tree-starencoder working (linear layer instead of alpha beta gamma)

code/pyproject.toml

@@ -40,3 +40,4 @@ distribution = true
 [tool.pdm.scripts]
 parse_dataset = {cmd = "src/parse_dataset.py"}
 train = {cmd = "src/training.py"}
+eval = {cmd = "src/eval_model.py"}

code/src/eval_config.json

@@ -0,0 +1,9 @@
+{
+    "extra_embeddings": false,
+    "data_dir": "./data/CodeSearchNet-parsed-starencoder/python",
+    "model_dir": "./outputs/original-starencoder",
+    "seed": 420,
+    "mlm_probability": 0.15,
+    "batch_size": 32,
+    "fp16": true
+}

code/src/eval_model.py

@@ -0,0 +1,188 @@
+import torch
+import json
+import logging
+import numpy as np
+from pathlib import Path
+from datasets import load_from_disk
+from safetensors.torch import load_file
+from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoConfig, DataCollatorForLanguageModeling
+from tqdm import tqdm
+from typing import Dict
+
+from tree_starencoder import TreeStarEncoderForPreTraining
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+
+def load_config(config_path: Path) -> dict:
+    with open(config_path, 'r') as f:
+        return json.load(f)
+
+def compute_metrics(predictions: torch.Tensor, labels: torch.Tensor, mask_positions: torch.Tensor) -> Dict:
+    """Compute MLM metrics including accuracy and top-k accuracy."""
+    # Get predictions only for masked tokens
+    masked_predictions = predictions[mask_positions]  # Shape: [num_masked_tokens, vocab_size]
+    masked_labels = labels[mask_positions]  # Shape: [num_masked_tokens]
+    
+    # Calculate top-k accuracy using raw logits (before softmax)
+    top1_acc = (masked_predictions.argmax(dim=-1) == masked_labels).float().mean().item()
+    top5_acc = (masked_predictions.topk(k=5, dim=-1).indices == masked_labels.unsqueeze(-1)).any(dim=-1).float().mean().item()
+    
+    # Calculate per-token loss
+    loss_fct = torch.nn.CrossEntropyLoss(reduction='none')
+    # Don't apply softmax here - CrossEntropyLoss expects raw logits
+    token_losses = loss_fct(masked_predictions, masked_labels)
+    
+    return {
+        'top1_accuracy': top1_acc * 100,  # Convert to percentage
+        'top5_accuracy': top5_acc * 100,  # Convert to percentage
+        'mean_token_loss': token_losses.mean().item(),
+        'max_token_loss': token_losses.max().item(),
+        'min_token_loss': token_losses.min().item(),
+        'std_token_loss': token_losses.std().item(),
+    }
+    
+def evaluate_model(model, dataset, tokenizer, device, batch_size=8, mlm_probability=0.15):
+    model.eval()
+    
+    # Set seed for reproducible masking
+    torch.manual_seed(42)
+    np.random.seed(42)
+    
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm=True,
+        mlm_probability=mlm_probability,
+    )
+    
+    all_metrics = []
+    total_loss = 0
+    total_samples = 0
+    
+    # Create a DataLoader that applies masking on the fly
+    dataloader = torch.utils.data.DataLoader(
+        dataset, 
+        batch_size=batch_size,
+        collate_fn=data_collator,
+        generator=torch.Generator().manual_seed(42)
+    )
+    
+    with torch.no_grad():
+        for batch_idx, batch in enumerate(tqdm(dataloader, desc="Evaluating")):
+            inputs = {k: v.to(device) for k, v in batch.items()}
+            
+            outputs = model(**inputs)
+            loss = outputs['loss']
+            logits = outputs['logits']
+            
+            masked_positions = inputs['input_ids'] == tokenizer.mask_token_id
+            masked_count = masked_positions.sum().item()
+            
+            # Add debugging for first batch
+            if batch_idx == 0:
+                logger.info(f"Number of masked tokens in first batch: {masked_count}")
+                logger.info(f"Logits shape: {logits.shape}")
+                logger.info(f"Sample logits min/max: {logits[0,0].min().item():.4f}/{logits[0,0].max().item():.4f}")
+            
+            if masked_count == 0:
+                continue
+                
+            if torch.isnan(logits).any() or torch.isinf(logits).any():
