text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
batch = pad_without_fast_tokenizer_warning(
self.tokenizer,
features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return... | 10,609 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorForTokenClassification(DataCollatorMixin):
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
padding (`bool... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
- `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 ar... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.0 (Volta).
label_pad_token_id (`int`, *optional*, defaults to -100):
The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
re... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
no_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]
batch = pad_without_fast_tokenizer_warning(
self.tokenizer,
no_labels_features,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=s... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if padding_side == "right":
batch[label_name] = [
to_list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
]
else:
batch[label_name] = [
[self.label_pad_token_id] * (sequence_length - len(label)) + to_list... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
batch = pad_without_fast_tokenizer_warning(
self.tokenizer,
features,
padding=self.padding,
... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
sequence_length = tf.convert_to_tensor(batch["input_ids"]).shape[1]
padding_side = self.tokenizer.padding_side
if padding_side == "right":
batch["labels"] = [
list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
]
el... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def numpy_call(self, features):
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
batch = pad_without_fast_tokenizer_warning(
self.tokenizer,
features,
... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
sequence_length = np.array(batch["input_ids"]).shape[1]
padding_side = self.tokenizer.padding_side
if padding_side == "right":
batch["labels"] = [
list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
]
else:
... | 10,610 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorForSeq2Seq:
"""
Data collator that will dynamically pad the inputs received, as well as the labels.
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
model ([`PreTrainedModel`], *optional*):
... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
- `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 ar... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
7.0 (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).
r... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
label_name = "label" if "label" in features[0].keys() else "labels"
labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
# reconvert list[None] to None if necessary
# this might occur when we pass {..., "labels": None}
if labels is not None... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# we have to pad the labels manually as we cannot rely on `tokenizer.pad` and we need them to be of the same length to return tensors
no_padding = self.padding is False or self.padding == PaddingStrategy.DO_NOT_PAD
if labels is not None:
if no_padding:
if isinstance(features[... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
) | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
padding_side = self.tokenizer.padding_side
if isinstance(features[0][label_name], list):
batch["labels"] = [
label + [self.label_pad_token_id] * (max_label_length - len(label))
if padding_side == "right"
else [se... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
label,
]
)
for label in labels
] | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# reintroduce side effects via tokenizer that return respective datatypes for the `return_tensors` argument
if batch.get("labels", None) is not None:
if return_tensors == "pt":
import torch
batch["labels"] = torch.tensor(batch["labels"], dtype=torch.int64)
... | 10,611 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorForLanguageModeling(DataCollatorMixin):
"""
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length. | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
Args:
tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
The tokenizer used for encoding the data.
mlm (`bool`, *optional*, defaults to `True`):
Whether or not to use masked language modeling. If set to `False`, the labels are the same as the inputs
w... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
random_replace_prob (`float`, *optional*, defaults to 0.1):
The probability with which masked tokens are replaced by random tokens from the tokenizer's vocabulary.
Defaults to 0.1, meaning 10% of the masked tokens will be replaced with random tokens. The remaining
masked tokens (1 - ... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
<Tip>
For best performance, this data collator should be used with a dataset having items that are dictionaries or
BatchEncoding, with the `"special_tokens_mask"` key, as returned by a [`PreTrainedTokenizer`] or a
[`PreTrainedTokenizerFast`] with the argument `return_special_tokens_mask=True`.
<Exampl... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
3. No `[MASK]` replacement, only random tokens:
- `mask_replace_prob=0.0`, `random_replace_prob=1.0`.
- Expect all masked tokens to be replaced with random tokens. No `[MASK]` replacements or unchanged tokens.
4. Balanced replacement:
- `mask_replace_prob=0.5`, `random_replace_prob=0.4`.
... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def __post_init__(self):
if self.mlm and self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. "
"You should pass `mlm=False` to train on causal language modeling instead."
... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
self.tf_mask_tokens = tf.function(self.tf_mask_tokens, jit_compile=True)
@staticmethod
def tf_bernoulli(shape, probability):
import tensorflow as tf
prob_matrix = tf.fill(shape, probability)
return tf.cast(prob_matrix - tf.random.uniform(shape, 0, 1) >= 0, tf.bool)
def tf_mask_tok... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
input_shape = tf.shape(inputs)
# 1 for a special token, 0 for a normal token in the special tokens mask
# We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
masked_indices = self.tf_bernoulli(input_shape, self.mlm_probability) & ~special_tokens_mas... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
remaining_prob = 1 - self.mask_replace_prob
# scaling the random_replace_prob to the remaining probability for example if
# mask_replace_prob = 0.8 and random_replace_prob = 0.1,
# then random_replace_prob_scaled = 0.1 / 0.2 = 0.5
random_replace_prob_scaled = self.random_replace_prob / r... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def tf_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
import tensorflow as tf
# Handle dict or lists with proper padding and conversion to tensor.
if isinstance(examples[0], Mapping):
batch = pad_without_fast_tokenizer_warning(
s... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# If special token mask has been preprocessed, pop it from the dict.
special_tokens_mask = batch.pop("special_tokens_mask", None)
if self.mlm:
if special_tokens_mask is None:
special_tokens_mask = [
self.tokenizer.get_special_tokens_mask(val, already_has_s... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
labels = batch["input_ids"]
if self.tokenizer.pad_token_id is not None:
# Replace self.tokenizer.pad_token_id with -100
labels = tf.where(labels == self.tokenizer.pad_token_id, -100, labels)
else:
labels = tf.identity(labels) # Makes a copy, just ... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def torch_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
# Handle dict or lists with proper padding and conversion to tensor.
if isinstance(examples[0], Mapping):
batch = pad_without_fast_tokenizer_warning(
self.tokenizer, examples, retur... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# If special token mask has been preprocessed, pop it from the dict.
special_tokens_mask = batch.pop("special_tokens_mask", None)
if self.mlm:
batch["input_ids"], batch["labels"] = self.torch_mask_tokens(
batch["input_ids"], special_tokens_mask=special_tokens_mask
... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
labels = inputs.clone()
# We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
probability_matrix = torch.full(labels.shape, self.mlm_probability)
if special_tokens_mask is None:
special_tokens_mask = [
self.tokenizer.get_... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# mask_replace_prob% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = torch.bernoulli(torch.full(labels.shape, self.mask_replace_prob)).bool() & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# random_replace_prob% of the time, we replace masked input tokens with random word
indices_random = (
torch.bernoulli(torch.full(labels.shape, random_replace_prob_scaled)).bool()
& masked_indices
& ~indices_replaced
)
random_words = torch.randint(len(self.tok... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def numpy_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
# Handle dict or lists with proper padding and conversion to tensor.
if isinstance(examples[0], Mapping):
batch = pad_without_fast_tokenizer_warning(
self.tokenizer, examples, retur... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# If special token mask has been preprocessed, pop it from the dict.
special_tokens_mask = batch.pop("special_tokens_mask", None)
if self.mlm:
batch["input_ids"], batch["labels"] = self.numpy_mask_tokens(
batch["input_ids"], special_tokens_mask=special_tokens_mask
... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def numpy_mask_tokens(self, inputs: Any, special_tokens_mask: Optional[Any] = None) -> Tuple[Any, Any]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
"""
labels = np.copy(inputs)
# We sample a few tokens in each sequence... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
probability_matrix[special_tokens_mask] = 0
# Numpy doesn't have bernoulli, so we use a binomial with 1 trial
masked_indices = np.random.binomial(1, probability_matrix, size=probability_matrix.shape).astype(bool)
labels[~masked_indices] = -100 # We only compute loss on masked tokens
# ... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
remaining_prob = 1 - self.mask_replace_prob
# scaling the random_replace_prob to the remaining probability for example if
# mask_replace_prob = 0.8 and random_replace_prob = 0.1,
# then random_replace_prob_scaled = 0.1 / 0.2 = 0.5
random_replace_prob_scaled = self.random_replace_prob / r... | 10,612 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorForWholeWordMask(DataCollatorForLanguageModeling):
"""
Data collator used for language modeling that masks entire words.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for masked language modeling
<Tip>
This collator relies on deta... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
batch_input = _torch_collate_batch(input_ids, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
mask_labels = []
for e in examples:
ref_tokens = []
for id in tolist(e["input_ids"]):
token = self.tokenizer._convert_id_to_token(id)
