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Data processing utilities for Ruby method datasets.
This module provides functions to load, preprocess, and prepare Ruby method
data for GNN training. Includes custom Dataset class for AST to graph conversion.
"""
import json
import random
import os
import logging
from pathlib import Path
from typing import List, Dict, Any, Tuple, Optional, Union
try:
import torch
from torch_geometric.data import Data
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
def load_methods_json(filepath: str) -> List[Dict[str, Any]]:
"""
Load Ruby methods from JSON file.
Args:
filepath: Path to the JSON file containing method data
Returns:
List of method dictionaries
"""
with open(filepath, 'r') as f:
return json.load(f)
def methods_to_dataframe(methods: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
Convert list of method dictionaries to a structured format.
Args:
methods: List of method dictionaries
Returns:
List of method dictionaries (pass-through for compatibility)
"""
return methods
def filter_methods_by_length(methods: List[Dict[str, Any]], min_lines: int = 5, max_lines: int = 100) -> List[Dict[str, Any]]:
"""
Filter methods by source code length.
Args:
methods: List of method dictionaries
min_lines: Minimum number of lines
max_lines: Maximum number of lines
Returns:
Filtered list of methods
"""
filtered = []
for method in methods:
if 'raw_source' in method:
line_count = len(method['raw_source'].split('\n'))
if min_lines <= line_count <= max_lines:
method['line_count'] = line_count
filtered.append(method)
return filtered
"""
Filter methods by source code length.
Args:
df: DataFrame containing method data
min_lines: Minimum number of lines
max_lines: Maximum number of lines
Returns:
Filtered DataFrame
"""
df['line_count'] = df['raw_source'].apply(lambda x: len(x.split('\n')))
return df[(df['line_count'] >= min_lines) & (df['line_count'] <= max_lines)]
class ASTNodeEncoder:
"""
Encoder for mapping AST node types to feature vectors.
This class maintains a vocabulary of AST node types found in Ruby code
and maps them to dense feature vectors for GNN processing.
"""
def __init__(self):
"""Initialize the node encoder with common Ruby AST node types."""
# Common Ruby AST node types based on the parser gem
self.node_types = [
'def', 'defs', 'args', 'arg', 'begin', 'end', 'lvasgn', 'ivasgn', 'gvasgn',
'cvasgn', 'send', 'block', 'if', 'unless', 'while', 'until', 'for', 'case',
'when', 'rescue', 'ensure', 'retry', 'break', 'next', 'redo', 'return',
'yield', 'super', 'zsuper', 'lambda', 'proc', 'and', 'or', 'not', 'true',
'false', 'nil', 'self', 'int', 'float', 'str', 'sym', 'regexp', 'array',
'hash', 'pair', 'splat', 'kwsplat', 'block_pass', 'const', 'cbase',
'lvar', 'ivar', 'gvar', 'cvar', 'casgn', 'masgn', 'mlhs', 'op_asgn',
'and_asgn', 'or_asgn', 'back_ref', 'nth_ref', 'class', 'sclass', 'module',
'defined?', 'alias', 'undef', 'range', 'irange', 'erange', 'regopt'
]
# Create mapping from node type to index
self.type_to_idx = {node_type: idx for idx, node_type in enumerate(self.node_types)}
self.unknown_idx = len(self.node_types) # Index for unknown node types
self.vocab_size = len(self.node_types) + 1 # +1 for unknown
def encode_node_type(self, node_type: str) -> int:
"""
Encode a node type to its integer index.
Args:
node_type: The AST node type string
Returns:
Integer index for the node type
"""
return self.type_to_idx.get(node_type, self.unknown_idx)
def create_node_features(self, node_type: str) -> List[float]:
"""
Create feature vector for a node type.
Args:
node_type: The AST node type string
Returns:
Feature vector as list of floats
"""
# Simple one-hot encoding for now
features = [0.0] * self.vocab_size
idx = self.encode_node_type(node_type)
features[idx] = 1.0
return features
class ASTGraphConverter:
"""
Converter for transforming AST JSON to graph representation.
This class parses the AST JSON structure and converts it into
a graph format suitable for GNN processing.
"""
def __init__(self):
"""Initialize the AST to graph converter."""
self.node_encoder = ASTNodeEncoder()
self.reset()
def reset(self):
"""Reset the converter state for processing a new AST."""
self.nodes = [] # List of node features
self.edges = [] # List of edge tuples (parent_idx, child_idx)
self.edge_attrs = [] # List of edge attributes [child_index, depth, num_siblings]
self.node_depths = [] # Depth of each node in the tree
self.node_child_indices = [] # Position of each node among its siblings
self.node_count = 0
def parse_ast_json(self, ast_json: str) -> Dict[str, Any]:
"""
Parse AST JSON string and convert to graph representation.
Args:
ast_json: JSON string representing the AST
Returns:
Dictionary containing node features, edge indices, and edge attributes.
edge_attr contains [child_index, depth, num_siblings] per edge.
node_pos contains [child_index, depth] per node for positional encoding.
