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

⨍ Functions 1,559◇ Types & classes 451↳ Endpoints 1

Inverts the composition operation between existing and injected weights.

src/adapters/methods/lora.py:381

Methodcompose_average

For averaging the output representations of multiple adapters.

src/adapters/methods/adapter_layer_base.py:558

Methodcompose_fuse

For fusing multiple adapters using adapter fusion. NOTE: This method has no default implementation.

src/adapters/methods/adapter_layer_base.py:401

Methodcompose_fuse

Performs adapter fusion with the given adapters for the given input.

src/adapters/methods/bottleneck.py:254

Methodcompose_multi_task

For splitting to multiple adapters along the task_ids.

src/adapters/methods/adapter_layer_base.py:466

Methodcompose_parallel

For parallel execution of the adapters on the same input. This means that the input is repeated N times before feeding it to the adap

src/adapters/methods/adapter_layer_base.py:490

Methodcompose_single

(self, adapter_setup: str, state: PrefixTuningState, lvl: int = 0)

src/adapters/methods/prefix_tuning.py:557

Methodcompose_single

(self, adapter_setup: str, state: LoRAState, lvl: int = 0)

src/adapters/methods/lora.py:939

Methodcompose_split

For splitting to multiple adapters along the sequence length dimension. NOTE: This method has no default implementation.

src/adapters/methods/adapter_layer_base.py:411

Methodcompose_split

Splits the given input between the given adapters.

src/adapters/methods/bottleneck.py:305

Methodcompose_stack

For sequentially stacking multiple adapters.

src/adapters/methods/adapter_layer_base.py:384

Functioncompute_metrics

(p: EvalPrediction)

examples/pytorch/adapterfusion/run_fusion_glue.py:194

Functioncompute_metrics

(eval_predictions)

examples/pytorch/multiple-choice/run_swag.py:435

Functioncompute_metrics

examples/pytorch/translation/run_translation.py:576

Functioncompute_metrics

examples/pytorch/token-classification/run_ner.py:534

Functioncompute_metrics

(p: EvalPrediction)

examples/pytorch/text-classification/run_glue.py:516

Functioncompute_metrics

(p: EvalPrediction)

examples/pytorch/question-answering/run_qa.py:635

Functioncompute_metrics

(p: EvalPrediction)

examples/pytorch/question-answering/run_seq2seq_qa.py:626

Functioncompute_metrics

examples/pytorch/summarization/run_summarization.py:648

Functioncompute_metrics

examples/pytorch/language-modeling/run_mlm.py:602

Functioncompute_metrics

examples/pytorch/language-modeling/run_clm.py:578

Methodconvert_static_to_flex_head

Loads a prediction head module from the given state dict, which contains a static head checkpoint. Args: state_dict (dic

src/adapters/loading.py:1056

Methodcopy_from

(self, module: Linear4bit)

src/adapters/methods/lora.py:983

Methodcopy_from

(self, module: Linear8bitLt)

src/adapters/methods/lora.py:1022

Methodcreate_custom_forward

src/adapters/models/mt5/modeling_mt5.py:424

Methodcreate_custom_forward

src/adapters/models/t5/modeling_t5.py:428

Methodcreate_optimizer

Setup the optimizer. We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the

src/adapters/trainer.py:101

Methodcreate_twin_models

tests/test_methods/test_on_custom_interface.py:78

Methodcustom_forward

src/adapters/models/mt5/modeling_mt5.py:425

Methodcustom_forward

src/adapters/models/t5/modeling_t5.py:429

Methoddecoder_invertible_adapters_forward

(hidden_states, rev=False)

src/adapters/models/encoder_decoder/mixin_encoder_decoder.py:51

Methoddecorator

src/adapters/heads/model_mixin.py:65

Methoddelete_adapter

Deletes the adapter with the specified name from the model. Args: adapter_name (str): The name of the adapter.

src/adapters/model_mixin.py:2160

Methoddelete_embeddings

src/adapters/model_mixin.py:402

Methoddelete_fusion_layer

(self, adapter_names: Union[List, str])

src/adapters/methods/adapter_layer_base.py:171

Methoddelete_invertible_adapter

(self, adapter_name: str)

src/adapters/model_mixin.py:221

src/adapters/methods/lora.py:134

src/adapters/methods/lora.py:286

src/adapters/methods/lora.py:369

Converts all PrefixTuning modules into FlatPrefixTuning modules.

