Create Transformer.py

PolyFusion/Transformer.py

@@ -0,0 +1,603 @@
+# fingerprint_mlm_training.py
+import os
+import json
+import time
+import shutil
+import sys
+import csv
+
+# Increase max CSV field size limit (some fingerprint fields can be long)
+csv.field_size_limit(sys.maxsize)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from torch.utils.data import Dataset, DataLoader
+
+from transformers import TrainingArguments, Trainer
+from transformers.trainer_callback import TrainerCallback
+from sklearn.metrics import accuracy_score, f1_score
+from typing import List
+
+# ---------------------------
+# Configuration / Constants
+# ---------------------------
+# MLM mask probability
+P_MASK = 0.15
+
+# Fingerprint specifics
+FINGERPRINT_KEY = "morgan_r3_bits"   # inside the JSON stored under 'fingerprints' column
+FP_LENGTH = 2048                      # expected fingerprint vector length (bits)
+# Vocabulary: {0, 1, MASK} where 0/1 are real bits and MASK token id = 2 used as masked input
+MASK_TOKEN_ID = 2
+VOCAB_SIZE = 3
+
+# Model / encoder hyperparams
+HIDDEN_DIM = 256
+TRANSFORMER_NUM_LAYERS = 4
+TRANSFORMER_NHEAD = 8
+TRANSFORMER_FF = 1024
+DROPOUT = 0.1
+
+# Training / data hyperparams
+TRAIN_BATCH_SIZE = 16   # number of molecules per batch
+EVAL_BATCH_SIZE = 8
+GRADIENT_ACCUMULATION_STEPS = 4
+NUM_EPOCHS = 25
+LEARNING_RATE = 1e-4
+WEIGHT_DECAY = 0.01
+
+# File locations (changed as requested)
+CSV_PATH = "Polymer_Foundational_Model/polymer_structures_unified_processed.csv"
+OUTPUT_DIR = "./fingerprint_mlm_output_5M"
+BEST_MODEL_DIR = os.path.join(OUTPUT_DIR, "best")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ---------------------------
+# 1. Load Data (chunked to avoid OOM) - read fingerprints column
+# ---------------------------
+TARGET_ROWS = 5000000
+CHUNKSIZE = 50000
+
+fp_lists: List[List[int]] = []
+rows_read = 0
+
+# Expect 'fingerprints' column value to be a JSON string we can json.loads()
+# that contains e.g. {"morgan_r3_bits": ["0","1","0",...]}
+for chunk in pd.read_csv(CSV_PATH, engine="python", chunksize=CHUNKSIZE):
+    # some files might already have parsed JSON-like dicts; ensure we handle strings
+    fps_chunk = chunk["fingerprints"]
+    for fpval in fps_chunk:
+        if pd.isna(fpval):
+            # skip or use zeros
+            fp_lists.append([0] * FP_LENGTH)
+            continue
+
+        # If it's already a dict-like object, use directly; else parse JSON string
+        if isinstance(fpval, str):
+            try:
+                fp_json = json.loads(fpval)
+            except Exception:
+                # fallback: try to fix common quoting issues
+                try:
+                    fp_json = json.loads(fpval.replace("'", '"'))
+                except Exception:
+                    # as last fallback, treat the string as a comma separated "0,1,0,..."
+                    parts = [p.strip().strip('"').strip("'") for p in fpval.split(",")]
+                    bits = [1 if p in ("1", "True", "true") else 0 for p in parts[:FP_LENGTH]]
+                    if len(bits) < FP_LENGTH:
+                        bits += [0] * (FP_LENGTH - len(bits))
+                    fp_lists.append(bits)
+                    continue
+        elif isinstance(fpval, dict):
+            fp_json = fpval
+        else:
+            # Unknown type, zero pad
+            fp_lists.append([0] * FP_LENGTH)
+            continue
+
+        # Extract the fingerprint bit list
+        bits = fp_json.get(FINGERPRINT_KEY, None)
+        if bits is None:
+            # fallback if top-level is already list
+            if isinstance(fp_json, list):
+                bits = fp_json
+            else:
+                # default zero vector
+                bits = [0] * FP_LENGTH
+
+        # bits may be list of strings "0"/"1" or ints
+        # normalize to ints and ensure length
+        normalized = []
+        for b in bits:
+            if isinstance(b, str):
+                b_clean = b.strip().strip('"').strip("'")
+                normalized.append(1 if b_clean in ("1", "True", "true") else 0)
+            elif isinstance(b, (int, np.integer)):
+                normalized.append(1 if int(b) != 0 else 0)
+            else:
+                normalized.append(0)
+            if len(normalized) >= FP_LENGTH:
+                break
+
+        if len(normalized) < FP_LENGTH:
+            # pad with zeros
+            normalized.extend([0] * (FP_LENGTH - len(normalized)))
+
+        fp_lists.append(normalized[:FP_LENGTH])
+
+    rows_read += len(chunk)
+    if rows_read >= TARGET_ROWS:
+        break
+
+print(f"Loaded {len(fp_lists)} fingerprint vectors (using FP_LENGTH={FP_LENGTH}).")
