Functions — github.com/adapter-hub/adapters

Methodforward

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

src/adapters/models/bart/modeling_bart.py:144

Methodforward

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

src/adapters/models/bart/modeling_bart.py:209

Methodforward

( self, input_ids: torch.LongTensor = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,

src/adapters/models/bart/mixin_bart.py:75

Methodforward

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/

src/adapters/models/bart/adapter_model.py:49

Methodforward

( self, input_ids=None, attention_mask=None, position_ids=None, past_k

src/adapters/models/mistral/adapter_model.py:57

Methodforward

( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Te

src/adapters/models/mistral/modeling_mistral.py:55

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,

src/adapters/models/mistral/modeling_mistral.py:124

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = Non

src/adapters/models/xmod/modeling_xmod.py:33

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/xmod/modeling_xmod.py:140

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, lang_ids: torch.Tensor)

src/adapters/models/xmod/modeling_xmod.py:148

Methodforward

(self, *args, **kwargs)

src/adapters/models/xmod/mixin_xmod.py:43

Methodforward

( self, input_ids: Optional[torch.Tensor] = None, lang_ids: Optional[torch.LongTensor]

src/adapters/models/xmod/adapter_model.py:46

Methodforward

( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False )

src/adapters/models/vit/modeling_vit.py:40

Methodforward

( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None,

src/adapters/models/vit/modeling_vit.py:93

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/vit/modeling_vit.py:105

Methodforward

( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor]

src/adapters/models/vit/adapter_model.py:36

Methodforward

(self, hidden_states, input_tensor)

src/adapters/models/deberta/modeling_deberta.py:36

Methodforward

(self, hidden_states, input_tensor)

src/adapters/models/deberta/modeling_deberta.py:44

Methodforward

(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None)

src/adapters/models/deberta/modeling_deberta.py:52

Methodforward

Call the module Args: hidden_states (`torch.FloatTensor`): Input states to the module usually the output

src/adapters/models/deberta/modeling_deberta.py:114

Methodforward

( self, input_ids=None, attention_mask=None, token_type_ids=None, posi

src/adapters/models/deberta/adapter_model.py:40

Methodforward

Input shape: Batch x Time x Channel

src/adapters/models/clip/modeling_clip.py:34

Methodforward

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

src/adapters/models/clip/modeling_clip.py:99

Methodforward

( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.Flo

src/adapters/models/clip/adapter_model.py:31

Methodforward

(self, hidden_states, input_tensor)

src/adapters/models/deberta_v2/modeling_deberta_v2.py:37

Methodforward

(self, hidden_states, input_tensor)

src/adapters/models/deberta_v2/modeling_deberta_v2.py:46

Methodforward

(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None)

src/adapters/models/deberta_v2/modeling_deberta_v2.py:55

Methodforward

Call the module Args: hidden_states (`torch.FloatTensor`): Input states to the module usually the output

src/adapters/models/deberta_v2/modeling_deberta_v2.py:113

Methodforward

( self, input_ids=None, attention_mask=None, token_type_ids=None, posi

src/adapters/models/deberta_v2/adapter_model.py:40

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = Non

src/adapters/models/bert/modeling_bert.py:39

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,

src/adapters/models/bert/modeling_bert.py:156

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/bert/modeling_bert.py:266

Methodforward

( self, input_ids=None, attention_mask=None, token_type_ids=None, posi

src/adapters/models/bert/adapter_model.py:45

Methodforward

( self, *args, input_features: Optional[torch.FloatTensor] = None, labels: Opt

src/adapters/models/whisper/mixin_whisper.py:129

Methodforward

Input shape: Batch x Time x Channel

src/adapters/models/whisper/modeling_whisper.py:44

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, layer_head_

src/adapters/models/whisper/modeling_whisper.py:123

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,

src/adapters/models/whisper/modeling_whisper.py:165

Methodforward

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/

src/adapters/models/whisper/adapter_model.py:56

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = Non

src/adapters/models/xlm_roberta/modeling_xlm_roberta.py:44

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None,

src/adapters/models/xlm_roberta/modeling_xlm_roberta.py:161

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/xlm_roberta/modeling_xlm_roberta.py:274

