wehub-resource-sync
167 lines
8.0 KiB
Python
167 lines
8.0 KiB
Python
# -*- coding:utf-8 -*-
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# Author: hankcs
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# Date: 2020-08-13 21:39
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import logging
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from typing import Dict, Any, Union, Iterable, List
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import torch
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from torch.optim import Adam
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from torch.optim.lr_scheduler import ExponentialLR
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from torch.utils.data import DataLoader
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from hanlp.common.dataset import SamplerBuilder, PadSequenceDataLoader
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from hanlp.common.transform import VocabDict, TransformList
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from hanlp.components.mtl.tasks import Task
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from hanlp.components.parsers.biaffine.biaffine_dep import BiaffineDependencyParser
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from hanlp.components.parsers.biaffine.biaffine_model import BiaffineDecoder
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from hanlp.datasets.parsing.loaders.conll_dataset import append_bos
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from hanlp.layers.scalar_mix import ScalarMixWithDropoutBuilder
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from hanlp.metrics.metric import Metric
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from hanlp.metrics.mtl import MetricDict
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from hanlp.utils.time_util import CountdownTimer
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from hanlp_common.constant import EOS
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from hanlp_common.util import merge_locals_kwargs
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class BiaffineDependencyParsing(Task, BiaffineDependencyParser):
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def __init__(self,
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trn: str = None,
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dev: str = None,
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tst: str = None,
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sampler_builder: SamplerBuilder = None,
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dependencies: str = None,
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scalar_mix: ScalarMixWithDropoutBuilder = None,
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use_raw_hidden_states=False,
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lr=2e-3, separate_optimizer=False,
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cls_is_bos=True,
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sep_is_eos=False,
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punct=False,
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tree=False,
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proj=False,
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n_mlp_arc=500,
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n_mlp_rel=100,
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mlp_dropout=.33,
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mu=.9,
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nu=.9,
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epsilon=1e-12,
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decay=.75,
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decay_steps=5000,
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use_pos=False,
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max_seq_len=None,
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**kwargs) -> None:
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"""Biaffine dependency parsing (:cite:`dozat:17a`).
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Args:
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trn: Path to training set.
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dev: Path to dev set.
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tst: Path to test set.
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sampler_builder: A builder which builds a sampler.
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dependencies: Its dependencies on other tasks.
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scalar_mix: A builder which builds a `ScalarMixWithDropout` object.
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use_raw_hidden_states: Whether to use raw hidden states from transformer without any pooling.
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lr: Learning rate for this task.
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separate_optimizer: Use customized separate optimizer for this task.
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cls_is_bos: ``True`` to treat the first token as ``BOS``.
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sep_is_eos: ``True`` to treat the last token as ``EOS``.
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punct: ``True`` to include punctuations in evaluation.
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tree: ``True`` to enforce tree constraint.
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proj: ``True`` for projective parsing.
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n_mlp_arc: Number of features for arc representation.
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n_mlp_rel: Number of features for rel representation.
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mlp_dropout: Dropout applied to MLPs.
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mu: First coefficient used for computing running averages of gradient and its square in Adam.
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nu: Second coefficient used for computing running averages of gradient and its square in Adam.
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epsilon: Term added to the denominator to improve numerical stability
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decay: Decay rate for exceptional lr scheduler.
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decay_steps: Decay every ``decay_steps`` steps.
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use_pos: Use pos feature.
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max_seq_len: Prune samples longer than this length.
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**kwargs: Not used.
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"""
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super().__init__(**merge_locals_kwargs(locals(), kwargs))
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self.vocabs = VocabDict()
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def update_metrics(self, batch: Dict[str, Any],
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output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
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prediction: Dict[str, Any], metric: Union[MetricDict, Metric]):
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BiaffineDependencyParser.update_metric(self, *prediction, batch['arc'], batch['rel_id'], output[1],
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batch.get('punct_mask', None), metric, batch)
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def decode_output(self,
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output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
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mask: torch.BoolTensor,
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batch: Dict[str, Any],
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decoder, **kwargs) -> Union[Dict[str, Any], Any]:
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(arc_scores, rel_scores), mask = output
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return BiaffineDependencyParser.decode(self, arc_scores, rel_scores, mask, batch)
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def compute_loss(self, batch: Dict[str, Any],
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output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any], criterion) -> \
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Union[torch.FloatTensor, Dict[str, torch.FloatTensor]]:
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(arc_scores, rel_scores), mask = output
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return BiaffineDependencyParser.compute_loss(self, arc_scores, rel_scores, batch['arc'], batch['rel_id'], mask,
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criterion,
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batch)
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def build_model(self, encoder_size, training=True, **kwargs) -> torch.nn.Module:
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return BiaffineDecoder(encoder_size, self.config.n_mlp_arc, self.config.n_mlp_rel, self.config.mlp_dropout,
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len(self.vocabs.rel))
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def build_metric(self, **kwargs):
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return BiaffineDependencyParser.build_metric(self, **kwargs)
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def build_dataloader(self, data, transform: TransformList = None, training=False, device=None,
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logger: logging.Logger = None, gradient_accumulation=1, **kwargs) -> DataLoader:
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transform.insert(0, append_bos)
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dataset = BiaffineDependencyParser.build_dataset(self, data, transform)
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dataset.purge_cache()
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if self.vocabs.mutable:
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BiaffineDependencyParser.build_vocabs(self, dataset, logger, transformer=True)
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if isinstance(data, str):
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timer = CountdownTimer(len(dataset))
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BiaffineDependencyParser.cache_dataset(self, dataset, timer, training, logger)
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max_seq_len = self.config.get('max_seq_len', None)
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if max_seq_len and isinstance(data, str):
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dataset.prune(lambda x: len(x['token_input_ids']) > max_seq_len, logger)
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return PadSequenceDataLoader(
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batch_sampler=self.sampler_builder.build(self.compute_lens(data, dataset),
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shuffle=training, gradient_accumulation=gradient_accumulation),
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device=device,
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dataset=dataset,
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pad=self.get_pad_dict())
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def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tensor], mask: torch.BoolTensor,
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decoder: torch.nn.Module):
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logits = super().feed_batch(h, batch, mask, decoder)
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mask = mask.clone()
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mask[:, 0] = 0
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return logits, mask
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def build_optimizer(self, decoder: torch.nn.Module, **kwargs):
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config = self.config
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optimizer = Adam(decoder.parameters(),
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config.lr,
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(config.mu, config.nu),
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config.epsilon)
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scheduler = ExponentialLR(optimizer, config.decay ** (1 / config.decay_steps))
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return optimizer, scheduler
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def input_is_flat(self, data) -> bool:
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return BiaffineDependencyParser.input_is_flat(self, data, self.config.use_pos)
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def prediction_to_result(self, prediction: Dict[str, Any], batch: Dict[str, Any]) -> List:
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arcs, rels = prediction
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arcs = arcs[:, 1:] # Skip the ROOT
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rels = rels[:, 1:]
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arcs = arcs.tolist()
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rels = rels.tolist()
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vocab = self.vocabs['rel'].idx_to_token
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for arcs_per_sent, rels_per_sent, tokens in zip(arcs, rels, batch['token']):
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tokens = tokens[1:]
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sent_len = len(tokens)
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result = list(zip(arcs_per_sent[:sent_len], [vocab[r] for r in rels_per_sent[:sent_len]]))
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yield result
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def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
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return [{'FORM': token + ([EOS] if sep_is_eos else [])} for token in inputs]
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