modify some style
This commit is contained in:
BUAADreamer
parent
31420f7b31
commit
d29f3798f6
|
|
@ -6,7 +6,6 @@ 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 AutoProcessor, PreTrainedTokenizer
|
||||
|
|
@ -18,7 +17,7 @@ logger = get_logger(__name__)
|
|||
|
||||
|
||||
def preprocess_pretrain_dataset(
|
||||
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
|
||||
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
|
||||
) -> Dict[str, List[List[int]]]:
|
||||
# build grouped texts with format `X1 X2 X3 ...` if packing is enabled
|
||||
text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
|
||||
|
|
@ -35,7 +34,7 @@ def preprocess_pretrain_dataset(
|
|||
block_size = data_args.cutoff_len
|
||||
total_length = (total_length // block_size) * block_size
|
||||
result = {
|
||||
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
||||
k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
|
||||
for k, t in concatenated_examples.items()
|
||||
}
|
||||
if data_args.template == "gemma":
|
||||
|
|
@ -46,11 +45,11 @@ def preprocess_pretrain_dataset(
|
|||
|
||||
|
||||
def preprocess_supervised_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
data_args: "DataArguments",
|
||||
processor: "AutoProcessor" = None,
|
||||
examples: Dict[str, List[Any]],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
data_args: "DataArguments",
|
||||
processor: "AutoProcessor" = None,
|
||||
) -> 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.
|
||||
|
|
@ -63,14 +62,14 @@ def preprocess_supervised_dataset(
|
|||
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,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
template.encode_multiturn(
|
||||
tokenizer,
|
||||
messages,
|
||||
examples["system"][i],
|
||||
examples["tools"][i],
|
||||
data_args.cutoff_len,
|
||||
data_args.reserved_label_len,
|
||||
)
|
||||
):
|
||||
if data_args.train_on_prompt:
|
||||
source_mask = source_ids
|
||||
|
|
@ -96,10 +95,10 @@ def preprocess_supervised_dataset(
|
|||
|
||||
|
||||
def preprocess_packed_supervised_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
data_args: "DataArguments",
|
||||
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>`
|
||||
|
|
@ -111,7 +110,7 @@ def preprocess_packed_supervised_dataset(
|
|||
|
||||
messages = examples["prompt"][i] + examples["response"][i]
|
||||
for source_ids, target_ids in template.encode_multiturn(
|
||||
tokenizer, messages, examples["system"][i], examples["tools"][i]
|
||||
tokenizer, messages, examples["system"][i], examples["tools"][i]
|
||||
):
|
||||
if data_args.train_on_prompt:
|
||||
source_mask = source_ids
|
||||
|
|
@ -133,19 +132,19 @@ def preprocess_packed_supervised_dataset(
|
|||
total_length = (total_length // block_size) * block_size
|
||||
# split by chunks of cutoff_len
|
||||
for i in range(0, total_length, block_size):
|
||||
if not all(label == IGNORE_INDEX for label in labels[i : i + block_size]):
|
||||
model_inputs["input_ids"].append(input_ids[i : i + block_size])
|
||||
if not all(label == IGNORE_INDEX for label in labels[i: i + 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])
|
||||
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",
|
||||
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": []}
|
||||
|
|
@ -179,10 +178,10 @@ def preprocess_unsupervised_dataset(
|
|||
|
||||
|
||||
def preprocess_pairwise_dataset(
|
||||
examples: Dict[str, List[Any]],
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
data_args: "DataArguments",
|
||||
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": []}
|
||||
|
|
@ -246,12 +245,12 @@ def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer:
|
|||
|
||||
|
||||
def get_preprocess_and_print_func(
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
data_args: "DataArguments",
|
||||
training_args: "Seq2SeqTrainingArguments",
|
||||
stage: Literal["pt", "sft", "rm", "ppo"],
|
||||
processor: Optional["AutoProcessor"] = None,
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
data_args: "DataArguments",
|
||||
training_args: "Seq2SeqTrainingArguments",
|
||||
stage: Literal["pt", "sft", "rm", "ppo"],
|
||||
processor: Optional["AutoProcessor"] = None,
|
||||
) -> Tuple[Callable, Callable]:
|
||||
if stage == "pt":
|
||||
preprocess_func = partial(preprocess_pretrain_dataset, tokenizer=tokenizer, data_args=data_args)
|
||||
|
|
@ -280,5 +279,4 @@ def get_preprocess_and_print_func(
|
|||
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
|
||||
|
|
|
|||
Loading…
Reference in New Issue