Source code for hanlp.components.classifiers.transformer_classifier

# -*- coding:utf-8 -*-
# Author: hankcs
# Date: 2020-06-08 16:31
import logging
from abc import ABC
from typing import Callable, Union
from typing import List

import torch
from torch import nn
from torch.utils.data import DataLoader

from hanlp_common.constant import IDX
from hanlp.common.dataset import TableDataset, SortingSampler, PadSequenceDataLoader, TransformableDataset
from hanlp.common.torch_component import TorchComponent
from hanlp.common.vocab import Vocab
from hanlp.components.distillation.schedulers import LinearTeacherAnnealingScheduler
from hanlp.layers.scalar_mix import ScalarMixWithDropoutBuilder
from hanlp.layers.transformers.encoder import TransformerEncoder
from hanlp.layers.transformers.pt_imports import PreTrainedModel, AutoTokenizer, BertTokenizer, AutoTokenizer_
from hanlp.layers.transformers.utils import transformer_sliding_window, build_optimizer_scheduler_with_transformer
from hanlp.metrics.accuracy import CategoricalAccuracy
from hanlp.transform.transformer_tokenizer import TransformerTextTokenizer
from hanlp.utils.time_util import CountdownTimer
from hanlp_common.util import merge_locals_kwargs, merge_dict, isdebugging


class TransformerClassificationModel(nn.Module):

    def __init__(self,
                 transformer: PreTrainedModel,
                 num_labels: int,
                 max_seq_length=512) -> None:
        super().__init__()
        self.max_seq_length = max_seq_length
        self.transformer = transformer
        self.dropout = nn.Dropout(transformer.config.hidden_dropout_prob)
        self.classifier = nn.Linear(transformer.config.hidden_size, num_labels)

    def forward(self, input_ids, attention_mask, token_type_ids):
        seq_length = input_ids.size(-1)
        if seq_length > self.max_seq_length:
            sequence_output = transformer_sliding_window(self.transformer, input_ids,
                                                         max_pieces=self.max_seq_length, ret_cls='max')
        else:
            sequence_output = self.transformer(input_ids, attention_mask, token_type_ids)[0][:, 0, :]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        return logits


class TransformerComponent(TorchComponent, ABC):
    def __init__(self, **kwargs) -> None:
        """ The base class for transorfmer based components. If offers methods to build transformer tokenizers
        , optimizers and models.

        Args:
            **kwargs: Passed to config.
        """
        super().__init__(**kwargs)
        self.transformer_tokenizer = None

    def build_optimizer(self,
                        trn,
                        epochs,
                        lr,
                        adam_epsilon,
                        weight_decay,
                        warmup_steps,
                        transformer_lr=None,
                        teacher=None,
                        **kwargs):
        num_training_steps = len(trn) * epochs // self.config.get('gradient_accumulation', 1)
        if transformer_lr is None:
            transformer_lr = lr
        transformer = self.model.encoder.transformer
        optimizer, scheduler = build_optimizer_scheduler_with_transformer(self.model, transformer,
                                                                          lr, transformer_lr,
                                                                          num_training_steps, warmup_steps,
                                                                          weight_decay, adam_epsilon)
        if teacher:
            lambda_scheduler = LinearTeacherAnnealingScheduler(num_training_steps)
            scheduler = (scheduler, lambda_scheduler)
        return optimizer, scheduler

    def fit(self, trn_data, dev_data, save_dir,
            transformer=None,
            lr=5e-5,
            transformer_lr=None,
            adam_epsilon=1e-8,
            weight_decay=0,
            warmup_steps=0.1,
            batch_size=32,
            gradient_accumulation=1,
            grad_norm=5.0,
            transformer_grad_norm=None,
            average_subwords=False,
            scalar_mix: Union[ScalarMixWithDropoutBuilder, int] = None,
            word_dropout=None,
            hidden_dropout=None,
            max_seq_len=None,
            ret_raw_hidden_states=False,
            batch_max_tokens=None,
            epochs=3,
            logger=None,
            devices: Union[float, int, List[int]] = None,
            **kwargs):
        return super().fit(**merge_locals_kwargs(locals(), kwargs))

    def on_config_ready(self, **kwargs):
        super().on_config_ready(**kwargs)
        if 'albert_chinese' in self.config.transformer:
            self.transformer_tokenizer = BertTokenizer.from_pretrained(self.config.transformer, use_fast=True)
        else:
            self.transformer_tokenizer = AutoTokenizer_.from_pretrained(self.config.transformer, use_fast=True)

    def build_transformer(self, training=True):
        transformer = TransformerEncoder(self.config.transformer, self.transformer_tokenizer,
                                         self.config.average_subwords,
                                         self.config.scalar_mix, self.config.word_dropout,
                                         self.config.max_seq_len, self.config.ret_raw_hidden_states,
                                         training=training)
        transformer_layers = self.config.get('transformer_layers', None)
        if transformer_layers:
            transformer.transformer.encoder.layer = transformer.transformer.encoder.layer[:-transformer_layers]
        return transformer


