id
int64 0
328k
| repository_name
stringlengths 7
58
| file_path
stringlengths 9
302
| class_name
stringlengths 5
256
| human_written_code
stringlengths 16
2.16M
| class_skeleton
stringlengths 18
1.49M
⌀ | total_program_units
int64 1
1.76k
| total_doc_str
int64 0
771
| AvgCountLine
float64 0
7.89k
| AvgCountLineBlank
float64 0
297
| AvgCountLineCode
float64 0
7.89k
| AvgCountLineComment
float64 0
7.89k
| AvgCyclomatic
float64 0
130
| CommentToCodeRatio
float64 0
168
| CountClassBase
float64 0
40
| CountClassCoupled
float64 0
583
| CountClassCoupledModified
float64 0
575
| CountClassDerived
float64 0
5.35k
| CountDeclInstanceMethod
float64 0
529
| CountDeclInstanceVariable
float64 0
296
| CountDeclMethod
float64 0
599
| CountDeclMethodAll
float64 0
1.12k
| CountLine
float64 1
40.4k
| CountLineBlank
float64 0
8.16k
| CountLineCode
float64 1
25.7k
| CountLineCodeDecl
float64 1
8.15k
| CountLineCodeExe
float64 0
24.2k
| CountLineComment
float64 0
16.5k
| CountStmt
float64 1
9.71k
| CountStmtDecl
float64 1
8.15k
| CountStmtExe
float64 0
9.69k
| MaxCyclomatic
float64 0
759
| MaxInheritanceTree
float64 0
16
| MaxNesting
float64 0
34
| SumCyclomatic
float64 0
2.9k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3,500
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeEmbeddings
|
import torch
from torch import nn
class LukeEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.padding_idx = config.pad_token_id
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if position_ids is None:
if input_ids is not None:
position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx).to(input_ids.device)
else:
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
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)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device)
return position_ids.unsqueeze(0).expand(input_shape)
|
class LukeEmbeddings(nn.Module):
'''
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
'''
def __init__(self, config):
pass
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
pass
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
'''
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
'''
pass
| 4
| 2
| 21
| 3
| 15
| 3
| 3
| 0.29
| 1
| 1
| 0
| 0
| 3
| 6
| 3
| 13
| 71
| 13
| 45
| 23
| 35
| 13
| 33
| 17
| 29
| 6
| 1
| 2
| 8
|
3,501
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeEncoder
|
from torch import nn
class LukeEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LukeLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(self, word_hidden_states, entity_hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True):
all_word_hidden_states = () if output_hidden_states else None
all_entity_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(word_hidden_states, entity_hidden_states, attention_mask, layer_head_mask, output_attentions)
word_hidden_states = layer_outputs[0]
if entity_hidden_states is not None:
entity_hidden_states = layer_outputs[1]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[2],)
if output_hidden_states:
all_word_hidden_states = all_word_hidden_states + (word_hidden_states,)
all_entity_hidden_states = all_entity_hidden_states + (entity_hidden_states,)
if not return_dict:
return tuple((v for v in [word_hidden_states, all_word_hidden_states, all_self_attentions, entity_hidden_states, all_entity_hidden_states] if v is not None))
return BaseLukeModelOutput(last_hidden_state=word_hidden_states, hidden_states=all_word_hidden_states, attentions=all_self_attentions, entity_last_hidden_state=entity_hidden_states, entity_hidden_states=all_entity_hidden_states)
|
class LukeEncoder(nn.Module):
def __init__(self, config):
pass
def forward(self, word_hidden_states, entity_hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False, return_dict=True):
pass
| 3
| 0
| 37
| 4
| 34
| 0
| 7
| 0
| 1
| 6
| 2
| 0
| 2
| 3
| 2
| 12
| 76
| 8
| 68
| 21
| 56
| 0
| 29
| 12
| 26
| 12
| 1
| 2
| 13
|
3,502
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeEntityEmbeddings
|
import torch
from typing import Optional, Union
from torch import nn
from .configuration_luke import LukeConfig
class LukeEntityEmbeddings(nn.Module):
def __init__(self, config: LukeConfig):
super().__init__()
self.config = config
self.entity_embeddings = nn.Embedding(config.entity_vocab_size, config.entity_emb_size, padding_idx=0)
if config.entity_emb_size != config.hidden_size:
self.entity_embedding_dense = nn.Linear(config.entity_emb_size, config.hidden_size, bias=False)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_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 forward(self, entity_ids: torch.LongTensor, position_ids: torch.LongTensor, token_type_ids: Optional[torch.LongTensor]=None):
if token_type_ids is None:
token_type_ids = torch.zeros_like(entity_ids)
entity_embeddings = self.entity_embeddings(entity_ids)
if self.config.entity_emb_size != self.config.hidden_size:
entity_embeddings = self.entity_embedding_dense(entity_embeddings)
position_embeddings = self.position_embeddings(position_ids.clamp(min=0))
position_embedding_mask = (position_ids != -1).type_as(position_embeddings).unsqueeze(-1)
position_embeddings = position_embeddings * position_embedding_mask
position_embeddings = torch.sum(position_embeddings, dim=-2)
position_embeddings = position_embeddings / position_embedding_mask.sum(dim=-2).clamp(min=1e-07)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = entity_embeddings + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
|
class LukeEntityEmbeddings(nn.Module):
def __init__(self, config: LukeConfig):
pass
def forward(self, entity_ids: torch.LongTensor, position_ids: torch.LongTensor, token_type_ids: Optional[torch.LongTensor]=None):
pass
| 3
| 0
| 18
| 4
| 14
| 0
| 3
| 0
| 1
| 2
| 1
| 0
| 2
| 7
| 2
| 12
| 38
| 9
| 29
| 17
| 24
| 0
| 27
| 15
| 24
| 3
| 1
| 1
| 5
|
3,503
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForEntityClassification
|
import torch
from typing import Optional, Union
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring(custom_intro='\n The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity\n token) for entity classification tasks, such as Open Entity.\n ')
class LukeForEntityClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, EntityClassificationOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
used for the single-label classification. In this case, labels should contain the indices that should be in
`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
and 1 indicate false and true, respectively.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntityClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: person
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
feature_vector = outputs.entity_last_hidden_state[:, 0, :]
feature_vector = self.dropout(feature_vector)
logits = self.classifier(feature_vector)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if labels.ndim == 1:
loss = nn.functional.cross_entropy(logits, labels)
else:
loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
if not return_dict:
return tuple((v for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return EntityClassificationOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring(custom_intro='\n The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity\n token) for entity classification tasks, such as Open Entity.\n ')
class LukeForEntityClassification(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, EntityClassificationOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
used for the single-label classification. In this case, labels should contain the indices that should be in
`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
and 1 indicate false and true, respectively.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntityClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> model = LukeForEntityClassification.from_pretrained("studio-ousia/luke-large-finetuned-open-entity")
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: person
```'''
pass
| 5
| 1
| 51
| 7
| 32
| 13
| 3
| 0.39
| 1
| 5
| 2
| 0
| 2
| 4
| 2
| 3
| 106
| 14
| 66
| 28
| 45
| 26
| 23
| 11
| 20
| 5
| 2
| 2
| 6
|
3,504
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForEntityPairClassification
|
import torch
from typing import Optional, Union
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring(custom_intro='\n The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity\n tokens) for entity pair classification tasks, such as TACRED.\n ')
class LukeForEntityPairClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size * 2, config.num_labels, False)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, EntityPairClassificationOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
used for the single-label classification. In this case, labels should contain the indices that should be in
`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
and 1 indicate false and true, respectively.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntityPairClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [
... (0, 7),
... (17, 28),
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: per:cities_of_residence
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
feature_vector = torch.cat([outputs.entity_last_hidden_state[:, 0, :], outputs.entity_last_hidden_state[:, 1, :]], dim=1)
feature_vector = self.dropout(feature_vector)
logits = self.classifier(feature_vector)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if labels.ndim == 1:
loss = nn.functional.cross_entropy(logits, labels)
else:
loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
if not return_dict:
return tuple((v for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return EntityPairClassificationOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring(custom_intro='\n The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity\n tokens) for entity pair classification tasks, such as TACRED.\n ')
class LukeForEntityPairClassification(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, EntityPairClassificationOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)` or `(batch_size, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size,)`, the cross entropy loss is
used for the single-label classification. In this case, labels should contain the indices that should be in
`[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, num_labels)`, the binary cross entropy
loss is used for the multi-label classification. In this case, labels should only contain `[0, 1]`, where 0
and 1 indicate false and true, respectively.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntityPairClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [
... (0, 7),
... (17, 28),
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: per:cities_of_residence
```'''
pass
| 5
| 1
| 54
| 7
| 33
| 15
| 3
| 0.43
| 1
| 5
| 2
| 0
| 2
| 4
| 2
| 3
| 111
| 14
| 68
| 28
| 47
| 29
| 23
| 11
| 20
| 5
| 2
| 2
| 6
|
3,505
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForEntitySpanClassification
|
from ...utils import ModelOutput, auto_docstring, logging
import torch
from typing import Optional, Union
from torch import nn
@auto_docstring(custom_intro='\n The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks\n such as named entity recognition.\n ')
class LukeForEntitySpanClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.LongTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, entity_start_positions: Optional[torch.LongTensor]=None, entity_end_positions: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, EntitySpanClassificationOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
entity_start_positions (`torch.LongTensor`):
The start positions of entities in the word token sequence.
entity_end_positions (`torch.LongTensor`):
The end positions of entities in the word token sequence.
labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross
entropy loss is used for the single-label classification. In this case, labels should contain the indices
that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length,
num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case,
labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntitySpanClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
>>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
>>> text = "Beyoncé lives in Los Angeles"
# List all possible entity spans in the text
>>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens
>>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens
>>> entity_spans = []
>>> for i, start_pos in enumerate(word_start_positions):
... for end_pos in word_end_positions[i:]:
... entity_spans.append((start_pos, end_pos))
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
>>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
... if predicted_class_idx != 0:
... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
Beyoncé PER
Los Angeles LOC
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
hidden_size = outputs.last_hidden_state.size(-1)
entity_start_positions = entity_start_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
if entity_start_positions.device != outputs.last_hidden_state.device:
entity_start_positions = entity_start_positions.to(outputs.last_hidden_state.device)
start_states = torch.gather(outputs.last_hidden_state, -2, entity_start_positions)
entity_end_positions = entity_end_positions.unsqueeze(-1).expand(-1, -1, hidden_size)
if entity_end_positions.device != outputs.last_hidden_state.device:
entity_end_positions = entity_end_positions.to(outputs.last_hidden_state.device)
end_states = torch.gather(outputs.last_hidden_state, -2, entity_end_positions)
feature_vector = torch.cat([start_states, end_states, outputs.entity_last_hidden_state], dim=2)
feature_vector = self.dropout(feature_vector)
logits = self.classifier(feature_vector)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if labels.ndim == 2:
loss = nn.functional.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
else:
loss = nn.functional.binary_cross_entropy_with_logits(logits.view(-1), labels.view(-1).type_as(logits))
if not return_dict:
return tuple((v for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return EntitySpanClassificationOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring(custom_intro='\n The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks\n such as named entity recognition.\n ')
class LukeForEntitySpanClassification(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.LongTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, entity_start_positions: Optional[torch.LongTensor]=None, entity_end_positions: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, EntitySpanClassificationOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
entity_start_positions (`torch.LongTensor`):
The start positions of entities in the word token sequence.
entity_end_positions (`torch.LongTensor`):
The end positions of entities in the word token sequence.
labels (`torch.LongTensor` of shape `(batch_size, entity_length)` or `(batch_size, entity_length, num_labels)`, *optional*):
Labels for computing the classification loss. If the shape is `(batch_size, entity_length)`, the cross
entropy loss is used for the single-label classification. In this case, labels should contain the indices
that should be in `[0, ..., config.num_labels - 1]`. If the shape is `(batch_size, entity_length,
num_labels)`, the binary cross entropy loss is used for the multi-label classification. In this case,
labels should only contain `[0, 1]`, where 0 and 1 indicate false and true, respectively.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeForEntitySpanClassification
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
>>> model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
>>> text = "Beyoncé lives in Los Angeles"
# List all possible entity spans in the text
>>> word_start_positions = [0, 8, 14, 17, 21] # character-based start positions of word tokens
>>> word_end_positions = [7, 13, 16, 20, 28] # character-based end positions of word tokens
>>> entity_spans = []
>>> for i, start_pos in enumerate(word_start_positions):
... for end_pos in word_end_positions[i:]:
... entity_spans.append((start_pos, end_pos))
>>> inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> predicted_class_indices = logits.argmax(-1).squeeze().tolist()
>>> for span, predicted_class_idx in zip(entity_spans, predicted_class_indices):
... if predicted_class_idx != 0:
... print(text[span[0] : span[1]], model.config.id2label[predicted_class_idx])
Beyoncé PER
Los Angeles LOC
```'''
pass
| 5
| 1
| 67
| 10
| 37
| 20
| 4
| 0.51
| 1
| 5
| 2
| 0
| 2
| 4
| 2
| 3
| 137
| 21
| 77
| 33
| 54
| 39
| 32
| 14
| 29
| 7
| 2
| 2
| 8
|
3,506
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForMaskedLM
|
import torch
from typing import Optional, Union
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring(custom_intro='\n The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and\n masked entity prediction.\n ')
class LukeForMaskedLM(LukePreTrainedModel):
_tied_weights_keys = ['lm_head.decoder.weight', 'lm_head.decoder.bias', 'entity_predictions.decoder.weight']
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.lm_head = LukeLMHead(config)
self.entity_predictions = EntityPredictionHead(config)
self.loss_fn = nn.CrossEntropyLoss()
self.post_init()
def tie_weights(self):
super().tie_weights()
self._tie_or_clone_weights(self.entity_predictions.decoder, self.luke.entity_embeddings.entity_embeddings)
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.LongTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, entity_labels: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeMaskedLMOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
entity_labels (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
loss = None
mlm_loss = None
logits = self.lm_head(outputs.last_hidden_state)
if labels is not None:
labels = labels.to(logits.device)
mlm_loss = self.loss_fn(logits.view(-1, self.config.vocab_size), labels.view(-1))
if loss is None:
loss = mlm_loss
mep_loss = None
entity_logits = None
if outputs.entity_last_hidden_state is not None:
entity_logits = self.entity_predictions(outputs.entity_last_hidden_state)
if entity_labels is not None:
mep_loss = self.loss_fn(entity_logits.view(-1, self.config.entity_vocab_size), entity_labels.view(-1))
if loss is None:
loss = mep_loss
else:
loss = loss + mep_loss
if not return_dict:
return tuple((v for v in [loss, mlm_loss, mep_loss, logits, entity_logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return LukeMaskedLMOutput(loss=loss, mlm_loss=mlm_loss, mep_loss=mep_loss, logits=logits, entity_logits=entity_logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring(custom_intro='\n The LUKE model with a language modeling head and entity prediction head on top for masked language modeling and\n masked entity prediction.\n ')
class LukeForMaskedLM(LukePreTrainedModel):
def __init__(self, config):
pass
def tie_weights(self):
pass
def get_output_embeddings(self):
pass
def set_output_embeddings(self, new_embeddings):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.LongTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, entity_labels: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeMaskedLMOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
entity_labels (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
'''
pass
| 8
| 1
| 23
| 3
| 18
| 3
| 2
| 0.14
| 1
| 7
| 4
| 0
| 5
| 4
| 5
| 6
| 126
| 18
| 95
| 35
| 70
| 13
| 39
| 17
| 33
| 8
| 2
| 3
| 12
|
3,507
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForMultipleChoice
|
from typing import Optional, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch import nn
import torch
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring
class LukeForMultipleChoice(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.luke = LukeModel(config)
self.dropout = nn.Dropout(config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeMultipleChoiceModelOutput]:
"""
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`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]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask 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_embeds = inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None
entity_ids = entity_ids.view(-1, entity_ids.size(-1)) if entity_ids is not None else None
entity_attention_mask = entity_attention_mask.view(-1, entity_attention_mask.size(-1)) if entity_attention_mask is not None else None
entity_token_type_ids = entity_token_type_ids.view(-1, entity_token_type_ids.size(-1)) if entity_token_type_ids is not None else None
entity_position_ids = entity_position_ids.view(-1, entity_position_ids.size(-2), entity_position_ids.size(-1)) if entity_position_ids is not None else None
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
pooled_output = outputs.pooler_output
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
labels = labels.to(reshaped_logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
return tuple((v for v in [loss, reshaped_logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return LukeMultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring
class LukeForMultipleChoice(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeMultipleChoiceModelOutput]:
'''
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
'''
pass
| 5
| 1
| 57
| 5
| 48
| 4
| 8
| 0.08
| 1
| 5
| 2
| 0
| 2
| 3
| 2
| 3
| 121
| 11
| 102
| 30
| 77
| 8
| 32
| 13
| 29
| 14
| 2
| 1
| 16
|
3,508
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForQuestionAnswering
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch import nn
import torch
from ...utils import ModelOutput, auto_docstring, logging
from typing import Optional, Union
@auto_docstring
class LukeForQuestionAnswering(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.FloatTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, start_positions: Optional[torch.LongTensor]=None, end_positions: Optional[torch.LongTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeQuestionAnsweringModelOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
sequence_output = outputs.last_hidden_state
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
total_loss = None
if start_positions is not None and end_positions is not None:
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
return tuple((v for v in [total_loss, start_logits, end_logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return LukeQuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring
class LukeForQuestionAnswering(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.FloatTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, start_positions: Optional[torch.LongTensor]=None, end_positions: Optional[torch.LongTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeQuestionAnsweringModelOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
'''
pass
| 5
| 1
| 51
| 5
| 40
| 7
| 4
| 0.15
| 1
| 5
| 2
| 0
| 2
| 3
| 2
| 3
| 110
| 11
| 86
| 33
| 60
| 13
| 31
| 15
| 28
| 6
| 2
| 2
| 7
|
3,509
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForSequenceClassification
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch import nn
import torch
from ...utils import ModelOutput, auto_docstring, logging
from typing import Optional, Union
@auto_docstring(custom_intro='\n The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the\n pooled output) e.g. for GLUE tasks.\n ')
class LukeForSequenceClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config)
self.dropout = nn.Dropout(config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeSequenceClassifierOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`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
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
pooled_output = outputs.pooler_output
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = 'single_label_classification'
else:
self.config.problem_type = 'multi_label_classification'
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 == 'single_label_classification':
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == 'multi_label_classification':
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
return tuple((v for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return LukeSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring(custom_intro='\n The LUKE Model transformer with a sequence classification/regression head on top (a linear layer on top of the\n pooled output) e.g. for GLUE tasks.\n ')
class LukeForSequenceClassification(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeSequenceClassifierOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
'''
pass
| 5
| 1
| 48
| 4
| 40
| 4
| 7
| 0.09
| 1
| 6
| 2
| 0
| 2
| 4
| 2
| 3
| 104
| 9
| 87
| 29
| 62
| 8
| 34
| 12
| 31
| 11
| 2
| 3
| 13
|
3,510
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeForTokenClassification
|
import torch
from ...utils import ModelOutput, auto_docstring, logging
from typing import Optional, Union
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch import nn
@auto_docstring(custom_intro='\n The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To\n solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this\n class.\n ')
class LukeForTokenClassification(LukePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.luke = LukeModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.post_init()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeTokenClassifierOutput]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.luke(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, entity_ids=entity_ids, entity_attention_mask=entity_attention_mask, entity_token_type_ids=entity_token_type_ids, entity_position_ids=entity_position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True)
sequence_output = outputs.last_hidden_state
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
return tuple((v for v in [loss, logits, outputs.hidden_states, outputs.entity_hidden_states, outputs.attentions] if v is not None))
return LukeTokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, entity_hidden_states=outputs.entity_hidden_states, attentions=outputs.attentions)
|
@auto_docstring(custom_intro='\n The LUKE Model with a token classification head on top (a linear layer on top of the hidden-states output). To\n solve Named-Entity Recognition (NER) task using LUKE, `LukeForEntitySpanClassification` is more suitable than this\n class.\n ')
class LukeForTokenClassification(LukePreTrainedModel):
def __init__(self, config):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, LukeTokenClassifierOutput]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
'''
pass
| 5
| 1
| 40
| 4
| 32
| 4
| 3
| 0.11
| 1
| 5
| 2
| 0
| 2
| 4
| 2
| 3
| 87
| 9
| 70
| 29
| 45
| 8
| 22
| 12
| 19
| 4
| 2
| 1
| 6
|
3,511
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeIntermediate
|
from ...activations import ACT2FN, gelu
import torch
from torch import nn
class LukeIntermediate(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.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
|
class LukeIntermediate(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass
| 3
| 0
| 6
| 0
| 6
| 0
| 2
| 0
| 1
| 3
| 0
| 0
| 2
| 2
| 2
| 12
| 13
| 1
| 12
| 5
| 9
| 0
| 11
| 5
| 8
| 2
| 1
| 1
| 3
|
3,512
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeLMHead
|
import torch
from torch import nn
from ...activations import ACT2FN, gelu
class LukeLMHead(nn.Module):
"""Roberta Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
def forward(self, features, **kwargs):
x = self.dense(features)
x = gelu(x)
x = self.layer_norm(x)
x = self.decoder(x)
return x
def _tie_weights(self):
if self.decoder.bias.device.type == 'meta':
self.decoder.bias = self.bias
else:
self.bias = self.decoder.bias
|
class LukeLMHead(nn.Module):
'''Roberta Head for masked language modeling.'''
def __init__(self, config):
pass
def forward(self, features, **kwargs):
pass
def _tie_weights(self):
pass
| 4
| 1
| 8
| 1
| 6
| 1
| 1
| 0.21
| 1
| 1
| 0
| 0
| 3
| 4
| 3
| 13
| 29
| 6
| 19
| 9
| 15
| 4
| 18
| 9
| 14
| 2
| 1
| 1
| 4
|
3,513
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeLayer
|
from ...modeling_layers import GradientCheckpointingLayer
import torch
from ...pytorch_utils import apply_chunking_to_forward
class LukeLayer(GradientCheckpointingLayer):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = LukeAttention(config)
self.intermediate = LukeIntermediate(config)
self.output = LukeOutput(config)
def forward(self, word_hidden_states, entity_hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
word_size = word_hidden_states.size(1)
self_attention_outputs = self.attention(word_hidden_states, entity_hidden_states, attention_mask, head_mask, output_attentions=output_attentions)
if entity_hidden_states is None:
concat_attention_output = self_attention_outputs[0]
else:
concat_attention_output = torch.cat(self_attention_outputs[:2], dim=1)
outputs = self_attention_outputs[2:]
layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, concat_attention_output)
word_layer_output = layer_output[:, :word_size, :]
if entity_hidden_states is None:
entity_layer_output = None
else:
entity_layer_output = layer_output[:, word_size:, :]
outputs = (word_layer_output, entity_layer_output) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
|
class LukeLayer(GradientCheckpointingLayer):
def __init__(self, config):
pass
def forward(self, word_hidden_states, entity_hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
pass
def feed_forward_chunk(self, attention_output):
pass
| 4
| 0
| 16
| 2
| 14
| 0
| 2
| 0.02
| 1
| 4
| 3
| 0
| 3
| 5
| 3
| 13
| 50
| 7
| 43
| 25
| 32
| 1
| 26
| 18
| 22
| 3
| 1
| 1
| 5
|
3,514
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeMaskedLMOutput
|
from ...utils import ModelOutput, auto_docstring, logging
from dataclasses import dataclass
import torch
from typing import Optional, Union
@dataclass
@auto_docstring(custom_intro="\n Base class for model's outputs, with potential hidden states and attentions.\n ")
class LukeMaskedLMOutput(ModelOutput):
"""
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
The sum of masked language modeling (MLM) loss and entity prediction loss.
mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked entity prediction (MEP) loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
"""
loss: Optional[torch.FloatTensor] = None
mlm_loss: Optional[torch.FloatTensor] = None
mep_loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
entity_logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
|
@dataclass
@auto_docstring(custom_intro="\n Base class for model's outputs, with potential hidden states and attentions.\n ")
class LukeMaskedLMOutput(ModelOutput):
'''
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
The sum of masked language modeling (MLM) loss and entity prediction loss.
mlm_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked language modeling (MLM) loss.
mep_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Masked entity prediction (MEP) loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
entity_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the entity prediction head (scores for each entity vocabulary token before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 40
| 4
| 9
| 9
| 8
| 27
| 9
| 9
| 8
| 0
| 1
| 0
| 0
|
3,515
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeModel
|
from ...utils import ModelOutput, auto_docstring, logging
from .configuration_luke import LukeConfig
import torch
from typing import Optional, Union
@auto_docstring(custom_intro='\n The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any\n ')
class LukeModel(LukePreTrainedModel):
def __init__(self, config: LukeConfig, add_pooling_layer: bool=True):
"""
add_pooling_layer (bool, *optional*, defaults to `True`):
Whether to add a pooling layer
"""
super().__init__(config)
self.config = config
self.embeddings = LukeEmbeddings(config)
self.entity_embeddings = LukeEntityEmbeddings(config)
self.encoder = LukeEncoder(config)
self.pooler = LukePooler(config) if add_pooling_layer else None
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def get_entity_embeddings(self):
return self.entity_embeddings.entity_embeddings
def set_entity_embeddings(self, value):
self.entity_embeddings.entity_embeddings = value
def _prune_heads(self, heads_to_prune):
raise NotImplementedError('LUKE does not support the pruning of attention heads')
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, BaseLukeModelOutputWithPooling]:
"""
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeModel
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
# Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
>>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Input Wikipedia entities to obtain enriched contextualized representations of word tokens
>>> text = "Beyoncé lives in Los Angeles."
>>> entities = [
... "Beyoncé",
... "Los Angeles",
... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
>>> entity_spans = [
... (0, 7),
... (17, 28),
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> encoding = tokenizer(
... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
... )
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds')
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if entity_ids is not None:
entity_seq_length = entity_ids.size(1)
if entity_attention_mask is None:
entity_attention_mask = torch.ones((batch_size, entity_seq_length), device=device)
if entity_token_type_ids is None:
entity_token_type_ids = torch.zeros((batch_size, entity_seq_length), dtype=torch.long, device=device)
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
word_embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
extended_attention_mask = self.get_extended_attention_mask(attention_mask, entity_attention_mask)
if entity_ids is None:
entity_embedding_output = None
else:
entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids)
encoder_outputs = self.encoder(word_embedding_output, entity_embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseLukeModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, entity_last_hidden_state=encoder_outputs.entity_last_hidden_state, entity_hidden_states=encoder_outputs.entity_hidden_states)
def get_extended_attention_mask(self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]):
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
word_attention_mask (`torch.LongTensor`):
Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
entity_attention_mask (`torch.LongTensor`, *optional*):
Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
"""
attention_mask = word_attention_mask
if entity_attention_mask is not None:
attention_mask = torch.cat([attention_mask, entity_attention_mask], dim=-1)
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(f'Wrong shape for attention_mask (shape {attention_mask.shape})')
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
return extended_attention_mask
|
@auto_docstring(custom_intro='\n The bare LUKE model transformer outputting raw hidden-states for both word tokens and entities without any\n ')
class LukeModel(LukePreTrainedModel):
def __init__(self, config: LukeConfig, add_pooling_layer: bool=True):
'''
add_pooling_layer (bool, *optional*, defaults to `True`):
Whether to add a pooling layer
'''
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def get_entity_embeddings(self):
pass
def set_entity_embeddings(self, value):
pass
def _prune_heads(self, heads_to_prune):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, entity_ids: Optional[torch.LongTensor]=None, entity_attention_mask: Optional[torch.FloatTensor]=None, entity_token_type_ids: Optional[torch.LongTensor]=None, entity_position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple, BaseLukeModelOutputWithPooling]:
'''
entity_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`):
Indices of entity tokens in the entity vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
entity_attention_mask (`torch.FloatTensor` of shape `(batch_size, entity_length)`, *optional*):
Mask to avoid performing attention on padding entity token indices. Mask values selected in `[0, 1]`:
- 1 for entity tokens that are **not masked**,
- 0 for entity tokens that are **masked**.
entity_token_type_ids (`torch.LongTensor` of shape `(batch_size, entity_length)`, *optional*):
Segment token indices to indicate first and second portions of the entity token inputs. Indices are
selected in `[0, 1]`:
- 0 corresponds to a *portion A* entity token,
- 1 corresponds to a *portion B* entity token.
entity_position_ids (`torch.LongTensor` of shape `(batch_size, entity_length, max_mention_length)`, *optional*):
Indices of positions of each input entity in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
Examples:
```python
>>> from transformers import AutoTokenizer, LukeModel
>>> tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base")
>>> model = LukeModel.from_pretrained("studio-ousia/luke-base")
# Compute the contextualized entity representation corresponding to the entity mention "Beyoncé"
>>> text = "Beyoncé lives in Los Angeles."
>>> entity_spans = [(0, 7)] # character-based entity span corresponding to "Beyoncé"
>>> encoding = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
# Input Wikipedia entities to obtain enriched contextualized representations of word tokens
>>> text = "Beyoncé lives in Los Angeles."
>>> entities = [
... "Beyoncé",
... "Los Angeles",
... ] # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
>>> entity_spans = [
... (0, 7),
... (17, 28),
... ] # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
>>> encoding = tokenizer(
... text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt"
... )
>>> outputs = model(**encoding)
>>> word_last_hidden_state = outputs.last_hidden_state
>>> entity_last_hidden_state = outputs.entity_last_hidden_state
```'''
pass
def get_extended_attention_mask(self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]):
'''
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
word_attention_mask (`torch.LongTensor`):
Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
entity_attention_mask (`torch.LongTensor`, *optional*):
Attention mask for entity tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
Returns:
`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
'''
pass
| 11
| 3
| 24
| 3
| 14
| 7
| 3
| 0.49
| 1
| 10
| 6
| 0
| 8
| 5
| 8
| 9
| 198
| 34
| 111
| 44
| 83
| 54
| 64
| 26
| 55
| 16
| 2
| 2
| 27
|
3,516
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeMultipleChoiceModelOutput
|
from dataclasses import dataclass
import torch
from typing import Optional, Union
from ...utils import ModelOutput, auto_docstring, logging
@dataclass
@auto_docstring(custom_intro='\n Outputs of multiple choice models.\n ')
class LukeMultipleChoiceModelOutput(ModelOutput):
"""
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification scores (before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Outputs of multiple choice models.\n ')
class LukeMultipleChoiceModelOutput(ModelOutput):
'''
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification scores (before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.67
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 33
| 5
| 6
| 6
| 5
| 22
| 6
| 6
| 5
| 0
| 1
| 0
| 0
|
3,517
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeOutput
|
import torch
from torch import nn
class LukeOutput(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)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
|
class LukeOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass
| 3
| 0
| 5
| 0
| 5
| 0
| 1
| 0
| 1
| 2
| 0
| 0
| 2
| 3
| 2
| 12
| 12
| 1
| 11
| 6
| 8
| 0
| 11
| 6
| 8
| 1
| 1
| 0
| 2
|
3,518
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukePooler
|
import torch
from torch import nn
class LukePooler(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: torch.Tensor) -> torch.Tensor:
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
|
class LukePooler(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
pass
| 3
| 0
| 6
| 0
| 5
| 1
| 1
| 0.2
| 1
| 2
| 0
| 0
| 2
| 2
| 2
| 12
| 13
| 1
| 10
| 7
| 7
| 2
| 10
| 7
| 7
| 1
| 1
| 0
| 2
|
3,519
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukePreTrainedModel
|
from ...modeling_utils import PreTrainedModel
from .configuration_luke import LukeConfig
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring
class LukePreTrainedModel(PreTrainedModel):
config: LukeConfig
base_model_prefix = 'luke'
supports_gradient_checkpointing = True
_no_split_modules = ['LukeAttention', 'LukeEntityEmbeddings']
def _init_weights(self, module: nn.Module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
if module.embedding_dim == 1:
module.weight.data.zero_()
else:
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
|
@auto_docstring
class LukePreTrainedModel(PreTrainedModel):
def _init_weights(self, module: nn.Module):
'''Initialize the weights'''
pass
| 3
| 1
| 16
| 0
| 15
| 2
| 7
| 0.3
| 1
| 0
| 0
| 9
| 1
| 0
| 1
| 1
| 27
| 2
| 20
| 6
| 18
| 6
| 17
| 6
| 15
| 7
| 1
| 2
| 7
|
3,520
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeQuestionAnsweringModelOutput
|
from dataclasses import dataclass
import torch
from typing import Optional, Union
from ...utils import ModelOutput, auto_docstring, logging
@dataclass
@auto_docstring(custom_intro='\n Outputs of question answering models.\n ')
class LukeQuestionAnsweringModelOutput(ModelOutput):
"""
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: Optional[torch.FloatTensor] = None
end_logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Outputs of question answering models.\n ')
class LukeQuestionAnsweringModelOutput(ModelOutput):
'''
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.29
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 34
| 4
| 7
| 7
| 6
| 23
| 7
| 7
| 6
| 0
| 1
| 0
| 0
|
3,521
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeSelfAttention
|
import torch
from torch import nn
import math
class LukeSelfAttention(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 number of attention heads {config.num_attention_heads}.')
