path: root/stanza/models/parser.py

"""
Entry point for training and evaluating a dependency parser.

This implementation combines a deep biaffine graph-based parser with linearization and distance features.
For details please refer to paper: https://nlp.stanford.edu/pubs/qi2018universal.pdf.
"""

"""
Training and evaluation for the parser.
"""

import sys
import os
import shutil
import time
import argparse
import logging
import numpy as np
import random
import torch
from torch import nn, optim

import stanza.models.depparse.data as data
from stanza.models.depparse.data import DataLoader
from stanza.models.depparse.trainer import Trainer
from stanza.models.depparse import scorer
from stanza.models.common import utils
from stanza.models.common import pretrain
from stanza.models.common.data import augment_punct
from stanza.models.common.doc import *
from stanza.utils.conll import CoNLL
from stanza.models import _training_logging

logger = logging.getLogger('stanza')

def parse_args(args=None):
    parser = argparse.ArgumentParser()
    parser.add_argument('--data_dir', type=str, default='data/depparse', help='Root dir for saving models.')
    parser.add_argument('--wordvec_dir', type=str, default='extern_data/word2vec', help='Directory of word vectors.')
    parser.add_argument('--wordvec_file', type=str, default=None, help='Word vectors filename.')
    parser.add_argument('--wordvec_pretrain_file', type=str, default=None, help='Exact name of the pretrain file to read')
    parser.add_argument('--train_file', type=str, default=None, help='Input file for data loader.')
    parser.add_argument('--eval_file', type=str, default=None, help='Input file for data loader.')
    parser.add_argument('--output_file', type=str, default=None, help='Output CoNLL-U file.')
    parser.add_argument('--gold_file', type=str, default=None, help='Output CoNLL-U file.')

    parser.add_argument('--mode', default='train', choices=['train', 'predict'])
    parser.add_argument('--lang', type=str, help='Language')
    parser.add_argument('--shorthand', type=str, help="Treebank shorthand")

    parser.add_argument('--hidden_dim', type=int, default=400)
    parser.add_argument('--char_hidden_dim', type=int, default=400)
    parser.add_argument('--deep_biaff_hidden_dim', type=int, default=400)
    parser.add_argument('--composite_deep_biaff_hidden_dim', type=int, default=100)
    parser.add_argument('--word_emb_dim', type=int, default=75)
    parser.add_argument('--char_emb_dim', type=int, default=100)
    parser.add_argument('--tag_emb_dim', type=int, default=50)
    parser.add_argument('--transformed_dim', type=int, default=125)
    parser.add_argument('--num_layers', type=int, default=3)
    parser.add_argument('--char_num_layers', type=int, default=1)
    parser.add_argument('--pretrain_max_vocab', type=int, default=250000)
    parser.add_argument('--word_dropout', type=float, default=0.33)
    parser.add_argument('--dropout', type=float, default=0.5)
    parser.add_argument('--rec_dropout', type=float, default=0, help="Recurrent dropout")
    parser.add_argument('--char_rec_dropout', type=float, default=0, help="Recurrent dropout")
    parser.add_argument('--no_char', dest='char', action='store_false', help="Turn off character model.")
    parser.add_argument('--no_pretrain', dest='pretrain', action='store_false', help="Turn off pretrained embeddings.")
    parser.add_argument('--no_linearization', dest='linearization', action='store_false', help="Turn off linearization term.")
    parser.add_argument('--no_distance', dest='distance', action='store_false', help="Turn off distance term.")

