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With the recent and accelerated advances in machine learning (ML), machines can understand natural language, engage in conversations, draw images, create videos and more. Modern ML models are programmed and trained using ML programming frameworks, such as TensorFlow, JAX, PyTorch, among many others. These libraries provide high-level instructions to ML practitioners, such as linear algebra operations (e.g., matrix multiplication, convolution, etc.) and neural network layers (e.g., 2D convolution layers, transformer layers). Importantly, practitioners need not worry about how to make their models run efficiently on hardware because an ML framework will automatically optimize the user's model through an underlying compiler. The efficiency of the ML workload, thus, depends on how good the compiler is. A compiler typically relies on heuristics to solve complex optimization problems, often resulting in suboptimal performance.
In this blog post, we present exciting advancements in ML for ML. In particular, we show how we use ML to improve efficiency of ML workloads! Prior works, both internal and external, have shown that we can use ML to improve performance of ML programs by selecting better ML compiler decisions. Although there exist a few datasets for program performance prediction, they target small sub-programs, such as basic blocks or kernels. We introduce “TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs” (presented at NeurIPS 2023), which we recently released to fuel more research in ML for program optimization. We hosted a Kaggle competition on the dataset, which recently completed with 792 participants on 616 teams from 66 countries. Furthermore, in “Learning Large Graph Property Prediction via Graph Segment Training”, we cover a novel method to scale graph neural network (GNN) training to handle large programs represented as graphs. The technique both enables training arbitrarily large graphs on a device with limited memory capacity and improves generalization of the model.
In this blog post, we present exciting advancements in ML for ML. In particular, we show how we use ML to improve efficiency of ML workloads! Prior works, both internal and external, have shown that we can use ML to improve performance of ML programs by selecting better ML compiler decisions. Although there exist a few datasets for program performance prediction, they target small sub-programs, such as basic blocks or kernels. We introduce “TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs” (presented at NeurIPS 2023), which we recently released to fuel more research in ML for program optimization. We hosted a Kaggle competition on the dataset, which recently completed with 792 participants on 616 teams from 66 countries. Furthermore, in “Learning Large Graph Property Prediction via Graph Segment Training”, we cover a novel method to scale graph neural network (GNN) training to handle large programs represented as graphs. The technique both enables training arbitrarily large graphs on a device with limited memory capacity and improves generalization of the model.
Advancements in machine learning for machine learning
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