Python 代码实现高性能异构分布式多模态大模型系统
数据预处理模块
该模块负责加载、清洗、转换和准备数据，以便进行模型训练和推理。假设我们处理的是图像和文本数据。
import tensorflow as tf
import cv2
import os
def load_image(image_path):
image = cv2.imread(image_path)
image = cv2.resize(image, (224, 224))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image / 255.0
def load_text(text_path):
with open(text_path, 'r') as file:
text = file.read()
return text
def preprocess_data(image_dir, text_dir):
image_files = [os.path.join(image_dir, f) for f in os.listdir(image_dir)]
text_files = [os.path.join(text_dir, f) for f in os.listdir(text_dir)]
images = [load_image(f) for f in image_files]
texts = [load_text(f) for f in text_files]
return images, texts
模型训练模块
该模块负责分布式模型训练，包括数据并行和模型并行训练方式。
import tensorflow as tf
def build_model():
image_input = tf.keras.layers.Input(shape=(224, 224, 3))
text_input = tf.keras.layers.Input(shape=(100,))
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')(image_input)
x = tf.keras.layers.MaxPooling2D((2, 2))(x)
x = tf.keras.layers.Flatten()(x)
y = tf.keras.layers.Embedding(input_dim=10000, output_dim=64)(text_input)
y = tf.keras.layers.LSTM(64)(y)
combined = tf.keras.layers.concatenate([x, y])
z = tf.keras.layers.Dense(64, activation='relu')(combined)
z = tf.keras.layers.Dense(1, activation='sigmoid')(z)
model = tf.keras.models.Model(inputs=[image_input, text_input], outputs=z)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model
def train_model(images, texts, labels):
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = build_model()
model.fit([images, texts], labels, epochs=10, batch_size=32)
模型推理模块
该模块负责分布式模型推理，处理来自不同节点的推理请求。
import tensorflow as tf
from tensorflow.keras.models import load_model
def load_trained_model(model_path):
return load_model(model_path)
def predict(model, image, text):
return model.predict([image, text])
# TensorFlow Serving configuration can be done through Docker or Kubernetes
分布式通信模块
该模块负责在不同计算节点之间传递数据和控制信息，使用 MPI 或 gRPC 等通信框架。
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
data = None
if rank == 0:
data = {'key1': 1, 'key2': 2}
comm.bcast(data, root=0)
异构计算资源管理模块
该模块负责管理和分配不同类型的计算资源，如 CPU、GPU、TPU。
import tensorflow as tf
import torch
def get_device():
if tf.config.list_physical_devices('GPU'):
return 'GPU'
elif torch.cuda.is_available():
return 'CUDA'
else:
return 'CPU'
device = get_device()
print(f'Using device: {device}')
多模态数据处理模块
该模块负责处理多模态数据的融合和特征提取。
import tensorflow as tf
def multimodal_feature_extraction(image, text):
image_features = tf.keras.applications.ResNet50(include_top=False, input_shape=(224, 224, 3))(image)
text_features = tf.keras.layers.Embedding(input_dim=10000, output_dim=64)(text)
return image_features, text_features
结果汇总与可视化模块
该模块负责汇总各计算节点的结果并进行可视化。
import matplotlib.pyplot as plt
def visualize_results(results):
plt.figure(figsize=(10, 5))
plt.plot(results['epochs'], results['accuracy'], label='Accuracy')
plt.plot(results['epochs'], results['loss'], label='Loss')
plt.xlabel('Epochs')
plt.ylabel('Metrics')
plt.legend()
plt.show()
C++ 代码实现高性能异构分布式多模态大模型系统
数据预处理模块
假设我们处理图像和文本数据，使用 OpenCV 和标准 C++ 库进行数据预处理。
#include <opencv2/opencv.hpp>
#include <fstream>
#include <vector>
#include <string>
std::vector<cv::Mat> loadImages(const std::string& image_dir) {
std::vector<cv::Mat> images;
for (const auto& entry : std::filesystem::directory_iterator(image_dir)) {
cv::Mat image = cv::imread(entry.path().string());
cv::resize(image, image, cv::Size(224, 224));
images.push_back(image);
}
return images;
}
std::vector<std::string> loadTexts(const std::string& text_dir) {
std::vector<std::string> texts;
for (const auto& entry : std::filesystem::directory_iterator(text_dir)) {
std::ifstream file(entry.path().string());
std::string text((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
texts.push_back(text);
}
return texts;
}
模型训练模块
使用 TensorFlow C++ API 进行模型训练。注意，TensorFlow 的 C++ API 需要 TensorFlow 库的支持，并且编译配置比较复杂。
#include <tensorflow/core/public/session.h>
#include <tensorflow/core/protobuf/meta_graph.pb.h>
tensorflow::Session* loadModel(const std::string& model_path) {
