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P-tuning

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P-tuning

P-tuning adds trainable prompt embeddings to the input that is optimized by a prompt encoder to find a better prompt, eliminating the need to manually design prompts. The prompt tokens can be added anywhere in the input sequence, and p-tuning also introduces anchor tokens for improving performance.

The abstract from the paper is:

While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), we show that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning — which employs trainable continuous prompt embeddings. On the knowledge probing (LAMA) benchmark, the best GPT recovers 64\% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning. Importantly, we find that P-tuning also improves BERTs’ performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark..

PromptEncoderConfig

class peft.PromptEncoderConfig

< >

( peft_type: Union = None auto_mapping: Optional = None base_model_name_or_path: Optional = None revision: Optional = None task_type: Union = None inference_mode: bool = False num_virtual_tokens: int = None token_dim: int = None num_transformer_submodules: Optional = None num_attention_heads: Optional = None num_layers: Optional = None encoder_reparameterization_type: Union = <PromptEncoderReparameterizationType.MLP: 'MLP'> encoder_hidden_size: int = None encoder_num_layers: int = 2 encoder_dropout: float = 0.0 )

Parameters

  • encoder_reparameterization_type (Union[PromptEncoderReparameterizationType, str]) — The type of reparameterization to use.
  • encoder_hidden_size (int) — The hidden size of the prompt encoder.
  • encoder_num_layers (int) — The number of layers of the prompt encoder.
  • encoder_dropout (float) — The dropout probability of the prompt encoder.

This is the configuration class to store the configuration of a PromptEncoder.

PromptEncoder

class peft.PromptEncoder

< >

( config )

Parameters

The prompt encoder network that is used to generate the virtual token embeddings for p-tuning.

Example:

>>> from peft import PromptEncoder, PromptEncoderConfig

>>> config = PromptEncoderConfig(
...     peft_type="P_TUNING",
...     task_type="SEQ_2_SEQ_LM",
...     num_virtual_tokens=20,
...     token_dim=768,
...     num_transformer_submodules=1,
...     num_attention_heads=12,
...     num_layers=12,
...     encoder_reparameterization_type="MLP",
...     encoder_hidden_size=768,
... )

>>> prompt_encoder = PromptEncoder(config)

Attributes:

  • embedding (torch.nn.Embedding) — The embedding layer of the prompt encoder.
  • mlp_head (torch.nn.Sequential) — The MLP head of the prompt encoder if inference_mode=False.
  • lstm_head (torch.nn.LSTM) — The LSTM head of the prompt encoder if inference_mode=False and encoder_reparameterization_type="LSTM".
  • token_dim (int) — The hidden embedding dimension of the base transformer model.
  • input_size (int) — The input size of the prompt encoder.
  • output_size (int) — The output size of the prompt encoder.
  • hidden_size (int) — The hidden size of the prompt encoder.
  • total_virtual_tokens (int): The total number of virtual tokens of the prompt encoder.
  • encoder_type (Union[PromptEncoderReparameterizationType, str]): The encoder type of the prompt encoder.

Input shape: (batch_size, total_virtual_tokens)

Output shape: (batch_size, total_virtual_tokens, token_dim)

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