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import copy
from tqdm import tqdm
from abc import ABC, abstractmethod
from typing import override, Union
import torch
from torch.utils.data import Dataset, DataLoader
from torch_geometric.data import Data, Batch
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, DataCollatorWithPadding
from trl.trainer.sft_trainer import DataCollatorForLanguageModeling
from utils.json_utils import load_json, load_jsonl
class GraphChatDataset(Dataset):
"""A custom PyTorch Dataset to handle mixed PyTorch Geometric Data and text prompts.
It avoids PyArrow serialization issues by keeping everything in native Python lists.
"""
def __init__(self, data_list: list[dict]):
self.data_list = data_list
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
# Return a shallow copy of the dictionary.
# This is CRITICAL because `collate_fn` uses `.pop()`,
# which would otherwise mutate the underlying data and cause errors in the 2nd epoch.
return {k: v for k, v in self.data_list[idx].items()}
def map(self, fn, fn_kwargs=None, batched=False, remove_columns=None):
"""Mimics the Hugging Face dataset.map() interface for seamless integration."""
if fn_kwargs is None:
fn_kwargs = {}
new_data_list = []
for item in tqdm(self.data_list, desc="Mapping dataset"):
# Apply the tokenize_fn
new_item = fn(item, **fn_kwargs)
# Since tokenize_fn explicitly returns the exact dictionary structure needed,
# we can safely ignore `remove_columns` and just append the returned item.
new_data_list.append(new_item)
return GraphChatDataset(new_data_list)
# ==========================================
# Tokenization Strategies
# ==========================================
class ClsTokenizeStrategy:
def __init__(self, tokenizer: AutoTokenizer = None):
self.tokenizer = tokenizer
def __call__(self, example):
"""Tokenizes text into input_ids and attention_mask."""
return self.tokenizer(
example["funcs"],
truncation=True,
padding=False,
max_length=None,
)
class CompletionTokenizeStrategy:
def __init__(self, tokenizer: AutoTokenizer, max_length=None, data_formats: str="pyg_graph"):
self.tokenizer = tokenizer
self.max_length = max_length
# Route tokenization logic based on data_formats during initialization
if data_formats == "pyg_graph":
self._tokenize_fn = self._tokenize_pyg_graph
else:
self._tokenize_fn = self._tokenize_text_only
def __call__(self, example):
return self._tokenize_fn(example)
def _base_tokenize(self, example):
"""Tokenizes a single example into input_ids, attention_mask, and labels."""
prompt = example["prompts"]
completion = example["completions"]
# 1. Construct the "question only" message list -> to calculate prompt length
prompt_ids = self.tokenizer.apply_chat_template(
prompt,
tokenize=True,
add_generation_prompt=True,
truncation=False,
enable_thinking=False,
).input_ids
# 2. Construct the "full conversation" message list -> input_ids
input_ids = self.tokenizer.apply_chat_template(
prompt+completion,
tokenize=True,
truncation=False,
enable_thinking=False,
).input_ids
# 3. Generate labels and apply masking
labels = copy.deepcopy(input_ids)
prompt_len = len(prompt_ids)
# Set the labels of the prompt tokens to -100 to ignore them in loss computation
for i in range(len(labels)):
if i < prompt_len:
labels[i] = -100
# 4. Truncate if max_length is specified
if not self.max_length is None:
if len(input_ids) > self.max_length:
input_ids = input_ids[:self.max_length]
labels = labels[:self.max_length] # no need to shift, when feed to hugging face CausalLM, it will automatically shift the labels by one to the left internally
# 5. Create attention mask and position ids
attention_mask = [1] * len(input_ids)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels,
"indices": example["indices"],
}
def _tokenize_pyg_graph(self, example):
features = self._base_tokenize(example)
features["graphs"] = example["graphs"]
return features
def _tokenize_text_only(self, example):
return self._base_tokenize(example)
class PromptTokenizeStrategy:
def __init__(self, tokenizer: AutoTokenizer, answer_template: str, max_length=None, data_formats: str = "pyg_graph"):
self.tokenizer = tokenizer
self.answer_template = answer_template
self.max_length = max_length
# Route tokenization logic based on data_formats during initialization
if data_formats == "pyg_graph":
self._tokenize_fn = self._tokenize_pyg_graph
else:
self._tokenize_fn = self._tokenize_text_only
def __call__(self, example):
return self._tokenize_fn(example)
def _base_tokenize(self, example):
"""Tokenizes a single example into prompt_ids and prompt_attention_mask."""