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.use_entity_aware_attention = config.use_entity_aware_attention
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
if self.use_entity_aware_attention:
self.w2e_query = nn.Linear(config.hidden_size, self.all_head_size)
self.e2w_query = nn.Linear(config.hidden_size, self.all_head_size)
self.e2e_query = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, word_hidden_states, entity_hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
word_size = word_hidden_states.size(1)
if entity_hidden_states is None:
concat_hidden_states = word_hidden_states
else:
concat_hidden_states = torch.cat([word_hidden_states, entity_hidden_states], dim=1)
key_layer = self.transpose_for_scores(self.key(concat_hidden_states))
value_layer = self.transpose_for_scores(self.value(concat_hidden_states))
if self.use_entity_aware_attention and entity_hidden_states is not None:
w2w_query_layer = self.transpose_for_scores(self.query(word_hidden_states))
w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_hidden_states))
e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states))
e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states))
w2w_key_layer = key_layer[:, :, :word_size, :]
e2w_key_layer = key_layer[:, :, :word_size, :]
w2e_key_layer = key_layer[:, :, word_size:, :]
e2e_key_layer = key_layer[:, :, word_size:, :]
w2w_attention_scores = torch.matmul(w2w_query_layer, w2w_key_layer.transpose(-1, -2))
w2e_attention_scores = torch.matmul(w2e_query_layer, w2e_key_layer.transpose(-1, -2))
e2w_attention_scores = torch.matmul(e2w_query_layer, e2w_key_layer.transpose(-1, -2))
e2e_attention_scores = torch.matmul(e2e_query_layer, e2e_key_layer.transpose(-1, -2))
word_attention_scores = torch.cat([w2w_attention_scores, w2e_attention_scores], dim=3)
entity_attention_scores = torch.cat([e2w_attention_scores, e2e_attention_scores], dim=3)
attention_scores = torch.cat([word_attention_scores, entity_attention_scores], dim=2)
else:
query_layer = self.transpose_for_scores(self.query(concat_hidden_states))
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
attention_probs = self.dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
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] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
output_word_hidden_states = context_layer[:, :word_size, :]
if entity_hidden_states is None:
output_entity_hidden_states = None
else:
output_entity_hidden_states = context_layer[:, word_size:, :]
if output_attentions:
outputs = (output_word_hidden_states, output_entity_hidden_states, attention_probs)
else:
outputs = (output_word_hidden_states, output_entity_hidden_states)
return outputs
|
class LukeSelfAttention(nn.Module):
def __init__(self, config):
pass
def transpose_for_scores(self, x):
pass
def forward(self, word_hidden_states, entity_hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
pass
| 4
| 0
| 36
| 7
| 26
| 3
| 4
| 0.13
| 1
| 3
| 0
| 0
| 3
| 11
| 3
| 13
| 111
| 22
| 79
| 49
| 68
| 10
| 65
| 42
| 61
| 7
| 1
| 1
| 11
|
3,522
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeSelfOutput
|
import torch
from torch import nn
class LukeSelfOutput(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 forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
|
class LukeSelfOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
pass
| 3
| 0
| 5
| 0
| 5
| 0
| 1
| 0
| 1
| 2
| 0
| 0
| 2
| 3
| 2
| 12
| 12
| 1
| 11
| 6
| 8
| 0
| 11
| 6
| 8
| 1
| 1
| 0
| 2
|
3,523
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeSequenceClassifierOutput
|
from ...utils import ModelOutput, auto_docstring, logging
from dataclasses import dataclass
import torch
from typing import Optional, Union
@dataclass
@auto_docstring(custom_intro='\n Outputs of sentence classification models.\n ')
class LukeSequenceClassifierOutput(ModelOutput):
"""
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Outputs of sentence classification models.\n ')
class LukeSequenceClassifierOutput(ModelOutput):
'''
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 31
| 4
| 6
| 6
| 5
| 21
| 6
| 6
| 5
| 0
| 1
| 0
| 0
|
3,524
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/modeling_luke.py
|
transformers.models.luke.modeling_luke.LukeTokenClassifierOutput
|
from ...utils import ModelOutput, auto_docstring, logging
from dataclasses import dataclass
import torch
from typing import Optional, Union
@dataclass
@auto_docstring(custom_intro='\n Base class for outputs of token classification models.\n ')
class LukeTokenClassifierOutput(ModelOutput):
"""
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
"""
loss: Optional[torch.FloatTensor] = None
logits: Optional[torch.FloatTensor] = None
hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
entity_hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
attentions: Optional[tuple[torch.FloatTensor, ...]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Base class for outputs of token classification models.\n ')
class LukeTokenClassifierOutput(ModelOutput):
'''
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
entity_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, entity_length, hidden_size)`. Entity hidden-states of the model at the output of each
layer plus the initial entity embedding outputs.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 31
| 4
| 6
| 6
| 5
| 21
| 6
| 6
| 5
| 0
| 1
| 0
| 0
|
3,525
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/luke/tokenization_luke.py
|
transformers.models.luke.tokenization_luke.LukeTokenizer
|
import itertools
import numpy as np
from ...utils import add_end_docstrings, is_torch_tensor, logging
import os
import json
from collections.abc import Mapping
from ...tokenization_utils_base import ENCODE_KWARGS_DOCSTRING, AddedToken, BatchEncoding, EncodedInput, PaddingStrategy, TensorType, TextInput, TextInputPair, TruncationStrategy, to_py_obj
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from typing import Optional, Union
class LukeTokenizer(PreTrainedTokenizer):
"""
Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import LukeTokenizer
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
</Tip>
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. It also creates entity sequences, namely
`entity_ids`, `entity_attention_mask`, `entity_token_type_ids`, and `entity_position_ids` to be used by the LUKE
model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
entity_vocab_file (`str`):
Path to the entity vocabulary file.
task (`str`, *optional*):
Task for which you want to prepare sequences. One of `"entity_classification"`,
`"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity
sequence is automatically created based on the given entity span(s).
max_entity_length (`int`, *optional*, defaults to 32):
The maximum length of `entity_ids`.
max_mention_length (`int`, *optional*, defaults to 30):
The maximum number of tokens inside an entity span.
entity_token_1 (`str`, *optional*, defaults to `<ent>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
`task` is set to `"entity_classification"` or `"entity_pair_classification"`.
entity_token_2 (`str`, *optional*, defaults to `<ent2>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
`task` is set to `"entity_pair_classification"`.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
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.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (LUKE tokenizer detect beginning of words by the preceding space).
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ['input_ids', 'attention_mask']
def __init__(self, vocab_file, merges_file, entity_vocab_file, task=None, max_entity_length=32, max_mention_length=30, entity_token_1='<ent>', entity_token_2='<ent2>', entity_unk_token='[UNK]', entity_pad_token='[PAD]', entity_mask_token='[MASK]', entity_mask2_token='[MASK2]', errors='replace', bos_token='<s>', eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', add_prefix_space=False, **kwargs):
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
with open(vocab_file, encoding='utf-8') as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
with open(merges_file, encoding='utf-8') as merges_handle:
bpe_merges = merges_handle.read().split('\n')[1:-1]
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
self.cache = {}
self.add_prefix_space = add_prefix_space
self.pat = re.compile("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+")
entity_token_1 = AddedToken(entity_token_1, lstrip=False, rstrip=False) if isinstance(entity_token_1, str) else entity_token_1
entity_token_2 = AddedToken(entity_token_2, lstrip=False, rstrip=False) if isinstance(entity_token_2, str) else entity_token_2
kwargs['additional_special_tokens'] = kwargs.get('additional_special_tokens', [])
kwargs['additional_special_tokens'] += [entity_token_1, entity_token_2]
with open(entity_vocab_file, encoding='utf-8') as entity_vocab_handle:
self.entity_vocab = json.load(entity_vocab_handle)
for entity_special_token in [entity_unk_token, entity_pad_token, entity_mask_token, entity_mask2_token]:
if entity_special_token not in self.entity_vocab:
raise ValueError(f'Specified entity special token ``{entity_special_token}`` is not found in entity_vocab. Probably an incorrect entity vocab file is loaded: {entity_vocab_file}.')
self.entity_unk_token_id = self.entity_vocab[entity_unk_token]
self.entity_pad_token_id = self.entity_vocab[entity_pad_token]
self.entity_mask_token_id = self.entity_vocab[entity_mask_token]
self.entity_mask2_token_id = self.entity_vocab[entity_mask2_token]
self.task = task
if task is None or task == 'entity_span_classification':
self.max_entity_length = max_entity_length
elif task == 'entity_classification':
self.max_entity_length = 1
elif task == 'entity_pair_classification':
self.max_entity_length = 2
else:
raise ValueError(f"Task {task} not supported. Select task from ['entity_classification', 'entity_pair_classification', 'entity_span_classification'] only.")
self.max_mention_length = max_mention_length
super().__init__(errors=errors, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, task=task, max_entity_length=32, max_mention_length=30, entity_token_1='<ent>', entity_token_2='<ent2>', entity_unk_token=entity_unk_token, entity_pad_token=entity_pad_token, entity_mask_token=entity_mask_token, entity_mask2_token=entity_mask2_token, **kwargs)
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
vocab = dict(self.encoder).copy()
vocab.update(self.added_tokens_encoder)
return vocab
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
i = j
if word[i] == first and i < len(word) - 1 and (word[i + 1] == second):
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
if len(word) == 1:
break
else:
pairs = get_pairs(word)
word = ' '.join(word)
self.cache[token] = word
return word
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
token = ''.join((self.byte_encoder[b] for b in token.encode('utf-8')))
bpe_tokens.extend((bpe_token for bpe_token in self.bpe(token).split(' ')))
return bpe_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.decoder.get(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
text = ''.join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors)
return text
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. A LUKE sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None, already_has_special_tokens: bool=False) -> list[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
if token_ids_1 is None:
return [1] + [0] * len(token_ids_0) + [1]
return [1] + [0] * len(token_ids_0) + [1, 1] + [0] * len(token_ids_1) + [1]
def create_token_type_ids_from_sequences(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None) -> list[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. LUKE does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of zeros.
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop('add_prefix_space', self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(text) > 0 and (not text[0].isspace())):
text = ' ' + text
return (text, kwargs)
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(self, text: Union[TextInput, list[TextInput]], text_pair: Optional[Union[TextInput, list[TextInput]]]=None, entity_spans: Optional[Union[EntitySpanInput, list[EntitySpanInput]]]=None, entity_spans_pair: Optional[Union[EntitySpanInput, list[EntitySpanInput]]]=None, entities: Optional[Union[EntityInput, list[EntityInput]]]=None, entities_pair: Optional[Union[EntityInput, list[EntityInput]]]=None, add_special_tokens: bool=True, padding: Union[bool, str, PaddingStrategy]=False, truncation: Union[bool, str, TruncationStrategy]=None, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, is_split_into_words: Optional[bool]=False, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True, **kwargs) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences, depending on the task you want to prepare them for.
Args:
text (`str`, `list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.
text_pair (`str`, `list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.
entity_spans (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
with two integers denoting character-based start and end positions of entities. If you specify
`"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor,
the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each
sequence must be equal to the length of each sequence of `entities`.
entity_spans_pair (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
with two integers denoting character-based start and end positions of entities. If you specify the
`task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the
length of each sequence must be equal to the length of each sequence of `entities_pair`.
entities (`list[str]`, `list[list[str]]`, *optional*):
The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
each sequence must be equal to the length of each sequence of `entity_spans`. If you specify
`entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences
is automatically constructed by filling it with the [MASK] entity.
entities_pair (`list[str]`, `list[list[str]]`, *optional*):
The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify
`entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity
sequences is automatically constructed by filling it with the [MASK] entity.
max_entity_length (`int`, *optional*):
The maximum length of `entity_ids`.
"""
is_valid_single_text = isinstance(text, str)
is_valid_batch_text = isinstance(text, (list, tuple)) and (len(text) == 0 or isinstance(text[0], str))
if not (is_valid_single_text or is_valid_batch_text):
raise ValueError('text input must be of type `str` (single example) or `list[str]` (batch).')
is_valid_single_text_pair = isinstance(text_pair, str)
is_valid_batch_text_pair = isinstance(text_pair, (list, tuple)) and (len(text_pair) == 0 or isinstance(text_pair[0], str))
if not (text_pair is None or is_valid_single_text_pair or is_valid_batch_text_pair):
raise ValueError('text_pair input must be of type `str` (single example) or `list[str]` (batch).')
is_batched = bool(isinstance(text, (list, tuple)))
if is_batched:
batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
if entities is None:
batch_entities_or_entities_pairs = None
else:
batch_entities_or_entities_pairs = list(zip(entities, entities_pair)) if entities_pair is not None else entities
if entity_spans is None:
batch_entity_spans_or_entity_spans_pairs = None
else:
batch_entity_spans_or_entity_spans_pairs = list(zip(entity_spans, entity_spans_pair)) if entity_spans_pair is not None else entity_spans
return self.batch_encode_plus(batch_text_or_text_pairs=batch_text_or_text_pairs, batch_entity_spans_or_entity_spans_pairs=batch_entity_spans_or_entity_spans_pairs, batch_entities_or_entities_pairs=batch_entities_or_entities_pairs, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs)
else:
return self.encode_plus(text=text, text_pair=text_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, entities=entities, entities_pair=entities_pair, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, max_entity_length=max_entity_length, stride=stride, is_split_into_words=is_split_into_words, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_tensors=return_tensors, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, **kwargs)
def _encode_plus(self, text: Union[TextInput], text_pair: Optional[Union[TextInput]]=None, entity_spans: Optional[EntitySpanInput]=None, entity_spans_pair: Optional[EntitySpanInput]=None, entities: Optional[EntityInput]=None, entities_pair: Optional[EntityInput]=None, add_special_tokens: bool=True, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy=TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, is_split_into_words: Optional[bool]=False, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True, **kwargs) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError('return_offset_mapping is not available when using Python tokenizers. To use this feature, change your tokenizer to one deriving from transformers.PreTrainedTokenizerFast. More information on available tokenizers at https://github.com/huggingface/transformers/pull/2674')
if is_split_into_words:
raise NotImplementedError('is_split_into_words is not supported in this tokenizer.')
first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans = self._create_input_sequence(text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs)
return self.prepare_for_model(first_ids, pair_ids=second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=padding_strategy.value, truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_tensors=return_tensors, prepend_batch_axis=True, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, verbose=verbose)
def _batch_encode_plus(self, batch_text_or_text_pairs: Union[list[TextInput], list[TextInputPair]], batch_entity_spans_or_entity_spans_pairs: Optional[Union[list[EntitySpanInput], list[tuple[EntitySpanInput, EntitySpanInput]]]]=None, batch_entities_or_entities_pairs: Optional[Union[list[EntityInput], list[tuple[EntityInput, EntityInput]]]]=None, add_special_tokens: bool=True, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy=TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, is_split_into_words: Optional[bool]=False, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True, **kwargs) -> BatchEncoding:
if return_offsets_mapping:
raise NotImplementedError('return_offset_mapping is not available when using Python tokenizers. To use this feature, change your tokenizer to one deriving from transformers.PreTrainedTokenizerFast.')
if is_split_into_words:
raise NotImplementedError('is_split_into_words is not supported in this tokenizer.')
input_ids = []
entity_ids = []
entity_token_spans = []
for index, text_or_text_pair in enumerate(batch_text_or_text_pairs):
if not isinstance(text_or_text_pair, (list, tuple)):
text, text_pair = (text_or_text_pair, None)
else:
text, text_pair = text_or_text_pair
entities, entities_pair = (None, None)
if batch_entities_or_entities_pairs is not None:
entities_or_entities_pairs = batch_entities_or_entities_pairs[index]
if entities_or_entities_pairs:
if isinstance(entities_or_entities_pairs[0], str):
entities, entities_pair = (entities_or_entities_pairs, None)
else:
entities, entities_pair = entities_or_entities_pairs
entity_spans, entity_spans_pair = (None, None)
if batch_entity_spans_or_entity_spans_pairs is not None:
entity_spans_or_entity_spans_pairs = batch_entity_spans_or_entity_spans_pairs[index]
if len(entity_spans_or_entity_spans_pairs) > 0 and isinstance(entity_spans_or_entity_spans_pairs[0], list):
entity_spans, entity_spans_pair = entity_spans_or_entity_spans_pairs
else:
entity_spans, entity_spans_pair = (entity_spans_or_entity_spans_pairs, None)
first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans = self._create_input_sequence(text=text, text_pair=text_pair, entities=entities, entities_pair=entities_pair, entity_spans=entity_spans, entity_spans_pair=entity_spans_pair, **kwargs)
input_ids.append((first_ids, second_ids))
entity_ids.append((first_entity_ids, second_entity_ids))
entity_token_spans.append((first_entity_token_spans, second_entity_token_spans))
batch_outputs = self._batch_prepare_for_model(input_ids, batch_entity_ids_pairs=entity_ids, batch_entity_token_spans_pairs=entity_token_spans, add_special_tokens=add_special_tokens, padding_strategy=padding_strategy, truncation_strategy=truncation_strategy, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_attention_mask=return_attention_mask, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=return_tensors, verbose=verbose)
return BatchEncoding(batch_outputs)
def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
if not isinstance(entity_spans, list):
raise TypeError('entity_spans should be given as a list')
elif len(entity_spans) > 0 and (not isinstance(entity_spans[0], tuple)):
raise ValueError('entity_spans should be given as a list of tuples containing the start and end character indices')
if entities is not None:
if not isinstance(entities, list):
raise ValueError('If you specify entities, they should be given as a list')
if len(entities) > 0 and (not isinstance(entities[0], str)):
raise ValueError('If you specify entities, they should be given as a list of entity names')
if len(entities) != len(entity_spans):
raise ValueError('If you specify entities, entities and entity_spans must be the same length')
def _create_input_sequence(self, text: Union[TextInput], text_pair: Optional[Union[TextInput]]=None, entities: Optional[EntityInput]=None, entities_pair: Optional[EntityInput]=None, entity_spans: Optional[EntitySpanInput]=None, entity_spans_pair: Optional[EntitySpanInput]=None, **kwargs) -> tuple[list, list, list, list, list, list]:
def get_input_ids(text):
tokens = self.tokenize(text, **kwargs)
return self.convert_tokens_to_ids(tokens)
def get_input_ids_and_entity_token_spans(text, entity_spans):
if entity_spans is None:
return (get_input_ids(text), None)
cur = 0
input_ids = []
entity_token_spans = [None] * len(entity_spans)
split_char_positions = sorted(frozenset(itertools.chain(*entity_spans)))
char_pos2token_pos = {}
for split_char_position in split_char_positions:
orig_split_char_position = split_char_position
if split_char_position > 0 and text[split_char_position - 1] == ' ':
split_char_position -= 1
if cur != split_char_position:
input_ids += get_input_ids(text[cur:split_char_position])
cur = split_char_position
char_pos2token_pos[orig_split_char_position] = len(input_ids)
input_ids += get_input_ids(text[cur:])
entity_token_spans = [(char_pos2token_pos[char_start], char_pos2token_pos[char_end]) for char_start, char_end in entity_spans]
return (input_ids, entity_token_spans)
first_ids, second_ids = (None, None)
first_entity_ids, second_entity_ids = (None, None)
first_entity_token_spans, second_entity_token_spans = (None, None)
if self.task is None:
if entity_spans is None:
first_ids = get_input_ids(text)
else:
self._check_entity_input_format(entities, entity_spans)
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
if entities is None:
first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
else:
first_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities]
if text_pair is not None:
if entity_spans_pair is None:
second_ids = get_input_ids(text_pair)
else:
self._check_entity_input_format(entities_pair, entity_spans_pair)
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:
second_entity_ids = [self.entity_vocab.get(entity, self.entity_unk_token_id) for entity in entities_pair]
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 end character indices of an entity')
first_entity_ids = [self.entity_mask_token_id]
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
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_ids[:entity_token_start] + [self.additional_special_tokens_ids[0]] + first_ids[entity_token_start:]
first_entity_token_spans = [(entity_token_start, entity_token_end + 2)]
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('Entity spans should be provided as a list of two tuples, each tuple containing the start and end character indices of an entity')
head_span, tail_span = entity_spans
first_entity_ids = [self.entity_mask_token_id, self.entity_mask2_token_id]
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
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_span[0]:
first_entity_token_spans[0] = (head_token_span[0], head_token_span[1] + 2)
first_entity_token_spans[1] = (tail_token_span[0] + 2, tail_token_span[1] + 4)
token_span_with_special_token_ids = reversed(token_span_with_special_token_ids)
else:
first_entity_token_spans[0] = (head_token_span[0] + 2, head_token_span[1] + 4)
first_entity_token_spans[1] = (tail_token_span[0], tail_token_span[1] + 2)
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:]
elif self.task == 'entity_span_classification':
if not (isinstance(entity_spans, list) and len(entity_spans) > 0 and isinstance(entity_spans[0], tuple)):
raise ValueError('Entity spans should be provided as a list of tuples, each tuple containing the start and end character indices of an entity')
first_ids, first_entity_token_spans = get_input_ids_and_entity_token_spans(text, entity_spans)
first_entity_ids = [self.entity_mask_token_id] * len(entity_spans)
else:
raise ValueError(f'Task {self.task} not supported')
return (first_ids, second_ids, first_entity_ids, second_entity_ids, first_entity_token_spans, second_entity_token_spans)
@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[tuple[Optional[list[tuple[int, int]]], Optional[list[tuple[int, int]]]]], add_special_tokens: bool=True, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy=TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[str]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, 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 overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens
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.
"""
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_spans, second_entity_token_spans = entity_token_span_pairs
outputs = self.prepare_for_model(first_ids, second_ids, entity_ids=first_entity_ids, pair_entity_ids=second_entity_ids, entity_token_spans=first_entity_token_spans, pair_entity_token_spans=second_entity_token_spans, add_special_tokens=add_special_tokens, padding=PaddingStrategy.DO_NOT_PAD.value, truncation=truncation_strategy.value, max_length=max_length, max_entity_length=max_entity_length, stride=stride, pad_to_multiple_of=None, padding_side=None, return_attention_mask=False, return_token_type_ids=return_token_type_ids, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_length=return_length, return_tensors=None, prepend_batch_axis=False, verbose=verbose)
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, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_attention_mask=return_attention_mask)
batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
return batch_outputs
@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_spans: Optional[list[tuple[int, int]]]=None, pair_entity_token_spans: Optional[list[tuple[int, int]]]=None, add_special_tokens: bool=True, padding: Union[bool, str, PaddingStrategy]=False, truncation: Union[bool, str, TruncationStrategy]=None, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, 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 sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing
while taking into account the special tokens and manages a moving window (with user defined stride) for
overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first*
or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an
error.
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_entity_ids (`list[int]`, *optional*):
Entity ids of the second sequence.
entity_token_spans (`list[tuple[int, int]]`, *optional*):
Entity spans of the first sequence.
pair_entity_token_spans (`list[tuple[int, int]]`, *optional*):
Entity spans of the second sequence.
max_entity_length (`int`, *optional*):
The maximum length of the entity sequence.
"""
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_multiple_of, verbose=verbose, **kwargs)
pair = bool(pair_ids is not None)
len_ids = len(ids)
len_pair_ids = len(pair_ids) if pair else 0
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.')
if return_overflowing_tokens and truncation_strategy == TruncationStrategy.LONGEST_FIRST and (pair_ids is not None):
raise ValueError('Not possible to return overflowing tokens for pair of sequences with the `longest_first`. Please select another truncation strategy than `longest_first`, for instance `only_second` or `only_first`.')
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 = {}
total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
overflowing_tokens = []
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and (total_len > max_length):
ids, pair_ids, overflowing_tokens = self.truncate_sequences(ids, pair_ids=pair_ids, num_tokens_to_remove=total_len - max_length, truncation_strategy=truncation_strategy, stride=stride)
if return_overflowing_tokens:
encoded_inputs['overflowing_tokens'] = overflowing_tokens
encoded_inputs['num_truncated_tokens'] = total_len - max_length
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
pair_entity_token_offset = len(ids) + 3
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
entity_token_offset = 0
pair_entity_token_offset = len(ids)
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_tokens_mask(ids, pair_ids)
else:
encoded_inputs['special_tokens_mask'] = [0] * len(sequence)
if not max_entity_length:
max_entity_length = self.max_entity_length
if entity_ids is not None:
total_entity_len = 0
num_invalid_entities = 0
valid_entity_ids = [ent_id for ent_id, span in zip(entity_ids, entity_token_spans) if span[1] <= len(ids)]
valid_entity_token_spans = [span for span in entity_token_spans if span[1] <= len(ids)]
total_entity_len += len(valid_entity_ids)
num_invalid_entities += len(entity_ids) - len(valid_entity_ids)
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)]
valid_pair_entity_token_spans = [span for span in pair_entity_token_spans if span[1] <= len(pair_ids)]
total_entity_len += len(valid_pair_entity_ids)
num_invalid_entities += len(pair_entity_ids) - len(valid_pair_entity_ids)
if num_invalid_entities != 0:
logger.warning(f'{num_invalid_entities} entities are ignored because their entity spans are invalid due to the truncation of input tokens')
if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and total_entity_len > max_entity_length:
valid_entity_ids, valid_pair_entity_ids, overflowing_entities = self.truncate_sequences(valid_entity_ids, pair_ids=valid_pair_entity_ids, num_tokens_to_remove=total_entity_len - max_entity_length, truncation_strategy=truncation_strategy, stride=stride)
valid_entity_token_spans = valid_entity_token_spans[:len(valid_entity_ids)]
if valid_pair_entity_token_spans is not None:
valid_pair_entity_token_spans = valid_pair_entity_token_spans[:len(valid_pair_entity_ids)]
if return_overflowing_tokens:
encoded_inputs['overflowing_entities'] = overflowing_entities
encoded_inputs['num_truncated_entities'] = total_entity_len - max_entity_length
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, offset in ((valid_entity_token_spans, entity_token_offset), (valid_pair_entity_token_spans, pair_entity_token_offset)):
if token_spans is not None:
for start, end in token_spans:
start += offset
end += offset
position_ids = list(range(start, end))[:self.max_mention_length]
position_ids += [-1] * (self.max_mention_length - end + start)
entity_position_ids.append(position_ids)
entity_start_positions.append(start)
entity_end_positions.append(end - 1)
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:
encoded_inputs['entity_token_type_ids'] = [0] * len(encoded_inputs['entity_ids'])
self._eventual_warn_about_too_long_sequence(encoded_inputs['input_ids'], max_length, verbose)
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, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_attention_mask=return_attention_mask)
if return_length:
encoded_inputs['length'] = len(encoded_inputs['input_ids'])
batch_outputs = BatchEncoding(encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis)
return batch_outputs
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_length: Optional[int]=None, max_entity_length: Optional[int]=None, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_attention_mask: Optional[bool]=None, return_tensors: Optional[Union[str, TensorType]]=None, verbose: bool=True) -> BatchEncoding:
"""
Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with
`self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
are dictionary of numpy arrays or PyTorch tensors the result will use the same type unless
you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the
specific device of your tensors however.
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 (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function. Instead of `list[int]` you can have tensors (numpy arrays, or PyTorch tensors),
see the note above for the return type.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: 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 argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
max_entity_length (`int`, *optional*):
The maximum length of the entity sequence.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
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.
return_attention_mask (`bool`, *optional*):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention
masks?](../glossary#attention-mask)
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'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.
"""
if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):
encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0]}
if self.model_input_names[0] not in encoded_inputs:
raise ValueError(f'You should supply an encoding or a list of encodings to this method that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}')
required_input = encoded_inputs[self.model_input_names[0]]
if not required_input:
if return_attention_mask:
encoded_inputs['attention_mask'] = []
return encoded_inputs
first_element = required_input[0]
if isinstance(first_element, (list, tuple)):
index = 0
while len(required_input[index]) == 0:
index += 1
if index < len(required_input):
first_element = required_input[index][0]
if not isinstance(first_element, (int, list, tuple)):
if is_torch_tensor(first_element):
return_tensors = 'pt' if return_tensors is None else return_tensors
elif isinstance(first_element, np.ndarray):
return_tensors = 'np' if return_tensors is None else return_tensors
else:
raise ValueError(f'type of {first_element} unknown: {type(first_element)}. Should be one of a python, numpy, or pytorch object.')
for key, value in encoded_inputs.items():
encoded_inputs[key] = to_py_obj(value)
padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(padding=padding, max_length=max_length, verbose=verbose)
if max_entity_length is None:
max_entity_length = self.max_entity_length
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, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_attention_mask=return_attention_mask)
return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
batch_size = len(required_input)
if any((len(v) != batch_size for v in encoded_inputs.values())):
raise ValueError('Some items in the output dictionary have a different batch size than others.')
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 = PaddingStrategy.MAX_LENGTH
batch_outputs = {}
for i in range(batch_size):
inputs = {k: v[i] for k, v in encoded_inputs.items()}
outputs = self._pad(inputs, max_length=max_length, max_entity_length=max_entity_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, padding_side=padding_side, return_attention_mask=return_attention_mask)
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, EncodedInput], BatchEncoding], max_length: Optional[int]=None, max_entity_length: Optional[int]=None, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_attention_mask: Optional[bool]=None) -> dict:
"""
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
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.
max_entity_length: The maximum length of the entity sequence.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- '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 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.
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
"""
entities_provided = bool('entity_ids' in encoded_inputs)
if return_attention_mask is None:
return_attention_mask = 'attention_mask' in self.model_input_names
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 != 0):
max_length = (max_length // pad_to_multiple_of + 1) * pad_to_multiple_of
if entities_provided and max_entity_length is not None and (pad_to_multiple_of is not None) and (max_entity_length % pad_to_multiple_of != 0):
max_entity_length = (max_entity_length // pad_to_multiple_of + 1) * pad_to_multiple_of
needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and (len(encoded_inputs['input_ids']) != max_length or (entities_provided and len(encoded_inputs['entity_ids']) != max_entity_length))
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):
encoded_inputs['entity_attention_mask'] = [1] * len(encoded_inputs['entity_ids'])
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'])
if padding_side == 'right':
if return_attention_mask:
encoded_inputs['attention_mask'] = encoded_inputs['attention_mask'] + [0] * difference
if entities_provided:
encoded_inputs['entity_attention_mask'] = encoded_inputs['entity_attention_mask'] + [0] * entity_difference
if 'token_type_ids' in encoded_inputs:
encoded_inputs['token_type_ids'] = encoded_inputs['token_type_ids'] + [0] * difference
if entities_provided:
encoded_inputs['entity_token_type_ids'] = 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'] = encoded_inputs['input_ids'] + [self.pad_token_id] * difference
if entities_provided:
encoded_inputs['entity_ids'] = encoded_inputs['entity_ids'] + [self.entity_pad_token_id] * entity_difference
encoded_inputs['entity_position_ids'] = encoded_inputs['entity_position_ids'] + [[-1] * self.max_mention_length] * entity_difference
if self.task == 'entity_span_classification':
encoded_inputs['entity_start_positions'] = encoded_inputs['entity_start_positions'] + [0] * entity_difference
encoded_inputs['entity_end_positions'] = encoded_inputs['entity_end_positions'] + [0] * entity_difference
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_inputs['entity_attention_mask']
if 'token_type_ids' in encoded_inputs:
encoded_inputs['token_type_ids'] = [0] * difference + encoded_inputs['token_type_ids']
if entities_provided:
encoded_inputs['entity_token_type_ids'] = [0] * entity_difference + encoded_inputs['entity_token_type_ids']
if 'special_tokens_mask' in encoded_inputs:
encoded_inputs['special_tokens_mask'] = [1] * difference + encoded_inputs['special_tokens_mask']
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']
encoded_inputs['entity_position_ids'] = [[-1] * self.max_mention_length] * entity_difference + encoded_inputs['entity_position_ids']
if self.task == 'entity_span_classification':
encoded_inputs['entity_start_positions'] = [0] * entity_difference + encoded_inputs['entity_start_positions']
encoded_inputs['entity_end_positions'] = [0] * entity_difference + encoded_inputs['entity_end_positions']
else:
raise ValueError('Invalid padding strategy:' + str(padding_side))
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(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
merge_file = os.path.join(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])
with open(vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + '\n')
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'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive. Please check that the tokenizer is not corrupted!')
index = token_index
writer.write(' '.join(bpe_tokens) + '\n')
index += 1
entity_vocab_file = os.path.join(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['entity_vocab_file'])
with open(entity_vocab_file, 'w', encoding='utf-8') as f:
f.write(json.dumps(self.entity_vocab, indent=2, sort_keys=True, ensure_ascii=False) + '\n')
return (vocab_file, merge_file, entity_vocab_file)
|
class LukeTokenizer(PreTrainedTokenizer):
'''
Constructs a LUKE tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import LukeTokenizer
>>> tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]
```
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).
</Tip>
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. It also creates entity sequences, namely
`entity_ids`, `entity_attention_mask`, `entity_token_type_ids`, and `entity_position_ids` to be used by the LUKE
model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
entity_vocab_file (`str`):
Path to the entity vocabulary file.
task (`str`, *optional*):
Task for which you want to prepare sequences. One of `"entity_classification"`,
`"entity_pair_classification"`, or `"entity_span_classification"`. If you specify this argument, the entity
sequence is automatically created based on the given entity span(s).
max_entity_length (`int`, *optional*, defaults to 32):
The maximum length of `entity_ids`.
max_mention_length (`int`, *optional*, defaults to 30):
The maximum number of tokens inside an entity span.
entity_token_1 (`str`, *optional*, defaults to `<ent>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
`task` is set to `"entity_classification"` or `"entity_pair_classification"`.
entity_token_2 (`str`, *optional*, defaults to `<ent2>`):
The special token used to represent an entity span in a word token sequence. This token is only used when
`task` is set to `"entity_pair_classification"`.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the `cls_token`.
</Tip>
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
<Tip>
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the `sep_token`.
</Tip>
sep_token (`str`, *optional*, defaults to `"</s>"`):
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.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (LUKE tokenizer detect beginning of words by the preceding space).
'''
def __init__(self, vocab_file, merges_file, entity_vocab_file, task=None, max_entity_length=32, max_mention_length=30, entity_token_1='<ent>', entity_token_2='<ent2>', entity_unk_token='[UNK]', entity_pad_token='[PAD]', entity_mask_token='[MASK]', entity_mask2_token='[MASK2]', errors='replace', bos_token='<s>', eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', add_prefix_space=False, **kwargs):
pass
@property
def vocab_size(self):
pass
def get_vocab(self):
pass
def bpe(self, token):
pass
def _tokenize(self, text):
'''Tokenize a string.'''
pass
def _convert_token_to_id(self, token):
'''Converts a token (str) in an id using the vocab.'''
pass
def _convert_id_to_token(self, index):
'''Converts an index (integer) in a token (str) using the vocab.'''
pass
def convert_tokens_to_string(self, tokens):
'''Converts a sequence of tokens (string) in a single string.'''
pass
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. A LUKE sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
'''
pass
def get_special_tokens_mask(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None, already_has_special_tokens: bool=False) -> list[int]:
'''
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
'''
pass
def create_token_type_ids_from_sequences(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None) -> list[int]:
'''
Create a mask from the two sequences passed to be used in a sequence-pair classification task. LUKE does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of zeros.