Methodforward

( self, input_ids=None, attention_mask=None, token_type_ids=None, posi

src/adapters/models/xlm_roberta/adapter_model.py:42

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/bert_generation/modeling_bert_generation.py:37

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = Non

src/adapters/models/bert_generation/modeling_bert_generation.py:46

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/bert_generation/modeling_bert_generation.py:166

Methodforward

( self, input_ids=None, attention_mask=None, position_ids=None, head_m

src/adapters/models/bert_generation/adapter_model.py:40

Methodforward

Parameters: query: torch.tensor(bs, seq_length, dim) key: torch.tensor(bs, seq_length, dim) value: torch.

src/adapters/models/distilbert/modeling_distilbert.py:49

Methodforward

Parameters: query: torch.tensor(bs, seq_length, dim) key: torch.tensor(bs, seq_length, dim) value: torch.

src/adapters/models/distilbert/modeling_distilbert.py:124

Methodforward

Parameters: query: torch.tensor(bs, seq_length, dim) key: torch.tensor(bs, seq_length, dim) value: torch.

src/adapters/models/distilbert/modeling_distilbert.py:200

Methodforward

( self, input_ids=None, attention_mask=None, head_mask=None, inputs_em

src/adapters/models/distilbert/adapter_model.py:63

Methodforward

(self, *args, **kwargs)

src/adapters/models/distilbert/mixin_distilbert.py:45

Methodforward

( self, hidden_states: torch.FloatTensor, layer_past: Optional[Cache] = None,

src/adapters/models/gptj/modeling_gptj.py:31

Methodforward

( self, hidden_states: Optional[torch.FloatTensor], layer_past: Optional[Cache] = None

src/adapters/models/gptj/modeling_gptj.py:109

Methodforward

( self, input_ids=None, past_key_values=None, attention_mask=None, tok

src/adapters/models/gptj/adapter_model.py:56

Methodforward

( self, hidden_states: Optional[Tuple[torch.FloatTensor]], past_key_value: Optional[Ca

src/adapters/models/gpt2/modeling_gpt2.py:36

Methodforward

( self, hidden_states: Optional[Tuple[torch.FloatTensor]], past_key_value: Optional[Ca

src/adapters/models/gpt2/modeling_gpt2.py:130

Methodforward

( self, input_ids=None, past_key_values=None, attention_mask=None, tok

src/adapters/models/gpt2/adapter_model.py:68

Methodforward

( self, input_ids=None, attention_mask=None, token_type_ids=None, posi

src/adapters/models/electra/adapter_model.py:42

Methodforward

( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = Non

src/adapters/models/electra/modeling_electra.py:15

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/electra/modeling_electra.py:136

Methodforward

(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor)

src/adapters/models/electra/modeling_electra.py:144

Methodforward

r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/

src/adapters/models/mbart/adapter_model.py:50

Methodforward

Input shape: Batch x Time x Channel

src/adapters/models/mbart/modeling_mbart.py:46

Methodforward

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

src/adapters/models/mbart/modeling_mbart.py:140

Methodforward

Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (

src/adapters/models/mbart/modeling_mbart.py:203

Methodforward

( self, input_ids=None, attention_mask=None, decoder_input_ids=None, d

src/adapters/models/mt5/adapter_model.py:64

Methodforward

(self, hidden_states)

src/adapters/models/mt5/modeling_mt5.py:45

Methodforward

Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).

