scripts/train_sft.py

"""
SFT Training Script for Qwen2.5-VL-3B-Instruct on Physics CoT Data.
Aligned with RL-with-Cold-Start 7B reference configuration.

Key changes from previous version:
  - Full fine-tuning (no LoRA) for stronger cold-start
  - Vision encoder NOT frozen (freeze_aligner=false in reference)
  - 3 epochs (not 16) to avoid overfitting
  - Higher image resolution (max_pixels=1204224) matching reference
  - Larger effective batch size (grad_accum=16)
  - DeepSpeed ZeRO-2 for memory efficiency
  - Lower learning rate (1e-5) appropriate for full FT
"""
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,
)


# ===== Configuration =====
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/sft_qwen25vl_3b_fullft"

# Training hyperparameters (aligned with 7B reference)
NUM_EPOCHS = 3               # Reference uses 3 epochs
LEARNING_RATE = 1e-5         # Full FT uses lower LR than LoRA
PER_DEVICE_BATCH_SIZE = 1    # Small batch for VLM
GRAD_ACCUM_STEPS = 8        # Effective batch = 1 * 8 GPUs * 8 = 64
MAX_LENGTH = 4096            # Max total sequence length
FREEZE_VISION = False        # Reference: freeze_aligner=false


class PhysicsCoTDataset(Dataset):
    """Dataset for Qwen2.5-VL SFT with physics CoT."""

    def __init__(self, data_path, processor, max_length=4096):
        self.processor = processor
        self.max_length = max_length

        with open(data_path, 'r', encoding='utf-8') as f:
            self.records = [json.loads(line) for line in f]

        print(f"Loaded {len(self.records)} records from {data_path}")

    def __len__(self):
        return len(self.records)

    def __getitem__(self, idx):
        record = self.records[idx]
        messages = record['messages']

        # Extract image path from user message
        user_msg = messages[0]
        image_path = None
        text_content = ""

        for content in user_msg['content']:
            if content['type'] == 'image':
                image_path = content['image'].replace('file://', '')
            elif content['type'] == 'text':
                text_content = content['text']

        # Extract assistant response
        assistant_msg = messages[1]
        assistant_text = assistant_msg['content'][0]['text']

        # Load image
        image = Image.open(image_path).convert('RGB')
        # Ensure minimum image size for Qwen2.5-VL vision encoder (factor=28)
        # Strategy: scale up proportionally (preserve aspect ratio), then pad with white
        MIN_DIM = 56  # Must be >= 28, use 56 for safety (2*factor)
        w, h = image.size
        if w < MIN_DIM or h < MIN_DIM:
            # Scale proportionally so the smaller dimension reaches MIN_DIM
            scale = max(MIN_DIM / w, MIN_DIM / h)
            new_w = int(w * scale)
            new_h = int(h * scale)
            image = image.resize((new_w, new_h), Image.LANCZOS)
            # Pad with white if any dimension still < MIN_DIM (shouldn't happen, but safety)
            if new_w < MIN_DIM or new_h < MIN_DIM:
                from PIL import ImageOps
                padded = Image.new('RGB', (max(new_w, MIN_DIM), max(new_h, MIN_DIM)), (255, 255, 255))
                padded.paste(image, (0, 0))
                image = padded

        # Build conversation for apply_chat_template
        conversation = [
            {
                "role": "user",
                "content": [
                    {"type": "image", "image": image},
                    {"type": "text", "text": text_content},
                ],
            },
            {
                "role": "assistant",
                "content": [
                    {"type": "text", "text": assistant_text},
                ],
            },
        ]

        # Use processor to create inputs
        text = self.processor.apply_chat_template(
            conversation,
            tokenize=False,
            add_generation_prompt=False,
        )

        inputs = self.processor(
            text=[text],
            images=[image],
            padding=False,
            truncation=True,
            max_length=self.max_length,
            return_tensors="pt",
        )

        # Squeeze batch dimension
        input_ids = inputs['input_ids'].squeeze(0)
        attention_mask = inputs['attention_mask'].squeeze(0)

        # Create labels: mask user tokens (only train on assistant response)
        labels = input_ids.clone()

        # Find the assistant turn start token and mask everything before it
        assistant_token_str = "<|im_start|>assistant\n"
        assistant_token_ids = self.processor.tokenizer.encode(
            assistant_token_str, add_special_tokens=False
        )
        input_ids_list = input_ids.tolist()
        assistant_start = -1
        for i in range(len(input_ids_list) - len(assistant_token_ids) + 1):
            if input_ids_list[i:i + len(assistant_token_ids)] == assistant_token_ids:
                assistant_start = i + len(assistant_token_ids)
                break

        if assistant_start > 0:
            labels[:assistant_start] = -100  # Mask user prompt
        else:
            raise ValueError(f"FATAL: assistant start token not found in sample {idx}.")

