""" dbert.py Provides the dBert class that implements Reader using BERT contextual embeddings to disambiguate heteronyms. """ import logging import os from pathlib import Path import numpy as np import torch from speach.ttlig import RubyFrag, RubyToken from transformers import ( AutoModelForTokenClassification, BertJapaneseTokenizer, DataCollatorForTokenClassification, EarlyStoppingCallback, Trainer, TrainingArguments, ) from config import config from config.config import logger from yomikata import utils from yomikata.reader import Reader from yomikata.utils import LabelEncoder logging.getLogger("transformers").setLevel(logging.ERROR) logging.getLogger("transformers.trainer").setLevel(logging.ERROR) logging.getLogger("datasets").setLevel(logging.ERROR) class dBert(Reader): def __init__( self, artifacts_dir: Path = Path(config.STORES_DIR, "dbert"), reinitialize: bool = False, device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), ) -> None: # Set the device self.device = device logger.info(f"Running on {self.device}") if self.device.type == "cuda": logger.info(torch.cuda.get_device_name(0)) # Hardcoded parameters self.max_length = 128 # Load the model self.artifacts_dir = artifacts_dir if reinitialize: # load tokenizer from upstream huggingface repository default_model = "cl-tohoku/bert-base-japanese-v2" self.tokenizer = BertJapaneseTokenizer.from_pretrained(default_model) logger.info(f"Using {default_model} tokenizer") # load the heteronyms list self.heteronyms = config.HETERONYMS # make the label encoder label_list = [""] for i, heteronym in enumerate(self.heteronyms.keys()): for j, reading in enumerate(self.heteronyms[heteronym]): label_list.append(heteronym + ":" + reading) self.label_encoder = LabelEncoder() self.label_encoder.fit(label_list) logger.info("Made label encoder with default heteronyms") # add surface forms to tokenizer vocab surfaces = list( set([x.split(":")[0] for x in self.label_encoder.classes if x != ""]) ) new_tokens = [ surface for surface in surfaces if surface not in (list(self.tokenizer.vocab.keys()) + list(self.tokenizer.get_added_vocab())) ] self.tokenizer.add_tokens(new_tokens) if len(new_tokens) > 0: logger.info(f"Added {len(new_tokens)} surface forms to tokenizer vocab") # check that new tokens were added properly assert [ self.tokenizer.decode( self.tokenizer.encode( [surface], add_special_tokens=False, ) ) for surface in surfaces ] == surfaces self.surfaceIDs = self.tokenizer.encode( list(set([x.split(":")[0] for x in self.label_encoder.classes if x != ""])), add_special_tokens=False, ) assert len(self.surfaceIDs) == len(surfaces) # Load model from upstream huggingface repository self.model = AutoModelForTokenClassification.from_pretrained( default_model, num_labels=len(self.label_encoder.classes) ) self.model.resize_token_embeddings(len(self.tokenizer)) logger.info(f"Using model {default_model}") self.save(artifacts_dir) else: self.load(artifacts_dir) def load(self, directory): self.tokenizer = BertJapaneseTokenizer.from_pretrained(directory) self.model = AutoModelForTokenClassification.from_pretrained(directory).to(self.device) self.label_encoder = LabelEncoder.load(Path(directory, "label_encoder.json")) self.heteronyms = utils.load_dict(Path(directory, "heteronyms.json")) self.surfaceIDs = self.tokenizer.encode( list(set([x.split(":")[0] for x in self.label_encoder.classes if x != ""])), add_special_tokens=False, ) logger.info(f"Loaded model from directory {directory}") def save(self, directory): self.tokenizer.save_pretrained(directory) self.model.save_pretrained(directory) self.label_encoder.save(Path(directory, "label_encoder.json")) utils.save_dict(self.heteronyms, Path(directory, "heteronyms.json")) logger.info(f"Saved model to directory {directory}") def batch_preprocess_function(self, entries, pad=False): inputs = [entry for entry in entries["sentence"]] furiganas = [entry for entry in entries["furigana"]] if pad: tokenized_inputs = self.tokenizer( inputs, max_length=self.max_length, truncation=True, padding="max_length", # return_tensors="np", ) else: tokenized_inputs = self.tokenizer( inputs, max_length=self.max_length, truncation=True, ) labels = [] for i, input_ids in enumerate(tokenized_inputs["input_ids"]): furigana_temp = furiganas[i] label_ids = [] assert inputs[i] == utils.remove_furigana(furiganas[i]) for j, input_id in enumerate(input_ids): if input_id not in self.surfaceIDs: label = -100 else: surface = self.tokenizer.decode([input_id]) try: reading_start_idx = furigana_temp.index(surface) + len(surface) furigana_temp = furigana_temp[reading_start_idx + 1 :] reading_end_idx = furigana_temp.index("}") reading = furigana_temp[:reading_end_idx] furigana_temp = furigana_temp[reading_end_idx + 1 :] label = self.label_encoder.class_to_index[surface + ":" + reading] except KeyError: # this means there's an unknown reading label = 0 except ValueError: # this means that the surface form is not present in the furigana # probably it got split between two different words label = 0 label_ids.append(label) assert len(label_ids) == len(input_ids) labels.append(label_ids) assert len(labels) == len(tokenized_inputs["input_ids"]) return { "input_ids": tokenized_inputs["input_ids"], "attention_mask": tokenized_inputs["attention_mask"], "labels": labels, } def train(self, dataset, training_args={}) -> dict: dataset = dataset.map( self.batch_preprocess_function, batched=True, fn_kwargs={"pad": False} ) dataset = dataset.filter( lambda