aDPtorch/src/federated-hierarchical2_main_fp16.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6

import os
import copy
import time
import pickle
import numpy as np
from tqdm import tqdm

import torch
from tensorboardX import SummaryWriter

from options import args_parser
from update import LocalUpdate, test_inference
from models import MLP, CNNMnist, CNNFashion_Mnist, CNNCifar
from utils import get_dataset, average_weights, exp_details, set_device, build_model, fl_train
import math
import random


if __name__ == '__main__':
    start_time = time.time()

    # define paths
    path_project = os.path.abspath('..')
    logger = SummaryWriter('../logs')

    args = args_parser()
    exp_details(args)

    # Select CPU or GPU
    device = set_device(args)

    # load dataset and user groups
    train_dataset, test_dataset, user_groupsold = get_dataset(args)

    # user_groups = user_groupsold
    # keylist = list(user_groups.keys())
    # ======= Shuffle dataset ======= 
    keys =  list(user_groupsold.keys())
    random.shuffle(keys)
    user_groups = dict()
    for key in keys:
        user_groups.update({key:user_groupsold[key]})
    # print(user_groups.keys()) 
    keylist = list(user_groups.keys())
    print("keylist: ", keylist)
    # ======= Splitting into clusters. FL groups ======= 
    if args.num_clusters != 2:
        exit("Confirm that the number of clusters is 2?")
    cluster_size = int(args.num_users / args.num_clusters)

    # Cluster 1
    # A1 = np.arange(cluster_size, dtaype=int)
    A1 = keylist[:cluster_size]
    # A1 = np.random.choice(keylist, cluster_size, replace=False)
    print("A1: ", A1)
    user_groupsA = {k:user_groups[k] for k in A1 if k in user_groups}
    print("Size of cluster 1: ", len(user_groupsA))
    # Cluster 2
    # B1 = np.arange(cluster_size, cluster_size+cluster_size, dtype=int)
    B1 = keylist[cluster_size:2*cluster_size]
    # B1 = np.random.choice(keylist, cluster_size, replace=False)
    print("B1: ", B1)
    user_groupsB = {k:user_groups[k] for k in B1 if k in user_groups}
    print("Size of cluster 2: ", len(user_groupsB))

    # MODEL PARAM SUMMARY
    global_model = build_model(args, train_dataset)
    pytorch_total_params = sum(p.numel() for p in global_model.parameters())
    print("Model total number of parameters: ", pytorch_total_params)

    # from torchsummary import summary
    # summary(global_model, (1, 28, 28))
    # global_model.parameters()

    # Set the model to train and send it to device.
    global_model.to(device)
    # Set model to use Floating Point 16
    global_model.to(dtype=torch.float16)  ##########################
    global_model.train()
    print(global_model)

    # copy weights
    global_weights = global_model.state_dict()


    # ======= Set the cluster models to train and send it to device. =======
    # Cluster A
    cluster_modelA = build_model(args, train_dataset)
    cluster_modelA.to(device)
    cluster_modelA.to(dtype=torch.float16)    ######################
    cluster_modelA.train()
    # copy weights
    cluster_modelA_weights = cluster_modelA.state_dict()
    
    # Cluster B
    cluster_modelB = build_model(args, train_dataset)
    cluster_modelB.to(device)
    cluster_modelB.to(dtype=torch.float16)    ######################
    cluster_modelB.train()
    # copy weights
    cluster_modelB_weights = cluster_modelB.state_dict()


    train_loss, train_accuracy = [], []
    val_acc_list, net_list = [], []
    cv_loss, cv_acc = [], []
    print_every = 1
    val_loss_pre, counter = 0, 0
    testacc_check, epoch = 0, 0 
    # idx = np.random.randint(0,99)

    # for epoch in tqdm(range(args.epochs)):
    for epoch in range(args.epochs):
    # while testacc_check < args.test_acc or epoch < args.epochs:
    # while epoch < args.epochs: 
        local_weights, local_losses, local_accuracies= [], [], []
        print(f'\n | Global Training Round : {epoch+1} |\n')
        
        # ============== TRAIN ==============
        global_model.train()
        
        # ===== Cluster A ===== 
        A_model, A_weights, A_losses = fl_train(args, train_dataset, cluster_modelA, A1, user_groupsA, args.Cepochs, logger, cluster_dtype=torch.float16)        
        local_weights.append(copy.deepcopy(A_weights))
        local_losses.append(copy.deepcopy(A_losses))    
        cluster_modelA = global_model# = A_model    
        # ===== Cluster B ===== 
        B_model, B_weights, B_losses = fl_train(args, train_dataset, cluster_modelB, B1, user_groupsB, args.Cepochs, logger, cluster_dtype=torch.float16)
        local_weights.append(copy.deepcopy(B_weights))
        local_losses.append(copy.deepcopy(B_losses))
        cluster_modelB = global_model# = B_model 
        
        
        # averaging global weights
        global_weights = average_weights(local_weights)

        # update global weights
        global_model.load_state_dict(global_weights)

        loss_avg = sum(local_losses) / len(local_losses)
        train_loss.append(loss_avg)
        
        # ============== EVAL ============== 
        # Calculate avg training accuracy over all users at every epoch
        list_acc, list_loss = [], []
        global_model.eval()
        # print("========== idx ========== ", idx)
        for c in range(args.num_users):
        # for c in range(cluster_size):
        # C = np.random.choice(keylist, int(args.frac * args.num_users), replace=False) # random set of clients
        # print("C: ", C)
        # for c in C:
            local_model = LocalUpdate(args=args, dataset=train_dataset,
                                      idxs=user_groups[c], logger=logger)
            acc, loss = local_model.inference(model=global_model, dtype=torch.float16)
            list_acc.append(acc)
            list_loss.append(loss)
        train_accuracy.append(sum(list_acc)/len(list_acc))
        # Add
        testacc_check = 100*train_accuracy[-1]
        epoch = epoch + 1

        # print global training loss after every 'i' rounds
        if (epoch+1) % print_every == 0:
            print(f' \nAvg Training Stats after {epoch+1} global rounds:')
            print(f'Training Loss : {np.mean(np.array(train_loss))}')
            print('Train Accuracy: {:.2f}% \n'.format(100*train_accuracy[-1]))
            

    print('\n Total Run Time: {0:0.4f}'.format(time.time()-start_time))

    # Test inference after completion of training
    test_acc, test_loss = test_inference(args, global_model, test_dataset, dtype=torch.float16)

    # print(f' \n Results after {args.epochs} global rounds of training:')
    print(f"\nAvg Training Stats after {epoch} global rounds:")
    print("|---- Avg Train Accuracy: {:.2f}%".format(100*train_accuracy[-1]))
    print("|---- Test Accuracy: {:.2f}%".format(100*test_acc))

    # Saving the objects train_loss and train_accuracy:
    file_name = '../save/objects_fp16/HFL2_{}_{}_{}_lr[{}]_C[{}]_iid[{}]_E[{}]_B[{}]_FP16.pkl'.\
    format(args.dataset, args.model, epoch, args.lr, args.frac, args.iid,
           args.local_ep, args.local_bs)

    with open(file_name, 'wb') as f:
        pickle.dump([train_loss, train_accuracy], f)
        
    print('\n Total Run Time: {0:0.4f}'.format(time.time()-start_time))