tensorflow::Session* session;
tensorflow::Status status = tensorflow::NewSession(tensorflow::SessionOptions(), &session);
if (!status.ok()) {
throw std::runtime_error("Error creating TensorFlow session: " + status.ToString());
}
tensorflow::MetaGraphDef graph_def;
status = tensorflow::ReadBinaryProto(tensorflow::Env::Default(), model_path, &graph_def);
if (!status.ok()) {
throw std::runtime_error("Error loading graph: " + status.ToString());
}
status = session->Create(graph_def.graph_def());
if (!status.ok()) {
throw std::runtime_error("Error adding graph to session: " + status.ToString());
}
return session;
}
void trainModel(tensorflow::Session* session, const std::vector<cv::Mat>& images, const std::vector<std::string>& texts) {
// Implement training logic using TensorFlow session run
// This is a simplified example
for (int i = 0; i < images.size(); ++i) {
// Prepare input tensors and run session
}
}
模型推理模块
使用 TensorFlow Serving 或自定义推理服务。
#include <tensorflow_serving/apis/prediction_service.grpc.pb.h>
#include <grpcpp/grpcpp.h>
void predict(const std::string& server_address, const cv::Mat& image, const std::string& text) {
auto channel = grpc::CreateChannel(server_address, grpc::InsecureChannelCredentials());
std::unique_ptr<tensorflow::serving::PredictionService::Stub> stub(tensorflow::serving::PredictionService::NewStub(channel));
tensorflow::serving::PredictRequest request;
tensorflow::serving::PredictResponse response;
// Fill request with image and text data
// ...
grpc::ClientContext context;
grpc::Status status = stub->Predict(&context, request, &response);
if (!status.ok()) {
throw std::runtime_error("Prediction failed: " + status.error_message());
}
// Process response
}
分布式通信模块
使用 MPI 进行分布式通信。
#include <mpi.h>
#include <vector>
#include <string>
void distributeData(const std::vector<cv::Mat>& images, const std::vector<std::string>& texts) {
int rank;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
if (rank == 0) {
// Master node: distribute data
for (int i = 1; i < size; ++i) {
// Send data to worker nodes
MPI_Send(...);
}
} else {
// Worker node: receive data
MPI_Recv(...);
}
}
异构计算资源管理模块
管理不同类型的计算资源（CPU、GPU）。
#include <tensorflow/core/public/session_options.h>
#include <cuda_runtime.h>
void checkAvailableDevices() {
int nDevices;
cudaGetDeviceCount(&nDevices);
for (int i = 0; i < nDevices; i++) {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, i);
std::cout << "Device Number: " << i << "\n";
std::cout << " Device name: " << prop.name << "\n";
std::cout << " Memory Clock Rate (KHz): " << prop.memoryClockRate << "\n";
std::cout << " Memory Bus Width (bits): " << prop.memoryBusWidth << "\n";
std::cout << " Peak Memory Bandwidth (GB/s): " << 2.0*prop.memoryClockRate*(prop.memoryBusWidth/8)/1.0e6 << "\n";
}
}
多模态数据处理模块
融合多模态数据特征。
#include <tensorflow/core/framework/tensor.h>
#include <tensorflow/core/platform/env.h>
#include <opencv2/opencv.hpp>
tensorflow::Tensor preprocessImage(const cv::Mat& image) {
tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, image.rows, image.cols, image.channels()}));
auto input_tensor_mapped = input_tensor.tensor<float, 4>();
for (int y = 0; y < image.rows; ++y) {
for (int x = 0; x < image.cols; ++x) {
for (int c = 0; c < image.channels(); ++c) {
input_tensor_mapped(0, y, x, c) = image.at<cv::Vec3f>(y, x)[c];
}
}
}
return input_tensor;
}
tensorflow::Tensor preprocessText(const std::string& text) {
// Implement text preprocessing to tensor conversion
}
结果汇总与可视化模块
汇总各节点的结果并进行可视化。
#include <mpi.h>
#include <vector>
#include <iostream>
void gatherResults(const std::vector<double>& local_results) {
int rank, size;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
std::vector<double> global_results(local_results.size() * size);
MPI_Gather(local_results.data(), local_results.size(), MPI_DOUBLE,
global_results.data(), local_results.size(), MPI_DOUBLE,
0, MPI_COMM_WORLD);
if (rank == 0) {
// Master node: process and visualize results
for (const auto& result : global_results) {
std::cout << result << " ";
}
std::cout << std::endl;
}
}
