Notebook 4 v2: Complete bulletproof fixer training with all error fixes integrated

notebook4_fixer_training_v2_FIXED.py

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+#!/usr/bin/env python
+# ============================================================
+# NOTEBOOK 4/4: Fixer Model Training - BULLETPROOF VERSION
+# ============================================================
+# Run on Kaggle with T4 GPU
+# This notebook is SELF-CONTAINED - can restart kernel and run all cells
+# Estimated time: ~3-4 hours on T4
+# Saves model to HF Hub: ayshajavd/codet5p-vuln-fixer
+# ============================================================
+# ALL PREVIOUS ERRORS FIXED:
+# 1. Tokenizer: RobertaTokenizer.from_pretrained(..., use_fast=False)
+# 2. NaN loss: fp16 DISABLED (avoid unscale_fp16 error), LR=5e-5, max_grad_norm=1.0
+# 3. OOM: batch_size=2, gradient_accumulation=16, single GPU forced
+# 4. DataParallel: model.to('cuda:0') + trainer.args._n_gpu=1
+# 5. Deprecation: no no_cuda param, use warmup_ratio instead of warmup_steps
+# 6. Padding warning: padding='max_length' in tokenizer
+# 7. CodeBLEU: tree-sitter-c parser pre-installed check
+# ============================================================
+
+# %% [CELL 1] Install + Login
+import subprocess
+subprocess.run(["pip", "install", "-q", "transformers", "datasets", "scikit-learn",
+                "accelerate", "huggingface_hub", "evaluate", "sentencepiece",
+                "sacrebleu", "rouge_score", "codebleu", "tree-sitter-c", "problog"], capture_output=True)
+
+from huggingface_hub import login
+import os
+try:
+    from kaggle_secrets import UserSecretsClient
+    token = UserSecretsClient().get_secret("HF_TOKEN")
+except:
+    token = os.environ.get("HF_TOKEN", None)
+if token:
+    login(token=token)
+    print("✅ Logged in to HF Hub")
+else:
+    print("⚠️ No HF token found. Set HF_TOKEN environment variable or Kaggle secret.")
+
+# %% [CELL 2] Imports + Config
+import json, numpy as np, torch
+from datasets import load_dataset
+from transformers import (
+    AutoModelForSeq2SeqLM, RobertaTokenizer,
+    Seq2SeqTrainingArguments, Seq2SeqTrainer,
+    DataCollatorForSeq2Seq, EarlyStoppingCallback,
+)
+import evaluate
+from huggingface_hub import HfApi
+
+MODEL_NAME = "Salesforce/codet5p-220m"
+HUB_MODEL_ID = "ayshajavd/codet5p-vuln-fixer"
+DATASET_ID = "ayshajavd/code-security-vulnerability-dataset"
+MAX_SOURCE_LENGTH = 512
+MAX_TARGET_LENGTH = 512
+SEED = 42
+
+CWE_NAMES = {
+    "safe":"Safe Code","CWE-20":"Improper Input Validation","CWE-22":"Path Traversal",
+    "CWE-78":"OS Command Injection","CWE-79":"Cross-Site Scripting",
+    "CWE-89":"SQL Injection","CWE-94":"Code Injection","CWE-119":"Buffer Overflow",
+    "CWE-125":"Out-of-bounds Read","CWE-190":"Integer Overflow",
+    "CWE-200":"Information Exposure","CWE-264":"Permissions Issues",
+    "CWE-269":"Privilege Management","CWE-276":"Incorrect Permissions",
+    "CWE-284":"Access Control","CWE-287":"Authentication",
+    "CWE-310":"Cryptographic Issues","CWE-327":"Broken Crypto",
+    "CWE-330":"Insufficient Randomness","CWE-352":"CSRF",
+    "CWE-362":"Race Condition","CWE-399":"Resource Management",
+    "CWE-401":"Memory Leak","CWE-416":"Use After Free",
+    "CWE-434":"File Upload","CWE-476":"NULL Pointer Dereference",
+    "CWE-502":"Insecure Deserialization","CWE-601":"Open Redirect",
+    "CWE-787":"Out-of-bounds Write","CWE-798":"Hardcoded Credentials","CWE-918":"SSRF",
+}
+
+if torch.cuda.is_available():
+    print(f"✅ GPU: {torch.cuda.get_device_name(0)} ({torch.cuda.get_device_properties(0).total_memory/1e9:.1f}GB)")
+else:
+    print("⚠️ No GPU!")
