language_oneshot_matching.py

# part of the code blocks are modified from: https://github.com/litian96/FedProx/blob/master/flearn/models/shakespeare/stacked_lstm.py
# credit goes to: Tian Li (litian96 @ GitHub)
import json
import logging
import numpy as np
import time
import math
import pickle
import copy

import torch
import torch.nn as nn
import torch.optim as optim

from language_utils import *
from language_fedma import layerwise_fedma
from language_fedma import patch_weights

import language_model


logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)


BATCH_SIZE = 50
TRAIN_DATA_DIR = "./datum/leaf/data/shakespeare/data/train/"
TEST_DATA_DIR = "./datum/leaf/data/shakespeare/data/test/"

TRAIN_DATA_NAME = "all_data_niid_0_keep_10000_train_9.json"
TEST_DATA_NAME = "all_data_niid_0_keep_10000_test_9.json"

#TRIAL_USER_NAME = ["THE_FIRST_PART_OF_KING_HENRY_THE_FOURTH_HOT", "KING_RICHARD_THE_SECOND_KING_RICHARD"]

TRIAL_EPOCH=10

# since we used a relatively "fixed" model for shakespeare dataset
# we thus hardcode it here
NUM_LAYERS=3 # we start from 1-layer LSTM now (so the 3 layers now is encoder|hidden LSTM|decoder)

#NUM_LAYERS=4 # 2-layer LSTM (4 layers: encoder|hidden LSTM1|hidden LSTM2|decoder)

def evaluate(data_source):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    total_loss = 0.
    ntokens = len(corpus.dictionary)
    hidden = model.init_hidden(eval_batch_size)
    
    with torch.no_grad():
        for i in range(0, data_source.size(0) - 1, args.bptt):
            data, targets = get_batch(data_source, i)
            if args.model == 'Transformer':
                output = model(data)
            else:
                output, hidden = model(data, hidden)
                hidden = repackage_hidden(hidden)
            output_flat = output.view(-1, ntokens)
            total_loss += len(data) * criterion(output_flat, targets).item()
    return total_loss / (len(data_source) - 1)


def collect_weights(models):
    logger.info("Collecting weights ...")
    collected_weights = []
    for model_index, model in enumerate(models):
        param_vals = []
        for param_index, (name, param) in enumerate(model.named_parameters()):
            logger.info("Layer index: {}, Layer name: {}, Layer shape: {}".format(param_index, name, param.size()))
            param_vals.append(param.cpu().detach().numpy())
        collected_weights.append(param_vals)
    return collected_weights


def fed_avg(weights, factors):
    """
    compute FedAvg
    param::weights locally trained model weights
    param::factors factors determined by the number of data points collected
    """
    fed_avg_wegiths = []
    n_clients = len(weights)
    for w_index, w in enumerate(weights[0]):
        layer_fed_avg_weight = sum([factors[i]*weights[i][w_index] for i in range(n_clients)])
        fed_avg_wegiths.append(layer_fed_avg_weight)
    return fed_avg_wegiths


def multi_rounds_fedavg(avged_weight, train_data, test_data, global_test_data, global_test_label, n_clients, factors, lr, comm_round):
    for cr in range(comm_round):
        logger.info("Start commround of FedAvg {} ....".format(cr))
        nets_list = []
        for client_index in range(n_clients):
            logger.info("Start local training process for client {}, communication round: {} ....".format(client_index, cr))

            client_user_name = TRIAL_USER_NAME[client_index]
            num_samples_train = len(train_data["user_data"][client_user_name]['x'])
            num_samples_test = len(test_data["user_data"][client_user_name]['x'])

            #num_samples_list.append(num_samples_train)
            user_train_data = train_data["user_data"][client_user_name]
            user_test_data = test_data["user_data"][client_user_name]

            model = language_model.RNNModel('LSTM', 80, 8, 256, 1, 0.2, tie_weights=False).to(device)

