text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
second_ids, second_entity_token_spans = get_input_ids_and_entity_token_spans(
text_pair, entity_spans_pair
)
if entities_pair is None:
second_entity_ids = [self.entity_mask_token_id] * len(entity_spans_pair)
else... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
elif self.task == "entity_classification":
if not (isinstance(entity_spans, list) and len(entity_spans) == 1 and isinstance(entity_spans[0], tuple)):
raise ValueError(
"Entity spans should be a list containing a single tuple "
"containing the start and... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# add special tokens to input ids
entity_token_start, entity_token_end = first_entity_token_spans[0]
first_ids = (
first_ids[:entity_token_end] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_end:]
)
first_ids = (
first_i... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
elif self.task == "entity_pair_classification":
if not (
isinstance(entity_spans, list)
and len(entity_spans) == 2
and isinstance(entity_spans[0], tuple)
and isinstance(entity_spans[1], tuple)
):
raise ValueError(
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
head_token_span, tail_token_span = first_entity_token_spans
token_span_with_special_token_ids = [
(head_token_span, self.additional_special_tokens_ids[0]),
(tail_token_span, self.additional_special_tokens_ids[1]),
]
if head_token_span[0] < tail_token_s... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
for (entity_token_start, entity_token_end), special_token_id in token_span_with_special_token_ids:
first_ids = first_ids[:entity_token_end] + [special_token_id] + first_ids[entity_token_end:]
first_ids = first_ids[:entity_token_start] + [special_token_id] + first_ids[entity_token_start:]... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return (
first_ids,
second_ids,
first_entity_ids,
second_entity_ids,
first_entity_token_spans,
second_entity_token_spans,
) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _batch_prepare_for_model(
self,
batch_ids_pairs: List[Tuple[List[int], None]],
batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]],
batch_entity_token_spans_pairs: List... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_special_tokens_mask: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overf... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
batch_entity_ids_pairs: list of entity ids or entity ids pairs
batch_entity_token_spans_pairs: list of entity spans or entity spans pairs
max_entity_length: The maximum length of the entity sequence.
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
batch_outputs = {}
for input_ids, entity_ids, entity_token_span_pairs in zip(
batch_ids_pairs, batch_entity_ids_pairs, batch_entity_token_spans_pairs
):
first_ids, second_ids = input_ids
first_entity_ids, second_entity_ids = entity_ids
first_entity_token_s... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
stride=stride,
pad_to_multiple_of=None, # we pad in batch afterward
padding_side=None, # we pad in batch afterward
return_attention_mask=False, # we pad in batch afterward
return_token_type_ids=return_token_type_ids,
return_overflowing_t... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def prepare_for_model(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
entity_ids: Optional[List[int]] = None,
pair_entity_ids: Optional[List[int]] = None,
entity_token_s... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
prepend_batch_axis: bool = False,
**kwargs,
) -> BatchEncoding:
"""
Prepares a s... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence.
pair_ids (`List[int]`, *optional*):
Tokenized input ids of the second sequence.
entity_ids (`List[int]`, *optional*):
Entity ids of the first sequence.
