swift/template/utils.py

# Copyright (c) ModelScope Contributors. All rights reserved.
import re
import torch
from transformers import PreTrainedTokenizerBase, StoppingCriteria
from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union

from swift.utils import get_logger

logger = get_logger()

Tool = Dict[str, Union[str, Dict]]
History = List[Union[Tuple[str, str], List[str]]]
Message = Dict[str, Union[str, List[Dict[str, Any]], List[int], None]]
Messages = List[Message]
Prompt = List[Union[str, List[int], List[str]]]
Word = Union[str, List[int]]
Context = Word


class ContextType:
    RESPONSE = 'response'
    SUFFIX = 'suffix'
    OTHER = 'other'


class StopWordsCriteria(StoppingCriteria):
    """Adding extra stop words in template to prevent unstoppable generation
        Like suffixes and chat seps in the template.
    """

    def __init__(self, tokenizer: PreTrainedTokenizerBase, stop_words: List[Word], **tokenizer_kwargs) -> None:
        self.tokenizer = tokenizer
        self.stop_words = stop_words
        self.tokenizer_kwargs = tokenizer_kwargs
        self.start_idx = -1
        self.is_done = None

    def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor, **kwargs) -> torch.Tensor:
        if self.start_idx == -1:
            self.start_idx = len(input_ids[0]) - 1
            self.is_done = torch.full((input_ids.shape[0], ), False, device=input_ids.device, dtype=torch.bool)
        # [-20:]: Assuming the end tokens do not exceed 20 tokens,
        #   to avoid input_ids being too long and affecting efficiency.
        start_idx = max(self.start_idx, input_ids.shape[1] - 20)
        text_list = self.tokenizer.batch_decode(input_ids[:, start_idx:], **self.tokenizer_kwargs)
        for i, text in enumerate(text_list):
            if self.is_done[i]:
                continue
            is_finished = False
            for stop_word in self.stop_words:
                if isinstance(stop_word, str) and stop_word in text or isinstance(
                        stop_word, list) and input_ids[i][-len(stop_word):].tolist() == stop_word:
                    is_finished = True
                    break
            self.is_done[i] = is_finished
        return self.is_done


def fetch_one(element: Union[Tuple, List, Set, Dict, Any], item_type: Optional[Type] = None) -> Any:
    if isinstance(element, (tuple, set, list)):
        for ele in element:
            out = fetch_one(ele)
            if out and (item_type is None or isinstance(out, item_type)):
                return out
    elif isinstance(element, dict):
        return fetch_one(list(element.values()))
    else:
        return element


def findall(token_list: List[int], sub_token_list: Union[int, List[int]]) -> List[int]:
    """Find the index of a token in the token_list."""
    if isinstance(sub_token_list, int):
        sub_token_list = [sub_token_list]
    res = []
    idx = -1
    try:
        while True:
            idx = token_list.index(sub_token_list[0], idx + 1)
            if len(sub_token_list) == 1 or sub_token_list == token_list[idx:idx + len(sub_token_list)]:
                res.append(idx)
    except ValueError:
        pass
    return res


def align_image_inputs(input_ids: List[int], labels: List[int], new_input_ids,
                       image_token: int) -> Tuple[List[int], List[int]]:
    if isinstance(new_input_ids, torch.Tensor):
        new_input_ids = new_input_ids.tolist()

    # Find the tokens after the image_token in input_ids, and then align them.
    i, j = 0, 0
    while i < len(input_ids):
        x = input_ids[i]
        if x == image_token:
            assert i + 1 < len(input_ids), f'input_ids[-10:]: {input_ids[-10:]}'
            assert i - 1 >= 0, f'input_ids[:10]: {input_ids[:10]}'
            # [1, 2, 3(i-1), image_token(i), 4(i+1) ,5, 6]
            # [1, 2, 3(j_begin), a(j'), a, a, a, 4(j) ,5, 6]
            j_begin = j - 1
            for k in range(5):  # Increase robustness.
                if j_begin + k < len(new_input_ids) and new_input_ids[j_begin + k] == input_ids[i - 1]:
                    j_begin += k
                    break
                if j_begin - k >= 0 and new_input_ids[j_begin - k] == input_ids[i - 1]:
                    j_begin -= k
                    break
            else:
                raise ValueError(f'new_input_ids: {new_input_ids}, input_ids: {input_ids}')
            j_begin += 1
            while j < len(new_input_ids) and new_input_ids[j] != input_ids[i + 1]:
                j += 1
            input_ids = input_ids[:i] + new_input_ids[j_begin:j] + input_ids[i + 1:]
            if labels:
                labels = labels[:i] + [-100] * (j - j_begin) + labels[i + 1:]
            i += j - j_begin
        else:
            j += 1
        i += 1
    return input_ids, labels