+
+# ---------------------------
+# 2. Train/Val Split
+# ---------------------------
+train_idx, val_idx = train_test_split(list(range(len(fp_lists))), test_size=0.2, random_state=42)
+train_fps = [torch.tensor(fp_lists[i], dtype=torch.long) for i in train_idx]
+val_fps   = [torch.tensor(fp_lists[i], dtype=torch.long) for i in val_idx]
+
+# ---------------------------
+# Compute class weights (for weighted CE to mitigate bit imbalance)
+# (we compute but will not apply them to match previous MLM-style loss behavior)
+# ---------------------------
+# We'll compute weights for classes {0,1} only (targets).
+counts = np.ones((2,), dtype=np.float64)  # initialize with 1 to avoid zero division
+for fp in train_fps:
+    vals = fp.cpu().numpy().astype(int)
+    counts[0] += np.sum(vals == 0)
+    counts[1] += np.sum(vals == 1)
+
+freq = counts / counts.sum()
+inv_freq = 1.0 / (freq + 1e-12)
+class_weights_arr = inv_freq / inv_freq.mean()
+class_weights = torch.tensor(class_weights_arr, dtype=torch.float)  # shape [2]
+print("Class weights (for bit 0 and bit 1):", class_weights.numpy())
+
+# ---------------------------
+# 3. Dataset and Collator (fingerprint MLM)
+# ---------------------------
+class FingerprintDataset(Dataset):
+    def __init__(self, fps: List[torch.Tensor]):
+        self.fps = fps
+
+    def __len__(self):
+        return len(self.fps)
+
+    def __getitem__(self, idx):
+        # Return the tensor directly (not wrapped in a dict). This avoids mismatches
+        # when HF's Trainer / collators pass around items in different formats.
+        return self.fps[idx]
+
+def collate_batch(batch):
+    """
+    Collate a batch of fingerprint tensors into:
+      - z: [B, L] long, masked/corrupted input tokens (values 0,1, or MASK_TOKEN_ID)
+      - labels_z: [B, L] long, with -100 for unselected positions and 0/1 for masked positions (targets)
+      - attention_mask: [B, L] bool (all True here since fixed length)
+
+    This collator is defensive: it accepts
+      - list of torch.Tensors
+      - list of dicts containing key 'fp'
+      - HF-style list of dict-like items where a tensor-like value is present
+    """
+    B = len(batch)
+    if B == 0:
+        return {"z": torch.zeros((0, FP_LENGTH), dtype=torch.long),
+                "labels_z": torch.zeros((0, FP_LENGTH), dtype=torch.long),
+                "attention_mask": torch.zeros((0, FP_LENGTH), dtype=torch.bool)}
+
+    # Normalize items -> list of tensors
+    tensors = []
+    for item in batch:
+        if isinstance(item, torch.Tensor):
+            tensors.append(item)
+        elif isinstance(item, dict):
+            # Prefer 'fp' if present
+            if "fp" in item:
+                val = item["fp"]
+                if not isinstance(val, torch.Tensor):
+                    val = torch.tensor(val, dtype=torch.long)
+                tensors.append(val)
+            else:
+                # Try to find any tensor-like value inside dict
+                found = None
+                for v in item.values():
+                    if isinstance(v, torch.Tensor):
+                        found = v
+                        break
+                    elif isinstance(v, np.ndarray):
+                        found = torch.tensor(v, dtype=torch.long)
+                        break
+                    elif isinstance(v, list):
+                        # possible list of ints
+                        try:
+                            found = torch.tensor(v, dtype=torch.long)
+                            break
+                        except Exception:
+                            continue
+                if found is None:
+                    raise KeyError("collate_batch: couldn't find 'fp' tensor in dataset item; item keys: {}".format(list(item.keys())))
+                tensors.append(found)
+        else:
+            # fallback: try to convert numpy/sequence to tensor
+            try:
+                tensors.append(torch.tensor(item, dtype=torch.long))
+            except Exception:
+                raise TypeError(f"collate_batch: unsupported batch item type: {type(item)}")