+
+# %% [CELL 3] Load Model + Tokenizer - BULLETPROOF
+print("=" * 60)
+print("🔄 Loading CodeT5+ 220M Tokenizer + Model")
+print("=" * 60)
+
+# BULLETPROOF: Use RobertaTokenizer slow path (use_fast=False)
+# This avoids the 'extra_special_tokens' TypeError in newer transformers
+tokenizer = RobertaTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
+
+# CRITICAL: Verify token IDs match model's config.json
+print(f"\n🔍 Tokenizer Verification:")
+print(f"   pad_token_id: {tokenizer.pad_token_id}  (expected: 0)")
+print(f"   bos_token_id: {tokenizer.bos_token_id}  (expected: 1)")
+print(f"   eos_token_id: {tokenizer.eos_token_id}  (expected: 2)")
+print(f"   unk_token_id: {tokenizer.unk_token_id}  (expected: 3)")
+print(f"   mask_token_id: {tokenizer.mask_token_id}  (expected: 4)")
+print(f"   vocab_size: {len(tokenizer)}  (expected: 32100)")
+
+# Verify sentinel tokens exist
+extra_id_0 = tokenizer.convert_tokens_to_ids("<extra_id_0>")
+extra_id_99 = tokenizer.convert_tokens_to_ids("<extra_id_99>")
+print(f"   <extra_id_0> id: {extra_id_0}  (should NOT be {tokenizer.unk_token_id})")
+print(f"   <extra_id_99> id: {extra_id_99}  (should NOT be {tokenizer.unk_token_id})")
+
+assert tokenizer.pad_token_id == 0, f"FATAL: pad_token_id={tokenizer.pad_token_id}, expected 0"
+assert len(tokenizer) == 32100, f"FATAL: vocab_size={len(tokenizer)}, expected 32100"
+assert extra_id_0 != tokenizer.unk_token_id, "FATAL: <extra_id_0> mapped to <unk>"
+print("✅ All tokenizer verifications PASSED")
+
+# Load model - use float32 (NOT fp16) to avoid unscale_fp16 error
+# T5ForConditionalGeneration works fine with default dtype
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+# Force single GPU to prevent DataParallel OOM issues
+if torch.cuda.is_available():
+    model = model.to('cuda:0')
+    torch.cuda.set_device(0)
+    print(f"✅ Model moved to cuda:0")
+
+print(f"✅ Model loaded: {sum(p.numel() for p in model.parameters()):,} parameters")
+
+# %% [CELL 4] Load + Filter Dataset
+print("\n" + "=" * 60)
+print("📂 Loading Dataset")
+print("=" * 60)
+
+ds = load_dataset(DATASET_ID)
+
+def filter_has_fix(example):
+    """Only keep vulnerable samples that have a fix"""
+    return (example['is_vulnerable'] == True and
+            example['code_fixed'] is not None and
+            len(example['code_fixed'].strip()) > 10)
+
+print("Filtering to samples with fixes...")
+ds_fixer = {}
+for split in ['train', 'validation', 'test']:
+    ds_fixer[split] = ds[split].filter(filter_has_fix, num_proc=2)
+    print(f"  {split}: {len(ds_fixer[split]):,} samples with fixes")
+
+# %% [CELL 5] Tokenize with CWE-aware Input
+print("\n" + "=" * 60)
+print("🔤 Tokenizing with CWE-aware input format")
+print("=" * 60)
+
+def tokenize_fn(examples):
+    """
+    Input: "fix <CWE-NAME> vulnerability in <language>: <code>"
+    Target: fixed code
+    """
+    inputs = []
+    for code, cwe, lang in zip(examples['code'], examples['cwe_id'], examples['language']):
+        cwe_name = CWE_NAMES.get(cwe, cwe)
+        prefix = f"fix {cwe_name} vulnerability in {lang.lower()}: "
+        inputs.append(prefix + code)
+
+    # FIXED: use padding='max_length' to avoid the warning
+    model_inputs = tokenizer(
+        inputs,
+        max_length=MAX_SOURCE_LENGTH,
+        truncation=True,
+        padding='max_length',
+    )
+    labels = tokenizer(
+        examples['code_fixed'],
+        max_length=MAX_TARGET_LENGTH,
+        truncation=True,
+        padding='max_length',
+    )
+    model_inputs['labels'] = labels['input_ids']
+    return model_inputs
+
+print("Tokenizing...")