pair... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_mu... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if return_token_type_ids and not add_special_tokens:
raise ValueError(
"Asking to return token_type_ids while setting add_special_tokens to False "
"results in an undefined behavior. Please set add_special_tokens to True or "
"set return_token_type_ids to None... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Load from model defaults
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
encoded_inputs = {}
# Compute the... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Truncation: Handle max sequence length and max_entity_length
overflowing_tokens = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
# truncate words up to max_length
ids, pair_ids, overflowing_tokens = self.truncate_seque... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Add special tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
entity_token_offset = 1 # 1 * <s> token
pair_entity_token_offset = len(ids) + 3 ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Build output dictionary
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if return_special_tokens_mask:
if add_special_tokens:
encoded_inputs["special_tokens_mask"] = self.get_special_toke... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
valid_pair_entity_ids, valid_pair_entity_token_spans = None, None
if pair_entity_ids is not None:
valid_pair_entity_ids = [
ent_id
for ent_id, span in zip(pair_entity_ids, pair_entity_token_spans)
if span[1] <= len(pair_ids)
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length:
# truncate entities up to max_entity_length
valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences(
valid_entity_ids,
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if return_overflowing_tokens:
encoded_inputs["overflowing_entities"] = overflowing_entities
encoded_inputs["num_truncated_entities"] = total_entity_len - max_entity_length | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
final_entity_ids = valid_entity_ids + valid_pair_entity_ids if valid_pair_entity_ids else valid_entity_ids
encoded_inputs["entity_ids"] = list(final_entity_ids)
entity_position_ids = []
entity_start_positions = []
entity_end_positions = []
for token_spans, off... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
entity_end_positions.append(end - 1) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
encoded_inputs["entity_position_ids"] = entity_position_ids
if self.task == "entity_span_classification":
encoded_inputs["entity_start_positions"] = entity_start_positions
encoded_inputs["entity_end_positions"] = entity_end_positions
if return_token_type_ids:
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Padding
if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
encoded_inputs = self.pad(
encoded_inputs,
max_length=max_length,
max_entity_length=max_entity_length,
padding=padding_strategy.value,
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
def pad(
self,
encoded_inputs: Union[
BatchEncoding,
List[BatchEncoding],
Dict[str, EncodedInput],
Dict[str, List[EncodedInput]],
List[Dict[str, EncodedInput]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_l... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
`self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the result will use the same type unless
you provide a different tensor type with `return_tensors`. In the case of PyTorc... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`):
Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of
tokenized inputs (li... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if 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 ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
padding_side:
The side on which the model should have padding applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
verbose (`bool`, *optional*, defaults to `True`):
Whether or not to print more information and warnings.
""... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# The model's main input name, usually `input_ids`, has be passed for padding
if self.model_input_names[0] not in encoded_inputs:
raise ValueError(
"You should supply an encoding or a list of encodings to this method "
f"that includes {self.model_input_names[0]}, but ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
index = 0
while len(required_input[index]) == 0:
index += 1
if in... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
raise ValueError(
f"type of {first_element} unknown: {type(first_element)}. "
"Should be one of a python, numpy, pytorch or tensorflow object."
) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
for key, value in encoded_inputs.items():
encoded_inputs[key] = to_py_obj(value)
# Convert padding_strategy in PaddingStrategy
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
padding=padding, max_length=max_length, verbose=verbose
)
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
required_input = encoded_inputs[self.model_input_names[0]]
if required_input and not isinstance(required_input[0], (list, tuple)):
encoded_inputs = self._pad(
encoded_inputs,
max_length=max_length,
max_entity_length=max_entity_length,
p... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if padding_strategy == PaddingStrategy.LONGEST:
max_length = max(len(inputs) for inputs in required_input)
max_entity_length = (
max(len(inputs) for inputs in encoded_inputs["entity_ids"]) if "entity_ids" in encoded_inputs else 0
)
padding_strategy = Paddi... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
return BatchEncoding(batch_outputs, tensor_type=return_tensors)
def _pad(
self,
encoded_inputs: Union[Dict[str, Encod... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and optionally padding length (see below).
Will truncate by taking into account the special tokens.... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(encoded_inputs["input_ids"])
if entities_provided:
max_entity_length = len(encoded_inputs["entity_ids"])
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != ... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