"""
self.reset()
try:
ast_data = json.loads(ast_json)
self._process_node(ast_data, parent_idx=None, depth=0, child_index=0, num_siblings=1)
# Convert to appropriate format
if not self.nodes:
# Handle empty AST case
node_features = [[0.0] * self.node_encoder.vocab_size]
edge_index = [[], []] # Empty edge list
edge_attr = []
node_pos = [[0, 0]]
else:
node_features = self.nodes
if self.edges:
# Transpose edge list to [2, num_edges] format
edge_index = [[], []]
for parent, child in self.edges:
edge_index[0].append(parent)
edge_index[1].append(child)
else:
edge_index = [[], []]
edge_attr = self.edge_attrs
node_pos = list(zip(self.node_child_indices, self.node_depths))
return {
'x': node_features,
'edge_index': edge_index,
'edge_attr': edge_attr,
'node_pos': node_pos,
'num_nodes': len(self.nodes) if self.nodes else 1
}
except (json.JSONDecodeError, Exception):
# Handle malformed JSON or other errors gracefully
return {
'x': [[0.0] * self.node_encoder.vocab_size],
'edge_index': [[], []],
'edge_attr': [],
'node_pos': [[0, 0]],
'num_nodes': 1
}
def _process_node(self, node: Union[Dict, List, str, int, float, None],
parent_idx: Optional[int] = None, depth: int = 0,
child_index: int = 0, num_siblings: int = 1) -> int:
"""
Recursively process an AST node and its children.
Args:
node: The AST node (dict, list, or primitive)
parent_idx: Index of the parent node
depth: Depth of the current node in the AST
child_index: Position of this node among its siblings (0-based)
num_siblings: Total number of siblings (including this node)
Returns:
Index of the current node
"""
if isinstance(node, dict) and 'type' in node:
# This is an AST node with a type
node_type = node['type']
current_idx = self.node_count
self.node_count += 1
# Create node features
features = self.node_encoder.create_node_features(node_type)
self.nodes.append(features)
self.node_depths.append(depth)
self.node_child_indices.append(child_index)
# Add edge from parent to current node
if parent_idx is not None:
self.edges.append((parent_idx, current_idx))
self.edge_attrs.append([child_index, depth, num_siblings])
# Process children with positional information
if 'children' in node:
children = node['children']
n_children = len(children)
for i, child in enumerate(children):
self._process_node(child, current_idx, depth=depth + 1,
child_index=i, num_siblings=n_children)
return current_idx
elif isinstance(node, list):
# Process list of nodes
n_items = len(node)
for i, child in enumerate(node):
self._process_node(child, parent_idx, depth=depth,
child_index=i, num_siblings=n_items)
return parent_idx if parent_idx is not None else -1
else:
# Leaf node (string, int, float, None)
if parent_idx is not None:
current_idx = self.node_count
self.node_count += 1
# Create a generic leaf node
leaf_type = 'leaf_' + type(node).__name__
features = self.node_encoder.create_node_features(leaf_type)
self.nodes.append(features)
self.node_depths.append(depth)
self.node_child_indices.append(child_index)
# Add edge from parent to leaf
self.edges.append((parent_idx, current_idx))
self.edge_attrs.append([child_index, depth, num_siblings])
return current_idx
return -1
def load_jsonl_file(filepath: str, limit: Optional[int] = None) -> List[Dict[str, Any]]:
"""
Load data from a JSONL file.
Args:
filepath: Path to the JSONL file
limit: Optional maximum number of lines to load.
Returns:
List of dictionaries from the JSONL file
"""
data = []
with open(filepath, 'r', encoding='utf-8') as f:
for i, line in enumerate(f):
if limit is not None and i >= limit:
break
line = line.strip()
if line:
try:
data.append(json.loads(line))
except json.JSONDecodeError:
continue # Skip malformed lines
return data
class RubyASTDataset:
"""
Dataset class for loading Ruby AST data and converting to graph format.
This class loads JSONL files containing Ruby method data and converts
the AST representations to graph objects suitable for GNN training.
"""
def __init__(self, jsonl_path: str, transform=None, limit: Optional[int] = None):
"""
Initialize the dataset.
Args:
jsonl_path: Path to the JSONL file containing method data
transform: Optional transform to apply to each sample
limit: Optional maximum number of samples to load.
"""
self.jsonl_path = jsonl_path
self.transform = transform
self.converter = ASTGraphConverter()
# Load the data
self.data = load_jsonl_file(jsonl_path, limit=limit)
print(f"Loaded {len(self.data)} samples from {jsonl_path}")
def __len__(self) -> int:
"""Return the number of samples in the dataset."""
return len(self.data)
def __getitem__(self, idx: int) -> Dict[str, Any]:
"""
Get a sample from the dataset.
Args:
idx: Index of the sample
Returns:
Dictionary containing graph data and target
"""
if idx < 0 or idx >= len(self.data):
raise IndexError(f"Index {idx} out of range for dataset of size {len(self.data)}")
sample = self.data[idx]
# Convert AST to graph
graph_data = self.converter.parse_ast_json(sample['ast_json'])
# Create the data object
result = {
'x': graph_data['x'],
'edge_index': graph_data['edge_index'],
'y': [sample.get('complexity_score', 5.0)], # Default complexity score if missing
'num_nodes': graph_data['num_nodes'],
'id': sample.get('id', f'sample_{idx}'),
'repo_name': sample.get('repo_name', ''),
'file_path': sample.get('file_path', '')
}
# Apply transform if provided
if self.transform:
result = self.transform(result)
return result
def get_feature_dim(self) -> int:
"""Return the dimension of node features."""
return self.converter.node_encoder.vocab_size
def collate_graphs(batch: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Collate function for batching graph data.
