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|
- #!/usr/bin/env python3 -u
- # Copyright (c) 2017-present, Facebook, Inc.
- # All rights reserved.
- #
- # This source code is licensed under the license found in the LICENSE file in
- # the root directory of this source tree. An additional grant of patent rights
- # can be found in the PATENTS file in the same directory.
- """
- Evaluate the perplexity of a trained language model.
- """
- import numpy as np
- import torch
- from fairseq import options, progress_bar, tasks, utils
- from fairseq.meters import StopwatchMeter, TimeMeter
- from fairseq.sequence_scorer import SequenceScorer
- from fairseq.utils import import_user_module
- class WordStat(object):
- def __init__(self, word, is_bpe):
- self.word = word
- self.is_bpe = is_bpe
- self.log_prob = 0
- self.next_word_prob = 0
- self.count = 0
- self.missing_next_words = 0
- def add(self, log_prob, next_word_prob):
- """ increments counters for the sum of log probs of current word and next
- word (given context ending at current word). Since the next word might be at the end of the example,
- or it might be not counted because it is not an ending subword unit,
- also keeps track of how many of those we have seen """
- if next_word_prob is not None:
- self.next_word_prob += next_word_prob
- else:
- self.missing_next_words += 1
- self.log_prob += log_prob
- self.count += 1
- def __str__(self):
- return '{}\t{}\t{}\t{}\t{}\t{}'.format(self.word, self.count, self.log_prob, self.is_bpe,
- self.next_word_prob, self.count - self.missing_next_words)
- def main(parsed_args):
- assert parsed_args.path is not None, '--path required for evaluation!'
- import_user_module(parsed_args)
- print(parsed_args)
- use_cuda = torch.cuda.is_available() and not parsed_args.cpu
- task = tasks.setup_task(parsed_args)
- # Load ensemble
- print('| loading model(s) from {}'.format(parsed_args.path))
- models, args = utils.load_ensemble_for_inference(
- parsed_args.path.split(':'), task, model_arg_overrides=eval(parsed_args.model_overrides),
- )
- for arg in vars(parsed_args).keys():
- if arg not in {'self_target', 'future_target', 'past_target', 'tokens_per_sample', 'output_size_dictionary'}:
- setattr(args, arg, getattr(parsed_args, arg))
- task = tasks.setup_task(args)
- # Load dataset splits
- task.load_dataset(args.gen_subset)
- print('| {} {} {} examples'.format(args.data, args.gen_subset, len(task.dataset(args.gen_subset))))
- # Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
- for model in models:
- model.make_generation_fast_()
- if args.fp16:
- model.half()
- assert len(models) > 0
- print('num. model params: {}'.format(sum(p.numel() for p in models[0].parameters())))
- itr = task.get_batch_iterator(
- dataset=task.dataset(args.gen_subset),
- max_tokens=args.max_tokens or 36000,
- max_sentences=args.max_sentences,
- max_positions=utils.resolve_max_positions(*[
- model.max_positions() for model in models
- ]),
- ignore_invalid_inputs=True,
- num_shards=args.num_shards,
- shard_id=args.shard_id,
- num_workers=args.num_workers,
- ).next_epoch_itr(shuffle=False)
- gen_timer = StopwatchMeter()
- scorer = SequenceScorer(models, task.target_dictionary)
- if use_cuda:
- scorer.cuda()
- score_sum = 0.
- count = 0
- if args.remove_bpe is not None:
- bpe_cont = args.remove_bpe.rstrip()
- bpe_toks = set(i for i in range(len(task.dictionary)) if task.dictionary[i].endswith(bpe_cont))
- bpe_len = len(bpe_cont)
- else:
- bpe_toks = None
- bpe_len = 0
- word_stats = dict()
- with progress_bar.build_progress_bar(args, itr) as t:
- results = scorer.score_batched_itr(t, cuda=use_cuda, timer=gen_timer)
- wps_meter = TimeMeter()
- for _, src_tokens, __, hypos in results:
- for hypo in hypos:
- pos_scores = hypo['positional_scores']
- skipped_toks = 0
- if bpe_toks is not None:
- for i in range(len(hypo['tokens']) - 1):
- if hypo['tokens'][i].item() in bpe_toks:
- skipped_toks += 1
- pos_scores[i + 1] += pos_scores[i]
- pos_scores[i] = 0
- inf_scores = pos_scores.eq(float('inf')) | pos_scores.eq(float('-inf'))
- if inf_scores.any():
- print('| Skipping tokens with inf scores:',
- task.target_dictionary.string(hypo['tokens'][inf_scores.nonzero()]))
- pos_scores = pos_scores[(~inf_scores).nonzero()]
- score_sum += pos_scores.sum().cpu()
- count += pos_scores.numel() - skipped_toks
- if args.output_word_probs or args.output_word_stats:
- w = ''
- word_prob = []
- is_bpe = False
- for i in range(len(hypo['tokens'])):
- w_ind = hypo['tokens'][i].item()
- w += task.dictionary[w_ind]
- if bpe_toks is not None and w_ind in bpe_toks:
- w = w[:-bpe_len]
- is_bpe = True
- else:
- word_prob.append((w, pos_scores[i].item()))
- next_prob = None
- ind = i + 1
- while ind < len(hypo['tokens']):
- if pos_scores[ind].item() != 0:
- next_prob = pos_scores[ind]
- break
- ind += 1
- word_stats.setdefault(w, WordStat(w, is_bpe)).add(pos_scores[i].item(), next_prob)
- is_bpe = False
- w = ''
- if args.output_word_probs:
- print('\t'.join('{} [{:2f}]'.format(x[0], x[1]) for x in word_prob))
- wps_meter.update(src_tokens.size(0))
- t.log({'wps': round(wps_meter.avg)})
- avg_nll_loss = -score_sum / count
- print('| Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format(gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
- print('| Loss: {:.4f}, Perplexity: {:.2f}'.format(avg_nll_loss, np.exp(avg_nll_loss)))
- if args.output_word_stats:
- for ws in sorted(word_stats.values(), key=lambda x: x.count, reverse=True):
- print(ws)
- def cli_main():
- parser = options.get_eval_lm_parser()
- args = options.parse_args_and_arch(parser)
- main(args)
- if __name__ == '__main__':
- cli_main()
|