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from functools import partial
from itertools import chain
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Tuple
from ..extras.constants import IGNORE_INDEX
from ..extras.logging import get_logger
from .utils import Role
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from ..hparams import DataArguments
from .template import Template
logger = get_logger(__name__)
def preprocess_pretrain_dataset(
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
) -> Dict[str, List[List[int]]]:
# build grouped texts with format `X1 X2 X3 ...`
text_examples = [examples["prompt"][i][0]["content"] for i in range(len(examples["prompt"]))]
tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
for i in range(len(tokenized_examples["input_ids"])):
tokenized_examples["input_ids"][i] += [tokenizer.eos_token_id]
tokenized_examples["attention_mask"][i] += [1]
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]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# 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 i in range(len(examples["prompt"])):
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
continue
messages = examples["prompt"][i] + examples["response"][i]
input_ids, labels = [], []
for turn_idx, (source_ids, target_ids) in enumerate(
template.encode_multiturn(
tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
)
):
if data_args.train_on_prompt:
source_mask = source_ids
elif 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]
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_packed_supervised_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
# and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
input_ids, labels = [], []
for i in range(len(examples["prompt"])):
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
continue
messages = examples["prompt"][i] + examples["response"][i]
for turn_idx, (source_ids, target_ids) in enumerate(
template.encode_multiturn(tokenizer, messages, examples["system"][i], examples["tools"][i])
):
if data_args.train_on_prompt:
source_mask = source_ids
elif 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]
total_length = len(input_ids)
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
for i in range(0, total_length, block_size):
model_inputs["input_ids"].append(input_ids[i : i + block_size])
model_inputs["attention_mask"].append([1] * block_size)
model_inputs["labels"].append(labels[i : i + block_size])
return model_inputs
def preprocess_unsupervised_dataset(
examples: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build inputs with format `<bos> X` and labels with format `Y <eos>`
model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
for i in range(len(examples["prompt"])):
if len(examples["prompt"][i]) % 2 != 1:
continue
if len(examples["response"][i]) == 1:
messages = examples["prompt"][i] + examples["response"][i]
else:
messages = examples["prompt"][i] + [{"role": Role.ASSISTANT, "content": ""}]
input_ids, labels = template.encode_oneturn(
tokenizer, messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
)
if template.efficient_eos:
labels += [tokenizer.eos_token_id]
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: Dict[str, List[Any]],
tokenizer: "PreTrainedTokenizer",
template: "Template",
data_args: "DataArguments",
) -> Dict[str, List[List[int]]]:
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
for i in range(len(examples["prompt"])):
if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
continue
chosen_messages = examples["prompt"][i] + [examples["response"][i][0]]
rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
prompt_ids, chosen_ids = template.encode_oneturn(
tokenizer, chosen_messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
)
_, rejected_ids = template.encode_oneturn(
tokenizer, rejected_messages, examples["system"][i], examples["tools"][i], data_args.cutoff_len
)
if template.efficient_eos:
chosen_ids += [tokenizer.eos_token_id]
rejected_ids += [tokenizer.eos_token_id]
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: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
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: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
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: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
def get_preprocess_and_print_func(
tokenizer: "PreTrainedTokenizer",
template: "Template",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
stage: Literal["pt", "sft", "rm", "ppo"],
) -> Tuple[Callable, Callable]:
if stage == "pt":
preprocess_func = partial(preprocess_pretrain_dataset, tokenizer=tokenizer, data_args=data_args)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
elif stage == "sft" and not training_args.predict_with_generate:
if data_args.sft_packing:
preprocess_func = partial(
preprocess_packed_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
)
else:
preprocess_func = partial(
preprocess_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
)
print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
elif stage == "rm":
preprocess_func = partial(
preprocess_pairwise_dataset, tokenizer=tokenizer, template=template, data_args=data_args
)
print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
else:
preprocess_func = partial(
preprocess_unsupervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
return preprocess_func, print_function
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