training/generate.py

import logging
import re
from typing import List, Tuple
import torch

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(pretrained_model_name_or_path, padding_side="left")
    model = AutoModelForCausalLM.from_pretrained(
        pretrained_model_name_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True
    )
    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:
        ValueError: 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
    ):
        """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 256.
            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.to(self.model.device),
            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"]