    parser.add_argument('--sample_train', type=float, default=1.0, help='Subsample training data.')
    parser.add_argument('--optim', type=str, default='adam', help='sgd, adagrad, adam or adamax.')
    parser.add_argument('--lr', type=float, default=3e-3, help='Learning rate')
    parser.add_argument('--beta2', type=float, default=0.95)

    parser.add_argument('--max_steps', type=int, default=50000)
    parser.add_argument('--eval_interval', type=int, default=100)
    parser.add_argument('--max_steps_before_stop', type=int, default=3000)
    parser.add_argument('--batch_size', type=int, default=5000)
    parser.add_argument('--max_grad_norm', type=float, default=1.0, help='Gradient clipping.')
    parser.add_argument('--log_step', type=int, default=20, help='Print log every k steps.')
    parser.add_argument('--save_dir', type=str, default='saved_models/depparse', help='Root dir for saving models.')
    parser.add_argument('--save_name', type=str, default=None, help="File name to save the model")

    parser.add_argument('--seed', type=int, default=1234)
    parser.add_argument('--cuda', type=bool, default=torch.cuda.is_available())
    parser.add_argument('--cpu', action='store_true', help='Ignore CUDA.')

    parser.add_argument('--augment_nopunct', type=float, default=None, help='Augment the training data by copying this fraction of punct-ending sentences as non-punct.  Default of None will aim for roughly 10%')

    args = parser.parse_args(args=args)
    return args

def main(args=None):
    args = parse_args(args=args)

    if args.cpu:
        args.cuda = False
    utils.set_random_seed(args.seed, args.cuda)

    args = vars(args)
    logger.info("Running parser in {} mode".format(args['mode']))

    if args['mode'] == 'train':
        train(args)
    else:
        evaluate(args)

# TODO: refactor with tagger
def model_file_name(args):
    if args['save_name'] is not None:
        save_name = args['save_name']
    else:
        save_name = args['shorthand'] + "_parser.pt"

    return os.path.join(args['save_dir'], save_name)

def load_pretrain(args):
    pt = None
    if args['pretrain']:
        pretrain_file = pretrain.find_pretrain_file(args['wordvec_pretrain_file'], args['save_dir'], args['shorthand'], args['lang'])
        if os.path.exists(pretrain_file):
            vec_file = None
        else:
            vec_file = args['wordvec_file'] if args['wordvec_file'] else utils.get_wordvec_file(args['wordvec_dir'], args['shorthand'])
        pt = pretrain.Pretrain(pretrain_file, vec_file, args['pretrain_max_vocab'])
    return pt

def train(args):
    model_file = model_file_name(args)
    utils.ensure_dir(os.path.split(model_file)[0])

    # load pretrained vectors if needed
    pretrain = load_pretrain(args)

    # load data
    logger.info("Loading data with batch size {}...".format(args['batch_size']))
    train_data, _ = CoNLL.conll2dict(input_file=args['train_file'])
    # possibly augment the training data with some amount of fake data
    # based on the options chosen
    logger.info("Original data size: {}".format(len(train_data)))
    train_data.extend(augment_punct(train_data, args['augment_nopunct'],
                                    keep_original_sentences=False))
    logger.info("Augmented data size: {}".format(len(train_data)))
    train_doc = Document(train_data)
    train_batch = DataLoader(train_doc, args['batch_size'], args, pretrain, evaluation=False)
    vocab = train_batch.vocab
    dev_doc = CoNLL.conll2doc(input_file=args['eval_file'])
    dev_batch = DataLoader(dev_doc, args['batch_size'], args, pretrain, vocab=vocab, evaluation=True, sort_during_eval=True)

    # pred and gold path
    system_pred_file = args['output_file']
    gold_file = args['gold_file']

    # skip training if the language does not have training or dev data
    if len(train_batch) == 0 or len(dev_batch) == 0:
        logger.info("Skip training because no data available...")
        sys.exit(0)

    logger.info("Training parser...")
    trainer = Trainer(args=args, vocab=vocab, pretrain=pretrain, use_cuda=args['cuda'])

    global_step = 0
    max_steps = args['max_steps']
    dev_score_history = []
    best_dev_preds = []
    current_lr = args['lr']
    global_start_time = time.time()
    format_str = 'Finished STEP {}/{}, loss = {:.6f} ({:.3f} sec/batch), lr: {:.6f}'

