Hi, I am trying to pretrain a T5 model from scratch but couldn’t find a proper implementation guide. I’m not sure which data collator I should use. I believe that DataCollatorForLanguageModeling is meant for BERT-style MLM, and DataCollatorForSeq2Seq expects you to already have input–output pairs.
I once found another implementation called DataCollatorForT5MLM in examples/flax/language-modeling/run_t5_mlm_flax.py in the Transformers repo, but I can’t find it anymore, probably it has been removed. However, based on my understanding, this seems to be the correct implementation. I am adding the code below, please let me know if this is the right implementation.
class DataCollatorForT5MLM: class DataCollatorForT5MLM: """ Data collator used for T5 span-masked language modeling. It is made sure that after masking the inputs are of length `data_args.max_seq_length` and targets are also of fixed length. For more information on how T5 span-masked language modeling works, one can take a look at the `official paper <https://huggingface.co/papers/1910.10683>`__ or the `official code for preprocessing <https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py>`__ . Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data. noise_density (:obj:`float`): The probability with which to (randomly) mask tokens in the input. mean_noise_span_length (:obj:`float`): The average span length of the masked tokens. input_length (:obj:`int`): The expected input length after masking. target_length (:obj:`int`): The expected target length after masking. pad_token_id: (:obj:`int`): The pad token id of the model decoder_start_token_id: (:obj:`int): The decoder start token id of the model """ def __init__(self, tokenizer, noise_density, mean_noise_span_length, input_length, target_length, pad_token_id, decoder_start_token_id): self.tokenizer = tokenizer self.noise_density = noise_density self.mean_noise_span_length = mean_noise_span_length self.input_length = input_length self.target_length = target_length self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id def shift_tokens_right(self,input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int) -> torch.Tensor: """ Shift input ids one token to the right (used for preparing decoder inputs). """ shifted_input_ids = input_ids.new_zeros(input_ids.shape) # Shift everything one step to the right shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() # Put decoder_start_token_id at position 0 shifted_input_ids[:, 0] = decoder_start_token_id # Replace -100 values with pad_token_id shifted_input_ids = shifted_input_ids.masked_fill(shifted_input_ids == -100, pad_token_id) return shifted_input_ids def __call__(self, examples: list[dict[str, np.ndarray]]) -> BatchEncoding: # convert list to dict and tensorize input batch = BatchEncoding( {k: torch.tensor([examples[i][k] for i in range(len(examples))], dtype=torch.long) for k, v in examples[0].items()} ) input_ids = batch["input_ids"] batch_size, expandend_input_length = input_ids.shape mask_indices = np.asarray([self.random_spans_noise_mask(expandend_input_length) for i in range(batch_size)]) labels_mask = ~mask_indices input_ids_sentinel = self.create_sentinel_ids(mask_indices.astype(np.int8)) labels_sentinel = self.create_sentinel_ids(labels_mask.astype(np.int8)) batch["input_ids"] = self.filter_input_ids(input_ids, input_ids_sentinel) batch["labels"] = self.filter_input_ids(input_ids, labels_sentinel) if batch["input_ids"].shape[-1] != self.input_length: raise ValueError( f"`input_ids` are incorrectly preprocessed. `input_ids` length is {batch['input_ids'].shape[-1]}, but" f" should be {self.input_length}." ) if batch["labels"].shape[-1] != self.target_length: raise ValueError( f"`labels` are incorrectly preprocessed. `labels` length is {batch['labels'].shape[-1]}, but should be" f" {self.target_length}." ) # to check that tokens are correctly preprocessed, one can run `self.tokenizer.batch_decode(input_ids)` and `self.tokenizer.batch_decode(labels)` here... batch["decoder_input_ids"] = self.shift_tokens_right( batch["labels"], self.pad_token_id, self.decoder_start_token_id ) # print(f"Shape of batch['input_ids']: {batch['input_ids'].shape}") # print(f"Shape of batch['labels']: {batch['labels'].shape}") # print(f"Shape of batch['decoder_input_ids']: {batch['decoder_input_ids'].shape}") return batch def create_sentinel_ids(self, mask_indices): """ Sentinel ids creation given the indices that should be masked. The start indices of each mask are replaced by the sentinel ids in increasing order. Consecutive mask indices to be deleted are replaced with `-1`. """ start_indices = mask_indices - np.roll(mask_indices, 1, axis=-1) * mask_indices start_indices[:, 0] = mask_indices[:, 0] sentinel_ids = np.where(start_indices != 0, np.cumsum(start_indices, axis=-1), start_indices) sentinel_ids = np.where(sentinel_ids != 