#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Splicing multiple pre-tokenized sequence data points
"""
import bisect
import random
import warnings
from copy import deepcopy
from typing import Any, Callable, Dict, Iterable, List, Tuple, Union
from datasets import dataset_dict
from torch.utils.data import ConcatDataset, Dataset, IterableDataset
from transformers import AutoTokenizer
from transformers.models.llama.tokenization_llama import LlamaTokenizer
from transformers.tokenization_utils import PreTrainedTokenizer
from colossalai.logging import get_dist_logger
from .conversation import Conversation, default_conversation
logger = get_dist_logger()
IGNORE_INDEX = -100
DSType = Union[Dataset, ConcatDataset, dataset_dict.Dataset]
def supervised_tokenize_pretrain(
data_point: Dict[str, str], tokenizer: LlamaTokenizer, ignore_index: int = None, max_length: int = 4096
) -> Dict[str, Union[int, str, List[int]]]:
"""
A tokenization function to tokenize an original pretraining data point as following:
{"source": "", "target": "Beijing, the capital of the People's Republic of China, ...", "category": "geography"}
"""
assert tokenizer.add_bos_token is False and tokenizer.add_eos_token is False, (
"Initially set `tokenizer.add_bos_token` and `tokenizer.add_eos_token` to False, "
"add <bos> and <eos> manually later"
)
if ignore_index is None:
ignore_index = IGNORE_INDEX
source_text = data_point["source"] # `str`
target_text = data_point["target"] # `str`
is_null_source = len(source_text) == 0
source_text = tokenizer.bos_token + source_text
target_text += tokenizer.eos_token
sequence_text = source_text + target_text
tokenized = tokenizer([source_text, sequence_text])["input_ids"]
sequence_input_ids = tokenized[1]
sequence_labels = deepcopy(sequence_input_ids)
source_length = len(tokenized[0])
if not is_null_source:
sequence_labels[:source_length] = [ignore_index for _ in range(source_length)]
# sequence truncation.
if len(sequence_input_ids) > max_length:
sequence_input_ids = sequence_input_ids[:max_length]
sequence_labels = sequence_labels[:max_length]
return dict(
input_ids=sequence_input_ids,
labels=sequence_labels,
seq_length=len(sequence_input_ids),
seq_category=data_point["category"],
)
def supervised_tokenize_sft(
data_point: Dict[str, str],
tokenizer: AutoTokenizer,
conversation_template: Conversation = default_conversation,
ignore_index: int = None,
max_length: int = 4096,
) -> Dict[str, Union[int, str, List[int]]]:
"""
A tokenization function to tokenize an original supervised data point as following:
{"messages": [{"from": "human", "content": "xxx"}, {"from": "assistant", "content": "xxx"}]}
"""
assert tokenizer.add_bos_token is False and tokenizer.add_eos_token is False, (
"Initially set `tokenizer.add_bos_token` and `tokenizer.add_eos_token` to False, "
"add <bos> and <eos> manually later"
)
assert (
tokenizer.bos_token == conversation_template.seps[0] and tokenizer.eos_token == conversation_template.seps[1]
), f"`bos_token`{tokenizer.bos_token} and `eos_token`{tokenizer.eos_token} should be the same with `conversation_template.seps`{conversation_template.seps}."
if ignore_index is None:
ignore_index = IGNORE_INDEX
messages = data_point["messages"]
template = deepcopy(conversation_template)
template.messages = []
for mess in messages:
from_str = mess["from"]
if from_str.lower() == "human":
from_str = template.roles[0]
elif from_str.lower() == "assistant":
from_str = template.roles[1]
else:
raise ValueError(f"Unsupported role {from_str.lower()}")
template.append_message(from_str, mess["content"])
if len(template.messages) % 2 != 0:
template.messages = template.messages[0:-1]
# `target_turn_index` is the number of turns which exceeds `max_length - 1` for the first time.
turns = [i for i in range(1, len(messages) // 2 + 1)]
target_turn_index = bisect.bisect_right(
turns,
max_length - 1,
key=lambda x: len(tokenizer([template.get_prompt(2 * x)], add_special_tokens=False)["input_ids"][0]),
)
# The tokenized length for first turn already exceeds `max_length - 1`.
if target_turn_index - 1 < 0:
return dict(
input_ids=None,
labels=None,
inputs_decode=None,
labels_decode=None,
seq_length=None,
seq_category=None,
)
target_turn = turns[target_turn_index - 1]
prompt = template.get_prompt(2 * target_turn)
tokenized = tokenizer([prompt], add_special_tokens=False)["input_ids"][0]
template.messages = template.messages[0 : 2 * target_turn]
starts = []
ends = []
gpt_bos = False if template.messages[0][0] == template.roles[0] else True
gpt_eos = False if template.messages[0][0] == template.roles[0] else True
for i, token_id in enumerate(tokenized):
if token_id == tokenizer.bos_token_id:
if gpt_bos:
starts.append(i)
gpt_bos = not gpt_bos
elif token_id == tokenizer.eos_token_id:
if gpt_eos:
ends.append(i)
gpt_eos = not gpt_eos
if len(starts) != target_turn or len(ends) != target_turn:
logger.info(
"Please check whether the tokenizer add additional `bos_token` and `eos_token`.\n\nOr the original message contains `bos_token` or `eos_token`."