ref_tok... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def tf_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
import tensorflow as tf
if isinstance(examples[0], Mapping):
input_ids = [e["input_ids"] for e in examples]
else:
input_ids = examples
examples = [{"input_ids": e} for... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# For Chinese tokens, we need extra inf to mark sub-word, e.g [喜,欢]-> [喜，##欢]
if "chinese_ref" in e:
ref_pos = tolist(e["chinese_ref"])
len_seq = len(e["input_ids"])
for i in range(len_seq):
if i in ref_pos:
ref_toke... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
batch_input = _numpy_collate_batch(input_ids, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
mask_labels = []
for e in examples:
ref_tokens = []
for id in tolist(e["input_ids"]):
token = self.tokenizer._convert_id_to_token(id)
ref_tok... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def _whole_word_mask(self, input_tokens: List[str], max_predictions=512):
"""
Get 0/1 labels for masked tokens with whole word mask proxy
"""
if not isinstance(self.tokenizer, (BertTokenizer, BertTokenizerFast)):
warnings.warn(
"DataCollatorForWholeWordMask is... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
random.shuffle(cand_indexes)
num_to_predict = min(max_predictions, max(1, int(round(len(input_tokens) * self.mlm_probability))))
masked_lms = []
covered_indexes = set()
for index_set in cand_indexes:
if len(masked_lms) >= num_to_predict:
break
# If... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if len(covered_indexes) != len(masked_lms):
raise ValueError("Length of covered_indexes is not equal to length of masked_lms.")
mask_labels = [1 if i in covered_indexes else 0 for i in range(len(input_tokens))]
return mask_labels
def torch_mask_tokens(self, inputs: Any, mask_labels: Any... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the"
" --mlm flag if you want to use this tokenizer."
)
labels = inputs.clone()
# We sample a... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
masked_indices = probability_matrix.bool()
labels[~masked_indices] = -100 # We only compute loss on masked tokens
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def tf_mask_tokens(self, inputs: Any, mask_labels: Any) -> Tuple[Any, Any]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
'mask_labels' means we use whole word mask (wwm), we directly mask idxs according to it's ref.
"""
... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
special_tokens_mask = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels
]
masked_indices = masked_indices & ~tf.cast(special_tokens_mask, dtype=tf.bool)
if self.tokenizer.pad_token is not None:
padding_mask = inputs == self.t... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# 10% of the time, we replace masked input tokens with random word
indices_random = self.tf_bernoulli(input_shape, 0.5) & masked_indices & ~indices_replaced
random_words = tf.random.uniform(input_shape, maxval=len(self.tokenizer), dtype=tf.int64)
inputs = tf.where(indices_random, random_words, i... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def numpy_mask_tokens(self, inputs: Any, mask_labels: Any) -> Tuple[Any, Any]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. Set
'mask_labels' means we use whole word mask (wwm), we directly mask idxs according to it's ref.
"""
... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
special_tokens_mask = [
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
]
masked_indices[np.array(special_tokens_mask, dtype=bool)] = 0
if self.tokenizer.pad_token is not None:
padding_mask = labels == self.tokenizer... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# 10% of the time, we replace masked input tokens with random word
# indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
indices_random = (
np.random.binomial(1, 0.5, size=labels.shape).astype(bool) & masked_indices & ~indices_replaced
... | 10,613 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorForSOP(DataCollatorForLanguageModeling):
"""
Data collator used for sentence order prediction task.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for both masked language modeling and sentence order prediction
"""
def __init__(self... | 10,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
token_type_ids = [example["token_type_ids"] for example in examples]
# size of segment_ids varied because randomness, padding zero to the end as the original implementation
token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)
sop_label_list ... | 10,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the"
" --mlm flag if you want to use this tokenizer."
) | 10,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
labels = inputs.clone()
# We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
probability_matrix = torch.full(labels.shape, self.mlm_probability)
special_tokens_mask = [
self.tokenizer.get_special_tok... | 10,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
attention_padding_mask = labels.eq(self.tokenizer.pad_token_id)
attention_mask.masked_fill_(attention_padding_mask, value=1.0)
labels[~masked_indices] = -100 # We only compute loss on masked tokens, -100 is default for CE compute | 10,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
# 10% of the time, we replace... | 10,614 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorForPermutationLanguageModeling(DataCollatorMixin):
"""
Data collator used for permutation language modeling.