prompt = example["prompts"]
# Construct the "question only" message list -> to calculate prompt length
prompt_ids = self.tokenizer.apply_chat_template(
prompt,
tokenize=True,
add_generation_prompt=True,
truncation=False,
enable_thinking=False,
).input_ids
# Create prompt_ids for constrained decoding
answer_ids = self.tokenizer(self.answer_template, add_special_tokens=False).input_ids
prompt_ids = prompt_ids + answer_ids
prompt_attention_mask = [1] * len(prompt_ids)
return {
"prompts": prompt_ids,
"prompt_attention_mask": prompt_attention_mask,
"indices": example["indices"],
}
def _tokenize_pyg_graph(self, example):
features = self._base_tokenize(example)
features["graphs"] = example["graphs"]
return features
def _tokenize_text_only(self, example):
return self._base_tokenize(example)
# ==========================================
# Collation Strategies
# ==========================================
class ClsDataCollator:
def __init__(self, tokenizer: AutoTokenizer = None, data_formats: str = "pyg_graph"):
# Route collation logic based on data_formats during initialization
if data_formats == "no_graph":
self.data_collator = DataCollatorWithPadding(
tokenizer=tokenizer,
padding="longest",
)
self._collate_fn = self._collate_text_only
else:
self._collate_fn = self._collate_pyg_graph
def __call__(self, batch):
return self._collate_fn(batch)
def _collate_text_only(self, batch):
"""Custom collate function to handle text and indices"""
indices = [item.pop("indices") for item in batch]
labels = [item.pop("labels") for item in batch]
# Collate input_ids and attention_mask with dynamic padding
batch = self.data_collator(batch)
# Put the indices and labels back into the batch
batch["indices"] = indices
batch["labels"] = torch.tensor(labels, dtype=torch.long)
return batch
def _collate_pyg_graph(self, batch):
"""Custom collate function to handle graphs and indices"""
# Extract fields to process them separately
indices = [item.pop("indices") for item in batch]
graphs = [item.pop("graphs") for item in batch]
labels = [item.pop("labels") for item in batch]
# Reassemble the batch
batch_dict = {
"indices": indices,
"labels": torch.tensor(labels, dtype=torch.long),
"graphs": Batch.from_data_list(graphs)
}
return batch_dict
class CompletionDataCollator:
def __init__(self, tokenizer: AutoTokenizer, data_formats: str = "pyg_graph"):
# DataCollatorForLanguageModeling uses right-padding only.
# During SFT, both `input_ids` and `labels` are fed directly to `model.forward()` to compute the loss.
# Since `model.forward()` does not automatically skip padding tokens when generating `position_ids`,
# it simply assigns them sequentially based on the entire sequence length.
# Therefore, right-padding is required to keep the `position_ids` of the valid text aligned across the batch.
self.data_collator = DataCollatorForLanguageModeling(
pad_token_id=tokenizer.pad_token_id,
padding_free=False,
pad_to_multiple_of=None,
return_tensors="pt",
)
# Route collation logic based on data_formats during initialization
if data_formats == "pyg_graph":
self._collate_fn = self._collate_pyg_graph
else:
self._collate_fn = self._collate_text_only
def __call__(self, example):
return self._collate_fn(example)
def _collate_pyg_graph(self, batch):
"""Custom collate function to handle mixed data types (indices, graphs, and texts)"""
# Extract non-standard fields to process them separately
indices = [item.pop("indices") for item in batch]
graphs = [item.pop("graphs") for item in batch]
# Process input_ids, attention_mask, and labels with DataCollatorForLanguageModeling as labels needs to be padded with -100
completions = self.data_collator(batch)
# Reassemble the batch
completions["indices"] = indices
completions["graphs"] = Batch.from_data_list(graphs)
return completions
def _collate_text_only(self, batch):
"""Custom collate function to handle mixed data types (indices and texts)"""
# Extract non-standard fields to process them separately
indices = [item.pop("indices") for item in batch]
# Process input_ids, attention_mask, and labels with DataCollatorForLanguageModeling as labels needs to be padded with -100
completions = self.data_collator(batch)
# Reassemble the batch
completions["indices"] = indices
return completions
class PromptDataCollator:
def __init__(self, tokenizer: AutoTokenizer, data_formats: str = "pyg_graph"):