        # Also mask padding
        labels[attention_mask == 0] = -100

        return {
            'input_ids': input_ids,
            'attention_mask': attention_mask,
            'labels': labels,
            'pixel_values': inputs.get('pixel_values', torch.tensor([])).squeeze(0) if 'pixel_values' in inputs else None,
            'image_grid_thw': inputs.get('image_grid_thw', torch.tensor([])).squeeze(0) if 'image_grid_thw' in inputs else None,
        }


class VLMDataCollator:
    """Custom data collator for variable-length VLM inputs."""

    def __init__(self, processor):
        self.processor = processor
        self.pad_token_id = processor.tokenizer.pad_token_id or processor.tokenizer.eos_token_id

    def __call__(self, features):
        max_len = max(f['input_ids'].size(0) for f in features)

        input_ids = []
        attention_mask = []
        labels = []
        pixel_values = []
        image_grid_thw = []

        for f in features:
            seq_len = f['input_ids'].size(0)
            pad_len = max_len - seq_len

            input_ids.append(torch.cat([
                f['input_ids'],
                torch.full((pad_len,), self.pad_token_id, dtype=f['input_ids'].dtype)
            ]))
            attention_mask.append(torch.cat([
                f['attention_mask'],
                torch.zeros(pad_len, dtype=f['attention_mask'].dtype)
            ]))
            labels.append(torch.cat([
                f['labels'],
                torch.full((pad_len,), -100, dtype=f['labels'].dtype)
            ]))

            if f.get('pixel_values') is not None:
                pixel_values.append(f['pixel_values'])
            if f.get('image_grid_thw') is not None:
                image_grid_thw.append(f['image_grid_thw'])

        batch = {
            'input_ids': torch.stack(input_ids),
            'attention_mask': torch.stack(attention_mask),
            'labels': torch.stack(labels),
        }

        if pixel_values:
            batch['pixel_values'] = torch.cat(pixel_values, dim=0)
        if image_grid_thw:
            batch['image_grid_thw'] = torch.stack(image_grid_thw)

        return batch


def main():
    print(f"Loading model: {MODEL_NAME}")
    print(f"Data: {DATA_PATH}")
    print(f"Output: {OUTPUT_DIR}")
    print(f"Full FT (no LoRA), Freeze Vision: {FREEZE_VISION}")
    print(f"Epochs: {NUM_EPOCHS}, LR: {LEARNING_RATE}, Batch: {PER_DEVICE_BATCH_SIZE} x {GRAD_ACCUM_STEPS}")

    # Load processor (higher resolution matching 7B reference)
    processor = AutoProcessor.from_pretrained(
        MODEL_NAME,
        min_pixels=3136,          # 56x56
        max_pixels=1204224,       # ~1100x1100, matching reference MAX_PIXELS
    )

    # Load model
    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        MODEL_NAME,
        torch_dtype=torch.bfloat16,
        attn_implementation="sdpa",
    )

    # Vision encoder: NOT frozen (matching reference freeze_aligner=false)
    if FREEZE_VISION:
        for name, param in model.named_parameters():
            if 'visual' in name:
                param.requires_grad = False
        print("Froze vision encoder parameters")
    else:
        print("Vision encoder is trainable (matching 7B reference)")

    # Full fine-tuning: enable input grads for gradient checkpointing
    model.enable_input_require_grads()

    # Create dataset
    dataset = PhysicsCoTDataset(data_path=DATA_PATH, processor=processor, max_length=MAX_LENGTH)

    # Training arguments
    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,             # Matching reference
        weight_decay=0.01,
        bf16=True,
        logging_steps=10,
        save_strategy="steps",
        save_steps=20,                 # Matching reference
        save_total_limit=2,            # Matching reference
        eval_steps=20,                 # Matching reference
        dataloader_num_workers=4,
        gradient_checkpointing=True,
        gradient_checkpointing_kwargs={'use_reentrant': False},
        remove_unused_columns=False,
        report_to="none",
        deepspeed="ds_zero2.json",     # DeepSpeed ZeRO-2 for full FT
        save_only_model=True,          # Matching reference
    )

    # Collator
    collator = VLMDataCollator(processor)

    # Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset,
        data_collator=collator,
    )

    # Train
    print("\n===== Starting SFT Training (Full FT, aligned with 7B reference) =====")
    trainer.train()

    # Save final model
    print("\n===== Saving final model =====")
    trainer.save_model(os.path.join(OUTPUT_DIR, "final"))
    processor.save_pretrained(os.path.join(OUTPUT_DIR, "final"))
    print(f"Final model saved to: {os.path.join(OUTPUT_DIR, 'final')}")

    print("\n===== SFT Training Complete =====")


if __name__ == "__main__":
    main()