'''
pass
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
pass
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def __call__(self, text: Union[TextInput, list[TextInput]], text_pair: Optional[Union[TextInput, list[TextInput]]]=None, entity_spans: Optional[Union[EntitySpanInput, list[EntitySpanInput]]]=None, entity_spans_pair: Optional[Union[EntitySpanInput, list[EntitySpanInput]]]=None, entities: Optional[Union[EntityInput, list[EntityInput]]]=None, entities_pair: Optional[Union[EntityInput, list[EntityInput]]]=None, add_special_tokens: bool=True, padding: Union[bool, str, PaddingStrategy]=False, truncation: Union[bool, str, TruncationStrategy]=None, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, is_split_into_words: Optional[bool]=False, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True, **kwargs) -> BatchEncoding:
'''
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences, depending on the task you want to prepare them for.
Args:
text (`str`, `list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.
text_pair (`str`, `list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence must be a string. Note that this
tokenizer does not support tokenization based on pretokenized strings.
entity_spans (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
with two integers denoting character-based start and end positions of entities. If you specify
`"entity_classification"` or `"entity_pair_classification"` as the `task` argument in the constructor,
the length of each sequence must be 1 or 2, respectively. If you specify `entities`, the length of each
sequence must be equal to the length of each sequence of `entities`.
entity_spans_pair (`list[tuple[int, int]]`, `list[list[tuple[int, int]]]`, *optional*):
The sequence or batch of sequences of entity spans to be encoded. Each sequence consists of tuples each
with two integers denoting character-based start and end positions of entities. If you specify the
`task` argument in the constructor, this argument is ignored. If you specify `entities_pair`, the
length of each sequence must be equal to the length of each sequence of `entities_pair`.
entities (`list[str]`, `list[list[str]]`, *optional*):
The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
each sequence must be equal to the length of each sequence of `entity_spans`. If you specify
`entity_spans` without specifying this argument, the entity sequence or the batch of entity sequences
is automatically constructed by filling it with the [MASK] entity.
entities_pair (`list[str]`, `list[list[str]]`, *optional*):
The sequence or batch of sequences of entities to be encoded. Each sequence consists of strings
representing entities, i.e., special entities (e.g., [MASK]) or entity titles of Wikipedia (e.g., Los
Angeles). This argument is ignored if you specify the `task` argument in the constructor. The length of
each sequence must be equal to the length of each sequence of `entity_spans_pair`. If you specify
`entity_spans_pair` without specifying this argument, the entity sequence or the batch of entity
sequences is automatically constructed by filling it with the [MASK] entity.
max_entity_length (`int`, *optional*):
The maximum length of `entity_ids`.
'''
pass
def _encode_plus(self, text: Union[TextInput], text_pair: Optional[Union[TextInput]]=None, entity_spans: Optional[EntitySpanInput]=None, entity_spans_pair: Optional[EntitySpanInput]=None, entities: Optional[EntityInput]=None, entities_pair: Optional[EntityInput]=None, add_special_tokens: bool=True, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy=TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, is_split_into_words: Optional[bool]=False, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True, **kwargs) -> BatchEncoding:
pass
def _batch_encode_plus(self, batch_text_or_text_pairs: Union[list[TextInput], list[TextInputPair]], batch_entity_spans_or_entity_spans_pairs: Optional[Union[list[EntitySpanInput], list[tuple[EntitySpanInput, EntitySpanInput]]]]=None, batch_entities_or_entities_pairs: Optional[Union[list[EntityInput], list[tuple[EntityInput, EntityInput]]]]=None, add_special_tokens: bool=True, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy=TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, is_split_into_words: Optional[bool]=False, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, return_special_tokens_mask: bool=False, return_offsets_mapping: bool=False, return_length: bool=False, verbose: bool=True, **kwargs) -> BatchEncoding:
pass
def _check_entity_input_format(self, entities: Optional[EntityInput], entity_spans: Optional[EntitySpanInput]):
pass
def _create_input_sequence(self, text: Union[TextInput], text_pair: Optional[Union[TextInput]]=None, entities: Optional[EntityInput]=None, entities_pair: Optional[EntityInput]=None, entity_spans: Optional[EntitySpanInput]=None, entity_spans_pair: Optional[EntitySpanInput]=None, **kwargs) -> tuple[list, list, list, list, list, list]:
pass
def get_input_ids(text):
pass
def get_input_ids_and_entity_token_spans(text, entity_spans):
pass
@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[tuple[Optional[list[tuple[int, int]]], Optional[list[tuple[int, int]]]]], add_special_tokens: bool=True, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, truncation_strategy: TruncationStrategy=TruncationStrategy.DO_NOT_TRUNCATE, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[str]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, return_overflowing_tokens: bool=False, 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 overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens
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.
'''
pass
@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_spans: Optional[list[tuple[int, int]]]=None, pair_entity_token_spans: Optional[list[tuple[int, int]]]=None, add_special_tokens: bool=True, padding: Union[bool, str, PaddingStrategy]=False, truncation: Union[bool, str, TruncationStrategy]=None, max_length: Optional[int]=None, max_entity_length: Optional[int]=None, stride: int=0, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_tensors: Optional[Union[str, TensorType]]=None, return_token_type_ids: Optional[bool]=None, return_attention_mask: Optional[bool]=None, 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 sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
entity spans so that it can be used by the model. It adds special tokens, truncates sequences if overflowing
while taking into account the special tokens and manages a moving window (with user defined stride) for
overflowing tokens. Please Note, for *pair_ids* different than `None` and *truncation_strategy = longest_first*
or `True`, it is not possible to return overflowing tokens. Such a combination of arguments will raise an
error.
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_entity_ids (`list[int]`, *optional*):
Entity ids of the second sequence.
entity_token_spans (`list[tuple[int, int]]`, *optional*):
Entity spans of the first sequence.
pair_entity_token_spans (`list[tuple[int, int]]`, *optional*):
Entity spans of the second sequence.
max_entity_length (`int`, *optional*):
The maximum length of the entity sequence.
'''
pass
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_length: Optional[int]=None, max_entity_length: Optional[int]=None, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_attention_mask: Optional[bool]=None, return_tensors: Optional[Union[str, TensorType]]=None, verbose: bool=True) -> BatchEncoding:
'''
Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch. Padding side (left/right) padding token ids are defined at the tokenizer level (with
`self.padding_side`, `self.pad_token_id` and `self.pad_token_type_id`) .. note:: If the `encoded_inputs` passed
are dictionary of numpy arrays or PyTorch tensors the result will use the same type unless
you provide a different tensor type with `return_tensors`. In the case of PyTorch tensors, you will lose the
specific device of your tensors however.
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 (list of [`BatchEncoding`], *dict[str, list[list[int]]]* or *list[dict[str,
list[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
collate function. Instead of `list[int]` you can have tensors (numpy arrays, or PyTorch tensors),
see the note above for the return type.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: 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 argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
max_entity_length (`int`, *optional*):
The maximum length of the entity sequence.
pad_to_multiple_of (`int`, *optional*):
If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
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.
return_attention_mask (`bool`, *optional*):
Whether to return the attention mask. If left to the default, will return the attention mask according
to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention
masks?](../glossary#attention-mask)
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `'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.
'''
pass
def _pad(self, encoded_inputs: Union[dict[str, EncodedInput], BatchEncoding], max_length: Optional[int]=None, max_entity_length: Optional[int]=None, padding_strategy: PaddingStrategy=PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int]=None, padding_side: Optional[str]=None, return_attention_mask: Optional[bool]=None) -> dict:
'''
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
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.
max_entity_length: The maximum length of the entity sequence.
padding_strategy: PaddingStrategy to use for padding.
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side:
- '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 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.
return_attention_mask:
(optional) Set to False to avoid returning attention mask (default: set to model specifics)
'''
pass
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
pass
| 29
| 13
| 61
| 6
| 46
| 9
| 8
| 0.29
| 1
| 22
| 2
| 0
| 22
| 17
| 22
| 111
| 1,555
| 172
| 1,084
| 357
| 865
| 310
| 497
| 148
| 472
| 30
| 3
| 4
| 182
|
3,526
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/configuration_lxmert.py
|
transformers.models.lxmert.configuration_lxmert.LxmertConfig
|
from ...configuration_utils import PretrainedConfig
class LxmertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
to instantiate a LXMERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defaults will yield a similar configuration to that of the Lxmert
[unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LxmertModel`] or [`TFLxmertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_qa_labels (`int`, *optional*, defaults to 9500):
This represents the total number of different question answering (QA) labels there are. If using more than
one dataset with QA, the user will need to account for the total number of labels that all of the datasets
have in total.
num_object_labels (`int`, *optional*, defaults to 1600):
This represents the total number of semantically unique objects that lxmert will be able to classify a
pooled-object feature as belonging too.
num_attr_labels (`int`, *optional*, defaults to 400):
This represents the total number of semantically unique attributes that lxmert will be able to classify a
pooled-object feature as possessing.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the *token_type_ids* passed into [`BertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
l_layers (`int`, *optional*, defaults to 9):
Number of hidden layers in the Transformer language encoder.
x_layers (`int`, *optional*, defaults to 5):
Number of hidden layers in the Transformer cross modality encoder.
r_layers (`int`, *optional*, defaults to 5):
Number of hidden layers in the Transformer visual encoder.
visual_feat_dim (`int`, *optional*, defaults to 2048):
This represents the last dimension of the pooled-object features used as input for the model, representing
the size of each object feature itself.
visual_pos_dim (`int`, *optional*, defaults to 4):
This represents the number of spatial features that are mixed into the visual features. The default is set
to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
decided to train with multiple vision-based loss objectives.
task_matched (`bool`, *optional*, defaults to `True`):
This task is used for sentence-image matching. If the sentence correctly describes the image the label will
be 1. If the sentence does not correctly describe the image, the label will be 0.
task_mask_lm (`bool`, *optional*, defaults to `True`):
Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
objective.
task_obj_predict (`bool`, *optional*, defaults to `True`):
Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
task_qa (`bool`, *optional*, defaults to `True`):
Whether or not to add the question-answering loss to the objective
visual_obj_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the object-prediction loss objective
visual_attr_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the attribute-prediction loss objective
visual_feat_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the feature-regression loss objective
"""
model_type = 'lxmert'
attribute_map = {}
def __init__(self, vocab_size=30522, hidden_size=768, num_attention_heads=12, num_qa_labels=9500, num_object_labels=1600, num_attr_labels=400, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, l_layers=9, x_layers=5, r_layers=5, visual_feat_dim=2048, visual_pos_dim=4, visual_loss_normalizer=6.67, task_matched=True, task_mask_lm=True, task_obj_predict=True, task_qa=True, visual_obj_loss=True, visual_attr_loss=True, visual_feat_loss=True, **kwargs):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.num_qa_labels = num_qa_labels
self.num_object_labels = num_object_labels
self.num_attr_labels = num_attr_labels
self.l_layers = l_layers
self.x_layers = x_layers
self.r_layers = r_layers
self.visual_feat_dim = visual_feat_dim
self.visual_pos_dim = visual_pos_dim
self.visual_loss_normalizer = visual_loss_normalizer
self.task_matched = task_matched
self.task_mask_lm = task_mask_lm
self.task_obj_predict = task_obj_predict
self.task_qa = task_qa
self.visual_obj_loss = visual_obj_loss
self.visual_attr_loss = visual_attr_loss
self.visual_feat_loss = visual_feat_loss
self.num_hidden_layers = {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
super().__init__(**kwargs)
|
class LxmertConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`LxmertModel`] or a [`TFLxmertModel`]. It is used
to instantiate a LXMERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defaults will yield a similar configuration to that of the Lxmert
[unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LXMERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LxmertModel`] or [`TFLxmertModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_qa_labels (`int`, *optional*, defaults to 9500):
This represents the total number of different question answering (QA) labels there are. If using more than
one dataset with QA, the user will need to account for the total number of labels that all of the datasets
have in total.
num_object_labels (`int`, *optional*, defaults to 1600):
This represents the total number of semantically unique objects that lxmert will be able to classify a
pooled-object feature as belonging too.
num_attr_labels (`int`, *optional*, defaults to 400):
This represents the total number of semantically unique attributes that lxmert will be able to classify a
pooled-object feature as possessing.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the *token_type_ids* passed into [`BertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
l_layers (`int`, *optional*, defaults to 9):
Number of hidden layers in the Transformer language encoder.
x_layers (`int`, *optional*, defaults to 5):
Number of hidden layers in the Transformer cross modality encoder.
r_layers (`int`, *optional*, defaults to 5):
Number of hidden layers in the Transformer visual encoder.
visual_feat_dim (`int`, *optional*, defaults to 2048):
This represents the last dimension of the pooled-object features used as input for the model, representing
the size of each object feature itself.
visual_pos_dim (`int`, *optional*, defaults to 4):
This represents the number of spatial features that are mixed into the visual features. The default is set
to 4 because most commonly this will represent the location of a bounding box. i.e., (x, y, width, height)
visual_loss_normalizer (`float`, *optional*, defaults to 6.67):
This represents the scaling factor in which each visual loss is multiplied by if during pretraining, one
decided to train with multiple vision-based loss objectives.
task_matched (`bool`, *optional*, defaults to `True`):
This task is used for sentence-image matching. If the sentence correctly describes the image the label will
be 1. If the sentence does not correctly describe the image, the label will be 0.
task_mask_lm (`bool`, *optional*, defaults to `True`):
Whether or not to add masked language modeling (as used in pretraining models such as BERT) to the loss
objective.
task_obj_predict (`bool`, *optional*, defaults to `True`):
Whether or not to add object prediction, attribute prediction and feature regression to the loss objective.
task_qa (`bool`, *optional*, defaults to `True`):
Whether or not to add the question-answering loss to the objective
visual_obj_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the object-prediction loss objective
visual_attr_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the attribute-prediction loss objective
visual_feat_loss (`bool`, *optional*, defaults to `True`):
Whether or not to calculate the feature-regression loss objective
'''
def __init__(self, vocab_size=30522, hidden_size=768, num_attention_heads=12, num_qa_labels=9500, num_object_labels=1600, num_attr_labels=400, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, l_layers=9, x_layers=5, r_layers=5, visual_feat_dim=2048, visual_pos_dim=4, visual_loss_normalizer=6.67, task_matched=True, task_mask_lm=True, task_obj_predict=True, task_qa=True, visual_obj_loss=True, visual_attr_loss=True, visual_feat_loss=True, **kwargs):
pass
| 2
| 1
| 60
| 0
| 60
| 0
| 1
| 1.19
| 1
| 1
| 0
| 0
| 1
| 28
| 1
| 1
| 143
| 5
| 63
| 62
| 31
| 75
| 33
| 32
| 31
| 1
| 1
| 0
| 1
|
3,527
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.GeLU
|
from torch import nn
from ...activations import ACT2FN, gelu
class GeLU(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return gelu(x)
|
class GeLU(nn.Module):
def __init__(self):
pass
def forward(self, x):
pass
| 3
| 0
| 2
| 0
| 2
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 2
| 0
| 2
| 12
| 6
| 1
| 5
| 3
| 2
| 0
| 5
| 3
| 2
| 1
| 1
| 0
| 2
|
3,528
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertAttention
|
import math
import torch
from torch import nn
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
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 heads ({config.num_attention_heads})')
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.head_size = self.num_attention_heads * self.attention_head_size
if ctx_dim is None:
ctx_dim = config.hidden_size
self.query = nn.Linear(config.hidden_size, self.head_size)
self.key = nn.Linear(ctx_dim, self.head_size)
self.value = nn.Linear(ctx_dim, self.head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states, context, attention_mask=None, output_attentions=False):
batch_size, seq_length, _ = hidden_states.shape
query_layer = self.query(hidden_states).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
key_layer = self.key(context).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
value_layer = self.value(context).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
attention_scores = attention_scores + attention_mask
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
attention_probs = self.dropout(attention_probs)
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] + (self.head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
|
class LxmertAttention(nn.Module):
def __init__(self, config, ctx_dim=None):
pass
def forward(self, hidden_states, context, attention_mask=None, output_attentions=False):
pass
| 3
| 0
| 19
| 3
| 14
| 2
| 2
| 0.14
| 1
| 3
| 0
| 0
| 3
| 7
| 3
| 13
| 59
| 10
| 43
| 23
| 39
| 6
| 37
| 23
| 33
| 3
| 1
| 1
| 7
|
3,529
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertAttentionOutput
|
from torch import nn
class LxmertAttentionOutput(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=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
|
class LxmertAttentionOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states, input_tensor):
pass
| 3
| 0
| 5
| 0
| 5
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 2
| 3
| 2
| 12
| 12
| 1
| 11
| 6
| 8
| 0
| 11
| 6
| 8
| 1
| 1
| 0
| 2
|
3,530
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertCrossAttentionLayer
|
from torch import nn
class LxmertCrossAttentionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.att = LxmertAttention(config)
self.output = LxmertAttentionOutput(config)
def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None, output_attentions=False):
output = self.att(input_tensor, ctx_tensor, ctx_att_mask, output_attentions=output_attentions)
if output_attentions:
attention_probs = output[1]
attention_output = self.output(output[0], input_tensor)
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
|
class LxmertCrossAttentionLayer(nn.Module):
def __init__(self, config):
pass
def forward(self, input_tensor, ctx_tensor, ctx_att_mask=None, output_attentions=False):
pass
| 3
| 0
| 6
| 0
| 6
| 0
| 2
| 0
| 1
| 3
| 2
| 0
| 2
| 2
| 2
| 12
| 13
| 1
| 12
| 9
| 9
| 0
| 12
| 9
| 9
| 3
| 1
| 1
| 4
|
3,531
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertEmbeddings
|
import torch
from torch import nn
class LxmertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size, padding_idx=0)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size, padding_idx=0)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids, token_type_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
device = input_ids.device
else:
input_shape = inputs_embeds.size()[:-1]
device = inputs_embeds.device
seq_length = input_shape[1]
position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
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)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
|
class LxmertEmbeddings(nn.Module):
'''Construct the embeddings from word, position and token_type embeddings.'''
def __init__(self, config):
pass
def forward(self, input_ids, token_type_ids=None, inputs_embeds=None):
pass
| 3
| 1
| 17
| 3
| 14
| 1
| 3
| 0.11
| 1
| 1
| 0
| 0
| 2
| 5
| 2
| 12
| 38
| 7
| 28
| 15
| 25
| 3
| 27
| 15
| 24
| 4
| 1
| 1
| 5
|
3,532
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertEncoder
|
from torch import nn
class LxmertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.visn_fc = LxmertVisualFeatureEncoder(config)
self.config = config
self.num_l_layers = config.l_layers
self.num_x_layers = config.x_layers
self.num_r_layers = config.r_layers
self.layer = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_l_layers)])
self.x_layers = nn.ModuleList([LxmertXLayer(config) for _ in range(self.num_x_layers)])
self.r_layers = nn.ModuleList([LxmertLayer(config) for _ in range(self.num_r_layers)])
def forward(self, lang_feats, lang_attention_mask, visual_feats, visual_pos, visual_attention_mask=None, output_attentions=None):
vision_hidden_states = ()
language_hidden_states = ()
vision_attentions = () if output_attentions or self.config.output_attentions else None
language_attentions = () if output_attentions or self.config.output_attentions else None
cross_encoder_attentions = () if output_attentions or self.config.output_attentions else None
visual_feats = self.visn_fc(visual_feats, visual_pos)
for layer_module in self.layer:
l_outputs = layer_module(lang_feats, lang_attention_mask, output_attentions=output_attentions)
lang_feats = l_outputs[0]
language_hidden_states = language_hidden_states + (lang_feats,)
if language_attentions is not None:
language_attentions = language_attentions + (l_outputs[1],)
for layer_module in self.r_layers:
v_outputs = layer_module(visual_feats, visual_attention_mask, output_attentions=output_attentions)
visual_feats = v_outputs[0]
vision_hidden_states = vision_hidden_states + (visual_feats,)
if vision_attentions is not None:
vision_attentions = vision_attentions + (v_outputs[1],)
for layer_module in self.x_layers:
x_outputs = layer_module(lang_feats, lang_attention_mask, visual_feats, visual_attention_mask, output_attentions=output_attentions)
lang_feats, visual_feats = x_outputs[:2]
vision_hidden_states = vision_hidden_states + (visual_feats,)
language_hidden_states = language_hidden_states + (lang_feats,)
if cross_encoder_attentions is not None:
cross_encoder_attentions = cross_encoder_attentions + (x_outputs[2],)
visual_encoder_outputs = (vision_hidden_states, vision_attentions if output_attentions else None)
lang_encoder_outputs = (language_hidden_states, language_attentions if output_attentions else None)
return (visual_encoder_outputs, lang_encoder_outputs, cross_encoder_attentions if output_attentions else None)
|
class LxmertEncoder(nn.Module):
def __init__(self, config):
pass
def forward(self, lang_feats, lang_attention_mask, visual_feats, visual_pos, visual_attention_mask=None, output_attentions=None):
pass
| 3
| 0
| 39
| 4
| 32
| 4
| 7
| 0.11
| 1
| 5
| 3
| 0
| 2
| 8
| 2
| 12
| 79
| 8
| 64
| 30
| 53
| 7
| 40
| 22
| 37
| 13
| 1
| 2
| 14
|
3,533
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertForPreTraining
|
from ...utils import ModelOutput, auto_docstring, logging
from torch import nn
import torch
from torch.nn import CrossEntropyLoss, SmoothL1Loss
import warnings
from typing import Optional, Union
@auto_docstring
class LxmertForPreTraining(LxmertPreTrainedModel):
_tied_weights_keys = ['cls.predictions.decoder.weight']
def __init__(self, config):
super().__init__(config)
self.config = config
self.num_qa_labels = config.num_qa_labels
self.visual_loss_normalizer = config.visual_loss_normalizer
self.task_mask_lm = config.task_mask_lm
self.task_obj_predict = config.task_obj_predict
self.task_matched = config.task_matched
self.task_qa = config.task_qa
self.lxmert = LxmertModel(config)
self.cls = LxmertPreTrainingHeads(config, self.lxmert.embeddings.word_embeddings.weight)
if self.task_obj_predict:
self.obj_predict_head = LxmertVisualObjHead(config)
if self.task_qa:
self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)
self.post_init()
self.loss_fcts = {'l2': SmoothL1Loss(reduction='none'), 'visual_ce': CrossEntropyLoss(reduction='none'), 'ce': CrossEntropyLoss()}
visual_losses = {}
if config.visual_obj_loss:
visual_losses['obj'] = {'shape': (-1,), 'num': config.num_object_labels, 'loss': 'visual_ce'}
if config.visual_attr_loss:
visual_losses['attr'] = {'shape': (-1,), 'num': config.num_attr_labels, 'loss': 'visual_ce'}
if config.visual_feat_loss:
visual_losses['feat'] = {'shape': (-1, config.visual_feat_dim), 'num': config.visual_feat_dim, 'loss': 'l2'}
self.visual_losses = visual_losses
def _tie_weights(self):
self.cls.predictions.decoder.weight = self.lxmert.embeddings.word_embeddings.weight
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int]=None, mean_resizing: bool=True) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
self.cls.predictions.bias = self._resize_bias(self.cls.predictions.bias, new_num_tokens)
return new_embeddings
def _resize_bias(self, bias, new_num_tokens: int):
old_num_tokens = bias.shape[0]
if new_num_tokens <= old_num_tokens:
new_bias = bias[:new_num_tokens]
else:
extra_bias = torch.zeros(new_num_tokens - old_num_tokens, device=bias.device)
new_bias = torch.cat([bias, extra_bias])
new_bias = nn.Parameter(new_bias)
return new_bias
def resize_num_qa_labels(self, num_labels):
"""
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
will add newly initialized weights. Reducing the size will remove weights from the end
Args:
num_labels (`int`, *optional*):
New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
Return:
`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
"""
cur_qa_logit_layer = self.get_qa_logit_layer()
if num_labels is None or cur_qa_logit_layer is None:
return
new_qa_logit_layer = self._resize_qa_labels(num_labels)
self.config.num_qa_labels = num_labels
self.num_qa_labels = num_labels
return new_qa_logit_layer
def _resize_qa_labels(self, num_labels):
cur_qa_logit_layer = self.get_qa_logit_layer()
new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
self._set_qa_logit_layer(new_qa_logit_layer)
return self.get_qa_logit_layer()
def get_qa_logit_layer(self) -> nn.Module:
"""
Returns the linear layer that produces question answering logits.
Returns:
`nn.Module`: A torch module mapping the question answering prediction hidden states or `None` if LXMERT
does not have a visual answering head.
"""
if hasattr(self, 'answer_head'):
return self.answer_head.logit_fc[-1]
def _set_qa_logit_layer(self, qa_logit_layer):
self.answer_head.logit_fc[-1] = qa_logit_layer
def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
if num_labels is None:
return cur_qa_logit_layer
cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
if cur_qa_labels == num_labels:
return cur_qa_logit_layer
if getattr(cur_qa_logit_layer, 'bias', None) is not None:
new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
else:
new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)
new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)
self._init_weights(new_qa_logit_layer)
num_labels_to_copy = min(cur_qa_labels, num_labels)
new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
if getattr(cur_qa_logit_layer, 'bias', None) is not None:
new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]
return new_qa_logit_layer
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, visual_feats: Optional[torch.FloatTensor]=None, visual_pos: Optional[torch.FloatTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, visual_attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, obj_labels: Optional[dict[str, tuple[torch.FloatTensor, torch.FloatTensor]]]=None, matched_label: Optional[torch.LongTensor]=None, ans: Optional[torch.Tensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **kwargs) -> Union[LxmertForPreTrainingOutput, tuple[torch.FloatTensor]]:
"""
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
This input represents spatial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
obj_labels (`dict[Str: tuple[Torch.FloatTensor, Torch.FloatTensor]]`, *optional*):
each key is named after each one of the visual losses and each element of the tuple is of the shape
`(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and
the label score respectively
matched_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the whether or not the text input matches the image (classification) loss. Input
should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
- 0 indicates that the sentence does not match the image,
- 1 indicates that the sentence does match the image.
ans (`Torch.Tensor` of shape `(batch_size)`, *optional*):
a one hot representation hof the correct answer *optional*
"""
if 'masked_lm_labels' in kwargs:
warnings.warn('The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.', FutureWarning)
labels = kwargs.pop('masked_lm_labels')
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
device = input_ids.device if input_ids is not None else inputs_embeds.device
lxmert_output = self.lxmert(input_ids=input_ids, visual_feats=visual_feats, visual_pos=visual_pos, token_type_ids=token_type_ids, attention_mask=attention_mask, visual_attention_mask=visual_attention_mask, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, output_attentions=output_attentions, return_dict=return_dict)
lang_output, visual_output, pooled_output = (lxmert_output[0], lxmert_output[1], lxmert_output[2])
lang_prediction_scores, cross_relationship_score = self.cls(lang_output, pooled_output)
if self.task_qa:
answer_score = self.answer_head(pooled_output)
else:
answer_score = pooled_output[0][0]
total_loss = None if labels is None and matched_label is None and (obj_labels is None) and (ans is None) else torch.tensor(0.0, device=device)
if labels is not None and self.task_mask_lm:
masked_lm_loss = self.loss_fcts['ce'](lang_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
total_loss += masked_lm_loss
if matched_label is not None and self.task_matched:
matched_loss = self.loss_fcts['ce'](cross_relationship_score.view(-1, 2), matched_label.view(-1))
total_loss += matched_loss
if obj_labels is not None and self.task_obj_predict:
total_visual_loss = torch.tensor(0.0, device=input_ids.device)
visual_prediction_scores_dict = self.obj_predict_head(visual_output)
for key, key_info in self.visual_losses.items():
label, mask_conf = obj_labels[key]
output_dim = key_info['num']
loss_fct_name = key_info['loss']
label_shape = key_info['shape']
weight = self.visual_loss_normalizer
visual_loss_fct = self.loss_fcts[loss_fct_name]
visual_prediction_scores = visual_prediction_scores_dict[key]
visual_loss = visual_loss_fct(visual_prediction_scores.view(-1, output_dim), label.view(label_shape))
if visual_loss.dim() > 1:
visual_loss = visual_loss.mean(1)
visual_loss = (visual_loss * mask_conf.view(-1)).mean() * weight
total_visual_loss += visual_loss
total_loss += total_visual_loss
if ans is not None and self.task_qa:
answer_loss = self.loss_fcts['ce'](answer_score.view(-1, self.num_qa_labels), ans.view(-1))
total_loss += answer_loss
if not return_dict:
output = (lang_prediction_scores, cross_relationship_score, answer_score) + lxmert_output[3:]
return (total_loss,) + output if total_loss is not None else output
return LxmertForPreTrainingOutput(loss=total_loss, prediction_logits=lang_prediction_scores, cross_relationship_score=cross_relationship_score, question_answering_score=answer_score, language_hidden_states=lxmert_output.language_hidden_states, vision_hidden_states=lxmert_output.vision_hidden_states, language_attentions=lxmert_output.language_attentions, vision_attentions=lxmert_output.vision_attentions, cross_encoder_attentions=lxmert_output.cross_encoder_attentions)
|
@auto_docstring
class LxmertForPreTraining(LxmertPreTrainedModel):
def __init__(self, config):
pass
def _tie_weights(self):
pass
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int]=None, mean_resizing: bool=True) -> nn.Embedding:
pass
def _resize_bias(self, bias, new_num_tokens: int):
pass
def resize_num_qa_labels(self, num_labels):
'''
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
will add newly initialized weights. Reducing the size will remove weights from the end
Args:
num_labels (`int`, *optional*):
New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
Return:
`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
'''
pass
def _resize_qa_labels(self, num_labels):
pass
def get_qa_logit_layer(self) -> nn.Module:
'''
Returns the linear layer that produces question answering logits.
Returns:
`nn.Module`: A torch module mapping the question answering prediction hidden states or `None` if LXMERT
does not have a visual answering head.
'''
pass
def _set_qa_logit_layer(self, qa_logit_layer):
pass
def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, visual_feats: Optional[torch.FloatTensor]=None, visual_pos: Optional[torch.FloatTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, visual_attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, obj_labels: Optional[dict[str, tuple[torch.FloatTensor, torch.FloatTensor]]]=None, matched_label: Optional[torch.LongTensor]=None, ans: Optional[torch.Tensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **kwargs) -> Union[LxmertForPreTrainingOutput, tuple[torch.FloatTensor]]:
'''
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
This input represents spatial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
obj_labels (`dict[Str: tuple[Torch.FloatTensor, Torch.FloatTensor]]`, *optional*):
each key is named after each one of the visual losses and each element of the tuple is of the shape
`(batch_size, num_features)` and `(batch_size, num_features, visual_feature_dim)` for each the label id and
the label score respectively
matched_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the whether or not the text input matches the image (classification) loss. Input
should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
- 0 indicates that the sentence does not match the image,
- 1 indicates that the sentence does match the image.
ans (`Torch.Tensor` of shape `(batch_size)`, *optional*):
a one hot representation hof the correct answer *optional*
'''
pass
| 13
| 3
| 30
| 3
| 22
| 5
| 4
| 0.24
| 1
| 11
| 5
| 0
| 9
| 13
| 9
| 10
| 280
| 35
| 199
| 77
| 168
| 47
| 116
| 57
| 106
| 14
| 2
| 3
| 34
|
3,534
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput
|
from ...utils import ModelOutput, auto_docstring, logging
import torch
from dataclasses import dataclass
from typing import Optional, Union
@dataclass
@auto_docstring(custom_intro='\n Output type of [`LxmertForPreTraining`].\n ')
class LxmertForPreTrainingOutput(ModelOutput):
"""
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
cross_relationship_score (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the textual matching objective (classification) head (scores of True/False
continuation before SoftMax).
question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`):
Prediction scores of question answering objective (classification).
language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
prediction_logits: Optional[torch.FloatTensor] = None
cross_relationship_score: Optional[torch.FloatTensor] = None
question_answering_score: Optional[torch.FloatTensor] = None
language_hidden_states: Optional[tuple[torch.FloatTensor]] = None
vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
language_attentions: Optional[tuple[torch.FloatTensor]] = None
vision_attentions: Optional[tuple[torch.FloatTensor]] = None
cross_encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Output type of [`LxmertForPreTraining`].\n ')
class LxmertForPreTrainingOutput(ModelOutput):
'''
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
cross_relationship_score (`torch.FloatTensor` of shape `(batch_size, 2)`):
Prediction scores of the textual matching objective (classification) head (scores of True/False
continuation before SoftMax).
question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`):
Prediction scores of question answering objective (classification).
language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 45
| 3
| 10
| 10
| 9
| 32
| 10
| 10
| 9
| 0
| 1
| 0
| 0
|
3,535
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnswering
|
from torch import nn
import torch
from typing import Optional, Union
from ...utils import ModelOutput, auto_docstring, logging
from torch.nn import CrossEntropyLoss, SmoothL1Loss
@auto_docstring(custom_intro='\n Lxmert Model with a visual-answering head on top for downstream QA tasks\n ')
class LxmertForQuestionAnswering(LxmertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.num_qa_labels = config.num_qa_labels
self.visual_loss_normalizer = config.visual_loss_normalizer
self.lxmert = LxmertModel(config)
self.answer_head = LxmertVisualAnswerHead(config, self.num_qa_labels)
self.post_init()
self.loss = CrossEntropyLoss()
def resize_num_qa_labels(self, num_labels):
"""
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
will add newly initialized weights. Reducing the size will remove weights from the end
Args:
num_labels (`int`, *optional*):
New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
Return:
`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
"""
cur_qa_logit_layer = self.get_qa_logit_layer()
if num_labels is None or cur_qa_logit_layer is None:
return
new_qa_logit_layer = self._resize_qa_labels(num_labels)
self.config.num_qa_labels = num_labels
self.num_qa_labels = num_labels
return new_qa_logit_layer
def _resize_qa_labels(self, num_labels):
cur_qa_logit_layer = self.get_qa_logit_layer()
new_qa_logit_layer = self._get_resized_qa_labels(cur_qa_logit_layer, num_labels)
self._set_qa_logit_layer(new_qa_logit_layer)
return self.get_qa_logit_layer()
def get_qa_logit_layer(self) -> nn.Module:
"""
Returns the linear layer that produces question answering logits
Returns:
`nn.Module`: A torch module mapping the question answering prediction hidden states. `None`: A NoneType
object if Lxmert does not have the visual answering head.
