBuffer cmd = cmdBuffers[currentCmdBuffer];
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currentCmdBuffer = (currentCmdBuffer + 1) % cmdBuffers.size();
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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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vk::MemoryBarrier barrier(vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eShaderRead,
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vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eShaderRead);
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// 1. FORWARD PASS
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for (int i = 0; i < numLayers; i++) {
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if (layerSources[i].empty()) continue;
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int totalIn = calculateTotalInputSize(i);
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int firstSrc = layerSources[i][0];
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TrainParams p = {0, (uint32_t)totalIn, (uint32_t)sizes[i], wOff[i], bOff[i],
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oOff[firstSrc], oOff[i], (float)lr};
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cmd.pushConstants(pipeLayout, vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams), &p);
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cmd.dispatch((sizes[i] + 255) / 256, 1, 1);
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cmd.pipelineBarrier(vk::PipelineStageFlagBits::eComputeShader,
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vk::PipelineStageFlagBits::eComputeShader, {}, {barrier}, {}, {});
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}
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// 2. OUTPUT ERROR
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{
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TrainParams p = {1, 0, (uint32_t)sizes.back(), 0, 0, 0, oOff.back(), (float)lr};
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cmd.pushConstants(pipeLayout, vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams), &p);
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cmd.dispatch((sizes.back() + 255) / 256, 1, 1);
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cmd.pipelineBarrier(vk::PipelineStageFlagBits::eComputeShader,
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vk::PipelineStageFlagBits::eComputeShader, {}, {barrier}, {}, {});
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}
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// 3. BACKWARD PASS
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for (int i = numLayers - 1; i >= 0; i--) {
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if (layerSources[i].empty()) continue;
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int totalIn = calculateTotalInputSize(i);
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int firstSrc = layerSources[i][0];
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TrainParams p = {2, (uint32_t)totalIn, (uint32_t)sizes[i], wOff[i], bOff[i],
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oOff[firstSrc], oOff[i], (float)lr};
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cmd.pushConstants(pipeLayout, vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams), &p);
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cmd.dispatch((totalIn + 255) / 256, 1, 1);
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cmd.pipelineBarrier(vk::PipelineStageFlagBits::eComputeShader,
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vk::PipelineStageFlagBits::eComputeShader, {}, {barrier}, {}, {});
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}
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// 4. UPDATE WEIGHTS
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for (int i = 0; i < numLayers; i++) {
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if (layerSources[i].empty()) continue;
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int totalIn = calculateTotalInputSize(i);
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int firstSrc = layerSources[i][0];
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TrainParams p = {3, (uint32_t)totalIn, (uint32_t)sizes[i], wOff[i], bOff[i],
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oOff[firstSrc], oOff[i], (float)lr};
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cmd.pushConstants(pipeLayout, vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams), &p);
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cmd.dispatch((sizes[i] + 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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// Расчет MSE
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float* out = (float*)pO + oOff.back();
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double mse = 0;
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for (int i = 0; i < sizes.back(); i++) {
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double d = (double)target[i] - (double)out[i];
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mse += d * d;
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}
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return mse / sizes.back();
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}
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std::vector<double> NeuralNetwork::feedForward(const std::map<int, std::vector<double>>& inputs) {
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if (!useVulkan) return {};
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for (auto const& [layerIdx, data] : inputs) {
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if (layerIdx >= 0 && layerIdx < numLayers) {
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float* ptr = (float*)pO + oOff[layerIdx];
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size_t copySize = std::min(data.size(), (size_t)sizes[layerIdx]);
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memcpy(ptr, data.data(), copySize * sizeof(float));
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}
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}
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if (cmdBuffers.empty()) {
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vk::CommandBufferAllocateInfo ai(cmdPool, vk::CommandBufferLevel::ePrimary, 4);
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cmdBuffers = device.allocateCommandBuffers(ai);
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}
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vk::CommandBuffer cmd = cmdBuffers[currentCmdBuffer];
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currentCmdBuffer = (currentCmdBuffer + 1) % cmdBuffers.size();
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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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vk::MemoryBarrier barrier(vk::AccessFlagBits::eShaderWrite, vk::AccessFlagBits::eShaderRead);
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for (int i = 0; i < numLayers; i++) {
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if (layerSources[i].empty()) continue;
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int totalIn = calculateTotalInputSize(i);
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int firstSrc = layerSources[i][0];
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TrainParams p = {0, (uint32_t)totalIn, (uint32_t)sizes[i], wOff[i], bOff[i],
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oOff[firstSrc], oOff[i], 0.0f};
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cmd.pushConstants(pipeLayout, vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams), &p);
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cmd.dispatch((sizes[i] + 255) / 256, 1, 1);
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cmd.pipelineBarrier(vk::PipelineStageFlagBits::eComputeShader,
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vk::PipelineStageFlagBits::eComputeShader, {}, {barrier}, {}, {});
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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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std::vector<double> result(sizes.back());
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float* out = (float*)pO + oOff.back();
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for (int i = 0; i < sizes.back(); i++) result[i] = (double)out[i];
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return result;
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}
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void NeuralNetwork::initVulkan() {
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vk::ApplicationInfo app{"Xenith", 1, nullptr, 0, VK_API_VERSION_1_1};
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instance = vk::createInstance({{}, &app});
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physDev = instance.enumeratePhysicalDevices()[0];
