genienlp/decanlp/predict.py

#
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import os
from .utils.generic_dataset import Query
from .text import torchtext
from argparse import ArgumentParser
import ujson as json
import torch
import numpy as np
import random
import sys
import logging
from pprint import pformat

from .util import set_seed, preprocess_examples, load_config_json
from .metrics import compute_metrics
from .utils.embeddings import load_embeddings
from .tasks.registry import get_tasks
from . import models

logger = logging.getLogger(__name__)

def get_all_splits(args, new_vocab):
    splits = []
    for task in args.tasks:
        logger.info(f'Loading {task}')
        kwargs = {}
        if not 'train' in args.evaluate:
            kwargs['train'] = None
        if not 'valid' in args.evaluate:
            kwargs['validation'] = None
        if not 'test' in args.evaluate:
            kwargs['test'] = None

        kwargs['skip_cache_bool'] = args.skip_cache_bool
        kwargs['cached_path'] = args.cached
        s = task.get_splits(new_vocab, root=args.data, **kwargs)[0]
        preprocess_examples(args, [task], [s], new_vocab, train=False)
        splits.append(s)
    return splits


def prepare_data(args, FIELD):
    new_vocab = torchtext.data.ReversibleField(batch_first=True, init_token='<init>', eos_token='<eos>', lower=args.lower, include_lengths=True)
    splits = get_all_splits(args, new_vocab)
    new_vocab.build_vocab(*splits)
    logger.info(f'Vocabulary has {len(FIELD.vocab)} tokens from training')
    args.max_generative_vocab = min(len(FIELD.vocab), args.max_generative_vocab)
    FIELD.append_vocab(new_vocab)
    logger.info(f'Vocabulary has expanded to {len(FIELD.vocab)} tokens')
    vectors = load_embeddings(args)
    FIELD.vocab.load_vectors(vectors, True)
    FIELD.decoder_to_vocab = {idx: FIELD.vocab.stoi[word] for idx, word in enumerate(FIELD.decoder_itos)}
    FIELD.vocab_to_decoder = {idx: FIELD.decoder_stoi[word] for idx, word in enumerate(FIELD.vocab.itos) if word in FIELD.decoder_stoi}
    splits = get_all_splits(args, FIELD)

    return FIELD, splits


def to_iter(data, bs, device):
    Iterator = torchtext.data.Iterator
    it = Iterator(data, batch_size=bs, 
       device=device, batch_size_fn=None, 
       train=False, repeat=False, sort=False,
       shuffle=False, reverse=False)

    return it


def run(args, field, val_sets, model):
    device = set_seed(args)
    logger.info(f'Preparing iterators')
    if len(args.val_batch_size) == 1 and len(val_sets) > 1:
        args.val_batch_size *= len(val_sets)
    iters = [(name, to_iter(x, bs, device)) for name, x, bs in zip(args.tasks, val_sets, args.val_batch_size)]
 
    def mult(ps):
        r = 0
        for p in ps:
            this_r = 1
            for s in p.size():
                this_r *= s
            r += this_r
        return r
    params = list(filter(lambda p: p.requires_grad, model.parameters()))
    num_param = mult(params)
    logger.info(f'{args.model} has {num_param:,} parameters')
    model.to(device)