            #### we need to load the prev avg weight to the model
            new_state_dict = {}
            for param_idx, (key_name, param) in enumerate(model.state_dict().items()):
                temp_dict = {key_name: torch.from_numpy(avged_weight[param_idx])}
                new_state_dict.update(temp_dict)
            model.load_state_dict(new_state_dict)
            ####
            optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=0.0001)
            total_loss = 0.0
            for epoch in range(5):
                model.train()
                epoch_start_time = time.time()

                hidden_train = model.init_hidden(BATCH_SIZE)
                for i in range(int(num_samples_train / BATCH_SIZE)):
                    input_data, target_data = process_x(user_train_data['x'][BATCH_SIZE*i:BATCH_SIZE*(i+1)]), process_y(user_train_data['y'][BATCH_SIZE*i:BATCH_SIZE*(i+1)])
      
                    data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
                    optimizer.zero_grad()

                    hidden_train = repackage_hidden(hidden_train)
                    output, hidden_train = model(data, hidden_train)

                    loss = criterion(output.t(), torch.max(targets, 1)[1])
                    loss.backward()
                    optimizer.step()
                    total_loss += loss.item()

                    cur_loss = total_loss
                    total_loss = 0

            eval_batch_size = 10
            model.eval()
            total_val_loss = 0.
            ntokens = 80
            hidden_test = model.init_hidden(eval_batch_size)
            correct_prediction = 0
            with torch.no_grad():
                for i in range(int(num_samples_test / eval_batch_size)):
                    input_data, target_data = process_x(user_test_data['x'][eval_batch_size*i:eval_batch_size*(i+1)]), process_y(user_test_data['y'][eval_batch_size*i:eval_batch_size*(i+1)])
                    data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)

                    hidden_test = repackage_hidden(hidden_test)
                    output, hidden_test = model(data, hidden_test)
                    loss = criterion(output.t(), torch.max(targets, 1)[1])
                    _, pred_label = torch.max(output.t(), 1)
                    correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()

                    total_val_loss += loss.item()
            logger.info('-' * 89)
            logger.info('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(epoch, (time.time() - epoch_start_time),
                                                   total_val_loss, correct_prediction, num_samples_test, correct_prediction/num_samples_test*100.0))
            logger.info('-' * 89)

            nets_list.append(model)

        collected_weights = collect_weights(nets_list)
        avged_weight = fed_avg(collected_weights, factors)
        # we now test the performance of the avged model
        new_state_dict = {}
        avged_model = language_model.RNNModel('LSTM', 80, 8, 256, 1, 0.2, tie_weights=False) # we now start from 1 layer
        for param_idx, (key_name, param) in enumerate(avged_model.state_dict().items()):
            temp_dict = {key_name: torch.from_numpy(avged_weight[param_idx])}
            new_state_dict.update(temp_dict)
        avged_model.load_state_dict(new_state_dict)
        # we will need to construct a new global test set based on all test data on each of the clients

        global_eval_batch_size = 10
        ######################################################
        # here we measure the performance of averaged model
        ######################################################
        logger.info("We measure the performamce of FedAvg here ....")
        avged_model.to(device)
        avged_model.eval()
        total_val_loss = 0.
        ntokens = 80
        hidden_test = avged_model.init_hidden(global_eval_batch_size)
        global_correct_prediction = 0
        with torch.no_grad():
            for i in range(int(global_num_samples_test / global_eval_batch_size)):
                input_data, target_data = process_x(global_test_data[global_eval_batch_size*i:global_eval_batch_size*(i+1)]), process_y(global_test_label[global_eval_batch_size*i:global_eval_batch_size*(i+1)])
                data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
                hidden_test = repackage_hidden(hidden_test)
                output, hidden_test = avged_model(data, hidden_test)
                loss = criterion(output.t(), torch.max(targets, 1)[1])
                _, pred_label = torch.max(output.t(), 1)
                global_correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()
                total_val_loss += loss.item()
        logger.info('*' * 89)
        logger.info('|FedAvg-ACC Comm Round: {} | On Global Testset | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(cr, total_val_loss, global_correct_prediction, global_num_samples_test, global_correct_prediction/global_num_samples_test*100.0))
        logger.info('*' * 89)