def _split_str_by_regex(text: str, regex_delimiters: List[str]) -> List[str]:
    combined_pattern = '|'.join(f'({pattern})' for pattern in regex_delimiters)
    parts = re.split(combined_pattern, text, flags=re.DOTALL)
    parts = [part for part in parts if part is not None]
    if parts[0] == '':
        parts.pop(0)
    else:
        parts.insert(0, '')
    assert len(parts) % 2 == 0, f'result: {parts}'
    assert ''.join(parts) == text, f'split_result: {parts}, text: {text}'
    return parts


def split_str_parts_by(text: str, delimiters: List[str], regex_mode: bool = False) -> List[Dict[str, str]]:
    """Split the text field into parts.

    Args:
        text: A text to be split.
        delimiters: The delimiters.

    Returns:
        The split text in list of dicts.
    """
    assert isinstance(text, str), f'text: {text}'
    delimiters_origin = delimiters
    if not regex_mode:
        delimiters = [re.escape(delimiter) for delimiter in delimiters]
    parts = _split_str_by_regex(text, delimiters) if delimiters else ['', text]
    res = []
    if regex_mode:
        parts = [part for part in parts if part]
        for part in parts:
            for delimiter, delimiter_origin in zip(delimiters, delimiters_origin):
                if re.match(delimiter, part, re.DOTALL):
                    break
            else:
                delimiter_origin = ''
            res.append({'key': delimiter_origin, 'content': part})
    else:
        for key, content in zip(parts[::2], parts[1::2]):
            res.append({'key': key, 'content': content})
    return res


def get_last_user_round(messages):
    """Get the index of the last occurrence of user role"""
    for i in range(len(messages) - 1, -1, -1):
        if messages[i]['role'] == 'user':
            return i
    return -1


def history_to_messages(history: History,
                        system: Optional[str] = None,
                        roles: Optional[List[List[str]]] = None) -> 'Messages':
    """
    history: [['query1', 'response1'], ['query2', 'response2']]
        or [['query1', 'response1'], ['query2', None]]
    """
    messages = []
    if not roles:
        roles = [['user', 'assistant']] * len(history)
    else:
        assert len(roles) == len(history), f'len(roles): {len(roles)}, len(history): {len(history)}'
    if system is not None:
        messages.append({'role': 'system', 'content': system})

    for role, h in zip(roles, history):
        assert isinstance(h, (list, tuple))
        if h[0] is not None:
            messages.append({'role': role[0], 'content': h[0]})
        if h[1] is not None:
            messages.append({'role': role[1], 'content': h[1]})
    return messages


def messages_to_history(messages: 'Messages') -> Dict[str, Any]:
    system = None
    messages = messages.copy()
    if messages[0]['role'] == 'system':
        system = messages[0]['content']
        messages = messages[1::]
    if len(messages) % 2 == 1:
        messages.append({'role': 'assistant', 'content': None})
    history = []
    history_roles = []
    for user_message, assistant_message in zip(messages[::2], messages[1::2]):
        assert user_message['role'] in {'tool', 'user'}, f'user_message {user_message}'
        assert assistant_message['role'] == 'assistant', f'assistant_message: {assistant_message}'
        history.append([user_message['content'], assistant_message['content']])
        history_roles.append([user_message['role'], assistant_message['role']])
    query, response = history.pop() if history else (None, None)
    query_role = history_roles.pop()[0] if history_roles else None
    return {
        'history': history,
        'history_roles': history_roles,
        'query': query,
        'query_role': query_role,
        'response': response,
        'system': system,
    }


def update_generation_config_eos_token(generation_config, template):
    if generation_config is None:
        return
    stop_words = template.template_meta.stop_words
    eos_token_id = generation_config.eos_token_id
    if eos_token_id is None:
        eos_token_id = []
    elif isinstance(eos_token_id, int):
        eos_token_id = [eos_token_id]
    modified = False
    for stop_word in stop_words:
        if stop_word is None:
            continue
        if isinstance(stop_word, str):
            stop_word = template._tokenize(stop_word)
        if isinstance(stop_word, (list, tuple)) and len(stop_word) == 1 and stop_word[0] not in eos_token_id:
            eos_token_id.append(stop_word[0])
            modified = True
    if modified:
        generation_config.eos_token_id = eos_token_id