# Initialize attention mask if not present.
if return_attention_mask and "attention_mask" not in encoded_inputs:
encoded_inputs["attention_mask"] = [1] * len(encoded_inputs["input_ids"])
if entities_provided and return_attention_mask and "entity_attention_mask" not in encoded_inputs:
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
if needs_to_be_padded:
difference = max_length - len(encoded_inputs["input_ids"])
padding_side = padding_side if padding_side is not None else self.padding_side
if entities_provided:
entity_difference = max_entity_length - len(encoded_inputs["entity_ids"])
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
encoded_inputs["entity_token_type_ids"] + [0] * entity_difference
)
if "special_tokens_mask" in encoded_inputs:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs["input_ids"] = encod... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
encoded_inputs["entity_start_positions"] + [0] * entity_difference
)
encoded_inputs["entity_end_positions"] = (
encoded_inputs["entity_end_positions"] + [0] * entity_difference
) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
elif padding_side == "left":
if return_attention_mask:
encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
if entities_provided:
encoded_inputs["entity_attention_mask"] = [0] * entity_difference + encoded_... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
encoded_inputs["input_ids"] = [self.pad_token_id] * difference + encoded_inputs["input_ids"]
if entities_provided:
encoded_inputs["entity_ids"] = [self.entity_pad_token_id] * entity_difference + encoded_inputs[
"entity_ids"
]
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
raise ValueError("Invalid padding strategy:" + str(padding_side)) | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return encoded_inputs
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Sa... | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
return vocab_file, merge_file, entity_vocab_file | 3,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/luke/tokenization_luke.py |
class DebertaLayerNorm(nn.Module):
"""LayerNorm module in the TF style (epsilon inside the square root)."""
def __init__(self, size, eps=1e-12):
super().__init__()
self.weight = nn.Parameter(torch.ones(size))
self.bias = nn.Parameter(torch.zeros(size))
self.variance_epsilon = ep... | 3,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
de... | 3,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DisentangledSelfAttention(nn.Module):
"""
Disentangled self-attention module
Parameters:
config (`str`):
A model config class instance with the configuration to build a new model. The schema is similar to
*BertConfig*, for more details, please refer [`DebertaConfig`]
... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
self.relative_attention = getattr(config, "relative_attention", False)
self.talking_head = getattr(config, "talking_head", False)
if self.talking_head:
self.head_logits_proj = nn.Linear(config.num_attention_heads, config.num_attention_heads, bias=False)
self.head_weights_proj = ... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if "c2p" in self.pos_att_type:
self.pos_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
if "p2c" in self.pos_att_type:
self.pos_q_proj = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_pr... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
Args:
hidden_states (`torch.FloatTensor`):
Input states to the module usually the output from previous layer, it will be the Q,K and V in
*Attention(Q,K,V)*
attention_mask (`torch.BoolTensor`):
An attention mask matrix of shape [*B*, *N*, *N*] whe... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
rel_embeddings (`torch.FloatTensor`):
The embedding of relative distances. It's a tensor of shape [\\(2 \\times
\\text{max_relative_positions}\\), *hidden_size*].
"""
if query_states is None:
qp = self.in_proj(hidden_states) # .split(self.all_head_size, dim... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])
value_layer = value_layer + self.transpose_for_scores(self.v_bias[None, None, :])
rel_att: int = 0
# Take the dot product between "query" and "key" to get the raw attention scores.
scale_factor = 1 + len(s... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
# bxhxlxd
if self.head_logits_proj is not None:
attention_scores = self.head_logits_proj(attention_scores.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
attention_mask = attention_mask.bool()
attention_scores = attention_scores.masked_fill(~(attention_mask), torch.finfo(query_layer.dtype)... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (-1,)
context_layer = context_layer.view(new_context_layer_shape)
if not output_attentions:
return (... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
def disentangled_att_bias(
self,
query_layer: torch.Tensor,
key_layer: torch.Tensor,
relative_pos: torch.Tensor,
rel_embeddings: torch.Tensor,
scale_factor: int,
):
if relative_pos is None:
relative_pos = build_relative_position(query_layer, key_la... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
# content->position
if "c2p" in self.pos_att_type:
pos_key_layer = self.pos_proj(rel_embeddings)
pos_key_layer = self.transpose_for_scores(pos_key_layer)
c2p_att = torch.matmul(query_layer, pos_key_layer.transpose(-1, -2))
c2p_pos = torch.clamp(relative_pos + att_... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
# position->content
if "p2c" in self.pos_att_type:
pos_query_layer = self.pos_q_proj(rel_embeddings)
pos_query_layer = self.transpose_for_scores(pos_query_layer)
pos_query_layer /= scaled_size_sqrt(pos_query_layer, scale_factor)
r_pos = build_rpos(
... | 3,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
pad_token_id = getattr(config, "pad_token_id", 0)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
... | 3,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if self.embedding_size != config.hidden_size:
self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
