Upload fine_tune_with_validation.py

fine_tune_with_validation.py

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+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import tiktoken  # New: OpenAI's fast BPE tokenizer (open-source, auto-downloads once)
+from tqdm import tqdm
+import shutil
+import math
+from pathlib import Path
+import re
+
+from gpt_pytorch import GPTPyTorch  # Your model import
+
+# ============================= SETTINGS =============================
+TRAIN_SEQ_LEN = 256  # Context length (increased for better coherence)
+BATCH_SIZE = 12
+EPOCHS = 50
+LEARNING_RATE = 6e-6
+WEIGHT_DECAY = 0.01
+GRAD_CLIP = 1.0
+KEEP_LAST_EPOCHS = 3
+VAL_SPLIT_RATIO = 0.05  # 5% of data used for validation
+
+# === MODEL PATHS ===
+BASE_MODEL_PATH = Path("models/JiRack_H12_L6_V50257_D768_MSL8192_FF768x4.pt")
+LAST_TRAINED_PATH = Path("models/JiRack_last_H12_L6_V50257_D768_MSL8192_FF768x4.pt")
+BACKUP_DIR = Path("models/backups")
+BACKUP_DIR.mkdir(exist_ok=True)
+
+# === DATASET AUTO-CLEANING ===
+RAW_PATH = Path("datasets/dialogues_text_clean.txt")
+CLEAN_PATH = Path("datasets/dialogues_text_clean.txt")
+
+force_clean = False
+if not CLEAN_PATH.exists():
+    print("Clean dataset not found. Performing initial cleaning...")
+    force_clean = True
+else:
+    try:
+        if RAW_PATH.stat().st_mtime > CLEAN_PATH.stat().st_mtime:
+            print("Changes detected in the source dataset. Performing re-cleaning...")
+            force_clean = True
+        else:
+            print(f"Using existing clean dataset → {CLEAN_PATH}")
+    except FileNotFoundError:
+        print("File system synchronization error. Performing re-cleaning for safety...")
+        force_clean = True
+
+if force_clean:
+    if not RAW_PATH.exists():
+        raise FileNotFoundError(f"ERROR: Source file {RAW_PATH} not found. Check the path.")
+
+    print("Cleaning dataset from garbage (extra spaces, incorrect separators)...")
+    text = RAW_PATH.read_text(encoding="utf-8")
+
+    text = re.sub(r' {2,}', ' ', text)
+    text = text.replace(" \n", "\n").replace("\n ", "\n")
+
+    CLEAN_PATH.write_text(text, encoding="utf-8")
+    print(f"Dataset successfully cleaned and saved → {CLEAN_PATH}")
+
+DATASET_PATH = CLEAN_PATH
+
+OUTPUT_DIR = Path("build/fine_tuning_output")
+MODEL_SAVE_NAME = "gpt_finetuned.pt"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# ============================= DATASET =============================
+class TextDataset(Dataset):
+    def __init__(self, text_file, seq_len=TRAIN_SEQ_LEN, encoding_name="gpt2", split_type='train', val_ratio=VAL_SPLIT_RATIO):
+        self.seq_len = seq_len
+        
+        # New: tiktoken – exact GPT-2 encoding, fast, auto-downloads small .tiktoken file once
+        print(f"Loading tiktoken encoding '{encoding_name}' (small file auto-downloads on first run if needed)...")
+        self.enc = tiktoken.get_encoding(encoding_name)  # "gpt2" is built-in and matches GPT-2 vocab perfectly
+        
+        self.split_type = split_type
+
+        print(f"Loading text from {text_file} for {split_type} split...")
+        text = Path(text_file).read_text(encoding="utf-8")
+        tokens = self.enc.encode(text)  # List of ints (exact same as GPT2Tokenizer)
+
+        if len(tokens) < seq_len * 2:
+            raise ValueError("Text too short!")
+
+        all_inputs = []
+        all_labels = []
+
+        for i in range(0, len(tokens) - seq_len, seq_len):
+            all_inputs.append(tokens[i:i + seq_len])
+            all_labels.append(tokens[i + 1:i + seq_len + 1])
+
+        total_sequences = len(all_inputs)
+        val_size = int(total_sequences * val_ratio)
+        train_size = total_sequences - val_size
+
+        if self.split_type == 'train':
+            self.inputs = all_inputs[:train_size]
+            self.labels = all_labels[:train_size]
+        elif self.split_type == 'val':
+            self.inputs = all_inputs[train_size:]
+            self.labels = all_labels[train_size:]
+        else:
+            raise ValueError("Invalid split_type. Must be 'train' or 'val'.")
+
+        print(f"Created {len(self.inputs):,} sequences for {self.split_type} split.")