Args:
batch: List of graph data dictionaries
Returns:
Batched graph data
"""
if not batch:
raise ValueError("Cannot collate empty batch")
# Collect all node features and edge indices
all_x = []
all_edge_index = [[], []] # [source_nodes, target_nodes]
all_y = []
batch_idx = []
node_offset = 0
metadata = {
'ids': [],
'repo_names': [],
'file_paths': []
}
for i, sample in enumerate(batch):
# Node features
all_x.extend(sample['x'])
# Edge indices (offset by current node count)
edges = sample['edge_index']
if len(edges[0]) > 0: # Only offset if there are edges
for j in range(len(edges[0])):
all_edge_index[0].append(edges[0][j] + node_offset)
all_edge_index[1].append(edges[1][j] + node_offset)
# Target values
all_y.extend(sample['y'])
# Batch indices for each node
num_nodes = sample['num_nodes']
batch_idx.extend([i] * num_nodes)
node_offset += num_nodes
# Metadata
metadata['ids'].append(sample['id'])
metadata['repo_names'].append(sample['repo_name'])
metadata['file_paths'].append(sample['file_path'])
return {
'x': all_x,
'edge_index': all_edge_index,
'y': all_y,
'batch': batch_idx,
'num_graphs': len(batch),
'metadata': metadata
}
class SimpleDataLoader:
"""
Simple DataLoader implementation for batching data.
This provides a basic implementation that can be used when PyTorch
DataLoader is not available, and can easily be replaced with the real
PyTorch DataLoader when dependencies are installed.
"""
def __init__(self, dataset, batch_size: int = 1, shuffle: bool = False, collate_fn=None):
"""
Initialize the DataLoader.
Args:
dataset: Dataset to load from
batch_size: Number of samples per batch
shuffle: Whether to shuffle the data
collate_fn: Function to collate samples into batches
"""
self.dataset = dataset
self.batch_size = batch_size
self.shuffle = shuffle
self.collate_fn = collate_fn or collate_graphs
# Create indices
self.indices = list(range(len(dataset)))
if shuffle:
import random
random.shuffle(self.indices)
def __len__(self) -> int:
"""Return number of batches."""
return (len(self.dataset) + self.batch_size - 1) // self.batch_size
def __iter__(self):
"""Iterate over batches."""
for i in range(0, len(self.dataset), self.batch_size):
batch_indices = self.indices[i:i + self.batch_size]
batch = [self.dataset[idx] for idx in batch_indices]
yield self.collate_fn(batch)
class PairedDataset:
"""
Dataset class for loading paired Ruby AST and text description data.
This class loads the paired_data.jsonl file containing Ruby method data
and converts AST representations to graph objects paired with text descriptions.
For each method, it randomly samples one description from the available descriptions.
"""
def __init__(self, jsonl_path: str, transform=None, seed: Optional[int] = None, limit: Optional[int] = None):
"""
Initialize the paired dataset.
Args:
jsonl_path: Path to the paired_data.jsonl file
transform: Optional transform to apply to each sample
seed: Random seed for consistent description sampling
limit: Optional maximum number of samples to load.
"""
self.jsonl_path = jsonl_path
self.transform = transform
self.converter = ASTGraphConverter()
if seed is not None:
random.seed(seed)
# Load the data
self.data = load_jsonl_file(jsonl_path, limit=limit)
print(f"Loaded {len(self.data)} samples from {jsonl_path}")
def __len__(self) -> int:
"""Return the number of samples in the dataset."""
return len(self.data)
def __getitem__(self, idx: int) -> Tuple[Dict[str, Any], str]:
"""
Get a sample from the dataset.
Args:
idx: Index of the sample
Returns:
Tuple of (graph_data, text_description)
"""
if idx < 0 or idx >= len(self.data):
raise IndexError(f"Index {idx} out of range for dataset of size {len(self.data)}")
sample = self.data[idx]
# Convert AST to graph
graph_data = self.converter.parse_ast_json(sample['ast_json'])
# Randomly sample one description
descriptions = sample.get('descriptions', [])
if descriptions:
description = random.choice(descriptions)
text_description = description['text']
else:
# Fallback to method name if no descriptions available
text_description = sample.get('method_name', 'unknown_method')
# Create the graph data object
graph_result = {
'x': graph_data['x'],
'edge_index': graph_data['edge_index'],
'num_nodes': graph_data['num_nodes'],
'id': sample.get('id', f'sample_{idx}'),
'repo_name': sample.get('repo_name', ''),
'file_path': sample.get('file_path', '')
}
# Apply transform if provided
if self.transform:
graph_result = self.transform(graph_result)
return graph_result, text_description
def get_feature_dim(self) -> int:
"""Return the dimension of node features."""
return self.converter.node_encoder.vocab_size
def collate_paired_data(batch: List[Tuple[Dict[str, Any], str]]) -> Tuple[Dict[str, Any], List[str]]:
"""
Collate function for batching paired graph and text data.
