origin_train_model.py

"""Model training."""
import argparse
import os
import time

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

import common
import datasets
import made

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Device', DEVICE)

parser = argparse.ArgumentParser()

# Training.
parser.add_argument('--dataset', type=str, default='dmv-tiny', help='Dataset.')
parser.add_argument('--num-gpus', type=int, default=0, help='#gpus.')
parser.add_argument('--bs', type=int, default=1024, help='Batch size.')
parser.add_argument(
    '--warmups',
    type=int,
    default=0,
    help='Learning rate warmup steps.  Crucial for Transformer.')
parser.add_argument('--epochs',
                    type=int,
                    default=20,
                    help='Number of epochs to train for.')
parser.add_argument('--constant-lr',
                    type=float,
                    default=None,
                    help='Constant LR?')
parser.add_argument(
    '--column-masking',
    action='store_true',
    help='Column masking training, which permits wildcard skipping'\
    ' at querying time.')

# MADE.
parser.add_argument('--fc-hiddens',
                    type=int,
                    default=128,
                    help='Hidden units in FC.')
parser.add_argument('--layers', type=int, default=4, help='# layers in FC.')
parser.add_argument('--residual', action='store_true', help='ResMade?')
parser.add_argument('--direct-io', action='store_true', help='Do direct IO?')
parser.add_argument(
    '--inv-order',
    action='store_true',
    help='Set this flag iff using MADE and specifying --order. Flag --order '\
    'lists natural indices, e.g., [0 2 1] means variable 2 appears second.'\
    'MADE, however, is implemented to take in an argument the inverse '\
    'semantics (element i indicates the position of variable i).  Transformer'\
    ' does not have this issue and thus should not have this flag on.')
parser.add_argument(
    '--input-encoding',
    type=str,
    default='binary',
    help='Input encoding for MADE/ResMADE, {binary, one_hot, embed}.')
parser.add_argument(
    '--output-encoding',
    type=str,
    default='one_hot',
    help='Iutput encoding for MADE/ResMADE, {one_hot, embed}.  If embed, '
    'then input encoding should be set to embed as well.')

# Ordering.
parser.add_argument('--num-orderings',
                    type=int,
                    default=1,
                    help='Number of orderings.')
parser.add_argument(
    '--order',
    nargs='+',
    type=int,
    required=False,
    help=
    'Use a specific ordering.  '\
    'Format: e.g., [0 2 1] means variable 2 appears second.'
)
parser.add_argument('--cuda-num',
                    type=int,
                    default=None,
                    help='the number of cuda used')

args = parser.parse_args()

def Entropy(name, data, bases=None):
    import scipy.stats
    s = 'Entropy of {}:'.format(name)
    ret = []
    for base in bases:
        assert base == 2 or base == 'e' or base is None
        e = scipy.stats.entropy(data, base=base if base != 'e' else None)
        ret.append(e)
        unit = 'nats' if (base == 'e' or base is None) else 'bits'
        s += ' {:.4f} {}'.format(e, unit)
    print(s)
    return ret


def RunEpoch(split,
             model,
             opt,
             train_data,
             val_data=None,
             batch_size=100,
             upto=None,
             epoch_num=None,
             verbose=False,
             log_every=10,
             return_losses=False,
             table_bits=None):
    torch.set_grad_enabled(split == 'train')
    model.train() if split == 'train' else model.eval()
    dataset = train_data if split == 'train' else val_data
    losses = []

    loader = torch.utils.data.DataLoader(dataset,
                                         batch_size=batch_size,
                                         shuffle=(split == 'train'))

    # How many orderings to run for the same batch?
    nsamples = 1
    if hasattr(model, 'orderings'):
        nsamples = len(model.orderings)

    for step, (idx, xb) in enumerate(loader):
        if split == 'train':
            base_lr = 8e-4
            for param_group in opt.param_groups:
                if args.constant_lr:
                    lr = args.constant_lr
                elif args.warmups:
                    t = args.warmups
                    d_model = model.embed_size
                    global_steps = len(loader) * epoch_num + step + 1
                    lr = (d_model**-0.5) * min(
                        (global_steps**-.5), global_steps * (t**-1.5))
                else:
                    lr = 1e-2

                param_group['lr'] = lr

        if upto and step >= upto:
            break

        xb = xb.to(DEVICE).to(torch.float32)

        # Forward pass, potentially through several orderings.
        xbhat = None
        model_logits = []
        num_orders_to_forward = 1
        if split == 'test' and nsamples > 1:
            # At test, we want to test the 'true' nll under all orderings.
            num_orders_to_forward = nsamples

        for i in range(num_orders_to_forward):
            if hasattr(model, 'update_masks'):
                # We want to update_masks even for first ever batch.
                model.update_masks()

            model_out = model(xb)
            model_logits.append(model_out)
            if xbhat is None:
                xbhat = torch.zeros_like(model_out)
            xbhat += model_out

