text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
token_type_ids: Optional[torc... | 9,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FNetForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer.fr... | 9,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
... | 9,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output
return NextSentencePredictorOutput(
loss=next_sentence_loss,
logits=seq_relationship_scores,
hi... | 9,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForSequenceClassification(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_si... | 9,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optiona... | 9,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_... | 9,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 9,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForMultipleChoice(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final... | 9,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
inpu... | 9,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] | 9,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs... | 9,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoic... | 9,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForTokenClassification(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size... | 9,762 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[t... | 9,762 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,762 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequ... | 9,762 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetForQuestionAnswering(FNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.fnet = FNetModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply fin... | 9,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
@add_start_docstrings_to_model_forward(FNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opt... | 9,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled spa... | 9,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logi... | 9,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 9,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_st... | 9,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/modeling_fnet.py |
class FNetTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" FNetTokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`AlbertTokenizerFast`]. Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokeniz... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
Args:
vocab_file (`str`):
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
contains the vocabulary necessary to instantiate a tokenizer.
do_lower_case (`bool`, *optional*, defaults to `False`):
Whether or not to low... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "token_type_ids"]
slow_tokenizer_class = FNetTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=False,
remove_space=True,
keep_accents=True,
unk_tok... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_t... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An FNet sequence has ... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An FNet
sequence pair mask has the following format:
... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
... | 9,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fnet/tokenization_fnet_fast.py |
class CanineConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CanineModel`]. It is used to instantiate an
CANINE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the deep Transformer encoder.
num_attention_heads (`int`, *optional*, defaults t... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 16384):
The maximum sequence length that this model might ever be used with.
type_vocab_size (`int`, *op... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
End of stream token id.
downsampling_rate (`int`, *optional*, defaults to 4):
The rate at which to downsample the original character sequence length before applying the deep Transformer
encoder.
upsampling_kernel_size (`int`, *optional*, defaults to 4):
The kernel siz... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
Example:
```python
>>> from transformers import CanineConfig, CanineModel
>>> # Initializing a CANINE google/canine-s style configuration
>>> configuration = CanineConfig()
>>> # Initializing a model (with random weights) from the google/canine-s style configuration
>>> model = CanineModel(co... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=16384,
type_vocab_size=16,
... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob... | 9,765 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/configuration_canine.py |
class CanineModelOutputWithPooling(ModelOutput):
"""
Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly
different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow
Transformer encoders. | 9,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final
shallow Transformer encoder).
pooler_output (`torch.FloatTensor` of shape `(ba... | 9,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length //
config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the
initial input to each Transformer encoder. The hidden states of the shallow encoders have l... | 9,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
attention softmax, used to compute the weighted average in the self-attention heads.
""" | 9,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None | 9,766 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineEmbeddings(nn.Module):
"""Construct the character, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.config = config
# character embeddings
shard_embedding_size = config.hidden_size // config.num_hash_functions
for i... | 9,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 9,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
result_tensors = []
for prime in primes:
hashed = ((input_ids + 1) * prime) % num_buckets
result_tensors.append(hashed)
return result_tensors
def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int):
"""Converts IDs (e.g. codep... | 9,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.FloatTensor:
if input_ids is not None:
... | 9,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.char_position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddi... | 9,767 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CharactersToMolecules(nn.Module):
"""Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions."""
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
in_channels=config.hidden_size,
out_channels=config.... | 9,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# char_encoding has shape [batch, char_seq, hidden_size]
# We transpose it to be [batch, hidden_size, char_seq]
char_encoding = torch.transpose(char_encoding, 1, 2)
downsampled = self.conv(char_encoding)
downsampled = torch.transpose(downsampled, 1, 2)
downsampled = self.activati... | 9,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
result = self.LayerNorm(result)
return result | 9,768 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class ConvProjection(nn.Module):
"""
Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size
characters.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.conv = nn.Conv1d(
in_... | 9,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def forward(
self,
inputs: torch.Tensor,
final_seq_char_positions: Optional[torch.Tensor] = None,
) -> torch.Tensor:
# inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final]
# we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq]
... | 9,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
pad = nn.ConstantPad1d((pad_beg, pad_end), 0)
# `result`: shape (batch_size, char_seq_len, hidden_size)
result = self.conv(pad(inputs))
result = torch.transpose(result, 1, 2)
result = self.activation(result)
result = self.LayerNorm(result)
result = self.dropout(result)
... | 9,769 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numb... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = confi... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def forward(
self,
from_tensor: torch.Tensor,
to_tensor: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = from_tensor.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, d... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
if attention_mask.ndim == 3:
# if attention_mask is 3D, do the following:
attention_mask = torch.unsqueeze(attention_mask, dim=1)
# Since attention... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_pr... | 9,770 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def... | 9,771 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineAttention(nn.Module):
"""
Additional arguments related to local attention: | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
- **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention.
