code/support_check/model_finetune/gemma3-finetune.py

import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
import ast
import json
import os
from datetime import datetime

import torch
from datasets import Dataset

from unsloth import FastModel
from unsloth.chat_templates import (
    get_chat_template,
    standardize_data_formats,
    train_on_responses_only,
)
from trl import SFTConfig, SFTTrainer

model_name = "unsloth/gemma-3-4b-it"
data_path = "/home/mshahidul/readctrl/code/support_check/support_check_bn/finetune_dataset_subclaim_support_bn.json"
test_size = 0.3
seed = 3407
finetune_mode = "subclaim_list"  # "single_subclaim" or "subclaim_list"
prompt_language = "en"  # "bn" (Bangla) or "en" (English)
run_mode = "finetune_and_eval"  # "finetune_and_eval" or "eval_base_only"
save_fp16_merged = False  # whether to save merged fp16 model after finetuning


def get_model_size_from_name(name):
    base = name.split("/")[-1]
    for part in base.split("-"):
        token = part.lower()
        if token.endswith("b") or token.endswith("m"):
            return part
    return "unknown"


model_size = get_model_size_from_name(model_name)


def formatting_prompts_func(examples):
    convos = examples["conversations"]
    texts = [
        tokenizer.apply_chat_template(
            convo,
            tokenize=False,
            add_generation_prompt=False,
        ).removeprefix("<bos>")
        for convo in convos
    ]
    return {"text": texts}


def parse_label_array(raw_text):
    text = (raw_text or "").strip()
    if not text:
        return []

    if "```" in text:
        text = text.replace("```json", "").replace("```", "").strip()

    start = text.find("[")
    end = text.rfind("]")
    if start != -1 and end != -1 and end > start:
        text = text[start : end + 1]

    parsed = None
    for parser in (json.loads, ast.literal_eval):
        try:
            parsed = parser(text)
            break
        except Exception:
            continue

    if not isinstance(parsed, list):
        return []

    normalized = []
    for item in parsed:
        if not isinstance(item, str):
            normalized.append("not_supported")
            continue
        label = item.strip().lower().replace("-", "_").replace(" ", "_")
        if label not in {"supported", "not_supported"}:
            label = "not_supported"
        normalized.append(label)
    return normalized


def parse_single_label(raw_text):
    text = (raw_text or "").strip().lower()
    if "supported" in text and "not_supported" not in text:
        return "supported"
    if "not_supported" in text:
        return "not_supported"
    if "supported" in text:
        return "supported"
    return None


def normalize_label(label):
    if label is None:
        return None
    label = str(label).strip().lower().replace("-", "_").replace(" ", "_")
    if label not in {"supported", "not_supported"}:
        return None
    return label


def build_single_user_prompt(input_text, subclaim):
    if prompt_language == "en":
        return (
            "You will be given a medical case description and one subclaim. "
            "Determine whether the subclaim is supported by the text.\n\n"
            f"Text:\n{input_text}\n\n"
            f"Subclaim:\n{subclaim}\n\n"
            "Reply with exactly one word: 'supported' or 'not_supported'."
        )
    # Bangla (default)
    return (
        "আপনাকে একটি মেডিকেল কেস বর্ণনা এবং একটি সাবক্লেইম দেওয়া হবে। "
        "সাবক্লেইমটি টেক্সট দ্বারা সমর্থিত কি না তা নির্ধারণ করুন।\n\n"
        f"টেক্সট:\n{input_text}\n\n"
        f"সাবক্লেইম:\n{subclaim}\n\n"
        "শুধু একটি শব্দ দিয়ে উত্তর দিন: 'supported' অথবা 'not_supported'."
    )


def build_list_user_prompt(input_text, subclaims):
    numbered = "\n".join(f"{idx + 1}. {sc}" for idx, sc in enumerate(subclaims))
    if prompt_language == "en":
        return (
            "You will be given a medical case description and a list of subclaims. "
            "Determine for each subclaim whether it is supported by the text.\n\n"
            f"Text:\n{input_text}\n\n"
            f"List of subclaims:\n{numbered}\n\n"
            "Give the label for each subclaim in order. "
            "Reply with a JSON array only, e.g.:\n"
            '["supported", "not_supported", ...]\n'
            "Do not write anything else."
        )
    # Bangla (default)
    return (
        "আপনাকে একটি মেডিকেল কেস বর্ণনা এবং একাধিক সাবক্লেইমের তালিকা দেওয়া হবে। "
        "প্রতিটি সাবক্লেইম টেক্সট দ্বারা সমর্থিত কি না তা নির্ধারণ করুন।\n\n"
        f"টেক্সট:\n{input_text}\n\n"
        f"সাবক্লেইমগুলোর তালিকা:\n{numbered}\n\n"
        "প্রতিটি সাবক্লেইমের জন্য ক্রমানুসারে লেবেল দিন। "
        "নির্দিষ্টভাবে একটি JSON array আকারে উত্তর দিন, যেমন:\n"
        '["supported", "not_supported", ...]\n'
        "অন্য কিছু লিখবেন না।"
    )