[docs]class TransformerClassifier(TransformerComponent): def __init__(self, **kwargs) -> None: """A classifier using transformer as encoder. Args: **kwargs: Passed to config. """ super().__init__(**kwargs) self.model: TransformerClassificationModel = None
[docs] def build_criterion(self, **kwargs): criterion = nn.CrossEntropyLoss() return criterion
[docs] def build_metric(self, **kwargs): return CategoricalAccuracy()
[docs] def execute_training_loop(self, trn: DataLoader, dev: DataLoader, epochs, criterion, optimizer, metric, save_dir, logger: logging.Logger, devices, **kwargs): best_epoch, best_metric = 0, -1 timer = CountdownTimer(epochs) ratio_width = len(f'{len(trn)}/{len(trn)}') for epoch in range(1, epochs + 1): logger.info(f"[yellow]Epoch {epoch} / {epochs}:[/yellow]") self.fit_dataloader(trn, criterion, optimizer, metric, logger) if dev: self.evaluate_dataloader(dev, criterion, metric, logger, ratio_width=ratio_width) report = f'{timer.elapsed_human}/{timer.total_time_human}' dev_score = metric.get_metric() if dev_score > best_metric: self.save_weights(save_dir) best_metric = dev_score report += ' [red]saved[/red]' timer.log(report, ratio_percentage=False, newline=True, ratio=False)
@property def label_vocab(self): return self.vocabs[self.config.label_key]
[docs] def fit_dataloader(self, trn: DataLoader, criterion, optimizer, metric, logger: logging.Logger, **kwargs): self.model.train() timer = CountdownTimer(len(trn)) optimizer, scheduler = optimizer total_loss = 0 metric.reset() for batch in trn: optimizer.zero_grad() logits = self.feed_batch(batch) target = batch['label_id'] loss = self.compute_loss(criterion, logits, target, batch) loss.backward() optimizer.step() scheduler.step() total_loss += loss.item() self.update_metric(metric, logits, target) timer.log(f'loss: {total_loss / (timer.current + 1):.4f} acc: {metric.get_metric():.2%}', ratio_percentage=None, logger=logger) del loss return total_loss / timer.total
def update_metric(self, metric, logits: torch.Tensor, target, output=None): metric(logits, target) if output: label_ids = logits.argmax(-1) return label_ids def compute_loss(self, criterion, logits, target, batch): loss = criterion(logits, target) return loss def feed_batch(self, batch) -> torch.LongTensor: logits = self.model(*[batch[key] for key in ['input_ids', 'attention_mask', 'token_type_ids']]) return logits # noinspection PyMethodOverriding
[docs] def evaluate_dataloader(self, data: DataLoader, criterion: Callable, metric, logger, ratio_width=None, filename=None, output=None, **kwargs): self.model.eval() timer = CountdownTimer(len(data)) total_loss = 0 metric.reset() num_samples = 0 if output: output = open(output, 'w') for batch in data: logits = self.feed_batch(batch) target = batch['label_id'] loss = self.compute_loss(criterion, logits, target, batch) total_loss += loss.item() label_ids = self.update_metric(metric, logits, target, output) if output: labels = [self.vocabs[self.config.label_key].idx_to_token[i] for i in label_ids.tolist()] for i, label in enumerate(labels): # text_a text_b pred gold columns = [batch[self.config.text_a_key][i]] if self.config.text_b_key: columns.append(batch[self.config.text_b_key][i]) columns.append(label) columns.append(batch[self.config.label_key][i]) output.write('\t'.join(columns)) output.write('\n') num_samples += len(target) report = f'loss: {total_loss / (timer.current + 1):.4f} acc: {metric.get_metric():.2%}' if filename: report = f'{filename} {report} {num_samples / timer.elapsed:.0f} samples/sec' timer.log(report, ratio_percentage=None, logger=logger, ratio_width=ratio_width) if output: output.close() return total_loss / timer.total
# noinspection PyMethodOverriding
[docs] def build_model(self, transformer, training=True, **kwargs) -> torch.nn.Module: # config: PretrainedConfig = AutoConfig.from_pretrained(transformer) # config.num_labels = len(self.vocabs.label) # config.hidden_dropout_prob = self.config.hidden_dropout_prob transformer = self.build_transformer(training=training).transformer model = TransformerClassificationModel(transformer, len(self.vocabs.label)) # truncated_normal_(model.classifier.weight, mean=0.02, std=0.05) return model
