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modules.py
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modules.py
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import json
import logging
import re
from copy import deepcopy
from typing import Dict, List, Iterable, Optional, Tuple, Union, Any
from overrides import overrides
import numpy
import torch
import transformers
from torch import nn
from torch.nn import functional as F
from allennlp.common.file_utils import cached_path
from allennlp.common.util import pad_sequence_to_length, JsonDict
from allennlp.data import Instance, DatasetReader, TextFieldTensors, Vocabulary
from allennlp.data.fields import TextField, LabelField, MetadataField
from allennlp.data.tokenizers import Token, Tokenizer, PretrainedTransformerTokenizer
from allennlp.data.token_indexers import TokenIndexer
from allennlp.data.token_indexers.token_indexer import IndexedTokenList
from allennlp.models import Model
from allennlp.modules import (TokenEmbedder, TextFieldEmbedder,
Seq2SeqEncoder, Seq2VecEncoder, FeedForward)
from allennlp.modules.token_embedders import Embedding, PretrainedTransformerEmbedder
from allennlp.nn.util import get_text_field_mask, masked_mean, masked_max
from allennlp.training.optimizers import Optimizer, make_parameter_groups
from allennlp.training.metrics import Metric, CategoricalAccuracy
from allennlp.predictors.predictor import Predictor
logger = logging.getLogger(__name__)
@DatasetReader.register('text_entity')
class TextEntityDatasetReader(DatasetReader):
def __init__(self,
tokenizer: Optional[Tokenizer],
token_indexers: Dict[str, TokenIndexer],
do_mask_entity_mentions: bool = False,
mask_token: Optional[str] = None,
lazy: bool = False) -> None:
super().__init__(lazy=lazy)
self.tokenizer = tokenizer
self.token_indexers = token_indexers
self.do_mask_entity_mentions = do_mask_entity_mentions
self.mask_token = mask_token
@overrides
def text_to_instance(self,
text: str,
entity: Optional[str] = None,
metadata: Dict[str, Any] = None) -> Instance:
fields = dict()
if self.do_mask_entity_mentions:
assert self.mask_token is not None
assert entity is not None
for mention in entity.replace('_', ' ').split():
if isinstance(self.tokenizer, PretrainedTransformerTokenizer):
mask_length = len(self.tokenizer.tokenize(mention)) \
- self.tokenizer.num_special_tokens_for_sequence()
else:
mask_length = len(self.tokenizer.tokenize(mention))
text = text.replace(mention, self.mask_token * mask_length)
text = text.replace(mention.lower(), self.mask_token * mask_length)
if self.tokenizer is not None:
tokens = self.tokenizer.tokenize(text)
fields['text'] = TextField(tokens, self.token_indexers)
if entity is not None:
fields['entity'] = LabelField(entity, 'entities')
else:
fields['entity'] = LabelField(-1, 'entities', skip_indexing=True)
if metadata is not None:
fields['metadata'] = MetadataField(metadata)
return Instance(fields)
@overrides
def _read(self,
file_path: str) -> Iterable[Instance]:
file_path = cached_path(file_path)
logger.info(f'Reading a dataset file at {file_path}')
if torch.distributed.is_initialized():
rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
else:
rank = 0
world_size = 1
with open(file_path) as dataset_file:
for i, line in enumerate(dataset_file):
item = json.loads(line)
instance = self.text_to_instance(
text=item['text'],
entity=item['entity'],
metadata=item
)
if i % world_size == rank:
if i < 20:
logger.info(f'Example: {instance}')
yield instance
@Model.register('quiz')
class QuizGuesser(Model):
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
seq2vec_encoder: Seq2VecEncoder,
seq2seq_encoder: Optional[Seq2SeqEncoder] = None,
feedforward: Optional[FeedForward] = None,
dropout: float = 0.0,
do_batch_norm: bool = False) -> None:
super(QuizGuesser, self).__init__(vocab)
self.text_field_embedder = text_field_embedder
self.seq2seq_encoder = seq2seq_encoder
self.seq2vec_encoder = seq2vec_encoder
self.feedforward = feedforward
if self.feedforward is not None:
entity_embedding_dim = self.feedforward.get_output_dim()
else:
entity_embedding_dim = self.seq2vec_encoder.get_output_dim()
num_entities = vocab.get_vocab_size('entities')
self.entity_embedder = Embedding(entity_embedding_dim, num_entities,
vocab_namespace='entities',
