BatGPT-15B-sirius / tokenization_batgpt.py
Charlie
First commit from MLP Lab
fbed214
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