    using_amsgrad = False
    last_best_step = 0
    # start training
    train_loss = 0
    while True:
        do_break = False
        for i, batch in enumerate(train_batch):
            start_time = time.time()
            global_step += 1
            loss = trainer.update(batch, eval=False) # update step
            train_loss += loss
            if global_step % args['log_step'] == 0:
                duration = time.time() - start_time
                logger.info(format_str.format(global_step, max_steps, loss, duration, current_lr))

            if global_step % args['eval_interval'] == 0:
                # eval on dev
                logger.info("Evaluating on dev set...")
                dev_preds = []
                for batch in dev_batch:
                    preds = trainer.predict(batch)
                    dev_preds += preds
                dev_preds = utils.unsort(dev_preds, dev_batch.data_orig_idx)

                dev_batch.doc.set([HEAD, DEPREL], [y for x in dev_preds for y in x])
                CoNLL.write_doc2conll(dev_batch.doc, system_pred_file)
                _, _, dev_score = scorer.score(system_pred_file, gold_file)

                train_loss = train_loss / args['eval_interval'] # avg loss per batch
                logger.info("step {}: train_loss = {:.6f}, dev_score = {:.4f}".format(global_step, train_loss, dev_score))
                train_loss = 0

                # save best model
                if len(dev_score_history) == 0 or dev_score > max(dev_score_history):
                    last_best_step = global_step
                    trainer.save(model_file)
                    logger.info("new best model saved.")
                    best_dev_preds = dev_preds

                dev_score_history += [dev_score]

            if global_step - last_best_step >= args['max_steps_before_stop']:
                if not using_amsgrad:
                    logger.info("Switching to AMSGrad")
                    last_best_step = global_step
                    using_amsgrad = True
                    trainer.optimizer = optim.Adam(trainer.model.parameters(), amsgrad=True, lr=args['lr'], betas=(.9, args['beta2']), eps=1e-6)
                else:
                    do_break = True
                    break

            if global_step >= args['max_steps']:
                do_break = True
                break

        if do_break: break

        train_batch.reshuffle()

    logger.info("Training ended with {} steps.".format(global_step))

    best_f, best_eval = max(dev_score_history)*100, np.argmax(dev_score_history)+1
    logger.info("Best dev F1 = {:.2f}, at iteration = {}".format(best_f, best_eval * args['eval_interval']))

def evaluate(args):
    # file paths
    system_pred_file = args['output_file']
    gold_file = args['gold_file']

    model_file = model_file_name(args)
    # load pretrained vectors if needed
    pretrain = load_pretrain(args)

    # load model
    logger.info("Loading model from: {}".format(model_file))
    use_cuda = args['cuda'] and not args['cpu']
    trainer = Trainer(pretrain=pretrain, model_file=model_file, use_cuda=use_cuda)
    loaded_args, vocab = trainer.args, trainer.vocab

    # load config
    for k in args:
        if k.endswith('_dir') or k.endswith('_file') or k in ['shorthand'] or k == 'mode':
            loaded_args[k] = args[k]

    # load data
    logger.info("Loading data with batch size {}...".format(args['batch_size']))
    doc = CoNLL.conll2doc(input_file=args['eval_file'])
    batch = DataLoader(doc, args['batch_size'], loaded_args, pretrain, vocab=vocab, evaluation=True, sort_during_eval=True)

    if len(batch) > 0:
        logger.info("Start evaluation...")
        preds = []
        for i, b in enumerate(batch):
            preds += trainer.predict(b)
    else:
        # skip eval if dev data does not exist
        preds = []
    preds = utils.unsort(preds, batch.data_orig_idx)

    # write to file and score
    batch.doc.set([HEAD, DEPREL], [y for x in preds for y in x])
    CoNLL.write_doc2conll(batch.doc, system_pred_file)

    if gold_file is not None:
        _, _, score = scorer.score(system_pred_file, gold_file)

        logger.info("Parser score:")
        logger.info("{} {:.2f}".format(args['shorthand'], score*100))

if __name__ == '__main__':
    main()