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tokenizer.py
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tokenizer.py
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from sentencepiece import SentencePieceProcessor
import os
import torch
class ExLlamaTokenizer:
def __init__(self, tokenizer_model_path):
self.path = tokenizer_model_path
self.tokenizer = SentencePieceProcessor(model_file = self.path)
self.unk_token = "<unk>"
self.bos_token = "<s>"
self.eos_token = "</s>"
self.unk_token_id = self.tokenizer.unk_id() # is the same as pad token id...
self.eos_token_id = self.tokenizer.eos_id()
self.bos_token_id = self.tokenizer.bos_id()
self.pad_token_id = 0 # self.tokenizer.pad_id()
self.newline_token_id = 13
self.special_characters = [(self.bos_token, self.bos_token_id), (self.eos_token, self.eos_token_id), (self.unk_token, self.unk_token_id)] # for tokenzier encoding
# Encode string
def encode(self, text, return_mask = False, max_seq_len = 2048, add_bos = False, add_eos = False, encode_special_characters = False):
if isinstance(text, list):
# text is a list of strings
list_ids = self.tokenizer.EncodeAsIds(text)
# pad bos and eos
if add_bos:
for ids in list_ids: ids.insert(0, self.bos_token_id)
if add_eos:
for ids in list_ids: ids.append(self.eos_token_id)
max_length = max([len(ids) for ids in list_ids])
needs_mask = False
padded_ids = []
for ids in list_ids:
if len(ids) != len(list_ids[0]): needs_mask = True
padding = torch.full((max_length - len(ids),), self.pad_token_id)
sequence = torch.tensor(ids)
padded_ids.append(torch.cat((padding, sequence), dim = 0).long())
stacked_ids = torch.stack(padded_ids, dim = 0)
if return_mask:
if needs_mask:
mask_padding = torch.full((stacked_ids.shape[0], max_seq_len - stacked_ids.shape[1]), True, dtype = torch.bool, device = "cpu")
mask = stacked_ids != 0
mask = torch.cat((mask, mask_padding), dim = 1)
return stacked_ids, mask
else:
return stacked_ids, None
else:
return stacked_ids
else:
# text is a single string
split_text = [text]
# look for special characters
if encode_special_characters:
for special_character, special_token_id in self.special_characters:
temp_text = []
for segment in split_text:
if isinstance(segment, str) and special_character in segment:
# for each special character, append the text before the special character, then append the special character ID, then the rest of the text
parts = segment.split(special_character)
new_parts = []
for i, part in enumerate(parts):
new_parts.append(part)
if i < len(parts) - 1: # add the special token id between parts, but not after the last part
new_parts.append(special_token_id)
temp_text.extend(new_parts)
else:
temp_text.append(segment)
split_text = temp_text
ids = []
for text_chunk in split_text:
if isinstance(text_chunk, str):
ids += self.tokenizer.EncodeAsIds(text_chunk)
else:
ids.append(text_chunk)
# pad bos and eos
if add_bos:
ids = [self.bos_token_id] + ids
if add_eos:
ids = ids + [self.eos_token_id]
stacked_ids = torch.tensor(ids).unsqueeze(0)
if return_mask:
return stacked_ids, None
else:
return stacked_ids
def decode(self, ids, decode_special_characters=False):
special_ids = {id_: char for char, id_ in self.special_characters} # create a lookup dictionary
if ids.dim() > 1:
texts = []
for i in range(ids.shape[0]):
seq = ids[i].tolist()
seq = [t for t in seq if t != self.pad_token_id]
if decode_special_characters:
text_parts = []
normal_ids = [] # list of lists
current_normal_ids = [] # current list of normal IDs
for idx, id_ in enumerate(seq):
if id_ in special_ids:
# Save the current list of normal IDs, then start a new one
normal_ids.append(current_normal_ids)
current_normal_ids = []
# Store special token as a string
text_parts.append(special_ids[id_])
else:
current_normal_ids.append(id_)
normal_ids.append(current_normal_ids) # save the last segment of normal IDs
decoded_segments = [self.tokenizer.Decode(segment) for segment in normal_ids]
for idx, decoded_segment in enumerate(decoded_segments):
text_parts.insert(2*idx, decoded_segment)
texts.append("".join(text_parts))
else:
if self.eos_token_id in seq: # to not mess up special char decoding
seq = seq[:seq.index(self.eos_token_id)]
texts.append(self.tokenizer.Decode(seq))
return texts
else:
ids = ids.tolist()
if decode_special_characters:
text_parts = []
normal_ids = [] # list of lists
current_normal_ids = [] # current list of normal IDs
for idx, id_ in enumerate(ids):
if id_ in special_ids:
# Save the current list of normal IDs, then start a new one
normal_ids.append(current_normal_ids)
current_normal_ids = []
# Store special token as a string
text_parts.append(special_ids[id_])
else:
current_normal_ids.append(id_)
normal_ids.append(current_normal_ids) # save the last segment of normal IDs
decoded_segments = [self.tokenizer.Decode(segment) for segment in normal_ids]
for idx, decoded_segment in enumerate(decoded_segments):
text_parts.insert(2*idx, decoded_segment)
text = "".join(text_parts)
else:
text = self.tokenizer.Decode(ids)
return text
def num_tokens(self, text, encode_special_characters = False):
if encode_special_characters:
ids = self.encode(text, encode_special_characters = True)
return ids.size(1)
else:
ids = self.tokenizer.Encode(text)
return len(ids)