forked from p04798526/LLaMA-Factory-Mirror
modify some style
This commit is contained in:
BUAADreamer
parent
31420f7b31
commit
d29f3798f6
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@ -6,7 +6,6 @@ from ..extras.constants import IGNORE_INDEX
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from ..extras.logging import get_logger
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from .utils import Role
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments
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from transformers.tokenization_utils import AutoProcessor, PreTrainedTokenizer
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@ -18,7 +17,7 @@ logger = get_logger(__name__)
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def preprocess_pretrain_dataset(
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examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
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examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
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) -> Dict[str, List[List[int]]]:
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# build grouped texts with format `X1 X2 X3 ...` if packing is enabled
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text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
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@ -35,7 +34,7 @@ def preprocess_pretrain_dataset(
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block_size = data_args.cutoff_len
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total_length = (total_length // block_size) * block_size
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result = {
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k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
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k: [t[i: i + block_size] for i in range(0, total_length, block_size)]
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for k, t in concatenated_examples.items()
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}
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if data_args.template == "gemma":
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@ -46,11 +45,11 @@ def preprocess_pretrain_dataset(
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def preprocess_supervised_dataset(
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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processor: "AutoProcessor" = None,
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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processor: "AutoProcessor" = None,
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) -> Dict[str, List[List[int]]]:
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# build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
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# for multiturn examples, we only mask the prompt part in each prompt-response pair.
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@ -63,14 +62,14 @@ def preprocess_supervised_dataset(
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messages = examples["prompt"][i] + examples["response"][i]
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input_ids, labels = [], []
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for turn_idx, (source_ids, target_ids) in enumerate(
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template.encode_multiturn(
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tokenizer,
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messages,
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examples["system"][i],
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examples["tools"][i],
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data_args.cutoff_len,
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data_args.reserved_label_len,
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)
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template.encode_multiturn(
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tokenizer,
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messages,
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examples["system"][i],
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examples["tools"][i],
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data_args.cutoff_len,
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data_args.reserved_label_len,
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)
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):
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if data_args.train_on_prompt:
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source_mask = source_ids
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@ -96,10 +95,10 @@ def preprocess_supervised_dataset(
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def preprocess_packed_supervised_dataset(
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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) -> Dict[str, List[List[int]]]:
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# build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
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# and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
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@ -111,7 +110,7 @@ def preprocess_packed_supervised_dataset(
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messages = examples["prompt"][i] + examples["response"][i]
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for source_ids, target_ids in template.encode_multiturn(
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tokenizer, messages, examples["system"][i], examples["tools"][i]
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tokenizer, messages, examples["system"][i], examples["tools"][i]
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):
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if data_args.train_on_prompt:
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source_mask = source_ids
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@ -133,19 +132,19 @@ def preprocess_packed_supervised_dataset(
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total_length = (total_length // block_size) * block_size
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# split by chunks of cutoff_len
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for i in range(0, total_length, block_size):
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if not all(label == IGNORE_INDEX for label in labels[i : i + block_size]):
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model_inputs["input_ids"].append(input_ids[i : i + block_size])
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if not all(label == IGNORE_INDEX for label in labels[i: i + block_size]):
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model_inputs["input_ids"].append(input_ids[i: i + block_size])
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model_inputs["attention_mask"].append([1] * block_size)
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model_inputs["labels"].append(labels[i : i + block_size])
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model_inputs["labels"].append(labels[i: i + block_size])
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return model_inputs
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def preprocess_unsupervised_dataset(
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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) -> Dict[str, List[List[int]]]:
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# build inputs with format `<bos> X` and labels with format `Y <eos>`
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model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
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@ -179,10 +178,10 @@ def preprocess_unsupervised_dataset(
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def preprocess_pairwise_dataset(
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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examples: Dict[str, List[Any]],
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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) -> Dict[str, List[List[int]]]:
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# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
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model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
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@ -246,12 +245,12 @@ def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer:
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def get_preprocess_and_print_func(
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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stage: Literal["pt", "sft", "rm", "ppo"],
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processor: Optional["AutoProcessor"] = None,
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tokenizer: "PreTrainedTokenizer",
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template: "Template",
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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stage: Literal["pt", "sft", "rm", "ppo"],
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processor: Optional["AutoProcessor"] = None,
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) -> Tuple[Callable, Callable]:
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if stage == "pt":
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preprocess_func = partial(preprocess_pretrain_dataset, tokenizer=tokenizer, data_args=data_args)
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@ -280,5 +279,4 @@ def get_preprocess_and_print_func(
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preprocess_unsupervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
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)
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print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
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return preprocess_func, print_function
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