Spaces:
Running
Running
File size: 6,635 Bytes
c1de90b | 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 | from __future__ import annotations
import argparse
import inspect
from pathlib import Path
from .data import TokenizedCodeDataset, load_jsonl_records, split_records
from .modeling import load_model_and_tokenizer
def _csv(value: str) -> list[str]:
return [part.strip() for part in value.split(",") if part.strip()]
def _import_training_stack():
try:
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from transformers import DataCollatorForSeq2Seq, Trainer, TrainingArguments
except Exception as exc:
raise RuntimeError(
"Missing training dependencies. Install them with: pip install -r requirements.txt"
) from exc
return LoraConfig, get_peft_model, prepare_model_for_kbit_training, DataCollatorForSeq2Seq, Trainer, TrainingArguments
def _build_training_args(TrainingArguments, args, torch):
signature = inspect.signature(TrainingArguments.__init__).parameters
cuda = torch.cuda.is_available()
use_bf16 = cuda and torch.cuda.is_bf16_supported()
use_fp16 = cuda and not use_bf16
kwargs = {
"output_dir": args.output_dir,
"per_device_train_batch_size": args.batch_size,
"gradient_accumulation_steps": args.gradient_accumulation_steps,
"num_train_epochs": args.epochs,
"learning_rate": args.learning_rate,
"logging_steps": args.logging_steps,
"save_steps": args.save_steps,
"save_total_limit": args.save_total_limit,
"warmup_ratio": args.warmup_ratio,
"weight_decay": args.weight_decay,
"optim": "adamw_torch",
"report_to": "none",
"remove_unused_columns": False,
"gradient_checkpointing": args.gradient_checkpointing,
}
if "bf16" in signature:
kwargs["bf16"] = use_bf16
if "fp16" in signature:
kwargs["fp16"] = use_fp16
if args.validation_size > 0:
strategy_name = "eval_strategy" if "eval_strategy" in signature else "evaluation_strategy"
kwargs[strategy_name] = "steps"
kwargs["eval_steps"] = args.eval_steps
return TrainingArguments(**kwargs)
def main() -> int:
parser = argparse.ArgumentParser(description="Fine-tune Gemma for code generation with LoRA.")
parser.add_argument("--data", required=True, help="Path to JSONL training data.")
parser.add_argument("--base-model", default="google/gemma-3-1b-it", help="Base Hugging Face model id.")
parser.add_argument("--output-dir", default="outputs/gemma-code-lora", help="Where to save the LoRA adapter.")
parser.add_argument("--max-records", type=int, default=None, help="Optional limit for quick tests.")
parser.add_argument("--validation-size", type=float, default=0.0, help="Fraction of data for validation.")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--max-length", type=int, default=2048)
parser.add_argument("--epochs", type=float, default=2.0)
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument("--gradient-accumulation-steps", type=int, default=8)
parser.add_argument("--learning-rate", type=float, default=2e-4)
parser.add_argument("--warmup-ratio", type=float, default=0.03)
parser.add_argument("--weight-decay", type=float, default=0.0)
parser.add_argument("--logging-steps", type=int, default=10)
parser.add_argument("--save-steps", type=int, default=100)
parser.add_argument("--eval-steps", type=int, default=100)
parser.add_argument("--save-total-limit", type=int, default=2)
parser.add_argument("--lora-r", type=int, default=16)
parser.add_argument("--lora-alpha", type=int, default=32)
parser.add_argument("--lora-dropout", type=float, default=0.05)
parser.add_argument(
"--target-modules",
default="q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj",
help="Comma-separated LoRA target module names.",
)
parser.add_argument("--quantization", choices=["none", "4bit", "8bit"], default="none")
parser.add_argument("--dtype", choices=["auto", "float32", "float16", "bfloat16"], default="auto")
parser.add_argument("--gradient-checkpointing", action="store_true")
parser.add_argument("--trust-remote-code", action="store_true")
parser.add_argument("--resume-from-checkpoint", default=None)
args = parser.parse_args()
LoraConfig, get_peft_model, prepare_model_for_kbit_training, DataCollatorForSeq2Seq, Trainer, TrainingArguments = (
_import_training_stack()
)
records = load_jsonl_records(args.data, max_records=args.max_records)
train_records, eval_records = split_records(records, args.validation_size, args.seed)
model, tokenizer, torch = load_model_and_tokenizer(
args.base_model,
quantization=args.quantization,
dtype=args.dtype,
trust_remote_code=args.trust_remote_code,
for_training=True,
)
model.config.use_cache = False
if args.quantization != "none":
model = prepare_model_for_kbit_training(model)
lora_config = LoraConfig(
task_type="CAUSAL_LM",
r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=_csv(args.target_modules),
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
train_dataset = TokenizedCodeDataset(train_records, tokenizer, args.max_length)
eval_dataset = TokenizedCodeDataset(eval_records, tokenizer, args.max_length) if eval_records else None
data_collator = DataCollatorForSeq2Seq(
tokenizer=tokenizer,
model=model,
label_pad_token_id=-100,
pad_to_multiple_of=8,
)
training_args = _build_training_args(TrainingArguments, args, torch)
trainer_kwargs = {
"model": model,
"args": training_args,
"train_dataset": train_dataset,
"eval_dataset": eval_dataset,
"data_collator": data_collator,
}
trainer_signature = inspect.signature(Trainer.__init__).parameters
if "processing_class" in trainer_signature:
trainer_kwargs["processing_class"] = tokenizer
else:
trainer_kwargs["tokenizer"] = tokenizer
trainer = Trainer(**trainer_kwargs)
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
trainer.save_model(str(output_dir))
tokenizer.save_pretrained(str(output_dir))
print(f"Saved LoRA adapter to {output_dir}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
|