# -*- coding: utf-8 -*-
import os
from typing import Dict, Iterable, Set, Union
import torch
from supar.config import Config
from supar.models.const.aj.model import AttachJuxtaposeConstituencyModel
from supar.models.const.aj.transform import AttachJuxtaposeTree
from supar.parser import Parser
from supar.utils import Dataset, Embedding
from supar.utils.common import BOS, EOS, NUL, PAD, UNK
from supar.utils.field import Field, RawField, SubwordField
from supar.utils.logging import get_logger
from supar.utils.metric import SpanMetric
from supar.utils.tokenizer import TransformerTokenizer
from supar.utils.transform import Batch
logger = get_logger(__name__)
[docs]class AttachJuxtaposeConstituencyParser(Parser):
r"""
The implementation of AttachJuxtapose Constituency Parser :cite:`yang-deng-2020-aj`.
"""
NAME = 'attach-juxtapose-constituency'
MODEL = AttachJuxtaposeConstituencyModel
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.TREE = self.transform.TREE
self.NODE = self.transform.NODE
self.PARENT = self.transform.PARENT
self.NEW = self.transform.NEW
[docs] def train(
self,
train: Union[str, Iterable],
dev: Union[str, Iterable],
test: Union[str, Iterable],
epochs: int = 1000,
patience: int = 100,
batch_size: int = 5000,
update_steps: int = 1,
buckets: int = 32,
workers: int = 0,
amp: bool = False,
cache: bool = False,
beam_size: int = 1,
delete: Set = {'TOP', 'S1', '-NONE-', ',', ':', '``', "''", '.', '?', '!', ''},
equal: Dict = {'ADVP': 'PRT'},
verbose: bool = True,
**kwargs
):
return super().train(**Config().update(locals()))
[docs] def evaluate(
self,
data: Union[str, Iterable],
batch_size: int = 5000,
buckets: int = 8,
workers: int = 0,
amp: bool = False,
cache: bool = False,
beam_size: int = 1,
delete: Set = {'TOP', 'S1', '-NONE-', ',', ':', '``', "''", '.', '?', '!', ''},
equal: Dict = {'ADVP': 'PRT'},
verbose: bool = True,
**kwargs
):
return super().evaluate(**Config().update(locals()))
[docs] def predict(
self,
data: Union[str, Iterable],
pred: str = None,
lang: str = None,
prob: bool = False,
batch_size: int = 5000,
buckets: int = 8,
workers: int = 0,
amp: bool = False,
cache: bool = False,
beam_size: int = 1,
verbose: bool = True,
**kwargs
):
return super().predict(**Config().update(locals()))
def train_step(self, batch: Batch) -> torch.Tensor:
words, *feats, _, nodes, parents, news = batch
mask = batch.mask[:, 2:]
x = self.model(words, feats)[:, 1:-1]
loss = self.model.loss(x, nodes, parents, news, mask)
return loss
@torch.no_grad()
def eval_step(self, batch: Batch) -> SpanMetric:
words, *feats, trees, nodes, parents, news = batch
mask = batch.mask[:, 2:]
x = self.model(words, feats)[:, 1:-1]
loss = self.model.loss(x, nodes, parents, news, mask)
chart_preds = self.model.decode(x, mask, self.args.beam_size)
preds = [AttachJuxtaposeTree.build(tree, [(i, j, self.NEW.vocab[label]) for i, j, label in chart], {UNK, NUL})
for tree, chart in zip(trees, chart_preds)]
return SpanMetric(loss,
[AttachJuxtaposeTree.factorize(tree, self.args.delete, self.args.equal) for tree in preds],
[AttachJuxtaposeTree.factorize(tree, self.args.delete, self.args.equal) for tree in trees])
@torch.no_grad()
def pred_step(self, batch: Batch) -> Batch:
words, *feats, trees = batch
mask = batch.mask[:, 2:]
x = self.model(words, feats)[:, 1:-1]
chart_preds = self.model.decode(x, mask, self.args.beam_size)
batch.trees = [AttachJuxtaposeTree.build(tree, [(i, j, self.NEW.vocab[label]) for i, j, label in chart], {UNK, NUL})
for tree, chart in zip(trees, chart_preds)]
if self.args.prob:
raise NotImplementedError("Returning action probs are currently not supported yet.")
return batch
[docs] @classmethod
def build(cls, path, min_freq=2, fix_len=20, **kwargs):
r"""
Build a brand-new Parser, including initialization of all data fields and model parameters.