def multi_rounds_fedprox(avged_weight, train_data, test_data, global_test_data, global_test_label, n_clients, factors, lr, mu, comm_round):
    for cr in range(comm_round):
        logger.info("Start commround of FedAvg {} ....".format(cr))
        nets_list = []
        for client_index in range(n_clients):
            logger.info("Start local training process for client {}, communication round: {} ....".format(client_index, cr))

            client_user_name = TRIAL_USER_NAME[client_index]
            num_samples_train = len(train_data["user_data"][client_user_name]['x'])
            num_samples_test = len(test_data["user_data"][client_user_name]['x'])

            #num_samples_list.append(num_samples_train)
            user_train_data = train_data["user_data"][client_user_name]
            user_test_data = test_data["user_data"][client_user_name]

            model = language_model.RNNModel('LSTM', 80, 8, 256, 1, 0.2, tie_weights=False).to(device)

            global_weight_collector = []
            #### we need to load the prev avg weight to the model
            new_state_dict = {}
            for param_idx, (key_name, param) in enumerate(model.state_dict().items()):
                temp_dict = {key_name: torch.from_numpy(avged_weight[param_idx])}
                global_weight_collector.append(torch.from_numpy(avged_weight[param_idx]).to(device))
                new_state_dict.update(temp_dict)
            model.load_state_dict(new_state_dict)
            ####

            optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=0.0001)
            total_loss = 0.0
            for epoch in range(1):
                model.train()
                epoch_start_time = time.time()

                hidden_train = model.init_hidden(BATCH_SIZE)
                for i in range(int(num_samples_train / BATCH_SIZE)):
                    input_data, target_data = process_x(user_train_data['x'][BATCH_SIZE*i:BATCH_SIZE*(i+1)]), process_y(user_train_data['y'][BATCH_SIZE*i:BATCH_SIZE*(i+1)])
      
                    data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
                    optimizer.zero_grad()

                    hidden_train = repackage_hidden(hidden_train)
                    output, hidden_train = model(data, hidden_train)

                    loss = criterion(output.t(), torch.max(targets, 1)[1])

                    ###################################we implement FedProx Here##################################
                    fed_prox_reg = 0.0
                    for param_index, param in enumerate(model.parameters()):
                        fed_prox_reg += ((mu / 2) * torch.norm((param - global_weight_collector[param_index]))**2)
                    loss += fed_prox_reg
                    ##############################################################################################

                    loss.backward()
                    optimizer.step()
                    total_loss += loss.item()

                    cur_loss = total_loss
                    total_loss = 0

            eval_batch_size = 10
            model.eval()
            total_val_loss = 0.
            ntokens = 80
            hidden_test = model.init_hidden(eval_batch_size)
            correct_prediction = 0
            with torch.no_grad():
                for i in range(int(num_samples_test / eval_batch_size)):
                    input_data, target_data = process_x(user_test_data['x'][eval_batch_size*i:eval_batch_size*(i+1)]), process_y(user_test_data['y'][eval_batch_size*i:eval_batch_size*(i+1)])
                    data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)

                    hidden_test = repackage_hidden(hidden_test)
                    output, hidden_test = model(data, hidden_test)
                    loss = criterion(output.t(), torch.max(targets, 1)[1])
                    _, pred_label = torch.max(output.t(), 1)
                    correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()

                    total_val_loss += loss.item()
            logger.info('-' * 89)
            logger.info('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(epoch, (time.time() - epoch_start_time),
                                                   total_val_loss, correct_prediction, num_samples_test, correct_prediction/num_samples_test*100.0))
            logger.info('-' * 89)

            nets_list.append(model)

        collected_weights = collect_weights(nets_list)
        avged_weight = fed_avg(collected_weights, factors)
        # we now test the performance of the avged model
        new_state_dict = {}
        avged_model = language_model.RNNModel('LSTM', 80, 8, 256, 1, 0.2, tie_weights=False) # we now start from 1 layer
        for param_idx, (key_name, param) in enumerate(avged_model.state_dict().items()):
            temp_dict = {key_name: torch.from_numpy(avged_weight[param_idx])}
            new_state_dict.update(temp_dict)
        avged_model.load_state_dict(new_state_dict)
        # we will need to construct a new global test set based on all test data on each of the clients