    decaScore = []
    model.eval()
    with torch.no_grad():
        for task, it in iters:
            logger.info(task.name)
            if args.eval_dir:
                prediction_file_name = os.path.join(args.eval_dir, os.path.join(args.evaluate, task.name + '.txt'))
                answer_file_name = os.path.join(args.eval_dir, os.path.join(args.evaluate, task.name + '.gold.txt'))
                results_file_name = answer_file_name.replace('gold', 'results')
            else:
                prediction_file_name = os.path.join(os.path.splitext(args.best_checkpoint)[0], args.evaluate, task.name + '.txt')
                answer_file_name = os.path.join(os.path.splitext(args.best_checkpoint)[0], args.evaluate, task.name + '.gold.txt')
                results_file_name = answer_file_name.replace('gold', 'results')
            if 'sql' in task.name or 'squad' in task.name:
                ids_file_name = answer_file_name.replace('gold', 'ids')
            if os.path.exists(prediction_file_name):
                logger.warning('** ', prediction_file_name, ' already exists -- this is where predictions are stored **')
                if args.overwrite:
                    logger.warning('**** overwriting ', prediction_file_name, ' ****')
            if os.path.exists(answer_file_name):
                logger.warning('** ', answer_file_name, ' already exists -- this is where ground truth answers are stored **')
                if args.overwrite:
                    logger.warning('**** overwriting ', answer_file_name, ' ****')
            if os.path.exists(results_file_name):
                logger.warning('** ', results_file_name, ' already exists -- this is where metrics are stored **')
                if args.overwrite:
                    logger.warning('**** overwriting ', results_file_name, ' ****')
                else:
                    with open(results_file_name) as results_file:
                        if not args.silent:
                            for l in results_file:
                                logger.debug(l)
                        metrics = json.loads(results_file.readlines()[0])
                        decaScore.append(metrics[task.metrics[0]])
                    continue

            for x in [prediction_file_name, answer_file_name, results_file_name]:
                os.makedirs(os.path.dirname(x), exist_ok=True)
    
            if not os.path.exists(prediction_file_name) or args.overwrite:
                with open(prediction_file_name, 'w') as prediction_file:
                    predictions = []
                    ids = []
                    for batch_idx, batch in enumerate(it):
                        _, p = model(batch, iteration=1)

                        p = field.reverse(p, detokenize=task.detokenize)

                        for i, pp in enumerate(p):
                            if 'sql' in task.name:
                                ids.append(int(batch.wikisql_id[i]))
                            if 'squad' in task.name:
                                ids.append(it.dataset.q_ids[int(batch.squad_id[i])])
                            prediction_file.write(json.dumps(pp) + '\n')
                            predictions.append(pp) 
                if 'sql' in task.name:
                    with open(ids_file_name, 'w') as id_file:
                        for i in ids:
                            id_file.write(json.dumps(i) + '\n')
                if 'squad' in task.name:
                    with open(ids_file_name, 'w') as id_file:
                        for i in ids:
                            id_file.write(i + '\n')
            else:
                with open(prediction_file_name) as prediction_file:
                    predictions = [x.strip() for x in prediction_file.readlines()] 
                if 'sql' in task.name or 'squad' in task.name:
                    with open(ids_file_name) as id_file:
                        ids = [int(x.strip()) for x in id_file.readlines()]
   
            def from_all_answers(an):
                return [it.dataset.all_answers[sid] for sid in an.tolist()] 
    
            if not os.path.exists(answer_file_name) or args.overwrite:
                with open(answer_file_name, 'w') as answer_file:
                    answers = []
                    for batch_idx, batch in enumerate(it):
                        if hasattr(batch, 'wikisql_id'):
                            a = from_all_answers(batch.wikisql_id.data.cpu())
                        elif hasattr(batch, 'squad_id'):
                            a = from_all_answers(batch.squad_id.data.cpu())
                        elif hasattr(batch, 'woz_id'):
                            a = from_all_answers(batch.woz_id.data.cpu())
                        else:
                            a = field.reverse(batch.answer.data, detokenize=task.detokenize)
                        for aa in a:
                            answers.append(aa) 
                            answer_file.write(json.dumps(aa) + '\n')
            else:
                with open(answer_file_name) as answer_file:
                    answers = [json.loads(x.strip()) for x in answer_file.readlines()] 
    