Args:
path (str):
The path of the model to be saved.
min_freq (str):
The minimum frequency needed to include a token in the vocabulary. Default: 2.
fix_len (int):
The max length of all subword pieces. The excess part of each piece will be truncated.
Required if using CharLSTM/BERT.
Default: 20.
kwargs (Dict):
A dict holding the unconsumed arguments.
"""
args = Config(**locals())
os.makedirs(os.path.dirname(path) or './', exist_ok=True)
if os.path.exists(path) and not args.build:
parser = cls.load(**args)
parser.model = cls.MODEL(**parser.args)
parser.model.load_pretrained(parser.transform.WORD[0].embed).to(parser.device)
return parser
logger.info("Building the fields")
TAG, CHAR, ELMO, BERT = None, None, None, None
if args.encoder == 'bert':
t = TransformerTokenizer(args.bert)
WORD = SubwordField('words', pad=t.pad, unk=t.unk, bos=t.bos, eos=t.eos, fix_len=args.fix_len, tokenize=t)
WORD.vocab = t.vocab
else:
WORD = Field('words', pad=PAD, unk=UNK, bos=BOS, eos=EOS, lower=True)
if 'tag' in args.feat:
TAG = Field('tags', bos=BOS, eos=EOS)
if 'char' in args.feat:
CHAR = SubwordField('chars', pad=PAD, unk=UNK, bos=BOS, eos=EOS, fix_len=args.fix_len)
if 'elmo' in args.feat:
from allennlp.modules.elmo import batch_to_ids
ELMO = RawField('elmo')
ELMO.compose = lambda x: batch_to_ids(x).to(WORD.device)
if 'bert' in args.feat:
t = TransformerTokenizer(args.bert)
BERT = SubwordField('bert', pad=t.pad, unk=t.unk, bos=t.bos, eos=t.eos, fix_len=args.fix_len, tokenize=t)
BERT.vocab = t.vocab
TREE = RawField('trees')
NODE, PARENT, NEW = Field('node', use_vocab=False), Field('parent', unk=UNK), Field('new', unk=UNK)
transform = AttachJuxtaposeTree(WORD=(WORD, CHAR, ELMO, BERT), POS=TAG, TREE=TREE, NODE=NODE, PARENT=PARENT, NEW=NEW)
train = Dataset(transform, args.train, **args)
if args.encoder != 'bert':
WORD.build(train, args.min_freq, (Embedding.load(args.embed) if args.embed else None), lambda x: x / torch.std(x))
if TAG is not None:
TAG.build(train)
if CHAR is not None:
CHAR.build(train)
PARENT, NEW = PARENT.build(train), NEW.build(train)
PARENT.vocab = NEW.vocab.update(PARENT.vocab)
args.update({
'n_words': len(WORD.vocab) if args.encoder == 'bert' else WORD.vocab.n_init,
'n_labels': len(NEW.vocab),
'n_tags': len(TAG.vocab) if TAG is not None else None,
'n_chars': len(CHAR.vocab) if CHAR is not None else None,
'char_pad_index': CHAR.pad_index if CHAR is not None else None,
'bert_pad_index': BERT.pad_index if BERT is not None else None,
'pad_index': WORD.pad_index,
'unk_index': WORD.unk_index,
'bos_index': WORD.bos_index,
'eos_index': WORD.eos_index,
'nul_index': NEW.vocab[NUL]
})
logger.info(f"{transform}")
logger.info("Building the model")
model = cls.MODEL(**args).load_pretrained(WORD.embed if hasattr(WORD, 'embed') else None)
logger.info(f"{model}\n")
parser = cls(args, model, transform)
parser.model.to(parser.device)
return parser