        global_eval_batch_size = 10
        ######################################################
        # here we measure the performance of averaged model
        ######################################################
        logger.info("We measure the performamce of FedAvg here ....")
        avged_model.to(device)
        avged_model.eval()
        total_val_loss = 0.
        ntokens = 80
        hidden_test = avged_model.init_hidden(global_eval_batch_size)
        global_correct_prediction = 0
        with torch.no_grad():
            for i in range(int(global_num_samples_test / global_eval_batch_size)):
                input_data, target_data = process_x(global_test_data[global_eval_batch_size*i:global_eval_batch_size*(i+1)]), process_y(global_test_label[global_eval_batch_size*i:global_eval_batch_size*(i+1)])
                data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
                hidden_test = repackage_hidden(hidden_test)
                output, hidden_test = avged_model(data, hidden_test)
                loss = criterion(output.t(), torch.max(targets, 1)[1])
                _, pred_label = torch.max(output.t(), 1)
                global_correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()
                total_val_loss += loss.item()
        logger.info('*' * 89)
        logger.info('|FedProx-ACC Comm Round: {} | On Global Testset | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(cr, total_val_loss, global_correct_prediction, global_num_samples_test, global_correct_prediction/global_num_samples_test*100.0))
        logger.info('*' * 89)


if __name__ == "__main__":
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    logger.info("Experiment running on device: {}".format(device))

    with open(TRAIN_DATA_DIR+TRAIN_DATA_NAME) as json_file:
        train_data = json.load(json_file)

    with open(TEST_DATA_DIR+TEST_DATA_NAME) as json_file:
        test_data = json.load(json_file)

    lr = 0.8
    clip = 0.25
    n_clients = 2
    retrain_flag = False


    TRIAL_USER_NAME = train_data["users"][0:n_clients] #this can be of length in range from 1 to 132
    start_time = time.time()
    total_loss = 0.0

    num_samples_list = [len(train_data["user_data"][TRIAL_USER_NAME[client_index]]['x']) for client_index in range(n_clients)]
    nets_list = []
    criterion = nn.CrossEntropyLoss()

    for client_index in range(n_clients):
        if retrain_flag:
            logger.info("Start local training process for client {} ....".format(client_index))
            client_user_name = TRIAL_USER_NAME[client_index]
            num_samples_train = len(train_data["user_data"][client_user_name]['x'])
            num_samples_test = len(test_data["user_data"][client_user_name]['x'])

            user_train_data = train_data["user_data"][client_user_name]
            user_test_data = test_data["user_data"][client_user_name]

            model = language_model.RNNModel('LSTM', 80, 8, 256, 1, 0.2, tie_weights=False).to(device)
            #optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=0.0001, amsgrad=True)
            optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=0.0001)

            for epoch in range(TRIAL_EPOCH):
                model.train()
                epoch_start_time = time.time()

                hidden_train = model.init_hidden(BATCH_SIZE)
                for i in range(int(num_samples_train / BATCH_SIZE)):
                    input_data, target_data = process_x(user_train_data['x'][BATCH_SIZE*i:BATCH_SIZE*(i+1)]), process_y(user_train_data['y'][BATCH_SIZE*i:BATCH_SIZE*(i+1)])
      
                    data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
                    optimizer.zero_grad()

                    hidden_train = repackage_hidden(hidden_train)
                    output, hidden_train = model(data, hidden_train)

                    loss = criterion(output.t(), torch.max(targets, 1)[1])
                    loss.backward()
                    optimizer.step()
                    total_loss += loss.item()

                    cur_loss = total_loss
                    elapsed = time.time() - start_time
                    #logger.info('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.2f} | ms/batch {:5.2f} | '
                    #        'loss {:5.2f} | ppl {:8.2f}'.format(
                    #    epoch, i, num_samples_train // BATCH_SIZE, lr,
                    #    elapsed * 1000, cur_loss, math.exp(cur_loss)))
                    total_loss = 0
                    start_time = time.time()

