Deep Learning with Dynamic Computation Graphs (1702.02181v2)

Abstract: Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynamic batching, which not only batches together operations between different input graphs of dissimilar shape, but also between different nodes within a single input graph. The technique allows us to create static graphs, using popular libraries, that emulate dynamic computation graphs of arbitrary shape and size. We further present a high-level library of compositional blocks that simplifies the creation of dynamic graph models. Using the library, we demonstrate concise and batch-wise parallel implementations for a variety of models from the literature.

• The paper introduces dynamic batching, a breakthrough that emulates dynamic computation graph processing within static deep learning frameworks.
• It organizes graph nodes by depth and batches operations, effectively reducing kernel invocation overhead and optimizing GPU performance.
• The development of TensorFlow Fold in the paper simplifies the creation and deployment of models that handle irregular, dynamic data structures.

Deep Learning with Dynamic Computation Graphs

This paper presents an innovative approach to address the challenges associated with neural networks that compute over dynamic graph structures. This method, termed "dynamic batching," facilitates the efficient processing of dynamic computation graphs (DCGs) in popular deep learning libraries like TensorFlow, which are traditionally optimized for static graphs. The authors propose a solution that circumvents the limitations of batch processing with DCGs by allowing these libraries to emulate dynamic graph computations through static data-flow graphs.

Core Contributions

The primary contribution of the paper is the introduction of dynamic batching, a technique that efficiently batches operations across inputs with varying graph structures. This is significant because DCGs are prevalent in domains such as natural language processing, where input data may naturally form complex structures like parse trees, and cheminformatics, where molecular graphs are common.

1. Dynamic Batching Algorithm:
   • The algorithm organizes computation nodes by their depth and batches operations occurring at the same depth, inserting additional operations for data movement, such as concatenation and gathering. This structure enables the static emulation of DCGs.
2. TensorFlow Fold Library:
   • A high-level library called TensorFlow Fold is developed, providing a set of compositional blocks for constructing and deploying models utilizing DCGs. This library abstracts the complexities of manual batching, allowing neural network models to be implemented more concisely and flexibly.
3. Experimental Validation:
   • The paper includes a rigorous benchmark analysis comparing dynamic vs. manual batching. The results indicate significant speedups using dynamic batching, particularly when applied to deep learning models on GPUs, with over 120x improvements observed in certain test scenarios.

Technical Insights and Implications

Dynamic batching addresses the inefficiencies typically associated with neural models working on inputs of varying sizes and topologies. By minimizing kernel invocation overhead and exploiting within-tree batching, this method enables more efficient GPU utilization and supports the scale-up of batch sizes without incurring overhead typically associated with DCGs.

The approach resolves some existing constraints in libraries like TensorFlow, which enforce static data-flow graphs. By allowing variable input shapes, TensorFlow Fold provides a pivotal step towards flexible model design, promoting innovation in areas where data naturally exhibits structural variability.

Future Directions

While the paper establishes a robust foundation for working with DCGs, several avenues for further research are apparent:

• Enhancing Model Architectures: Further exploration of architectures that leverage dynamic graph capabilities for tasks such as graph neural networks or attention mechanisms can build upon this work.
• Optimizing Data Movement Operations: Refinements to operations introduced for data movement, such as optimization of the gather and concat operations, could further enhance computational efficiency.
• Generality Across Frameworks: Expanding compatibility to other frameworks beyond TensorFlow could democratize access to these optimizations and propel broader adoption.

Conclusion

The introduction of dynamic batching represents a substantial advancement in deploying DCGs effectively within static deep learning frameworks. The developments presented in this paper pave the way for streamlined, efficient processing of irregular data structures in neural network applications, facilitating advancements in AI domains such as NLP and cheminformatics. TensorFlow Fold, with its combinatorial approach to model construction, further encapsulates the shift towards flexible yet efficient deep learning frameworks, potentially sparking innovative research methodologies and outcomes.