import logging import re from typing import List, Tuple from ctransformers import AutoModelForCausalLM import numpy as np from transformers import ( # AutoModelForCausalLM, AutoTokenizer, Pipeline, PreTrainedModel, PreTrainedTokenizer, ) from transformers.utils import is_tf_available if is_tf_available(): import tensorflow as tf from .consts import END_KEY, PROMPT_FOR_GENERATION_FORMAT, RESPONSE_KEY logger = logging.getLogger(__name__) def load_model_tokenizer_for_generate( pretrained_model_name_or_path: str, ) -> Tuple[PreTrainedModel, PreTrainedTokenizer]: """Loads the model and tokenizer so that it can be used for generating responses. Args: pretrained_model_name_or_path (str): name or path for model Returns: Tuple[PreTrainedModel, PreTrainedTokenizer]: model and tokenizer """ tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left")#, cache_dir="/media/siiva/DataStore/LLMs/cache/dollyV2") # model = AutoModelForCausalLM.from_pretrained( # pretrained_model_name_or_path, device_map="auto", trust_remote_code=True)#, cache_dir="/media/siiva/DataStore/LLMs/cache/dollyV2" #) model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, model_type='dolly-v2') # tokenizer = AutoTokenizer.from_pretrained('gpt2') return model, tokenizer 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 RuntimeError(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 ): """Initialize the pipeline Args: do_sample (bool, optional): Whether or not to use sampling. Defaults to True. max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128. top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92. top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering. Defaults to 0. """ 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_full_text: bool = None, **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 } if return_full_text is not None: postprocess_params["return_full_text"] = return_full_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) if input_ids.shape[1] == 0: input_ids = None attention_mask = None in_b = 1 else: in_b = input_ids.shape[0] 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, ) out_b = generated_sequence.shape[0] if self.framework == "pt": generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:]) elif self.framework == "tf": generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:])) 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_full_text: bool = False): generated_sequence = model_outputs["generated_sequence"][0] instruction_text = model_outputs["instruction_text"] generated_sequence: List[List[int]] = generated_sequence.numpy().tolist() records = [] for sequence in generated_sequence: # 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. try: response_pos = sequence.index(response_key_token_id) except ValueError: logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}") response_pos = None 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. try: end_pos = sequence.index(end_key_token_id) except ValueError: end_pos = None decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip() if not decoded: # 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() else: logger.warn(f"Failed to find response in:\n{fully_decoded}") # If the full text is requested, then append the decoded text to the original instruction. # This technically isn't the full text, as we format the instruction in the prompt the model has been # trained on, but to the client it will appear to be the full text. if return_full_text: decoded = f"{instruction_text}\n{decoded}" rec = {"generated_text": decoded} records.append(rec) return records def generate_response( instruction: str, *, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, **kwargs, ) -> str: """Given an instruction, uses the model and tokenizer to generate a response. This formats the instruction in the instruction format that the model was fine-tuned on. Args: instruction (str): _description_ model (PreTrainedModel): the model to use tokenizer (PreTrainedTokenizer): the tokenizer to use Returns: str: response """ generation_pipeline = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer, **kwargs) return generation_pipeline(instruction)[0]["generated_text"]