                eval_batch_size = 10
                model.eval()
                total_val_loss = 0.
                ntokens = 80
                hidden_test = model.init_hidden(eval_batch_size)
                correct_prediction = 0
                with torch.no_grad():
                    for i in range(int(num_samples_test / eval_batch_size)):
                        input_data, target_data = process_x(user_test_data['x'][eval_batch_size*i:eval_batch_size*(i+1)]), process_y(user_test_data['y'][eval_batch_size*i:eval_batch_size*(i+1)])
                        data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)

                        hidden_test = repackage_hidden(hidden_test)
                        output, hidden_test = model(data, hidden_test)
                        loss = criterion(output.t(), torch.max(targets, 1)[1])
                        _, pred_label = torch.max(output.t(), 1)
                        correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()

                        total_val_loss += loss.item()
                logger.info('-' * 89)
                logger.info('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(epoch, (time.time() - epoch_start_time),
                                                       total_val_loss, correct_prediction, num_samples_test, correct_prediction/num_samples_test*100.0))
                logger.info('-' * 89)
            
            nets_list.append(model)

            # we save the trained model here:
            with open("trained_model_client_{}".format(client_index), 'wb') as trained_model_file:
                pickle.dump(model, trained_model_file)
        else:
            # load the trained model from local disk, and forward
            with open("trained_model_client_{}".format(client_index), 'rb') as trained_model_file:
                model = pickle.load(trained_model_file)
                nets_list.append(model)

    collected_weights = collect_weights(nets_list)
    #factors = [1/n_clients for _ in range(n_clients)] # we now just conduct naive averaging
    factors = [ns/sum(num_samples_list) for ns in num_samples_list]
    avged_weight = fed_avg(collected_weights, factors)

    # we now test the performance of the avged model
    new_state_dict = {}
    
    avged_model = language_model.RNNModel('LSTM', 80, 8, 256, 1, 0.2, tie_weights=False) # we now start from 1 layer
    for param_idx, (key_name, param) in enumerate(avged_model.state_dict().items()):
        temp_dict = {key_name: torch.from_numpy(avged_weight[param_idx])}
        new_state_dict.update(temp_dict)
    avged_model.load_state_dict(new_state_dict)

    # we will need to construct a new global test set based on all test data on each of the clients
    global_test_data = []
    global_test_label = []
    global_num_samples_test = 0
    for client_index in range(n_clients):
        client_user_name = TRIAL_USER_NAME[client_index]
        global_num_samples_test += len(test_data["user_data"][client_user_name]['x'])
        global_test_data += test_data["user_data"][client_user_name]['x']
        global_test_label += test_data["user_data"][client_user_name]['y']

    global_eval_batch_size = 10

    ######################################################
    # here we measure the performance of averaged model
    ######################################################
    avged_model.to(device)
    avged_model.eval()
    total_val_loss = 0.
    ntokens = 80
    hidden_test = avged_model.init_hidden(global_eval_batch_size)
    global_correct_prediction = 0
    with torch.no_grad():
        for i in range(int(global_num_samples_test / global_eval_batch_size)):
            input_data, target_data = process_x(global_test_data[global_eval_batch_size*i:global_eval_batch_size*(i+1)]), process_y(global_test_label[global_eval_batch_size*i:global_eval_batch_size*(i+1)])
            data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
            hidden_test = repackage_hidden(hidden_test)
            output, hidden_test = avged_model(data, hidden_test)
            loss = criterion(output.t(), torch.max(targets, 1)[1])
            _, pred_label = torch.max(output.t(), 1)
            global_correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()
            total_val_loss += loss.item()
    logger.info('*' * 89)
    logger.info('| On Global Testset | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(total_val_loss, global_correct_prediction, global_num_samples_test, global_correct_prediction/global_num_samples_test*100.0))
    logger.info('*' * 89)

