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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | #!/usr/bin/env python3
"""
BioRLHF DPO Training Script
Direct Preference Optimization on biological reasoning
Usage:
python dpo_train.py --sft_model ./kmp_sft_model_v2
"""
import argparse
import os
import torch
from datasets import load_dataset, Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model, PeftModel, prepare_model_for_kbit_training
from trl import DPOTrainer, DPOConfig
import wandb
import json
def parse_args():
parser = argparse.ArgumentParser(description='DPO Training for BioRLHF')
parser.add_argument('--sft_model', type=str, default='./kmp_sft_model_v2',
help='Path to SFT fine-tuned model')
parser.add_argument('--base_model', type=str, default='mistralai/Mistral-7B-v0.3',
help='Base model name')
parser.add_argument('--dataset', type=str, default='kmp_dpo_preferences.json',
help='Path to preference dataset')
parser.add_argument('--output_dir', type=str, default='./kmp_dpo_model',
help='Output directory')
parser.add_argument('--epochs', type=int, default=3,
help='Number of training epochs')
parser.add_argument('--batch_size', type=int, default=2,
help='Per-device batch size')
parser.add_argument('--grad_accum', type=int, default=4,
help='Gradient accumulation steps')
parser.add_argument('--lr', type=float, default=5e-5,
help='Learning rate')
parser.add_argument('--beta', type=float, default=0.1,
help='DPO beta parameter')
parser.add_argument('--max_length', type=int, default=1024,
help='Maximum sequence length')
parser.add_argument('--wandb_project', type=str, default='biorlhf')
parser.add_argument('--wandb_run', type=str, default='kmp_dpo_v1')
parser.add_argument('--no_wandb', action='store_true')
return parser.parse_args()
def main():
args = parse_args()
print("="*60)
print("BioRLHF DPO Training")
print("="*60)
print(f"SFT Model: {args.sft_model}")
print(f"Base Model: {args.base_model}")
print(f"Dataset: {args.dataset}")
print(f"Output: {args.output_dir}")
print(f"Beta: {args.beta}")
print("="*60)
# Initialize wandb
if not args.no_wandb:
wandb.init(project=args.wandb_project, name=args.wandb_run, config=vars(args))
# Load preference dataset
print("\nLoading preference dataset...")
with open(args.dataset, 'r') as f:
raw_data = json.load(f)
dataset = Dataset.from_list(raw_data)
print(f"Preference pairs: {len(dataset)}")
# Split
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
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,
)
# Load base model
print(f"\nLoading base model: {args.base_model}")
model = AutoModelForCausalLM.from_pretrained(
args.base_model,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
# Load SFT LoRA adapters
print(f"\nLoading SFT adapters from: {args.sft_model}")
model = PeftModel.from_pretrained(model, args.sft_model)
model = model.merge_and_unload() # Merge SFT adapters into base
# Prepare for new LoRA training
model = prepare_model_for_kbit_training(model)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.sft_model, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left" # DPO needs left padding
# New LoRA config for DPO
print("\nConfiguring LoRA for DPO...")
lora_config = LoraConfig(
r=16,
lora_alpha=32,
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()
# Reference model (frozen copy)
print("\nLoading reference model...")
ref_model = AutoModelForCausalLM.from_pretrained(
args.base_model,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
ref_model = PeftModel.from_pretrained(ref_model, args.sft_model)
ref_model = ref_model.merge_and_unload()
# DPO Config
print("\nConfiguring DPO training...")
dpo_config = DPOConfig(
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,
beta=args.beta,
warmup_ratio=0.1,
lr_scheduler_type="cosine",
logging_steps=5,
save_steps=25,
eval_steps=25,
eval_strategy="steps",
save_total_limit=2,
bf16=True,
gradient_checkpointing=True,
report_to="wandb" if not args.no_wandb else "none",
run_name=args.wandb_run,
max_length=args.max_length,
max_prompt_length=512,
)
# Create DPO Trainer
print("\nInitializing DPO trainer...")
trainer = DPOTrainer(
model=model,
ref_model=ref_model,
args=dpo_config,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=tokenizer,
)
# Train
print("\n" + "="*60)
print("Starting DPO training...")
print("="*60)
trainer.train()
# Save
print(f"\nSaving model to {args.output_dir}")
trainer.save_model(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
if not args.no_wandb:
wandb.finish()
print("\n" + "="*60)
print("DPO Training complete!")
print("="*60)
if __name__ == "__main__":
main()
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