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SFT Training: math | fullft | vision=freeze
"""
import os
import json
import torch
from PIL import Image
from torch.utils.data import Dataset
from transformers import (
Qwen2_5_VLForConditionalGeneration,
AutoProcessor,
TrainingArguments,
Trainer,
)
MODEL_NAME = "/workspace/rl4phyx/models/Qwen2.5-VL-3B-Instruct"
DATA_PATH = "/workspace/rl4phyx/RL4Phyx/SFT/sft_train/coldstart_formatted.jsonl"
OUTPUT_DIR = "/workspace/rl4phyx/RL4Phyx/SFT/checkpoints/fullft_math_f"
NUM_EPOCHS = 3
LEARNING_RATE = 1e-5
PER_DEVICE_BATCH_SIZE = 1
GRAD_ACCUM_STEPS = 16
MAX_LENGTH = 4096
FREEZE_VISION = True
# === Dataset and Collator classes imported from template ===
exec(open('/workspace/rl4phyx/RL4Phyx/SFT/_sft_classes.py').read())
def main():
print("Config: math | fullft | vision=freeze")
print(f"Data: {DATA_PATH}")
print(f"Output: {OUTPUT_DIR}")
processor = AutoProcessor.from_pretrained(
MODEL_NAME, min_pixels=3136, max_pixels=1204224,
)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_NAME, torch_dtype=torch.bfloat16, attn_implementation="sdpa",
)
if FREEZE_VISION:
for name, param in model.named_parameters():
if 'visual' in name:
param.requires_grad = False
print("Froze vision encoder")
else:
print("Vision encoder trainable")
model.enable_input_require_grads()
dataset = PhysicsCoTDataset(data_path=DATA_PATH, processor=processor, max_length=MAX_LENGTH)
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
num_train_epochs=NUM_EPOCHS,
per_device_train_batch_size=PER_DEVICE_BATCH_SIZE,
gradient_accumulation_steps=GRAD_ACCUM_STEPS,
learning_rate=LEARNING_RATE,
lr_scheduler_type="cosine",
warmup_ratio=0.03,
weight_decay=0.01,
bf16=True,
logging_steps=10,
save_strategy="steps",
save_steps=20,
save_total_limit=2,
dataloader_num_workers=4,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={'use_reentrant': False},
remove_unused_columns=False,
report_to="none",
deepspeed="ds_zero2.json",
save_only_model=False,
)
collator = VLMDataCollator(processor)
trainer = Trainer(model=model, args=training_args, train_dataset=dataset, data_collator=collator)
print("\n===== Starting SFT Training =====")
trainer.train()
print("\n===== Saving final model =====")
trainer.save_model(os.path.join(OUTPUT_DIR, "final"))
processor.save_pretrained(os.path.join(OUTPUT_DIR, "final"))
print("\n===== SFT Training Complete =====")
if __name__ == "__main__":
main()
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