    #######################################################
    # start to conduct FedMA process
    #######################################################
    gamma = 1e-3
    sigma = 1.0
    sigma0 = 1.0
    it=5

    matching_shapes = []
    assignments_list = []
    for i in range(NUM_LAYERS-1):
        matched_weights, assignments, next_layer_shape, pop_counts = layerwise_fedma(batch_weights=collected_weights, 
                                                                        layer_index=i, 
                                                                        sigma_layers=sigma, 
                                                                        sigma0_layers=sigma0, 
                                                                        gamma_layers=gamma, 
                                                                        it=it,
                                                                        n_layers=NUM_LAYERS,
                                                                        matching_shapes=matching_shapes)
        matching_shapes.append(next_layer_shape)
        assignments_list.append(assignments)

        if i == 0:
            avg_encoding_weights = np.zeros(matched_weights[0].T.shape) # (80, 8)
            for client_index in range(n_clients):
                #avg_encoding_weights += 1/2 * patch_weights(collected_weights[client_index][0], next_layer_shape, assignments[client_index])
                avg_encoding_weights += patch_weights(collected_weights[client_index][0], next_layer_shape, assignments[client_index])
            avg_encoding_weights *= (1/pop_counts)
        elif i == 1:
            avg_h_weights = np.zeros((next_layer_shape*4, next_layer_shape), dtype=np.float32)
            avg_h_bias = np.zeros(next_layer_shape*4, dtype=np.float32)

            avg_i_weights = np.zeros((next_layer_shape*4, matching_shapes[i-1]), dtype=np.float32)
            avg_i_bias = np.zeros(next_layer_shape*4, dtype=np.float32)
            
            for client_index in range(n_clients):
                h_weights = collected_weights[client_index][i+1]
                h_bias = collected_weights[client_index][i+3]
                permutated_h_weights = patch_h_weights(h_weights, next_layer_shape, assignments[client_index])
                permutated_h_bias = patch_biases(h_bias, next_layer_shape, assignments[client_index])
                avg_h_weights += (1/n_clients * permutated_h_weights)
                avg_h_bias += (1/n_clients * permutated_h_bias)

                i_weights = collected_weights[client_index][i]
                i_bias = collected_weights[client_index][i+2]
                permutated_i_weights = perm_i_weights(i_weights, next_layer_shape, assignments[client_index])
                permutated_i_bias = patch_biases(i_bias, next_layer_shape, assignments[client_index])      
                #avg_i_weights += (1/n_clients * permutated_i_weights)
                #avg_i_bias += (1/n_clients * permutated_i_bias)
                avg_i_weights += permutated_i_weights
                avg_i_bias += permutated_i_bias
            #coef_pop = 1/np.hstack((popularity_counts, popularity_counts, popularity_counts, popularity_counts)).astype(np.float32)
            coef_pop = 1/np.hstack((pop_counts, pop_counts, pop_counts, pop_counts)).astype(np.float32)
            avg_i_weights = (avg_i_weights.T*coef_pop).T
            avg_i_bias *= coef_pop

        # we need to permutate the next layer first:
        retrain_nets_list = []
        for client_index in range(n_clients):
            if i == 0:
                temp_retrained_weights = []
                next_layer_weights = collected_weights[client_index][i+1]
                patched_next_layer_weights = patch_weights(next_layer_weights, next_layer_shape, assignments[client_index])

