"""
DataCollator for axolotl to pad labels and position_ids for packed sequences
"""
from dataclasses import dataclass
from typing import Any, Dict, Optional, Sequence, Union
import numpy as np
import torch
import transformers
from transformers import PreTrainedTokenizerBase
from transformers.utils import PaddingStrategy
IGNORE_INDEX = -100
@dataclass
class DataCollatorForSeq2Seq:
"""
Data collator that will dynamically pad the inputs received, as well as the labels and position_ids
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
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.
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).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
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).
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).
return_tensors (`str`):
The type of Tensor to return. Allowable values are "np", "pt" and "tf".
"""
tokenizer: PreTrainedTokenizerBase
model: Optional[Any] = None
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
label_pad_token_id: int = -100
position_pad_token_id: int = 0
return_tensors: str = "pt"
def __call__(self, features, return_tensors=None):
labels = None
if return_tensors is None:
return_tensors = self.return_tensors
for feature_name, pad_token_id in [
("labels", self.label_pad_token_id),
("position_ids", self.position_pad_token_id),
]:
feat = (
[feature[feature_name] for feature in features]
if feature_name in features[0].keys()
else None
)
labels = feat if feat and feature_name == "labels" else labels
# We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
# same length to return tensors.
if feat is not None:
max_feature_length = max(len(l) for l in feat) # noqa: E741
if self.pad_to_multiple_of is not None:
max_feature_length = (
(max_feature_length + self.pad_to_multiple_of - 1)
// self.pad_to_multiple_of
* self.pad_to_multiple_of
)
padding_side = self.tokenizer.padding_side
for feature in features:
remainder = [pad_token_id] * (
max_feature_length - len(feature[feature_name])
)
if isinstance(feature[feature_name], list):
feature[feature_name] = (
feature[feature_name] + remainder
if padding_side == "right"
else remainder + feature[feature_name]
)
elif padding_side == "right":
feature[feature_name] = np.concatenate(
[feature[feature_name], remainder]
).astype(np.int64)
else:
feature[feature_name] = np.concatenate(
[remainder, feature[feature_name]]
).astype(np.int64)
features = self.tokenizer.pad(
features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors=return_tensors,
)
# prepare decoder_input_ids
if (
labels is not None
and self.model is not None
and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
):
decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(
labels=features["labels"]
)
features["decoder_input_ids"] = decoder_input_ids
return features
@dataclass
class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
"""
Collator for multipack specific to the using the BatchSampler
"""
def __call__(self, features, return_tensors=None):
chunked_data = {}
for feature in features[0].keys():
if feature == "length":
continue
if feature == "attention_mask":
arrays = [
(1) * np.array(item[feature])
for item in features
if feature in item
]
chunked_data[feature] = np.concatenate(arrays)
else:
arrays = [
np.array(item[feature]) for item in features if feature in item
]
chunked_data[feature] = np.concatenate(arrays)
features = [chunked_data]
return super().__call__(features, return_tensors=return_tensors)
@dataclass
class MambaDataCollator:
"""
Collator for State Space Models (Mamba)
"""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
input_ids, labels = tuple(
[torch.LongTensor(instance[key]) for instance in instances]
for key in ("input_ids", "labels")
)
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids,
batch_first=True,
padding_value=self.tokenizer.pad_token_id,
)
labels = torch.nn.utils.rnn.pad_sequence(
labels, batch_first=True, padding_value=IGNORE_INDEX
)
return {
"input_ids": input_ids,
"labels": labels,
}
@dataclass
class PretrainingBatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
"""
Collator for multipack specific to the using the BatchSampler
"""
def __call__(self, features, return_tensors=None):
chunked_data = {}
for feature in features.keys():
if feature == "length":
continue
if feature == "attention_mask":
arrays = [(1) * np.array(item) for item in features[feature]]
chunked_data[feature] = np.concatenate(arrays)
else:
arrays = [np.array(item) for item in features[feature]]
chunked_data[feature] = np.concatenate(arrays)
features = [chunked_data]
return super().__call__(features, return_tensors=return_tensors)