import re import numpy as np from transformers import Pipeline, PreTrainedTokenizer INSTRUCTION_KEY = "### Instruction:" RESPONSE_KEY = "### Response:" END_KEY = "### End" INTRO_BLURB = ( "Below is an instruction that describes a task. Write a response that appropriately completes the request." ) # This is the prompt that is used for generating responses using an already trained model. It ends with the response # key, where the job of the model is to provide the completion that follows it (i.e. the response itself). PROMPT_FOR_GENERATION_FORMAT = """{intro} {instruction_key} {instruction} {response_key} """.format( intro=INTRO_BLURB, instruction_key=INSTRUCTION_KEY, instruction="{instruction}", response_key=RESPONSE_KEY, ) def get_special_token_id(tokenizer: PreTrainedTokenizer, key: str) -> int: """Gets the token ID for a given string that has been added to the tokenizer as a special token. When training, we configure the tokenizer so that the sequences like "### Instruction:" and "### End" are treated specially and converted to a single, new token. This retrieves the token ID each of these keys map to. Args: tokenizer (PreTrainedTokenizer): the tokenizer key (str): the key to convert to a single token Raises: RuntimeError: if more than one ID was generated Returns: int: the token ID for the given key """ token_ids = tokenizer.encode(key) if len(token_ids) > 1: raise ValueError(f"Expected only a single token for '{key}' but found {token_ids}") return token_ids[0] class InstructionTextGenerationPipeline(Pipeline): def __init__( self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs ): super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs) def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs): preprocess_params = {} # newer versions of the tokenizer configure the response key as a special token. newer versions still may # append a newline to yield a single token. find whatever token is configured for the response key. tokenizer_response_key = next( (token for token in self.tokenizer.additional_special_tokens if token.startswith(RESPONSE_KEY)), None ) response_key_token_id = None end_key_token_id = None if tokenizer_response_key: try: response_key_token_id = get_special_token_id(self.tokenizer, tokenizer_response_key) end_key_token_id = get_special_token_id(self.tokenizer, END_KEY) # Ensure generation stops once it generates "### End" generate_kwargs["eos_token_id"] = end_key_token_id except ValueError: pass forward_params = generate_kwargs postprocess_params = { "response_key_token_id": response_key_token_id, "end_key_token_id": end_key_token_id, "return_instruction_text": return_instruction_text, } return preprocess_params, forward_params, postprocess_params def preprocess(self, instruction_text, **generate_kwargs): prompt_text = PROMPT_FOR_GENERATION_FORMAT.format(instruction=instruction_text) inputs = self.tokenizer( prompt_text, return_tensors="pt", ) inputs["prompt_text"] = prompt_text inputs["instruction_text"] = instruction_text return inputs def _forward(self, model_inputs, **generate_kwargs): input_ids = model_inputs["input_ids"] attention_mask = model_inputs.get("attention_mask", None) generated_sequence = self.model.generate( input_ids=input_ids.to(self.model.device), attention_mask=attention_mask, pad_token_id=self.tokenizer.pad_token_id, **generate_kwargs, )[0].cpu() instruction_text = model_inputs.pop("instruction_text") return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text} def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_instruction_text): sequence = model_outputs["generated_sequence"] instruction_text = model_outputs["instruction_text"] # The response will be set to this variable if we can identify it. decoded = None # If we have token IDs for the response and end, then we can find the tokens and only decode between them. if response_key_token_id and end_key_token_id: # Find where "### Response:" is first found in the generated tokens. Considering this is part of the # prompt, we should definitely find it. We will return the tokens found after this token. response_pos = None response_positions = np.where(sequence == response_key_token_id)[0] if len(response_positions) == 0: pass else: response_pos = response_positions[0] if response_pos: # Next find where "### End" is located. The model has been trained to end its responses with this # sequence (or actually, the token ID it maps to, since it is a special token). We may not find # this token, as the response could be truncated. If we don't find it then just return everything # to the end. Note that even though we set eos_token_id, we still see the this token at the end. end_pos = None end_positions = np.where(sequence == end_key_token_id)[0] if len(end_positions) > 0: end_pos = end_positions[0] decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip() else: # Otherwise we'll decode everything and use a regex to find the response and end. fully_decoded = self.tokenizer.decode(sequence) # The response appears after "### Response:". The model has been trained to append "### End" at the # end. m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL) if m: decoded = m.group(1).strip() else: # The model might not generate the "### End" sequence before reaching the max tokens. In this case, # return everything after "### Response:". m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL) if m: decoded = m.group(1).strip() if return_instruction_text: return {"instruction_text": instruction_text, "generated_text": decoded} return decoded