            if len(answers) > 0:
                if not os.path.exists(results_file_name) or args.overwrite:
                    metrics, answers = compute_metrics(predictions, answers, task.metrics, args=args)
                    with open(results_file_name, 'w') as results_file:
                        results_file.write(json.dumps(metrics) + '\n')
                else:
                    with open(results_file_name) as results_file:
                        metrics = json.loads(results_file.readlines()[0])
    
                if not args.silent:
                    for i, (p, a) in enumerate(zip(predictions, answers)):
                        logger.info(f'Prediction {i+1}: {p}\nAnswer {i+1}: {a}\n')
                    logger.info(metrics)
                decaScore.append(metrics[task.metrics[0]])

    logger.info(f'Evaluated Tasks:\n')
    for i, (task, _) in enumerate(iters):
        logger.info(f'{task.name}: {decaScore[i]}')
    logger.info(f'-------------------')
    logger.info(f'DecaScore:  {sum(decaScore)}\n')
    logger.info(f'\nSummary: | {sum(decaScore)} | {" | ".join([str(x) for x in decaScore])} |\n')


def get_args(argv):
    parser = ArgumentParser(prog=argv[0])
    parser.add_argument('--path', required=True)
    parser.add_argument('--evaluate', type=str, required=True)
    parser.add_argument('--tasks', default=['almond', 'squad', 'iwslt.en.de', 'cnn_dailymail', 'multinli.in.out', 'sst', 'srl', 'zre', 'woz.en', 'wikisql', 'schema'], dest='task_names', nargs='+')
    parser.add_argument('--devices', default=[0], nargs='+', type=int, help='a list of devices that can be used (multi-gpu currently WIP)')
    parser.add_argument('--seed', default=123, type=int, help='Random seed.')
    parser.add_argument('--data', default='./decaNLP/.data/', type=str, help='where to load data from.')
    parser.add_argument('--embeddings', default='./decaNLP/.embeddings', type=str, help='where to save embeddings.')
    parser.add_argument('--checkpoint_name', default='best.pth', help='Checkpoint file to use (relative to --path, defaults to best.pth)')
    parser.add_argument('--bleu', action='store_true', help='whether to use the bleu metric (always on for iwslt)')
    parser.add_argument('--rouge', action='store_true', help='whether to use the bleu metric (always on for cnn, dailymail, and cnn_dailymail)')
    parser.add_argument('--overwrite', action='store_true', help='whether to overwrite previously written predictions')
    parser.add_argument('--silent', action='store_true', help='whether to print predictions to stdout')

    parser.add_argument('--skip_cache', action='store_true', dest='skip_cache_bool', help='whether use exisiting cached splits or generate new ones')
    parser.add_argument('--eval_dir', type=str, default=None, help='use this directory to store eval results')
    parser.add_argument('--cached', default='', type=str, help='where to save cached files')

    args = parser.parse_args(argv[1:])
    args.tasks = get_tasks(args.task_names)

    load_config_json(args)
    return args


def main(argv=sys.argv):
    args = get_args(argv)
    logger.info(f'Arguments:\n{pformat(vars(args))}')

    np.random.seed(args.seed)
    random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    logger.info(f'Loading from {args.best_checkpoint}')

    if torch.cuda.is_available():
        save_dict = torch.load(args.best_checkpoint)
    else:
        save_dict = torch.load(args.best_checkpoint, map_location='cpu')

    field = save_dict['field']
    logger.info(f'Initializing Model')
    Model = getattr(models, args.model)
    model = Model(field, args)
    model_dict = save_dict['model_state_dict']
    backwards_compatible_cove_dict = {}
    for k, v in model_dict.items():
        if 'cove.rnn.' in k:
            k = k.replace('cove.rnn.', 'cove.rnn1.')
        backwards_compatible_cove_dict[k] = v
    model_dict = backwards_compatible_cove_dict
    model.load_state_dict(model_dict)
    field, splits = prepare_data(args, field)
    model.set_embeddings(field.vocab.vectors)

    run(args, field, splits, model)

if __name__ == '__main__':
    main()