from transformers import AutoTokenizer, AutoModelForSequenceClassification, get_linear_schedule_with_warmup import datasets import pandas as pd import pyarrow import pytorch_lightning as pl import torchmetrics import torch.nn as nn import torch import types import multiprocessing from .text_cleaning import clean_text_funcs class RRUMDataset(): scalar_features = ['channel_sim'] _image_features = ['regret_thumbnail', 'recommendation_thumbnail'] # not used atm def __init__(self, data, with_transcript, cross_encoder_model_name_or_path, label_col="label", label_map=None, balance_label_counts=False, max_length=128, do_train_test_split=False, test_size=0.25, seed=42, keep_video_ids_for_predictions=False, encode_on_the_fly=False, clean_text=False, processing_batch_size=1000, processing_num_proc=1): self._with_transcript = with_transcript self.tokenizer = AutoTokenizer.from_pretrained( cross_encoder_model_name_or_path) self.label_col = label_col self.label_map = label_map self.balance_label_counts = balance_label_counts self.max_length = max_length self.seed = seed self.keep_video_ids_for_predictions = keep_video_ids_for_predictions self.clean_text = clean_text self.processing_batch_size = processing_batch_size self.processing_num_proc = multiprocessing.cpu_count( ) if not processing_num_proc else processing_num_proc self.text_types = ['title', 'description'] + \ (['transcript'] if self._with_transcript else []) self._text_features = [ 'regret_title', 'recommendation_title', 'regret_description', 'recommendation_description'] + (['regret_transcript', 'recommendation_transcript'] if self._with_transcript else []) # LOAD DATA INTO DATASET self.streaming_dataset = False if isinstance(data, pd.DataFrame): self.dataset = datasets.Dataset.from_pandas(data) elif isinstance(data, types.GeneratorType): examples_iterable = datasets.iterable_dataset.ExamplesIterable( self._streaming_generate_examples, {"iterable": data}) self.dataset = datasets.IterableDataset(examples_iterable) self._stream_dataset_example = next(iter(self.dataset)) self._stream_dataset_column_names = list( self._stream_dataset_example.keys()) self.streaming_dataset = True elif isinstance(data, pyarrow.Table): self.dataset = datasets.Dataset(data) else: raise ValueError( f'Type of data is {type(data)} when pd.DataFrame, pyarrow.Table, or generator of pyarrow.RecordBatch is allowed') # PREPROCESS DATASET self._preprocess() # ENCODE DATASET self.train_dataset = None self.test_dataset = None if self.streaming_dataset: # IterableDataset doesn't have train_test_split method if self.label_col: self.train_dataset = self._encode_streaming(self.dataset) print('Streaming dataset available in .train_dataset') else: self.test_dataset = self._encode_streaming(self.dataset) print( 'Streaming dataset available in .test_dataset because label_col=None') else: # dataset into train_dataset and/or test_dataset if do_train_test_split: ds = self.dataset.train_test_split( test_size=test_size, shuffle=True, seed=self.seed, stratify_by_column=self.label_col) self.train_dataset = ds['train'] self.test_dataset = ds['test'] print( f'Dataset was splitted into train and test with test_size={test_size}') else: if self.label_col: self.train_dataset = self.dataset else: self.test_dataset = self.dataset if encode_on_the_fly: if self.train_dataset: self.train_dataset.set_transform(self._encode_on_the_fly) print('On-the-fly encoded dataset available in .train_dataset') if self.test_dataset: self.test_dataset.set_transform(self._encode_on_the_fly) print('On-the-fly encoded dataset available in .test_dataset') else: if self.train_dataset: self.train_dataset = self._encode(self.train_dataset) print('Pre-encoded dataset available in .train_dataset') if self.test_dataset: self.test_dataset = self._encode(self.test_dataset) print('Pre-encoded dataset available in .test_dataset') def __len__(self): if self.streaming_dataset: raise ValueError( f'Streaming dataset does not support len() method') return len(self.dataset) def __getitem__(self, index): if self.streaming_dataset: return next(iter(self.dataset)) return self.dataset[index] def _streaming_generate_examples(self, iterable): id_ = 0 # TODO: make sure GeneratorType is pyarrow.RecordBatch if isinstance(iterable, types.GeneratorType): for examples in iterable: for ex in examples.to_pylist(): yield id_, ex id_ += 1 def _preprocess(self): if self._with_transcript: self.dataset = self.dataset.filter( lambda example: example['regret_transcript'] is not None and example['recommendation_transcript'] is not None) else: self.dataset = self.dataset.filter( lambda example: example['regret_transcript'] is None or example['recommendation_transcript'] is None) if self.label_col: if self.streaming_dataset: if self.label_col in self._stream_dataset_column_names and isinstance(self._stream_dataset_example[self.label_col], str): if not self.label_map: raise ValueError( f'"label_map" dict was not provided and is needed to encode string labels for streaming datasets') # cast_column method had issues with streaming dataset self.dataset = self.dataset.map( self._streaming_rename_labels) else: if self.dataset.features[self.label_col].dtype == 'string': if not self.label_map: self.label_map = {k: v for v, k in enumerate( self.dataset.unique(self.label_col))} self.dataset = self.dataset.filter( lambda example: example[self.label_col] in self.label_map.keys()) self.dataset = self.dataset.cast_column(self.label_col, datasets.ClassLabel( num_classes=len(self.label_map), names=list(self.label_map.keys()))) self.dataset = self.dataset.filter(lambda example: not any(x in [None, ""] for x in [ example[key] for key in self._text_features + self.scalar_features + ([self.label_col] if self.label_col else [])])) # dropna if self.balance_label_counts and self.label_col and not self.streaming_dataset: label_datasets = {} for label in list(self.label_map.values()): label_dataset = self.dataset.filter( lambda example: example[self.label_col] == label) label_datasets[len(label_dataset)] = label_dataset min_label_count = min(label_datasets) sampled_datasets = [dataset.train_test_split(train_size=min_label_count, shuffle=True, seed=self.seed)[ 'train'] if len(dataset) != min_label_count else dataset for dataset in label_datasets.values()] self.dataset = datasets.concatenate_datasets(sampled_datasets) if self.clean_text: self.dataset = self.dataset.map(self._clean_text, batched=not self.streaming_dataset, batch_size=self.processing_batch_size) self.dataset = self.dataset.map(self._truncate_and_strip_text, batched=not self.streaming_dataset, batch_size=self.processing_batch_size) def _streaming_rename_labels(self, example): # rename labels according to label_map if not already correct labels if isinstance(example[self.label_col], list): example[self.label_col] = [self.label_map.get( ex, None) for ex in example[self.label_col] if ex not in self.label_map.values()] elif isinstance(example[self.label_col], str) and example[self.label_col] not in self.label_map.values(): example[self.label_col] = self.label_map.get( example[self.label_col], None) else: raise ValueError( f'Type of example label is {type(example[self.label_col])} when list or string is allowed') return example def _clean_text(self, example): for feat in self._text_features: example[feat] = clean_text_funcs(example[feat])[0] if isinstance( example[feat], str) else clean_text_funcs(example[feat]) return example def _truncate_and_strip_text(self, example): # tokenizer will truncate to max_length tokens anyway so to save RAM let's truncate to max_length words already beforehand # one word is usually one or more tokens so should be safe to truncate this way without losing information for feat in self._text_features: if isinstance(example[feat], list): example[feat] = [ ' '.join(text.split()[:self.max_length]).strip() for text in example[feat] if text] elif isinstance(example[feat], str): example[feat] = ' '.join(example[feat].split()[ :self.max_length]).strip() elif example[feat] is None: return None else: raise ValueError( f'Type of example is {type(example[feat])} when list or string is allowed') return example def _encode(self, dataset): encoded_dataset = None for text_type in self.text_types: encoded_text_type = dataset.map(lambda regret, recommendation: self.tokenizer(regret, recommendation, padding="max_length", truncation=True, max_length=self.max_length), batched=True, batch_size=self.processing_batch_size, num_proc=self.processing_num_proc, input_columns=[f'regret_{text_type}', f'recommendation_{text_type}'], remove_columns=dataset.column_names) encoded_text_type = encoded_text_type.rename_columns( {col: f'{text_type}_{col}' for col in encoded_text_type.column_names}) # e.g. input_ids -> title_input_ids so we have separate input_ids for each text_type if encoded_dataset: encoded_dataset = datasets.concatenate_datasets( [encoded_dataset, encoded_text_type], axis=1) else: encoded_dataset = encoded_text_type # copy scalar features and label from original dataset to the encoded dataset for scalar_feat in self.scalar_features: encoded_dataset = encoded_dataset.add_column( name=scalar_feat, column=dataset[scalar_feat]) if self.label_col: encoded_dataset = encoded_dataset.add_column( name=self.label_col, column=dataset[self.label_col]) if self.keep_video_ids_for_predictions: for id in ['regret_id', "recommendation_id"]: encoded_dataset = encoded_dataset.add_column( name=id, column=dataset[id]) encoded_dataset.set_format( type='torch', columns=encoded_dataset.column_names) return