+
+    # Stack into [B, L]
+    all_inputs = torch.stack(tensors, dim=0).long()  # [B, L], long (0/1)
+    device = all_inputs.device
+
+    # Prepare masks and labels
+    labels_z = torch.full_like(all_inputs, fill_value=-100, dtype=torch.long)  # -100 ignored by CE
+    z_masked = all_inputs.clone()
+
+    for i in range(B):
+        z = all_inputs[i]  # [L]
+        n_positions = z.size(0)
+        # select positions to supervise (mask) with probability P_MASK
+        is_selected = torch.rand(n_positions) < P_MASK
+
+        # ensure not all selected
+        if is_selected.all():
+            is_selected[torch.randint(0, n_positions, (1,))] = False
+
+        sel_idx = torch.nonzero(is_selected).squeeze(-1)
+        if sel_idx.numel() > 0:
+            labels_z[i, sel_idx] = z[sel_idx]  # store true bit labels
+
+            # BERT-style corruption per selected position
+            probs = torch.rand(sel_idx.size(0))
+            mask_choice = probs < 0.8
+            rand_choice = (probs >= 0.8) & (probs < 0.9)
+            # keep_choice = probs >= 0.9
+
+            if mask_choice.any():
+                z_masked[i, sel_idx[mask_choice]] = MASK_TOKEN_ID  # mask token id
+
+            if rand_choice.any():
+                # replace with random 0 or 1
+                rand_bits = torch.randint(0, 2, (rand_choice.sum().item(),), dtype=torch.long)
+                z_masked[i, sel_idx[rand_choice]] = rand_bits
+
+            # keep_choice -> leave original bit
+
+    attention_mask = torch.ones_like(all_inputs, dtype=torch.bool)  # full attention (fixed length)
+
+    return {"z": z_masked, "labels_z": labels_z, "attention_mask": attention_mask}
+
+train_dataset = FingerprintDataset(train_fps)
+val_dataset   = FingerprintDataset(val_fps)
+train_loader = DataLoader(train_dataset, batch_size=TRAIN_BATCH_SIZE, shuffle=True, collate_fn=collate_batch, drop_last=False)
+val_loader   = DataLoader(val_dataset, batch_size=EVAL_BATCH_SIZE, shuffle=False, collate_fn=collate_batch, drop_last=False)
+
+# ---------------------------
+# 4. Model Definition (Fingerprint Encoder + MLM head)
+# ---------------------------
+
+class FingerprintEncoder(nn.Module):
+    """
+    Simple encoder for fingerprint token sequences:
+      - token embedding (vocab size VOCAB_SIZE)
+      - positional embedding
+      - Transformer encoder stack
+      - returns per-position embeddings [B, L, hidden_dim]
+    """
+    def __init__(self, vocab_size=VOCAB_SIZE, hidden_dim=HIDDEN_DIM, seq_len=FP_LENGTH,
+                 num_layers=TRANSFORMER_NUM_LAYERS, nhead=TRANSFORMER_NHEAD, dim_feedforward=TRANSFORMER_FF,
+                 dropout=DROPOUT):
+        super().__init__()
+        self.token_emb = nn.Embedding(vocab_size, hidden_dim)
+        self.pos_emb = nn.Embedding(seq_len, hidden_dim)
+        encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout, batch_first=True)
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.hidden_dim = hidden_dim
+        self.seq_len = seq_len
+
+    def forward(self, input_ids, attention_mask=None):
+        """
+        input_ids: [B, L] long (values 0,1, or MASK_TOKEN_ID)
+        attention_mask: [B, L] bool (True for valid positions)
+        returns: embeddings [B, L, hidden_dim]
+        """
+        B, L = input_ids.shape
+        x = self.token_emb(input_ids)  # [B, L, hidden]
+        # positional indices 0..L-1 broadcast to batch
+        pos_ids = torch.arange(L, device=input_ids.device).unsqueeze(0).expand(B, -1)
+        x = x + self.pos_emb(pos_ids)
+        # transformer expects batch_first=True (we set that)
+        if attention_mask is not None:
+            # transformer encoder in PyTorch doesn't use attention_mask in same way as HF; provide key_padding_mask
+            key_padding_mask = ~attention_mask  # True where to mask
+        else:
+            key_padding_mask = None
+
+        out = self.transformer(x, src_key_padding_mask=key_padding_mask)
+        return out  # [B, L, hidden_dim]
+
+
+class MaskedFingerprintModel(nn.Module):
+    """
+    Encoder + MLM head for fingerprint masked language modeling.