Args:
batch: List of (graph_data, text_description) tuples
Returns:
Tuple of (batched_graph_data, list_of_text_descriptions)
"""
if not batch:
raise ValueError("Cannot collate empty batch")
# Separate graph data and text descriptions
graph_batch = [item[0] for item in batch]
text_batch = [item[1] for item in batch]
# Collate graph data manually (similar to collate_graphs but without 'y' field)
all_x = []
all_edge_index = [[], []] # [source_nodes, target_nodes]
batch_idx = []
node_offset = 0
metadata = {
'ids': [],
'repo_names': [],
'file_paths': []
}
for i, sample in enumerate(graph_batch):
# Node features
all_x.extend(sample['x'])
# Edge indices (offset by current node count)
edges = sample['edge_index']
if len(edges[0]) > 0: # Only offset if there are edges
for j in range(len(edges[0])):
all_edge_index[0].append(edges[0][j] + node_offset)
all_edge_index[1].append(edges[1][j] + node_offset)
# Batch indices for each node
num_nodes = sample['num_nodes']
batch_idx.extend([i] * num_nodes)
node_offset += num_nodes
# Metadata
metadata['ids'].append(sample['id'])
metadata['repo_names'].append(sample['repo_name'])
metadata['file_paths'].append(sample['file_path'])
batched_graphs = {
'x': all_x,
'edge_index': all_edge_index,
'batch': batch_idx,
'num_graphs': len(batch),
'metadata': metadata
}
return batched_graphs, text_batch
class PairedDataLoader:
"""
DataLoader for paired graph and text data.
Extends SimpleDataLoader to handle paired (graph, text) data.
"""
def __init__(self, dataset, batch_size: int = 1, shuffle: bool = False):
"""
Initialize the PairedDataLoader.
Args:
dataset: PairedDataset to load from
batch_size: Number of samples per batch
shuffle: Whether to shuffle the data
"""
self.dataset = dataset
self.batch_size = batch_size
self.shuffle = shuffle
# Create indices
self.indices = list(range(len(dataset)))
if shuffle:
random.shuffle(self.indices)
def __len__(self) -> int:
"""Return number of batches."""
return (len(self.dataset) + self.batch_size - 1) // self.batch_size
def __iter__(self):
"""Iterate over batches."""
for i in range(0, len(self.dataset), self.batch_size):
batch_indices = self.indices[i:i + self.batch_size]
batch = [self.dataset[idx] for idx in batch_indices]
yield collate_paired_data(batch)
class PrecomputedRubyASTDataset:
"""
Dataset class for loading precomputed Ruby AST graph data.
This class can load .pt files containing pre-converted PyTorch Geometric
Data objects for speed, but also supports processing .jsonl files as a fallback.
"""
def __init__(self, path: str, transform=None):
"""
Initialize the dataset.
Args:
path: Path to the .pt or .jsonl file containing graph data.
transform: Optional transform to apply to each sample.
"""
self.path = path
self.transform = transform
if not TORCH_AVAILABLE:
raise ImportError("PyTorch and PyG are required for this dataset.")
if path.endswith('.pt'):
# Load the precomputed data into RAM
self.data = torch.load(path, weights_only=False)
print(f"Loaded {len(self.data)} precomputed graphs from {path}")
elif path.endswith('.jsonl'):
print(f"Processing JSONL file into graphs: {path}")
jsonl_data = load_jsonl_file(path)
converter = ASTGraphConverter()
self.data = []
for sample in jsonl_data:
graph_data = converter.parse_ast_json(sample['ast_json'])
x = torch.tensor(graph_data['x'], dtype=torch.float)
edge_index = torch.tensor(graph_data['edge_index'], dtype=torch.long)
y = torch.tensor([sample.get('complexity_score', 5.0)], dtype=torch.float)
data_obj = Data(x=x, edge_index=edge_index, y=y)
# Add positional attributes — always set so PyG collation is consistent
ea = graph_data.get('edge_attr', [])
data_obj.edge_attr = torch.tensor(
ea if ea else [], dtype=torch.float,
).reshape(-1, 3) if ea else torch.zeros((0, 3), dtype=torch.float)
np_ = graph_data.get('node_pos', [])
data_obj.node_pos = torch.tensor(
np_ if np_ else [[0, 0]], dtype=torch.float,
)
self.data.append(data_obj)
print(f"Converted {len(self.data)} graphs from {path}")
else:
raise ValueError(f"Unsupported file type: {path}. Please provide a .pt or .jsonl file.")
def __len__(self) -> int:
"""Return the number of samples in the dataset."""
return len(self.data)
def __getitem__(self, idx: int):
"""
Get a sample from the dataset.
Args:
idx: Index of the sample
Returns:
PyTorch Geometric Data object
"""
if idx < 0 or idx >= len(self.data):
raise IndexError(f"Index {idx} out of range for dataset of size {len(self.data)}")
sample = self.data[idx]
if self.transform:
sample = self.transform(sample)
return sample
class PreCollatedDataset:
"""
Dataset class for loading pre-collated batches of graph data.
This class loads a .pt file where each item is an already-collated
`torch_geometric.data.Batch` object. This is the most efficient
way to load data as it eliminates all real-time collation overhead.
"""
def __init__(self, pt_path: str):
"""
Initialize the dataset.
Args:
pt_path: Path to the .pt file containing pre-collated batches.