# DataCollatorWithPadding pads the sequences based on the tokenizer settings.
# ---------------------------------------------------
# In `model.generate()`, padding tokens are automatically ignored when computing `position_ids`.
# This prevents `position_ids` from being misaligned by leading pad tokens during generation.
# Left-padding is strictly required for batched generation to ensure the causal LM always
# predicts the next token from the last valid (non-pad) token.
# ---------------------------------------------------
# If `model.forward()` is explicitly used to fetch next-token logits instead of `generate()`,
# left-padding MUST still be used. With right-padding, `logits[:, -1, :]` would incorrectly
# point to a padding token rather than the actual end of the text sequence.
# ---------------------------------------------------
# In our case, because we need to concat prefix and get next-token logits during inference.
# For simplicity, we use a batch size of 1 during testing, though batching is also supported.
self.data_collator = DataCollatorWithPadding(
tokenizer=tokenizer,
padding="longest"
)
# Route collation logic based on data_formats during initialization
if data_formats == "pyg_graph":
self._collate_fn = self._collate_pyg_graph
else:
self._collate_fn = self._collate_text_only
def __call__(self, example):
return self._collate_fn(example)
def _collate_pyg_graph(self, batch):
"""Custom collate function to handle mixed data types (indices, graphs, and texts)"""
# Extract non-standard fields to process them separately
indices = [item.pop("indices") for item in batch]
graphs = [item.pop("graphs") for item in batch]
# Process prompts and prompt_attention_mask with DataCollatorWithPadding
prompts = self.data_collator([{"input_ids": item.pop("prompts"), "attention_mask": item.pop("prompt_attention_mask")} for item in batch])
# Reassemble the batch
return {
"prompts": prompts["input_ids"],
"prompt_attention_mask": prompts["attention_mask"],
"indices": indices,
"graphs": Batch.from_data_list(graphs),
}
def _collate_text_only(self, batch):
"""Custom collate function to handle mixed data types (indices and texts)"""
# Extract non-standard fields to process them separately
indices = [item.pop("indices") for item in batch]
# Process prompts and prompt_attention_mask with DataCollatorWithPadding
prompts = self.data_collator([{"input_ids": item.pop("prompts"), "attention_mask": item.pop("prompt_attention_mask")} for item in batch])
# Reassemble the batch
return {
"prompts": prompts["input_ids"],
"prompt_attention_mask": prompts["attention_mask"],
"indices": indices,
}
# ==========================================
# Data Module
# ==========================================
class DataModule(ABC):
def __init__(
self,
config,
):
"""Initialize the DataModule for dataset processing.
Args:
config (BaseConfig): Configuration object containing parameters for data processing.
"""
# Initialize the config
self.config = config
# Initialize the tokenizer to filter samples by max tokens and convert the graph text into tokens
self.tokenizer = AutoTokenizer.from_pretrained(
config.func_filter_model_name,
padding_side="left",
)
# 1. initialize the dataset and directories
self._prepare_dataset_and_directory()
# 2. cut into subsets
self._generate_splits()
@abstractmethod
def _prepare_dataset_and_directory(self):
"""TODO: Prepare the raw dataset and create save directories."""
pass
def _generate_splits(self):
"""Generates train, validation, and test splits based on the valid_func_length_indices.
If the splits already exist, it loads them from the file.
If not, it generates the splits and saves them to the file.
"""
print("Loading train, val, test indices from file:", f"{self.indices_directory}/test_indices.json")
self.train_indices = load_json(f"{self.indices_directory}/train_indices.json")
self.val_indices = load_json(f"{self.indices_directory}/val_indices.json")
self.test_indices = load_json(f"{self.indices_directory}/test_indices.json")
def graph_to_context(self, graph: dict) -> tuple[str, str]:
"""
Convert node and edge information from a graph as context.
Args:
graph (Data): A PyTorch Geometric Data object representing the graph.