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auto props = physDev.getQueueFamilyProperties();
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int qIdx = -1;
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for (int i = 0; i < props.size(); i++)
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if (props[i].queueFlags & vk::QueueFlagBits::eCompute) {
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qIdx = i;
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break;
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}
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float priority = 1.0f;
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device = physDev.createDevice({{}, 1, new vk::DeviceQueueCreateInfo({}, (uint32_t)qIdx, 1, &priority)});
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queue = device.getQueue(qIdx, 0);
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cmdPool = device.createCommandPool({{}, (uint32_t)qIdx});
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}
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void NeuralNetwork::initVulkanResources() {
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auto createBuf = [&](size_t sz, vk::Buffer& b, vk::DeviceMemory& m, void** ptr) {
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if (sz == 0) sz = 1;
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b = device.createBuffer({{}, sz * sizeof(float), vk::BufferUsageFlagBits::eStorageBuffer});
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auto req = device.getBufferMemoryRequirements(b);
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m = device.allocateMemory({req.size, findMemoryType(req.memoryTypeBits,
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vk::MemoryPropertyFlagBits::eHostVisible |
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vk::MemoryPropertyFlagBits::eHostCoherent)});
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device.bindBufferMemory(b, m, 0);
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*ptr = device.mapMemory(m, 0, sz * sizeof(float));
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};
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createBuf(h_weights.size(), gpuW, memW, &pW);
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createBuf(h_biases.size(), gpuB, memB, &pB);
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createBuf(h_outputs.size(), gpuO, memO, &pO);
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createBuf(h_errors.size(), gpuE, memE, &pE);
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createBuf(sizes.back(), gpuT, memT, &pT);
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vk::DescriptorSetLayoutBinding binds[] = {
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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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{4, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute}
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};
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dsLayout = device.createDescriptorSetLayout({{}, 5, binds});
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vk::DescriptorPoolSize ps(vk::DescriptorType::eStorageBuffer, 5);
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descriptorPool = device.createDescriptorPool({{}, 1, 1, &ps});
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descriptorSet = device.allocateDescriptorSets({descriptorPool, 1, &dsLayout})[0];
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vk::DescriptorBufferInfo db[] = {
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{gpuW,0,VK_WHOLE_SIZE}, {gpuB,0,VK_WHOLE_SIZE}, {gpuO,0,VK_WHOLE_SIZE},
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{gpuE,0,VK_WHOLE_SIZE}, {gpuT,0,VK_WHOLE_SIZE}
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};
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vk::WriteDescriptorSet wds[] = {
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{descriptorSet,0,0,1,vk::DescriptorType::eStorageBuffer,nullptr,db+0},
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{descriptorSet,1,0,1,vk::DescriptorType::eStorageBuffer,nullptr,db+1},
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{descriptorSet,2,0,1,vk::DescriptorType::eStorageBuffer,nullptr,db+2},
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{descriptorSet,3,0,1,vk::DescriptorType::eStorageBuffer,nullptr,db+3},
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{descriptorSet,4,0,1,vk::DescriptorType::eStorageBuffer,nullptr,db+4}
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};
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device.updateDescriptorSets(5, wds, 0, nullptr);
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auto code = readFile("Xenith/shader.comp.spv");
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if (code.empty()) {
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std::cerr << "ERROR: Failed to load shader.comp.spv!\n";
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exit(1);
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}
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shaderModule = device.createShaderModule({{}, code.size(), (uint32_t*)code.data()});
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vk::PushConstantRange pr(vk::ShaderStageFlagBits::eCompute, 0, sizeof(TrainParams));
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pipeLayout = device.createPipelineLayout({{}, 1, &dsLayout, 1, &pr});
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pipeline = device.createComputePipeline(nullptr, {{}, {{}, vk::ShaderStageFlagBits::eCompute,
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shaderModule, "main"}, pipeLayout}).value;
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}
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uint32_t NeuralNetwork::findMemoryType(uint32_t f, vk::MemoryPropertyFlags p) {
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auto props = physDev.getMemoryProperties();
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for (uint32_t i = 0; i < props.memoryTypeCount; i++)
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if ((f & (1 << i)) && (props.memoryTypes[i].propertyFlags & p) == p) return i;
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return 0;
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}
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std::vector<char> NeuralNetwork::readFile(const std::string& n) {
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std::ifstream f(n, std::ios::ate | std::ios::binary);
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if (!f.is_open()) {
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std::cerr << "ERROR: Cannot open file: " << n << "\n";
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return {};
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}
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size_t s = (size_t)f.tellg();
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std::vector<char> b(s);
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f.seekg(0);
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f.read(b.data(), s);
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return b;
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}
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void NeuralNetwork::syncToCPU() {
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memcpy(h_weights.data(), pW, h_weights.size() * 4);
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memcpy(h_biases.data(), pB, h_biases.size() * 4);
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}
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void NeuralNetwork::syncToGPU() {
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memcpy(pW, h_weights.data(), h_weights.size() * 4);
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memcpy(pB, h_biases.data(), h_biases.size() * 4);
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}
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NeuralNetwork::~NeuralNetwork() {
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if (useVulkan) {
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device.waitIdle();
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if (!cmdBuffers.empty()) {
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device.freeCommandBuffers(cmdPool, cmdBuffers);
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}
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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.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.destroyBuffer(gpuT); device.freeMemory(memT);
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device.destroyDescriptorPool(descriptorPool);
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device.destroyDescriptorSetLayout(dsLayout);
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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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} |