mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 12:42:51 +08:00
277 lines
11 KiB
Python
277 lines
11 KiB
Python
from functools import partial
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from itertools import chain
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Tuple
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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 PreTrainedTokenizer
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from ..hparams import DataArguments
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from .template import Template
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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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) -> 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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if not data_args.packing:
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return tokenizer(text_examples, add_special_tokens=False, max_length=data_args.cutoff_len)
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tokenized_examples = tokenizer(text_examples, add_special_tokens=False)
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concatenated_examples = {k: list(chain(*tokenized_examples[k])) for k in tokenized_examples.keys()}
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total_length = len(concatenated_examples[list(concatenated_examples.keys())[0]])
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block_size = data_args.cutoff_len
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# we drop the small remainder, and if the total_length < block_size, we exclude this batch
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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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result = {
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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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for i in range(len(result["input_ids"])):
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result["input_ids"][i][0] = tokenizer.bos_token_id
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return result
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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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) -> 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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model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
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for i in range(len(examples["prompt"])):
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if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
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continue
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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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):
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if data_args.train_on_prompt:
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source_mask = source_ids
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elif turn_idx != 0 and template.efficient_eos:
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source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
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else:
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source_mask = [IGNORE_INDEX] * len(source_ids)
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input_ids += source_ids + target_ids
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labels += source_mask + target_ids
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if template.efficient_eos:
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input_ids += [tokenizer.eos_token_id]
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labels += [tokenizer.eos_token_id]
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model_inputs["input_ids"].append(input_ids)
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model_inputs["attention_mask"].append([1] * len(input_ids))
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model_inputs["labels"].append(labels)
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return model_inputs
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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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) -> 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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model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
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input_ids, labels = [], []
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for i in range(len(examples["prompt"])):
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if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
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continue
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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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):
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if data_args.train_on_prompt:
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source_mask = source_ids
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elif len(input_ids) != 0 and template.efficient_eos:
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source_mask = [tokenizer.eos_token_id] + [IGNORE_INDEX] * (len(source_ids) - 1)
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else:
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source_mask = [IGNORE_INDEX] * len(source_ids)
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input_ids += source_ids + target_ids
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labels += source_mask + target_ids
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if template.efficient_eos:
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input_ids += [tokenizer.eos_token_id]
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labels += [tokenizer.eos_token_id]
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total_length = len(input_ids)
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block_size = data_args.cutoff_len
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# we drop the small remainder, and if the total_length < block_size, we exclude this batch
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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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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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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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) -> 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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for i in range(len(examples["prompt"])):
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if len(examples["prompt"][i]) % 2 != 1:
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continue
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if len(examples["response"][i]) == 1:
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messages = examples["prompt"][i] + examples["response"][i]
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else:
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messages = examples["prompt"][i] + [{"role": Role.ASSISTANT.value, "content": ""}]
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input_ids, labels = template.encode_oneturn(
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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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if template.efficient_eos:
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labels += [tokenizer.eos_token_id]
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model_inputs["input_ids"].append(input_ids)
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model_inputs["attention_mask"].append([1] * len(input_ids))
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model_inputs["labels"].append(labels)
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return model_inputs
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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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) -> 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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for i in range(len(examples["prompt"])):
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if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
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continue
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chosen_messages = examples["prompt"][i] + [examples["response"][i][0]]
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rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
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prompt_ids, chosen_ids = template.encode_oneturn(
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tokenizer,
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chosen_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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_, rejected_ids = template.encode_oneturn(
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tokenizer,
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rejected_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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if template.efficient_eos:
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chosen_ids += [tokenizer.eos_token_id]
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rejected_ids += [tokenizer.eos_token_id]
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model_inputs["prompt_ids"].append(prompt_ids)
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model_inputs["chosen_ids"].append(chosen_ids)
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model_inputs["rejected_ids"].append(rejected_ids)
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return model_inputs
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def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
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print("input_ids:\n{}".format(example["input_ids"]))
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print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
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print("label_ids:\n{}".format(example["labels"]))
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print(
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"labels:\n{}".format(
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tokenizer.decode(list(filter(lambda x: x != IGNORE_INDEX, example["labels"])), skip_special_tokens=False)
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)
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)
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def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
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print("prompt_ids:\n{}".format(example["prompt_ids"]))
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print("prompt:\n{}".format(tokenizer.decode(example["prompt_ids"], skip_special_tokens=False)))
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print("chosen_ids:\n{}".format(example["chosen_ids"]))
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print("chosen:\n{}".format(tokenizer.decode(example["chosen_ids"], skip_special_tokens=False)))
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print("rejected_ids:\n{}".format(example["rejected_ids"]))
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print("rejected:\n{}".format(tokenizer.decode(example["rejected_ids"], skip_special_tokens=False)))
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def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer: "PreTrainedTokenizer") -> None:
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print("input_ids:\n{}".format(example["input_ids"]))
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print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
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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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) -> 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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print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
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elif stage == "sft" and not training_args.predict_with_generate:
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if data_args.packing:
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preprocess_func = partial(
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preprocess_packed_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
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)
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else:
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preprocess_func = partial(
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preprocess_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
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)
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print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
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elif stage == "rm":
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preprocess_func = partial(
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preprocess_pairwise_dataset, tokenizer=tokenizer, template=template, data_args=data_args
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)
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print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
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else:
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preprocess_func = partial(
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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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