# utils.collators.batching – Axolotl

# utils.collators.batching

`utils.collators.batching`

Data collators for axolotl to pad labels and position_ids for packed sequences

## Classes

Name | Description
--- | ---
BatchSamplerDataCollatorForSeq2Seq | Collator for multipack specific to the using the BatchSampler
DataCollatorForSeq2Seq | Data collator that will dynamically pad the inputs received, as well as the labels and position_ids
PretrainingBatchSamplerDataCollatorForSeq2Seq | Collator for multipack specific to the using the BatchSampler
V2BatchSamplerDataCollatorForSeq2Seq | Collator for multipack specific to the using the BatchSampler

### BatchSamplerDataCollatorForSeq2Seq

    utils.collators.batching.BatchSamplerDataCollatorForSeq2Seq(
        tokenizer,
        model=None,
        padding=True,
        max_length=None,
        pad_to_multiple_of=None,
        label_pad_token_id=-100,
        position_pad_token_id=0,
        return_tensors='pt',
    )

Collator for multipack specific to the using the BatchSampler

### DataCollatorForSeq2Seq

    utils.collators.batching.DataCollatorForSeq2Seq(
        tokenizer,
        model=None,
        padding=True,
        max_length=None,
        pad_to_multiple_of=None,
        label_pad_token_id=-100,
        position_pad_token_id=0,
        return_tensors='pt',
    )

Data collator that will dynamically pad the inputs received, as well as the labels and position_ids

#### Parameters

Name | Type | Description | Default
--- | --- | --- | ---
tokenizer | [`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`] | The tokenizer used for encoding the data. | required
model | [`PreTrainedModel`] | The model that is being trained. If set and has the prepare_decoder_input_ids_from_labels, use it to prepare the decoder_input_ids This is useful when using label_smoothing to avoid calculating loss twice. | `None`

padding

`bool`, `str` or [`~utils.PaddingStrategy`], optional, defaults to `True`

Select a strategy to pad the returned sequences (according to the model’s padding side and padding index) among: - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single sequence is provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).

`True`

max_length | `int`, optional | Maximum length of the returned list and optionally padding length (see above). | `None`
pad_to_multiple_of | `int`, optional | If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). | `None`
label_pad_token_id | `int`, optional, defaults to -100 | The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions). | `-100`
return_tensors | `str` | The type of Tensor to return. Allowable values are “np”, “pt” and “tf”. | `'pt'`

### PretrainingBatchSamplerDataCollatorForSeq2Seq

    utils.collators.batching.PretrainingBatchSamplerDataCollatorForSeq2Seq(
        *args,
        multipack_attn=True,
        **kwargs,
    )

Collator for multipack specific to the using the BatchSampler

### V2BatchSamplerDataCollatorForSeq2Seq

    utils.collators.batching.V2BatchSamplerDataCollatorForSeq2Seq(
        tokenizer,
        model=None,
        padding=True,
        max_length=None,
        pad_to_multiple_of=None,
        label_pad_token_id=-100,
        position_pad_token_id=0,
        return_tensors='pt',
        squash_position_ids=False,
    )

Collator for multipack specific to the using the BatchSampler