Returns:
tuple[str, str]: Node and edge information in string format.
"""
node_info = "Node ID\t Node Type\t CODE\n"
for i, (label, code) in enumerate(zip(graph.node_label, graph.CODE)):
node_info += f"{i}\t {label}\t {code}\n"
edge_info = "Source\t Target\t Edge Type\n"
for i in range(graph.edge_index.size(1)):
source = graph.edge_index[0, i].item()
target = graph.edge_index[1, i].item()
label = graph.edge_label[i]
edge_info += f"{source}\t {target}\t {label}\n"
return node_info, edge_info
class PrimeVulDataModule(DataModule):
@override
def _prepare_dataset_and_directory(self):
"""Prepares the PrimeVul dataset by loading cwe info and formatting into paired dicts.
Each pair consists of two consecutive dicts, where the first is vulnerable and second is safe.
"""
# Initialize the directories
self.indices_directory = f"datasets/processed/primevul/data_splits/"
# Load the dataset and graph
self.dataset = (
load_jsonl("datasets/raw/primevul/primevul_train.jsonl") +
load_jsonl("datasets/raw/primevul/primevul_valid.jsonl") +
load_jsonl("datasets/raw/primevul/primevul_test.jsonl")
)
# Conditionally load and process graphs
graphs = self._prepare_pyg_graphs()
# Add graph and index into the dataset
for i, item in enumerate(self.dataset):
item["index"] = i
if graphs is not None:
item["graph"] = graphs[i]
# Initialize the index to label mapping
self.index_to_label = {1: "true", 0: "false"} # 1 is vulnerable, 0 is secure
def _prepare_pyg_graphs(self) -> Union[list[Data], None]:
"""Prepares PyTorch Geometric Data objects from the raw graphs in JSONL format.
"""
if self.config.disable_graph_data:
return None
print("Loading raw graphs from JSONL files...")
graphs = (
load_jsonl("datasets/processed/primevul/data_graphs/cpg/processed/primevul_train.jsonl") +
load_jsonl("datasets/processed/primevul/data_graphs/cpg/processed/primevul_valid.jsonl") +
load_jsonl("datasets/processed/primevul/data_graphs/cpg/processed/primevul_test.jsonl")
)
# If the embedding model is configured, generate node and edge embeddings
embedding_enabled = getattr(self.config, "gnn_embedding_model_name", None) is not None
if embedding_enabled:
# Create embedding save directory if it doesn't exist
embedding_dir = "datasets/processed/primevul/data_graphs/cpg/embeddings"
os.makedirs(embedding_dir, exist_ok=True)
node_emb_path = os.path.join(embedding_dir, "node_embeddings.pt")
edge_map_path = os.path.join(embedding_dir, "edge_label_to_attr.pt")
# Check if the saved embeddings and mapping already exist
if os.path.exists(node_emb_path) and os.path.exists(edge_map_path):
print(f"Loading cached node embeddings from {node_emb_path}...")
node_embeddings = torch.load(node_emb_path, map_location="cpu", weights_only=True)
print(f"Loading cached edge_label_to_attr mapping from {edge_map_path}...")
edge_label_to_attr = torch.load(edge_map_path, map_location="cpu", weights_only=True)
else:
print("Cached embeddings not found. Initializing the embedding model...")
embedding_model = SentenceTransformer(
self.config.gnn_embedding_model_name,
trust_remote_code=True,
model_kwargs={"torch_dtype": self.config.gnn_dtype},
)
# Generate global edge mapping based on all graphs
print("Extracting unique edge labels across all graphs...")
unique_edge_labels = set()
for graph in graphs:
# Extract labels from the 'edges' list
unique_edge_labels.update([edge["label"] for edge in graph["edges"]])
unique_edge_labels = list(unique_edge_labels)
edge_label_to_attr = {}
if unique_edge_labels:
print("Generating embeddings for unique edge labels...")