)
return dict(
input_ids=None,
labels=None,
inputs_decode=None,
labels_decode=None,
seq_length=None,
seq_category=None,
)
tokenized = [tokenizer.bos_token_id] + tokenized
labels = [ignore_index] * len(tokenized)
for start, end in zip(starts, ends):
labels[start + 1 : end + 2] = tokenized[start + 1 : end + 2]
labels_decode = deepcopy(labels)
for i, z in enumerate(labels_decode):
if z == ignore_index:
labels_decode[i] = tokenizer.unk_token_id
# `inputs_decode` and `labels_decode` can be used to check whether the tokenization method is true.
return dict(
input_ids=tokenized,
labels=labels,
inputs_decode=tokenizer.decode(tokenized),
labels_decode=tokenizer.decode(labels_decode),
seq_length=len(tokenized),
seq_category=data_point["category"] if "category" in data_point else "None",
)
class ClosedToConstantLengthSplicedDataset(IterableDataset):
"""
Define an iterable dataset that returns a (close to) constant length data point spliced from multiple
original independent (pre-tokenized) data points.
"""
def __init__(
self,
dataset: DSType,
tokenizer: PreTrainedTokenizer,
max_length: int = 4096,
num_packed_sequences: int = 8,
fetch_sequence_func: Callable[[Any], Tuple[List[int], List[int]]] = None,
input_ids_field: str = "input_ids",
labels_field: str = "labels",
infinite: bool = False,
shuffle: bool = True,
error_strict: bool = False,
) -> None:
self.tokenizer = tokenizer
self.dataset = dataset
self.max_length = max_length
self.infinite = infinite
self.max_buffer_size = max_length * num_packed_sequences # e.g., 4096 * 16
self.shuffle = shuffle
# Callable[[Dict[str, Any]], Tuple[List[int], List[int]]],
# A function that fetch sequence input_ids and labels from the original data point
if fetch_sequence_func is None:
self.fetch_sequence_func = lambda data_point: (data_point[input_ids_field], data_point[labels_field])
else:
self.fetch_sequence_func = fetch_sequence_func
self.input_ids_field = input_ids_field
self.labels_field = labels_field
self.error_strict = error_strict
self.current_size = 0 # `int`, current packed data size.
def __len__(self) -> int:
return len(self.dataset)
def __iter__(self) -> Iterable[Dict[str, List[int]]]:
iterator = iter(self.dataset)
more_data_points = True
while more_data_points is True:
buffer, buffer_len = [], 0
while True:
# ending condition.
if buffer_len >= self.max_buffer_size:
break
try:
# `Tuple[List[int], List[int]]`
seq_input_ids, seq_labels = self.fetch_sequence_func(next(iterator))
buffer.append({self.input_ids_field: seq_input_ids, self.labels_field: seq_labels})
buffer_len += len(buffer[-1][self.input_ids_field])
except StopIteration:
if self.infinite is True:
iterator = iter(self.dataset)
warnings.warn("The dataset reached end and the iterator is reset to the start.")
else:
more_data_points = False
break
examples = [] # `List[Dict[str, List[int]]]`, save buffered spliced data points.
spliced_input_ids, spliced_labels = [], [] # `List[int]`, `List[int]`
for i, data_point in enumerate(buffer):
# TODO(2023-09-18) check errors for each unspliced tokenized data point
seq_input_ids = data_point[self.input_ids_field]
seq_labels = data_point[self.labels_field]
# Handle special case:
# If the length of an original data point (i.e., input_ids length of a data point before splicing)
# exceeds `max_length`, truncate it.
if len(seq_input_ids) > self.max_length:
truncated_seq_input_ids = seq_input_ids[: self.max_length]
truncated_label_ids = seq_labels[: self.max_length]
if set(truncated_label_ids) == {IGNORE_INDEX}:
if self.error_strict is True:
raise ValueError(
f"Find an out-of-bounds length({len(seq_input_ids)}) data point "
f"with all label values as {IGNORE_INDEX}."
)
else:
warnings.warn(f"Filter an error truncated data point (labels all {IGNORE_INDEX})")
continue # Skip the current error data point.
spliced_data_point = {
self.input_ids_field: truncated_seq_input_ids,
self.labels_field: truncated_label_ids,
}
examples.append(spliced_data_point)
warnings.warn("Find a data point to be truncated.")
continue
# Pre action judgment.
if len(spliced_input_ids) + len(seq_input_ids) > self.max_length:
spliced_data_point = {
self.input_ids_field: spliced_input_ids,
self.labels_field: spliced_labels,
} # `Dict[str, List[int]]`
# Update.
spliced_input_ids, spliced_labels = [], []
spliced_input_ids.extend(seq_input_ids)
spliced_labels.extend(seq_labels)
examples.append(spliced_data_point)
else:
spliced_input_ids.extend(seq_input_ids)
spliced_labels.extend(seq_labels)
# For residual spliced data point at the end of the data set
if self.infinite is False and more_data_points is False and len(spliced_input_ids) > 0:
examples.append({self.input_ids_field: spliced_input_ids, self.labels_field: spliced_labels})
if self.shuffle:
random.shuffle(examples)
for spliced_data_point in examples:
# TODO(2023-09-18): check errors for each spliced tokenized data point.
self.current_size += 1
yield spliced_data_point