# noinspection PyMethodOverriding
[docs] def build_dataloader(self, data, batch_size, shuffle, device, text_a_key, text_b_key, label_key, logger: logging.Logger = None, sorting=True, **kwargs) -> DataLoader: if not batch_size: batch_size = self.config.batch_size dataset = self.build_dataset(data) dataset.append_transform(self.vocabs) if self.vocabs.mutable: if not any([text_a_key, text_b_key]): if len(dataset.headers) == 2: self.config.text_a_key = dataset.headers[0] self.config.label_key = dataset.headers[1] elif len(dataset.headers) >= 3: self.config.text_a_key, self.config.text_b_key, self.config.label_key = dataset.headers[0], \ dataset.headers[1], \ dataset.headers[-1] else: raise ValueError('Wrong dataset format') report = {'text_a_key', 'text_b_key', 'label_key'} report = dict((k, self.config[k]) for k in report) report = [f'{k}={v}' for k, v in report.items() if v] report = ', '.join(report) logger.info(f'Guess [bold][blue]{report}[/blue][/bold] according to the headers of training dataset: ' f'[blue]{dataset}[/blue]') self.build_vocabs(dataset, logger) dataset.purge_cache() # if self.config.transform: # dataset.append_transform(self.config.transform) dataset.append_transform(TransformerTextTokenizer(tokenizer=self.transformer_tokenizer, text_a_key=self.config.text_a_key, text_b_key=self.config.text_b_key, max_seq_length=self.config.max_seq_length, truncate_long_sequences=self.config.truncate_long_sequences, output_key='')) batch_sampler = None if sorting and not isdebugging(): if dataset.cache and len(dataset) > 1000: timer = CountdownTimer(len(dataset)) lens = [] for idx, sample in enumerate(dataset): lens.append(len(sample['input_ids'])) timer.log('Pre-processing and caching dataset [blink][yellow]...[/yellow][/blink]', ratio_percentage=None) else: lens = [len(sample['input_ids']) for sample in dataset] batch_sampler = SortingSampler(lens, batch_size=batch_size, shuffle=shuffle, batch_max_tokens=self.config.batch_max_tokens) return PadSequenceDataLoader(dataset, batch_size, shuffle, batch_sampler=batch_sampler, device=device)
def build_dataset(self, data) -> TransformableDataset: if isinstance(data, str): dataset = TableDataset(data, cache=True) elif isinstance(data, TableDataset): dataset = data elif isinstance(data, list): dataset = TableDataset(data) else: raise ValueError(f'Unsupported data {data}') return dataset
[docs] def predict(self, data: Union[str, List[str]], batch_size: int = None, **kwargs): if not data: return [] flat = isinstance(data, str) or isinstance(data, tuple) if flat: data = [data] samples = [] for idx, d in enumerate(data): sample = {IDX: idx} if self.config.text_b_key: sample[self.config.text_a_key] = d[0] sample[self.config.text_b_key] = d[1] else: sample[self.config.text_a_key] = d samples.append(sample) dataloader = self.build_dataloader(samples, sorting=False, **merge_dict(self.config, batch_size=batch_size, shuffle=False, device=self.device, overwrite=True) ) labels = [None] * len(data) vocab = self.vocabs.label for batch in dataloader: logits = self.feed_batch(batch) pred = logits.argmax(-1) pred = pred.tolist() for idx, tag in zip(batch[IDX], pred): labels[idx] = vocab.idx_to_token[tag] if flat: return labels[0] return labels
[docs] def fit(self, trn_data, dev_data, save_dir, text_a_key=None, text_b_key=None, label_key=None, transformer=None, max_seq_len=512, truncate_long_sequences=True, # hidden_dropout_prob=0.0, lr=5e-5, transformer_lr=None, adam_epsilon=1e-6, weight_decay=0, warmup_steps=0.1, batch_size=32, batch_max_tokens=None, epochs=3, logger=None, # transform=None, devices: Union[float, int, List[int]] = None, **kwargs): return super().fit(**merge_locals_kwargs(locals(), kwargs))
[docs] def build_vocabs(self, trn, logger, **kwargs): self.vocabs.label = Vocab(pad_token=None, unk_token=None) for each in trn: pass self.vocabs.lock() self.vocabs.summary(logger)