else:
self.embed_proj = None
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidde... | 3,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if self.position_embeddings is not None:
position_embeddings = self.posi... | 3,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if mask is not None:
if mask.dim() != embeddings.dim():
if mask.dim() == 4:
mask = mask.squeeze(1).squeeze(1)
mask = mask.unsqueeze(2)
mask = mask.to(embeddings.dtype)
embeddings = embeddings * mask
embeddings = self.dropo... | 3,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = DisentangledSelfAttention(config)
self.output = DebertaSelfOutput(config)
self.config = config
def forward(
self,
hidden_states,
attention_mask,
output_a... | 3,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.int... | 3,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = DebertaLayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | 3,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = DebertaAttention(config)
self.intermediate = DebertaIntermediate(config)
self.output = DebertaOutput(config)
def forward(
self,
hidden_states,
attention_mask,
... | 3,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if output_attentions:
return (layer_output, att_matrix)
else:
return (layer_output, None) | 3,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaEncoder(PreTrainedModel):
"""Modified BertEncoder with relative position bias support"""
def __init__(self, config):
super().__init__(config)
self.layer = nn.ModuleList([DebertaLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention = getattr(config... | 3,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
def get_attention_mask(self, attention_mask):
if attention_mask.dim() <= 2:
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
elif attention_mask.dim() == 3:
... | 3,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_hidden_states: bool = True,
output_attentions: bool = False,
query_states=None,
relative_pos=None,
return_dict: bool = True,
):
attention_mask = self.get_atten... | 3,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
rel_embeddings = self.get_rel_embedding()
for i, layer_module in enumerate(self.layer):
if self.gradient_checkpointing and self.training:
hidden_states, att_m = self._gradient_checkpointing_func(
layer_module.__call__,
next_kv,
... | 3,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if query_states is not None:
query_states = hidden_states
else:
next_kv = hidden_states
if output_attentions:
all_attentions = all_attentions + (att_m,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_st... | 3,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DebertaConfig
base_model_prefix = "deberta"
_keys_to_ignore_on_load_unexpected = ["position_embeddin... | 3,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0... | 3,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaModel(DebertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = DebertaEmbeddings(config)
self.encoder = DebertaEncoder(config)
self.z_steps = 0
self.config = config
# Initialize weights and apply final processing
... | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torc... | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
embedding_output = self.embeddings(
input_ids=input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
mask=attention_mask,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
at... | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
if self.z_steps > 1:
hidden_states = encoded_layers[-2]
layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
query_states = encoded_layers[-1]
rel_embeddings = self.encoder.get_rel_embedding()
attention_mask = self.encoder.get_attention_mask(attentio... | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
attentions=encoder_outputs.attentions,
) | 3,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class LegacyDebertaPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
self.dense = nn.Linear(config.hidden_size, self.embedding_size)
if isinstance(config.hidden_act, str):
... | 3,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class LegacyDebertaLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LegacyDebertaPredictionHeadTransform(config)
self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
# The output weights are the same as the input emb... | 3,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class LegacyDebertaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = LegacyDebertaLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return predi... | 3,936 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaLMPredictionHead(nn.Module):
"""https://github.com/microsoft/DeBERTa/blob/master/DeBERTa/deberta/bert.py#L270"""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
... | 3,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
# note that the input embeddings must be passed as an argument
def forward(self, hidden_states, word_embeddings):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(
hidden_states
) # original use... | 3,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.lm_head = DebertaLMPredictionHead(config)
# note that the input embeddings must be passed as an argument
def forward(self, sequence_output, word_embeddings):
prediction_scores = self.lm_head(seq... | 3,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
class DebertaForMaskedLM(DebertaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.legacy = config.legacy
self.deberta = DebertaModel(config)
if self.legacy:
... | 3,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
def set_output_embeddings(self, new_embeddings):
if self.legacy:
self.cls.predictions.decoder = new_embeddings
self.cls.predictions.bias = new_embeddings.bias
else:
self.lm_predictions.lm_head.dense = new_embeddings
self.lm_predictions.lm_head.bias = new_e... | 3,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deberta/modeling_deberta.py |
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