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"pause_on_instability": false } \ No newline at end of file diff --git a/code/src/train_codebert_mlm.py b/code/src/train_codebert_mlm.py index 41b1912..c7293f7 100644 --- a/code/src/train_codebert_mlm.py +++ b/code/src/train_codebert_mlm.py @@ -13,7 +13,7 @@ import torch from torch import Tensor from torch.optim import AdamW from torch.utils.data import DataLoader, Dataset -from datasets import load_dataset, disable_caching, DatasetDict +from datasets import load_dataset, DatasetDict from huggingface_hub import list_repo_files, hf_hub_download from transformers import ( RobertaForMaskedLM, @@ -61,14 +61,14 @@ def set_seed(seed: int) -> None: if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) -def setup_wandb(config: Dict[str, Any]) -> None: +def setup_wandb(config: Dict[str, Any], exec_file: str = 'train_codebert_mlm.py') -> None: curr_time: str = datetime.datetime.now().strftime('%Y-%m-%d %H:%M') - wandb.init(project='codebert-training', name=curr_time, config=config) + wandb.init(project='codebert-training', name=f'tree_{curr_time}', config=config) wandb.save('train_codebert_mlm.py') def setup_directories(current_dir: Path) -> Path: curr_time: str = datetime.datetime.now().strftime('%Y-%m-%d %H:%M') - output_dir: Path = current_dir.parent.parent / 'outputs' / curr_time + output_dir: Path = current_dir.parent.parent / 'outputs' / f'tree_{curr_time}' output_dir.mkdir(parents=True, exist_ok=True) return output_dir @@ -130,10 +130,11 @@ def create_dataloaders( def setup_model_and_optimizer( config: Dict[str, Any], - current_dir: Path + models_dir: Path ) -> Tuple[PreTrainedModel, AdamW]: - os.environ['HF_HOME'] = str(current_dir.parent / 'models') - model_config = RobertaConfig.from_pretrained('roberta-base') + if not models_dir.exists(): + logger.info("Downloading the model...") + model_config = RobertaConfig.from_pretrained('roberta-base', cache_dir=models_dir) model: PreTrainedModel = RobertaForMaskedLM(model_config) model = torch.compile(model) wandb.watch(model) @@ -212,8 +213,6 @@ def evaluate(model: PreTrainedModel, dataloader: DataLoader) -> Tuple[float, flo return avg_loss, avg_acc def main() -> None: - disable_caching() - current_dir: Path = Path(__file__).parent output_dir: Path = setup_directories(current_dir) config: Dict[str, Any] = load_config(current_dir / 'config.json') @@ -228,10 +227,16 @@ def main() -> None: tokenizer: PreTrainedTokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base', clean_up_tokenization_spaces=True) logger.info(f'Tokenizer loaded: {tokenizer}') + + ######################## Reproducing last training here ######################## + # Remove first 186513 batches + dataset['train'] = dataset['train'].select(range(186_513 * 32, len(dataset['train']))) + ################################################################################ train_dataloader, valid_dataloader = create_dataloaders(dataset, tokenizer, config, device) - model, optimizer = setup_model_and_optimizer(config, current_dir) + models_dir: Path = current_dir.parent / 'models' / 'roberta-base' + model, optimizer = setup_model_and_optimizer(config, models_dir) num_training_steps: int = config['epochs'] * len(train_dataloader) scheduler = get_linear_schedule_with_warmup( @@ -239,6 +244,16 @@ def main() -> None: num_warmup_steps=config['warmup_steps'], num_training_steps=num_training_steps ) + + ######################## Reproducing last training here ######################## + # Change opitmizer learning rate to 0.00021575814536340852 + optimizer = AdamW(model.parameters(), lr=0.00021575814536340852, weight_decay=config['weight_decay']) + # Set warmup_steps to 0 + scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=num_training_steps) + # Load the best model weights + state_dict = torch.load('/sql/msc-patryk-bartkowiak/outputs/2024-10-21_20:15/best_model.pt', weights_only=True, map_location=device) + model.load_state_dict(state_dict) + ################################################################################ train_and_evaluate(model, train_dataloader, valid_dataloader, optimizer, scheduler, config, output_dir) diff --git a/code/src/train_tree_codebert_mlm.py b/code/src/train_tree_codebert_mlm.py new file mode 100644 index 0000000..80fa5ec --- /dev/null +++ b/code/src/train_tree_codebert_mlm.py @@ -0,0 +1,494 @@ +import wandb + +import sys +import math +import torch +import torch.nn as nn +from torch import Tensor +from torch.optim import AdamW +from dataclasses import dataclass +from tqdm import tqdm +from pathlib import Path +from typing import List, Dict, Any, Optional, Tuple +import ast +import numpy as np +from torch.utils.data import Dataset, DataLoader +from datasets import DatasetDict +from transformers import ( + RobertaForMaskedLM, + RobertaConfig, + RobertaTokenizer, + get_linear_schedule_with_warmup, + PreTrainedTokenizer, + PreTrainedModel, +) + +sys.setrecursionlimit(3000) # Increase recursion limit + +# Import existing training functionality +from train_codebert_mlm import ( + set_seed, setup_wandb, setup_directories, load_config, + setup_device, download_dataset, load_and_prepare_dataset, logger +) + +@dataclass +class ASTNodeInfo: + """Stores structural information about an AST node.""" + node_type: str + start_token_idx: int + end_token_idx: int + depth: int + sibling_pos: int + parent_idx: int + +def ast_collate_fn(batch): + """Custom collate function with improved error handling.""" + # Remove failed parses (where attention_mask is all zeros) + valid_batch = [ + item for item in batch + if item['attention_mask'].sum() > 0 and len(item['ast_nodes']) > 0 + ] + + if not valid_batch: + # Return minimal batch if no valid items + return { + 'input_ids': torch.zeros((1, 512), dtype=torch.long), + 'attention_mask': torch.zeros((1, 512), dtype=torch.long), + 'labels': torch.zeros((1, 512), dtype=torch.long), + 'ast_nodes': [[]] + } + + # Stack tensors + input_ids = torch.stack([item['input_ids'] for item in valid_batch]) + attention_mask = torch.stack([item['attention_mask'] for item in valid_batch]) + labels = torch.stack([item['labels'] for item in valid_batch]) + + # Collect AST nodes + ast_nodes = [item['ast_nodes'] for item in valid_batch] + + return { + 'input_ids': input_ids, + 'attention_mask': attention_mask, + 'labels': labels, + 'ast_nodes': ast_nodes + } + +class TreePositionalEmbedding(nn.Module): + """Generates tree-aware positional embeddings for code tokens.""" + + def __init__(self, d_model: int = 768, max_depth: int = 32): + super().__init__() + self.d_model = d_model + self.max_depth = max_depth + + self.depth_embedding = nn.Embedding(max_depth, d_model) + self.sibling_embedding = nn.Embedding(max_depth, d_model) + self.combine = nn.Linear(d_model * 2, d_model) + + self._initialize_embeddings() + + def _initialize_embeddings(self): + position = torch.arange(self.max_depth).unsqueeze(1).float() + div_term = torch.exp(torch.arange(0, self.d_model, 2).float() * + (-math.log(10000.0) / self.d_model)) + + pe = torch.zeros(self.max_depth, self.d_model) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + with torch.no_grad(): + self.depth_embedding.weight.copy_(pe) + self.sibling_embedding.weight.copy_(pe) + + def forward(self, input_ids: torch.Tensor, ast_nodes_batch: List[List[ASTNodeInfo]]) -> torch.Tensor: + """Process batched input with corresponding AST nodes.""" + batch_size, seq_len = input_ids.shape + device = input_ids.device + embeddings = torch.zeros((batch_size, seq_len, self.d_model), device=device) + + # Process each item in the batch + for batch_idx in range(batch_size): + ast_nodes = ast_nodes_batch[batch_idx] + # Process each position in the sequence + for i in range(seq_len): + containing_nodes = [ + node for node in ast_nodes + if node.start_token_idx <= i < node.end_token_idx + ] + + if containing_nodes: + node = max(containing_nodes, key=lambda n: n.depth) + depth = min(node.depth, self.max_depth - 1) + sibling_pos = min(node.sibling_pos, self.max_depth - 1) + + depth_emb = self.depth_embedding(torch.tensor(depth, device=device)) + sibling_emb = self.sibling_embedding(torch.tensor(sibling_pos, device=device)) + embeddings[batch_idx, i] = self.combine(torch.cat([depth_emb, sibling_emb])) + + return embeddings + +class TreeCodeBERTForPreTraining(RobertaForMaskedLM): + """CodeBERT model enhanced with normalized embedding weights for stable training.""" + + def __init__(self, config: RobertaConfig, max_depth: int = 32, max_seq_length: int = 512): + super().__init__(config) + + self.tree_pos_embeddings = TreePositionalEmbedding( + d_model=config.hidden_size, + max_depth=max_depth + ) + + self.seq_pos_embeddings = nn.Embedding(max_seq_length, config.hidden_size) + + # Initialize sequential position embeddings with sinusoidal pattern + position = torch.arange(max_seq_length).unsqueeze(1) + div_term = torch.exp(torch.arange(0, config.hidden_size, 2) * (-math.log(10000.0) / config.hidden_size)) + pe = torch.zeros(max_seq_length, config.hidden_size) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + self.seq_pos_embeddings.weight.data.copy_(pe) + + # Initialize weights with small random values around 0 + self.alpha = nn.Parameter(torch.randn(1) * 0.02) + self.beta = nn.Parameter(torch.randn(1) * 0.02) + self.gamma = nn.Parameter(torch.randn(1) * 0.02) + + self.embedding_combination_layer_norm = nn.LayerNorm(config.hidden_size) + self.final_layer_norm = nn.LayerNorm(config.hidden_size) + + # Add dropout for regularization + self.embedding_dropout = nn.Dropout(config.hidden_dropout_prob) + + def get_normalized_weights(self) -> torch.Tensor: + """ + Compute softmax-normalized weights for embedding combination. + Returns tensor of shape (3,) containing normalized [alpha, beta, gamma]. + """ + weights = torch.stack([self.alpha, self.beta, self.gamma]) + return torch.softmax(weights, dim=0) + + def forward( + self, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + ast_nodes: Optional[List[List[ASTNodeInfo]]] = None, + output_attentions: bool = False, + **kwargs + ) -> Dict[str, torch.Tensor]: + # Move tensors to device + device = input_ids.device + + # Get embeddings + token_embeddings = self.roberta.embeddings.word_embeddings(input_ids) + + seq_positions = torch.arange(input_ids.size(1), device=device) + seq_embeddings = self.seq_pos_embeddings(seq_positions).unsqueeze(0).expand(input_ids.size(0), -1, -1) + + # Get normalized weights + norm_weights = self.get_normalized_weights() + + # Combine embeddings based on presence of AST nodes + if ast_nodes is not None: + tree_embeddings = self.tree_pos_embeddings(input_ids, ast_nodes) + combined_embeddings = ( + norm_weights[0] * token_embeddings + + norm_weights[1] * tree_embeddings + + norm_weights[2] * seq_embeddings + ) + else: + # Redistribute tree weight to other components when no AST available + token_seq_weights = torch.softmax(torch.stack([self.alpha, self.gamma]), dim=0) + combined_embeddings = ( + token_seq_weights[0] * token_embeddings + + token_seq_weights[1] * seq_embeddings + ) + + # Apply layer normalization and dropout + combined_embeddings = self.embedding_combination_layer_norm(combined_embeddings) + combined_embeddings = self.embedding_dropout(combined_embeddings) + combined_embeddings = self.final_layer_norm(combined_embeddings) + + # Forward pass through transformer + outputs = self.roberta( + inputs_embeds=combined_embeddings, + attention_mask=attention_mask, + output_attentions=output_attentions, + **kwargs + ) + + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + # Calculate loss if labels provided + masked_lm_loss = None + if labels is not None: + loss_fct = nn.CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), + labels.view(-1) + ) + + # Get normalized weights for logging + norm_weights_cpu = norm_weights.detach().cpu() + + return { + "loss": masked_lm_loss, + "logits": prediction_scores, + "hidden_states": outputs.hidden_states if hasattr(outputs, "hidden_states") else None, + "attentions": outputs.attentions, + "embedding_weights": { + "token": norm_weights_cpu[0].item(), + "tree": norm_weights_cpu[1].item(), + "sequential": norm_weights_cpu[2].item() + } + } + +class TreeEnhancedDataset(Dataset): + """Dataset that processes code into tokens and AST nodes with improved error handling.""" + def __init__(self, dataset: Dataset, tokenizer: PreTrainedTokenizer, max_length: int): + self.dataset = dataset + self.tokenizer = tokenizer + self.max_length = max_length + self.max_ast_size = 1000 # Limit maximum AST size to prevent memory issues + + def __len__(self) -> int: + return len(self.dataset) + + def create_dummy_data(self) -> Dict[str, Any]: + """Create dummy data for invalid/problematic code samples.""" + pad_token_id = self.tokenizer.pad_token_id or 0 + dummy_tensor = torch.full((self.max_length,), pad_token_id, dtype=torch.long) + return { + 'input_ids': dummy_tensor, + 'attention_mask': torch.zeros(self.max_length, dtype=torch.long), + 'labels': dummy_tensor.clone(), + 'ast_nodes': [] + } + + def __getitem__(self, idx: int) -> Dict[str, Any]: + try: + content: str = self.dataset[idx]['content'] + + # Skip extremely long files + if len(content) > 50000: # ~1000 lines + return self.create_dummy_data() + + # Basic code validation + if not content.strip() or '\0' in content: + return self.create_dummy_data() + + # Tokenize first + encoding = self.tokenizer( + content, + max_length=self.max_length, + truncation=True, + padding='max_length', + return_tensors='pt' + ) + + try: + # Parse AST with timeout + tree = ast.parse(content) + + # Get AST nodes info + nodes_info = [] + def visit_node(node: ast.AST, depth: int, parent_idx: int, sibling_pos: int): + if len(nodes_info) >= self.max_ast_size: + return + + current_idx = len(nodes_info) + if hasattr(node, 'lineno'): + nodes_info.append(ASTNodeInfo( + node_type=type(node).__name__, + start_token_idx=node.lineno, + end_token_idx=getattr(node, 'end_lineno', node.lineno), + depth=min(depth, 31), # Limit depth to prevent issues + sibling_pos=min(sibling_pos, 31), + parent_idx=parent_idx + )) + + # Only process children if we haven't hit the limit + if len(nodes_info) < self.max_ast_size: + for i, child in enumerate(ast.iter_child_nodes(node)): + visit_node(child, depth + 1, current_idx, i) + + visit_node(tree, 0, -1, 0) + + # If we hit the AST size limit, use dummy data + if len(nodes_info) >= self.max_ast_size: + return self.create_dummy_data() + + return { + 'input_ids': encoding['input_ids'].squeeze(0), + 'attention_mask': encoding['attention_mask'].squeeze(0), + 'labels': encoding['input_ids'].squeeze(0).clone(), + 'ast_nodes': nodes_info + } + + except (SyntaxError, ValueError, RecursionError, TimeoutError, MemoryError): + return self.create_dummy_data() + + except Exception as e: + return self.create_dummy_data() + +def create_tree_dataloaders( + dataset: DatasetDict, + tokenizer: PreTrainedTokenizer, + config: Dict[str, Any], + device: torch.device +) -> Tuple[DataLoader, DataLoader]: + """Create dataloaders with tree-enhanced datasets.""" + train_dataset = TreeEnhancedDataset(dataset['train'], tokenizer, max_length=512) + valid_dataset = TreeEnhancedDataset(dataset['test'], tokenizer, max_length=512) + + train_dataloader = DataLoader( + train_dataset, + batch_size=config['batch'], + shuffle=True, + collate_fn=ast_collate_fn, + generator=torch.Generator(device=device) + ) + valid_dataloader = DataLoader( + valid_dataset, + batch_size=config['batch'], + shuffle=False, + collate_fn=ast_collate_fn, + generator=torch.Generator(device=device) + ) + + return train_dataloader, valid_dataloader + +def train_and_evaluate( + model: PreTrainedModel, + train_dataloader: DataLoader, + valid_dataloader: DataLoader, + optimizer: AdamW, + scheduler: Any, + config: Dict[str, Any], + output_dir: Path +) -> None: + """Training loop with explicit tracking of alpha, beta, gamma weights.""" + num_training_steps: int = config['epochs'] * len(train_dataloader) + best_valid_loss: float = float('inf') + + with tqdm(total=num_training_steps, desc='Training') as pbar: + for epoch_idx in range(config['epochs']): + model.train() + + for train_idx, train_batch in enumerate(train_dataloader): + outputs = model(**train_batch) + train_loss = outputs["loss"] + + if train_loss is not None: + train_loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), config['max_grad_norm']) + optimizer.step() + scheduler.step() + optimizer.zero_grad() + + # Get current metrics + current_loss = train_loss.item() + weights = outputs["embedding_weights"] + + # Update progress bar with all three weights + pbar.update(1) + pbar.set_postfix({ + 'loss': f"{current_loss:.3f}", + 'α': f"{weights['token']:.2f}", + 'β': f"{weights['tree']:.2f}", + 'γ': f"{weights['sequential']:.2f}" + }) + + # Log all three weights separately + step = train_idx + len(train_dataloader) * epoch_idx + wandb.log({ + 'loss': current_loss, + 'token_weight': weights['token'], + 'tree_weight': weights['tree'], + 'sequential_weight': weights['sequential'], + 'step': step, + }) + + # Periodic evaluation + if train_idx != 0 and train_idx % config['eval_every'] == 0: + valid_loss, valid_acc = evaluate(model, valid_dataloader) + + wandb.log({ + 'valid_loss': valid_loss, + 'valid_acc': valid_acc, + 'step': step, + }) + + if valid_loss < best_valid_loss: + best_valid_loss = valid_loss + torch.save(model.state_dict(), output_dir / 'best_model.pt') + else: + pbar.update(1) + pbar.set_postfix({'loss': 'N/A'}) + + logger.info(f'Best validation loss: {best_valid_loss}') + +def evaluate(model: PreTrainedModel, dataloader: DataLoader) -> Tuple[float, float]: + """Evaluation function with simple metrics.""" + model.eval() + total_loss: float = 0.0 + total_acc: float = 0.0 + num_batches: int = 0 + + with torch.no_grad(): + for batch in dataloader: + outputs = model(**batch) + if outputs["loss"] is not None: + total_loss += outputs["loss"].item() + logits = outputs["logits"] + labels = batch['labels'] + predictions = logits.argmax(dim=-1) + total_acc += (predictions == labels).float().mean().item() + num_batches += 1 + + avg_loss: float = total_loss / max(num_batches, 1) + avg_acc: float = total_acc / max(num_batches, 1) + + model.train() + return avg_loss, avg_acc + +def main(): + # Setup identical to original training script + current_dir = Path(__file__).parent + output_dir = setup_directories(current_dir) + config = load_config(current_dir / 'config.json') + + setup_wandb(config, exec_file='src/train_tree_codebert_mlm.py') + set_seed(config['seed']) + device = setup_device() + + dataset_dir = current_dir.parent / 'data' / 'the-stack-python' + download_dataset(dataset_dir) + dataset = load_and_prepare_dataset(dataset_dir, config['seed']) + + tokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base') + + # Create tree-enhanced dataloaders + train_dataloader, valid_dataloader = create_tree_dataloaders(dataset, tokenizer, config, device) + + # Initialize tree-enhanced model + model_config = RobertaConfig.from_pretrained('microsoft/codebert-base') + model = TreeCodeBERTForPreTraining(model_config) + model = model.to(device) # Just move to device without compilation + # model = torch.compile(model) + + # Optimizer and scheduler setup identical to original + optimizer = AdamW(model.parameters(), lr=config['learning_rate'], weight_decay=config['weight_decay']) + num_training_steps = config['epochs'] * len(train_dataloader) + scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=config['warmup_steps'], + num_training_steps=num_training_steps + ) + + # Training loop (using original train_and_evaluate function) + train_and_evaluate(model, train_dataloader, valid_dataloader, optimizer, scheduler, config, output_dir) + +if __name__ == "__main__": + main() \ No newline at end of file