                for layer_index, layer_weight in enumerate(collected_weights[client_index]):
                    if layer_index == i:
                        #temp_retrained_weights.append(matched_weights[0].T)
                        temp_retrained_weights.append(avg_encoding_weights)
                    elif layer_index == (i+1):
                        temp_retrained_weights.append(patched_next_layer_weights)
                    else:
                        temp_retrained_weights.append(layer_weight)
                retrain_nets_list.append(temp_retrained_weights)
                #retrain_model = language_model.RNNModel('LSTM', 80, next_layer_shape, 256, 1, 0.2, tie_weights=False) # we now start from 1 layer
            elif i == 1:
                # we now get the match results based on the w_{i.} matrices we will need to use this to permutate three dimensions
                # i) col of w_{h.} matrices ii) row of w_{h.} matrices iii) input shape of the next layer
                # we process the next layer first:
                next_layer_weights = collected_weights[client_index][i+4]
                patched_next_layer_weights = patch_weights(next_layer_weights, next_layer_shape, assignments[client_index])
                # then we process the w_{h.} weights and biases in the same layer
                #h_weights = collected_weights[client_index][i+1]
                #h_bias = collected_weights[client_index][i+3]
                #permutated_h_weights = patch_h_weights(h_weights, next_layer_shape, assignments[client_index])
                #permutated_h_bias = patch_h_biases(h_bias, next_layer_shape, assignments[client_index])

                #temp_retrained_weights = [collected_weights[client_index][0], matched_weights[0], permutated_h_weights, matched_weights[1], permutated_h_bias, patched_next_layer_weights, collected_weights[client_index][-1]]
                #temp_retrained_weights = [collected_weights[client_index][0], matched_weights[0], avg_h_weights, matched_weights[1], avg_h_bias, patched_next_layer_weights, collected_weights[client_index][-1]]
                temp_retrained_weights = [collected_weights[client_index][0], avg_i_weights, avg_h_weights, avg_i_bias, avg_h_bias, patched_next_layer_weights, collected_weights[client_index][-1]]
                #retrain_model = language_model.RNNModel('LSTM', 80, matching_shapes[0], next_layer_shape, 1, 0.2, tie_weights=False) # we now start from 1 layer
                retrain_nets_list.append(temp_retrained_weights)
            #retrain_nets_list.append(retrain_model)
        #collected_weights = collect_weights(retrain_nets_list)
        collected_weights = retrain_nets_list

    
    avg_last_weight = np.zeros(collected_weights[0][-2].shape, dtype=np.float32)
    avg_last_bias = np.zeros(collected_weights[0][-1].shape, dtype=np.float32)

    for client_index in range(n_clients):
        avg_last_weight += (1/n_clients * collected_weights[client_index][-2])
        avg_last_bias += (1/n_clients * collected_weights[client_index][-1])
    global_matched_weights = [collected_weights[0][l_idx] for l_idx in range(len(collected_weights[0])-2)] + [avg_last_weight] + [avg_last_bias]

    global_matched_model = language_model.RNNModel('LSTM', 80, matching_shapes[0], matching_shapes[1], 1, 0.2, tie_weights=False)
    new_state_dict = {}
    for param_idx, (key_name, param) in enumerate(global_matched_model.state_dict().items()):
        temp_dict = {key_name: torch.from_numpy(global_matched_weights[param_idx])}
        new_state_dict.update(temp_dict)

    global_matched_model.load_state_dict(new_state_dict)
    global_matched_model.to(device)
    global_matched_model.eval()
    hidden_test = global_matched_model.init_hidden(global_eval_batch_size)
    global_correct_prediction = 0.0
    with torch.no_grad():
        for i in range(int(global_num_samples_test / global_eval_batch_size)):
            input_data, target_data = process_x(global_test_data[global_eval_batch_size*i:global_eval_batch_size*(i+1)]), process_y(global_test_label[global_eval_batch_size*i:global_eval_batch_size*(i+1)])
            data, targets = torch.from_numpy(input_data).to(device), torch.from_numpy(target_data).to(device)
            hidden_test = repackage_hidden(hidden_test)
            output, hidden_test = global_matched_model(data, hidden_test)
            loss = criterion(output.t(), torch.max(targets, 1)[1])
            _, pred_label = torch.max(output.t(), 1)
            global_correct_prediction += (pred_label == torch.max(targets, 1)[1]).sum().item()
            total_val_loss += loss.item()
    logger.info('*' * 89)
    logger.info('| Matched model on Global Testset | valid loss {:5.2f} | pred: {}/{} | acc: {:.4f}%'.format(total_val_loss, global_correct_prediction, global_num_samples_test, global_correct_prediction/global_num_samples_test*100.0))
    logger.info('*' * 89)