import torch, math from datasets.load import load_dataset, load_metric from transformers import ( AutoTokenizer, EvalPrediction, default_data_collator, ) import os, hashlib import numpy as np import logging, copy, re from datasets.formatting.formatting import LazyRow, LazyBatch task_to_keys = { "imdb": ("text", None) } logger = logging.getLogger(__name__) idx = 0 class IMDBDataset(): def __init__(self, tokenizer: AutoTokenizer, data_args, training_args) -> None: super().__init__() self.data_args = data_args self.training_args = training_args self.tokenizer = tokenizer self.is_regression = False raw_datasets = load_dataset("imdb") self.label_list = raw_datasets["train"].features["label"].names self.num_labels = len(self.label_list) # Preprocessing the raw_datasets self.sentence1_key, self.sentence2_key = task_to_keys[data_args.dataset_name] sc_template = f'''{'{' + self.sentence1_key + '}'}''' \ if self.sentence2_key is None else f'''{'{' + self.sentence1_key + '}'}{'{' + self.sentence2_key + '}'}''' self.tokenizer.template = self.template = [sc_template] print(f"-> using template:{self.template}") # Padding strategy if data_args.pad_to_max_length: self.padding = "max_length" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch self.padding = False # Some models have set the order of the labels to use, so let's make sure we do use it. if not self.is_regression: self.label2id = {l: i for i, l in enumerate(self.label_list)} self.id2label = {id: label for label, id in self.label2id.items()} if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) self.max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) if self.data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({self.data_args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) self.max_seq_length = min(self.data_args.max_seq_length, tokenizer.model_max_length) keys = ["unsupervised", "train", "test"] for key in keys: ''' cache_root = os.path.dirname(raw_datasets[key].cache_files[0]["filename"]) digest = hashlib.md5(str(tokenizer.prompt_template + tokenizer.key_template).encode("utf-8")).hexdigest() filename = f"{tokenizer.name_or_path}_{key}_{digest[:16]}.arrow".replace("/", "_") print(f"-> template:{tokenizer.prompt_template} filename:{filename}") cache_file_name = os.path.join(cache_root, filename) ''' raw_datasets[key] = raw_datasets[key].map( self.preprocess_function, batched=True, load_from_cache_file=True, #cache_file_name=cache_file_name, desc="Running tokenizer on dataset", remove_columns=None, ) idx = np.arange(len(raw_datasets[key])).tolist() raw_datasets[key] = raw_datasets[key].add_column("idx", idx) self.train_dataset = raw_datasets["train"] if self.data_args.max_train_samples is not None: self.data_args.max_train_samples = min(self.data_args.max_train_samples, len(self.train_dataset)) self.train_dataset = self.train_dataset.select(range(self.data_args.max_train_samples)) size = len(self.train_dataset) select = np.random.choice(size, math.ceil(size * training_args.poison_rate), replace=False) idx = torch.zeros([size]) idx[select] = 1 self.train_dataset.poison_idx = idx self.eval_dataset = raw_datasets["test"] if self.data_args.max_eval_samples is not None: self.data_args.max_eval_samples = min(self.data_args.max_eval_samples, len(self.eval_dataset)) self.eval_dataset = self.eval_dataset.select(range(self.data_args.max_eval_samples)) self.predict_dataset = raw_datasets["unsupervised"] if self.data_args.max_predict_samples is not None: self.predict_dataset = self.predict_dataset.select(range(self.data_args.max_predict_samples)) self.metric = load_metric("glue", "sst2") self.data_collator = default_data_collator def filter(self, examples, length=None): if type(examples) == list: return [self.filter(x, length) for x in examples] elif type(examples) == dict or type(examples) == LazyRow or type(examples) == LazyBatch: return {k: self.filter(v, length) for k, v in examples.items()} elif type(examples) == str: # txt = re.sub(r"[^a-zA-Z0-9\ \%#!.,]+", '', examples) txt = examples.replace(self.tokenizer.prompt_token, "T").replace(self.tokenizer.skey_token, "K").replace( self.tokenizer.predict_token, "P").replace("[X]", "Y").replace("[Y]", "Y") if length is not None: return txt[:length] return txt return examples def preprocess_function(self, examples, **kwargs): examples = self.filter(examples, length=300) # Tokenize the texts, args = [text1, text2, ...] _examples = copy.deepcopy(examples) args = ( (_examples[self.sentence1_key],) if self.sentence2_key is None else ( _examples[self.sentence1_key], _examples[self.sentence2_key]) ) result = self.tokenizer(*args, padding=self.padding, max_length=self.max_seq_length, truncation=True) return result def compute_metrics(self, p: EvalPrediction): preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions preds = np.argmax(preds, axis=1) return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}