"""
# Load the list of pre-collated batches into RAM
self.batches = torch.load(pt_path, weights_only=False)
print(f"Loaded {len(self.batches)} pre-collated batches from {pt_path}")
def __len__(self):
return len(self.batches)
def __getitem__(self, idx):
return self.batches[idx]
def create_data_loaders(train_path: str, val_path: str, batch_size: int = 32, shuffle: bool = True, num_workers: Optional[int] = None, pre_collated: bool = False):
"""
Create train and validation data loaders.
Supports two modes:
1. Standard loading from a dataset of individual graphs (`pre_collated=False`).
This uses a PyG DataLoader to perform real-time batching.
2. Pre-collated loading from a dataset of pre-batched graphs (`pre_collated=True`).
This is the most performant option, as it has near-zero CPU overhead.
Args:
train_path: Path to training .pt file.
val_path: Path to validation .pt file.
batch_size: Batch size (used only if `pre_collated=False`).
shuffle: Whether to shuffle training data.
num_workers: Number of workers for data loading (used only if `pre_collated=False`).
pre_collated: Whether the dataset files contain pre-collated batches.
Returns:
Tuple of (train_loader, val_loader)
"""
if not TORCH_AVAILABLE:
raise ImportError("PyTorch is required to create data loaders.")
if pre_collated:
# --- Pre-collated path (most efficient) ---
train_dataset = PreCollatedDataset(train_path)
val_dataset = PreCollatedDataset(val_path)
# The collate_fn simply returns the already-collated batch.
# The input `batch` is a list of size 1 containing our pre-made Batch object.
collate_fn = lambda x: x[0]
# DataLoader is just a simple iterator here, no real collation work.
# num_workers > 0 can actually be slower due to overhead of sending
# already-large batches between processes.
from torch.utils.data import DataLoader
train_loader = DataLoader(train_dataset, batch_size=1, shuffle=shuffle, num_workers=0, collate_fn=collate_fn)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0, collate_fn=collate_fn)
print("✅ Using pre-collated data loader (maximum performance).")
else:
# --- Standard real-time collation path ---
from torch_geometric.loader import DataLoader
train_dataset = PrecomputedRubyASTDataset(train_path)
val_dataset = PrecomputedRubyASTDataset(val_path)
if num_workers is None:
num_workers = os.cpu_count()
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=torch.cuda.is_available(),
persistent_workers=num_workers > 0
)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=torch.cuda.is_available(),
persistent_workers=num_workers > 0
)
print(f"✅ Using standard PyG DataLoader with {num_workers} workers.")
return train_loader, val_loader
def create_paired_data_loaders(paired_data_path: str, batch_size: int = 32, shuffle: bool = True, seed: Optional[int] = None):
"""
Create data loader for paired graph and text data.
Args:
paired_data_path: Path to paired_data.jsonl file
batch_size: Batch size for the loader
shuffle: Whether to shuffle the data
seed: Random seed for consistent description sampling
Returns:
PairedDataLoader instance
"""
dataset = PairedDataset(paired_data_path, seed=seed)
loader = PairedDataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
return loader
class AutoregressiveASTDataset:
"""
Dataset class for autoregressive AST generation training.
This class loads paired Ruby AST and text description data and converts
each AST into a sequence of (partial_graph, target_node) pairs for
autoregressive training. Each method generates multiple training examples.
"""
def __init__(self, paired_data_path: str, max_sequence_length: int = 50, seed: Optional[int] = None,
precomputed_embeddings_path: Optional[str] = None):
"""
Initialize the autoregressive dataset.
Args:
paired_data_path: Path to the paired_data.jsonl file
max_sequence_length: Maximum number of nodes per sequence
seed: Random seed for consistent description sampling
precomputed_embeddings_path: Path to pre-computed text embeddings file (optional)
"""
self.paired_data_path = paired_data_path
self.max_sequence_length = max_sequence_length
self.converter = ASTGraphConverter()
if seed is not None:
random.seed(seed)
# Load pre-computed embeddings if available
self.precomputed_embeddings = {}
if precomputed_embeddings_path and os.path.exists(precomputed_embeddings_path):
try:
if TORCH_AVAILABLE:
self.precomputed_embeddings = torch.load(precomputed_embeddings_path, map_location='cpu', weights_only=True)
print(f"✅ Loaded {len(self.precomputed_embeddings)} pre-computed text embeddings")
else:
print("⚠️ PyTorch not available, skipping pre-computed embeddings")
except Exception as e:
print(f"⚠️ Warning: Could not load pre-computed embeddings: {e}")
elif precomputed_embeddings_path:
print(f"⚠️ Warning: Pre-computed embeddings file not found: {precomputed_embeddings_path}")
# Load the paired data
self.paired_data = load_jsonl_file(paired_data_path)
# Generate sequential training pairs from all methods
self.sequential_pairs = []
self._generate_all_sequential_pairs()
print(f"Loaded {len(self.paired_data)} methods from {paired_data_path}")
print(f"Generated {len(self.sequential_pairs)} sequential training pairs")
def _generate_all_sequential_pairs(self):
"""Generate sequential training pairs from all ASTs in the dataset."""
for sample in self.paired_data:
try:
# Get text description
descriptions = sample.get('descriptions', [])
if descriptions:
description = random.choice(descriptions)
text_description = description['text']
else:
# Fallback to method name if no descriptions available
text_description = sample.get('method_name', 'unknown_method')
# Create sequential pairs for this AST
sequential_pairs = self._create_sequential_pairs(
sample['ast_json'],
text_description
)
# Add to global list
self.sequential_pairs.extend(sequential_pairs)
except Exception as e:
# Skip malformed samples gracefully
print(f"Warning: Skipping sample {sample.get('id', 'unknown')} due to error: {e}")
continue
def _create_sequential_pairs(self, ast_json: str, text_description: str) -> List[Dict[str, Any]]:
"""
Convert single AST into sequence of (partial_graph, target_node) pairs.