+    MLM head predicts over VOCAB_SIZE (0,1,MASK) like a token classification over the small vocab.
+    Loss is standard CrossEntropyLoss (ignore_index=-100) computed only on masked positions,
+    matching the "MLM with CrossEntropy" behavior used in the DebertaV2ForMaskedLM setup.
+    """
+    def __init__(self, hidden_dim=HIDDEN_DIM, vocab_size=VOCAB_SIZE):
+        super().__init__()
+        self.encoder = FingerprintEncoder(vocab_size=vocab_size, hidden_dim=hidden_dim)
+        # MLM head: predict logits over the small token vocabulary {0,1,MASK}
+        self.mlm_head = nn.Linear(hidden_dim, vocab_size)
+
+    def forward(self, z, attention_mask=None, labels_z=None):
+        """
+        z: [B, L] long inputs (0/1/MASK_TOKEN_ID)
+        labels_z: [B, L] long with -100 for unselected positions, else 0/1 targets
+        Returns:
+          - if labels_z provided -> loss (scalar tensor)
+          - else -> logits [B, L, VOCAB_SIZE]
+        """
+        embeddings = self.encoder(z, attention_mask=attention_mask)  # [B, L, hidden]
+        logits = self.mlm_head(embeddings)  # [B, L, VOCAB_SIZE]
+
+        if labels_z is not None:
+            mask = labels_z != -100  # [B, L]
+            if mask.sum() == 0:
+                # return zero loss tensor on same device
+                return torch.tensor(0.0, device=z.device)
+
+            logits_masked = logits[mask]  # [M, VOCAB_SIZE]
+            labels_masked = labels_z[mask]  # [M] values in {0,1}
+
+            # standard cross-entropy over the vocabulary (no class weighting, matching previous Deberta MLM behavior)
+            # labels_masked must be long
+            labels_masked = labels_masked.long()
+            loss_z = F.cross_entropy(logits_masked, labels_masked)
+
+            return loss_z
+
+        # inference -> return logits
+        return logits
+
+# instantiate model using MLM-style head and standard cross-entropy loss (no learned weighting/class-weights)
+model = MaskedFingerprintModel(hidden_dim=HIDDEN_DIM, vocab_size=VOCAB_SIZE)
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# ---------------------------
+# 5. Training Setup (Hugging Face Trainer)
+# ---------------------------
+training_args = TrainingArguments(
+    output_dir=OUTPUT_DIR,
+    overwrite_output_dir=True,
+    num_train_epochs=NUM_EPOCHS,
+    per_device_train_batch_size=TRAIN_BATCH_SIZE,
+    per_device_eval_batch_size=EVAL_BATCH_SIZE,
+eval_accumulation_steps=1000,   gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
+    eval_strategy="epoch",
+    logging_steps=500,
+    learning_rate=LEARNING_RATE,
+    weight_decay=WEIGHT_DECAY,
+    fp16=torch.cuda.is_available(),
+    save_strategy="no",            # callback will save best model
+    disable_tqdm=False,
+    logging_first_step=True,
+    report_to=[],
+    # NOTE: set to 0 to avoid DataLoader worker pickling/collate issues in some environments.
