{"time": "2023-03-18T17:52:54.425808", "lm": "spellbook", "url": "https://dashboard.scale.com/spellbook/api/v2/deploy/", "input": "Modify the initial portion of a huggingface decoder transformer such that it accepts a sentence encoding in addition to token embeddings.", "output": "To modify the initial portion of a Hugging Face decoder transformer to accept a sentence encoding in addition to token embeddings, you can create a custom class that inherits from the original decoder class and then override the necessary methods. Here's an example using the `BertDecoder` class from the `transformers` library:\n\n```python\nfrom transformers import BertConfig, BertModel\nfrom transformers.models.bert.modeling_bert import BertDecoder\n\nclass CustomBertDecoder(BertDecoder):\n def __init__(self, config):\n super().__init__(config)\n\n def forward(\n self,\n input_ids=None,\n sentence_encoding=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n head_mask=None,\n inputs_embeds=None,\n encoder_hidden_states=None,\n encoder_attention_mask=None,\n past_key_values=None,\n use_cache=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n # Add sentence encoding to the token embeddings\n if inputs_embeds is None:\n inputs_embeds = self.word_embeddings(input_ids)\n if sentence_encoding is not None:\n inputs_embeds = inputs_embeds + sentence_encoding.unsqueeze(1)\n\n return super().forward(\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n head_mask=head_mask,\n inputs_embeds=inputs_embeds,\n encoder_hidden_states=encoder_hidden_states,\n encoder_attention_mask=encoder_attention_mask,\n past_key_values=past_key_values,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n# Example usage\nconfig = BertConfig()\nmodel = BertModel(config)\ncustom_decoder = CustomBertDecoder(config)\n\n# Get sentence encoding from the encoder\nencoder_output = model(input_ids)\nsentence_encoding = encoder_output.last_hidden_state[:, 0]\n\n# Pass sentence encoding to the custom decoder\ndecoder_output = custom_decoder(input_ids=input_ids, sentence_encoding=sentence_encoding)\n```\n\nIn this example, we create a `CustomBertDecoder` class that inherits from `BertDecoder`. We override the `forward` method to accept an additional `sentence_encoding` argument. We then add the sentence encoding to the token embeddings before passing them to the original `forward` method of the `BertDecoder` class.\n\nThis example assumes that the sentence encoding is a vector that can be added to the token embeddings. You may need to adjust the dimensions or operations depending on your specific use case."}