Args:
ast_json: JSON string representing the AST
text_description: Text description for this method
Returns:
List of sequential training pairs
"""
pairs = []
try:
# Extract nodes in proper order along with their connections
nodes, connections = self._extract_nodes_and_connections_in_order(ast_json)
# Limit sequence length if needed
if len(nodes) > self.max_sequence_length:
nodes = nodes[:self.max_sequence_length]
# Also limit connections to only include those within the sequence
filtered_connections = []
for src, tgt in connections:
if src < self.max_sequence_length and tgt < self.max_sequence_length:
filtered_connections.append((src, tgt))
connections = filtered_connections
# Get pre-computed text embedding if available, otherwise store text
text_embedding = None
if text_description in self.precomputed_embeddings:
text_embedding = self.precomputed_embeddings[text_description]
# Create sequential pairs
for i in range(len(nodes)):
# Build partial graph with nodes 0 to i-1
partial_graph = self._build_partial_graph(nodes[:i])
# Target is the i-th node
target_node = nodes[i]
# Create target connections for this step
# This represents which existing nodes (0 to i-1) the new node i should connect to
target_connections = self._create_target_connections(i, connections)
pair = {
'text_description': text_description,
'text_embedding': text_embedding, # Pre-computed embedding if available
'partial_graph': partial_graph,
'target_node': target_node,
'target_connections': target_connections,
'step': i,
'total_steps': len(nodes)
}
pairs.append(pair)
except Exception as e:
# Return empty list for malformed ASTs
print(f"Warning: Failed to create sequential pairs: {e}")
return pairs
def _extract_nodes_and_connections_in_order(self, ast_json: str) -> Tuple[List[Dict[str, Any]], List[Tuple[int, int]]]:
"""
Extract nodes and their connections from AST in proper depth-first order.
Args:
ast_json: JSON string representing the AST
Returns:
Tuple of (nodes_list, connections_list) where connections are (parent_idx, child_idx) pairs
"""
try:
ast_data = json.loads(ast_json)
nodes = []
connections = []
self._traverse_ast_nodes_with_connections(ast_data, nodes, connections, parent_idx=None)
return nodes, connections
except (json.JSONDecodeError, Exception):
# Return empty lists for malformed JSON
return [], []
def _traverse_ast_nodes_with_connections(self, node: Union[Dict, List, str, int, float, None],
nodes: List[Dict[str, Any]],
connections: List[Tuple[int, int]],
parent_idx: Optional[int] = None):
"""
Recursively traverse AST and collect nodes and connections in depth-first order.
Args:
node: Current AST node
nodes: List to collect nodes
connections: List to collect connections as (parent_idx, child_idx) pairs
parent_idx: Index of parent node
"""
if isinstance(node, dict) and 'type' in node:
# This is an AST node with a type
current_idx = len(nodes)
node_info = {
'node_type': node['type'],
'features': self.converter.node_encoder.create_node_features(node['type']),
'raw_node': node # Keep reference for debugging
}
nodes.append(node_info)
# Add connection from parent to current node
if parent_idx is not None:
connections.append((parent_idx, current_idx))
# Traverse children
if 'children' in node:
for child in node['children']:
self._traverse_ast_nodes_with_connections(child, nodes, connections, current_idx)
elif isinstance(node, list):
# Process list of nodes
for child in node:
self._traverse_ast_nodes_with_connections(child, nodes, connections, parent_idx)
def _create_target_connections(self, node_idx: int, all_connections: List[Tuple[int, int]]) -> List[float]:
"""
Create target connection vector for a specific node being added.
Args:
node_idx: Index of the node being added to the graph
all_connections: List of all connections in the full AST as (parent_idx, child_idx) pairs
Returns:
Binary vector of length max_nodes indicating which existing nodes to connect to
"""
# Initialize with zeros for all possible connections
target_vector = [0.0] * 100 # max_nodes = 100 from model
# Find all connections where this node is the target (child)
# We want to know which existing nodes (with index < node_idx) should connect to this node
for parent_idx, child_idx in all_connections:
if child_idx == node_idx and parent_idx < node_idx and parent_idx < 100:
target_vector[parent_idx] = 1.0
return target_vector
def _traverse_ast_nodes(self, node: Union[Dict, List, str, int, float, None], nodes: List[Dict[str, Any]]):
"""
Recursively traverse AST and collect nodes in depth-first order.