+    dataloader_num_workers=0,
+)
+
+class ValLossCallback(TrainerCallback):
+    def __init__(self, trainer_ref=None):
+        self.best_val_loss = float("inf")
+        self.epochs_no_improve = 0
+        self.patience = 10
+        self.best_epoch = None
+        self.trainer_ref = trainer_ref
+
+    def on_epoch_end(self, args, state, control, **kwargs):
+        epoch_num = int(state.epoch)
+        train_loss = next((x["loss"] for x in reversed(state.log_history) if "loss" in x), None)
+        print(f"\n=== Epoch {epoch_num}/{args.num_train_epochs} ===")
+        if train_loss is not None:
+            print(f"Train Loss: {train_loss:.4f}")
+
+    def on_evaluate(self, args, state, control, metrics=None, **kwargs):
+        epoch_num = int(state.epoch) + 1
+
+        if self.trainer_ref is None:
+            print(f"[Eval] Epoch {epoch_num} - metrics (trainer_ref missing): {metrics}")
+            return
+
+        metric_val_loss = None
+        if metrics is not None:
+            metric_val_loss = metrics.get("eval_loss")
+
+        model_eval = self.trainer_ref.model
+        model_eval.eval()
+
+        device_local = next(model_eval.parameters()).device if any(p.numel() > 0 for p in model_eval.parameters()) else torch.device("cpu")
+
+        preds_bits = []
+        true_bits = []
+        total_loss = 0.0
+        n_batches = 0
+
+        logits_masked_list = []
+        labels_masked_list = []
+
+        with torch.no_grad():
+            for batch in val_loader:
+                z = batch["z"].to(device_local)  # [B, L]
+                labels_z = batch["labels_z"].to(device_local)
+                attention_mask = batch.get("attention_mask", torch.ones_like(z, dtype=torch.bool)).to(device_local)
+
+                # compute loss if possible (model returns scalar loss when labels_z provided)
+                try:
+                    loss = model_eval(z, attention_mask=attention_mask, labels_z=labels_z)
+                except Exception as e:
+                    loss = None
+
+                if isinstance(loss, torch.Tensor):
+                    total_loss += loss.item()
+                    n_batches += 1
+
+                logits = model_eval(z, attention_mask=attention_mask)  # [B, L, VOCAB_SIZE]
+
+                mask = labels_z != -100
+                if mask.sum().item() == 0:
+                    continue
+
+                logits_masked_list.append(logits[mask])
+                labels_masked_list.append(labels_z[mask])
+
+                pred_bits = torch.argmax(logits[mask], dim=-1)
+                true_b = labels_z[mask]
+
+                preds_bits.extend(pred_bits.cpu().tolist())
+                true_bits.extend(true_b.cpu().tolist())
+
+        avg_val_loss = metric_val_loss if metric_val_loss is not None else ((total_loss / n_batches) if n_batches > 0 else float("nan"))
+
+        accuracy = accuracy_score(true_bits, preds_bits) if len(true_bits) > 0 else 0.0
+        f1 = f1_score(true_bits, preds_bits, average="weighted") if len(true_bits) > 0 else 0.0
+
+        # perplexity from masked-token cross-entropy (computed over masked positions only)
+        if len(logits_masked_list) > 0:
+            all_logits_masked = torch.cat(logits_masked_list, dim=0)
+            all_labels_masked = torch.cat(labels_masked_list, dim=0)
+            # match previous MLM: standard cross-entropy over the vocabulary
+            loss_z_all = F.cross_entropy(all_logits_masked, all_labels_masked.long())
+            try:
+                perplexity = float(torch.exp(loss_z_all).cpu().item())
+            except Exception:
+                perplexity = float(np.exp(float(loss_z_all.cpu().item())))
+        else:
+            perplexity = float("nan")
+
+        print(f"\n--- Evaluation after Epoch {epoch_num} ---")
+        print(f"Validation Loss: {avg_val_loss:.4f}")
+        print(f"Validation Accuracy: {accuracy:.4f}")
+        print(f"Validation F1 (weighted): {f1:.4f}")
+        print(f"Validation Perplexity (classification head): {perplexity:.4f}")
+
+        # Check for improvement
+        if avg_val_loss is not None and not (isinstance(avg_val_loss, float) and np.isnan(avg_val_loss)) and avg_val_loss < self.best_val_loss - 1e-6:
+            self.best_val_loss = avg_val_loss
+            self.best_epoch = int(state.epoch)
+            self.epochs_no_improve = 0
+            os.makedirs(BEST_MODEL_DIR, exist_ok=True)
+            try:
+                torch.save(self.trainer_ref.model.state_dict(), os.path.join(BEST_MODEL_DIR, "pytorch_model.bin"))
+                print(f"Saved new best model (epoch {epoch_num}) to {os.path.join(BEST_MODEL_DIR, 'pytorch_model.bin')}")
+            except Exception as e:
+                print(f"Failed to save best model at epoch {epoch_num}: {e}")
+        else:
+            self.epochs_no_improve += 1
+
+        if self.epochs_no_improve >= self.patience:
+            print(f"Early stopping after {self.patience} epochs with no improvement.")