src/adapters/methods/prefix_tuning.py:132

Methodenable_adapters

Enables/ disables a set of adapter modules within the layer. Args: adapter_setup (AdapterCompositionBlock): The adapter setup to

src/adapters/methods/adapter_layer_base.py:174

Methodenable_adapters

( self, adapter_setup: AdapterCompositionBlock, unfreeze_adapters: bool, unfre

src/adapters/methods/prefix_tuning.py:428

Methodenable_invertible_adapters

(self, adapter_names)

src/adapters/model_mixin.py:230

Functionencode_batch

examples/pytorch/dependency-parsing/preprocessing.py:25

( self, eval_dataset: Optional[Dataset] = None, eval_examples=None, ignore_key

examples/pytorch/question-answering/trainer_seq2seq_qa.py:36

Methodextra_repr

src/adapters/heads/dependency_parsing.py:40

Methodextract_input_ids

tests/test_methods/base.py:76

(self, *args, **kwargs)

src/adapters/model_mixin.py:2659

(self, hidden_states: torch.Tensor)

src/adapters/methods/prompt_tuning.py:180

src/adapters/methods/modeling.py:28

(self, x, residual_input, output_gating=False)

src/adapters/methods/modeling.py:210

(self, x, residual_input, output_gating=False)

src/adapters/methods/modeling.py:309

(self, query, key, value, residual, output_attentions: bool = False)

src/adapters/methods/modeling.py:412

(self, x, c=[], rev=False)

src/adapters/methods/modeling.py:493

(self, x, c=[], rev=False)

src/adapters/methods/modeling.py:552

(self, x: torch.Tensor)

src/adapters/methods/modeling.py:728

(self, batch_size)

src/adapters/methods/prefix_tuning.py:58

(self, batch_size)

src/adapters/methods/prefix_tuning.py:102

(self, batch_size)

src/adapters/methods/prefix_tuning.py:141

(self, *args, **kwargs)

src/adapters/methods/prefix_tuning.py:258

( self, key_states, value_states, residual_input, attention_mask=None,

src/adapters/methods/prefix_tuning.py:625

(self, hidden_states: torch.Tensor, rev=False)

src/adapters/methods/invertible.py:60

src/adapters/methods/reft.py:55

(self, hidden_states: torch.Tensor)

src/adapters/methods/reft.py:171

(self, hidden_states: torch.Tensor)

src/adapters/methods/reft.py:213

Forward pass through the adapter layer. Args: hidden_states (torch.Tensor): Input hidden states to the adapter layer.

src/adapters/methods/bottleneck.py:384

src/adapters/methods/lora.py:44

src/adapters/methods/lora.py:54

(self, hidden_states: Optional[torch.Tensor], layer_input: torch.Tensor)

src/adapters/methods/lora.py:162

( self, hidden_states: Optional[torch.Tensor], layer_input: torch.Tensor, )

src/adapters/methods/lora.py:208

(self, hidden_states: Optional[torch.Tensor], layer_input: torch.Tensor)

src/adapters/methods/lora.py:385

(self, input_states: torch.Tensor)

src/adapters/methods/lora.py:947

(self, x: torch.Tensor)

src/adapters/methods/lora.py:1249

src/adapters/heads/dependency_parsing.py:52

( self, outputs, cls_output=None, attention_mask=None, return_dict=Fal

src/adapters/heads/dependency_parsing.py:91

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

src/adapters/heads/base.py:159

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

src/adapters/heads/base.py:227

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

src/adapters/heads/base.py:290

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

src/adapters/heads/base.py:404

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

src/adapters/heads/base.py:492

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

src/adapters/heads/language_modeling.py:102

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

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

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

src/adapters/models/beit/modeling_beit.py:41

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

src/adapters/models/beit/modeling_beit.py:114

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

src/adapters/models/roberta/adapter_model.py:41

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

src/adapters/models/roberta/modeling_roberta.py:44

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

src/adapters/models/roberta/modeling_roberta.py:161

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

src/adapters/models/roberta/modeling_roberta.py:274

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

src/adapters/models/albert/modeling_albert.py:36

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

src/adapters/models/albert/modeling_albert.py:117

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

src/adapters/models/albert/adapter_model.py:37

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

src/adapters/models/llama/modeling_llama.py:48

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

src/adapters/models/llama/modeling_llama.py:113

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

src/adapters/models/llama/adapter_model.py:58

Input shape: Batch x Time x Channel

src/adapters/models/plbart/modeling_plbart.py:50

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

src/adapters/models/plbart/modeling_plbart.py:153

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

src/adapters/models/plbart/modeling_plbart.py:218

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

src/adapters/models/plbart/mixin_plbart.py:68

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

src/adapters/models/plbart/adapter_model.py:54

Input shape: Batch x Time x Channel

src/adapters/models/bart/modeling_bart.py:42

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