File size: 6,234 Bytes
11a6bae | 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 | from __future__ import annotations
import os
import re
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional
from datasets import load_dataset
from torch.utils.data import Dataset as TorchDataset
from transformers import HfArgumentParser, set_seed
from trainer.tivd.online_trainer import (
TIVDConfig,
TIVDTrainer,
assert_qwen_tokenizer_compatibility,
build_student_model,
build_teacher_model,
build_tokenizer,
copy_training_sources,
render_math_prompt,
)
@dataclass
class DataArguments:
dataset_name: str = field(default="openai/gsm8k")
dataset_config_name: Optional[str] = field(default="main")
dataset_split: str = field(default="train")
question_column: str = field(default="question")
answer_column: str = field(default="answer")
final_answer_column: str = field(default="")
difficulty_column: str = field(default="")
topic_column: str = field(default="")
solution_columns: str = field(default="")
limit: Optional[int] = field(default=None)
class PromptListDataset(TorchDataset):
"""Simple Python dataset wrapper to avoid Arrow batched-indexing quirks in custom Trainer flows."""
def __init__(self, rows: list[dict]):
self.rows = rows
def __len__(self) -> int:
return len(self.rows)
def __getitem__(self, idx: int) -> dict:
return self.rows[idx]
def _parse_gsm8k_final_answer(answer_text: Optional[str]) -> Optional[str]:
if not answer_text:
return None
match = re.search(r"####\s*(.+)$", answer_text.strip(), flags=re.MULTILINE)
if match:
return match.group(1).strip()
return answer_text.strip().splitlines()[-1].strip()
def build_filtered_dataset(data_args: DataArguments, train_args: TIVDConfig) -> PromptListDataset:
load_kwargs = {"path": data_args.dataset_name, "split": data_args.dataset_split}
if data_args.dataset_config_name:
load_kwargs["name"] = data_args.dataset_config_name
dataset = load_dataset(**load_kwargs)
if data_args.difficulty_column and data_args.difficulty_column in dataset.column_names:
dataset = dataset.filter(
lambda ex: ex.get(data_args.difficulty_column) is not None
and float(ex[data_args.difficulty_column]) >= float(train_args.difficulty_threshold),
desc=f"Filtering difficulty >= {train_args.difficulty_threshold}",
)
if data_args.limit is not None:
dataset = dataset.select(range(min(len(dataset), data_args.limit)))
solution_columns = [col.strip() for col in data_args.solution_columns.split(",") if col.strip()]
rows: list[dict] = []
for example in dataset:
raw_answer = example.get(data_args.answer_column) if data_args.answer_column else None
if data_args.final_answer_column:
final_answer = example.get(data_args.final_answer_column)
else:
final_answer = _parse_gsm8k_final_answer(raw_answer)
row = {
"prompt": render_math_prompt(example[data_args.question_column]),
"question": example[data_args.question_column],
"final_answer": final_answer,
"answer": raw_answer,
"difficulty": float(example.get(data_args.difficulty_column, 0.0) or 0.0)
if data_args.difficulty_column and data_args.difficulty_column in example
else 0.0,
"topic": example.get(data_args.topic_column) if data_args.topic_column else None,
}
for col in solution_columns:
if col in example:
row[col] = example[col]
rows.append(row)
return PromptListDataset(rows)
def main() -> None:
parser = HfArgumentParser((TIVDConfig, DataArguments))
train_args, data_args = parser.parse_args_into_dataclasses()
train_args.remove_unused_columns = False
train_args.label_names = []
if train_args.wandb_project:
os.environ.setdefault("WANDB_PROJECT", train_args.wandb_project)
if train_args.wandb_run_name:
os.environ.setdefault("WANDB_NAME", train_args.wandb_run_name)
Path(train_args.output_dir).mkdir(parents=True, exist_ok=True)
set_seed(train_args.seed)
world_size = int(os.environ.get("WORLD_SIZE", "1"))
if train_args.use_vllm and train_args.vllm_mode == "server" and world_size > 1:
raise ValueError(
"For this trainer, server-mode vLLM should be run with a single training process. "
"Use accelerate launch --num_processes 1 so training stays on one GPU and the vLLM server on another, "
"or use --vllm_mode colocate for same-GPU execution."
)
student_tokenizer = build_tokenizer(train_args.student_model_name_or_path, train_args.trust_remote_code)
teacher_tokenizer = build_tokenizer(train_args.teacher_model_name_or_path, train_args.trust_remote_code)
assert_qwen_tokenizer_compatibility(student_tokenizer, teacher_tokenizer)
train_dataset = build_filtered_dataset(data_args, train_args)
student_model = build_student_model(train_args)
teacher_model = build_teacher_model(train_args)
copy_training_sources(train_args.output_dir, __file__, Path(__file__).parent / "online_trainer.py")
trainer = TIVDTrainer(
model=student_model,
args=train_args,
tokenizer=student_tokenizer,
teacher_model=teacher_model,
target_model=None,
train_dataset=train_dataset,
eval_dataset=None,
ref_model=None,
source_file_paths=[__file__, str(Path(__file__).parent / "online_trainer.py")],
)
train_result = trainer.train(resume_from_checkpoint=train_args.resume_from_checkpoint)
trainer.save_model(train_args.output_dir)
student_tokenizer.save_pretrained(train_args.output_dir)
metrics = train_result.metrics
metrics["train_examples"] = len(train_dataset)
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
if train_args.push_to_hub:
kwargs = {}
if train_args.hub_model_id:
kwargs["repo_id"] = train_args.hub_model_id
trainer.push_to_hub(**kwargs)
if __name__ == "__main__":
main()
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