import logging from typing import List import torch from datasets import IterableDataset from .prompt_tokenizers import PromptTokenizingStrategy, InvalidDataException # We want this to be a wrapper for an existing dataset that we have loaded # lets use the concept of middlewares to wrap each dataset, for example # ConstantLengthDataset(ShuffledDataset([TokenizedPromptDataset(alpaca_dataset)])) # let's check to ensure we don't truncate an item in the middle, we'll use # the collators later on to pad the datasets class TokenizedPromptDataset(IterableDataset): def __init__( self, prompt_tokenizer: PromptTokenizingStrategy, dataset: IterableDataset, ): self.prompt_tokenizer = prompt_tokenizer self.dataset = dataset def __iter__(self): iterator = iter(self.dataset) # Loop through the entire dataset for example in iterator: try: yield self.prompt_tokenizer.tokenize_prompt(example) except InvalidDataException: pass # TODO this isn't the best since it can't interleave datasets class ConstantLengthDataset(IterableDataset): """ Iterable dataset that returns constant length chunks of tokens from stream of text files. Args: tokenizer (Tokenizer): The processor used for proccessing the data. dataset (dataset.Dataset): Dataset with text files. seq_length (int): Length of token sequences to return. """ def __init__( self, tokenizer, datasets, seq_length=2048, ): self.tokenizer = tokenizer self.concat_token_id = tokenizer.eos_token_id self.datasets: List[IterableDataset] = datasets self.seq_length = seq_length vocab_size = len(tokenizer.get_vocab()) if vocab_size <= torch.iinfo(torch.int16).max: self.tokens_dtype = torch.int16 elif vocab_size <= torch.iinfo(torch.int32).max: self.tokens_dtype = torch.int32 else: self.tokens_dtype = torch.int64 def __iter__(self): buffer = {"input_ids": [], "attention_mask": [], "labels": []} buffer_len = 0 for dataset in self.datasets: iterator = iter(dataset) more_examples = True while more_examples: try: example = next(iterator) except StopIteration: more_examples = False example = None add_concat_token = False if example: example_len = len(example["input_ids"]) add_concat_token = example["input_ids"][-1] != self.concat_token_id else: example_len = 0 if ( not example_len or buffer_len + int(add_concat_token) + example_len > self.seq_length ): if buffer["input_ids"]: input_ids = torch.cat(buffer["input_ids"], dim=-1)[ : self.seq_length ] attention_mask = torch.cat(buffer["attention_mask"], dim=-1)[ : self.seq_length ] labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length] if labels.size() == input_ids.size() and attention_mask.size() == input_ids.size(): yield { "input_ids": input_ids, "labels": labels, "attention_mask": attention_mask, } else: logging.warning("dropping batch due to tensor size mismatch") buffer = {"input_ids": [], "attention_mask": [], "labels": []} buffer_len = 0 if example: # just going to drop data points that are too long if len(example["input_ids"]) <= self.seq_length: input_ids = example["input_ids"] attention_mask = example["attention_mask"] labels = example["labels"] if add_concat_token: input_ids.append(self.concat_token_id) attention_mask.append(1) labels.append(self.concat_token_id) input_ids_with_concat = torch.tensor(input_ids, dtype=self.tokens_dtype) attention_mask_with_concat = torch.tensor( attention_mask, dtype=self.tokens_dtype ) labels_with_concat = torch.tensor(labels, dtype=self.tokens_dtype) buffer["input_ids"].append(input_ids_with_concat) buffer["attention_mask"].append(attention_mask_with_concat) buffer["labels"].append(labels_with_concat) buffer_len += len(input_ids)