+
+    def __len__(self):
+        return len(self.inputs)
+
+    def __getitem__(self, idx):
+        return (torch.tensor(self.inputs[idx], dtype=torch.long),
+                torch.tensor(self.labels[idx], dtype=torch.long))
+
+# ============================= EVALUATION =============================
+def evaluate(model, dataloader, criterion, device):
+    model.eval()
+    total_loss = 0.0
+
+    with torch.no_grad():
+        for inputs, targets in dataloader:
+            inputs, targets = inputs.to(device), targets.to(device)
+            logits, _ = model(inputs)
+            loss = criterion(logits.view(-1, logits.size(-1)), targets.view(-1))
+            total_loss += loss.item()
+
+    avg_loss = total_loss / len(dataloader)
+    model.train()
+    return avg_loss
+
+# ============================= CLEANUP OLD EPOCHS =============================
+def cleanup_old_epochs(keep_last=KEEP_LAST_EPOCHS):
+    epochs = sorted([p for p in OUTPUT_DIR.glob("epoch*") if p.is_dir()],
+                    key=lambda x: int(x.name.replace("epoch", "")))
+    for old in epochs[:-keep_last]:
+        if old.exists():
+            shutil.rmtree(old)
+            print(f"Deleted old epoch: {old.name}")
+
+# ============================= TRAINING =============================
+def train():
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    print("Loading model...")
+    model = GPTPyTorch().to(device)
+
+    # Safer loading (silences FutureWarning)
+    load_kwargs = {"map_location": device, "weights_only": True}
+    if LAST_TRAINED_PATH.exists():
+        print(f"Resuming training from last model: {LAST_TRAINED_PATH}")
+        model.load_state_dict(torch.load(LAST_TRAINED_PATH, **load_kwargs))
+    elif BASE_MODEL_PATH.exists():
+        print(f"Starting from base model: {BASE_MODEL_PATH}")
+        model.load_state_dict(torch.load(BASE_MODEL_PATH, **load_kwargs))
+    else:
+        print("No models found — initializing from scratch")
+
+    model.train()
+
+    train_dataset = TextDataset(DATASET_PATH, seq_len=TRAIN_SEQ_LEN, encoding_name="gpt2", split_type='train', val_ratio=VAL_SPLIT_RATIO)
+    val_dataset = TextDataset(DATASET_PATH, seq_len=TRAIN_SEQ_LEN, encoding_name="gpt2", split_type='val', val_ratio=VAL_SPLIT_RATIO)
+
+    train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
+    val_dataloader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, drop_last=True)
+
+    optimizer = optim.AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)
+    criterion = nn.CrossEntropyLoss()
+
+    total_steps = len(train_dataloader) * EPOCHS
+    print(f"\n=== STARTING LONG-TERM TRAINING ===")
+    print(f"Epochs: {EPOCHS} | Steps (Train): {total_steps} | Examples (Train): {len(train_dataset)}")
+
+    global_step = 0
+    for epoch in range(1, EPOCHS + 1):
+        print(f"\n--- Epoch {epoch}/{EPOCHS} ---")
+        epoch_loss = 0.0
+
+        with tqdm(train_dataloader, desc=f"Epoch {epoch} [TRAIN]", leave=False) as pbar:
+            for inputs, targets in pbar:
+                inputs, targets = inputs.to(device), targets.to(device)
+
+                optimizer.zero_grad()
+                logits, _ = model(inputs)
+                loss = criterion(logits.view(-1, logits.size(-1)), targets.view(-1))
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
+                optimizer.step()
+
+                loss_val = loss.item()
+                epoch_loss += loss_val
+                global_step += 1
+
+                pbar.set_postfix({
+                    "loss": f"{loss_val:.3f}",
+                    "ppl": f"{math.exp(min(loss_val, 10)):.1f}",
+                    "step": f"{global_step}/{total_steps}"
+                })
+
+        avg_train_loss = epoch_loss / len(train_dataloader)
+        print(f" [TRAIN] Average loss: {avg_train_loss:.3f} | PPL: {math.exp(avg_train_loss):.1f}")
+
+        print(" [VALIDATION] Running evaluation...")
+        val_loss = evaluate(model, val_dataloader, criterion, device)
+        print(f" [VALIDATION] Average loss: {val_loss:.3f} | PPL: {math.exp(val_loss):.1f}")
+
+        epoch_dir = OUTPUT_DIR / f"epoch{epoch}"
+        epoch_dir.mkdir(exist_ok=True)
+        torch.save(model.state_dict(), epoch_dir / MODEL_SAVE_NAME)
+        print(f"Model saved: {epoch_dir / MODEL_SAVE_NAME}")
+        cleanup_old_epochs()
+
+    # Final saving – note: no tokenizer.save_pretrained anymore (tiktoken doesn't need it)
+    final_dir = OUTPUT_DIR / "final"
+    final_dir.mkdir(exist_ok=True)
+    torch.save(model.state_dict(), final_dir / MODEL_SAVE_NAME)
+
+    if LAST_TRAINED_PATH.exists():
+        backup_path = BACKUP_DIR / f"gpt_last_trained_backup_{int(os.path.getmtime(LAST_TRAINED_PATH))}.pt"
+        shutil.copy(LAST_TRAINED_PATH, backup_path)
+        print(f"Backup of previous model created → {backup_path.name}")
+
+    shutil.copy(final_dir / MODEL_SAVE_NAME, LAST_TRAINED_PATH)
+    print(f"Last trained model saved → {LAST_TRAINED_PATH}")
+
+    print(f"\nTRAINING COMPLETED! Model is ready:")
+    print(f" • For chat/inference: {final_dir / MODEL_SAVE_NAME}")
+    print(f" • For continued fine-tuning: {LAST_TRAINED_PATH}")
+
+if __name__ == "__main__":
+    if not RAW_PATH.exists():
+        print(f"ERROR: File {RAW_PATH} not found")
+        print("Place your text in datasets/dialogues_text.txt")
+    else:
+        train()