- collates batches of tensors, honoring their tokenizer's pad_token
- preprocesses batches for permutation language modeling with procedures specific to XLNet
"""
toke... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def tf_call(self, examples: List[Union[List[int], Any, Dict[str, Any]]]) -> Dict[str, Any]:
if isinstance(examples[0], Mapping):
examples = [e["input_ids"] for e in examples]
batch = _tf_collate_batch(examples, self.tokenizer)
inputs, perm_mask, target_mapping, labels = self.tf_mask_... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def torch_mask_tokens(self, inputs: Any) -> Tuple[Any, Any, Any, Any]:
"""
The masked tokens to be predicted for a particular sequence are determined by the following algorithm:
0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for permutation language modeling."
" Please add a mask token if you want to use this tokenizer."
)
if inputs.size(1) % 2 != 0:
... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
for i in range(labels.size(0)):
# Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
cur_len = 0
max_len = labels.size(1) | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
while cur_len < max_len:
# Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
span_length = torch.randint(1, self.max_span_length + 1, (1,)).item()
# Reserve a context of length `context_length = span_length / plm_proba... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Since we're replacing non-masked tokens with -100 in the labels tensor instead of skipping them altogether,
# the i-th predict corresponds to the i-th token.
target_mapping[i] = torch.eye(labels.size(1))
special_tokens_mask = torch.tensor(
[self.tokenizer.get_special_token... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
perm_mask = torch.zeros((labels.size(0), labels.size(1), labels.size(1)), dtype=torch.float32)
for i in range(labels.size(0)):
# Generate permutation indices i.e. sample a random factorisation order for the sequence. This will
# determine which tokens a given token can attend to (encode... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Create a linear factorisation order
perm_index = torch.arange(labels.size(1))
# Split this into two halves, assuming that half the sequence is reused each time
perm_index = perm_index.reshape((-1, labels.size(1) // 2)).transpose(0, 1)
# Permute the two halves such that ... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# The logic for whether the i-th token can attend on the j-th token based on the factorisation order:
# 0 (can attend): If perm_index[i] > perm_index[j] or j is neither masked nor a functional token
# 1 (cannot attend): If perm_index[i] <= perm_index[j] and j is either masked or a functional tok... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
return inputs.long(), perm_mask, target_mapping, labels.long()
def tf_mask_tokens(self, inputs: Any) -> Tuple[Any, Any, Any, Any]:
"""
The masked tokens to be predicted for a particular sequence are determined by the following algorithm: | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
2. Reserve a context of length `context_length = span_length / plm_probability` to ... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for permutation language modeling."
" Please add a mask token if you want to use this tokenizer."
)
if tf.shape(inputs)[1] % 2 != 0:
... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
for i in range(len(labels)):
# Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
cur_len = 0
max_len = tf.shape(labels)[1] | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
while cur_len < max_len:
# Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
span_length = randint(1, self.max_span_length + 1)
# Reserve a context of length `context_length = span_length / plm_probability` to surround... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Since we're replacing non-masked tokens with -100 in the labels tensor instead of skipping them altogether,
# the i-th predict corresponds to the i-th token.
target_mapping[i] = np.eye(labels_shape[1])
masked_indices = tf.cast(tf.convert_to_tensor(masked_indices), dtype=tf.bool)
... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Mask indicating non-functional tokens, where functional tokens are [SEP], [CLS], padding, etc.