0, (len(self.tokenizer) - sentinel_ids), 0) sentinel_ids -= mask_indices - start_indices return sentinel_ids def filter_input_ids(self, input_ids, sentinel_ids): """ Puts sentinel mask on `input_ids` and fuse consecutive mask tokens into a single mask token by deleting. This will reduce the sequence length from `expanded_inputs_length` to `input_length`. """ batch_size = input_ids.shape[0] input_ids_full = np.where(sentinel_ids != 0, sentinel_ids, input_ids) # input_ids tokens and sentinel tokens are >= 0, tokens < 0 are # masked tokens coming after sentinel tokens and should be removed input_ids = input_ids_full[input_ids_full >= 0].reshape((batch_size, -1)) input_ids = np.concatenate( [input_ids, np.full((batch_size, 1), self.tokenizer.eos_token_id, dtype=np.int32)], axis=-1 ) # return input_ids return torch.tensor(input_ids, dtype=torch.long) def random_spans_noise_mask(self, length): """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ . Noise mask consisting of random spans of noise tokens. The number of noise tokens and the number of noise spans and non-noise spans are determined deterministically as follows: num_noise_tokens = round(length * noise_density) num_nonnoise_spans = num_noise_spans = round(num_noise_tokens / mean_noise_span_length) Spans alternate between non-noise and noise, beginning with non-noise. Subject to the above restrictions, all masks are equally likely. Args: length: an int32 scalar (length of the incoming token sequence) noise_density: a float - approximate density of output mask mean_noise_span_length: a number Returns: a boolean tensor with shape [length] """ orig_length = length num_noise_tokens = int(np.round(length * self.noise_density)) num_nonnoise_tokens = length - num_noise_tokens # avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens. num_noise_tokens = min(max(num_noise_tokens, 1), length - 1) # num_noise_tokens should be less than num_noise_tokens and num_nonnoise_tokens num_noise_spans = int(np.round(min(num_noise_tokens, num_nonnoise_tokens) / self.mean_noise_span_length)) # avoid degeneracy by ensuring positive number of noise spans num_noise_spans = max(num_noise_spans, 1) # pick the lengths of the noise spans and the non-noise spans def _random_segmentation(num_items, num_segments): """Partition a sequence of items randomly into non-empty segments. Args: num_items: an integer scalar > 0 num_segments: an integer scalar in [1, num_items] Returns: a Tensor with shape [num_segments] containing positive integers that add up to num_items """ mask_indices = np.arange(num_items - 1) < (num_segments - 1) np.random.shuffle(mask_indices) first_in_segment = np.pad(mask_indices, [[1, 0]]) segment_id = np.cumsum(first_in_segment) # count length of sub segments assuming that list is sorted _, segment_length = np.unique(segment_id, return_counts=True) return segment_length noise_span_lengths = _random_segmentation(num_noise_tokens, num_noise_spans) nonnoise_span_lengths = _random_segmentation(num_nonnoise_tokens, num_noise_spans) interleaved_span_lengths = np.reshape( np.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2] ) span_starts = np.cumsum(interleaved_span_lengths)[:-1] span_start_indicator = np.zeros((length,), dtype=np.int8) span_start_indicator[span_starts] = True span_num = np.cumsum(span_start_indicator) is_noise = np.equal(span_num % 2, 1) return is_noise[:orig_length]
Trainer
training_args = TrainingArguments() output_dir="Saved_models/"+args.output_dir, per_device_train_batch_size=16, # Adjust based on GPU memory gradient_accumulation_steps=2, # To simulate a larger batch size max_steps=args.max_step, logging_steps=20000, save_steps=20000, save_total_limit=1, dataloader_pin_memory=False, bf16=True, # Use mixed precision for faster training and less memory usage ddp_find_unused_parameters=False, report_to="tensorboard", logging_strategy="steps", logging_dir="logs/new_log_test", load_best_model_at_end=True, eval_strategy="steps", metric_for_best_model="eval_loss", per_device_eval_batch_size=16, eval_steps=20000, warmup_steps = args.warm_up_step, eval_accumulation_steps=1, disable_tqdm=False, log_level="info",) #optimizer optimizer = Adafactor( model.parameters(), lr=args.lr, scale_parameter=False, relative_step=False, warmup_init=False) # Create the inverse square root scheduler scheduler = get_inverse_sqrt_schedule( optimizer, num_warmup_steps=training_args.warmup_steps,) print("trainer init") trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_train_dataset, eval_dataset=tokenized_eval_dataset, processing_class=tokenizer, data_collator=data_collator, # You'd put your custom T5 data collator here optimizers=(optimizer, scheduler) ) print("training started") trainer.train()
My second question is , if this implementation is correct, should I use the same DataCollator class for fine-tuning too, or should I use DataCollatorForSeq2Seq for translation work?