src/adapters/models/mt5/modeling_mt5.py:56

Methodforward

( self, hidden_states, attention_mask=None, position_bias=None, layer_

src/adapters/models/mt5/modeling_mt5.py:192

Methodforward

( self, hidden_states, key_value_states, attention_mask=None, position

src/adapters/models/mt5/modeling_mt5.py:222

Methodforward

( self, input_ids=None, attention_mask=None, encoder_hidden_states=None,

src/adapters/models/mt5/modeling_mt5.py:256

Methodforward

( self, *args, input_ids: Optional[torch.LongTensor] = None, labels: Optional[

src/adapters/models/t5/mixin_t5.py:116

Methodforward

( self, input_ids=None, attention_mask=None, decoder_input_ids=None, d

src/adapters/models/t5/adapter_model.py:64

Methodforward

(self, hidden_states)

src/adapters/models/t5/modeling_t5.py:45

Methodforward

Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).

src/adapters/models/t5/modeling_t5.py:56

Methodforward

( self, hidden_states, attention_mask=None, position_bias=None, layer_

src/adapters/models/t5/modeling_t5.py:196

Methodforward

( self, hidden_states, key_value_states, attention_mask=None, position

src/adapters/models/t5/modeling_t5.py:226

Methodforward

( self, input_ids=None, attention_mask=None, encoder_hidden_states=None,

src/adapters/models/t5/modeling_t5.py:260

Methodforward

(self, outputs, cls_output=None, attention_mask=None, return_dict=False, **kwargs)

tests/test_misc/test_adapter_custom_head.py:24

Methodforward

(self, x)

tests/test_misc/test_adapter_trainer/test_adapter_trainer.py:592

Methodforward

(self, x)

tests/test_methods/test_all_custom_interfaces.py:56

Methodforward

(self, x)

tests/test_methods/test_all_custom_interfaces.py:73

Methodforward

(self, x)

tests/test_methods/test_all_custom_interfaces.py:85

Methodforward

(self, pixel_values)

tests/test_methods/test_all_custom_interfaces.py:103

Methodforward

(self, pixel_values, **kwargs)

tests/test_methods/test_all_custom_interfaces.py:125

Methodforward_pre_hook

(module, input)

tests/test_methods/method_test_impl/heads/test_adapter_heads.py:428

Methodforward_pre_hook

(module, input)

tests/test_methods/method_test_impl/peft/test_adapter_common.py:119

Methodfreeze_adapter

Freezes/ unfreezes an adapter module. Args: adapter_name (str): The name of the adapter to freeze/ unfreeze. freeze (

src/adapters/methods/adapter_layer_base.py:192

Methodfreeze_embeddings_and_language_adapters

Freeze the embeddings and language adapters of the model. Usually, this is applied before the model is fine-tuned on a downstream tas

src/adapters/models/xmod/mixin_xmod.py:60

Methodfreeze_encoder

Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will not be updated during

src/adapters/models/whisper/adapter_model.py:45

Methodfrom_dict

Creates a config class from a Python dict.

src/adapters/configuration/adapter_config.py:58

Methodget_adapter

If self.base_model is self, must inherit from a class that implements this method, to preclude infinite recursion

src/adapters/model_mixin.py:2636

Methodget_adapter

Returns the adapter module with the given name. Args: adapter_name (str): The name of the adapter module.

src/adapters/methods/adapter_layer_base.py:204

Methodget_adapter

(self, adapter_name)

src/adapters/methods/prefix_tuning.py:450

Methodget_adapter_config

(self)

tests/test_misc/test_adapter_composition.py:271

Methodget_adapter_config

(self)

tests/test_misc/test_adapter_composition.py:278

Methodget_adapter_config

(self)

tests/test_misc/test_adapter_composition.py:285

Functionget_adapter_info

Retrieves information about a specific adapter. Args: adapter_id (str): The identifier of the adapter to retrieve. Returns:

src/adapters/utils.py:848

Methodget_context

(cls)

src/adapters/context.py:196

Methodget_contexts

(cls)

src/adapters/context.py:190

Functions1,559 in github.com/adapter-hub/adapters