def build_single_subclaim_examples(raw_records):
    examples = []
    for record in raw_records:
        input_text = record.get("input_text", "")
        model_output = record.get("model_output") or {}
        items = model_output.get("items") or []
        for item in items:
            subclaims = item.get("subclaims") or []
            for sc in subclaims:
                subclaim_text = sc.get("subclaim", "")
                label = normalize_label(sc.get("label"))
                if not label:
                    continue
                user_prompt = build_single_user_prompt(input_text, subclaim_text)
                examples.append(
                    {
                        "conversations": [
                            {"role": "user", "content": user_prompt},
                            {"role": "assistant", "content": label},
                        ],
                    }
                )
    return examples


def build_list_subclaim_examples(raw_records):
    examples = []
    for record in raw_records:
        input_text = record.get("input_text", "")
        model_output = record.get("model_output") or {}
        items = model_output.get("items") or []
        all_subclaims = []
        all_labels = []
        for item in items:
            subclaims = item.get("subclaims") or []
            for sc in subclaims:
                subclaim_text = sc.get("subclaim", "")
                label = normalize_label(sc.get("label"))
                if not label:
                    continue
                all_subclaims.append(subclaim_text)
                all_labels.append(label)
        if not all_subclaims:
            continue
        user_prompt = build_list_user_prompt(input_text, all_subclaims)
        examples.append(
            {
                "conversations": [
                    {"role": "user", "content": user_prompt},
                    {"role": "assistant", "content": json.dumps(all_labels)},
                ],
            }
        )
    return examples


def extract_conversation_pair(conversations):
    user_prompt = ""
    gold_response = ""
    for message in conversations:
        role = message.get("role") or message.get("from")
        content = message.get("content", "")
        if role == "user" and not user_prompt:
            user_prompt = content
        elif role == "assistant" and not gold_response:
            gold_response = content
    return user_prompt, gold_response


def generate_prediction(user_prompt):
    prompt = tokenizer.apply_chat_template(
        [{"role": "user", "content": user_prompt}],
        tokenize=False,
        add_generation_prompt=True,
    )
    inputs = tokenizer(text=prompt, return_tensors="pt").to(model.device)
    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            max_new_tokens=256,
            do_sample=False,
            temperature=0.0,
            use_cache=True,
        )
    generated_tokens = outputs[0][inputs["input_ids"].shape[1] :]
    return tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()


# 1. Load Model and Tokenizer
model, tokenizer = FastModel.from_pretrained(
    model_name=model_name,
    max_seq_length=4092,
    load_in_4bit=True,
)

# 2. Data Preparation
tokenizer = get_chat_template(tokenizer, chat_template="gemma-3")
with open(data_path, "r", encoding="utf-8") as f:
    raw_data = json.load(f)

raw_dataset = Dataset.from_list(raw_data)
split_dataset = raw_dataset.train_test_split(test_size=test_size, seed=seed, shuffle=True)
train_raw = split_dataset["train"]
test_raw = split_dataset["test"]

if finetune_mode == "single_subclaim":
    train_examples = build_single_subclaim_examples(train_raw)
elif finetune_mode == "subclaim_list":
    train_examples = build_list_subclaim_examples(train_raw)
else:
    raise ValueError(f"Unsupported finetune_mode: {finetune_mode}")

train_dataset = Dataset.from_list(train_examples)
train_dataset = train_dataset.map(formatting_prompts_func, batched=True)

# 3. Optional Finetuning
if run_mode == "finetune_and_eval":
    # Add LoRA adapters for finetuning
    model = FastModel.get_peft_model(
        model,
        r=8,
        target_modules=[
            "q_proj",
            "k_proj",
            "v_proj",
            "o_proj",
            "gate_proj",
            "up_proj",
            "down_proj",
        ],
        lora_alpha=16,
        lora_dropout=0,
        bias="none",
        random_state=seed,
    )