+                logger.warning(f"Found NaN or Inf in logits in batch {batch_idx}!")
+                continue
+                
+            batch_metrics = compute_metrics(logits, inputs['labels'], masked_positions)
+            all_metrics.append(batch_metrics)
+            
+            total_loss += loss.item() * inputs['input_ids'].size(0)
+            total_samples += inputs['input_ids'].size(0)
+    
+    # Calculate average metrics
+    avg_metrics = {
+        k: np.mean([m[k] for m in all_metrics])
+        for k in all_metrics[0].keys()
+    }
+    
+    # Calculate perplexity
+    avg_loss = total_loss / total_samples
+    perplexity = torch.exp(torch.tensor(avg_loss)).item()
+    
+    avg_metrics['perplexity'] = perplexity
+    avg_metrics['loss'] = avg_loss
+    
+    return avg_metrics
+
+def main():
+    # Setup paths
+    current_dir = Path(__file__).parent
+    config = load_config(current_dir / 'eval_config.json')
+    model_dir = Path(config['model_dir']) / 'final-model'
+    data_dir = Path(config['data_dir'])
+    results_dir = Path(config['model_dir']) / 'evaluation_results'
+    results_dir.mkdir(exist_ok=True)
+    
+    # Load model and tokenizer
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    
+    # Initialize model from config
+    model_config = AutoConfig.from_pretrained(model_dir / 'config.json')
+    model_config.max_position_embeddings = 1024
+
+    if config['extra_embeddings']:
+        model = TreeStarEncoderForPreTraining(config=model_config, log=False)
+    else:
+        model = AutoModelForMaskedLM.from_config(model_config)
+    
+    # Load weights from safetensors
+    state_dict = load_file(model_dir / 'model.safetensors')
+    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+    print("Missing keys:", missing_keys)
+    print("Unexpected keys:", unexpected_keys)
+
+    # Re-tie the word embeddings and decoder:
+    model.tie_weights()
+    
+    model = model.to(device)
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    
+    # Load dataset
+    dataset = load_from_disk(data_dir / 'test')
+    
+    # Remove unnecessary columns
+    columns_to_keep = ['input_ids', 'attention_mask']
+    if config['extra_embeddings']:
+        columns_to_keep.extend(['depths', 'sibling_idxs'])
+    dataset = dataset.remove_columns(
+        [col for col in dataset.column_names if col not in columns_to_keep]
+    )
+    
+    # Evaluate
+    logger.info('Starting evaluation...')
+    metrics = evaluate_model(
+        model=model,
+        dataset=dataset,
+        tokenizer=tokenizer,
+        device=device,
+        batch_size=config['batch_size'],
+        mlm_probability=config['mlm_probability'],
+    )
+    
+    # Log results
+    logger.info('Evaluation Results:')
+    for metric_name, value in metrics.items():
+        logger.info(f'{metric_name}: {value:.4f}')
+    
+    # Save results to JSON
+    results_file = results_dir / 'metrics.json'
+    with open(results_file, 'w') as f:
+        json.dump(metrics, f, indent=4)
+    logger.info(f'Results saved to {results_file}')
+    
+    logger.info('Evaluation completed!')
+
+if __name__ == '__main__':
+    main()

code/src/tree_starencoder.py

@@ -9,9 +9,10 @@ from transformers import AutoConfig, BertForMaskedLM
 from tree_codebert import TreePositionalEmbedding
 
 class TreeStarEncoderForPreTraining(BertForMaskedLM):
-    def __init__(self, config: AutoConfig, max_depth: int = 32, max_seq_length: int = 512):
+    def __init__(self, config: AutoConfig, max_depth: int = 32, max_seq_length: int = 512, log: bool = True):
         super().__init__(config)
         self.config = config
+        self.log = log
 
         self.fusion_layer = nn.Sequential(
             nn.Linear(config.hidden_size * 3, config.hidden_size),
@@ -93,6 +94,7 @@ class TreeStarEncoderForPreTraining(BertForMaskedLM):
                 labels.view(-1)
             )
 
+        if self.log:
             wandb.log({
                 "embeddings/token": self.alpha.item(),
                 "embeddings/tree": self.beta.item(),