"""
if hasattr(self, 'answer_head'):
return self.answer_head.logit_fc[-1]
def _set_qa_logit_layer(self, qa_logit_layer):
self.answer_head.logit_fc[-1] = qa_logit_layer
def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
if num_labels is None:
return cur_qa_logit_layer
cur_qa_labels, hidden_dim = cur_qa_logit_layer.weight.size()
if cur_qa_labels == num_labels:
return cur_qa_logit_layer
if getattr(cur_qa_logit_layer, 'bias', None) is not None:
new_qa_logit_layer = nn.Linear(hidden_dim, num_labels)
else:
new_qa_logit_layer = nn.Linear(hidden_dim, num_labels, bias=False)
new_qa_logit_layer.to(cur_qa_logit_layer.weight.device)
self._init_weights(new_qa_logit_layer)
num_labels_to_copy = min(cur_qa_labels, num_labels)
new_qa_logit_layer.weight.data[:num_labels_to_copy, :] = cur_qa_logit_layer.weight.data[:num_labels_to_copy, :]
if getattr(cur_qa_logit_layer, 'bias', None) is not None:
new_qa_logit_layer.bias.data[:num_labels_to_copy] = cur_qa_logit_layer.bias.data[:num_labels_to_copy]
return new_qa_logit_layer
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, visual_feats: Optional[torch.FloatTensor]=None, visual_pos: Optional[torch.FloatTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, visual_attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.Tensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[LxmertForQuestionAnsweringOutput, tuple[torch.FloatTensor]]:
"""
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
This input represents spatial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
labels (`Torch.Tensor` of shape `(batch_size)`, *optional*):
A one-hot representation of the correct answer
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
lxmert_output = self.lxmert(input_ids=input_ids, visual_feats=visual_feats, visual_pos=visual_pos, token_type_ids=token_type_ids, attention_mask=attention_mask, visual_attention_mask=visual_attention_mask, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, output_attentions=output_attentions, return_dict=return_dict)
pooled_output = lxmert_output[2]
answer_score = self.answer_head(pooled_output)
loss = None
if labels is not None:
loss = self.loss(answer_score.view(-1, self.num_qa_labels), labels.view(-1))
if not return_dict:
output = (answer_score,) + lxmert_output[3:]
return (loss,) + output if loss is not None else output
return LxmertForQuestionAnsweringOutput(loss=loss, question_answering_score=answer_score, language_hidden_states=lxmert_output.language_hidden_states, vision_hidden_states=lxmert_output.vision_hidden_states, language_attentions=lxmert_output.language_attentions, vision_attentions=lxmert_output.vision_attentions, cross_encoder_attentions=lxmert_output.cross_encoder_attentions)
|
@auto_docstring(custom_intro='\n Lxmert Model with a visual-answering head on top for downstream QA tasks\n ')
class LxmertForQuestionAnswering(LxmertPreTrainedModel):
def __init__(self, config):
pass
def resize_num_qa_labels(self, num_labels):
'''
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
will add newly initialized weights. Reducing the size will remove weights from the end
Args:
num_labels (`int`, *optional*):
New number of labels in the linear layer weight matrix. Increasing the size will add newly initialized
weights at the end. Reducing the size will remove weights from the end. If not provided or `None`, just
returns a pointer to the qa labels ``torch.nn.Linear``` module of the model without doing anything.
Return:
`torch.nn.Linear`: Pointer to the resized Linear layer or the old Linear layer
'''
pass
def _resize_qa_labels(self, num_labels):
pass
def get_qa_logit_layer(self) -> nn.Module:
'''
Returns the linear layer that produces question answering logits
Returns:
`nn.Module`: A torch module mapping the question answering prediction hidden states. `None`: A NoneType
object if Lxmert does not have the visual answering head.
'''
pass
def _set_qa_logit_layer(self, qa_logit_layer):
pass
def _get_resized_qa_labels(self, cur_qa_logit_layer, num_labels):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, visual_feats: Optional[torch.FloatTensor]=None, visual_pos: Optional[torch.FloatTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, visual_attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.Tensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[LxmertForQuestionAnsweringOutput, tuple[torch.FloatTensor]]:
'''
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
This input represents spatial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
labels (`Torch.Tensor` of shape `(batch_size)`, *optional*):
A one-hot representation of the correct answer
'''
pass
| 10
| 3
| 20
| 3
| 13
| 4
| 2
| 0.31
| 1
| 6
| 3
| 0
| 7
| 6
| 7
| 8
| 150
| 26
| 95
| 40
| 68
| 29
| 56
| 26
| 48
| 5
| 2
| 1
| 17
|
3,536
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput
|
from ...utils import ModelOutput, auto_docstring, logging
from dataclasses import dataclass
from typing import Optional, Union
import torch
@dataclass
@auto_docstring(custom_intro='\n Output type of [`LxmertForQuestionAnswering`].\n ')
class LxmertForQuestionAnsweringOutput(ModelOutput):
"""
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.k.
question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`, *optional*):
Prediction scores of question answering objective (classification).
language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
question_answering_score: Optional[torch.FloatTensor] = None
language_hidden_states: Optional[tuple[torch.FloatTensor]] = None
vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
language_attentions: Optional[tuple[torch.FloatTensor]] = None
vision_attentions: Optional[tuple[torch.FloatTensor]] = None
cross_encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Output type of [`LxmertForQuestionAnswering`].\n ')
class LxmertForQuestionAnsweringOutput(ModelOutput):
'''
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.k.
question_answering_score (`torch.FloatTensor` of shape `(batch_size, n_qa_answers)`, *optional*):
Prediction scores of question answering objective (classification).
language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.38
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 37
| 2
| 8
| 8
| 7
| 27
| 8
| 8
| 7
| 0
| 1
| 0
| 0
|
3,537
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertIntermediate
|
from torch import nn
from ...activations import ACT2FN, gelu
class LxmertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
|
class LxmertIntermediate(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states):
pass
| 3
| 0
| 4
| 0
| 4
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 2
| 2
| 2
| 12
| 10
| 1
| 9
| 5
| 6
| 0
| 9
| 5
| 6
| 1
| 1
| 0
| 2
|
3,538
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertLMPredictionHead
|
import torch
from torch import nn
class LxmertLMPredictionHead(nn.Module):
def __init__(self, config, lxmert_model_embedding_weights):
super().__init__()
self.transform = LxmertPredictionHeadTransform(config)
self.decoder = nn.Linear(lxmert_model_embedding_weights.size(1), lxmert_model_embedding_weights.size(0), bias=False)
self.decoder.weight = lxmert_model_embedding_weights
self.bias = nn.Parameter(torch.zeros(lxmert_model_embedding_weights.size(0)))
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states) + self.bias
return hidden_states
|
class LxmertLMPredictionHead(nn.Module):
def __init__(self, config, lxmert_model_embedding_weights):
pass
def forward(self, hidden_states):
pass
| 3
| 0
| 9
| 1
| 7
| 1
| 1
| 0.13
| 1
| 2
| 1
| 0
| 2
| 3
| 2
| 12
| 19
| 2
| 15
| 6
| 12
| 2
| 11
| 6
| 8
| 1
| 1
| 0
| 2
|
3,539
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertLayer
|
from torch import nn
class LxmertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = LxmertSelfAttentionLayer(config)
self.intermediate = LxmertIntermediate(config)
self.output = LxmertOutput(config)
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
attention_output = outputs[0]
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + outputs[1:]
return outputs
|
class LxmertLayer(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states, attention_mask=None, output_attentions=False):
pass
| 3
| 0
| 6
| 0
| 6
| 1
| 1
| 0.08
| 1
| 4
| 3
| 0
| 2
| 3
| 2
| 12
| 14
| 1
| 13
| 10
| 10
| 1
| 13
| 10
| 10
| 1
| 1
| 0
| 2
|
3,540
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertModel
|
from typing import Optional, Union
import torch
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring
class LxmertModel(LxmertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = LxmertEmbeddings(config)
self.encoder = LxmertEncoder(config)
self.pooler = LxmertPooler(config)
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings.word_embeddings = new_embeddings
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, visual_feats: Optional[torch.FloatTensor]=None, visual_pos: Optional[torch.FloatTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, visual_attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[LxmertModelOutput, tuple[torch.FloatTensor]]:
"""
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
This input represents spatial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds')
if visual_feats is None:
raise ValueError('`visual_feats` cannot be `None`')
if visual_pos is None:
raise ValueError('`visual_pos` cannot be `None`')
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(self.dtype).min
if visual_attention_mask is not None:
extended_visual_attention_mask = visual_attention_mask.unsqueeze(1).unsqueeze(2)
extended_visual_attention_mask = extended_visual_attention_mask.to(dtype=self.dtype)
extended_visual_attention_mask = (1.0 - extended_visual_attention_mask) * torch.finfo(self.dtype).min
else:
extended_visual_attention_mask = None
embedding_output = self.embeddings(input_ids, token_type_ids, inputs_embeds)
encoder_outputs = self.encoder(embedding_output, extended_attention_mask, visual_feats=visual_feats, visual_pos=visual_pos, visual_attention_mask=extended_visual_attention_mask, output_attentions=output_attentions)
visual_encoder_outputs, lang_encoder_outputs = encoder_outputs[:2]
vision_hidden_states = visual_encoder_outputs[0]
language_hidden_states = lang_encoder_outputs[0]
all_attentions = ()
if output_attentions:
language_attentions = lang_encoder_outputs[1]
vision_attentions = visual_encoder_outputs[1]
cross_encoder_attentions = encoder_outputs[2]
all_attentions = (language_attentions, vision_attentions, cross_encoder_attentions)
hidden_states = (language_hidden_states, vision_hidden_states) if output_hidden_states else ()
visual_output = vision_hidden_states[-1]
lang_output = language_hidden_states[-1]
pooled_output = self.pooler(lang_output)
if not return_dict:
return (lang_output, visual_output, pooled_output) + hidden_states + all_attentions
return LxmertModelOutput(pooled_output=pooled_output, language_output=lang_output, vision_output=visual_output, language_hidden_states=language_hidden_states if output_hidden_states else None, vision_hidden_states=vision_hidden_states if output_hidden_states else None, language_attentions=language_attentions if output_attentions else None, vision_attentions=vision_attentions if output_attentions else None, cross_encoder_attentions=cross_encoder_attentions if output_attentions else None)
|
@auto_docstring
class LxmertModel(LxmertPreTrainedModel):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, new_embeddings):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, visual_feats: Optional[torch.FloatTensor]=None, visual_pos: Optional[torch.FloatTensor]=None, attention_mask: Optional[torch.FloatTensor]=None, visual_attention_mask: Optional[torch.FloatTensor]=None, token_type_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[LxmertModelOutput, tuple[torch.FloatTensor]]:
'''
visual_feats (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_feat_dim)`):
This input represents visual features. They ROI pooled object features from bounding boxes using a
faster-RCNN model)
These are currently not provided by the transformers library.
visual_pos (`torch.FloatTensor` of shape `(batch_size, num_visual_features, visual_pos_dim)`):
This input represents spatial features corresponding to their relative (via index) visual features. The
pre-trained LXMERT model expects these spatial features to be normalized bounding boxes on a scale of 0 to
1.
These are currently not provided by the transformers library.
visual_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
'''
pass
| 7
| 1
| 31
| 4
| 23
| 4
| 6
| 0.14
| 1
| 7
| 4
| 0
| 4
| 3
| 4
| 5
| 132
| 18
| 100
| 38
| 77
| 14
| 56
| 25
| 51
| 21
| 2
| 1
| 24
|
3,541
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertModelOutput
|
from typing import Optional, Union
from ...utils import ModelOutput, auto_docstring, logging
import torch
from dataclasses import dataclass
@dataclass
@auto_docstring(custom_intro='\n Lxmert\'s outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,\n visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"\n encoder")\n ')
class LxmertModelOutput(ModelOutput):
"""
language_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the language encoder.
vision_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the visual encoder.
pooled_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear
language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
"""
language_output: Optional[torch.FloatTensor] = None
vision_output: Optional[torch.FloatTensor] = None
pooled_output: Optional[torch.FloatTensor] = None
language_hidden_states: Optional[tuple[torch.FloatTensor]] = None
vision_hidden_states: Optional[tuple[torch.FloatTensor]] = None
language_attentions: Optional[tuple[torch.FloatTensor]] = None
vision_attentions: Optional[tuple[torch.FloatTensor]] = None
cross_encoder_attentions: Optional[tuple[torch.FloatTensor]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Lxmert\'s outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,\n visual, and, cross-modality encoders. (note: the visual encoder in Lxmert is referred to as the "relation-ship"\n encoder")\n ')
class LxmertModelOutput(ModelOutput):
'''
language_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the language encoder.
vision_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the visual encoder.
pooled_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification, CLS, token) further processed
by a Linear layer and a Tanh activation function. The Linear
language_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for input features + one for the output of each cross-modality layer) of
shape `(batch_size, sequence_length, hidden_size)`.
language_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.44
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 43
| 3
| 9
| 9
| 8
| 31
| 9
| 9
| 8
| 0
| 1
| 0
| 0
|
3,542
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertOutput
|
from torch import nn
class LxmertOutput(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=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
|
class LxmertOutput(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states, input_tensor):
pass
| 3
| 0
| 5
| 0
| 5
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 2
| 3
| 2
| 12
| 12
| 1
| 11
| 6
| 8
| 0
| 11
| 6
| 8
| 1
| 1
| 0
| 2
|
3,543
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertPooler
|
from torch import nn
class LxmertPooler(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):
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
|
class LxmertPooler(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states):
pass
| 3
| 0
| 6
| 0
| 5
| 1
| 1
| 0.2
| 1
| 1
| 0
| 0
| 2
| 2
| 2
| 12
| 13
| 1
| 10
| 7
| 7
| 2
| 10
| 7
| 7
| 1
| 1
| 0
| 2
|
3,544
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertPreTrainedModel
|
from torch import nn
from .configuration_lxmert import LxmertConfig
from ...utils import ModelOutput, auto_docstring, logging
from ...modeling_utils import PreTrainedModel
@auto_docstring
class LxmertPreTrainedModel(PreTrainedModel):
config: LxmertConfig
base_model_prefix = 'lxmert'
_supports_param_buffer_assignment = False
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, LxmertLMPredictionHead):
module.bias.data.zero_()
|
@auto_docstring
class LxmertPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights'''
pass
| 3
| 1
| 15
| 0
| 12
| 3
| 6
| 0.41
| 1
| 0
| 0
| 3
| 1
| 0
| 1
| 1
| 26
| 2
| 17
| 6
| 15
| 7
| 15
| 6
| 13
| 6
| 1
| 2
| 6
|
3,545
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertPreTrainingHeads
|
from torch import nn
class LxmertPreTrainingHeads(nn.Module):
def __init__(self, config, lxmert_model_embedding_weights):
super().__init__()
self.predictions = LxmertLMPredictionHead(config, lxmert_model_embedding_weights)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return (prediction_scores, seq_relationship_score)
|
class LxmertPreTrainingHeads(nn.Module):
def __init__(self, config, lxmert_model_embedding_weights):
pass
def forward(self, sequence_output, pooled_output):
pass
| 3
| 0
| 4
| 0
| 4
| 0
| 1
| 0
| 1
| 2
| 1
| 0
| 2
| 2
| 2
| 12
| 10
| 1
| 9
| 7
| 6
| 0
| 9
| 7
| 6
| 1
| 1
| 0
| 2
|
3,546
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertPredictionHeadTransform
|
from ...activations import ACT2FN, gelu
from torch import nn
class LxmertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.transform_act_fn = ACT2FN[config.hidden_act]
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=1e-12)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
|
class LxmertPredictionHeadTransform(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states):
pass
| 3
| 0
| 5
| 0
| 5
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 2
| 3
| 2
| 12
| 12
| 1
| 11
| 6
| 8
| 0
| 11
| 6
| 8
| 1
| 1
| 0
| 2
|
3,547
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertSelfAttentionLayer
|
from torch import nn
class LxmertSelfAttentionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.self = LxmertAttention(config)
self.output = LxmertAttentionOutput(config)
def forward(self, input_tensor, attention_mask, output_attentions=False):
output = self.self(input_tensor, input_tensor, attention_mask, output_attentions=output_attentions)
if output_attentions:
attention_probs = output[1]
attention_output = self.output(output[0], input_tensor)
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
|
class LxmertSelfAttentionLayer(nn.Module):
def __init__(self, config):
pass
def forward(self, input_tensor, attention_mask, output_attentions=False):
pass
| 3
| 0
| 9
| 0
| 8
| 1
| 2
| 0.06
| 1
| 3
| 2
| 0
| 2
| 2
| 2
| 12
| 19
| 1
| 17
| 9
| 14
| 1
| 12
| 9
| 9
| 3
| 1
| 1
| 4
|
3,548
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertVisualAnswerHead
|
from torch import nn
class LxmertVisualAnswerHead(nn.Module):
def __init__(self, config, num_labels):
super().__init__()
hid_dim = config.hidden_size
self.logit_fc = nn.Sequential(nn.Linear(hid_dim, hid_dim * 2), GeLU(), nn.LayerNorm(hid_dim * 2, eps=1e-12), nn.Linear(hid_dim * 2, num_labels))
def forward(self, hidden_states):
return self.logit_fc(hidden_states)
|
class LxmertVisualAnswerHead(nn.Module):
def __init__(self, config, num_labels):
pass
def forward(self, hidden_states):
pass
| 3
| 0
| 6
| 0
| 6
| 0
| 1
| 0
| 1
| 2
| 1
| 0
| 2
| 1
| 2
| 12
| 13
| 1
| 12
| 5
| 9
| 0
| 7
| 5
| 4
| 1
| 1
| 0
| 2
|
3,549
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertVisualFeatureEncoder
|
from torch import nn
class LxmertVisualFeatureEncoder(nn.Module):
def __init__(self, config):
super().__init__()
feat_dim = config.visual_feat_dim
pos_dim = config.visual_pos_dim
self.visn_fc = nn.Linear(feat_dim, config.hidden_size)
self.visn_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
self.box_fc = nn.Linear(pos_dim, config.hidden_size)
self.box_layer_norm = nn.LayerNorm(config.hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, visual_feats, visual_pos):
x = self.visn_fc(visual_feats)
x = self.visn_layer_norm(x)
y = self.box_fc(visual_pos)
y = self.box_layer_norm(y)
output = (x + y) / 2
output = self.dropout(output)
return output
|
class LxmertVisualFeatureEncoder(nn.Module):
def __init__(self, config):
pass
def forward(self, visual_feats, visual_pos):
pass
| 3
| 0
| 12
| 2
| 9
| 1
| 1
| 0.11
| 1
| 1
| 0
| 0
| 2
| 5
| 2
| 12
| 25
| 5
| 18
| 13
| 15
| 2
| 18
| 13
| 15
| 1
| 1
| 0
| 2
|
3,550
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertVisualObjHead
|
from torch import nn
class LxmertVisualObjHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = LxmertPredictionHeadTransform(config)
visual_losses = {}
if config.visual_obj_loss:
visual_losses['obj'] = {'shape': (-1,), 'num': config.num_object_labels}
if config.visual_attr_loss:
visual_losses['attr'] = {'shape': (-1,), 'num': config.num_attr_labels}
if config.visual_feat_loss:
visual_losses['feat'] = {'shape': (-1, config.visual_feat_dim), 'num': config.visual_feat_dim}
self.visual_losses = visual_losses
self.decoder_dict = nn.ModuleDict({key: nn.Linear(config.hidden_size, self.visual_losses[key]['num']) for key in self.visual_losses})
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
output = {}
for key in self.visual_losses:
output[key] = self.decoder_dict[key](hidden_states)
return output
|
class LxmertVisualObjHead(nn.Module):
def __init__(self, config):
pass
def forward(self, hidden_states):
pass
| 3
| 0
| 14
| 1
| 12
| 2
| 3
| 0.13
| 1
| 2
| 1
| 0
| 2
| 3
| 2
| 12
| 29
| 2
| 24
| 9
| 21
| 3
| 19
| 9
| 16
| 4
| 1
| 1
| 6
|
3,551
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/modeling_lxmert.py
|
transformers.models.lxmert.modeling_lxmert.LxmertXLayer
|
from torch import nn
class LxmertXLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.visual_attention = LxmertCrossAttentionLayer(config)
self.lang_self_att = LxmertSelfAttentionLayer(config)
self.visn_self_att = LxmertSelfAttentionLayer(config)
self.lang_inter = LxmertIntermediate(config)
self.lang_output = LxmertOutput(config)
self.visn_inter = LxmertIntermediate(config)
self.visn_output = LxmertOutput(config)
def cross_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask, output_x_attentions=False):
lang_att_output = self.visual_attention(lang_input, visual_input, ctx_att_mask=visual_attention_mask, output_attentions=output_x_attentions)
visual_att_output = self.visual_attention(visual_input, lang_input, ctx_att_mask=lang_attention_mask, output_attentions=False)
return (lang_att_output, visual_att_output)
def self_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask):
lang_att_output = self.lang_self_att(lang_input, lang_attention_mask, output_attentions=False)
visual_att_output = self.visn_self_att(visual_input, visual_attention_mask, output_attentions=False)
return (lang_att_output[0], visual_att_output[0])
def output_fc(self, lang_input, visual_input):
lang_inter_output = self.lang_inter(lang_input)
visual_inter_output = self.visn_inter(visual_input)
lang_output = self.lang_output(lang_inter_output, lang_input)
visual_output = self.visn_output(visual_inter_output, visual_input)
return (lang_output, visual_output)
def forward(self, lang_feats, lang_attention_mask, visual_feats, visual_attention_mask, output_attentions=False):
lang_att_output, visual_att_output = self.cross_att(lang_input=lang_feats, lang_attention_mask=lang_attention_mask, visual_input=visual_feats, visual_attention_mask=visual_attention_mask, output_x_attentions=output_attentions)
attention_probs = lang_att_output[1:]
lang_att_output, visual_att_output = self.self_att(lang_att_output[0], lang_attention_mask, visual_att_output[0], visual_attention_mask)
lang_output, visual_output = self.output_fc(lang_att_output, visual_att_output)
return (lang_output, visual_output, attention_probs[0]) if output_attentions else (lang_output, visual_output)
|
class LxmertXLayer(nn.Module):
def __init__(self, config):
pass
def cross_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask, output_x_attentions=False):
pass
def self_att(self, lang_input, lang_attention_mask, visual_input, visual_attention_mask):
pass
def output_fc(self, lang_input, visual_input):
pass
def forward(self, lang_feats, lang_attention_mask, visual_feats, visual_attention_mask, output_attentions=False):
pass
| 6
| 0
| 17
| 1
| 14
| 1
| 1
| 0.1
| 1
| 5
| 4
| 0
| 5
| 7
| 5
| 15
| 89
| 9
| 73
| 38
| 53
| 7
| 30
| 24
| 24
| 2
| 1
| 0
| 6
|
3,552
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/tokenization_lxmert.py
|
transformers.models.lxmert.tokenization_lxmert.LxmertTokenizer
|
import os
from typing import Optional
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
import collections
class LxmertTokenizer(PreTrainedTokenizer):
"""
Construct a Lxmert tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original Lxmert).
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
"""
vocab_files_names = VOCAB_FILES_NAMES
def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, clean_up_tokenization_spaces=True, **kwargs):
if not os.path.isfile(vocab_file):
raise ValueError(f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained model use `tokenizer = LxmertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`")
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
super().__init__(do_lower_case=do_lower_case, do_basic_tokenize=do_basic_tokenize, never_split=never_split, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text, split_special_tokens=False):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens if not split_special_tokens else None):
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = ' '.join(tokens).replace(' ##', '').strip()
return out_string
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. A Lxmert sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None, already_has_special_tokens: bool=False) -> list[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
if token_ids_1 is not None:
return [1] + [0] * len(token_ids_0) + [1] + [0] * len(token_ids_1) + [1]
return [1] + [0] * len(token_ids_0) + [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
else:
vocab_file = (filename_prefix + '-' if filename_prefix else '') + save_directory
with open(vocab_file, 'w', encoding='utf-8') as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive. Please check that the vocabulary is not corrupted!')
index = token_index
writer.write(token + '\n')
index += 1
return (vocab_file,)
|
class LxmertTokenizer(PreTrainedTokenizer):
'''
Construct a Lxmert tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic tokenization before WordPiece.
never_split (`Iterable`, *optional*):
Collection of tokens which will never be split during tokenization. Only has an effect when
`do_basic_tokenize=True`
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original Lxmert).
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
'''
def __init__(self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, clean_up_tokenization_spaces=True, **kwargs):
pass
@property
def do_lower_case(self):
pass
@property
def vocab_size(self):
pass
def get_vocab(self):
pass
def _tokenize(self, text, split_special_tokens=False):
pass
def _convert_token_to_id(self, token):
'''Converts a token (str) in an id using the vocab.'''
pass
def _convert_id_to_token(self, index):
'''Converts an index (integer) in a token (str) using the vocab.'''
pass
def convert_tokens_to_string(self, tokens):
'''Converts a sequence of tokens (string) in a single string.'''
pass
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. A Lxmert sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
'''
pass
def get_special_tokens_mask(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None, already_has_special_tokens: bool=False) -> list[int]:
'''
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
'''
pass
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
pass
| 14
| 6
| 15
| 1
| 10
| 4
| 2
| 0.72
| 1
| 9
| 2
| 0
| 12
| 5
| 12
| 101
| 236
| 29
| 121
| 53
| 85
| 87
| 65
| 29
| 52
| 6
| 3
| 3
| 27
|
3,553
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/lxmert/tokenization_lxmert_fast.py
|
transformers.models.lxmert.tokenization_lxmert_fast.LxmertTokenizerFast
|
from .tokenization_lxmert import LxmertTokenizer
import json
from typing import Optional
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
class LxmertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Lxmert tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
clean_text (`bool`, *optional*, defaults to `True`):
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original Lxmert).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = LxmertTokenizer
def __init__(self, vocab_file=None, tokenizer_file=None, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, **kwargs):
super().__init__(vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs)
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if normalizer_state.get('lowercase', do_lower_case) != do_lower_case or normalizer_state.get('strip_accents', strip_accents) != strip_accents or normalizer_state.get('handle_chinese_chars', tokenize_chinese_chars) != tokenize_chinese_chars:
normalizer_class = getattr(normalizers, normalizer_state.pop('type'))
normalizer_state['lowercase'] = do_lower_case
normalizer_state['strip_accents'] = strip_accents
normalizer_state['handle_chinese_chars'] = tokenize_chinese_chars
self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state)
self.do_lower_case = do_lower_case
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Lxmert sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
if token_ids_1 is not None:
output += token_ids_1 + [self.sep_token_id]
return output
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
|
class LxmertTokenizerFast(PreTrainedTokenizerFast):
'''
Construct a "fast" Lxmert tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
clean_text (`bool`, *optional*, defaults to `True`):
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this
issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original Lxmert).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
'''
def __init__(self, vocab_file=None, tokenizer_file=None, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, **kwargs):
pass
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
'''
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Lxmert sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
'''
pass
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
pass
| 4
| 2
| 24
| 3
| 14
| 7
| 2
| 1.12
| 1
| 4
| 0
| 0
| 4
| 1
| 4
| 92
| 141
| 18
| 58
| 29
| 38
| 65
| 27
| 14
| 22
| 2
| 3
| 1
| 7
|
3,554
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/configuration_m2m_100.py
|
transformers.models.m2m_100.configuration_m2m_100.M2M100Config
|
from ...configuration_utils import PretrainedConfig
class M2M100Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the M2M100
[facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the M2M100 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`M2M100Model`] or
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import M2M100Config, M2M100Model
>>> # Initializing a M2M100 facebook/m2m100_418M style configuration
>>> configuration = M2M100Config()
>>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration
>>> model = M2M100Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = 'm2m_100'
keys_to_ignore_at_inference = ['past_key_values']
attribute_map = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__(self, vocab_size=128112, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.05, decoder_layerdrop=0.05, use_cache=True, is_encoder_decoder=True, activation_function='relu', d_model=1024, dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, **kwargs)
|
class M2M100Config(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the M2M100
[facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the M2M100 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`M2M100Model`] or
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
classifier_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for classifier.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
Example:
```python
>>> from transformers import M2M100Config, M2M100Model
>>> # Initializing a M2M100 facebook/m2m100_418M style configuration
>>> configuration = M2M100Config()
>>> # Initializing a model (with random weights) from the facebook/m2m100_418M style configuration
>>> model = M2M100Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```'''
def __init__(self, vocab_size=128112, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.05, decoder_layerdrop=0.05, use_cache=True, is_encoder_decoder=True, activation_function='relu', d_model=1024, dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=2, scale_embedding=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs):
pass
| 2
| 1
| 55
| 1
| 54
| 1
| 1
| 1.03
| 1
| 1
| 0
| 0
| 1
| 19
| 1
| 1
| 128
| 11
| 58
| 50
| 30
| 60
| 25
| 24
| 23
| 1
| 1
| 0
| 1
|
3,555
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/configuration_m2m_100.py
|
transformers.models.m2m_100.configuration_m2m_100.M2M100OnnxConfig
|
from typing import Any
from ... import PreTrainedTokenizer
from ...onnx.utils import compute_effective_axis_dimension
from ...onnx import OnnxConfig, OnnxSeq2SeqConfigWithPast
from ...utils import is_torch_available, logging
from collections import OrderedDict
from collections.abc import Mapping
class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict([('input_ids', {0: 'batch', 1: 'encoder_sequence'}), ('attention_mask', {0: 'batch', 1: 'encoder_sequence'})])
if self.use_past:
common_inputs['decoder_input_ids'] = {0: 'batch'}
common_inputs['decoder_attention_mask'] = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
common_inputs['decoder_input_ids'] = {0: 'batch', 1: 'decoder_sequence'}
common_inputs['decoder_attention_mask'] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction='inputs')
return common_inputs
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0)
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add)
dummy_input = [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
common_inputs = dict(tokenizer(dummy_input, return_tensors='pt'))
return common_inputs
def _generate_dummy_inputs_for_default_and_seq2seq_lm(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(tokenizer, batch_size, seq_length, is_pair)
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(tokenizer, batch_size, decoder_seq_length, is_pair)
decoder_inputs = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
common_inputs = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
else:
import torch
batch, encoder_seq_length = common_inputs['input_ids'].shape
decoder_seq_length = common_inputs['decoder_input_ids'].shape[1]
num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
encoder_shape = (batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads)
decoder_past_length = decoder_seq_length + 3
decoder_shape = (batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads)
common_inputs['decoder_attention_mask'] = torch.cat([common_inputs['decoder_attention_mask'], torch.ones(batch, decoder_past_length)], dim=1)
common_inputs['past_key_values'] = []
num_encoder_layers, num_decoder_layers = self.num_layers
min_num_layers = min(num_encoder_layers, num_decoder_layers)
max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
remaining_side_name = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(min_num_layers):
common_inputs['past_key_values'].append((torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape)))
shape = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(min_num_layers, max_num_layers):
common_inputs['past_key_values'].append((torch.zeros(shape), torch.zeros(shape)))
return common_inputs
generate_dummy_inputs = _generate_dummy_inputs_for_default_and_seq2seq_lm
|
class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
pass
def _generate_dummy_inputs_for_default_and_seq2seq_lm(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
pass
| 5
| 0
| 37
| 3
| 31
| 3
| 4
| 0.14
| 1
| 8
| 0
| 0
| 3
| 0
| 3
| 3
| 121
| 12
| 96
| 40
| 76
| 13
| 47
| 25
| 42
| 8
| 1
| 2
| 12
|
3,556
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100Attention
|
from .configuration_m2m_100 import M2M100Config
from ...utils.deprecation import deprecate_kwarg
from typing import Callable, Optional, Union
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from torch import nn
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
import torch
class M2M100Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, embed_dim: int, num_heads: int, dropout: float=0.0, is_decoder: bool=False, bias: bool=True, is_causal: bool=False, config: Optional[M2M100Config]=None, layer_idx: Optional[int]=None):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if self.head_dim * num_heads != self.embed_dim:
raise ValueError(f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads}).')
self.scaling = self.head_dim ** (-0.5)
self.is_decoder = is_decoder
self.is_causal = is_causal
self.layer_idx = layer_idx
if layer_idx is None and self.is_decoder:
logger.warning_once(f'Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` when creating this class.')