    # Training setup
    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=train_dataset,
        dataset_text_field="text",
        max_seq_length=2048,
        args=SFTConfig(
            per_device_train_batch_size=2,
            gradient_accumulation_steps=4,
            warmup_steps=5,
            max_steps=60,
            learning_rate=2e-4,
            fp16=not torch.cuda.is_bf16_supported(),
            bf16=torch.cuda.is_bf16_supported(),
            logging_steps=1,
            optim="adamw_8bit",
            weight_decay=0.01,
            lr_scheduler_type="linear",
            seed=seed,
            output_dir="outputs",
            report_to="none",
        ),
    )

    # Masking to train on assistant responses only
    trainer = train_on_responses_only(
        trainer,
        instruction_part="<start_of_turn>user\n",
        response_part="<start_of_turn>model\n",
    )

    # Execute training
    save_dir = f"/home/mshahidul/readctrl_model/support_checking_bn/{model_name.split('/')[-1]}"
    os.makedirs(save_dir, exist_ok=True)
    trainer.train()

    # Optional: save in float16 merged format
    if save_fp16_merged:
        model.save_pretrained_merged(save_dir, tokenizer, save_method="merged_16bit")
        tokenizer.save_pretrained(save_dir)

elif run_mode == "eval_base_only":
    # No finetuning; evaluate base model
    save_dir = f"BASE_MODEL:{model_name}"
else:
    raise ValueError(f"Unsupported run_mode: {run_mode}")

# 4. Test-set Inference + Accuracy
FastModel.for_inference(model)
model.eval()

model_info_dir = "/home/mshahidul/readctrl/code/support_check/model_info"
ablation_dir = "/home/mshahidul/readctrl/code/support_check/support_check_bn/ablation_studies"
os.makedirs(model_info_dir, exist_ok=True)
os.makedirs(ablation_dir, exist_ok=True)

timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
model_tag = model_name.split("/")[-1].replace(".", "_")

def evaluate_single_subclaim_mode(test_split):
    results = []
    total = 0
    correct = 0
    tp = fp = fn = tn = 0

    for idx, sample in enumerate(test_split):
        input_text = sample.get("input_text", "")
        model_output = sample.get("model_output") or {}
        items = model_output.get("items") or []

        for item in items:
            subclaims = item.get("subclaims") or []
            for sc in subclaims:
                subclaim_text = sc.get("subclaim", "")
                gold_label = normalize_label(sc.get("label"))
                if not gold_label:
                    continue

                user_prompt = build_single_user_prompt(input_text, subclaim_text)
                pred_text = generate_prediction(user_prompt)
                pred_label = parse_single_label(pred_text) or "not_supported"

                total += 1
                is_correct = pred_label == gold_label
                if is_correct:
                    correct += 1

                if gold_label == "supported" and pred_label == "supported":
                    tp += 1
                elif gold_label == "supported" and pred_label == "not_supported":
                    fn += 1
                elif gold_label == "not_supported" and pred_label == "supported":
                    fp += 1
                elif gold_label == "not_supported" and pred_label == "not_supported":
                    tn += 1

                results.append(
                    {
                        "sample_index": idx,
                        "input_text": input_text,
                        "subclaim": subclaim_text,
                        "gold_label": gold_label,
                        "predicted_label": pred_label,
                        "correct": is_correct,
                    }
                )

    accuracy = correct / total if total else 0.0
    precision = tp / (tp + fp) if (tp + fp) else 0.0
    recall = tp / (tp + fn) if (tp + fn) else 0.0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0

    metrics = {
        "mode": "single_subclaim",
        "model_name": model_name,
        "model_save_dir": save_dir,
        "dataset_path": data_path,
        "seed": seed,
        "test_size": test_size,
        "examples_evaluated": total,
        "accuracy": accuracy,
        "precision_supported": precision,
        "recall_supported": recall,
        "f1_supported": f1,
        "tp_supported": tp,
        "fp_supported": fp,
        "fn_supported": fn,
        "tn_supported": tn,
        "timestamp": timestamp,
    }
    return results, metrics


def evaluate_subclaim_list_mode(test_split):
    results = []
    total_samples = 0
    exact_match_correct = 0
    total_subclaims = 0
    correct_subclaims = 0
    tp = fp = fn = tn = 0