        if xbhat.shape == xb.shape:
            if mean:
                xb = (xb * std) + mean
            loss = F.binary_cross_entropy_with_logits(
                xbhat, xb, size_average=False) / xbhat.size()[0]
        else:
            if model.input_bins is None:
                # NOTE: we have to view() it in this order due to the mask
                # construction within MADE.  The masks there on the output unit
                # determine which unit sees what input vars.
                xbhat = xbhat.view(-1, model.nout // model.nin, model.nin)
                # Equivalent to:
                loss = F.cross_entropy(xbhat, xb.long(), reduction='none') \
                        .sum(-1).mean()
            else:
                if num_orders_to_forward == 1:
                    loss = model.nll(xbhat, xb).mean()
                else:
                    # Average across orderings & then across minibatch.
                    #
                    #   p(x) = 1/N sum_i p_i(x)
                    #   log(p(x)) = log(1/N) + log(sum_i p_i(x))
                    #             = log(1/N) + logsumexp ( log p_i(x) )
                    #             = log(1/N) + logsumexp ( - nll_i (x) )
                    #
                    # Used only at test time.
                    logps = []  # [batch size, num orders]
                    assert len(model_logits) == num_orders_to_forward, len(
                        model_logits)
                    for logits in model_logits:
                        # Note the minus.
                        logps.append(-model.nll(logits, xb))
                    logps = torch.stack(logps, dim=1)
                    logps = logps.logsumexp(dim=1) + torch.log(
                        torch.tensor(1.0 / nsamples, device=logps.device))
                    loss = (-logps).mean()

        losses.append(loss.item())

        if step % log_every == 0:
            if split == 'train':
                print(
                    'Epoch {} Iter {}, {} entropy gap {:.4f} bits (loss {:.3f}, data {:.3f}) {:.5f} lr'
                    .format(epoch_num, step, split,
                            loss.item() / np.log(2) - table_bits,
                            loss.item() / np.log(2), table_bits, lr))
            else:
                print('Epoch {} Iter {}, {} loss {:.4f} nats / {:.4f} bits'.
                      format(epoch_num, step, split, loss.item(),
                             loss.item() / np.log(2)))

        if split == 'train':
            opt.zero_grad()
            loss.backward()
            opt.step()

        if verbose:
            print('%s epoch average loss: %f' % (split, np.mean(losses)))
    if return_losses:
        return losses
    return np.mean(losses)


def ReportModel(model, blacklist=None):
    ps = []
    for name, p in model.named_parameters():
        if blacklist is None or blacklist not in name:
            ps.append(np.prod(p.size()))
    num_params = sum(ps)
    mb = num_params * 4 / 1024 / 1024
    print('Number of model parameters: {} (~= {:.1f}MB)'.format(num_params, mb))
    print(model)
    return mb


def InvertOrder(order):
    if order is None:
        return None
    # 'order'[i] maps nat_i -> position of nat_i
    # Inverse: position -> natural idx.  This it the 'true' ordering -- it's how
    # heuristic orders are generated + (less crucially) how Transformer works.
    nin = len(order)
    inv_ordering = [None] * nin
    for natural_idx in range(nin):
        inv_ordering[order[natural_idx]] = natural_idx
    return inv_ordering


def MakeMade(scale, cols_to_train, seed, fixed_ordering=None):
    if args.inv_order:
        print('Inverting order!')
        fixed_ordering = InvertOrder(fixed_ordering)

    model = made.MADE(
        nin=len(cols_to_train),
        hidden_sizes=[scale] *
        args.layers if args.layers > 0 else [512, 256, 512, 128, 1024],
        nout=sum([c.DistributionSize() for c in cols_to_train]),
        input_bins=[c.DistributionSize() for c in cols_to_train],
        input_encoding=args.input_encoding,
        output_encoding=args.output_encoding,
        embed_size=32,
        seed=seed,
        do_direct_io_connections=args.direct_io,
        natural_ordering=False if seed is not None and seed != 0 else True,
        residual_connections=args.residual,
        fixed_ordering=fixed_ordering,
        column_masking=args.column_masking,
    ).to(DEVICE)

    return model



def InitWeight(m):
    if type(m) == made.MaskedLinear or type(m) == nn.Linear:
        nn.init.xavier_uniform_(m.weight)
        nn.init.zeros_(m.bias)
    if type(m) == nn.Embedding:
        nn.init.normal_(m.weight, std=0.02)


def TrainTask(seed=0):
    torch.manual_seed(0)
    np.random.seed(0)

    if args.cuda_num is not None:
        torch.cuda.set_device(args.cuda_num)
        os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_num)

    assert args.dataset in ['dmv-tiny', 'dmv', 'covtype','kddcup', 'poker', 'census' , 'dmv-full', 'tpc', 'cup98']
    if args.dataset == 'dmv-tiny':
        table = datasets.LoadDmv('dmv-tiny.csv')
    elif args.dataset == 'dmv':
        table = datasets.LoadDmv()
    elif args.dataset == 'covtype':
        table = datasets.LoadCovtype()
    elif args.dataset == 'kddcup':
        table = datasets.LoadKddcup()
    elif args.dataset == 'poker':
        table = datasets.LoadPoker()
    elif args.dataset == 'census':
        table = datasets.LoadCensus()
    elif args.dataset == 'dmv-full':
        table = datasets.LoadDmvFull()
    elif args.dataset == 'tpc':
        table = datasets.LoadTpc()
    elif args.dataset == 'cup98':
        table = datasets.LoadCup98()


