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c7ebaa1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | #!/usr/bin/env python3
"""
BioRLHF SFT Training Script - Fixed for TRL 0.26
"""
import argparse
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from trl import SFTTrainer
import wandb
def parse_args():
parser = argparse.ArgumentParser(description='SFT Training for BioRLHF')
parser.add_argument('--model', type=str, default='mistralai/Mistral-7B-v0.3')
parser.add_argument('--dataset', type=str, default='kmp_sft_dataset.json')
parser.add_argument('--output_dir', type=str, default='./kmp_sft_model')
parser.add_argument('--epochs', type=int, default=3)
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--grad_accum', type=int, default=4)
parser.add_argument('--lr', type=float, default=2e-4)
parser.add_argument('--max_seq_length', type=int, default=2048)
parser.add_argument('--lora_r', type=int, default=32)
parser.add_argument('--lora_alpha', type=int, default=64)
parser.add_argument('--use_4bit', action='store_true', default=True)
parser.add_argument('--wandb_project', type=str, default='biorlhf')
parser.add_argument('--wandb_run', type=str, default='kmp_sft_v1')
parser.add_argument('--no_wandb', action='store_true')
return parser.parse_args()
def main():
args = parse_args()
print("="*60)
print("BioRLHF SFT Training")
print("="*60)
print(f"Model: {args.model}")
print(f"Dataset: {args.dataset}")
print(f"Output: {args.output_dir}")
print(f"Epochs: {args.epochs}")
print("="*60)
# Initialize wandb
if not args.no_wandb:
wandb.init(project=args.wandb_project, name=args.wandb_run, config=vars(args))
# Load dataset
print("\nLoading dataset...")
dataset = load_dataset('json', data_files=args.dataset)['train']
print(f"Dataset size: {len(dataset)} examples")
# Split into train/eval
dataset = dataset.train_test_split(test_size=0.1, seed=42)
train_dataset = dataset['train']
eval_dataset = dataset['test']
print(f"Train: {len(train_dataset)}, Eval: {len(eval_dataset)}")
# Quantization config
if args.use_4bit:
print("\nUsing 4-bit quantization...")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
else:
bnb_config = None
# Load model
print(f"\nLoading model: {args.model}")
model = AutoModelForCausalLM.from_pretrained(
args.model,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
# Prepare model for training
if args.use_4bit:
model = prepare_model_for_kbit_training(model)
# LoRA config
print("\nConfiguring LoRA...")
lora_config = LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Training arguments (using standard TrainingArguments)
training_args = TrainingArguments(
output_dir=args.output_dir,
num_train_epochs=args.epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
gradient_accumulation_steps=args.grad_accum,
learning_rate=args.lr,
weight_decay=0.01,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
logging_steps=10,
save_steps=50,
eval_steps=50,
eval_strategy="steps",
save_total_limit=3,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
bf16=True,
gradient_checkpointing=True,
report_to="wandb" if not args.no_wandb else "none",
run_name=args.wandb_run,
)
# Create trainer
print("\nInitializing trainer...")
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=tokenizer,
max_seq_length=args.max_seq_length,
)
# Train
print("\n" + "="*60)
print("Starting training...")
print("="*60)
trainer.train()
# Save final model
print(f"\nSaving model to {args.output_dir}")
trainer.save_model(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Save LoRA adapters separately
lora_output = os.path.join(args.output_dir, "lora_adapters")
model.save_pretrained(lora_output)
print(f"LoRA adapters saved to {lora_output}")
if not args.no_wandb:
wandb.finish()
print("\n" + "="*60)
print("Training complete!")
print("="*60)
if __name__ == "__main__":
main()
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