    for idx, sample in enumerate(test_split):
        input_text = sample.get("input_text", "")
        model_output = sample.get("model_output") or {}
        items = model_output.get("items") or []

        subclaims = []
        gold_labels = []
        for item in items:
            for sc in item.get("subclaims") or []:
                subclaim_text = sc.get("subclaim", "")
                label = normalize_label(sc.get("label"))
                if not label:
                    continue
                subclaims.append(subclaim_text)
                gold_labels.append(label)

        if not subclaims:
            continue

        user_prompt = build_list_user_prompt(input_text, subclaims)
        pred_text = generate_prediction(user_prompt)
        pred_labels = parse_label_array(pred_text)

        if not pred_labels:
            pred_labels = ["not_supported"] * len(gold_labels)

        if len(pred_labels) < len(gold_labels):
            pred_labels = pred_labels + ["not_supported"] * (len(gold_labels) - len(pred_labels))
        elif len(pred_labels) > len(gold_labels):
            pred_labels = pred_labels[: len(gold_labels)]

        sample_correct = 0
        for gold_label, pred_label in zip(gold_labels, pred_labels):
            total_subclaims += 1
            if pred_label == gold_label:
                correct_subclaims += 1
                sample_correct += 1

            if gold_label == "supported" and pred_label == "supported":
                tp += 1
            elif gold_label == "supported" and pred_label == "not_supported":
                fn += 1
            elif gold_label == "not_supported" and pred_label == "supported":
                fp += 1
            elif gold_label == "not_supported" and pred_label == "not_supported":
                tn += 1

        total_samples += 1
        exact_match = sample_correct == len(gold_labels)
        if exact_match:
            exact_match_correct += 1

        results.append(
            {
                "sample_index": idx,
                "input_text": input_text,
                "subclaims": subclaims,
                "gold_labels": gold_labels,
                "predicted_labels": pred_labels,
                "exact_match": exact_match,
                "per_sample_accuracy": sample_correct / len(gold_labels),
            }
        )

    accuracy = correct_subclaims / total_subclaims if total_subclaims else 0.0
    precision = tp / (tp + fp) if (tp + fp) else 0.0
    recall = tp / (tp + fn) if (tp + fn) else 0.0
    f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
    exact_match_accuracy = (
        exact_match_correct / total_samples if total_samples else 0.0
    )

    metrics = {
        "mode": "subclaim_list",
        "model_name": model_name,
        "model_save_dir": save_dir,
        "dataset_path": data_path,
        "seed": seed,
        "test_size": test_size,
        "test_samples_evaluated": total_samples,
        "total_subclaims": total_subclaims,
        "correct_subclaims": correct_subclaims,
        "subclaim_accuracy": accuracy,
        "exact_match_accuracy": exact_match_accuracy,
        "precision_supported": precision,
        "recall_supported": recall,
        "f1_supported": f1,
        "tp_supported": tp,
        "fp_supported": fp,
        "fn_supported": fn,
        "tn_supported": tn,
        "timestamp": timestamp,
    }
    return results, metrics


if finetune_mode == "single_subclaim":
    results, accuracy_summary = evaluate_single_subclaim_mode(test_raw)
else:
    results, accuracy_summary = evaluate_subclaim_list_mode(test_raw)

accuracy_summary["finetune_mode"] = finetune_mode
accuracy_summary["model_size"] = model_size
accuracy_summary["run_mode"] = run_mode

predictions_path = os.path.join(
    model_info_dir,
    f"{model_tag}_test_inference_{timestamp}.json",
)
accuracy_path = os.path.join(
    ablation_dir,
    f"{model_tag}_{finetune_mode}_{model_size}_{run_mode}_{timestamp}.json",
)

with open(predictions_path, "w", encoding="utf-8") as f:
    json.dump(results, f, ensure_ascii=False, indent=2)

with open(accuracy_path, "w", encoding="utf-8") as f:
    json.dump(accuracy_summary, f, ensure_ascii=False, indent=2)

print(f"Saved test inference to: {predictions_path}")
print(f"Saved test accuracy to: {accuracy_path}")
print(f"Accuracy: {accuracy_summary.get('accuracy', accuracy_summary.get('subclaim_accuracy', 0.0)):.4f}")
print(f"F1 (supported class): {accuracy_summary.get('f1_supported', 0.0):.4f}")