- **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to
attend
to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_atte... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
*to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to
skip when moving to the next block in `to_tensor`.
""" | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def __init__(
self,
config,
local=False,
always_attend_to_first_position: bool = False,
first_position_attends_to_all: bool = False,
attend_from_chunk_width: int = 128,
attend_from_chunk_stride: int = 128,
attend_to_chunk_width: int = 128,
attend_t... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# additional arguments related to local attention
self.local = local
if attend_from_chunk_width < attend_from_chunk_stride:
raise ValueError(
"`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped."
)
if atten... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(s... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def forward(
self,
hidden_states: Tuple[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
if not... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# Create chunks (windows) that we will attend *from* and then concatenate them.
from_chunks = []
if self.first_position_attends_to_all:
from_chunks.append((0, 1))
# We must skip this first position so that our output sequence is the
# correct lengt... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# Determine the chunks (windows) that will attend *to*.
to_chunks = []
if self.first_position_attends_to_all:
to_chunks.append((0, to_seq_length))
for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride):
chunk_end = min(to_seq_length, c... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# next, compute attention scores for each pair of windows and concatenate
attention_output_chunks = []
attention_probs_chunks = []
for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks):
from_tensor_chunk = from_tensor[:, from_start:from_end, :]... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
cls_position = to_tensor[:, 0:1, :]
to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1)
attention_outputs_chunk = self.self(
from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions
)
a... | 9,772 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineIntermediate(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.inte... | 9,773 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
d... | 9,774 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineLayer(nn.Module):
def __init__(
self,
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
):
su... | 9,775 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def forward(
self,
hidden_states: Tuple[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
self_a... | 9,775 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output | 9,775 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineEncoder(nn.Module):
def __init__(
self,
config,
local=False,
always_attend_to_first_position=False,
first_position_attends_to_all=False,
attend_from_chunk_width=128,
attend_from_chunk_stride=128,
attend_to_chunk_width=128,
attend_to... | 9,776 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def forward(
self,
hidden_states: Tuple[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Opt... | 9,776 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
output_attentions,
... | 9,776 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 9,776 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CaninePooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
# We "pool" the model by simp... | 9,777 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CaninePredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tr... | 9,778 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = CaninePredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Line... | 9,779 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = CanineLMPredictionHead(config)
def forward(
self,
sequence_output: Tuple[torch.Tensor],
) -> Tuple[torch.Tensor]:
prediction_scores = self.predictions(sequence_outpu... | 9,780 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CaninePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CanineConfig
load_tf_weights = load_tf_weights_in_canine
base_model_prefix = "canine"
supports_gr... | 9,781 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv1d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.no... | 9,781 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
class CanineModel(CaninePreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
shallow_config = copy.deepcopy(config)
shallow_config.num_hidden_layers = 1 | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
self.char_embeddings = CanineEmbeddings(config)
# shallow/low-dim transformer encoder to get a initial character encoding
self.initial_char_encoder = CanineEncoder(
shallow_config,
local=True,
always_attend_to_first_position=False,
first_position_attends_t... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
self.pooler = CaninePooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See ba... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
to_seq_length = to_mask.shape[1]
to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float()
# We don't assume that `from_tensor` is a mask (although it could be). We
# don't actually care if we attend *from* padding tokens (only *to* padding)
# tokens so we create a tenso... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# first, make char_attention_mask 3D by adding a channel dim
batch_size, char_seq_len = char_attention_mask.shape
poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len))
# next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len)
... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
molecules_without_extra_cls = molecules[:, 1:, :]
# `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size]
repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2)
# So far, we've repeated the elements sufficient for any `char_seq_length`
# that... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# `repeated`: [batch_size, char_seq_len, molecule_hidden_size]
return torch.cat([repeated, remainder_repeated], dim=-2) | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
@add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CanineModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: O... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
return_dict = return_dict if return_dict is not None else self.config.use_retu... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.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()
... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
molecule_attention... | 9,782 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/canine/modeling_canine.py |
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