instruct_pipeline.py

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, along with any additional context. 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