+tokenized = {}
+for split in ['train', 'validation', 'test']:
+    tokenized[split] = ds_fixer[split].map(
+        tokenize_fn, batched=True, batch_size=500, num_proc=2,
+        remove_columns=ds_fixer[split].column_names,
+    )
+    print(f"  {split}: {len(tokenized[split]):,} tokenized")
+
+# Verify a sample
+sample_input_ids = tokenized['train'][0]['input_ids']
+sample_label_ids = tokenized['train'][0]['labels']
+print(f"\n📋 Sample verification:")
+print(f"   input_ids length: {len(sample_input_ids)}")
+print(f"   labels length: {len(sample_label_ids)}")
+print(f"   input_ids[:10]: {sample_input_ids[:10]}")
+print(f"   labels[:10]: {sample_label_ids[:10]}")
+
+# Check label masking: pad tokens should be in input_ids but not in labels
+# (DataCollatorForSeq2Seq handles -100 replacement automatically)
+print(f"   pad_token_id in input: {tokenizer.pad_token_id in sample_input_ids}")
+print(f"   pad_token_id in labels (raw): {tokenizer.pad_token_id in sample_label_ids}")
+
+# %% [CELL 6] Metrics
+print("\n" + "=" * 60)
+print("📊 Loading Evaluation Metrics")
+print("=" * 60)
+
+bleu_metric = evaluate.load("sacrebleu")
+rouge_metric = evaluate.load("rouge")
+
+def compute_metrics(eval_preds):
+    preds, labels = eval_preds
+    # Replace -100 with pad_token_id for decoding
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
+
+    decoded_preds = [p.strip() for p in tokenizer.batch_decode(preds, skip_special_tokens=True)]
+    decoded_labels = [l.strip() for l in tokenizer.batch_decode(labels, skip_special_tokens=True)]
+
+    # BLEU
+    bleu_result = bleu_metric.compute(
+        predictions=decoded_preds,
+        references=[[l] for l in decoded_labels],
+    )
+
+    # ROUGE
+    rouge_result = rouge_metric.compute(
+        predictions=decoded_preds, references=decoded_labels,
+    )
+
+    # Exact match
+    exact_matches = sum(1 for p, l in zip(decoded_preds, decoded_labels) if p == l)
+    exact_match_rate = exact_matches / max(len(decoded_preds), 1)
+
+    # CodeBLEU (subset for speed, may fail if tree-sitter unavailable)
+    codebleu_score = 0.0
+    try:
+        from codebleu import calc_codebleu
+        n_eval = min(200, len(decoded_preds))
+        cb_result = calc_codebleu(
+            references=[[l] for l in decoded_labels[:n_eval]],
+            predictions=decoded_preds[:n_eval],
+            lang="c",
+            weights=(0.25, 0.25, 0.25, 0.25),
+        )
+        codebleu_score = cb_result['codebleu']
+    except Exception as e:
+        print(f"CodeBLEU failed (non-critical): {e}")
+
+    return {
+        "bleu": bleu_result["score"],
+        "rouge1": rouge_result["rouge1"],
+        "rouge2": rouge_result["rouge2"],
+        "rougeL": rouge_result["rougeL"],
+        "codebleu": codebleu_score,
+        "exact_match": exact_match_rate,
+    }
+
+print("✅ Metrics loaded")
+
+# %% [CELL 7] TRAINING - BULLETPROOF
+print("\n" + "=" * 60)
+print("🚀 FIXER MODEL TRAINING (Bulletproof v2)")
+print("   CodeT5+ 220M | CWE-aware input | BLEU+CodeBLEU eval")
+print("   10 epochs | lr=5e-5 | constant scheduler | beam_search=5")
+print("   fp16=OFF (avoids unscale error) | batch=2 | grad_accum=16")
+print("=" * 60)
+
+data_collator = DataCollatorForSeq2Seq(
+    tokenizer=tokenizer, model=model, padding=True, max_length=MAX_SOURCE_LENGTH,
+)
+
+# BULLETPROOF training args - every error from previous runs fixed:
+training_args = Seq2SeqTrainingArguments(
+    output_dir="./fixer_output",
+    num_train_epochs=10,
+    per_device_train_batch_size=2,       # T4 safe
+    per_device_eval_batch_size=2,
+    gradient_accumulation_steps=16,        # effective batch = 32
+    learning_rate=5e-5,                    # T5 recommended (1e-4 to 3e-5 range; 5e-5 stable)
+    lr_scheduler_type="constant",          # T5APR found constant > cosine for code repair
+    warmup_ratio=0.06,                     # ~6% of steps warmup
+    weight_decay=0.01,
+    max_grad_norm=1.0,                     # prevents gradient explosion
+    # fp16=False - DO NOT ENABLE: causes "Attempting to unscale FP16 gradients" error
+    # on newer accelerate + T4 GPU. Full float32 training is slower but stable.
+    eval_strategy="epoch",
+    save_strategy="epoch",
+    logging_strategy="steps",
+    logging_steps=50,
+    logging_first_step=True,
+    disable_tqdm=True,                     # plain text output for Kaggle
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_bleu",
+    greater_is_better=True,
+    save_total_limit=3,
+    seed=SEED,
+    predict_with_generate=True,
+    generation_max_length=MAX_TARGET_LENGTH,
+    generation_num_beams=5,
+    dataloader_num_workers=2,
+    report_to="none",
+    gradient_checkpointing=True,           # saves VRAM
+)
+
+trainer = Seq2SeqTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized['train'],
+    eval_dataset=tokenized['validation'],
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
+)
+
+# Force single GPU to avoid DataParallel OOM
+if torch.cuda.is_available():
+    trainer.args._n_gpu = 1
+    print("✅ Single GPU mode enforced (_n_gpu=1)")
+
+print("🏋️ Training starting...")
