# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2020-08-13 21:39
import logging
from typing import Dict, Any, Union, Iterable, List
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
from hanlp.common.dataset import SamplerBuilder, PadSequenceDataLoader
from hanlp.common.transform import VocabDict, TransformList
from hanlp.components.mtl.tasks import Task
from hanlp.components.parsers.biaffine.biaffine_model import BiaffineDecoder
from hanlp.components.parsers.biaffine.biaffine_sdp import BiaffineSemanticDependencyParser
from hanlp.layers.scalar_mix import ScalarMixWithDropoutBuilder
from hanlp.metrics.metric import Metric
from hanlp.metrics.mtl import MetricDict
from hanlp.utils.time_util import CountdownTimer
from hanlp_common.util import merge_locals_kwargs
[docs]class BiaffineSemanticDependencyParsing(Task, BiaffineSemanticDependencyParser):
def __init__(self,
trn: str = None,
dev: str = None,
tst: str = None,
sampler_builder: SamplerBuilder = None,
dependencies: str = None,
scalar_mix: ScalarMixWithDropoutBuilder = None,
use_raw_hidden_states=False,
lr=2e-3, separate_optimizer=False,
punct=False,
tree=True,
pad_rel=None,
apply_constraint=False,
single_root=True,
no_zero_head=None,
n_mlp_arc=500,
n_mlp_rel=100,
mlp_dropout=.33,
mu=.9,
nu=.9,
epsilon=1e-12,
decay=.75,
decay_steps=5000,
cls_is_bos=True,
use_pos=False,
**kwargs) -> None:
r"""Implementation of "Stanford's graph-based neural dependency parser at
the conll 2017 shared task" (:cite:`dozat2017stanford`) and "Establishing Strong Baselines for the New Decade"
(:cite:`he-choi-2019`).
Args:
trn: Path to training set.
dev: Path to dev set.
tst: Path to test set.
sampler_builder: A builder which builds a sampler.
dependencies: Its dependencies on other tasks.
scalar_mix: A builder which builds a `ScalarMixWithDropout` object.
use_raw_hidden_states: Whether to use raw hidden states from transformer without any pooling.
lr: Learning rate for this task.
separate_optimizer: Use customized separate optimizer for this task.
punct: ``True`` to include punctuations in evaluation.
pad_rel: Padding token for relations.
apply_constraint: Enforce constraints (see following parameters).
single_root: Force single root.
no_zero_head: Every token has at least one head.
n_mlp_arc: Number of features for arc representation.
n_mlp_rel: Number of features for rel representation.
mlp_dropout: Dropout applied to MLPs.
mu: First coefficient used for computing running averages of gradient and its square in Adam.
nu: Second coefficient used for computing running averages of gradient and its square in Adam.
epsilon: Term added to the denominator to improve numerical stability
decay: Decay rate for exceptional lr scheduler.
decay_steps: Decay every ``decay_steps`` steps.
cls_is_bos: ``True`` to treat the first token as ``BOS``.
use_pos: Use pos feature.
**kwargs: Not used.
"""
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.vocabs = VocabDict()
def update_metrics(self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
prediction: Dict[str, Any], metric: Union[MetricDict, Metric]):
BiaffineSemanticDependencyParser.update_metric(self, *prediction, batch['arc'], batch['rel_id'], output[1],
output[-1], metric, batch)
def decode_output(self,
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
mask: torch.BoolTensor,
batch: Dict[str, Any],
decoder, **kwargs) -> Union[Dict[str, Any], Any]:
(arc_scores, rel_scores), mask, punct_mask = output
return BiaffineSemanticDependencyParser.decode(self, arc_scores, rel_scores, mask, batch)
def compute_loss(self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any], criterion) -> \
Union[torch.FloatTensor, Dict[str, torch.FloatTensor]]:
(arc_scores, rel_scores), mask, punct_mask = output
return BiaffineSemanticDependencyParser.compute_loss(self, arc_scores, rel_scores, batch['arc'],
batch['rel_id'], mask, criterion,
batch)
[docs] def build_model(self, encoder_size, training=True, **kwargs) -> torch.nn.Module:
return BiaffineDecoder(encoder_size, self.config.n_mlp_arc, self.config.n_mlp_rel, self.config.mlp_dropout,
len(self.vocabs.rel))
[docs] def build_metric(self, **kwargs):
return BiaffineSemanticDependencyParser.build_metric(self, **kwargs)
[docs] def build_dataloader(self, data, transform: TransformList = None, training=False, device=None,
logger: logging.Logger = None, gradient_accumulation=1, **kwargs) -> DataLoader:
dataset = BiaffineSemanticDependencyParser.build_dataset(self, data, transform)
dataset.purge_cache()
if self.vocabs.mutable:
BiaffineSemanticDependencyParser.build_vocabs(self, dataset, logger, transformer=True)
if isinstance(data, str):
timer = CountdownTimer(len(dataset))
BiaffineSemanticDependencyParser.cache_dataset(self, dataset, timer, training, logger)
return PadSequenceDataLoader(
batch_sampler=self.sampler_builder.build(self.compute_lens(data, dataset),
shuffle=training, gradient_accumulation=gradient_accumulation),
device=device,
dataset=dataset,
pad=self.get_pad_dict())
def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tensor], mask: torch.BoolTensor,
decoder: torch.nn.Module):
logits = super().feed_batch(h, batch, mask, decoder)
arc_scores = logits[0]
mask = mask.clone()
mask[:, 0] = 0
mask = self.convert_to_3d_mask(arc_scores, mask)
punct_mask = self.convert_to_3d_puncts(batch.get('punct_mask', None), mask)
return logits, mask, punct_mask
[docs] def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
config = self.config
optimizer = Adam(decoder.parameters(),
config.lr,
(config.mu, config.nu),
config.epsilon)
scheduler = ExponentialLR(optimizer, config.decay ** (1 / config.decay_steps))
return optimizer, scheduler
def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict[str, Any]) -> List:
arcs, rels = prediction
arcs = arcs[:, 1:, :] # Skip the ROOT
rels = rels[:, 1:, :]
arcs = arcs.tolist()
rels = rels.tolist()
vocab = self.vocabs['rel'].idx_to_token
for arcs_per_sent, rels_per_sent, tokens in zip(arcs, rels, batch['token']):
tokens = tokens[1:]
sent_len = len(tokens)
result = []
for a, r in zip(arcs_per_sent[:sent_len], rels_per_sent[:sent_len]):
heads = [i for i in range(sent_len + 1) if a[i]]
deprels = [vocab[r[i]] for i in range(sent_len + 1) if a[i]]
result.append(list(zip(heads, deprels)))
yield result
[docs] def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
return BiaffineSemanticDependencyParser.build_samples(self, inputs, self.config.use_pos)
