Spaces:
Runtime error
Runtime error
File size: 9,873 Bytes
b87a3ce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
import tiktoken
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal, Union
from itertools import chain
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.extras.template import get_template_and_fix_tokenizer
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from llmtuner.hparams import DataArguments
def preprocess_dataset(
dataset: Union["Dataset", "IterableDataset"],
tokenizer: "PreTrainedTokenizer",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"]
) -> Union["Dataset", "IterableDataset"]:
column_names = list(next(iter(dataset)).keys())
template = get_template_and_fix_tokenizer(data_args.template, tokenizer)
def construct_example(examples: Dict[str, List[Any]]) -> Generator[Any, None, None]:
for i in range(len(examples["prompt"])):
query, response = examples["prompt"][i], examples["response"][i]
query = query + "\n" + examples["query"][i] if "query" in examples and examples["query"][i] else query
history = examples["history"][i] if "history" in examples else None
system = examples["system"][i] if "system" in examples else None
yield query, response, history, system
def preprocess_pretrain_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
# build grouped texts with format `X1 X2 X3 ...`
if isinstance(getattr(tokenizer, "tokenizer", None), tiktoken.Encoding):
kwargs = dict(allowed_special="all") # for tiktoken tokenizer (Qwen)
else:
kwargs = dict(add_special_tokens=True)
if hasattr(tokenizer, "add_bos_token") and hasattr(tokenizer, "add_eos_token"):
setattr(tokenizer, "add_bos_token", True) # for LLaMA tokenizer
setattr(tokenizer, "add_eos_token", True)
tokenized_examples = tokenizer(examples["prompt"], **kwargs)
concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
block_size = data_args.cutoff_len
# we drop the small remainder, and if the total_length < block_size, we exclude this batch
total_length = (total_length // block_size) * block_size
# split by chunks of cutoff_len
result = {
k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
return result
def preprocess_supervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
# for multiturn examples, we only mask the prompt part in each prompt-response pair.
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
for query, response, history, system in construct_example(examples):
input_ids, labels = [], []
for turn_idx, (source_ids, target_ids) in enumerate(template.encode_multiturn(
tokenizer, query, response, history, system
)):
total_len = len(source_ids) + len(target_ids)
max_source_len = int(data_args.cutoff_len * (len(source_ids) / total_len))
max_target_len = int(data_args.cutoff_len * (len(target_ids) / total_len))
if len(source_ids) > max_source_len:
source_ids = source_ids[:max_source_len]
if len(target_ids) > max_target_len:
target_ids = target_ids[:max_target_len]
if turn_idx != 0 and template.efficient_eos:
source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
else:
source_mask = [IGNORE_INDEX] * len(source_ids)
input_ids += source_ids + target_ids
labels += source_mask + target_ids
if template.efficient_eos:
input_ids += [tokenizer.eos_token_id]
labels += [tokenizer.eos_token_id]
if len(input_ids) > data_args.cutoff_len:
input_ids = input_ids[:data_args.cutoff_len]
labels = labels[:data_args.cutoff_len]
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
return model_inputs
def preprocess_unsupervised_dataset(examples: Dict[str, List[Any]]) -> Dict[str, Any]:
# build inputs with format `<bos> X` and labels with format `Y <eos>`
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
for query, response, history, system in construct_example(examples):
input_ids, labels = template.encode_oneturn(tokenizer, query, response, history, system)
if template.efficient_eos:
labels += [tokenizer.eos_token_id]
if len(input_ids) > data_args.cutoff_len:
input_ids = input_ids[:data_args.cutoff_len]
if len(labels) > data_args.cutoff_len:
labels = labels[:data_args.cutoff_len]
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
return model_inputs
def preprocess_pairwise_dataset(examples):
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
for query, response, history, system in construct_example(examples):
prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, query, response[0], history, system)
_, rejected_ids = template.encode_oneturn(tokenizer, query, response[1], history, system)
if template.efficient_eos:
chosen_ids += [tokenizer.eos_token_id]
rejected_ids += [tokenizer.eos_token_id]
total_len = len(prompt_ids) + max(len(chosen_ids), len(rejected_ids))
max_source_len = int(data_args.cutoff_len * (len(prompt_ids) / total_len))
max_target_len = int(data_args.cutoff_len * (max(len(chosen_ids), len(rejected_ids)) / total_len))
if len(prompt_ids) > max_source_len:
prompt_ids = prompt_ids[:max_source_len]
if len(chosen_ids) > max_target_len:
chosen_ids = chosen_ids[:max_target_len]
if len(rejected_ids) > max_target_len:
rejected_ids = rejected_ids[:max_target_len]
model_inputs["prompt_ids"].append(prompt_ids)
model_inputs["chosen_ids"].append(chosen_ids)
model_inputs["rejected_ids"].append(rejected_ids)
return model_inputs
def print_supervised_dataset_example(example):
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print("labels:\n{}".format(
tokenizer.decode(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False)
))
def print_pairwise_dataset_example(example):
print("prompt_ids:\n{}".format(example["prompt_ids"]))
print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False)))
print("chosen_ids:\n{}".format(example["chosen_ids"]))
print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False)))
print("rejected_ids:\n{}".format(example["rejected_ids"]))
print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False)))
def print_unsupervised_dataset_example(example):
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
if stage == "pt":
dataset = dataset.filter(lambda example: example["prompt"])
preprocess_function = preprocess_pretrain_dataset
print_function = print_unsupervised_dataset_example
elif stage == "sft" and not training_args.predict_with_generate:
dataset = dataset.filter(lambda example: example["prompt"] and example["response"])
preprocess_function = preprocess_supervised_dataset
print_function = print_supervised_dataset_example
elif stage == "rm":
dataset = dataset.filter(lambda example: example["prompt"] and len(example["response"]) > 1)
preprocess_function = preprocess_pairwise_dataset
print_function = print_pairwise_dataset_example
else:
dataset = dataset.filter(lambda example: example["prompt"])
preprocess_function = preprocess_unsupervised_dataset
print_function = print_unsupervised_dataset_example
with training_args.main_process_first(desc="dataset map pre-processing"):
kwargs = {}
if not data_args.streaming:
kwargs = dict(
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset"
)
dataset = dataset.map(
preprocess_function,
batched=True,
remove_columns=column_names,
**kwargs
)
print_function(next(iter(dataset)))
return dataset
|