+            control.should_training_stop = True
+
+
+# Create callback and Trainer
+callback = ValLossCallback()
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=val_dataset,
+    data_collator=collate_batch,
+    callbacks=[callback]
+)
+callback.trainer_ref = trainer
+
+# ---------------------------
+# 6. Run training
+# ---------------------------
+start_time = time.time()
+trainer.train()
+total_time = time.time() - start_time
+
+# ---------------------------
+# 7. Final Evaluation (evaluate best saved model on validation set)
+# ---------------------------
+
+best_model_path = os.path.join(BEST_MODEL_DIR, "pytorch_model.bin")
+if os.path.exists(best_model_path):
+    try:
+        model.load_state_dict(torch.load(best_model_path, map_location=device))
+        print(f"\nLoaded best model from {best_model_path}")
+    except Exception as e:
+        print(f"\nFailed to load best model from {best_model_path}: {e}")
+
+model.eval()
+preds_bits_all = []
+true_bits_all = []
+logits_masked_final = []
+labels_masked_final = []
+
+with torch.no_grad():
+    for batch in val_loader:
+        z = batch["z"].to(device)
+        labels_z = batch["labels_z"].to(device)
+        attention_mask = batch.get("attention_mask", torch.ones_like(z, dtype=torch.bool)).to(device)
+
+        logits = model(z, attention_mask=attention_mask)  # [B, L, VOCAB_SIZE]
+
+        mask = labels_z != -100
+        if mask.sum().item() == 0:
+            continue
+
+        logits_masked_final.append(logits[mask])
+        labels_masked_final.append(labels_z[mask])
+
+        pred_bits = torch.argmax(logits[mask], dim=-1)
+        true_b = labels_z[mask]
+
+        preds_bits_all.extend(pred_bits.cpu().tolist())
+        true_bits_all.extend(true_b.cpu().tolist())
+
+accuracy = accuracy_score(true_bits_all, preds_bits_all) if len(true_bits_all) > 0 else 0.0
+f1 = f1_score(true_bits_all, preds_bits_all, average="weighted") if len(true_bits_all) > 0 else 0.0
+
+if len(logits_masked_final) > 0:
+    all_logits_masked_final = torch.cat(logits_masked_final, dim=0)
+    all_labels_masked_final = torch.cat(labels_masked_final, dim=0)
+    loss_z_final = F.cross_entropy(all_logits_masked_final, all_labels_masked_final.long())
+    try:
+        perplexity_final = float(torch.exp(loss_z_final).cpu().item())
+    except Exception:
+        perplexity_final = float(np.exp(float(loss_z_final.cpu().item())))
+else:
+    perplexity_final = float("nan")
+
+best_val_loss = callback.best_val_loss if hasattr(callback, "best_val_loss") else float("nan")
+best_epoch_num = (int(callback.best_epoch) + 1) if callback.best_epoch is not None else None
+
+print(f"\n=== Final Results (evaluated on best saved model) ===")
+print(f"Total Training Time (s): {total_time:.2f}")
+if best_epoch_num is not None:
+    print(f"Best Epoch (1-based): {best_epoch_num}")
+else:
+    print("Best Epoch: (none saved)")
+
+print(f"Best Validation Loss: {best_val_loss:.4f}")
+print(f"Validation Accuracy: {accuracy:.4f}")
+print(f"Validation F1 (weighted): {f1:.4f}")
+print(f"Validation Perplexity (classification head): {perplexity_final:.4f}")
+
+total_params = sum(p.numel() for p in model.parameters())
+trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+non_trainable_params = total_params - trainable_params
+print(f"Total Parameters: {total_params}")
+print(f"Trainable Parameters: {trainable_params}")
+print(f"Non-trainable Parameters: {non_trainable_params}")