edge_embs = embedding_model.encode(
unique_edge_labels,
truncate_dim=self.config.gnn_edge_dim,
convert_to_tensor=True,
show_progress_bar=False,
)
# Map string label to CPU tensor
edge_label_to_attr = {
label: emb.cpu() for label, emb in zip(unique_edge_labels, edge_embs)
}
# Generate node embeddings for all graphs
node_embeddings = []
for graph in tqdm(graphs, desc="Generating node embeddings"):
# Extract CODE and label from the 'nodes' list
node_inputs = [f"{node['CODE']} {node['label']}".strip() for node in graph["nodes"]]
# Generate node embeddings
x = embedding_model.encode(
node_inputs,
truncate_dim=self.config.gnn_input_size,
convert_to_tensor=True,
show_progress_bar=False,
)
node_embeddings.append(x.cpu())
# Save the generated node embeddings and edge mapping
print("Saving generated node embeddings and edge mapping to disk...")
torch.save(node_embeddings, node_emb_path)
torch.save(edge_label_to_attr, edge_map_path)
# Inject embeddings and convert directly to PyG Data objects
pyg_graphs = []
for i, graph in enumerate(tqdm(graphs, desc="Converting to PyG Data")):
# Map original node IDs to continuous indices (0, 1, 2...) as required by PyG
id_to_idx = {node["id"]: idx for idx, node in enumerate(graph["nodes"])}
# Construct the PyG edge_index tensor with shape [2, num_edges]
src = [id_to_idx[edge["source"]] for edge in graph["edges"]]
dst = [id_to_idx[edge["target"]] for edge in graph["edges"]]
edge_index = torch.tensor([src, dst], dtype=torch.long)
# Basic PyG attributes
data_kwargs = {
"edge_index": edge_index,
"CODE": [node["CODE"] for node in graph["nodes"]],
"node_label": [node["label"] for node in graph["nodes"]],
"edge_label": [edge["label"] for edge in graph["edges"]],
}
# Add embeddings only when enabled
if embedding_enabled:
data_kwargs["x"] = node_embeddings[i][:, :self.config.gnn_input_size]
edge_attr = torch.stack([edge_label_to_attr[edge["label"]] for edge in graph["edges"]])
data_kwargs["edge_attr"] = edge_attr[:, :self.config.gnn_edge_dim]
# Construct the PyG Data object
pyg_graphs.append(Data(**data_kwargs))
return pyg_graphs
def get_cls_dataset(
self,
indices: list,
data_formats: str = "no_graph",
):
"""Returns a dataset based on the provided indices and data type.
Args:
indices (list): List of indices to select from the dataset.
data_formats (str): Data formulation mode. Options:
- "no_graph": Uses only func (no graph data).
- "pyg_graph": Returns PyG graph objects.
- "text_graph": Returns graph context (text) and func.
Returns:
dataset (List): A list containing the selected items and their attributes.
"""
# Define extraction functions to handle different data extraction logic
if data_formats == "no_graph":
def extract_fn(item):
return {"funcs": item["func"]}
elif data_formats == "pyg_graph":
def extract_fn(item):
return {"graphs": item["graph"]}
elif data_formats == "text_graph":
def extract_fn(item):
node_info, edge_info = self.graph_to_context(item["graph"])
return {
"node_info": node_info,
"edge_info": edge_info,
"funcs": item["func"],
}
dataset = []
for index in tqdm(indices):
item = self.dataset[index]
# Assemble common fields
data_dict = {
"indices": item["index"],
"labels": item["target"],
}
# Assemble specific fields
data_dict.update(extract_fn(item))
dataset.append(data_dict)
return dataset
def get_chat_dataset(
self,
indices: list,
question_template: str,
answer_template: str,
data_formats: str = "pyg_graph",
):
"""Prepares a Dataset object in the format of prompts and completions for the given indices.
Args:
indices (list): List of indices to select from the dataset.
question_template (str): The template for the question prompt.
answer_template (str): The template for the answer prompt.
data_formats (str): Data formulation mode. Options:
- "no_graph": Uses only func in the prompt (no graph data).
- "pyg_graph": Returns PyG graph objects + func in the prompt.
- "text_graph": Uses graph context (text) + func in the prompt.
Returns:
dataset (Dataset): A list containing the selected items and their attributes.