Args:
node: Current AST node
nodes: List to collect nodes
"""
if isinstance(node, dict) and 'type' in node:
# This is an AST node with a type
node_info = {
'node_type': node['type'],
'features': self.converter.node_encoder.create_node_features(node['type']),
'raw_node': node # Keep reference for debugging
}
nodes.append(node_info)
# Traverse children
if 'children' in node:
for child in node['children']:
self._traverse_ast_nodes(child, nodes)
elif isinstance(node, list):
# Process list of nodes
for child in node:
self._traverse_ast_nodes(child, nodes)
def _build_partial_graph(self, nodes: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Build partial graph from first i nodes.
Args:
nodes: List of nodes to include in partial graph
Returns:
Partial graph representation
"""
if not nodes:
# Empty graph case
return {
'x': [],
'edge_index': [[], []],
'num_nodes': 0
}
# Extract node features
node_features = [node['features'] for node in nodes]
# Create simple sequential connections (each node connects to next)
# This is a simplified approach - in practice you'd want to preserve
# the actual AST structure relationships
edge_list = []
for i in range(len(nodes) - 1):
edge_list.append([i, i + 1]) # Forward edge
edge_list.append([i + 1, i]) # Backward edge for undirected
if edge_list:
edge_index = [[], []]
for source, target in edge_list:
edge_index[0].append(source)
edge_index[1].append(target)
else:
edge_index = [[], []]
return {
'x': node_features,
'edge_index': edge_index,
'num_nodes': len(nodes)
}
def __len__(self) -> int:
"""Return the number of sequential training pairs."""
return len(self.sequential_pairs)
def __getitem__(self, idx: int) -> Dict[str, Any]:
"""
Get a sequential training pair.
Args:
idx: Index of the training pair
Returns:
Dictionary containing partial graph and target node data
"""
if idx < 0 or idx >= len(self.sequential_pairs):
raise IndexError(f"Index {idx} out of range for dataset of size {len(self.sequential_pairs)}")
return self.sequential_pairs[idx]
def get_feature_dim(self) -> int:
"""Return the dimension of node features."""
return self.converter.node_encoder.vocab_size
def collate_autoregressive_data(batch: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Collate function for batching autoregressive training data.
Args:
batch: List of sequential training pairs
Returns:
Batched autoregressive training data
"""
if not batch:
raise ValueError("Cannot collate empty batch")
# Separate different components
text_descriptions = [item['text_description'] for item in batch]
text_embeddings = [item.get('text_embedding') for item in batch]
steps = [item['step'] for item in batch]
total_steps = [item['total_steps'] for item in batch]
# Collate partial graphs
partial_graphs = [item['partial_graph'] for item in batch]
# Collate node features from partial graphs
all_x = []
all_edge_index = [[], []]
batch_idx = []
node_offset = 0
for i, graph in enumerate(partial_graphs):
# Node features
if graph['x']:
all_x.extend(graph['x'])
# Edge indices (offset by current node count)
edges = graph['edge_index']
if len(edges[0]) > 0:
for j in range(len(edges[0])):
all_edge_index[0].append(edges[0][j] + node_offset)
all_edge_index[1].append(edges[1][j] + node_offset)
# Batch indices for each node
num_nodes = graph['num_nodes']
batch_idx.extend([i] * num_nodes)
node_offset += num_nodes
# Target nodes and connections
target_nodes = [item['target_node'] for item in batch]
target_node_types = [node['node_type'] for node in target_nodes]
target_node_features = [node['features'] for node in target_nodes]
target_connections = [item['target_connections'] for item in batch]
return {
'text_descriptions': text_descriptions,
'text_embeddings': text_embeddings, # Can contain None values if not pre-computed
'partial_graphs': {
'x': all_x,
'edge_index': all_edge_index,
'batch': batch_idx,
'num_graphs': len(batch)
},
'target_node_types': target_node_types,
'target_node_features': target_node_features,
'target_connections': target_connections,
'steps': steps,
'total_steps': total_steps
}
class AutoregressiveDataLoader:
"""
DataLoader for autoregressive AST training data.
"""
def __init__(self, dataset: AutoregressiveASTDataset, batch_size: int = 8, shuffle: bool = True):
"""
Initialize the AutoregressiveDataLoader.
Args:
dataset: AutoregressiveASTDataset to load from
batch_size: Number of sequential pairs per batch
shuffle: Whether to shuffle the data
"""
self.dataset = dataset
self.batch_size = batch_size
self.shuffle = shuffle
# Create indices
self.indices = list(range(len(dataset)))
if shuffle:
random.shuffle(self.indices)
def __len__(self) -> int:
"""Return number of batches."""
return (len(self.dataset) + self.batch_size - 1) // self.batch_size
def __iter__(self):
"""Iterate over batches."""
for i in range(0, len(self.dataset), self.batch_size):
batch_indices = self.indices[i:i + self.batch_size]
batch = [self.dataset[idx] for idx in batch_indices]
yield collate_autoregressive_data(batch)
def create_autoregressive_data_loader(paired_data_path: str, batch_size: int = 8, shuffle: bool = True,
max_sequence_length: int = 50, seed: Optional[int] = None,
precomputed_embeddings_path: Optional[str] = None,
num_workers: Optional[int] = None, pin_memory: bool = True):
"""
Create data loader for autoregressive AST training.