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, cache_position: Optional[torch.Tensor]=None, **kwargs: Unpack[FlashAttentionKwargs]) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
is_cross_attention = key_value_states is not None
bsz, tgt_len = hidden_states.shape[:-1]
src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
q_input_shape = (bsz, tgt_len, -1, self.head_dim)
kv_input_shape = (bsz, src_len, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
is_updated = False
if past_key_values is not None:
if isinstance(past_key_values, EncoderDecoderCache):
is_updated = past_key_values.is_updated.get(self.layer_idx)
if is_cross_attention:
curr_past_key_value = past_key_values.cross_attention_cache
else:
curr_past_key_value = past_key_values.self_attention_cache
else:
curr_past_key_value = past_key_values
current_states = key_value_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_values is not None and is_updated:
key_states = curr_past_key_value.layers[self.layer_idx].keys
value_states = curr_past_key_value.layers[self.layer_idx].values
else:
key_states = self.k_proj(current_states)
value_states = self.v_proj(current_states)
key_states = key_states.view(*kv_input_shape).transpose(1, 2)
value_states = value_states.view(*kv_input_shape).transpose(1, 2)
if past_key_values is not None:
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = curr_past_key_value.update(key_states, value_states, self.layer_idx, {'cache_position': cache_position})
if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache):
past_key_values.is_updated[self.layer_idx] = True
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != 'eager':
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.dropout, scaling=self.scaling, output_attentions=output_attentions, head_mask=layer_head_mask, **kwargs)
attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
attn_output = self.out_proj(attn_output)
return (attn_output, attn_weights)
|
class M2M100Attention(nn.Module):
'''Multi-headed attention from 'Attention Is All You Need' paper'''
def __init__(self, embed_dim: int, num_heads: int, dropout: float=0.0, is_decoder: bool=False, bias: bool=True, is_causal: bool=False, config: Optional[M2M100Config]=None, layer_idx: Optional[int]=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, cache_position: Optional[torch.Tensor]=None, **kwargs: Unpack[FlashAttentionKwargs]) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
'''Input shape: Batch x Time x Channel'''
pass
| 4
| 2
| 50
| 7
| 35
| 8
| 5
| 0.24
| 1
| 7
| 1
| 2
| 3
| 12
| 3
| 13
| 156
| 23
| 107
| 44
| 86
| 26
| 68
| 27
| 64
| 12
| 1
| 2
| 15
|
3,557
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100Decoder
|
from torch import nn
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
import torch
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...integrations.fsdp import is_fsdp_managed_module
import math
from typing import Callable, Optional, Union
from .configuration_m2m_100 import M2M100Config
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
class M2M100Decoder(M2M100PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`M2M100DecoderLayer`]
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding]=None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = M2M100ScaledWordEmbedding(config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = M2M100SinusoidalPositionalEmbedding(config.max_position_embeddings, config.d_model, self.padding_idx)
self.layers = nn.ModuleList([M2M100DecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
self.post_init()
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None):
"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError('You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time')
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either decoder_input_ids or decoder_inputs_embeds')
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once('`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`...')
use_cache = False
if use_cache and past_key_values is None:
past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
if use_cache and isinstance(past_key_values, tuple):
logger.warning_once('Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.')
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
batch_size, seq_length = inputs_embeds.size()[:-1]
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device)
if attention_mask is None and (not is_torchdynamo_compiling()):
mask_seq_length = past_key_values_length + seq_length
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
self_attn_cache = past_key_values.self_attention_cache if isinstance(past_key_values, EncoderDecoderCache) else past_key_values
attention_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, self_attn_cache)
encoder_attention_mask = self._update_cross_attn_mask(encoder_hidden_states, encoder_attention_mask, input_shape, inputs_embeds)
positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if output_attentions else None
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ['head_mask', 'cross_attn_head_mask']):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(f'The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.')
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = torch.rand([])
skip_the_layer = self.training and dropout_probability < self.layerdrop
if not skip_the_layer or synced_gpus:
layer_outputs = decoder_layer(hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, past_key_values=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position)
hidden_states = layer_outputs[0]
if skip_the_layer:
continue
if output_attentions:
all_self_attns += (layer_outputs[1],)
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple((v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None))
return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions)
|
class M2M100Decoder(M2M100PreTrainedModel):
'''
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`M2M100DecoderLayer`]
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding]=None):
pass
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None):
'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
'''
pass
| 3
| 2
| 137
| 20
| 82
| 36
| 23
| 0.47
| 1
| 13
| 5
| 0
| 2
| 11
| 2
| 3
| 283
| 42
| 164
| 46
| 147
| 77
| 83
| 32
| 80
| 43
| 2
| 3
| 46
|
3,558
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100DecoderLayer
|
from ...utils.deprecation import deprecate_kwarg
from .configuration_m2m_100 import M2M100Config
from ...modeling_layers import GradientCheckpointingLayer
from torch import nn
import torch
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...activations import ACT2FN
class M2M100DecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: M2M100Config, layer_idx: Optional[int]=None):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100Attention(embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, is_causal=True, config=config, layer_idx=layer_idx)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = M2M100Attention(self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config, layer_idx=layer_idx)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, cross_attn_layer_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, output_attentions: Optional[bool]=False, use_cache: Optional[bool]=True, cache_position: Optional[torch.Tensor]=None) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_values (`Cache`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, self_attn_weights = self.self_attn(hidden_states=hidden_states, past_key_values=past_key_values, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, cache_position=cache_position)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.encoder_attn(hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_values=past_key_values, output_attentions=output_attentions)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
|
class M2M100DecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: M2M100Config, layer_idx: Optional[int]=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, cross_attn_layer_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, output_attentions: Optional[bool]=False, use_cache: Optional[bool]=True, cache_position: Optional[torch.Tensor]=None) -> torch.Tensor:
'''
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_values (`Cache`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
'''
pass
| 4
| 1
| 58
| 6
| 40
| 13
| 4
| 0.31
| 1
| 4
| 1
| 0
| 2
| 11
| 2
| 12
| 118
| 12
| 81
| 32
| 67
| 25
| 44
| 21
| 41
| 6
| 1
| 1
| 7
|
3,559
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100Encoder
|
from .configuration_m2m_100 import M2M100Config
from typing import Callable, Optional, Union
import torch
import math
from torch import nn
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...integrations.fsdp import is_fsdp_managed_module
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
class M2M100Encoder(M2M100PreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`M2M100EncoderLayer`].
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding]=None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = M2M100ScaledWordEmbedding(config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = M2M100SinusoidalPositionalEmbedding(config.max_position_embeddings, embed_dim, self.padding_idx)
self.layers = nn.ModuleList([M2M100EncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
self.post_init()
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds')
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
embed_pos = self.embed_positions(input_ids, inputs_embeds)
embed_pos = embed_pos.to(inputs_embeds.device)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
attention_mask = self._update_full_mask(attention_mask, inputs_embeds)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(f'The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.')
synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
dropout_probability = torch.rand([])
skip_the_layer = self.training and dropout_probability < self.layerdrop
if not skip_the_layer or synced_gpus:
layer_outputs = encoder_layer(hidden_states, attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, output_attentions=output_attentions)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple((v for v in [hidden_states, encoder_states, all_attentions] if v is not None))
return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
|
class M2M100Encoder(M2M100PreTrainedModel):
'''
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`M2M100EncoderLayer`].
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, config: M2M100Config, embed_tokens: Optional[nn.Embedding]=None):
pass
def forward(self, input_ids: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
pass
| 3
| 2
| 87
| 15
| 53
| 20
| 15
| 0.44
| 1
| 12
| 5
| 0
| 2
| 11
| 2
| 3
| 185
| 32
| 106
| 35
| 94
| 47
| 67
| 26
| 64
| 27
| 2
| 3
| 30
|
3,560
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100EncoderLayer
|
from torch import nn
import torch
from ...activations import ACT2FN
from .configuration_m2m_100 import M2M100Config
from ...modeling_layers import GradientCheckpointingLayer
class M2M100EncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100Attention(embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, config=config)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, output_attentions: bool=False) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16:
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
return (hidden_states, attn_weights)
|
class M2M100EncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: M2M100Config):
pass
def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_mask: torch.Tensor, output_attentions: bool=False) -> torch.Tensor:
'''
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
'''
pass
| 3
| 1
| 33
| 3
| 25
| 6
| 2
| 0.22
| 1
| 4
| 1
| 0
| 2
| 9
| 2
| 12
| 68
| 7
| 50
| 22
| 41
| 11
| 32
| 16
| 29
| 3
| 1
| 1
| 4
|
3,561
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100ForConditionalGeneration
|
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from .configuration_m2m_100 import M2M100Config
from torch import nn
from ...generation import GenerationMixin
import torch
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
from typing import Callable, Optional, Union
from torch.nn import CrossEntropyLoss
@auto_docstring(custom_intro='\n The M2M100 Model with a language modeling head. Can be used for summarization.\n ')
class M2M100ForConditionalGeneration(M2M100PreTrainedModel, GenerationMixin):
base_model_prefix = 'model'
_tied_weights_keys = ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight', 'lm_head.weight']
def __init__(self, config: M2M100Config):
super().__init__(config)
self.model = M2M100Model(config)
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Union[tuple[torch.Tensor], Seq2SeqLMOutput]:
"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example Translation:
```python
>>> from transformers import AutoTokenizer, M2M100ForConditionalGeneration
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
>>> text_to_translate = "Life is like a box of chocolates"
>>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
>>> # translate to French
>>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("fr"))
>>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position)
lm_logits = self.lm_head(outputs[0])
masked_lm_loss = None
if labels is not None:
labels = labels.to(lm_logits.device)
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return (masked_lm_loss,) + output if masked_lm_loss is not None else output
return Seq2SeqLMOutput(loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions)
|
@auto_docstring(custom_intro='\n The M2M100 Model with a language modeling head. Can be used for summarization.\n ')
class M2M100ForConditionalGeneration(M2M100PreTrainedModel, GenerationMixin):
def __init__(self, config: M2M100Config):
pass
def get_encoder(self):
pass
def get_decoder(self):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Union[tuple[torch.Tensor], Seq2SeqLMOutput]:
'''
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example Translation:
```python
>>> from transformers import AutoTokenizer, M2M100ForConditionalGeneration
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
>>> text_to_translate = "Life is like a box of chocolates"
>>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")
>>> # translate to French
>>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("fr"))
>>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
```
'''
pass
| 7
| 1
| 14
| 1
| 12
| 1
| 2
| 0.1
| 2
| 7
| 3
| 0
| 6
| 2
| 7
| 8
| 112
| 14
| 89
| 39
| 59
| 9
| 37
| 19
| 29
| 7
| 2
| 2
| 14
|
3,562
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100Model
|
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
import math
import torch
from typing import Callable, Optional, Union
from .configuration_m2m_100 import M2M100Config
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
@auto_docstring
class M2M100Model(M2M100PreTrainedModel):
_tied_weights_keys = ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight']
def __init__(self, config: M2M100Config):
super().__init__(config)
padding_idx, vocab_size = (config.pad_token_id, config.vocab_size)
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.shared = M2M100ScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale)
self.encoder = M2M100Encoder(config, self.shared)
self.decoder = M2M100Decoder(config, self.shared)
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
def get_encoder(self):
return self.encoder
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Union[tuple[torch.Tensor], Seq2SeqModelOutput]:
"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
elif return_dict and (not isinstance(encoder_outputs, BaseModelOutput)):
encoder_outputs = BaseModelOutput(last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
|
@auto_docstring
class M2M100Model(M2M100PreTrainedModel):
def __init__(self, config: M2M100Config):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def _tie_weights(self):
pass
def get_encoder(self):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[tuple[tuple[torch.FloatTensor]]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Union[tuple[torch.Tensor], Seq2SeqModelOutput]:
'''
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
M2M100 uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
'''
pass
| 9
| 1
| 15
| 1
| 13
| 0
| 3
| 0.03
| 1
| 9
| 6
| 0
| 7
| 3
| 7
| 8
| 118
| 15
| 100
| 33
| 69
| 3
| 38
| 15
| 30
| 10
| 2
| 1
| 19
|
3,563
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100PreTrainedModel
|
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
import torch
from typing import Callable, Optional, Union
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from torch import nn
from .configuration_m2m_100 import M2M100Config
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
@auto_docstring
class M2M100PreTrainedModel(PreTrainedModel):
config: M2M100Config
base_model_prefix = 'model'
supports_gradient_checkpointing = True
_no_split_modules = ['M2M100EncoderLayer', 'M2M100DecoderLayer']
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = False
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
def _update_full_mask(self, attention_mask: Union[torch.Tensor, None], inputs_embeds: torch.Tensor):
if attention_mask is not None:
if 'flash' in self.config._attn_implementation:
attention_mask = attention_mask if 0 in attention_mask else None
elif self.config._attn_implementation == 'sdpa':
attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
elif self.config._attn_implementation == 'flex_attention':
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
else:
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
return attention_mask
def _update_causal_mask(self, attention_mask: Optional[Union[torch.Tensor, 'BlockMask']], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache):
if self.config._attn_implementation == 'flex_attention':
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
elif attention_mask is None:
attention_mask = make_flex_block_causal_mask(torch.ones(size=(input_tensor.shape[0], input_tensor.shape[1]), device=attention_mask.device))
return attention_mask
if 'flash' in self.config._attn_implementation:
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
if self.config._attn_implementation == 'sdpa' and (not using_compilable_cache):
if AttentionMaskConverter._ignore_causal_mask_sdpa(attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training):
return None
dtype = input_tensor.dtype
sequence_length = input_tensor.shape[1]
if using_compilable_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0])
if self.config._attn_implementation == 'sdpa' and attention_mask is not None and (attention_mask.device.type in ['cuda', 'xpu', 'npu']):
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone()
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype)
return causal_mask
def _update_cross_attn_mask(self, encoder_hidden_states: Union[torch.Tensor, None], encoder_attention_mask: Union[torch.Tensor, None], input_shape: torch.Size, inputs_embeds: torch.Tensor):
if encoder_hidden_states is not None and encoder_attention_mask is not None:
if 'flash' in self.config._attn_implementation:
encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
elif self.config._attn_implementation == 'sdpa':
encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
elif self.config._attn_implementation == 'flex_attention':
if isinstance(encoder_attention_mask, torch.Tensor):
encoder_attention_mask = make_flex_block_causal_mask(encoder_attention_mask, query_length=input_shape[-1], is_causal=False)
else:
encoder_attention_mask = _prepare_4d_attention_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
return encoder_attention_mask
|
@auto_docstring
class M2M100PreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
pass
def _update_full_mask(self, attention_mask: Union[torch.Tensor, None], inputs_embeds: torch.Tensor):
pass
def _update_causal_mask(self, attention_mask: Optional[Union[torch.Tensor, 'BlockMask']], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache):
pass
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs):
'''
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
'''
pass
def _update_cross_attn_mask(self, encoder_hidden_states: Union[torch.Tensor, None], encoder_attention_mask: Union[torch.Tensor, None], input_shape: torch.Size, inputs_embeds: torch.Tensor):
pass
| 8
| 1
| 10
| 0
| 10
| 0
| 5
| 0
| 1
| 0
| 0
| 4
| 1
| 0
| 1
| 1
| 18
| 1
| 17
| 9
| 15
| 0
| 16
| 9
| 14
| 5
| 1
| 2
| 5
|
3,564
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100ScaledWordEmbedding
|
from torch import nn
import torch
from typing import Callable, Optional, Union
class M2M100ScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float]=1.0):
super().__init__(num_embeddings, embedding_dim, padding_idx)
self.embed_scale = embed_scale
def forward(self, input_ids: torch.Tensor):
return super().forward(input_ids) * self.embed_scale
|
class M2M100ScaledWordEmbedding(nn.Embedding):
'''
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
'''
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float]=1.0):
pass
def forward(self, input_ids: torch.Tensor):
pass
| 3
| 1
| 3
| 0
| 3
| 0
| 1
| 0.5
| 1
| 4
| 0
| 0
| 2
| 1
| 2
| 2
| 11
| 2
| 6
| 4
| 3
| 3
| 6
| 4
| 3
| 1
| 1
| 0
| 2
|
3,565
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/modeling_m2m_100.py
|
transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding
|
from torch import nn
from typing import Callable, Optional, Union
import math
import torch
class M2M100SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int]=None):
super().__init__()
self.offset = 2
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int]=None):
emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
if hasattr(self, 'weights'):
emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
self.register_buffer('weights', emb_weights, persistent=False)
@staticmethod
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int]=None):
"""
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb.to(torch.get_default_dtype())
@torch.no_grad()
def forward(self, input_ids: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, past_key_values_length: int=0):
if input_ids is not None:
bsz, seq_len = input_ids.size()
position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(input_ids.device)
else:
bsz, seq_len = inputs_embeds.size()[:-1]
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, self.padding_idx)
max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
if max_pos > self.weights.size(0):
self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
@staticmethod
def create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, padding_idx):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids = torch.arange(padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device)
return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
@staticmethod
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
"""
mask = input_ids.ne(padding_idx).int()
incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
return incremental_indices.long() + padding_idx
|
class M2M100SinusoidalPositionalEmbedding(nn.Module):
'''This module produces sinusoidal positional embeddings of any length.'''
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int]=None):
pass
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int]=None):
pass
@staticmethod
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int]=None):
'''
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
"Attention Is All You Need".
'''
pass
@torch.no_grad()
def forward(self, input_ids: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, past_key_values_length: int=0):
pass
@staticmethod
def create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, padding_idx):
'''
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
'''
pass
@staticmethod
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
'''
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
x: torch.Tensor x:
Returns: torch.Tensor
'''
pass
| 11
| 4
| 13
| 2
| 9
| 3
| 2
| 0.34
| 1
| 3
| 0
| 0
| 4
| 3
| 5
| 15
| 76
| 13
| 47
| 22
| 37
| 16
| 38
| 18
| 32
| 3
| 1
| 1
| 10
|
3,566
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/m2m_100/tokenization_m2m_100.py
|
transformers.models.m2m_100.tokenization_m2m_100.M2M100Tokenizer
|
from typing import Any, Optional, Union
import os
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils.import_utils import requires
from shutil import copyfile
from pathlib import Path
@requires(backends=('sentencepiece',))
class M2M100Tokenizer(PreTrainedTokenizer):
"""
Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
spm_file (`str`):
Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary.
src_lang (`str`, *optional*):
A string representing the source language.
tgt_lang (`str`, *optional*):
A string representing the target language.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
sep_token (`str`, *optional*, defaults to `"</s>"`):
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.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
language_codes (`str`, *optional*, defaults to `"m2m100"`):
What language codes to use. Should be one of `"m2m100"` or `"wmt21"`.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Examples:
```python
>>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
>>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
>>> outputs = model(**model_inputs) # should work
```"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ['input_ids', 'attention_mask']
prefix_tokens: list[int] = []
suffix_tokens: list[int] = []
def __init__(self, vocab_file, spm_file, src_lang=None, tgt_lang=None, bos_token='<s>', eos_token='</s>', sep_token='</s>', pad_token='<pad>', unk_token='<unk>', language_codes='m2m100', sp_model_kwargs: Optional[dict[str, Any]]=None, num_madeup_words=8, **kwargs) -> None:
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.language_codes = language_codes
fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
self.lang_code_to_token = {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}
additional_special_tokens = kwargs.pop('additional_special_tokens', [])
for lang_code in fairseq_language_code:
token = self.get_lang_token(lang_code)
if token not in additional_special_tokens and lang_code not in str(token) not in self.added_tokens_encoder:
additional_special_tokens.append(token)
self.vocab_file = vocab_file
self.encoder = load_json(vocab_file)
self.decoder = {v: k for k, v in self.encoder.items()}
self.spm_file = spm_file
self.sp_model = load_spm(spm_file, self.sp_model_kwargs)
self.encoder_size = len(self.encoder)
self.lang_token_to_id = {self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
self._src_lang = src_lang if src_lang is not None else 'en'
self.tgt_lang = tgt_lang
self.cur_lang_id = self.get_lang_id(self._src_lang)
self.num_madeup_words = num_madeup_words
super().__init__(src_lang=src_lang, tgt_lang=tgt_lang, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, unk_token=unk_token, pad_token=pad_token, language_codes=language_codes, sp_model_kwargs=self.sp_model_kwargs, additional_special_tokens=additional_special_tokens, num_madeup_words=num_madeup_words, **kwargs)
self.set_src_lang_special_tokens(self._src_lang)
@property
def vocab_size(self) -> int:
return len(self.encoder)
def get_vocab(self) -> dict:
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
@property
def src_lang(self) -> str:
return self._src_lang
@src_lang.setter
def src_lang(self, new_src_lang: str) -> None:
self._src_lang = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def _tokenize(self, text: str) -> list[str]:
return self.sp_model.encode(text, out_type=str)
def _convert_token_to_id(self, token):
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(token, self.encoder[self.unk_token])
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the decoder."""
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
current_sub_tokens = []
out_string = ''
for token in tokens:
if token in self.all_special_tokens:
out_string += self.sp_model.decode(current_sub_tokens) + token
current_sub_tokens = []
else:
current_sub_tokens.append(token)
out_string += self.sp_model.decode(current_sub_tokens)
return out_string.strip()
def get_special_tokens_mask(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None, already_has_special_tokens: bool=False) -> list[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1] * len(self.suffix_tokens)
if token_ids_1 is None:
return prefix_ones + [0] * len(token_ids_0) + suffix_ones
return prefix_ones + [0] * len(token_ids_0) + [0] * len(token_ids_1) + suffix_ones
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 MBART sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `X [eos, src_lang_code]`
- `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
separator.
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return self.prefix_tokens + token_ids_0 + self.suffix_tokens
return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
def __getstate__(self) -> dict:
state = self.__dict__.copy()
state['sp_model'] = None
return state
def __setstate__(self, d: dict) -> None:
self.__dict__ = d
if not hasattr(self, 'sp_model_kwargs'):
self.sp_model_kwargs = {}
self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
save_dir = Path(save_directory)
if not save_dir.is_dir():
raise OSError(f'{save_directory} should be a directory')
vocab_save_path = save_dir / ((filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file'])
spm_save_path = save_dir / ((filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file'])
save_json(self.encoder, vocab_save_path)
if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
copyfile(self.spm_file, spm_save_path)
elif not os.path.isfile(self.spm_file):
with open(spm_save_path, 'wb') as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
return (str(vocab_save_path), str(spm_save_path))
def prepare_seq2seq_batch(self, src_texts: list[str], src_lang: str='en', tgt_texts: Optional[list[str]]=None, tgt_lang: str='ro', **kwargs) -> BatchEncoding:
self.src_lang = src_lang
self.tgt_lang = tgt_lang
self.set_src_lang_special_tokens(self.src_lang)
return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
self.src_lang = src_lang
inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs)
tgt_lang_id = self.get_lang_id(tgt_lang)
inputs['forced_bos_token_id'] = tgt_lang_id
return inputs
def _switch_to_input_mode(self):
self.set_src_lang_special_tokens(self.src_lang)
def _switch_to_target_mode(self):
self.set_tgt_lang_special_tokens(self.tgt_lang)
def set_src_lang_special_tokens(self, src_lang: str) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
lang_token = self.get_lang_token(src_lang)
self.cur_lang_id = self.lang_token_to_id[lang_token]
self.prefix_tokens = [self.cur_lang_id]
self.suffix_tokens = [self.eos_token_id]
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
"""Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
lang_token = self.get_lang_token(tgt_lang)
self.cur_lang_id = self.lang_token_to_id[lang_token]
self.prefix_tokens = [self.cur_lang_id]
self.suffix_tokens = [self.eos_token_id]
def get_lang_token(self, lang: str) -> str:
return self.lang_code_to_token[lang]
def get_lang_id(self, lang: str) -> int:
lang_token = self.get_lang_token(lang)
return self.lang_token_to_id[lang_token]
|
@requires(backends=('sentencepiece',))
class M2M100Tokenizer(PreTrainedTokenizer):
'''
Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
spm_file (`str`):
Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
contains the vocabulary.
src_lang (`str`, *optional*):
A string representing the source language.
tgt_lang (`str`, *optional*):
A string representing the target language.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
sep_token (`str`, *optional*, defaults to `"</s>"`):
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.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
language_codes (`str`, *optional*, defaults to `"m2m100"`):
What language codes to use. Should be one of `"m2m100"` or `"wmt21"`.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
Examples:
```python
>>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="en", tgt_lang="ro")
>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
>>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
>>> outputs = model(**model_inputs) # should work
```'''
def __init__(self, vocab_file, spm_file, src_lang=None, tgt_lang=None, bos_token='<s>', eos_token='</s>', sep_token='</s>', pad_token='<pad>', unk_token='<unk>', language_codes='m2m100', sp_model_kwargs: Optional[dict[str, Any]]=None, num_madeup_words=8, **kwargs) -> None:
pass
@property
def vocab_size(self) -> int:
pass
def get_vocab(self) -> dict:
pass
@property
def src_lang(self) -> str:
pass
@src_lang.setter
def src_lang(self) -> str:
pass
def _tokenize(self, text: str) -> list[str]:
pass
def _convert_token_to_id(self, token):
pass
def _convert_id_to_token(self, index: int) -> str:
'''Converts an index (integer) in a token (str) using the decoder.'''
pass
def convert_tokens_to_string(self, tokens):
'''Converts a sequence of tokens (string) in a single string.'''
pass
def get_special_tokens_mask(self, token_ids_0: list[int], token_ids_1: Optional[list[int]]=None, already_has_special_tokens: bool=False) -> list[int]:
'''
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`list[int]`):
List of IDs.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
'''
pass
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 MBART sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `X [eos, src_lang_code]`
- `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
separator.
Args:
token_ids_0 (`list[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`list[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
'''
pass
def __getstate__(self) -> dict:
pass
def __setstate__(self, d: dict) -> None:
pass
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> tuple[str]:
pass
def prepare_seq2seq_batch(self, src_texts: list[str], src_lang: str='en', tgt_texts: Optional[list[str]]=None, tgt_lang: str='ro', **kwargs) -> BatchEncoding:
pass
def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
'''Used by translation pipeline, to prepare inputs for the generate function'''
pass
def _switch_to_input_mode(self):
pass
def _switch_to_target_mode(self):
pass
def set_src_lang_special_tokens(self, src_lang: str) -> None:
'''Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].'''
pass
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
'''Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].'''
pass
def get_lang_token(self, lang: str) -> str:
pass
def get_lang_id(self, lang: str) -> int:
pass
| 27
| 8
| 10
| 1
| 8
| 2
| 2
| 0.47
| 1
| 11
| 1
| 0
| 22
| 17
| 22
| 111
| 317
| 53
| 179
| 93
| 127
| 85
| 127
| 63
| 104
| 6
| 3
| 2
| 40
|
3,567
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/configuration_mamba.py
|
transformers.models.mamba.configuration_mamba.MambaConfig
|
import math
from ...configuration_utils import PretrainedConfig
class MambaConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MAMBA
[state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50280):
Vocabulary size of the MAMBA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MambaModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
state_size (`int`, *optional*, defaults to 16): shape of the state space latents.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the model.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
The id of the beginning of sentence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 0):
The id of the end of sentence token in the vocabulary.
expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
use_bias (`bool`, *optional*, defaults to `False`):
Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
use_conv_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use bias in the convolution layer of the mixer block.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.1):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
residual_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
time_step_scale (`float`, *optional*, defaults to 1.0):
Scale used used to scale `dt_proj.bias`.
time_step_min (`float`, *optional*, defaults to 0.001):
Minimum `time_step` used to bound `dt_proj.bias`.
time_step_max (`float`, *optional*, defaults to 0.1):
Maximum `time_step` used to bound `dt_proj.bias`.
time_step_init_scheme (`float`, *optional*, defaults to `"random"`):
Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`
time_step_floor (`float`, *optional*, defaults to 0.0001):
Minimum clamping value of the `dt_proj.bias` layer initialization.
rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
Whether or not to rescale `out_proj` weights when initializing.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the cache should be used.
use_mambapy (`bool`, *optional*, defaults to `False`):
Determines the fallback strategy during training if the CUDA-based official implementation of Mamba is not available. If `True`, the mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited.
Example:
```python
>>> from transformers import MambaConfig, MambaModel
>>> # Initializing a Mamba configuration
>>> configuration = MambaConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = MambaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = 'mamba'
def __init__(self, vocab_size=50280, hidden_size=768, state_size=16, num_hidden_layers=32, layer_norm_epsilon=1e-05, pad_token_id=0, bos_token_id=0, eos_token_id=0, expand=2, conv_kernel=4, use_bias=False, use_conv_bias=True, hidden_act='silu', initializer_range=0.1, residual_in_fp32=True, time_step_rank='auto', time_step_scale=1.0, time_step_min=0.001, time_step_max=0.1, time_step_init_scheme='random', time_step_floor=0.0001, rescale_prenorm_residual=False, use_cache=True, use_mambapy=False, **kwargs):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.state_size = state_size
self.num_hidden_layers = num_hidden_layers
self.layer_norm_epsilon = layer_norm_epsilon
self.conv_kernel = conv_kernel
self.expand = expand
self.intermediate_size = int(expand * self.hidden_size)
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.use_bias = use_bias
self.use_conv_bias = use_conv_bias
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == 'auto' else time_step_rank
self.time_step_scale = time_step_scale
self.time_step_min = time_step_min
self.time_step_max = time_step_max
self.time_step_init_scheme = time_step_init_scheme
self.time_step_floor = time_step_floor
self.rescale_prenorm_residual = rescale_prenorm_residual
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.use_mambapy = use_mambapy
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)
|
class MambaConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MAMBA
[state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 50280):
Vocabulary size of the MAMBA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MambaModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
state_size (`int`, *optional*, defaults to 16): shape of the state space latents.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the model.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
The id of the beginning of sentence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 0):
The id of the end of sentence token in the vocabulary.
expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
use_bias (`bool`, *optional*, defaults to `False`):
Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
use_conv_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use bias in the convolution layer of the mixer block.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.1):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
residual_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
time_step_scale (`float`, *optional*, defaults to 1.0):
Scale used used to scale `dt_proj.bias`.
time_step_min (`float`, *optional*, defaults to 0.001):
Minimum `time_step` used to bound `dt_proj.bias`.
time_step_max (`float`, *optional*, defaults to 0.1):
Maximum `time_step` used to bound `dt_proj.bias`.
time_step_init_scheme (`float`, *optional*, defaults to `"random"`):
Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`
time_step_floor (`float`, *optional*, defaults to 0.0001):
Minimum clamping value of the `dt_proj.bias` layer initialization.
rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
Whether or not to rescale `out_proj` weights when initializing.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the cache should be used.
use_mambapy (`bool`, *optional*, defaults to `False`):
Determines the fallback strategy during training if the CUDA-based official implementation of Mamba is not available. If `True`, the mamba.py implementation is used. If `False`, the naive and slower implementation is used. Consider switching to the naive version if memory is limited.