encoded_dataset def _encode_streaming(self, dataset): encoded_dataset = dataset.map(self._encode_on_the_fly, batched=True, batch_size=self.processing_batch_size, remove_columns=list(set(self._stream_dataset_column_names)-set(self.scalar_features + ( [self.label_col] if self.label_col else []) + (['regret_id', "recommendation_id"] if self.keep_video_ids_for_predictions else [])))) # IterableDataset doesn't have column_names attribute as normal Dataset encoded_dataset = encoded_dataset.with_format("torch") return encoded_dataset def _encode_on_the_fly(self, batch): for text_type in self.text_types: encoded_text_type = dict(self.tokenizer( batch[f'regret_{text_type}'], batch[f'recommendation_{text_type}'], padding="max_length", truncation=True, max_length=self.max_length, return_tensors="pt")) for encoded_key in encoded_text_type.copy(): encoded_text_type[f"{text_type}_{encoded_key}"] = encoded_text_type.pop(encoded_key) if not self.streaming_dataset else encoded_text_type.pop( encoded_key).squeeze(0) # e.g. input_ids -> title_input_ids so we have separate input_ids for each text_type del batch[f'regret_{text_type}'] del batch[f'recommendation_{text_type}'] batch.update(encoded_text_type) for scalar_feat in self.scalar_features: batch[scalar_feat] = torch.as_tensor( batch[scalar_feat]) if not self.streaming_dataset else torch.as_tensor(batch[scalar_feat]).squeeze(0) if self.label_col: batch[self.label_col] = torch.as_tensor( batch[self.label_col]) if not self.streaming_dataset else torch.as_tensor(batch[self.label_col]).squeeze(0) return batch class RRUM(pl.LightningModule): def __init__(self, text_types, scalar_features, label_col, cross_encoder_model_name_or_path, optimizer_config=None, freeze_policy=None, pos_weight=None): super().__init__() self.save_hyperparameters() self.text_types = text_types self.scalar_features = scalar_features self.label_col = label_col self.optimizer_config = optimizer_config self.cross_encoder_model_name_or_path = cross_encoder_model_name_or_path self.cross_encoders = nn.ModuleDict({}) for t in self.text_types: self.cross_encoders[t] = AutoModelForSequenceClassification.from_pretrained( self.cross_encoder_model_name_or_path) if freeze_policy is not None: for xe in self.cross_encoders.values(): for name, param in xe.named_parameters(): if freeze_policy(name): param.requires_grad = False cross_encoder_out_features = list(self.cross_encoders.values())[0]( torch.randint(1, 2, (1, 2))).logits.size(dim=1) self.lin1 = nn.Linear(len(self.cross_encoders) * cross_encoder_out_features + len(self.scalar_features), 1) self.ac_metric = torchmetrics.Accuracy() self.pr_metric = torchmetrics.Precision() self.re_metric = torchmetrics.Recall() self.auc_metric = torchmetrics.AUROC() if pos_weight: self.loss = nn.BCEWithLogitsLoss( pos_weight=torch.Tensor([pos_weight])) else: self.loss = nn.BCEWithLogitsLoss() def forward(self, x): cross_logits = {} for f in self.text_types: inputs = {key.split(f'{f}_')[1]: x[key] for key in x if f in key} # e.g. title_input_ids -> input_ids since we have separate input_ids for each text_type cross_logits[f] = self.cross_encoders[f](**inputs).logits x = torch.cat([*cross_logits.values()] + [x[scalar][:, None] for scalar in self.scalar_features], 1 ) del cross_logits x = self.lin1(x) return x def configure_optimizers(self): if self.optimizer_config: return self.optimizer_config(self) optimizer = torch.optim.AdamW(self.parameters(), lr=5e-5) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=int( self.trainer.estimated_stepping_batches * 0.05), num_training_steps=self.trainer.estimated_stepping_batches, ) scheduler = {'scheduler': scheduler, 'interval': 'step', 'frequency': 1} return [optimizer], [scheduler] def training_step(self, train_batch, batch_idx): y = train_batch[self.label_col].unsqueeze(1).float() logits = self(train_batch) loss = self.loss(logits, y) self.log('train_loss', loss) return loss def validation_step(self, val_batch, batch_idx): y = val_batch[self.label_col].unsqueeze(1).float() logits = self(val_batch) loss = self.loss(logits, y) self.ac_metric(logits, y.int()) self.pr_metric(logits, y.int()) self.re_metric(logits, y.int()) self.auc_metric(logits, y.int()) self.log('validation_accuracy', self.ac_metric) self.log('validation_precision', self.pr_metric) self.log('validation_recall', self.re_metric) self.log('validation_auc', self.auc_metric) self.log('val_loss', loss, prog_bar=True) def validation_epoch_end(self, outputs): self.log('validation_accuracy_ep', self.ac_metric) self.log('validation_precision_ep', self.pr_metric) self.log('validation_recall_ep', self.re_metric) self.log('validation_auc_ep', self.auc_metric)