padding_index=vocab.get_token_index(vocab._oov_token, namespace='entities'))
self.dropout = nn.Dropout(dropout)
if do_batch_norm:
self.batch_norm = nn.BatchNorm1d(num_entities)
self.accuracy = CategoricalAccuracy(top_k=1)
self.mean_reciprocal_rank = MeanReciprocalRank()
def forward(self,
text: TextFieldTensors,
entity: torch.LongTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
output_dict = dict()
mask = get_text_field_mask(text)
encoded_text = self.text_field_embedder(text)
if self.seq2seq_encoder is not None:
encoded_text = self.seq2seq_encoder(encoded_text, mask=mask)
encoded_text = self.seq2vec_encoder(encoded_text, mask=mask)
encoded_text = self.dropout(encoded_text)
if self.feedforward is not None:
encoded_text = self.feedforward(encoded_text)
logits = F.linear(encoded_text, self.entity_embedder.weight)
if hasattr(self, 'batch_norm'):
logits = self.batch_norm(logits)
loss = F.cross_entropy(logits, entity)
output_dict['loss'] = loss
if not self.training:
log_probs = F.log_softmax(logits, dim=1)
output_dict['log_probs'] = log_probs
self.accuracy(log_probs, entity)
self.mean_reciprocal_rank(log_probs, entity)
output_dict['metadata'] = metadata
return output_dict
@overrides
def make_output_human_readable(
self,
output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
top10_labels_list = []
rank_list = []
predictions = output_dict['log_probs']
if predictions.dim() == 2:
prediction_list = [predictions[i] for i in range(predictions.shape[0])]
else:
prediction_list = [predictions]
label_list = [metadata['entity'] for metadata in output_dict['metadata']]
for prediction, label in zip(prediction_list, label_list):
top_label_ids = prediction.argsort(dim=-1, descending=True)
top_labels = [self.vocab.get_index_to_token_vocabulary('entities')[i]
for i in top_label_ids.tolist()]
if label in top_labels:
rank = top_labels.index(label) + 1
else:
rank = None
top10_labels_list.append(top_labels[:10])
rank_list.append(rank)
output_dict['top10_labels'] = top10_labels_list
output_dict['rank'] = rank_list
del output_dict['log_probs'] # delete it since it's really big
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
if self.training:
metrics = dict()
else:
metrics = {
'acc': self.accuracy.get_metric(reset),
'mrr': self.mean_reciprocal_rank.get_metric(reset)
}
return metrics
@Predictor.register('quiz')
class QuizPredictor(Predictor):
def predict(self, sentence: str) -> JsonDict:
return self.predict_json({'sentence': sentence})
@overrides
def _json_to_instance(self,
json_dict: JsonDict) -> Instance:
text = json_dict['text']
entity = json_dict.get('entity')
metadata = json_dict.get('metadata')
return self._dataset_reader.text_to_instance(text, entity, metadata)
@overrides
def predictions_to_labeled_instances(self,
instance: Instance,
outputs: Dict[str, numpy.ndarray]) -> List[Instance]:
new_instance = deepcopy(instance)
pred_entity = numpy.argmax(outputs['log_probs'])
new_instance.add_field('entity',
LabelField(int(pred_entity), 'entities', skip_indexing=True))
return new_instance
@Metric.register('mean_reciprocal_rank')
class MeanReciprocalRank(Metric):
def __init__(self) -> None:
self.summed_reciprocal_ranks = 0.0
self.total_count = 0
def __call__(self,
predictions: torch.Tensor,
gold_labels: torch.Tensor,
mask: Optional[torch.Tensor] = None) -> None:
predictions, gold_labels, mask = \
self.detach_tensors(predictions, gold_labels, mask)
num_classes = predictions.size(-1)
predictions = predictions.view((-1, num_classes))
gold_labels = gold_labels.view(-1).long()
predicted_ids = predictions.argsort(-1, descending=True)
correct = predicted_ids.eq(gold_labels.unsqueeze(-1)).float()
reciprocals = torch.arange(1, num_classes + 1,
device=correct.device).float().reciprocal()
reciprocal_ranks = torch.matmul(correct, reciprocals)
if mask is not None:
self.summed_reciprocal_ranks += reciprocal_ranks[mask].sum().item()
self.total_count += mask.sum().item()
else:
self.summed_reciprocal_ranks += reciprocal_ranks.sum().item()
self.total_count += gold_labels.numel()
def get_metric(self,
reset: bool = False) -> float:
if self.total_count > 0.0:
mean_reciprocal_rank = self.summed_reciprocal_ranks / self.total_count
else:
mean_reciprocal_rank = 0.0
if reset:
self.reset()
return mean_reciprocal_rank
@overrides
def reset(self) -> None:
self.summed_reciprocal_ranks = 0.0
self.total_count = 0