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PyTorch Expert Exchange β Multi-Modal Tabular Deep Learning with PyTorch Frame
0:05 Introduction: Akihiro Nitta, a software engineer at Kumo.ai and core contributor to PyTorch Frame and PyTorch Geometric, presents PyTorch Frame.
0:48 Presentation Overview: The presentation covers challenges in modern tabular learning, the creation and features of PyTorch Frame, and its extension to multi-table use cases.
1:18 Tabular Learning Challenges: Traditional tree-based models struggle with modern tabular data containing text and images, and integrating them with deep learning models is not straightforward.
3:19 PyTorch Frame Introduction: PyTorch Frame is a modular framework for tabular learning that supports multi-modal features (numerical, categorical, timestamps, images, text, etc.).
3:45 Flexible design: It's designed to be flexible, capable of expressing existing models like TabNet, SAINT, FT-Transformer. It has 7 or 8 built in models.
4:22 Foundation Model & GNN Integration: PyTorch Frame integrates with foundation models (e.g., OpenAI's API for text embeddings) and Graph Neural Networks (GNNs).
4:55 Best Paper Award: PyTorch Frame received the best paper award at a NeurIPS workshop.
5:15 Data Materialization: The first step in using PyTorch Frame is data materialization, which encodes raw data frames into tensors and computes statistics for handling missing values.
6:17 TensorFrame: Data materialization produces a dictionary of tensors ("TensorFrame"), mapping column names to tensors, including specialized "multi-tensors" for sparse features.
6:53 Semantic Types: PyTorch Frame uses "semantic types" to represent different column types (numerical, categorical, text, image, etc.) and handles their encoding into tensors.
7:50 Text Handling: Two ways to handle text: "text_embedded" (pre-encode text during materialization) and "text_tokenized" (tokenize during materialization, fine-tune embeddings during model training).
10:23 MultiNestedTensor: A data structure to efficiently store variable-length features (like multi-categorical or tokenized text), similar to PyTorch's jagged tensors.
12:08 Model Architecture: The modeling part is divided into three stages: encoding (embed each column independently), column-wise interaction (mix column representations), and decoding (summarize column embeddings into final row embeddings).
13:53 Code Example: A code example demonstrates the three-stage model architecture, highlighting its flexibility and similarity to standard PyTorch modules.
14:54 Torch.Compile Appreciation: Use of torch.compile and distributed data parallel (DDP) drastically improve performance.
16:10 Multi-Table Scenarios: Real-world data often involves multiple related tables (e.g., users, sales, products in e-commerce).
16:47 Relational Data as a Graph: Relational data can be represented as a graph, where nodes are rows in tables, and edges represent primary/foreign key relationships.
18:51 GNNs for Relational Data: Graph Neural Networks (GNNs) are a natural fit for relational data, avoiding manual feature engineering (joining tables) and preserving information.
20:30 GNN Workflow: The GNN workflow involves sampling subgraphs, computing node embeddings, and performing message passing across tables using PyTorch Frame and PyTorch Geometric.
21:21 Code Example (GNN): Another code example showes how the model using PyTorch Geometric integrates with PyTorch Frame.
22:11 Heterogeneous Graphs: PyTorch Geometric's to_hetero function, using torch.fx, simplifies converting homogeneous graphs to heterogeneous graphs.
22:53 Summary, Next Steps: Use multimodel features, LLMs and GNN integration. Call to action: Run the examples, try the repository and contribute to the project.
23:35 Community and Roadmap: The project has a growing community. Future plans include adding more models/layers, integrating nestedtensor from PyTorch, and potentially using FBGEMM kernels.
27:11 Graphs and ANNs benchmarked against decision tree algorithms. Graphs and ANNs are performing great.
28:23 Motivation to make Pytorch Frame was to use the capabilities of GNNs on relational data, and to use rich embeddings from other columns such as text and image.