Args:
paired_data_path: Path to paired_data.jsonl file
batch_size: Number of sequential pairs per batch
shuffle: Whether to shuffle the data
max_sequence_length: Maximum sequence length per method
seed: Random seed for consistent description sampling
precomputed_embeddings_path: Path to pre-computed text embeddings file
num_workers: Number of worker processes for data loading (defaults to CPU count)
pin_memory: Whether to use pinned memory for faster GPU transfer
Returns:
DataLoader instance (PyTorch DataLoader if available, otherwise AutoregressiveDataLoader)
"""
dataset = AutoregressiveASTDataset(
paired_data_path,
max_sequence_length=max_sequence_length,
seed=seed,
precomputed_embeddings_path=precomputed_embeddings_path
)
# Use PyTorch DataLoader if available for better performance
if TORCH_AVAILABLE:
import os
if num_workers is None:
num_workers = os.cpu_count()
try:
from torch.utils.data import DataLoader
# Create PyTorch DataLoader with optimizations
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=pin_memory and torch.cuda.is_available(),
collate_fn=collate_autoregressive_data,
persistent_workers=num_workers > 0, # Keep workers alive between epochs
prefetch_factor=2 if num_workers > 0 else 2 # Prefetch batches
)
print(f"✅ Using optimized PyTorch DataLoader with {num_workers} workers, pin_memory={pin_memory and torch.cuda.is_available()}")
return loader
except Exception as e:
print(f"⚠️ Warning: Could not create PyTorch DataLoader ({e}), falling back to custom loader")
# Fallback to custom loader
loader = AutoregressiveDataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
print("ℹ️ Using custom AutoregressiveDataLoader")
return loader
class HierarchicalASTDataset(RubyASTDataset):
"""
Dataset for loading a single level of a hierarchical AST dataset.
This class inherits from RubyASTDataset to reuse the same AST-to-graph
conversion logic. It is used to load one of the `_level_N.jsonl` files.
"""
def __init__(self, jsonl_path: str, transform=None):
"""
Initialize the dataset for a specific AST level.
Args:
jsonl_path: Path to the JSONL file for a specific level.
transform: Optional transform to apply to each sample.
"""
super().__init__(jsonl_path, transform)
def create_hierarchical_data_loader(dataset_path: str, batch_size: int, shuffle: bool, num_workers: Optional[int] = None):
"""
Creates a data loader for a specific level of the hierarchical dataset.
Args:
dataset_path: The full path to the `_level_N.jsonl` file.
batch_size: The batch size for the data loader.
shuffle: Whether to shuffle the data.
num_workers: The number of worker processes for data loading.
Returns:
A DataLoader instance for the specified dataset level.
"""
dataset = HierarchicalASTDataset(dataset_path)
if TORCH_AVAILABLE:
try:
from torch_geometric.loader import DataLoader
if num_workers is None:
num_workers = os.cpu_count()
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=torch.cuda.is_available(),
persistent_workers=num_workers > 0,
collate_fn=collate_graphs # Reusing the existing collate function
)
logging.info(f"Created PyG DataLoader for {dataset_path} with {num_workers} workers.")
return loader
except ImportError:
logging.warning("PyTorch Geometric not found. Falling back to SimpleDataLoader.")
# Fallback to SimpleDataLoader
return SimpleDataLoader(dataset, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_graphs)
class HierarchicalPairedDataset(PairedDataset):
"""
Dataset for loading a single level of a hierarchical dataset with paired text.
This class inherits from PairedDataset to reuse the same logic for
processing graph data and randomly sampling text descriptions.
"""
def __init__(self, jsonl_path: str, transform=None, seed: Optional[int] = None, limit: Optional[int] = None):
"""
Initialize the dataset for a specific AST level.
Args:
jsonl_path: Path to the JSONL file for a specific level (e.g., train_paired_data_level_0.jsonl).
transform: Optional transform to apply to each sample.
seed: Random seed for consistent description sampling.
limit: Optional maximum number of samples to load.
"""
super().__init__(jsonl_path, transform, seed, limit)
def create_hierarchical_paired_data_loader(dataset_path: str, batch_size: int, shuffle: bool, num_workers: Optional[int] = None, limit: Optional[int] = None):
"""
Creates a data loader for a specific level of the hierarchical paired dataset.
Args:
dataset_path: The full path to the `_level_N.jsonl` file.
batch_size: The batch size for the data loader.
shuffle: Whether to shuffle the data.
num_workers: The number of worker processes for data loading.
limit: Optional maximum number of samples to load.
Returns:
A DataLoader instance for the specified dataset level.
"""
dataset = HierarchicalPairedDataset(dataset_path, limit=limit)
if TORCH_AVAILABLE:
try:
from torch.utils.data import DataLoader
if num_workers is None:
num_workers = 0 # Disabled for now to prevent file handle exhaustion
loader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=torch.cuda.is_available(),
persistent_workers=num_workers > 0,
collate_fn=collate_paired_data
)
logging.info(f"Created PyTorch DataLoader for {dataset_path} with {num_workers} workers.")
return loader
except (ImportError, Exception) as e:
logging.warning(f"PyTorch DataLoader creation failed ({e}). Falling back to PairedDataLoader.")
# Fallback to custom PairedDataLoader
return PairedDataLoader(dataset, batch_size=batch_size, shuffle=shuffle) |