non_func_mask = ~(padding_mask | special_tokens_mask)
inputs = tf.where(masked_indices, self.tokenizer.mask_token_id, inputs)
labels = tf.where(masked_indices, labels, -100) # We only compute loss ... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
for i in range(len(labels)):
# Generate permutation indices i.e. sample a random factorisation order for the sequence. This will
# determine which tokens a given token can attend to (encoded in `perm_mask`).
# Note: Length of token sequence being permuted has to be less than or equal... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Create a linear factorisation order
# tf.range is the equivalent of torch.arange
perm_index = tf.range(labels_shape[1])
# Split this into two halves, assuming that half the sequence is reused each time
perm_index = tf.transpose(tf.reshape(perm_index, (-1, labels_shape[1... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# The logic for whether the i-th token can attend on the j-th token based on the factorisation order:
# 0 (can attend): If perm_index[i] > perm_index[j] or j is neither masked nor a functional token
# 1 (cannot attend): If perm_index[i] <= perm_index[j] and j is either masked or a functional tok... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
return tf.cast(inputs, tf.int64), tf.cast(perm_mask, tf.float32), target_mapping, tf.cast(labels, tf.int64)
def numpy_mask_tokens(self, inputs: Any) -> Tuple[Any, Any, Any, Any]:
"""
The masked tokens to be predicted for a particular sequence are determined by the following algorithm: | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
2. Reserve a context of length `context_length = span_length / plm_probability` to ... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
" Please add a mask token if you want to use this tokenizer."
) | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
if inputs.shape[1] % 2 != 0:
raise ValueError(
"This collator requires that sequence lengths be even to create a leakage-free perm_mask. Please see"
" relevant comments in source code for details."
)
labels = np.copy(inputs)
# Creating the mask an... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
while cur_len < max_len:
# Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
span_length = randint(1, self.max_span_length + 1)
# Reserve a context of length `context_length = span_length / plm_probability` to surround... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Since we're replacing non-masked tokens with -100 in the labels tensor instead of skipping them altogether,
# the i-th predict corresponds to the i-th token.
target_mapping[i] = np.eye(labels.shape[1])
special_tokens_mask = np.array(
[self.tokenizer.get_special_tokens_mask... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
perm_mask = np.zeros((labels.shape[0], labels.shape[1], labels.shape[1]), dtype=np.float32)
for i in range(labels.shape[0]):
# Generate permutation indices i.e. sample a random factorisation order for the sequence. This will
# determine which tokens a given token can attend to (encoded ... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# Create a linear factorisation order
perm_index = np.arange(labels.shape[1])
# Split this into two halves, assuming that half the sequence is reused each time
perm_index = perm_index.reshape((-1, labels.shape[1] // 2)).T
# Permute the two halves such that they do not cro... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
# 0 (can attend): If perm_index[i] > perm_index[j] or j is neither masked nor a functional token
# 1 (cannot attend): If perm_index[i] <= perm_index[j] and j is either masked or a functional token
perm_mask[i] = (
perm_index.reshape((labels.shape[1], 1)) <= perm_index.reshape((1,... | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
return inputs.astype(np.int64), perm_mask, target_mapping, labels.astype(np.int64) | 10,615 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class DataCollatorWithFlattening(DefaultDataCollator):
"""
Data collator used for padding free approach. Does the following:
- concatate the entire mini batch into single long sequence [1, total_tokens]
- uses `separator_id` to separate sequences within the concatenated `labels`, default value is -100
... | 10,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
def __call__(self, features, return_tensors=None, separator_id=None):
if return_tensors is None:
return_tensors = self.return_tensors
if separator_id is None:
separator_id = self.separator_id
is_labels_provided = "labels" in features[0]
ret = {"input_ids": [], "la... | 10,616 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/data_collator.py |
class TextDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(
self,
tokenizer: PreTrainedTokenizer,
file_path: str,
block_size: int,
overwrite_cache=False,
cache_dir: Optional[str] = None,
):
... | 10,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lock_path = cached_features_file + ".lock"
with FileLock(lock_path):
if os.path.exists(cached_features_file) and not overwrite_cache:
start = time.ti... | 10,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
for i in range(0, len(tokenized_text) - block_size + 1, block_size): # Truncate in block of block_size
self.examples.append(
tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size])
)
# Note that we are losing the las... | 10,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
def __getitem__(self, i) -> torch.Tensor:
return torch.tensor(self.examples[i], dtype=torch.long) | 10,617 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
class LineByLineTextDataset(Dataset):
"""
This will be superseded by a framework-agnostic approach soon.
"""
def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
warnings.warn(
DEPRECATION_WARNING.format(
"https://github.com/huggingfac... | 10,618 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/data/datasets/language_modeling.py |
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