    table_bits = Entropy(
        table,
        table.data.fillna(value=0).groupby([c.name for c in table.columns
                                           ]).size(), [2])[0]
    fixed_ordering = None

    if args.order is not None:
        print('Using passed-in order:', args.order)
        fixed_ordering = args.order

    print(table.data.info())

    table_train = table


    if args.dataset in ['dmv-tiny', 'dmv', 'covtype', 'kddcup', 'poker', 'census', 'dmv-full', 'tpc', 'cup98']:
        model = MakeMade(
            scale=args.fc_hiddens,
            cols_to_train=table.columns,
            seed=seed,
            fixed_ordering=fixed_ordering,
        )
    else:
        assert False, args.dataset

    mb = ReportModel(model)


    print('Applying InitWeight()')
    model.apply(InitWeight)


    opt = torch.optim.Adam(list(model.parameters()), 2e-4)

    bs = args.bs
    log_every = 200

    train_data = common.TableDataset(table_train)

    train_losses = []
    train_start = time.time()
    for epoch in range(args.epochs):

        mean_epoch_train_loss = RunEpoch('train',
                                         model,
                                         opt,
                                         train_data=train_data,
                                         val_data=train_data,
                                         batch_size=bs,
                                         epoch_num=epoch,
                                         log_every=log_every,
                                         table_bits=table_bits)

        if epoch % 1 == 0:
            print('epoch {} train loss {:.4f} nats / {:.4f} bits'.format(
                epoch, mean_epoch_train_loss,
                mean_epoch_train_loss / np.log(2)))
            since_start = time.time() - train_start
            print('time since start: {:.1f} secs'.format(since_start))

        train_losses.append(mean_epoch_train_loss)


        if (epoch+1) % 5 == 0:
            all_losses = RunEpoch('test',
                                  model,
                                  train_data=train_data,
                                  val_data=train_data,
                                  opt=None,
                                  batch_size=1024,
                                  log_every=500,
                                  table_bits=table_bits,
                                  return_losses=True)
            model_nats = np.mean(all_losses)
            model_bits = model_nats / np.log(2)
            model.model_bits = model_bits
            if fixed_ordering is None:
                if seed is not None:
                    PATH = 'models/{}-{:.1f}MB-model{:.3f}-data{:.3f}-{}-{}epochs-seed{}.pt'.format(
                        args.dataset, mb, model.model_bits, table_bits, model.name(),
                        epoch+1, seed)
                else:
                    PATH = 'models/{}-{:.1f}MB-model{:.3f}-data{:.3f}-{}-{}epochs-seed{}-{}.pt'.format(
                        args.dataset, mb, model.model_bits, table_bits, model.name(),
                        epoch + 1, seed, time.time())
            else:
                annot = ''
                if args.inv_order:
                    annot = '-invOrder'

                PATH = 'models/{}-{:.1f}MB-model{:.3f}-data{:.3f}-{}-{}epochs-seed{}-order{}{}.pt'.format(
                    args.dataset, mb, model.model_bits, table_bits, model.name(),
                    epoch + 1, seed, '_'.join(map(str, fixed_ordering)), annot)
            os.makedirs(os.path.dirname(PATH), exist_ok=True)
            torch.save(model.state_dict(), PATH)
            print('Saved to:')
            print(PATH)

    print('Training done; evaluating likelihood on full data:')
    all_losses = RunEpoch('test',
                          model,
                          train_data=train_data,
                          val_data=train_data,
                          opt=None,
                          batch_size=1024,
                          log_every=500,
                          table_bits=table_bits,
                          return_losses=True)
    model_nats = np.mean(all_losses)
    model_bits = model_nats / np.log(2)
    model.model_bits = model_bits

    if fixed_ordering is None:
        if seed is not None:
            PATH = 'data_models/{}-{:.1f}MB-model{:.3f}-data{:.3f}-{}-{}epochs-seed{}.pt'.format(
                args.dataset, mb, model.model_bits, table_bits, model.name(),
                args.epochs, seed)
        else:
            PATH = 'data_models/{}-{:.1f}MB-model{:.3f}-data{:.3f}-{}-{}epochs-seed{}-{}.pt'.format(
                args.dataset, mb, model.model_bits, table_bits, model.name(),
                args.epochs, seed, time.time())
    else:
        annot = ''
        if args.inv_order:
            annot = '-invOrder'

        PATH = 'data_models/{}-{:.1f}MB-model{:.3f}-data{:.3f}-{}-{}epochs-seed{}-order{}{}.pt'.format(
            args.dataset, mb, model.model_bits, table_bits, model.name(),
            args.epochs, seed, '_'.join(map(str, fixed_ordering)), annot)
    os.makedirs(os.path.dirname(PATH), exist_ok=True)
    torch.save(model.state_dict(), PATH)
    print('Saved to:')
    print(PATH)


TrainTask()