Example:
```python
>>> from transformers import MambaConfig, MambaModel
>>> # Initializing a Mamba configuration
>>> configuration = MambaConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = MambaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```'''
def __init__(self, vocab_size=50280, hidden_size=768, state_size=16, num_hidden_layers=32, layer_norm_epsilon=1e-05, pad_token_id=0, bos_token_id=0, eos_token_id=0, expand=2, conv_kernel=4, use_bias=False, use_conv_bias=True, hidden_act='silu', initializer_range=0.1, residual_in_fp32=True, time_step_rank='auto', time_step_scale=1.0, time_step_min=0.001, time_step_max=0.1, time_step_init_scheme='random', time_step_floor=0.0001, rescale_prenorm_residual=False, use_cache=True, use_mambapy=False, **kwargs):
pass
| 2
| 1
| 55
| 1
| 54
| 0
| 2
| 1.14
| 1
| 2
| 0
| 0
| 1
| 25
| 1
| 1
| 132
| 12
| 56
| 55
| 27
| 64
| 29
| 28
| 27
| 2
| 1
| 0
| 2
|
3,568
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaBlock
|
from typing import Any, Optional, Union
import torch
from ...modeling_layers import GradientCheckpointingLayer
class MambaBlock(GradientCheckpointingLayer):
def __init__(self, config, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.residual_in_fp32 = config.residual_in_fp32
self.norm = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.mixer = MambaMixer(config, layer_idx=layer_idx)
def forward(self, hidden_states, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
residual = hidden_states
hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
if self.residual_in_fp32:
residual = residual.to(torch.float32)
hidden_states = self.mixer(hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask)
hidden_states = residual + hidden_states
return hidden_states
|
class MambaBlock(GradientCheckpointingLayer):
def __init__(self, config, layer_idx):
pass
def forward(self, hidden_states, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
pass
| 3
| 0
| 12
| 1
| 12
| 0
| 2
| 0
| 1
| 4
| 3
| 0
| 2
| 5
| 2
| 12
| 26
| 2
| 24
| 15
| 15
| 0
| 16
| 9
| 13
| 2
| 1
| 1
| 3
|
3,569
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaForCausalLM
|
from ...utils import ModelOutput, auto_docstring, logging
import torch
from torch.nn import CrossEntropyLoss
from torch import nn
from ...generation import GenerationMixin
from typing import Any, Optional, Union
@auto_docstring(custom_intro='\n The MAMBA Model transformer with a language modeling head on top (linear layer with weights tied to the input\n embeddings).\n ')
class MambaForCausalLM(MambaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ['lm_head.weight']
def __init__(self, config):
super().__init__(config)
self.backbone = MambaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.backbone.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
return self.backbone.set_input_embeddings(new_embeddings)
def _update_model_kwargs_for_generation(self, outputs: ModelOutput, model_kwargs: dict[str, Any], num_new_tokens: int=1, **kwargs) -> dict[str, Any]:
model_kwargs['cache_params'] = outputs.get('cache_params', None)
if model_kwargs.get('use_cache', True) and 'cache_position' in model_kwargs and (model_kwargs['cache_position'] is not None):
model_kwargs['cache_position'] = model_kwargs['cache_position'][-1:] + num_new_tokens
if 'attention_mask' in model_kwargs:
attention_mask = model_kwargs['attention_mask']
model_kwargs['attention_mask'] = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)
return model_kwargs
def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, use_cache=None, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None, **kwargs):
model_inputs = {'input_ids': input_ids.contiguous()}
if use_cache and cache_params is None:
cache_position = torch.arange(0, self.backbone.config.conv_kernel, device=input_ids.device)
if inputs_embeds is not None:
model_inputs = {'inputs_embeds': inputs_embeds}
max_batch_size = inputs_embeds.size(0)
else:
max_batch_size = input_ids.size(0)
cache_params = MambaCache(self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype)
if use_cache and cache_position[0] > 0:
model_inputs['input_ids'] = input_ids[:, -1].unsqueeze(-1).contiguous()
attention_mask = None
if not use_cache and inputs_embeds is not None:
model_inputs = {'inputs_embeds': inputs_embeds}
model_inputs.update({'cache_params': cache_params, 'use_cache': use_cache, 'cache_position': cache_position, 'attention_mask': attention_mask})
for key, value in kwargs.items():
if key not in model_inputs:
model_inputs[key] = value
return model_inputs
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, cache_params: Optional[MambaCache]=None, labels: Optional[torch.LongTensor]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, use_cache: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None, **kwargs) -> Union[tuple, MambaCausalLMOutput]:
"""
cache_params (`MambaCache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
mamba_outputs = self.backbone(input_ids, cache_params=cache_params, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, use_cache=use_cache, cache_position=cache_position, attention_mask=attention_mask)
hidden_states = mamba_outputs[0]
logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
loss = None
if labels is not None:
labels = labels.to(logits.device)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (logits,) + mamba_outputs[1:]
return (loss,) + output if loss is not None else output
return MambaCausalLMOutput(loss=loss, logits=logits, cache_params=mamba_outputs.cache_params, hidden_states=mamba_outputs.hidden_states)
|
@auto_docstring(custom_intro='\n The MAMBA Model transformer with a language modeling head on top (linear layer with weights tied to the input\n embeddings).\n ')
class MambaForCausalLM(MambaPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, new_embeddings):
pass
def _update_model_kwargs_for_generation(self, outputs: ModelOutput, model_kwargs: dict[str, Any], num_new_tokens: int=1, **kwargs) -> dict[str, Any]:
pass
def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, use_cache=None, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None, **kwargs):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, cache_params: Optional[MambaCache]=None, labels: Optional[torch.LongTensor]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, use_cache: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None, **kwargs) -> Union[tuple, MambaCausalLMOutput]:
'''
cache_params (`MambaCache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
'''
pass
| 9
| 1
| 17
| 2
| 13
| 2
| 2
| 0.15
| 2
| 10
| 3
| 0
| 8
| 2
| 8
| 9
| 151
| 20
| 115
| 46
| 77
| 17
| 53
| 22
| 44
| 6
| 2
| 3
| 19
|
3,570
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaMixer
|
from typing import Any, Optional, Union
import torch
from ...utils.import_utils import is_causal_conv1d_available, is_kernels_available, is_mamba_ssm_available, is_mambapy_available
from .configuration_mamba import MambaConfig
from torch import nn
from ...activations import ACT2FN
class MambaMixer(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
and is why Mamba is called **selective** state spaces)
"""
def __init__(self, config: MambaConfig, layer_idx: int):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.ssm_state_size = config.state_size
self.conv_kernel_size = config.conv_kernel
self.intermediate_size = config.intermediate_size
self.time_step_rank = int(config.time_step_rank)
self.layer_idx = layer_idx
self.use_conv_bias = config.use_conv_bias
self.conv1d = nn.Conv1d(in_channels=self.intermediate_size, out_channels=self.intermediate_size, bias=config.use_conv_bias, kernel_size=config.conv_kernel, groups=self.intermediate_size, padding=config.conv_kernel - 1)
self.activation = config.hidden_act
self.act = ACT2FN[config.hidden_act]
self.use_mambapy = config.use_mambapy
self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=config.use_bias)
self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :]
A = A.expand(self.intermediate_size, -1).contiguous()
self.A_log = nn.Parameter(torch.log(A))
self.D = nn.Parameter(torch.ones(self.intermediate_size))
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
self.use_bias = config.use_bias
self.warn_slow_implementation()
def warn_slow_implementation(self):
causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
is_fast_path_available = all((selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn))
if not is_fast_path_available:
if self.use_mambapy:
if is_mambapy_available():
logger.warning_once('The fast path is not available because one of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)` is None. Falling back to the mamba.py backend. To install follow https://github.com/state-spaces/mamba/#installation for mamba-ssm and install the kernels library using `pip install kernels` or https://github.com/Dao-AILab/causal-conv1d for causal-conv1d')
else:
raise ImportError('use_mambapy is set to True but the mambapy package is not installed. To install it follow https://github.com/alxndrTL/mamba.py.')
else:
logger.warning_once('The fast path is not available because one of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)` is None. Falling back to the sequential implementation of Mamba, as use_mambapy is set to False. To install follow https://github.com/state-spaces/mamba/#installation for mamba-ssm and install the kernels library using `pip install kernels` or https://github.com/Dao-AILab/causal-conv1d for causal-conv1d. For the mamba.py backend, follow https://github.com/alxndrTL/mamba.py.')
def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
projected_states = self.in_proj(hidden_states).transpose(1, 2)
if self.training and cache_params is None:
contextualized_states = mamba_inner_fn(projected_states, self.conv1d.weight, self.conv1d.bias if self.use_conv_bias else None, self.x_proj.weight, self.dt_proj.weight, self.out_proj.weight, self.out_proj.bias.float() if self.use_bias else None, -torch.exp(self.A_log.float()), None, None, self.D.float(), delta_bias=self.dt_proj.bias.float(), delta_softplus=True)
else:
causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
hidden_states, gate = projected_states.chunk(2, dim=1)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
if cache_params is not None and cache_position[0] > 0:
hidden_states = causal_conv1d_update(hidden_states.squeeze(-1), cache_params.conv_states[self.layer_idx], conv_weights, self.conv1d.bias, self.activation)
hidden_states = hidden_states.unsqueeze(-1)
else:
if cache_params is not None:
conv_states = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
cache_params.update_conv_state(self.layer_idx, conv_states, cache_position)
hidden_states = causal_conv1d_fn(hidden_states, conv_weights, self.conv1d.bias, activation=self.activation)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
time_step, B, C = torch.split(ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1)
discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)
A = -torch.exp(self.A_log.float())
time_proj_bias = self.dt_proj.bias.float() if hasattr(self.dt_proj, 'bias') else None
if cache_params is not None and cache_position[0] > 0:
scan_outputs = selective_state_update(cache_params.ssm_states[self.layer_idx], hidden_states[..., 0], discrete_time_step[..., 0], A, B[:, 0], C[:, 0], self.D, gate[..., 0], time_proj_bias, dt_softplus=True).unsqueeze(-1)
else:
scan_outputs, ssm_state = selective_scan_fn(hidden_states, discrete_time_step, A, B.transpose(1, 2), C.transpose(1, 2), self.D.float(), gate, time_proj_bias, delta_softplus=True, return_last_state=True)
if ssm_state is not None and cache_params is not None:
cache_params.update_ssm_state(self.layer_idx, ssm_state)
contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
return contextualized_states
def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
batch_size, seq_len, _ = input_states.shape
dtype = input_states.dtype
projected_states = self.in_proj(input_states).transpose(1, 2)
hidden_states, gate = projected_states.chunk(2, dim=1)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
if cache_params is not None:
ssm_state = cache_params.ssm_states[self.layer_idx].clone()
ssm_state = ssm_state.to(hidden_states.device)
if cache_position.shape[0] == self.conv_kernel_size:
conv_state = nn.functional.pad(hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0))
cache_params.update_conv_state(self.layer_idx, conv_state, cache_position)
hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
else:
conv_state = cache_params.update_conv_state(self.layer_idx, hidden_states, cache_position)
conv_state = conv_state.to(self.conv1d.weight.device)
hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
if self.use_conv_bias:
hidden_states += self.conv1d.bias
hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1)
else:
ssm_state = torch.zeros((batch_size, self.intermediate_size, self.ssm_state_size), device=hidden_states.device, dtype=dtype)
hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len])
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.unsqueeze(1)
ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
time_step, B, C = torch.split(ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1)
discrete_time_step = self.dt_proj(time_step)
discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2)
A = -torch.exp(self.A_log.float())
discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None])
discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float()
deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
if self.use_mambapy and self.training and (cache_params is None):
hs = pscan(discrete_A.transpose(1, 2), deltaB_u.transpose(1, 2))
scan_output = (hs @ C.unsqueeze(-1)).squeeze(3).transpose(1, 2)
scan_output = scan_output + hidden_states * self.D[None, :, None]
scan_output = scan_output * self.act(gate)
else:
scan_outputs = []
for i in range(seq_len):
ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :]
scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1))
scan_outputs.append(scan_output[:, :, 0])
scan_output = torch.stack(scan_outputs, dim=-1)
scan_output = scan_output + hidden_states * self.D[None, :, None]
scan_output = scan_output * self.act(gate)
if cache_params is not None:
cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
contextualized_states = self.out_proj(scan_output.transpose(1, 2))
return contextualized_states
def forward(self, hidden_states, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
causal_conv1d_update, causal_conv1d_fn = _lazy_load_causal_conv1d()
is_fast_path_available = all((selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn))
if is_fast_path_available and 'cuda' in self.x_proj.weight.device.type and (not torch._dynamo.is_compiling()):
return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
return self.slow_forward(hidden_states, cache_params, cache_position, attention_mask)
|
class MambaMixer(nn.Module):
'''
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
and is why Mamba is called **selective** state spaces)
'''
def __init__(self, config: MambaConfig, layer_idx: int):
pass
def warn_slow_implementation(self):
pass
def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
pass
def slow_forward(self, input_states, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
pass
def forward(self, hidden_states, cache_params: Optional[MambaCache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None):
pass
| 6
| 1
| 62
| 6
| 51
| 10
| 7
| 0.24
| 1
| 7
| 2
| 0
| 4
| 19
| 4
| 14
| 261
| 28
| 204
| 67
| 187
| 49
| 111
| 55
| 106
| 11
| 1
| 3
| 26
|
3,571
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaModel
|
import torch
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
from typing import Any, Optional, Union
@auto_docstring
class MambaModel(MambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([MambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.norm_f = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self._register_load_state_dict_pre_hook(self.load_hook)
self.post_init()
def load_hook(self, state_dict, prefix, *args):
for k in state_dict:
if 'embedding.' in k:
state_dict[k.replace('embedding.', 'embeddings.')] = state_dict.pop(k)
break
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = new_embeddings
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, cache_params: Optional[MambaCache]=None, use_cache: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None) -> Union[tuple, MambaOutput]:
"""
cache_params (`MambaCache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
"""
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
use_cache = use_cache if use_cache is not None else self.config.use_cache if not self.training else False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError('You must specify exactly one of input_ids or inputs_embeds')
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
if self.gradient_checkpointing and self.training and use_cache:
use_cache = False
if use_cache:
if cache_params is None:
cache_params = MambaCache(self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype)
cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device)
elif cache_position is None:
raise ValueError("You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will be initialized for you automatically")
else:
cache_params = None
hidden_states = inputs_embeds
all_hidden_states = () if output_hidden_states else None
for mixer_block in self.layers:
hidden_states = mixer_block(hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states = self.norm_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple((v for v in [hidden_states, cache_params, all_hidden_states] if v is not None))
return MambaOutput(last_hidden_state=hidden_states, cache_params=cache_params if use_cache else None, hidden_states=all_hidden_states)
|
@auto_docstring
class MambaModel(MambaPreTrainedModel):
def __init__(self, config):
pass
def load_hook(self, state_dict, prefix, *args):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, new_embeddings):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, cache_params: Optional[MambaCache]=None, use_cache: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None) -> Union[tuple, MambaOutput]:
'''
cache_params (`MambaCache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
'''
pass
| 8
| 1
| 19
| 2
| 16
| 1
| 5
| 0.06
| 1
| 9
| 4
| 0
| 5
| 5
| 5
| 6
| 106
| 16
| 86
| 26
| 64
| 5
| 48
| 14
| 42
| 18
| 2
| 2
| 24
|
3,572
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaOutput
|
from typing import Any, Optional, Union
import torch
from dataclasses import dataclass
from ...utils import ModelOutput, auto_docstring, logging
@dataclass
@auto_docstring(custom_intro='\n Class for the MAMBA model outputs.\n ')
class MambaOutput(ModelOutput):
"""
cache_params (`MambaCache`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
Includes both the State space model state matrices after the selective scan, and the Convolutional states
"""
last_hidden_state: Optional[torch.FloatTensor] = None
cache_params: Optional[MambaCache] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Class for the MAMBA model outputs.\n ')
class MambaOutput(ModelOutput):
'''
cache_params (`MambaCache`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
Includes both the State space model state matrices after the selective scan, and the Convolutional states
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 22
| 4
| 4
| 4
| 3
| 14
| 4
| 4
| 3
| 0
| 1
| 0
| 0
|
3,573
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaPreTrainedModel
|
from .configuration_mamba import MambaConfig
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
import math
import torch
from ...modeling_utils import PreTrainedModel
@auto_docstring
class MambaPreTrainedModel(PreTrainedModel):
config: MambaConfig
base_model_prefix = 'backbone'
_no_split_modules = ['MambaBlock', 'MambaMixer']
supports_gradient_checkpointing = True
_is_stateful = True
def _init_weights(self, module):
"""Initialize the weights."""
std = self.config.initializer_range
if isinstance(module, MambaMixer):
A = torch.arange(1, module.ssm_state_size + 1, dtype=torch.float32)[None, :]
A = A.expand(module.intermediate_size, -1).contiguous()
module.A_log.copy_(torch.log(A))
module.D.data.fill_(1.0)
dt_init_std = self.config.time_step_rank ** (-0.5) * self.config.time_step_scale
if self.config.time_step_init_scheme == 'constant':
nn.init.constant_(module.dt_proj.weight, dt_init_std)
elif self.config.time_step_init_scheme == 'random':
nn.init.uniform_(module.dt_proj.weight, -dt_init_std, dt_init_std)
dt = torch.exp(torch.rand(self.config.intermediate_size) * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + math.log(self.config.time_step_min)).clamp(min=self.config.time_step_floor)
inv_dt = dt + torch.log(-torch.expm1(-dt))
module.dt_proj.bias.copy_(inv_dt)
module.dt_proj.bias._no_reinit = True
nn.init.kaiming_uniform_(module.conv1d.weight, a=math.sqrt(5))
if module.conv1d.bias is not None:
if not getattr(module.conv1d.bias, '_no_reinit', False):
nn.init.zeros_(module.conv1d.bias)
nn.init.kaiming_uniform_(module.out_proj.weight, a=math.sqrt(5))
if self.config.rescale_prenorm_residual:
p = module.out_proj.weight
p /= math.sqrt(self.config.num_hidden_layers)
if isinstance(module, nn.Linear):
if not getattr(module.weight, '_no_reinit', False):
nn.init.normal_(module.weight, std=std)
if module.bias is not None:
if not getattr(module.bias, '_no_reinit', False):
nn.init.zeros_(module.bias)
elif isinstance(module, MambaRMSNorm):
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=std)
|
@auto_docstring
class MambaPreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights.'''
pass
| 3
| 1
| 46
| 4
| 30
| 12
| 11
| 0.44
| 1
| 1
| 1
| 2
| 1
| 0
| 1
| 1
| 58
| 6
| 36
| 11
| 34
| 16
| 30
| 11
| 28
| 11
| 1
| 4
| 11
|
3,574
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba/modeling_mamba.py
|
transformers.models.mamba.modeling_mamba.MambaRMSNorm
|
from torch import nn
import torch
class MambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
"""
MambaRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f'{self.weight.shape[0]}, eps={self.variance_epsilon}'
|
class MambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
'''
MambaRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
'''
pass
def forward(self, hidden_states):
pass
def extra_repr(self):
pass
| 4
| 1
| 5
| 0
| 4
| 1
| 1
| 0.23
| 1
| 1
| 0
| 0
| 3
| 2
| 3
| 13
| 18
| 2
| 13
| 8
| 9
| 3
| 13
| 8
| 9
| 1
| 1
| 0
| 3
|
3,575
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/configuration_mamba2.py
|
transformers.models.mamba2.configuration_mamba2.Mamba2Config
|
from ...configuration_utils import PretrainedConfig
import math
class Mamba2Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MAMBA2
[state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_heads (`int`, *optional*, defaults to 128):
Number of heads for the evolution matrices of mamba 2.
head_dim (`int`, *optional*, defaults to 64):
Dimension of each head.
vocab_size (`int`, *optional*, defaults to 32768):
Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Mamba2Model`].
hidden_size (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
state_size (`int`, *optional*, defaults to 128): shape of the state space latents.
num_hidden_layers (`int`, *optional*, defaults to 64):
Number of hidden layers in the model.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
The id of the beginning of sentence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the end of sentence token in the vocabulary.
expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
n_groups (`int`, *optional*, defaults to 8):
Number of groups for the evolution matrices of mamba 2.
use_bias (`bool`, *optional*, defaults to `False`):
Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
use_conv_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use bias in the convolution layer of the mixer block.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.1):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
residual_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
time_step_min (`float`, *optional*, defaults to 0.001):
Minimum `time_step` used to bound `dt_proj.bias`.
time_step_max (`float`, *optional*, defaults to 0.1):
Maximum `time_step` used to bound `dt_proj.bias`.
time_step_floor (`float`, *optional*, defaults to 0.0001):
Minimum clamping value of the `dt_proj.bias` layer initialization.
time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`):
Accepted range of time step values.
rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
Whether or not to rescale `out_proj` weights when initializing.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the cache should be used.
rms_norm (`bool`, *optional*, defaults to `True`):
Whether to use RMS norm or not.
chunk_size (`int`, *optional*, defaults to 256):
Size of the chunks that will comprise the sequence.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie word embeddings or not.
Example:
```python
>>> from transformers import Mamba2Config, Mamba2Model
>>> # Initializing a Mamba2 configuration
>>> configuration = Mamba2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = Mamba2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = 'mamba2'
def __init__(self, num_heads=128, head_dim=64, vocab_size=32768, hidden_size=4096, state_size=128, num_hidden_layers=64, layer_norm_epsilon=1e-05, pad_token_id=1, bos_token_id=0, eos_token_id=2, expand=2, conv_kernel=4, n_groups=8, use_bias=False, use_conv_bias=True, hidden_act='silu', initializer_range=0.1, residual_in_fp32=True, time_step_rank='auto', time_step_min=0.001, time_step_max=0.1, time_step_floor=0.0001, time_step_limit=(0.0, float('inf')), rescale_prenorm_residual=False, use_cache=True, rms_norm=True, chunk_size=256, tie_word_embeddings=False, **kwargs):
if hidden_size * expand != num_heads * head_dim:
raise ValueError(f'Inconsistent configuration: hidden_size * expand ({hidden_size * expand}) must equal num_heads * head_dim ({num_heads * head_dim}).')
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.state_size = state_size
self.num_hidden_layers = num_hidden_layers
self.layer_norm_epsilon = layer_norm_epsilon
self.conv_kernel = conv_kernel
self.expand = expand
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.use_bias = use_bias
self.use_conv_bias = use_conv_bias
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == 'auto' else time_step_rank
self.time_step_min = time_step_min
self.time_step_max = time_step_max
self.time_step_floor = time_step_floor
self.rescale_prenorm_residual = rescale_prenorm_residual
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.n_groups = n_groups
self.num_heads = num_heads
self.head_dim = head_dim
self.rms_norm = rms_norm
self.state_size = state_size
self.chunk_size = chunk_size
self.time_step_limit = time_step_limit
self.tie_word_embeddings = tie_word_embeddings
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)
|
class Mamba2Config(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the MAMBA2
[state-spaces/mamba2-2.8b](https://huggingface.co/state-spaces/mamba2-2.8b) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_heads (`int`, *optional*, defaults to 128):
Number of heads for the evolution matrices of mamba 2.
head_dim (`int`, *optional*, defaults to 64):
Dimension of each head.
vocab_size (`int`, *optional*, defaults to 32768):
Vocabulary size of the MAMBA2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Mamba2Model`].
hidden_size (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
state_size (`int`, *optional*, defaults to 128): shape of the state space latents.
num_hidden_layers (`int`, *optional*, defaults to 64):
Number of hidden layers in the model.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
The id of the beginning of sentence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the end of sentence token in the vocabulary.
expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
n_groups (`int`, *optional*, defaults to 8):
Number of groups for the evolution matrices of mamba 2.
use_bias (`bool`, *optional*, defaults to `False`):
Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
use_conv_bias (`bool`, *optional*, defaults to `True`):
Whether or not to use bias in the convolution layer of the mixer block.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
initializer_range (`float`, *optional*, defaults to 0.1):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
residual_in_fp32 (`bool`, *optional*, defaults to `True`):
Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
time_step_min (`float`, *optional*, defaults to 0.001):
Minimum `time_step` used to bound `dt_proj.bias`.
time_step_max (`float`, *optional*, defaults to 0.1):
Maximum `time_step` used to bound `dt_proj.bias`.
time_step_floor (`float`, *optional*, defaults to 0.0001):
Minimum clamping value of the `dt_proj.bias` layer initialization.
time_step_limit (`tuple`, *optional*, defaults to `(0.0, inf)`):
Accepted range of time step values.
rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
Whether or not to rescale `out_proj` weights when initializing.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the cache should be used.
rms_norm (`bool`, *optional*, defaults to `True`):
Whether to use RMS norm or not.
chunk_size (`int`, *optional*, defaults to 256):
Size of the chunks that will comprise the sequence.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie word embeddings or not.
Example:
```python
>>> from transformers import Mamba2Config, Mamba2Model
>>> # Initializing a Mamba2 configuration
>>> configuration = Mamba2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = Mamba2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```'''
def __init__(self, num_heads=128, head_dim=64, vocab_size=32768, hidden_size=4096, state_size=128, num_hidden_layers=64, layer_norm_epsilon=1e-05, pad_token_id=1, bos_token_id=0, eos_token_id=2, expand=2, conv_kernel=4, n_groups=8, use_bias=False, use_conv_bias=True, hidden_act='silu', initializer_range=0.1, residual_in_fp32=True, time_step_rank='auto', time_step_min=0.001, time_step_max=0.1, time_step_floor=0.0001, time_step_limit=(0.0, float('inf')), rescale_prenorm_residual=False, use_cache=True, rms_norm=True, chunk_size=256, tie_word_embeddings=False, **kwargs):
pass
| 2
| 1
| 70
| 2
| 68
| 0
| 2
| 1.03
| 1
| 2
| 0
| 0
| 1
| 28
| 1
| 1
| 155
| 13
| 70
| 62
| 37
| 72
| 33
| 31
| 31
| 2
| 1
| 0
| 2
|
3,576
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2Block
|
from typing import Optional, Union
from ...modeling_layers import GradientCheckpointingLayer
import torch
class Mamba2Block(GradientCheckpointingLayer):
def __init__(self, config, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.residual_in_fp32 = config.residual_in_fp32
self.norm = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.mixer = Mamba2Mixer(config, layer_idx=layer_idx)
def forward(self, hidden_states, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
residual = hidden_states
hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
if self.residual_in_fp32:
residual = residual.to(torch.float32)
hidden_states = self.mixer(hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask)
hidden_states = residual + hidden_states
return hidden_states
|
class Mamba2Block(GradientCheckpointingLayer):
def __init__(self, config, layer_idx):
pass
def forward(self, hidden_states, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
pass
| 3
| 0
| 12
| 1
| 12
| 0
| 2
| 0
| 1
| 5
| 3
| 0
| 2
| 5
| 2
| 12
| 26
| 2
| 24
| 15
| 15
| 0
| 16
| 9
| 13
| 2
| 1
| 1
| 3
|
3,577
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2Cache
|
from typing import Optional, Union
import torch
from .configuration_mamba2 import Mamba2Config
class Mamba2Cache:
"""
Arguments:
config: Mamba2Config
batch_size: int
dtype: torch.dtype
device: torch.device
Attributes:
dtype: (`torch.dtype`):
The default `dtype` used to initializing the cache.
conv_kernel_size: (`int`):
Model's convolution kernel size taken from config.
n_groups: (`int`):
Model's number of groups taken from the config - similar to tensor parallel in Transformer.
state_size: (`int`):
Model's SSM state size taken from config.
num_heads: (`int`):
The number of heads used in the linear attention / SSM.
head_dim: (`int`):
The respective dimension of the heads used in the linear attention / SSM.
intermediate_size: (`int`):
Model's intermediate_size based on (expand * hidden_dim) from config.
conv_states: (`torch.Tensor`):
A tensor of shape `[num_layers, batch_size, conv_kernel_size, intermediate_size + 2 * n_groups * state_size]` that holds convolutional states.
ssm_states: (`torch.Tensor`):
A tensor of shape `[num_layers, batch_size, num_heads, head_dim, state_size]` that holds ssm states.
"""
def __init__(self, config: Mamba2Config, batch_size: int, dtype: torch.dtype=torch.float16, device: Optional[str]=None):
self.dtype = dtype
self.conv_kernel_size = config.conv_kernel
self.n_groups = config.n_groups
self.state_size = config.state_size
self.num_heads = config.num_heads
self.head_dim = config.head_dim
self.intermediate_size = int(config.expand * config.hidden_size)
self.conv_states = torch.zeros(config.num_hidden_layers, batch_size, self.intermediate_size + 2 * self.n_groups * self.state_size, self.conv_kernel_size, device=device, dtype=dtype)
self.ssm_states = torch.zeros(config.num_hidden_layers, batch_size, self.num_heads, self.head_dim, self.state_size, device=device, dtype=dtype)
def update_conv_state(self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool=False) -> torch.Tensor:
if cache_init:
self.conv_states[layer_idx] = new_conv_state.to(self.conv_states.device)
else:
self.conv_states[layer_idx] = self.conv_states[layer_idx].roll(shifts=-1, dims=-1)
self.conv_states[layer_idx][:, :, -1] = new_conv_state[:, 0, :].to(self.conv_states.device)
return self.conv_states[layer_idx]
def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device)
return self.ssm_states[layer_idx]
def reset(self):
self.conv_states.zero_()
self.ssm_states.zero_()
|
class Mamba2Cache:
'''
Arguments:
config: Mamba2Config
batch_size: int
dtype: torch.dtype
device: torch.device
Attributes:
dtype: (`torch.dtype`):
The default `dtype` used to initializing the cache.
conv_kernel_size: (`int`):
Model's convolution kernel size taken from config.
n_groups: (`int`):
Model's number of groups taken from the config - similar to tensor parallel in Transformer.
state_size: (`int`):
Model's SSM state size taken from config.
num_heads: (`int`):
The number of heads used in the linear attention / SSM.
head_dim: (`int`):
The respective dimension of the heads used in the linear attention / SSM.
intermediate_size: (`int`):
Model's intermediate_size based on (expand * hidden_dim) from config.
conv_states: (`torch.Tensor`):
A tensor of shape `[num_layers, batch_size, conv_kernel_size, intermediate_size + 2 * n_groups * state_size]` that holds convolutional states.
ssm_states: (`torch.Tensor`):
A tensor of shape `[num_layers, batch_size, num_heads, head_dim, state_size]` that holds ssm states.
'''
def __init__(self, config: Mamba2Config, batch_size: int, dtype: torch.dtype=torch.float16, device: Optional[str]=None):
pass
def update_conv_state(self, layer_idx: int, new_conv_state: torch.Tensor, cache_init: bool=False) -> torch.Tensor:
pass
def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
pass
def reset(self):
pass
| 5
| 1
| 11
| 0
| 11
| 0
| 1
| 0.6
| 0
| 5
| 1
| 0
| 4
| 9
| 4
| 4
| 75
| 6
| 43
| 18
| 34
| 26
| 23
| 14
| 18
| 2
| 0
| 1
| 5
|
3,578
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2ForCausalLM
|
import torch
from ...utils import ModelOutput, auto_docstring, logging
from typing import Optional, Union
from ...generation import GenerationMixin
from torch import nn
@auto_docstring(custom_intro='\n The MAMBA2 Model transformer with a language modeling head on top (linear layer with weights not tied to the input\n embeddings).\n ')
class Mamba2ForCausalLM(Mamba2PreTrainedModel, GenerationMixin):
_tied_weights_keys = []
def __init__(self, config):
super().__init__(config)
self.backbone = Mamba2Model(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.backbone.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
return self.backbone.set_input_embeddings(new_embeddings)
def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, use_cache=None, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs):
model_inputs = {'input_ids': input_ids.contiguous()}
if use_cache and cache_params is None:
cache_position = torch.arange(0, self.backbone.config.conv_kernel, device=input_ids.device)
if inputs_embeds is not None:
model_inputs = {'inputs_embeds': inputs_embeds}
max_batch_size = inputs_embeds.size(0)
else:
max_batch_size = input_ids.size(0)
cache_params = Mamba2Cache(self.backbone.config, max_batch_size, device=self.device, dtype=self.dtype)
if use_cache and cache_position[0] > 0:
model_inputs['input_ids'] = input_ids[:, -1].unsqueeze(-1).contiguous()
attention_mask = None
if not use_cache and inputs_embeds is not None:
model_inputs = {'inputs_embeds': inputs_embeds}
model_inputs.update({'cache_params': cache_params, 'use_cache': use_cache, 'cache_position': cache_position, 'attention_mask': attention_mask})
for key, value in kwargs.items():
if key not in model_inputs:
model_inputs[key] = value
return model_inputs
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, cache_params: Optional[Mamba2Cache]=None, labels: Optional[torch.LongTensor]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, use_cache: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> Union[tuple, Mamba2CausalLMOutput]:
"""
cache_params (`Mamba2Cache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
If `cache_params` is passed, `cache_position` should also be passed.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
mamba2_outputs = self.backbone(input_ids, cache_params=cache_params, inputs_embeds=inputs_embeds, output_hidden_states=output_hidden_states, return_dict=return_dict, use_cache=use_cache, cache_position=cache_position, attention_mask=attention_mask)
hidden_states = mamba2_outputs[0]
logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + mamba2_outputs[1:]
return (loss,) + output if loss is not None else output
return Mamba2CausalLMOutput(loss=loss, logits=logits, cache_params=mamba2_outputs.cache_params, hidden_states=mamba2_outputs.hidden_states)
|
@auto_docstring(custom_intro='\n The MAMBA2 Model transformer with a language modeling head on top (linear layer with weights not tied to the input\n embeddings).\n ')
class Mamba2ForCausalLM(Mamba2PreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, new_embeddings):
pass
def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, use_cache=None, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, cache_params: Optional[Mamba2Cache]=None, labels: Optional[torch.LongTensor]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, use_cache: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> Union[tuple, Mamba2CausalLMOutput]:
'''
cache_params (`Mamba2Cache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
If `cache_params` is passed, `cache_position` should also be passed.
