File size: 4,751 Bytes
5a90f0c | 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 | #!/usr/bin/env python3
"""LoRA fine-tuning entrypoint for RunPod GPU pods."""
from __future__ import annotations
import json
import os
import re
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_CONFIG = ROOT / "config" / "training.yaml"
def load_config(path: Path) -> dict:
import yaml
text = path.read_text()
def repl(match: re.Match[str]) -> str:
var, default = match.group(1), match.group(2) or ""
return os.environ.get(var, default)
text = re.sub(r"\$\{([^}:]+)(?::-([^}]*))?\}", repl, text)
return yaml.safe_load(text)
def format_example(row: dict) -> dict:
messages = row["messages"]
return {"messages": messages}
def main() -> None:
import torch
from datasets import Dataset, load_dataset
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from trl import SFTTrainer
config_path = Path(os.environ.get("CONFIG_PATH", DEFAULT_CONFIG))
cfg = load_config(config_path)
train_cfg = cfg["training"]
data_dir = ROOT / "data" / "processed"
train_file = data_dir / "train.jsonl"
if not train_file.exists():
fallback = ROOT / cfg["dataset"]["output_file"]
if fallback.exists():
train_file = fallback
else:
raise SystemExit(f"No training data at {data_dir}/train.jsonl or {fallback}")
rows = [json.loads(line) for line in train_file.read_text().splitlines() if line.strip()]
dataset = Dataset.from_list([format_example(r) for r in rows])
val_file = data_dir / "val.jsonl"
eval_dataset = None
if val_file.exists():
val_rows = [json.loads(line) for line in val_file.read_text().splitlines() if line.strip()]
eval_dataset = Dataset.from_list([format_example(r) for r in val_rows])
base_model = os.environ.get("BASE_MODEL", train_cfg["base_model"])
output_dir = os.environ.get("OUTPUT_DIR", train_cfg["output_dir"])
Path(output_dir).mkdir(parents=True, exist_ok=True)
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
device_map="auto",
trust_remote_code=True,
)
lora_config = LoraConfig(
r=int(train_cfg["lora_r"]),
lora_alpha=int(train_cfg["lora_alpha"]),
lora_dropout=float(train_cfg["lora_dropout"]),
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)
model = get_peft_model(model, lora_config)
def formatting_func(batch):
texts = []
for messages in batch["messages"]:
texts.append(
tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
)
return {"text": texts}
formatted = dataset.map(formatting_func, batched=True, remove_columns=dataset.column_names)
formatted_eval = None
if eval_dataset is not None:
formatted_eval = eval_dataset.map(
formatting_func, batched=True, remove_columns=eval_dataset.column_names
)
max_steps = int(os.environ.get("RUNPOD_MAX_STEPS", "0"))
training_args = TrainingArguments(
output_dir=output_dir,
num_train_epochs=float(train_cfg["num_train_epochs"]) if not max_steps else 1.0,
max_steps=max_steps if max_steps > 0 else -1,
per_device_train_batch_size=int(train_cfg["per_device_train_batch_size"]),
gradient_accumulation_steps=int(train_cfg["gradient_accumulation_steps"]),
learning_rate=float(train_cfg["learning_rate"]),
warmup_ratio=float(train_cfg["warmup_ratio"]),
logging_steps=int(train_cfg["logging_steps"]),
save_steps=int(train_cfg["save_steps"]),
save_total_limit=2,
bf16=torch.cuda.is_available(),
report_to="none",
remove_unused_columns=False,
)
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=formatted,
eval_dataset=formatted_eval,
processing_class=tokenizer,
)
trainer.train()
trainer.save_model(output_dir)
tokenizer.save_pretrained(output_dir)
meta = {
"base_model": base_model,
"train_examples": len(rows),
"output_dir": output_dir,
}
Path(output_dir, "training_meta.json").write_text(json.dumps(meta, indent=2))
print(json.dumps(meta, indent=2))
if __name__ == "__main__":
main()
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