Melu_L2L_hyperTunning/hyper_tunning.py

import os
import torch
import torch.nn as nn
from ray import tune
import pickle
from options import config
from embedding_module import EmbeddingModule
import learn2learn as l2l
import random
from fast_adapt import fast_adapt
import gc
from learn2learn.optim.transforms import KroneckerTransform
from hyper_testing import hyper_test

# Define paths (for data)
master_path= "/media/external_10TB/10TB/maheri/melu_data5"

def load_data(data_dir=master_path,test_state='warm_state'):

    training_set_size = int(len(os.listdir("{}/warm_state".format(data_dir))) / 4)
    supp_xs_s = []
    supp_ys_s = []
    query_xs_s = []
    query_ys_s = []
    for idx in range(training_set_size):
        supp_xs_s.append(pickle.load(open("{}/warm_state/supp_x_{}.pkl".format(data_dir, idx), "rb")))
        supp_ys_s.append(pickle.load(open("{}/warm_state/supp_y_{}.pkl".format(data_dir, idx), "rb")))
        query_xs_s.append(pickle.load(open("{}/warm_state/query_x_{}.pkl".format(data_dir, idx), "rb")))
        query_ys_s.append(pickle.load(open("{}/warm_state/query_y_{}.pkl".format(data_dir, idx), "rb")))
    total_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s))
    del (supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)
    trainset = total_dataset

    test_set_size = int(len(os.listdir("{}/{}".format(data_dir, test_state))) / 4)
    supp_xs_s = []
    supp_ys_s = []
    query_xs_s = []
    query_ys_s = []
    for idx in range(test_set_size):
        supp_xs_s.append(pickle.load(open("{}/{}/supp_x_{}.pkl".format(data_dir, test_state, idx), "rb")))
        supp_ys_s.append(pickle.load(open("{}/{}/supp_y_{}.pkl".format(data_dir, test_state, idx), "rb")))
        query_xs_s.append(pickle.load(open("{}/{}/query_x_{}.pkl".format(data_dir, test_state, idx), "rb")))
        query_ys_s.append(pickle.load(open("{}/{}/query_y_{}.pkl".format(data_dir, test_state, idx), "rb")))
    test_dataset = list(zip(supp_xs_s, supp_ys_s, query_xs_s, query_ys_s))
    del (supp_xs_s, supp_ys_s, query_xs_s, query_ys_s)

    random.shuffle(test_dataset)
    val_size = int(test_set_size * 0.2)
    validationset = test_dataset[:val_size]
    testset = test_dataset[val_size:]

    return trainset, validationset,testset


def train_melu(conf, checkpoint_dir=None, data_dir=None):
    embedding_dim = config['embedding_dim']
    print("inajm1:",checkpoint_dir)
    fc1_in_dim = config['embedding_dim'] * 8
    fc2_in_dim = config['first_fc_hidden_dim']
    fc2_out_dim = config['second_fc_hidden_dim']

    fc1 = torch.nn.Linear(fc1_in_dim, fc2_in_dim)
    fc2 = torch.nn.Linear(fc2_in_dim, fc2_out_dim)
    linear_out = torch.nn.Linear(fc2_out_dim, 1)
    head = torch.nn.Sequential(fc1, fc2, linear_out)

    emb = EmbeddingModule(config).cuda()

    transform = None
    if conf['transformer'] == "kronoker":
        transform = KroneckerTransform(l2l.nn.KroneckerLinear)
    elif conf['transformer'] == "linear":
        transform = l2l.optim.ModuleTransform(torch.nn.Linear)

    # define meta algorithm
    if conf['meta_algo'] == "maml":
        head = l2l.algorithms.MAML(head, lr=conf['local_lr'], first_order=conf['first_order'])
    elif conf['meta_algo'] == 'metasgd':
        head = l2l.algorithms.MetaSGD(head, lr=conf['local_lr'], first_order=conf['first_order'])
    elif conf['meta_algo'] == 'gbml':
        head = l2l.algorithms.GBML(head, transform=transform, lr=conf['local_lr'],
                                   adapt_transform=conf['adapt_transform'], first_order=conf['first_order'])
    head.cuda()
    net = nn.Sequential(emb,head)

    criterion = nn.MSELoss()
    all_parameters =  list(emb.parameters()) + list(head.parameters())
    optimizer = torch.optim.Adam(all_parameters, lr=conf['lr'])

    if checkpoint_dir:
        model_state, optimizer_state = torch.load(
            os.path.join(checkpoint_dir, "checkpoint"))
        net.load_state_dict(model_state)
        optimizer.load_state_dict(optimizer_state)

    # loading data
    train_dataset,validation_dataset,test_dataset = load_data(data_dir,test_state=conf['test_state'])
    print(conf['test_state'])
    batch_size = conf['batch_size']
    num_batch = int(len(train_dataset) / batch_size)
    a, b, c, d = zip(*train_dataset)

    for epoch in range(config['num_epoch']):  # loop over the dataset multiple times
        for i in range(num_batch):
            optimizer.zero_grad()
            meta_train_error = 0.0

            # print("EPOCH: ", epoch, " BATCH: ", i)
            supp_xs = list(a[batch_size * i:batch_size * (i + 1)])
            supp_ys = list(b[batch_size * i:batch_size * (i + 1)])
            query_xs = list(c[batch_size * i:batch_size * (i + 1)])
            query_ys = list(d[batch_size * i:batch_size * (i + 1)])
            batch_sz = len(supp_xs)

            # iterate over all tasks
            for task in range(batch_sz):
                sxs = supp_xs[task].cuda()
                qxs = query_xs[task].cuda()
                sys = supp_ys[task].cuda()
                qys = query_ys[task].cuda()

                learner = head.clone()
                temp_sxs = emb(sxs)
                temp_qxs = emb(qxs)

                evaluation_error = fast_adapt(learner,
                                               temp_sxs,
                                               temp_qxs,
                                               sys,
                                               qys,
                                               conf['inner'])

                evaluation_error.backward()
                meta_train_error += evaluation_error.item()

                del(sxs,qxs,sys,qys)
                supp_xs[task].cpu()
                query_xs[task].cpu()
                supp_ys[task].cpu()
                query_ys[task].cpu()

            # Average the accumulated gradients and optimize (After each batch we will update params)
            for p in all_parameters:
                p.grad.data.mul_(1.0 / batch_sz)
            optimizer.step()
            del (supp_xs, supp_ys, query_xs, query_ys)
            gc.collect()

        # test results on the validation data
        val_loss,val_ndcg1,val_ndcg3 = hyper_test(emb,head,validation_dataset,adaptation_step=conf['inner'])

        with tune.checkpoint_dir(epoch) as checkpoint_dir:
            path = os.path.join(checkpoint_dir, "checkpoint")
            torch.save((net.state_dict(), optimizer.state_dict()), path)

        tune.report(loss=val_loss, ndcg1=val_ndcg1,ndcg3=val_ndcg3)
    print("Finished Training")