"""
# Define extraction functions to handle different data extraction logic
if data_formats == "text_graph":
def extract_fn(item):
return {
"prompts": [{
"content": question_template.format(
func=item["func"],
graph=self.graph_to_context(item["graph"]),
),
"role": "user",
}]
}
elif data_formats == "pyg_graph":
def extract_fn(item):
return {
"graphs": item["graph"],
"prompts": [{
"content": question_template.format(
func=item["func"],
),
"role": "user",
}]
}
elif data_formats == "no_graph":
def extract_fn(item):
return {
"prompts": [{
"content": question_template.format(
func=item["func"],
),
"role": "user",
}]
}
dataset = []
for index in tqdm(indices):
item = self.dataset[index]
# Assemble common fields
data_dict = {
"indices": item["index"], # used for result logging and loading, this needs to be a string.
"completions": [
{
"content": f"{answer_template}{self.index_to_label[item['target']]}",
"role": "assistant",
},
]
}
# Execute the externally bound function
data_dict.update(extract_fn(item))
dataset.append(data_dict)
return dataset
def get_dataloader(
self,
task_types: str,
indices: list,
batch_size: int,
tokenizer: AutoTokenizer = None,
data_formats: str = "pyg_graph",
question_template: str = None,
answer_template: str = None,
):
"""Returns a unified DataLoader based on the provided task type and indices.
Args:
task_types (str): The specific task formulation. Options:
- "cls": Classification task format.
- "completion": Completion-only format for chat models.
- "prompt": Prompt-only format for chat models.
indices (List): List of indices to select from the dataset.
batch_size (int): The batch size for the DataLoader.
tokenizer (AutoTokenizer, optional): The tokenizer to use. Required for text processing.
data_formats (str): Data formulation mode. Options:
- "no_graph": Uses text data only.
- "pyg_graph": Uses PyTorch Geometric graph objects.
- "text_graph": Uses text-based graph context (applicable for chat tasks).
question_template (str, optional): The template for the question prompt (used in chat tasks).
answer_template (str, optional): The template for the answer prompt (used in chat tasks).
Returns:
dataloader (DataLoader): A unified PyTorch DataLoader object configured for the specified task.
"""
# Define the strategy mapping for different task types
strategy_map = {
"cls": {
"dataset_fn": lambda: self.get_cls_dataset(indices, data_formats),
"tokenizer_cls": ClsTokenizeStrategy if data_formats == "no_graph" else None,
"collator_cls": ClsDataCollator,
"remove_cols": ["funcs"] if data_formats == "no_graph" else [],
},
"completion": {
"dataset_fn": lambda: self.get_chat_dataset(indices, question_template, answer_template, data_formats),
"tokenizer_cls": CompletionTokenizeStrategy,
"collator_cls": CompletionDataCollator,
"remove_cols": ["prompts", "completions"],
},
"prompt": {
"dataset_fn": lambda: self.get_chat_dataset(indices, question_template, answer_template, data_formats),
"tokenizer_cls": PromptTokenizeStrategy,
"collator_cls": PromptDataCollator,
"remove_cols": ["prompts", "completions"],
}
}
if task_types not in strategy_map:
raise ValueError(f"Invalid task_types: '{task_types}'. Choose from {list(strategy_map.keys())}")
cfg = strategy_map[task_types]
# Retrieve and wrap the dataset
dataset = GraphChatDataset(cfg["dataset_fn"]())
# Apply tokenization conditionally (skips mapping for PyG graphs in cls tasks)
if cfg["tokenizer_cls"] is not None:
# Prepare arguments specifically required by the tokenization strategies
tokenize_kwargs = {"tokenizer": tokenizer, "data_formats": data_formats}
if task_types in ["completion", "prompt"]:
tokenize_kwargs["max_length"] = None
if task_types == "prompt":
tokenize_kwargs["answer_template"] = answer_template
# Instantiate the strategy and map it to the dataset
tokenize_fn = cfg["tokenizer_cls"](**tokenize_kwargs)
dataset = dataset.map(
tokenize_fn,
batched=False, # Process sample by sample due to complex tokenization logic
remove_columns=cfg["remove_cols"],
)
# Instantiate the data collator and generate the DataLoader
collate_fn = cfg["collator_cls"](
tokenizer=tokenizer,
data_formats=data_formats,
)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False, # no shuffle as train_test_split already handles it
collate_fn=collate_fn,
pin_memory=True,
)
return dataloader
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