+trainer.train()
+
+# %% [CELL 8] Final Evaluation on Test Set
+print("\n" + "=" * 60)
+print("📊 FINAL EVALUATION ON TEST SET")
+print("=" * 60)
+
+test_results = trainer.predict(tokenized['test'])
+test_metrics = test_results.metrics
+
+print("\n🏆 TEST RESULTS:")
+for k, v in sorted(test_metrics.items()):
+    if isinstance(v, float):
+        print(f"   {k}: {v:.4f}")
+    else:
+        print(f"   {k}: {v}")
+
+# Save test metrics for later
+with open("./fixer_output/test_metrics.json", 'w') as f:
+    json.dump({k: float(v) if isinstance(v, (float, np.floating)) else v
+               for k, v in test_metrics.items()}, f, indent=2)
+print("✅ Test metrics saved")
+
+# %% [CELL 9] Example Fixes (Qualitative)
+print("\n" + "=" * 60)
+print("🔧 EXAMPLE FIXES (Qualitative Assessment)")
+print("=" * 60)
+
+test_samples = ds_fixer['test'].select(range(min(5, len(ds_fixer['test']))))
+
+for i, sample in enumerate(test_samples):
+    cwe_name = CWE_NAMES.get(sample['cwe_id'], sample['cwe_id'])
+    input_text = f"fix {cwe_name} vulnerability in {sample['language'].lower()}: {sample['code']}"
+
+    inputs = tokenizer(input_text, return_tensors="pt", max_length=MAX_SOURCE_LENGTH, truncation=True, padding=True)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, max_length=MAX_TARGET_LENGTH,
+            num_beams=5, early_stopping=True, no_repeat_ngram_size=3,
+        )
+
+    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    print(f"\n{'='*60}")
+    print(f"--- Example {i+1}: {sample['cwe_id']} ({sample['language']}) ---")
+    print(f"VULNERABLE:\n{sample['code'][:500]}")
+    print(f"\nEXPECTED FIX:\n{sample['code_fixed'][:500]}")
+    print(f"\nGENERATED FIX:\n{generated[:500]}")
+    match = "✅" if generated.strip() == sample['code_fixed'].strip() else "❌"
+    print(f"Exact Match: {match}")
+
+# %% [CELL 10] Save + Push to Hub
+print("\n" + "=" * 60)
+print("💾 Saving Fixer Model to HF Hub")
+print("=" * 60)
+
+model.save_pretrained("./fixer_final")
+tokenizer.save_pretrained("./fixer_final")
+
+# Save evaluation results + config
+eval_results = {
+    "model": MODEL_NAME,
+    "test_metrics": {k: float(v) if isinstance(v, (float, np.floating)) else v
+                     for k, v in test_metrics.items()},
+    "improvements": [
+        "CWE-aware input: 'fix <vulnerability> in <language>: <code>'",
+        "BLEU + CodeBLEU + ROUGE + exact match evaluation",
+        "Beam search (num_beams=5)",
+        "Only trained on samples with actual fixes",
+        "Constant LR schedule with warmup (T5APR-optimal)",
+        "Early stopping (patience=3)",
+        "fp16=OFF (stable on T4)",
+        "Gradient accumulation (eff_batch=32)",
+    ],
+    "training_data": {
+        "total_samples_with_fixes": len(ds_fixer['train']),
+        "source_dataset": DATASET_ID,
+    },
+}
+with open("./fixer_final/eval_results.json", 'w') as f:
+    json.dump(eval_results, f, indent=2)
+
+api = HfApi()
+api.upload_folder(
+    folder_path="./fixer_final",
+    repo_id=HUB_MODEL_ID,
+    commit_message="v2: Fixed tokenizer + stable training (fp32, constant LR, CWE-aware)",
+)
+
+print(f"\n✅ Fixer model pushed to: https://huggingface.co/{HUB_MODEL_ID}")
+
+# %% [CELL 11] Push test metrics separately
+api.upload_file(
+    path_or_fileobj="./fixer_output/test_metrics.json",
+    path_in_repo="test_metrics.json",
+    repo_id=HUB_MODEL_ID,
+    commit_message="Test metrics from v2 training",
+)
+print("✅ Test metrics pushed")
+
+print("\n" + "=" * 60)
+print("🎯 NOTEBOOK 4 COMPLETE!")
+print("=" * 60)