'''
pass
| 8
| 1
| 17
| 1
| 13
| 2
| 2
| 0.17
| 2
| 7
| 3
| 0
| 7
| 2
| 7
| 8
| 131
| 16
| 99
| 42
| 64
| 17
| 45
| 20
| 37
| 6
| 2
| 3
| 16
|
3,579
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2Mixer
|
from .configuration_mamba2 import Mamba2Config
from ...activations import ACT2FN
import torch
from typing import Optional, Union
from torch import nn
class Mamba2Mixer(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
and is why Mamba is called **selective** state spaces)
"""
def __init__(self, config: Mamba2Config, layer_idx: int):
super().__init__()
self.num_heads = config.num_heads
self.hidden_size = config.hidden_size
self.ssm_state_size = config.state_size
self.conv_kernel_size = config.conv_kernel
self.intermediate_size = int(config.expand * self.hidden_size)
self.time_step_rank = int(config.time_step_rank)
self.layer_idx = layer_idx
self.use_conv_bias = config.use_conv_bias
self.activation = config.hidden_act
self.act = ACT2FN[config.hidden_act]
self.layer_norm_epsilon = config.layer_norm_epsilon
self.rms_norm = config.rms_norm
self.n_groups = config.n_groups
self.head_dim = config.head_dim
self.chunk_size = config.chunk_size
self.time_step_limit = config.time_step_limit
self.time_step_min = config.time_step_min
self.time_step_max = config.time_step_max
self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
self.conv1d = nn.Conv1d(in_channels=self.conv_dim, out_channels=self.conv_dim, bias=config.use_conv_bias, kernel_size=config.conv_kernel, groups=self.conv_dim, padding=config.conv_kernel - 1)
projection_size = self.intermediate_size + self.conv_dim + self.num_heads
self.in_proj = nn.Linear(self.hidden_size, projection_size, bias=config.use_bias)
self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
A = torch.arange(1, self.num_heads + 1)
self.A_log = nn.Parameter(torch.log(A))
self.norm = MambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
self.D = nn.Parameter(torch.ones(self.num_heads))
self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
self.use_bias = config.use_bias
if not is_fast_path_available:
logger.warning_once('The fast path is not available because on of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d')
def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
projected_states = self.in_proj(hidden_states)
batch_size, seq_len, _ = hidden_states.shape
groups_time_state_size = self.n_groups * self.ssm_state_size
d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size - self.num_heads) // 2
if cache_params is not None and cache_position is not None and (cache_position[0] > 0):
_, _, gate, hidden_states_B_C, dt = projected_states.squeeze(1).split([d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1)
hidden_states_B_C = causal_conv1d_update(hidden_states_B_C, cache_params.conv_states[self.layer_idx], self.conv1d.weight.squeeze(1), self.conv1d.bias, self.activation)
hidden_states, B, C = torch.split(hidden_states_B_C, [self.intermediate_size, groups_time_state_size, groups_time_state_size], dim=-1)
A = -torch.exp(self.A_log.float())
A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
dt = dt[:, :, None].expand(-1, -1, self.head_dim)
dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
D = self.D[:, None, ...].expand(-1, self.head_dim)
B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
hidden_states = selective_state_update(cache_params.ssm_states[self.layer_idx], hidden_states_reshaped, dt, A, B, C, D, z=None, dt_bias=dt_bias, dt_softplus=True)
hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim)
hidden_states = self.norm(hidden_states, gate)
out = self.out_proj(hidden_states)[:, None, ...]
else:
A = -torch.exp(self.A_log.float())
dt_limit_kwargs = {} if self.time_step_limit == (0.0, float('inf')) else {'dt_limit': self.time_step_limit}
if self.training and cache_params is None:
out = mamba_split_conv1d_scan_combined(projected_states, self.conv1d.weight.squeeze(1), self.conv1d.bias, self.dt_bias, A, D=self.D, chunk_size=self.chunk_size, seq_idx=None, activation=self.activation, rmsnorm_weight=self.norm.weight, rmsnorm_eps=self.norm.variance_epsilon, outproj_weight=self.out_proj.weight, outproj_bias=self.out_proj.bias, headdim=self.head_dim, ngroups=self.n_groups, norm_before_gate=False, return_final_states=False, **dt_limit_kwargs)
else:
_, _, gate, hidden_states_B_C, dt = projected_states.split([d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1)
if cache_params is not None:
hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
conv_states = nn.functional.pad(hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0))
cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True)
if self.activation not in ['silu', 'swish']:
hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
else:
hidden_states_B_C = causal_conv1d_fn(x=hidden_states_B_C.transpose(1, 2), weight=self.conv1d.weight.squeeze(1), bias=self.conv1d.bias, activation=self.activation).transpose(1, 2)
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
hidden_states, B, C = torch.split(hidden_states_B_C, [self.intermediate_size, groups_time_state_size, groups_time_state_size], dim=-1)
scan_output, ssm_state = mamba_chunk_scan_combined(hidden_states.view(batch_size, seq_len, -1, self.head_dim), dt, A, B.view(batch_size, seq_len, self.n_groups, -1), C.view(batch_size, seq_len, self.n_groups, -1), chunk_size=self.chunk_size, D=self.D, z=None, seq_idx=None, return_final_states=True, dt_bias=self.dt_bias, dt_softplus=True, **dt_limit_kwargs)
if ssm_state is not None and cache_params is not None:
cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
scan_output = scan_output.view(batch_size, seq_len, -1)
scan_output = self.norm(scan_output, gate)
out = self.out_proj(scan_output)
return out
def torch_forward(self, hidden_states: torch.Tensor, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
batch_size, seq_len, _ = hidden_states.shape
dtype = hidden_states.dtype
hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
projected_states = self.in_proj(hidden_states)
d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size - self.num_heads) // 2
_, _, gate, hidden_states_B_C, dt = projected_states.split([d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1)
if cache_params is not None and cache_position is not None and (cache_position[0] > 0):
cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=hidden_states_B_C, cache_init=False)
conv_states = cache_params.conv_states[self.layer_idx].to(device=self.conv1d.weight.device)
hidden_states_B_C = torch.sum(conv_states * self.conv1d.weight.squeeze(1), dim=-1)
if self.use_conv_bias:
hidden_states_B_C = hidden_states_B_C + self.conv1d.bias
hidden_states_B_C = self.act(hidden_states_B_C)
else:
if cache_params is not None:
hidden_states_B_C_transposed = hidden_states_B_C.transpose(1, 2)
conv_states = nn.functional.pad(hidden_states_B_C_transposed, (cache_params.conv_kernel_size - hidden_states_B_C_transposed.shape[-1], 0))
cache_params.update_conv_state(layer_idx=self.layer_idx, new_conv_state=conv_states, cache_init=True)
hidden_states_B_C = self.act(self.conv1d(hidden_states_B_C.transpose(1, 2))[..., :seq_len].transpose(1, 2))
hidden_states_B_C = apply_mask_to_padding_states(hidden_states_B_C, attention_mask)
hidden_states, B, C = torch.split(hidden_states_B_C, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
A = -torch.exp(self.A_log.float())
if cache_params is not None and cache_position is not None and (cache_position[0] > 0):
cache_device = cache_params.ssm_states.device
dt = dt[:, 0, :][:, None, ...]
dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
dA = torch.exp(dt[..., None] * A).to(device=cache_device)
B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
B = B.reshape(batch_size, -1, B.shape[-1])
dB = dt[..., None] * B[..., None, :]
hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
dBx = (dB * hidden_states[..., None]).to(device=cache_device)
cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=cache_params.ssm_states[self.layer_idx] * dA + dBx)
C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
C = C.reshape(batch_size, -1, C.shape[-1])
ssm_states = cache_params.ssm_states[self.layer_idx].to(device=C.device, dtype=C.dtype)
ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)
C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)
y = torch.bmm(ssm_states_reshaped, C_reshaped)
y = y.view(batch_size, self.num_heads, self.head_dim)
D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
y = (y + hidden_states * D).to(y.dtype)
y = y.reshape(batch_size, -1)[:, None, ...]
else:
dt = nn.functional.softplus(dt + self.dt_bias)
dt = torch.clamp(dt, self.time_step_limit[0], self.time_step_limit[1])
hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
hidden_states = hidden_states * dt[..., None]
A = A.to(hidden_states.dtype) * dt
hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
A = A.permute(0, 3, 1, 2)
A_cumsum = torch.cumsum(A, dim=-1)
L = torch.exp(segment_sum(A))
G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :]
G = G_intermediate.sum(dim=-1)
M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
M = M_intermediate.sum(dim=-1)
Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3)
decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None]
states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2)
if cache_params is not None and cache_position is not None and (cache_position[0] > 0):
previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...].to(device=states.device)
else:
previous_states = torch.zeros_like(states[:, :1])
states = torch.cat([previous_states, states], dim=1)
decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
decay_chunk = decay_chunk.transpose(1, 3)
new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1)
states, ssm_state = (new_states[:, :-1], new_states[:, -1])
state_decay_out = torch.exp(A_cumsum)
C_times_states = C[..., None, :] * states[:, :, None, ...]
state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
Y_off = C_times_states.sum(-1) * state_decay_out_permuted[..., None]
y = Y_diag + Y_off
y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
y = y + D_residual
if pad_size > 0:
y = y[:, :seq_len, :, :]
y = y.reshape(batch_size, seq_len, -1)
if ssm_state is not None and cache_params is not None:
cache_params.update_ssm_state(layer_idx=self.layer_idx, new_ssm_state=ssm_state)
scan_output = self.norm(y, gate)
contextualized_states = self.out_proj(scan_output.to(dtype))
return contextualized_states
def forward(self, hidden_states, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
if is_fast_path_available and 'cuda' in self.in_proj.weight.device.type:
return self.cuda_kernels_forward(hidden_states, cache_params, cache_position, attention_mask)
return self.torch_forward(hidden_states, cache_params, cache_position, attention_mask)
|
class Mamba2Mixer(nn.Module):
'''
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
and is why Mamba is called **selective** state spaces)
'''
def __init__(self, config: Mamba2Config, layer_idx: int):
pass
def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
pass
def torch_forward(self, hidden_states: torch.Tensor, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
pass
def forward(self, hidden_states, cache_params: Optional[Mamba2Cache]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None):
pass
| 5
| 1
| 108
| 15
| 76
| 19
| 5
| 0.28
| 1
| 7
| 3
| 0
| 4
| 27
| 4
| 14
| 443
| 63
| 306
| 103
| 289
| 85
| 181
| 91
| 176
| 8
| 1
| 3
| 20
|
3,580
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2Model
|
import torch
from typing import Optional, Union
from torch import nn
from ...utils import ModelOutput, auto_docstring, logging
@auto_docstring
class Mamba2Model(Mamba2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([Mamba2Block(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.norm_f = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self._register_load_state_dict_pre_hook(self.load_hook)
self.post_init()
def load_hook(self, state_dict, prefix, *args):
for k in state_dict:
if 'embedding.' in k:
state_dict[k.replace('embedding.', 'embeddings.')] = state_dict.pop(k)
break
def get_input_embeddings(self):
return self.embeddings
def set_input_embeddings(self, new_embeddings):
self.embeddings = new_embeddings
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, cache_params: Optional[Mamba2Cache]=None, use_cache: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> Union[tuple, Mamba2Output]:
"""
cache_params (`Mamba2Cache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
If `cache_params` is passed, `cache_position` should also be passed.
"""
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
use_cache = use_cache if use_cache is not None else self.config.use_cache if not self.training else False
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError('You must specify exactly one of input_ids or inputs_embeds')
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
if self.gradient_checkpointing and self.training and use_cache:
use_cache = False
if use_cache:
if cache_params is None:
cache_params = Mamba2Cache(self.config, inputs_embeds.size(0), device=inputs_embeds.device, dtype=inputs_embeds.dtype)
cache_position = torch.arange(0, self.config.conv_kernel, device=inputs_embeds.device)
elif cache_position is None:
raise ValueError("You have to specify the `cache_position` manually when `use_cache=True` and `cache_params` is passed, you don't have to pass a `cache_params` if you are in prefilling stage because in that case it will be initialized for you automatically")
else:
cache_params = None
hidden_states = inputs_embeds
all_hidden_states = () if output_hidden_states else None
for mixer_block in self.layers:
hidden_states = mixer_block(hidden_states, cache_params=cache_params, cache_position=cache_position, attention_mask=attention_mask)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
hidden_states = self.norm_f(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple((v for v in [hidden_states, cache_params, all_hidden_states] if v is not None))
return Mamba2Output(last_hidden_state=hidden_states, cache_params=cache_params if use_cache else None, hidden_states=all_hidden_states)
|
@auto_docstring
class Mamba2Model(Mamba2PreTrainedModel):
def __init__(self, config):
pass
def load_hook(self, state_dict, prefix, *args):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, new_embeddings):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, cache_params: Optional[Mamba2Cache]=None, use_cache: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> Union[tuple, Mamba2Output]:
'''
cache_params (`Mamba2Cache`, *optional*):
If passed along, the model uses the previous state in all the blocks (which will give the output for the
`input_ids` provided as if the model add `state_input_ids + input_ids` as context).
use_cache (`bool`, *optional*):
If set to `True`, the `cache_params` is returned and can be used to quickly generate the next logits.
cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
If `cache_params` is passed, `cache_position` should also be passed.
'''
pass
| 8
| 1
| 19
| 2
| 16
| 1
| 5
| 0.06
| 1
| 10
| 4
| 0
| 5
| 5
| 5
| 6
| 107
| 16
| 87
| 27
| 64
| 5
| 48
| 14
| 42
| 18
| 2
| 2
| 24
|
3,581
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2Output
|
from dataclasses import dataclass
import torch
from typing import Optional, Union
from ...utils import ModelOutput, auto_docstring, logging
@dataclass
@auto_docstring(custom_intro='\n Class for the MAMBA2 model outputs.\n ')
class Mamba2Output(ModelOutput):
"""
cache_params (`Mamba2Cache`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
Includes both the State space model state matrices after the selective scan, and the Convolutional states
"""
last_hidden_state: Optional[torch.FloatTensor] = None
cache_params: Optional[Mamba2Cache] = None
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
@dataclass
@auto_docstring(custom_intro='\n Class for the MAMBA2 model outputs.\n ')
class Mamba2Output(ModelOutput):
'''
cache_params (`Mamba2Cache`):
The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
avoid providing the old `input_ids`.
Includes both the State space model state matrices after the selective scan, and the Convolutional states
'''
pass
| 3
| 1
| 0
| 0
| 0
| 0
| 0
| 3.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 22
| 4
| 4
| 4
| 3
| 14
| 4
| 4
| 3
| 0
| 1
| 0
| 0
|
3,582
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2PreTrainedModel
|
import torch
from ...modeling_utils import PreTrainedModel
from ...utils import ModelOutput, auto_docstring, logging
import math
from .configuration_mamba2 import Mamba2Config
from torch import nn
@auto_docstring
class Mamba2PreTrainedModel(PreTrainedModel):
config: Mamba2Config
base_model_prefix = 'backbone'
_no_split_modules = ['Mamba2Block']
supports_gradient_checkpointing = True
_is_stateful = True
def _init_weights(self, module):
"""Initialize the weights."""
std = self.config.initializer_range
if isinstance(module, Mamba2Mixer):
A = torch.arange(1, self.config.num_heads + 1)
module.A_log.copy_(torch.log(A))
module.D.data.fill_(1.0)
dt = torch.exp(torch.rand(self.config.num_heads) * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + math.log(self.config.time_step_min)).clamp(min=self.config.time_step_floor)
inv_dt = dt + torch.log(-torch.expm1(-dt))
module.dt_bias.copy_(inv_dt)
module.dt_bias._no_reinit = True
nn.init.kaiming_uniform_(module.conv1d.weight, a=math.sqrt(5))
if module.conv1d.bias is not None:
if not getattr(module.conv1d.bias, '_no_reinit', False):
nn.init.zeros_(module.conv1d.bias)
nn.init.kaiming_uniform_(module.out_proj.weight, a=math.sqrt(5))
if self.config.rescale_prenorm_residual:
p = module.out_proj.weight
p /= math.sqrt(self.config.num_hidden_layers)
if isinstance(module, nn.Linear):
if not getattr(module.weight, '_no_reinit', False):
nn.init.normal_(module.weight, std=std)
if module.bias is not None:
if not getattr(module.bias, '_no_reinit', False):
nn.init.zeros_(module.bias)
elif isinstance(module, (Mamba2RMSNorm, MambaRMSNormGated)):
module.weight.data.fill_(1.0)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=std)
|
@auto_docstring
class Mamba2PreTrainedModel(PreTrainedModel):
def _init_weights(self, module):
'''Initialize the weights.'''
pass
| 3
| 1
| 41
| 4
| 25
| 12
| 9
| 0.52
| 1
| 1
| 1
| 2
| 1
| 0
| 1
| 1
| 53
| 6
| 31
| 10
| 29
| 16
| 26
| 10
| 24
| 9
| 1
| 4
| 9
|
3,583
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.Mamba2RMSNorm
|
from torch import nn
import torch
class Mamba2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
"""
Mamba2RMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
|
class Mamba2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-06):
'''
Mamba2RMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
'''
pass
def forward(self, hidden_states):
pass
| 3
| 1
| 7
| 0
| 5
| 2
| 1
| 0.27
| 1
| 1
| 0
| 0
| 2
| 2
| 2
| 12
| 15
| 1
| 11
| 7
| 8
| 3
| 11
| 7
| 8
| 1
| 1
| 0
| 2
|
3,584
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/mamba2/modeling_mamba2.py
|
transformers.models.mamba2.modeling_mamba2.MambaRMSNormGated
|
import torch
from torch import nn
class MambaRMSNormGated(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-06):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
if gate is not None:
hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
|
class MambaRMSNormGated(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-06):
pass
def forward(self, hidden_states, gate=None):
pass
| 3
| 0
| 7
| 1
| 6
| 0
| 2
| 0
| 1
| 1
| 0
| 0
| 2
| 2
| 2
| 12
| 16
| 3
| 13
| 7
| 10
| 0
| 13
| 7
| 10
| 2
| 1
| 1
| 3
|
3,585
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/configuration_marian.py
|
transformers.models.marian.configuration_marian.MarianConfig
|
from ...configuration_utils import PretrainedConfig
class MarianConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Marian
[Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 58101):
Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 0):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
Examples:
```python
>>> from transformers import MarianModel, MarianConfig
>>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
>>> configuration = MarianConfig()
>>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
>>> model = MarianModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = 'marian'
keys_to_ignore_at_inference = ['past_key_values']
attribute_map = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__(self, vocab_size=58101, decoder_vocab_size=None, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function='gelu', d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=58100, scale_embedding=False, pad_token_id=58100, eos_token_id=0, forced_eos_token_id=0, share_encoder_decoder_embeddings=True, **kwargs):
self.vocab_size = vocab_size
self.decoder_vocab_size = decoder_vocab_size or vocab_size
self.max_position_embeddings = max_position_embeddings
self.d_model = d_model
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.init_std = init_std
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.use_cache = use_cache
self.num_hidden_layers = encoder_layers
self.scale_embedding = scale_embedding
self.share_encoder_decoder_embeddings = share_encoder_decoder_embeddings
super().__init__(pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs)
|
class MarianConfig(PretrainedConfig):
'''
This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Marian
[Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 58101):
Vocabulary size of the Marian model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MarianModel`] or [`TFMarianModel`].
d_model (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
encoder_layers (`int`, *optional*, defaults to 12):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer decoder.
decoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(d_model).
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models)
forced_eos_token_id (`int`, *optional*, defaults to 0):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
Examples:
```python
>>> from transformers import MarianModel, MarianConfig
>>> # Initializing a Marian Helsinki-NLP/opus-mt-en-de style configuration
>>> configuration = MarianConfig()
>>> # Initializing a model from the Helsinki-NLP/opus-mt-en-de style configuration
>>> model = MarianModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```'''
def __init__(self, vocab_size=58101, decoder_vocab_size=None, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function='gelu', d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, decoder_start_token_id=58100, scale_embedding=False, pad_token_id=58100, eos_token_id=0, forced_eos_token_id=0, share_encoder_decoder_embeddings=True, **kwargs):
pass
| 2
| 1
| 58
| 0
| 58
| 1
| 1
| 1.02
| 1
| 1
| 0
| 0
| 1
| 21
| 1
| 1
| 134
| 10
| 62
| 54
| 32
| 63
| 27
| 26
| 25
| 1
| 1
| 0
| 1
|
3,586
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/configuration_marian.py
|
transformers.models.marian.configuration_marian.MarianOnnxConfig
|
from ...onnx.utils import compute_effective_axis_dimension
from typing import Any
from collections import OrderedDict
from collections.abc import Mapping
from ... import PreTrainedTokenizer
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
from ...utils import is_torch_available, logging
class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ['default', 'seq2seq-lm']:
common_inputs = OrderedDict([('input_ids', {0: 'batch', 1: 'encoder_sequence'}), ('attention_mask', {0: 'batch', 1: 'encoder_sequence'})])
if self.use_past:
common_inputs['decoder_input_ids'] = {0: 'batch'}
common_inputs['decoder_attention_mask'] = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
common_inputs['decoder_input_ids'] = {0: 'batch', 1: 'decoder_sequence'}
common_inputs['decoder_attention_mask'] = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction='inputs')
elif self.task == 'causal-lm':
common_inputs = OrderedDict([('input_ids', {0: 'batch', 1: 'encoder_sequence'}), ('attention_mask', {0: 'batch', 1: 'encoder_sequence'})])
if self.use_past:
num_encoder_layers, _ = self.num_layers
for i in range(num_encoder_layers):
common_inputs[f'past_key_values.{i}.key'] = {0: 'batch', 2: 'past_sequence + sequence'}
common_inputs[f'past_key_values.{i}.value'] = {0: 'batch', 2: 'past_sequence + sequence'}
else:
common_inputs = OrderedDict([('input_ids', {0: 'batch', 1: 'encoder_sequence'}), ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}), ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}), ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'})])
return common_inputs
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ['default', 'seq2seq-lm']:
common_outputs = super().outputs
else:
common_outputs = super(OnnxConfigWithPast, self).outputs
if self.use_past:
num_encoder_layers, _ = self.num_layers
for i in range(num_encoder_layers):
common_outputs[f'present.{i}.key'] = {0: 'batch', 2: 'past_sequence + sequence'}
common_outputs[f'present.{i}.value'] = {0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def _generate_dummy_inputs_for_default_and_seq2seq_lm(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
encoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(tokenizer, batch_size, seq_length, is_pair)
decoder_seq_length = seq_length if not self.use_past else 1
decoder_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(tokenizer, batch_size, decoder_seq_length, is_pair)
decoder_inputs = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
common_inputs = dict(**encoder_inputs, **decoder_inputs)
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
else:
import torch
batch, encoder_seq_length = common_inputs['input_ids'].shape
decoder_seq_length = common_inputs['decoder_input_ids'].shape[1]
num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
encoder_shape = (batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads)
decoder_past_length = decoder_seq_length + 3
decoder_shape = (batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads)
common_inputs['decoder_attention_mask'] = torch.cat([common_inputs['decoder_attention_mask'], torch.ones(batch, decoder_past_length)], dim=1)
common_inputs['past_key_values'] = []
num_encoder_layers, num_decoder_layers = self.num_layers
min_num_layers = min(num_encoder_layers, num_decoder_layers)
max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
remaining_side_name = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(min_num_layers):
common_inputs['past_key_values'].append((torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape)))
shape = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(min_num_layers, max_num_layers):
common_inputs['past_key_values'].append((torch.zeros(shape), torch.zeros(shape)))
return common_inputs
def _generate_dummy_inputs_for_causal_lm(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
common_inputs = self._generate_dummy_inputs_for_encoder_and_decoder(tokenizer, batch_size, seq_length, is_pair)
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
else:
import torch
batch, seqlen = common_inputs['input_ids'].shape
past_key_values_length = seqlen + 2
num_encoder_layers, _ = self.num_layers
num_encoder_attention_heads, _ = self.num_attention_heads
past_shape = (batch, num_encoder_attention_heads, past_key_values_length, self._config.hidden_size // num_encoder_attention_heads)
mask_dtype = common_inputs['attention_mask'].dtype
common_inputs['attention_mask'] = torch.cat([common_inputs['attention_mask'], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1)
common_inputs['past_key_values'] = [(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)]
return common_inputs
def _generate_dummy_inputs_for_encoder_and_decoder(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0)
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add)
dummy_input = [' '.join([tokenizer.unk_token]) * seq_length] * batch_size
common_inputs = dict(tokenizer(dummy_input, return_tensors='pt'))
return common_inputs
def generate_dummy_inputs(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
if self.task in ['default', 'seq2seq-lm']:
common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair)
else:
common_inputs = self._generate_dummy_inputs_for_causal_lm(tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair)
return common_inputs
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
if self.task in ['default', 'seq2seq-lm']:
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
else:
flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(flattened_output, name, idx, t)
@property
def atol_for_validation(self) -> float:
return 0.0001
|
class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
pass
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
pass
def _generate_dummy_inputs_for_default_and_seq2seq_lm(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
pass
def _generate_dummy_inputs_for_causal_lm(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
pass
def _generate_dummy_inputs_for_encoder_and_decoder(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
pass
def generate_dummy_inputs(self, tokenizer: PreTrainedTokenizer, batch_size: int=-1, seq_length: int=-1, is_pair: bool=False) -> Mapping[str, Any]:
pass
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
pass
@property
def atol_for_validation(self) -> float:
pass
| 12
| 0
| 26
| 2
| 23
| 1
| 4
| 0.08
| 1
| 10
| 0
| 0
| 8
| 1
| 8
| 8
| 226
| 21
| 190
| 75
| 148
| 15
| 90
| 43
| 79
| 8
| 1
| 3
| 28
|
3,587
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py
|
transformers.models.marian.convert_marian_tatoeba_to_pytorch.TatoebaConverter
|
import datetime
import yaml
from transformers.models.marian.convert_marian_to_pytorch import FRONT_MATTER_TEMPLATE, convert, convert_opus_name_to_hf_name, download_and_unzip, get_system_metadata
import os
from tqdm import tqdm
import re
import json
from pathlib import Path
class TatoebaConverter:
"""
Convert Tatoeba-Challenge models to huggingface format.
Steps:
1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
one exists. e.g. aav-eng -> aav-en, heb-eng -> he-en
3. Select the best model for a particular pair, parse the yml for it and write a model card. By default the
best model is the one listed first in released-model-results, but it's also possible to specify the most
recent one.
"""
def __init__(self, save_dir='marian_converted'):
assert Path(DEFAULT_REPO).exists(), 'need git clone git@github.com:Helsinki-NLP/Tatoeba-Challenge.git'
self.download_lang_info()
self.model_results = json.load(open('Tatoeba-Challenge/models/released-model-results.json'))
self.alpha3_to_alpha2 = {}
for line in open(ISO_PATH):
parts = line.split('\t')
if len(parts[0]) == 3 and len(parts[3]) == 2:
self.alpha3_to_alpha2[parts[0]] = parts[3]
for line in LANG_CODE_PATH:
parts = line.split(',')
if len(parts[0]) == 3 and len(parts[1]) == 2:
self.alpha3_to_alpha2[parts[0]] = parts[1]
self.model_card_dir = Path(save_dir)
self.tag2name = {}
for key, value in GROUP_MEMBERS.items():
self.tag2name[key] = value[0]
def convert_models(self, tatoeba_ids, dry_run=False):
models_to_convert = [self.parse_metadata(x) for x in tatoeba_ids]
save_dir = Path('marian_ckpt')
dest_dir = Path(self.model_card_dir)
dest_dir.mkdir(exist_ok=True)
for model in tqdm(models_to_convert):
if 'SentencePiece' not in model['pre-processing']:
print(f"Skipping {model['release']} because it doesn't appear to use SentencePiece")
continue
if not os.path.exists(save_dir / model['_name']):
download_and_unzip(f"{TATOEBA_MODELS_URL}/{model['release']}", save_dir / model['_name'])
opus_language_groups_to_hf = convert_opus_name_to_hf_name
pair_name = opus_language_groups_to_hf(model['_name'])
convert(save_dir / model['_name'], dest_dir / f'opus-mt-{pair_name}')
self.write_model_card(model, dry_run=dry_run)
def expand_group_to_two_letter_codes(self, grp_name):
return [self.alpha3_to_alpha2.get(x, x) for x in GROUP_MEMBERS[grp_name][1]]
def is_group(self, code, name):
return 'languages' in name or len(GROUP_MEMBERS.get(code, [])) > 1
def get_tags(self, code, name):
if len(code) == 2:
assert 'languages' not in name, f'{code}: {name}'
return [code]
elif self.is_group(code, name):
group = self.expand_group_to_two_letter_codes(code)
group.append(code)
return group
else:
print(f'Three letter monolingual code: {code}')
return [code]
def resolve_lang_code(self, src, tgt) -> tuple[str, str]:
src_tags = self.get_tags(src, self.tag2name[src])
tgt_tags = self.get_tags(tgt, self.tag2name[tgt])
return (src_tags, tgt_tags)
@staticmethod
def model_type_info_from_model_name(name):
info = {'_has_backtranslated_data': False}
if '1m' in name:
info['_data_per_pair'] = str(1000000.0)
if '2m' in name:
info['_data_per_pair'] = str(2000000.0)
if '4m' in name:
info['_data_per_pair'] = str(4000000.0)
if '+bt' in name:
info['_has_backtranslated_data'] = True
if 'tuned4' in name:
info['_tuned'] = re.search('tuned4[^-]+', name).group()
return info
def write_model_card(self, model_dict, dry_run=False) -> str:
"""
Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
model_dir_url = f"{TATOEBA_MODELS_URL}/{model_dict['release']}"
long_pair = model_dict['_name'].split('-')
assert len(long_pair) == 2, f"got a translation pair {model_dict['_name']} that doesn't appear to be a pair"
short_src = self.alpha3_to_alpha2.get(long_pair[0], long_pair[0])
short_tgt = self.alpha3_to_alpha2.get(long_pair[1], long_pair[1])
model_dict['_hf_model_id'] = f'opus-mt-{short_src}-{short_tgt}'
a3_src, a3_tgt = model_dict['_name'].split('-')
resolved_src_tags, resolved_tgt_tags = self.resolve_lang_code(a3_src, a3_tgt)
a2_src_tags, a2_tgt_tags = ([], [])
for tag in resolved_src_tags:
if tag not in self.alpha3_to_alpha2:
a2_src_tags.append(tag)
for tag in resolved_tgt_tags:
if tag not in self.alpha3_to_alpha2:
a2_tgt_tags.append(tag)
lang_tags = dedup(a2_src_tags + a2_tgt_tags)
src_multilingual, tgt_multilingual = (len(a2_src_tags) > 1, len(a2_tgt_tags) > 1)
s, t = (','.join(a2_src_tags), ','.join(a2_tgt_tags))
metadata = {'hf_name': model_dict['_name'], 'source_languages': s, 'target_languages': t, 'opus_readme_url': f'{model_dir_url}/README.md', 'original_repo': 'Tatoeba-Challenge', 'tags': ['translation'], 'languages': lang_tags}
lang_tags = l2front_matter(lang_tags)
metadata['src_constituents'] = list(GROUP_MEMBERS[a3_src][1])
metadata['tgt_constituents'] = list(GROUP_MEMBERS[a3_tgt][1])
metadata['src_multilingual'] = src_multilingual
metadata['tgt_multilingual'] = tgt_multilingual
backtranslated_data = ''
if model_dict['_has_backtranslated_data']:
backtranslated_data = ' with backtranslations'
multilingual_data = ''
if '_data_per_pair' in model_dict:
multilingual_data = f"* data per pair in multilingual model: {model_dict['_data_per_pair']}\n"
tuned = ''
if '_tuned' in model_dict:
tuned = f"* multilingual model tuned for: {model_dict['_tuned']}\n"
model_base_filename = model_dict['release'].split('/')[-1]
download = f"* download original weights: [{model_base_filename}]({model_dir_url}/{model_dict['release']})\n"
langtoken = ''
if tgt_multilingual:
langtoken = '* a sentence-initial language token is required in the form of >>id<<(id = valid, usually three-letter target language ID)\n'
metadata.update(get_system_metadata(DEFAULT_REPO))
scorestable = ''
for k, v in model_dict.items():
if 'scores' in k:
this_score_table = f'* {k}\n|Test set|score|\n|---|---|\n'
pairs = sorted(v.items(), key=lambda x: x[1], reverse=True)
for pair in pairs:
this_score_table += f'|{pair[0]}|{pair[1]}|\n'
scorestable += this_score_table
datainfo = ''
if 'training-data' in model_dict:
datainfo += '* Training data: \n'
for k, v in model_dict['training-data'].items():
datainfo += f' * {str(k)}: {str(v)}\n'
if 'validation-data' in model_dict:
datainfo += '* Validation data: \n'
for k, v in model_dict['validation-data'].items():
datainfo += f' * {str(k)}: {str(v)}\n'
if 'test-data' in model_dict:
datainfo += '* Test data: \n'
for k, v in model_dict['test-data'].items():
datainfo += f' * {str(k)}: {str(v)}\n'
testsetfilename = model_dict['release'].replace('.zip', '.test.txt')
testscoresfilename = model_dict['release'].replace('.zip', '.eval.txt')
testset = f'* test set translations file: [test.txt]({model_dir_url}/{testsetfilename})\n'
testscores = f'* test set scores file: [eval.txt]({model_dir_url}/{testscoresfilename})\n'
readme_url = f"{TATOEBA_MODELS_URL}/{model_dict['_name']}/README.md"
extra_markdown = f"\n### {model_dict['_name']}\n\n* source language name: {self.tag2name[a3_src]}\n* target language name: {self.tag2name[a3_tgt]}\n* OPUS readme: [README.md]({readme_url})\n"
content = f"\n* model: {model_dict['modeltype']}\n* source language code{src_multilingual * 's'}: {', '.join(a2_src_tags)}\n* target language code{tgt_multilingual * 's'}: {', '.join(a2_tgt_tags)}\n* dataset: opus {backtranslated_data}\n* release date: {model_dict['release-date']}\n* pre-processing: {model_dict['pre-processing']}\n" + multilingual_data + tuned + download + langtoken + datainfo + testset + testscores + scorestable
content = FRONT_MATTER_TEMPLATE.format(lang_tags) + extra_markdown + content
items = '\n'.join([f'* {k}: {v}' for k, v in metadata.items()])
sec3 = '\n### System Info: \n' + items
content += sec3
if dry_run:
print('CONTENT:')
print(content)
print('METADATA:')
print(metadata)
return
sub_dir = self.model_card_dir / model_dict['_hf_model_id']
sub_dir.mkdir(exist_ok=True)
dest = sub_dir / 'README.md'
dest.open('w').write(content)
for k, v in metadata.items():
if isinstance(v, datetime.date):
metadata[k] = datetime.datetime.strftime(v, '%Y-%m-%d')
with open(sub_dir / 'metadata.json', 'w', encoding='utf-8') as writeobj:
json.dump(metadata, writeobj)
def download_lang_info(self):
global LANG_CODE_PATH
Path(LANG_CODE_PATH).parent.mkdir(exist_ok=True)
import wget
from huggingface_hub import hf_hub_download
if not os.path.exists(ISO_PATH):
wget.download(ISO_URL, ISO_PATH)
if not os.path.exists(LANG_CODE_PATH):
LANG_CODE_PATH = hf_hub_download(repo_id='huggingface/language_codes_marianMT', filename='language-codes-3b2.csv', repo_type='dataset')
def parse_metadata(self, model_name, repo_path=DEFAULT_MODEL_DIR, method='best'):
p = Path(repo_path) / model_name
def url_to_name(url):
return url.split('/')[-1].split('.')[0]
if model_name not in self.model_results:
method = 'newest'
if method == 'best':
results = [url_to_name(model['download']) for model in self.model_results[model_name]]
ymls = [f for f in os.listdir(p) if f.endswith('.yml') and f[:-4] in results]
ymls.sort(key=lambda x: results.index(x[:-4]))
metadata = yaml.safe_load(open(p / ymls[0]))
metadata.update(self.model_type_info_from_model_name(ymls[0][:-4]))
elif method == 'newest':
ymls = [f for f in os.listdir(p) if f.endswith('.yml')]
ymls.sort(key=lambda x: datetime.datetime.strptime(re.search('\\d\\d\\d\\d-\\d\\d?-\\d\\d?', x).group(), '%Y-%m-%d'))
metadata = yaml.safe_load(open(p / ymls[-1]))
metadata.update(self.model_type_info_from_model_name(ymls[-1][:-4]))
else:
raise NotImplementedError(f"Don't know argument method='{method}' to parse_metadata()")
metadata['_name'] = model_name
return metadata
|
class TatoebaConverter:
'''
Convert Tatoeba-Challenge models to huggingface format.
Steps:
1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
one exists. e.g. aav-eng -> aav-en, heb-eng -> he-en
3. Select the best model for a particular pair, parse the yml for it and write a model card. By default the
best model is the one listed first in released-model-results, but it's also possible to specify the most
recent one.