entry: any(x in entry["input_ids"] for x in list(self.surfaceIDs)) ) # put the model in training mode self.model.train() default_training_args = { "output_dir": self.artifacts_dir, "num_train_epochs": 10, "evaluation_strategy": "steps", "eval_steps": 10, "logging_strategy": "steps", "logging_steps": 10, "save_strategy": "steps", "save_steps": 10, "learning_rate": 2e-5, "per_device_train_batch_size": 128, "per_device_eval_batch_size": 128, "load_best_model_at_end": True, "metric_for_best_model": "loss", "weight_decay": 0.01, "save_total_limit": 3, "fp16": True, "report_to": "tensorboard", } default_training_args.update(training_args) training_args = default_training_args # Not padding in batch_preprocess_function so need data_collator for trainer data_collator = DataCollatorForTokenClassification(tokenizer=self.tokenizer, padding=True) if "val" in list(dataset): trainer = Trainer( model=self.model, args=TrainingArguments(**training_args), train_dataset=dataset["train"], eval_dataset=dataset["val"], tokenizer=self.tokenizer, callbacks=[ EarlyStoppingCallback(early_stopping_patience=5), ], data_collator=data_collator, ) else: trainer = Trainer( model=self.model, args=TrainingArguments(**training_args), train_dataset=dataset["train"], tokenizer=self.tokenizer, data_collator=data_collator, ) result = trainer.train() # Output some training information print(f"Time: {result.metrics['train_runtime']:.2f}") print(f"Samples/second: {result.metrics['train_samples_per_second']:.2f}") gpu_index = int(os.environ["CUDA_VISIBLE_DEVICES"]) utils.print_gpu_utilization(gpu_index) # Get metrics for each train/val/split self.model.eval() full_performance = {} for key in dataset.keys(): max_evals = min(100000, len(dataset[key])) # max_evals = len(dataset[key]) logger.info(f"getting predictions for {key}") subset = dataset[key].shuffle().select(range(max_evals)) prediction_output = trainer.predict(subset) logger.info(f"processing predictions for {key}") metrics = prediction_output[2] labels = prediction_output[1] predictions = np.argmax(prediction_output[0], axis=2) true_inputs = [ self.tokenizer.decode([input_id]) for row in subset["input_ids"] for input_id in row if input_id in self.surfaceIDs ] true_predictions = [ str(self.label_encoder.index_to_class[p]) for prediction, label in zip(predictions, labels) for (p, l) in zip(prediction, label) if l != -100 ] true_labels = [ str(self.label_encoder.index_to_class[l]) for prediction, label in zip(predictions, labels) for (p, l) in zip(prediction, label) if l != -100 ] logger.info("processing performance") performance = { heteronym: { "n": 0, "readings": { reading: { "n": 0, "found": { readingprime: 0 for readingprime in list(self.heteronyms[heteronym].keys()) + [""] }, } for reading in list(self.heteronyms[heteronym].keys()) + [""] }, } for heteronym in self.heteronyms.keys() } for i, surface in enumerate(true_inputs): performance[surface]["n"] += 1 true_reading = true_labels[i].split(":")[-1] performance[surface]["readings"][true_reading]["n"] += 1 if true_predictions[i] != "": if true_predictions[i].split(":")[0] != surface: logger.warning(f"big failure at {surface} {true_predictions[i]}") found_reading = "" else: found_reading = true_predictions[i].split(":")[1] else: found_reading = "" performance[surface]["readings"][true_reading]["found"][found_reading] += 1 # if found_reading != true_reading: # # pass # logger.info( # f"Predicted {found_reading} instead of {true_reading} in {subset["furigana"][furi_rows[i]]}" # ) n = 0 correct = 0 for surface in performance.keys(): for true_reading in performance[surface]["readings"].keys(): performance[surface]["readings"][true_reading]["accuracy"] = np.round( performance[surface]["readings"][true_reading]["found"][true_reading] / np.array(performance[surface]["readings"][true_reading]["n"]), 3, ) performance[surface]["accuracy"] = np.round( sum( performance[surface]["readings"][true_reading]["found"][true_reading] for true_reading in performance[surface]["readings"].keys() ) / np.array(performance[surface]["n"]), 3, ) correct += sum( performance[surface]["readings"][true_reading]["found"][true_reading] for true_reading in performance[surface]["readings"].keys() ) n += performance[surface]["n"] performance = { "metrics": metrics, "accuracy": round(correct / n, 3), "heteronym_performance": performance, } full_performance[key] = performance trainer.save_model() return full_performance def furigana(self, text: str) -> str: text = utils.standardize_text(text) text = utils.remove_furigana(text) text = text.replace("{", "").replace("}", "") self.model.eval() text_encoded = self.tokenizer( text, max_length=self.max_length, truncation=True, return_tensors="pt", ) input_ids = text_encoded["input_ids"].to(self.device) input_mask = text_encoded["attention_mask"].to(self.device) logits = self.model(input_ids=input_ids, attention_mask=input_mask).logits predictions = torch.argmax(logits, dim=2) output_ruby = [] for (i, p) in enumerate(predictions[0]): text = self.tokenizer.decode([input_ids[0][i]]) if text in ["[CLS]", "[SEP]"]: continue if text[:2] == "##": text = text[2:] if input_ids[0][i].item() in self.surfaceIDs: furi = self.label_encoder.index_to_class[p.item()] if furi == "": output_ruby.append(f"{{{text}}}") elif furi.split(":")[0] != text: output_ruby.append(f"{{{text}}}") else: output_ruby.append(RubyFrag(text=text, furi=furi.split(":")[1])) else: output_ruby.append(text) return RubyToken(groups=output_ruby).to_code()