tokenization_skywork.py

# Copyright (c) SkyworkAI and the HuggingFace Inc. team. All rights reserved.
# This code is built upon Huggingface's transformers repository.

"""Tokenization classes for Skywork."""
import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
from transformers.utils import logging

if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}


SPIECE_UNDERLINE = "▁"

B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"

DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""

class SkyworkTokenizer(PreTrainedTokenizer):

    vocab_files_names = VOCAB_FILES_NAMES
    # pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    # max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        pad_token=None,
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        add_bos_token=True,
        add_eos_token=False,
        clean_up_tokenization_spaces=False,
        legacy=True,
        **kwargs,
    ):
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
        self.legacy = legacy
        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(vocab_file)
        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            additional_special_tokens=["[USER]", "[BOT]", "[SEP]"],
            sp_model_kwargs=self.sp_model_kwargs,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            legacy=legacy,
            **kwargs,
        )
        if legacy:
            logger.warning_once(
                f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. "
            )

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
        return state

    def __setstate__(self, d):
        self.__dict__ = d
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

    @property
    def vocab_size(self):
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    def get_vocab(self):
        """Returns vocab as a dict"""
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
    def tokenize(self, text, **kwargs) -> List[str]:
        # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
        # the beginning of the text
        if not self.legacy:
            text = SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " ")
        return super().tokenize(text, **kwargs)

    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
    def _tokenize(self, text):
        if not self.legacy:
            is_first = text.startswith(SPIECE_UNDERLINE)
            if is_first:
                text = text[1:]

        tokens = self.sp_model.encode(text, out_type=str)

        if not self.legacy and not is_first and not text.startswith(" ") and tokens[0].startswith(SPIECE_UNDERLINE):
            tokens = ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
        return tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.IdToPiece(index)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens = []
        out_string = ""
        prev_is_special = False
        for i, token in enumerate(tokens):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special and i != 0:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []

        if token_ids_1 is None:
            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(token_ids_0))
            + eos_token_id
            + bos_token_id
            + ([0] * len(token_ids_1))
            + eos_token_id
        )

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)

        if token_ids_1 is not None:
            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)

        return output

    def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
        dialogue = list(conversation.iter_texts())
        if not all([is_user for is_user, msg in dialogue[::2]]) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]
        ):
            raise ValueError(
                "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"
            )

        dialog_tokens: List[int] = []
        if len(conversation.past_user_inputs) > 0:
            if not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0]:
                conversation.past_user_inputs[0] = (
                    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                )
        elif not dialogue[0][1].startswith(B_SYS) or E_SYS not in dialogue[0][1]:
            dialogue[0] = (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ", add_special_tokens=False
                )
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2], dialogue[1::2])
            ],
            [],
        )
        if not (dialogue[-1][0]):
            raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}")
        dialog_tokens += [self.bos_token_id] + self.encode(
            f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}", add_special_tokens=False
        )
        return dialog_tokens