'''
def __init__(self, save_dir='marian_converted'):
pass
def convert_models(self, tatoeba_ids, dry_run=False):
pass
def expand_group_to_two_letter_codes(self, grp_name):
pass
def is_group(self, code, name):
pass
def get_tags(self, code, name):
pass
def resolve_lang_code(self, src, tgt) -> tuple[str, str]:
pass
@staticmethod
def model_type_info_from_model_name(name):
pass
def write_model_card(self, model_dict, dry_run=False) -> str:
'''
Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
'''
pass
def download_lang_info(self):
pass
def parse_metadata(self, model_name, repo_path=DEFAULT_MODEL_DIR, method='best'):
pass
def url_to_name(url):
pass
| 13
| 2
| 23
| 2
| 20
| 1
| 5
| 0.12
| 0
| 7
| 0
| 0
| 9
| 4
| 10
| 10
| 277
| 35
| 220
| 73
| 204
| 26
| 178
| 71
| 163
| 21
| 0
| 3
| 51
|
3,588
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/convert_marian_to_pytorch.py
|
transformers.models.marian.convert_marian_to_pytorch.OpusState
|
import torch
from torch import nn
import warnings
from transformers import MarianConfig, MarianMTModel, MarianTokenizer
import numpy as np
class OpusState:
def __init__(self, source_dir, eos_token_id=0):
npz_path = find_model_file(source_dir)
self.state_dict = np.load(npz_path)
cfg = load_config_from_state_dict(self.state_dict)
if cfg['dim-vocabs'][0] != cfg['dim-vocabs'][1]:
raise ValueError
if 'Wpos' in self.state_dict:
raise ValueError('Wpos key in state dictionary')
self.state_dict = dict(self.state_dict)
if cfg['tied-embeddings-all']:
cfg['tied-embeddings-src'] = True
cfg['tied-embeddings'] = True
self.share_encoder_decoder_embeddings = cfg['tied-embeddings-src']
self.source_dir = source_dir
self.tokenizer = self.load_tokenizer()
tokenizer_has_eos_token_id = hasattr(self.tokenizer, 'eos_token_id') and self.tokenizer.eos_token_id is not None
eos_token_id = self.tokenizer.eos_token_id if tokenizer_has_eos_token_id else 0
if cfg['tied-embeddings-src']:
self.wemb, self.final_bias = add_emb_entries(self.state_dict['Wemb'], self.state_dict[BIAS_KEY], 1)
self.pad_token_id = self.wemb.shape[0] - 1
cfg['vocab_size'] = self.pad_token_id + 1
else:
self.wemb, _ = add_emb_entries(self.state_dict['encoder_Wemb'], self.state_dict[BIAS_KEY], 1)
self.dec_wemb, self.final_bias = add_emb_entries(self.state_dict['decoder_Wemb'], self.state_dict[BIAS_KEY], 1)
self.pad_token_id = self.wemb.shape[0] - 1
cfg['vocab_size'] = self.pad_token_id + 1
cfg['decoder_vocab_size'] = self.pad_token_id + 1
if cfg['vocab_size'] != self.tokenizer.vocab_size:
raise ValueError(f"Original vocab size {cfg['vocab_size']} and new vocab size {len(self.tokenizer.encoder)} mismatched.")
self.state_keys = list(self.state_dict.keys())
if 'Wtype' in self.state_dict:
raise ValueError('Wtype key in state dictionary')
self._check_layer_entries()
self.cfg = cfg
hidden_size, intermediate_shape = self.state_dict['encoder_l1_ffn_W1'].shape
if hidden_size != cfg['dim-emb']:
raise ValueError(f"Hidden size {hidden_size} and configured size {cfg['dim_emb']} mismatched")
decoder_yml = cast_marian_config(load_yaml(source_dir / 'decoder.yml'))
check_marian_cfg_assumptions(cfg)
self.hf_config = MarianConfig(vocab_size=cfg['vocab_size'], decoder_vocab_size=cfg.get('decoder_vocab_size', cfg['vocab_size']), share_encoder_decoder_embeddings=cfg['tied-embeddings-src'], decoder_layers=cfg['dec-depth'], encoder_layers=cfg['enc-depth'], decoder_attention_heads=cfg['transformer-heads'], encoder_attention_heads=cfg['transformer-heads'], decoder_ffn_dim=cfg['transformer-dim-ffn'], encoder_ffn_dim=cfg['transformer-dim-ffn'], d_model=cfg['dim-emb'], activation_function=cfg['transformer-ffn-activation'], pad_token_id=self.pad_token_id, eos_token_id=eos_token_id, forced_eos_token_id=eos_token_id, bos_token_id=0, max_position_embeddings=cfg['dim-emb'], scale_embedding=True, normalize_embedding='n' in cfg['transformer-preprocess'], static_position_embeddings=not cfg['transformer-train-position-embeddings'], tie_word_embeddings=cfg['tied-embeddings'], dropout=0.1, num_beams=decoder_yml['beam-size'], decoder_start_token_id=self.pad_token_id, bad_words_ids=[[self.pad_token_id]], max_length=512)
def _check_layer_entries(self):
self.encoder_l1 = self.sub_keys('encoder_l1')
self.decoder_l1 = self.sub_keys('decoder_l1')
self.decoder_l2 = self.sub_keys('decoder_l2')
if len(self.encoder_l1) != 16:
warnings.warn(f'Expected 16 keys for each encoder layer, got {len(self.encoder_l1)}')
if len(self.decoder_l1) != 26:
warnings.warn(f'Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}')
if len(self.decoder_l2) != 26:
warnings.warn(f'Expected 26 keys for each decoder layer, got {len(self.decoder_l1)}')
@property
def extra_keys(self):
extra = []
for k in self.state_keys:
if k.startswith('encoder_l') or k.startswith('decoder_l') or k in [CONFIG_KEY, 'Wemb', 'encoder_Wemb', 'decoder_Wemb', 'Wpos', 'decoder_ff_logit_out_b']:
continue
else:
extra.append(k)
return extra
def sub_keys(self, layer_prefix):
return [remove_prefix(k, layer_prefix) for k in self.state_dict if k.startswith(layer_prefix)]
def load_tokenizer(self):
add_special_tokens_to_vocab(self.source_dir, not self.share_encoder_decoder_embeddings)
return MarianTokenizer.from_pretrained(str(self.source_dir))
def load_marian_model(self) -> MarianMTModel:
state_dict, cfg = (self.state_dict, self.hf_config)
if not cfg.static_position_embeddings:
raise ValueError('config.static_position_embeddings should be True')
model = MarianMTModel(cfg)
if 'hidden_size' in cfg.to_dict():
raise ValueError('hidden_size is in config')
load_layers_(model.model.encoder.layers, state_dict, BART_CONVERTER)
load_layers_(model.model.decoder.layers, state_dict, BART_CONVERTER, is_decoder=True)
if self.cfg['tied-embeddings-src']:
wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
model.model.shared.weight = wemb_tensor
model.model.encoder.embed_tokens = model.model.decoder.embed_tokens = model.model.shared
else:
wemb_tensor = nn.Parameter(torch.FloatTensor(self.wemb))
model.model.encoder.embed_tokens.weight = wemb_tensor
decoder_wemb_tensor = nn.Parameter(torch.FloatTensor(self.dec_wemb))
bias_tensor = nn.Parameter(torch.FloatTensor(self.final_bias))
model.model.decoder.embed_tokens.weight = decoder_wemb_tensor
if self.cfg['tied-embeddings']:
model.lm_head.weight.data = model.model.decoder.embed_tokens.weight.data.clone()
model.final_logits_bias = bias_tensor
if 'Wpos' in state_dict:
print('Unexpected: got Wpos')
wpos_tensor = torch.tensor(state_dict['Wpos'])
model.model.encoder.embed_positions.weight = wpos_tensor
model.model.decoder.embed_positions.weight = wpos_tensor
if cfg.normalize_embedding:
if 'encoder_emb_ln_scale_pre' not in state_dict:
raise ValueError('encoder_emb_ln_scale_pre is not in state dictionary')
raise NotImplementedError('Need to convert layernorm_embedding')
if self.extra_keys:
raise ValueError(f'Failed to convert {self.extra_keys}')
if model.get_input_embeddings().padding_idx != self.pad_token_id:
raise ValueError(f'Padding tokens {model.get_input_embeddings().padding_idx} and {self.pad_token_id} mismatched')
return model
|
class OpusState:
def __init__(self, source_dir, eos_token_id=0):
pass
def _check_layer_entries(self):
pass
@property
def extra_keys(self):
pass
def sub_keys(self, layer_prefix):
pass
def load_tokenizer(self):
pass
def load_marian_model(self) -> MarianMTModel:
pass
| 8
| 0
| 28
| 3
| 24
| 2
| 5
| 0.07
| 0
| 8
| 0
| 0
| 6
| 14
| 6
| 6
| 173
| 20
| 144
| 35
| 136
| 10
| 98
| 34
| 91
| 10
| 0
| 2
| 28
|
3,589
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianAttention
|
from typing import Callable, Optional, Union
from torch import nn
import torch
from ...processing_utils import Unpack
from .configuration_marian import MarianConfig
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...utils.deprecation import deprecate_kwarg
class MarianAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, embed_dim: int, num_heads: int, dropout: float=0.0, is_decoder: bool=False, bias: bool=True, is_causal: bool=False, config: Optional[MarianConfig]=None, layer_idx: Optional[int]=None):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if self.head_dim * num_heads != self.embed_dim:
raise ValueError(f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads}).')
self.scaling = self.head_dim ** (-0.5)
self.is_decoder = is_decoder
self.is_causal = is_causal
self.layer_idx = layer_idx
if layer_idx is None and self.is_decoder:
logger.warning_once(f'Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` when creating this class.')
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, cache_position: Optional[torch.Tensor]=None, **kwargs: Unpack[FlashAttentionKwargs]) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
is_cross_attention = key_value_states is not None
bsz, tgt_len = hidden_states.shape[:-1]
src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
q_input_shape = (bsz, tgt_len, -1, self.head_dim)
kv_input_shape = (bsz, src_len, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
is_updated = False
if past_key_values is not None:
if isinstance(past_key_values, EncoderDecoderCache):
is_updated = past_key_values.is_updated.get(self.layer_idx)
if is_cross_attention:
curr_past_key_value = past_key_values.cross_attention_cache
else:
curr_past_key_value = past_key_values.self_attention_cache
else:
curr_past_key_value = past_key_values
current_states = key_value_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_values is not None and is_updated:
key_states = curr_past_key_value.layers[self.layer_idx].keys
value_states = curr_past_key_value.layers[self.layer_idx].values
else:
key_states = self.k_proj(current_states)
value_states = self.v_proj(current_states)
key_states = key_states.view(*kv_input_shape).transpose(1, 2)
value_states = value_states.view(*kv_input_shape).transpose(1, 2)
if past_key_values is not None:
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = curr_past_key_value.update(key_states, value_states, self.layer_idx, {'cache_position': cache_position})
if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache):
past_key_values.is_updated[self.layer_idx] = True
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != 'eager':
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.dropout, scaling=self.scaling, output_attentions=output_attentions, head_mask=layer_head_mask, **kwargs)
attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
attn_output = self.out_proj(attn_output)
return (attn_output, attn_weights)
|
class MarianAttention(nn.Module):
'''Multi-headed attention from 'Attention Is All You Need' paper'''
def __init__(self, embed_dim: int, num_heads: int, dropout: float=0.0, is_decoder: bool=False, bias: bool=True, is_causal: bool=False, config: Optional[MarianConfig]=None, layer_idx: Optional[int]=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, output_attentions: bool=False, cache_position: Optional[torch.Tensor]=None, **kwargs: Unpack[FlashAttentionKwargs]) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
'''Input shape: Batch x Time x Channel'''
pass
| 4
| 2
| 50
| 7
| 35
| 8
| 5
| 0.24
| 1
| 7
| 1
| 0
| 3
| 12
| 3
| 13
| 156
| 23
| 107
| 44
| 86
| 26
| 68
| 27
| 64
| 12
| 1
| 2
| 15
|
3,590
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianDecoder
|
from typing import Callable, Optional, Union
import math
from torch import nn
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
import torch
from .configuration_marian import MarianConfig
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
class MarianDecoder(MarianPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding]=None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.decoder_vocab_size, config.d_model, self.padding_idx)
self.embed_positions = MarianSinusoidalPositionalEmbedding(config.max_position_embeddings, config.d_model, self.padding_idx)
self.layers = nn.ModuleList([MarianDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)])
self.gradient_checkpointing = False
self.post_init()
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None, encoder_attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')
use_cache = False
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError('You must specify exactly one of input_ids or inputs_embeds')
elif input_ids is not None:
input = input_ids
input_shape = input.shape
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
input = inputs_embeds[:, :, -1]
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input)
inputs_embeds = inputs_embeds * self.embed_scale
if use_cache and past_key_values is None:
past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) if encoder_hidden_states is not None else DynamicCache(config=self.config)
if use_cache and isinstance(past_key_values, tuple):
logger.warning_once('Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.')
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
batch_size, seq_length = inputs_embeds.size()[:-1]
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device)
if attention_mask is None and (not is_torchdynamo_compiling()):
mask_seq_length = past_key_values_length + seq_length
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
self_attn_cache = past_key_values.self_attention_cache if isinstance(past_key_values, EncoderDecoderCache) else past_key_values
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, self_attn_cache)
encoder_attention_mask = self._update_cross_attn_mask(encoder_hidden_states, encoder_attention_mask, input_shape, inputs_embeds)
position_ids = self.embed_positions((batch_size, seq_length), past_key_values_length, position_ids=cache_position)
hidden_states = inputs_embeds + position_ids
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if output_attentions and encoder_hidden_states is not None else None
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ['head_mask', 'cross_attn_head_mask']):
if attn_mask is not None:
assert attn_mask.size()[0] == len(self.layers), f'The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.'
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
layer_outputs = decoder_layer(hidden_states, causal_mask, encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, cross_attn_layer_head_mask=cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, past_key_values=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple((v for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None))
return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions)
|
class MarianDecoder(MarianPreTrainedModel):
'''
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding]=None):
pass
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None, encoder_attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
'''
pass
| 3
| 2
| 59
| 8
| 35
| 16
| 10
| 0.5
| 1
| 12
| 4
| 0
| 4
| 9
| 4
| 6
| 246
| 38
| 139
| 42
| 120
| 69
| 74
| 28
| 69
| 36
| 2
| 3
| 41
|
3,591
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianDecoderLayer
|
from typing import Callable, Optional, Union
from torch import nn
import torch
from ...modeling_layers import GradientCheckpointingLayer
from .configuration_marian import MarianConfig
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...utils.deprecation import deprecate_kwarg
from ...activations import ACT2FN
class MarianDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: MarianConfig, layer_idx: Optional[int]=None):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MarianAttention(embed_dim=self.embed_dim, num_heads=config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, is_causal=True, config=config, layer_idx=layer_idx)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = MarianAttention(self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout, is_decoder=True, config=config, layer_idx=layer_idx)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, cross_attn_layer_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, output_attentions: Optional[bool]=False, use_cache: Optional[bool]=True, cache_position: Optional[torch.Tensor]=None) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_values (`Cache`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
residual = hidden_states
hidden_states, self_attn_weights = self.self_attn(hidden_states=hidden_states, past_key_values=past_key_values, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, cache_position=cache_position)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states, cross_attn_weights = self.encoder_attn(hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, layer_head_mask=cross_attn_layer_head_mask, past_key_values=past_key_values, output_attentions=output_attentions, cache_position=cache_position)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
|
class MarianDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: MarianConfig, layer_idx: Optional[int]=None):
pass
@deprecate_kwarg('past_key_value', new_name='past_key_values', version='4.58')
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.Tensor]=None, encoder_attention_mask: Optional[torch.Tensor]=None, layer_head_mask: Optional[torch.Tensor]=None, cross_attn_layer_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, output_attentions: Optional[bool]=False, use_cache: Optional[bool]=True, cache_position: Optional[torch.Tensor]=None) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
'''
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_values (`Cache`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
'''
pass
| 4
| 1
| 58
| 6
| 40
| 13
| 4
| 0.31
| 1
| 4
| 1
| 0
| 2
| 11
| 2
| 12
| 118
| 12
| 81
| 32
| 67
| 25
| 44
| 21
| 41
| 6
| 1
| 1
| 7
|
3,592
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianDecoderWrapper
|
class MarianDecoderWrapper(MarianPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = MarianDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
|
class MarianDecoderWrapper(MarianPreTrainedModel):
'''
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
'''
def __init__(self, config):
pass
def forward(self, *args, **kwargs):
pass
| 3
| 1
| 3
| 0
| 3
| 0
| 1
| 0.67
| 1
| 2
| 1
| 0
| 2
| 1
| 2
| 4
| 12
| 2
| 6
| 4
| 3
| 4
| 6
| 4
| 3
| 1
| 2
| 0
| 2
|
3,593
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianEncoder
|
import torch
from .configuration_marian import MarianConfig
from typing import Callable, Optional, Union
import math
from torch import nn
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
class MarianEncoder(MarianPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MarianEncoderLayer`].
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding]=None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
if embed_tokens is not None:
self.embed_tokens = embed_tokens
else:
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)
self.embed_positions = MarianSinusoidalPositionalEmbedding(config.max_position_embeddings, embed_dim, self.padding_idx)
self.layers = nn.ModuleList([MarianEncoderLayer(config) for _ in range(config.encoder_layers)])
self.gradient_checkpointing = False
self.post_init()
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple[torch.Tensor], BaseModelOutput]:
"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds')
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
attention_mask = self._update_full_mask(attention_mask, inputs_embeds)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
if head_mask is not None:
assert head_mask.size()[0] == len(self.layers), f'The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.'
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
layer_outputs = encoder_layer(hidden_states, attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, output_attentions=output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple((v for v in [hidden_states, encoder_states, all_attentions] if v is not None))
return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
|
class MarianEncoder(MarianPreTrainedModel):
'''
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MarianEncoderLayer`].
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
'''
def __init__(self, config: MarianConfig, embed_tokens: Optional[nn.Embedding]=None):
pass
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.LongTensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None) -> Union[tuple[torch.Tensor], BaseModelOutput]:
'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
pass
| 3
| 2
| 39
| 6
| 24
| 9
| 7
| 0.45
| 1
| 11
| 4
| 0
| 4
| 9
| 4
| 6
| 168
| 28
| 97
| 33
| 83
| 44
| 63
| 24
| 58
| 23
| 2
| 3
| 28
|
3,594
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianEncoderLayer
|
from typing import Callable, Optional, Union
from torch import nn
import torch
from ...modeling_layers import GradientCheckpointingLayer
from .configuration_marian import MarianConfig
from ...activations import ACT2FN
class MarianEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: MarianConfig, layer_idx: Optional[int]=None):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MarianAttention(embed_dim=self.embed_dim, num_heads=config.encoder_attention_heads, dropout=config.attention_dropout, config=config, layer_idx=layer_idx)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool]=False) -> tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
hidden_states = self.final_layer_norm(hidden_states)
if hidden_states.dtype == torch.float16 and (torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs
|
class MarianEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: MarianConfig, layer_idx: Optional[int]=None):
pass
def forward(self, hidden_states: torch.FloatTensor, attention_mask: torch.FloatTensor, layer_head_mask: torch.FloatTensor, output_attentions: Optional[bool]=False) -> tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
'''
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
'''
pass
| 3
| 1
| 33
| 3
| 25
| 6
| 2
| 0.22
| 1
| 3
| 1
| 0
| 2
| 9
| 2
| 12
| 68
| 7
| 50
| 22
| 41
| 11
| 32
| 16
| 29
| 3
| 1
| 1
| 4
|
3,595
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianForCausalLM
|
from typing import Callable, Optional, Union
from torch.nn import CrossEntropyLoss
from torch import nn
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...generation import GenerationMixin
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
import torch
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
class MarianForCausalLM(MarianPreTrainedModel, GenerationMixin):
_tied_weights_keys = ['lm_head.weight']
def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MarianDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None, encoder_attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.LongTensor]=None) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
"""
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, MarianForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model.decoder(input_ids=input_ids, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, head_mask=head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
labels = labels.to(logits.device)
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions)
|
class MarianForCausalLM(MarianPreTrainedModel, GenerationMixin):
def __init__(self, config):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def set_decoder(self, decoder):
pass
def get_decoder(self):
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, encoder_hidden_states: Optional[torch.FloatTensor]=None, encoder_attention_mask: Optional[torch.FloatTensor]=None, head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.LongTensor]=None) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
'''
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, MarianForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True
```'''
pass
| 8
| 1
| 19
| 3
| 9
| 8
| 2
| 0.84
| 2
| 6
| 2
| 0
| 8
| 2
| 9
| 11
| 186
| 33
| 83
| 37
| 56
| 70
| 42
| 20
| 32
| 7
| 2
| 1
| 16
|
3,596
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianMTModel
|
from typing import Callable, Optional, Union
from torch.nn import CrossEntropyLoss
from torch import nn
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...generation import GenerationMixin
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
import torch
from .configuration_marian import MarianConfig
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
@auto_docstring(custom_intro='\n The Marian Model with a language modeling head. Can be used for summarization.\n ')
class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
base_model_prefix = 'model'
_keys_to_ignore_on_load_missing = ['final_logits_bias', 'encoder.embed_positions.weight', 'decoder.embed_positions.weight']
_keys_to_ignore_on_save = ['model.encoder.embed_positions.weight', 'model.decoder.embed_positions.weight']
_tied_weights_keys = ['model.encoder.embed_tokens.weight', 'model.decoder.embed_tokens.weight', 'lm_head.weight']
def __init__(self, config: MarianConfig):
super().__init__(config)
self.model = MarianModel(config)
target_vocab_size = config.vocab_size if config.share_encoder_decoder_embeddings else config.decoder_vocab_size
self.register_buffer('final_logits_bias', torch.zeros((1, target_vocab_size)))
self.lm_head = nn.Linear(config.d_model, target_vocab_size, bias=False)
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int]=None, mean_resizing: bool=True) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
if self.config.share_encoder_decoder_embeddings:
self._resize_final_logits_bias(new_num_tokens)
return new_embeddings
def _resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of=None, *args) -> nn.Embedding:
old_embeddings = self.get_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens, pad_to_multiple_of)
self.set_input_embeddings(new_embeddings)
new_num_tokens = new_embeddings.weight.shape[0]
if self.config.share_encoder_decoder_embeddings:
self.config.decoder_vocab_size = new_num_tokens
if self.config.share_encoder_decoder_embeddings and self.get_output_embeddings() is not None and (not self.config.tie_word_embeddings):
old_lm_head = self.get_output_embeddings()
new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
self.set_output_embeddings(new_lm_head)
return self.get_input_embeddings()
def resize_decoder_token_embeddings(self, new_num_tokens):
if self.config.share_encoder_decoder_embeddings:
raise ValueError('`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` is `True`. Please use `resize_token_embeddings` instead.')
old_embeddings = self.model.get_decoder_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.model.set_decoder_input_embeddings(new_embeddings)
if self.get_output_embeddings() is not None and (not self.config.tie_word_embeddings):
old_lm_head = self.get_output_embeddings()
new_lm_head = self._get_resized_lm_head(old_lm_head, new_num_tokens)
self.set_output_embeddings(new_lm_head)
model_embeds = self.model.get_decoder_input_embeddings()
if new_num_tokens is None:
return model_embeds
self.config.decoder_vocab_size = new_num_tokens
self.tie_weights()
self._resize_final_logits_bias(new_num_tokens)
return model_embeds
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer('final_logits_bias', new_bias)
def set_output_embeddings(self, new_embeddings: nn.Embedding):
self.lm_head = new_embeddings
def tie_weights(self):
"""
Tie the weights between the input embeddings and the output embeddings.
If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
weights instead.
"""
output_embeddings = self.get_output_embeddings()
if output_embeddings is not None and getattr(self.config, 'tie_word_embeddings', True):
word_embeddings = self.get_decoder().get_input_embeddings()
self._tie_or_clone_weights(output_embeddings, word_embeddings)
if getattr(self.config, 'is_encoder_decoder', False) and getattr(self.config, 'tie_encoder_decoder', False):
if hasattr(self, self.base_model_prefix):
self = getattr(self, self.base_model_prefix)
tied_weights = self._tie_encoder_decoder_weights(self.encoder, self.decoder, self.base_model_prefix, 'encoder')
self._dynamic_tied_weights_keys = tied_weights
for module in self.modules():
if hasattr(module, '_tie_weights'):
module._tie_weights()
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[Union[tuple[torch.Tensor], BaseModelOutput]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Seq2SeqLMOutput:
"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, MarianMTModel
>>> src = "fr" # source language
>>> trg = "en" # target language
>>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
>>> model = MarianMTModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> sample_text = "où est l'arrêt de bus ?"
>>> batch = tokenizer([sample_text], return_tensors="pt")
>>> generated_ids = model.generate(**batch)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
"Where's the bus stop?"
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning('The `use_cache` argument is changed to `False` since `labels` is provided.')
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, head_mask=head_mask, decoder_head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position)
lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.decoder_vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return (masked_lm_loss,) + output if masked_lm_loss is not None else output
return Seq2SeqLMOutput(loss=masked_lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions)
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
|
@auto_docstring(custom_intro='\n The Marian Model with a language modeling head. Can be used for summarization.\n ')
class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
def __init__(self, config: MarianConfig):
pass
def get_encoder(self):
pass
def get_decoder(self):
pass
def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int]=None, mean_resizing: bool=True) -> nn.Embedding:
pass
def _resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of=None, *args) -> nn.Embedding:
pass
def resize_decoder_token_embeddings(self, new_num_tokens):
pass
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
pass
def set_output_embeddings(self, new_embeddings: nn.Embedding):
pass
def tie_weights(self):
'''
Tie the weights between the input embeddings and the output embeddings.
If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
weights instead.
'''
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[Union[tuple[torch.Tensor], BaseModelOutput]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Seq2SeqLMOutput:
'''
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, MarianMTModel
>>> src = "fr" # source language
>>> trg = "en" # target language
>>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
>>> model = MarianMTModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> sample_text = "où est l'arrêt de bus ?"
>>> batch = tokenizer([sample_text], return_tensors="pt")
>>> generated_ids = model.generate(**batch)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
"Where's the bus stop?"
```
'''
pass
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
pass
| 14
| 2
| 15
| 2
| 12
| 2
| 3
| 0.14
| 2
| 10
| 4
| 0
| 12
| 4
| 13
| 15
| 228
| 36
| 168
| 69
| 130
| 24
| 99
| 46
| 85
| 8
| 2
| 2
| 34
|
3,597
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianModel
|
from .configuration_marian import MarianConfig
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from typing import Callable, Optional, Union
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
from torch import nn
import copy
import torch
@auto_docstring
class MarianModel(MarianPreTrainedModel):
_tied_weights_keys = ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight']
def __init__(self, config: MarianConfig):
super().__init__(config)
padding_idx, vocab_size = (config.pad_token_id, config.vocab_size)
self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
if self.config.share_encoder_decoder_embeddings:
encoder_embed_tokens = decoder_embed_tokens = self.shared
else:
encoder_embed_tokens = copy.deepcopy(self.shared)
decoder_embed_tokens = copy.deepcopy(self.shared)
self.shared = None
self.encoder = MarianEncoder(config, encoder_embed_tokens)
self.decoder = MarianDecoder(config, decoder_embed_tokens)
self.post_init()
def get_input_embeddings(self):
return self.get_encoder().get_input_embeddings()
def set_input_embeddings(self, value):
if self.config.share_encoder_decoder_embeddings:
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
else:
self.encoder.embed_tokens = value
def get_decoder_input_embeddings(self):
if self.config.share_encoder_decoder_embeddings:
raise ValueError('`get_decoder_input_embeddings` should not be called if `config.share_encoder_decoder_embeddings` is `True`. Please use `get_input_embeddings` instead.')
return self.get_decoder().get_input_embeddings()
def set_decoder_input_embeddings(self, value):
if self.config.share_encoder_decoder_embeddings:
raise ValueError('`config.share_encoder_decoder_embeddings` is set to `True` meaning the decoder input embeddings are shared with the encoder. In order to set the decoder input embeddings, you should simply set the encoder input embeddings by calling `set_input_embeddings` with the appropriate embeddings.')
self.decoder.embed_tokens = value
def get_encoder(self):
return self.encoder
def resize_decoder_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
if self.config.share_encoder_decoder_embeddings:
raise ValueError('`resize_decoder_token_embeddings` should not be called if `config.share_encoder_decoder_embeddings` is `True`. Please use `resize_token_embeddings` instead.')
old_embeddings = self.get_decoder_input_embeddings()
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.set_decoder_input_embeddings(new_embeddings)
model_embeds = self.get_decoder_input_embeddings()
if new_num_tokens is None:
return model_embeds
self.config.decoder_vocab_size = new_num_tokens
self.tie_weights()
return model_embeds
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[Union[tuple[torch.Tensor], BaseModelOutput]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Seq2SeqModelOutput:
"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
Example:
```python
>>> from transformers import AutoTokenizer, MarianModel
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer(
... "<pad> Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
... return_tensors="pt",
... add_special_tokens=False,
... )
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 26, 512]
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
elif return_dict and (not isinstance(encoder_outputs, BaseModelOutput)):
encoder_outputs = BaseModelOutput(last_hidden_state=encoder_outputs[0], hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
|
@auto_docstring
class MarianModel(MarianPreTrainedModel):
def __init__(self, config: MarianConfig):
pass
def get_input_embeddings(self):
pass
def set_input_embeddings(self, value):
pass
def get_decoder_input_embeddings(self):
pass
def set_decoder_input_embeddings(self, value):
pass
def get_encoder(self):
pass
def resize_decoder_token_embeddings(self, new_num_tokens: int) -> nn.Embedding:
pass
@auto_docstring
def forward(self, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, decoder_input_ids: Optional[torch.LongTensor]=None, decoder_attention_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, decoder_head_mask: Optional[torch.Tensor]=None, cross_attn_head_mask: Optional[torch.Tensor]=None, encoder_outputs: Optional[Union[tuple[torch.Tensor], BaseModelOutput]]=None, past_key_values: Optional[Cache]=None, inputs_embeds: Optional[torch.FloatTensor]=None, decoder_inputs_embeds: Optional[torch.FloatTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, cache_position: Optional[torch.Tensor]=None) -> Seq2SeqModelOutput:
'''
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
Marian uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
`past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
`past_key_values`).
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
Example:
```python
>>> from transformers import AutoTokenizer, MarianModel
>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer(
... "<pad> Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
... return_tensors="pt",
... add_special_tokens=False,
... )
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 26, 512]
```'''
pass
| 11
| 1
| 19
| 2
| 14
| 3
| 3
| 0.22
| 1
| 10
| 5
| 0
| 9
| 3
| 9
| 11
| 181
| 28
| 126
| 38
| 97
| 28
| 58
| 20
| 48
| 10
| 2
| 1
| 24
|
3,598
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianPreTrainedModel
|
from typing import Callable, Optional, Union
from ...modeling_attn_mask_utils import AttentionMaskConverter, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
from torch import nn
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
import torch
from .configuration_marian import MarianConfig
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
@auto_docstring
class MarianPreTrainedModel(PreTrainedModel):
config: MarianConfig
base_model_prefix = 'model'
supports_gradient_checkpointing = True
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = True
def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, MarianSinusoidalPositionalEmbedding):
module._init_weight()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {'attention_mask': input_ids.ne(pad_token), 'input_ids': input_ids, 'decoder_input_ids': input_ids}
return dummy_inputs
def _update_full_mask(self, attention_mask: Union[torch.Tensor, None], inputs_embeds: torch.Tensor):
if attention_mask is not None:
if 'flash' in self.config._attn_implementation:
attention_mask = attention_mask if 0 in attention_mask else None
elif self.config._attn_implementation == 'sdpa':
attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
elif self.config._attn_implementation == 'flex_attention':
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
else:
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
return attention_mask
def _update_causal_mask(self, attention_mask: Optional[Union[torch.Tensor, 'BlockMask']], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache):
if self.config._attn_implementation == 'flex_attention':
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
elif attention_mask is None:
attention_mask = make_flex_block_causal_mask(torch.ones(size=(input_tensor.shape[0], input_tensor.shape[1]), device=attention_mask.device))
return attention_mask
if 'flash' in self.config._attn_implementation:
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
if self.config._attn_implementation == 'sdpa' and (not using_compilable_cache):
if AttentionMaskConverter._ignore_causal_mask_sdpa(attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, is_training=self.training):
return None
dtype = input_tensor.dtype
sequence_length = input_tensor.shape[1]
if using_compilable_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0])
if self.config._attn_implementation == 'sdpa' and attention_mask is not None and (attention_mask.device.type in ['cuda', 'xpu', 'npu']):
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone()
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(padding_mask, min_dtype)
return causal_mask
def _update_cross_attn_mask(self, encoder_hidden_states: Union[torch.Tensor, None], encoder_attention_mask: Union[torch.Tensor, None], input_shape: torch.Size, inputs_embeds: torch.Tensor):
if encoder_hidden_states is not None and encoder_attention_mask is not None:
if 'flash' in self.config._attn_implementation:
encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
elif self.config._attn_implementation == 'sdpa':
encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
elif self.config._attn_implementation == 'flex_attention':
if isinstance(encoder_attention_mask, torch.Tensor):
encoder_attention_mask = make_flex_block_causal_mask(encoder_attention_mask, query_length=input_shape[-1], is_causal=False)
else:
encoder_attention_mask = _prepare_4d_attention_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1])
return encoder_attention_mask
|
@auto_docstring
class MarianPreTrainedModel(PreTrainedModel):
def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
pass
@property
def dummy_inputs(self):
pass
def _update_full_mask(self, attention_mask: Union[torch.Tensor, None], inputs_embeds: torch.Tensor):
pass
def _update_causal_mask(self, attention_mask: Optional[Union[torch.Tensor, 'BlockMask']], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache):
pass
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs):
'''
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
'''
pass
def _update_cross_attn_mask(self, encoder_hidden_states: Union[torch.Tensor, None], encoder_attention_mask: Union[torch.Tensor, None], input_shape: torch.Size, inputs_embeds: torch.Tensor):
pass
| 10
| 1
| 11
| 0
| 11
| 0
| 4
| 0
| 1
| 1
| 1
| 6
| 2
| 0
| 2
| 2
| 28
| 2
| 26
| 11
| 22
| 0
| 19
| 10
| 16
| 6
| 1
| 2
| 7
|
3,599
|
huggingface/pytorch-pretrained-BERT
|
huggingface_pytorch-pretrained-BERT/src/transformers/models/marian/modeling_marian.py
|
transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding
|
from typing import Callable, Optional, Union
import numpy as np
from torch import nn
import torch
class MarianSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int]=None) -> None:
super().__init__(num_positions, embedding_dim)
def _init_weight(self):
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = self.weight.shape
position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
out = torch.empty(n_pos, dim, dtype=self.weight.dtype, requires_grad=False)
sentinel = dim // 2 if dim % 2 == 0 else dim // 2 + 1
out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2]))
out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2]))
self.weight = nn.Parameter(out, requires_grad=False)
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int=0, position_ids: Optional[torch.Tensor]=None) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
if position_ids is None:
bsz, seq_len = input_ids_shape[:2]
position_ids = torch.arange(past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device)
return super().forward(position_ids)
|
class MarianSinusoidalPositionalEmbedding(nn.Embedding):
'''This module produces sinusoidal positional embeddings of any length.'''
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int]=None) -> None:
pass
def _init_weight(self):
'''
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
'''
pass
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int=0, position_ids: Optional[torch.Tensor]=None) -> torch.Tensor:
'''`input_ids_shape` is expected to be [bsz x seqlen].'''
pass
| 5
| 3
| 8
| 0
| 7
| 2
| 1
| 0.3
| 1
| 4
| 0
| 0
| 2
| 1
| 3
| 3
| 32
| 3
| 23
| 12
| 17
| 7
| 17
| 10
| 13
| 2
| 1
| 0
| 4
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.