tokenization_batgpt.py

import os
import torch
from typing import List, Optional, Union, Dict, Tuple
from sentencepiece import SentencePieceProcessor
from transformers import PreTrainedTokenizer
from transformers.utils import logging, PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding

SPECIAL_TOKENS = ["<mask>", "<doc>", "<title>", "<para>", "<eop>", "<eot>", "<eod>"] + ["[User]", "[Assistant]", "[System]"] +  ["[Turn {}]".format(i+1) for i in range(100)]

class SPTokenizer:
    def __init__(self, model_path: str):
        # reload tokenizer
        assert os.path.isfile(model_path), model_path
        self.sp_model = SentencePieceProcessor(model_file=model_path)

        # BOS / EOS token IDs
        self.n_words: int = self.sp_model.vocab_size()
        self.bos_id: int = self.sp_model.bos_id()
        self.eos_id: int = self.sp_model.eos_id()
        self.pad_id: int = self.sp_model.unk_id()
        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()

        self.special_tokens = {}
        self.index_special_tokens = {}
        for token in SPECIAL_TOKENS:
            self.special_tokens[token] = self.n_words
            self.index_special_tokens[self.n_words] = token
            self.n_words += 1

    def tokenize(self, s: str):
        return self.sp_model.EncodeAsPieces(s)

    def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
        assert type(s) is str
        t = self.sp_model.encode(s)
        if bos:
            t = [self.bos_id] + t
        if eos:
            t = t + [self.eos_id]
        return t

    def decode(self, t: List[int]) -> str:
        return self.sp_model.decode(t)

    def decode_tokens(self, tokens: List[str]) -> str:
        text = self.sp_model.DecodePieces(tokens)
        return text

    def convert_token_to_id(self, token):
        """ Converts a token (str) in an id using the vocab. """
        if token in self.special_tokens:
            return self.special_tokens[token]
        return self.sp_model.PieceToId(token)

    def convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        if index in self.index_special_tokens or index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
            return ""
        return self.sp_model.IdToPiece(index)


class BatGPTTokenizer(PreTrainedTokenizer):
    vocab_files_names = {"vocab_file": "tokenizer.model"}

    model_input_names = ["input_ids", "attention_mask", "position_ids"]

    def __init__(self, vocab_file, padding_side="left", **kwargs):
        super().__init__(padding_side=padding_side, **kwargs)
        self.name = "BatGPTTokenizer"

        self.vocab_file = vocab_file
        self.tokenizer = SPTokenizer(vocab_file)
        self.special_tokens = {
            "<bos>": self.tokenizer.bos_id,
            "<eos>": self.tokenizer.eos_id,
            "<pad>": self.tokenizer.pad_id
        }

        # 
        self.unk_token = "<unk>"
        self.add_special_tokens({'additional_special_tokens': SPECIAL_TOKENS})

    def get_command(self, token):
        if token in self.special_tokens:
            return self.special_tokens[token]
        assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
        return self.tokenizer.special_tokens[token]

    @property
    def pad_token(self) -> str:
        return "<unk>"

    @property
    def pad_token_id(self):
        return self.get_command("<pad>")

    @property
    def eos_token(self) -> str:
        return "</s>"

    @property
    def eos_token_id(self):
        return self.get_command("<eos>")

    @property
    def vocab_size(self):
        return self.tokenizer.n_words

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

    def _tokenize(self, text, **kwargs):
        return self.tokenizer.tokenize(text)

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

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

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        return self.tokenizer.decode_tokens(tokens)

    def save_vocabulary(self, save_directory, filename_prefix=None):
        if os.path.isdir(save_directory):
            vocab_file = os.path.join(
                save_directory, self.vocab_files_names["vocab_file"]
            )
        else:
            vocab_file = save_directory

        with open(self.vocab_file, 'rb') as fin:
            proto_str = fin.read()

        with open(vocab_file, "wb") as writer:
            writer.write(proto_str)

        return (vocab_file,)

    def get_prefix_tokens(self):
        prefix_tokens = [self.get_command("<doc>"), self.get_command("<para>")]
        return prefix_tokens

    def build_inputs(self, query, history=None, system_prompt=None):
        if history is None:
            history = []
        role_user = "[User]"
        role_assistant = "[Assistant]"
        if system_prompt:
            prompt = "[System]\n\n {}\n\n<eot>".format(system_prompt)
        else:
            prompt = ""
        for i, (old_query, response) in enumerate(history):
            prompt += "[Turn {}]\n\n{} {}\n\n{} {}\n\n<eop>".format(i + 1, role_user, old_query, role_assistant, response)
        prompt += "[Turn {}]\n\n{} {}\n\n{}".format(len(history) + 1, role_user, query, role_assistant)
        inputs = self([prompt], return_tensors="pt")
        return inputs

    def build_stream_inputs(self, query: str, history: List[Tuple[str, str]] = None, system_prompt = None):
        role_user = "[User]"
        role_assistant = "[Assistant]"
        if history:
            prompt = "\n\n[Turn {}]\n\n{} {}\n\n{}".format(len(history) + 1, role_user, query, role_assistant)
            input_ids = self.encode(prompt, add_special_tokens=False)
            input_ids = input_ids[1:]
            inputs = self.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False)
        else:
            if system_prompt:
                prompt = "[System]\n\n {}\n\n[Turn {}]\n\n{} {}\n\n{} ".format(system_prompt, len(history) + 1, role_user, query, role_assistant)
            else:
                prompt = "[Turn {}]\n\n{} {}\n\n{} ".format(len(history) + 1, role_user, query, role_assistant)
            inputs = self([prompt], return_tensors="pt")
        return inputs

    def build_inputs_with_special_tokens(
            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        prefix_tokens = self.get_prefix_tokens()
        token_ids_0 = prefix_tokens + token_ids_0
        if token_ids_1 is not None:
            token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
        return token_ids_0

    def _pad(
            self,
            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
            max_length: Optional[int] = None,
            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
            pad_to_multiple_of: Optional[int] = None,
            return_attention_mask: Optional[bool] = None,
    ) -> dict:
        # Load from model defaults
        assert self.padding_side == "left"

        required_input = encoded_inputs[self.model_input_names[0]]
        seq_length = len(required_input)

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = len(required_input)

        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length

        # Initialize attention mask if not present.
        if "attention_mask" not in encoded_inputs:
            encoded_inputs["attention_mask"] = [1] * seq_length

        if "position_ids" not in encoded_inputs:
            encoded_inputs["position_ids"] = list(range(seq_length))

        if needs_to_be_padded:
            difference = max_length - len(required_input)

            if "attention_mask" in encoded_inputs:
                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
            if "position_ids" in encoded_inputs:
                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input

        return encoded_inputs