Training large language models (LLMs) efficiently while preserving model quality poses significant challenges, particularly with subbyte precision supported by state-of-the-art GPUs. Current mixed-precision training approaches either apply uniform precision to all GEMM operations or rely on heuristic-based methods that fail to generalize during training, leading to suboptimal convergence and instability.

This paper introduces two extensions to the popular PyTorch machine learning framework, TorchDynamo and TorchInductor, which implement the torch.compile feature released in PyTorch 2. TorchDynamo is a Python-level just-in-time (JIT) compiler that enables graph compilation in PyTorch programs without sacrificing the flexibility of Python. It achieves this by dynamically modifying Python bytecode before execution and extracting sequences of PyTorch operations into an FX graph, which is then JIT compiled using one of many extensible backends. TorchInductor is the default compiler backend for TorchDynamo, which translates PyTorch programs into OpenAI's Triton for GPUs and C++ for CPUs. Results show that TorchDynamo is able to capture graphs more robustly than prior approaches while adding minimal overhead, and TorchInductor is able to provide a 2.27× inference and 1.41× training geometric mean speedup on an NVIDIA A100 GPU across 180+ real-world models, which outperforms six other compilers. These extensions provide a new way to apply optimizations through compilers in eager mode frameworks like PyTorch.

Counting the number of small subgraphs, called motifs, is a fundamental problem in social network analysis and graph mining. Many real-world networks are directed and temporal, where edges have timestamps. Motif counting in directed, temporal graphs is especially challenging because there are a plethora of different kinds of patterns. Temporal motif counts reveal much richer information and there is a need for scalable algorithms for motif counting. A major challenge in counting is that there can be trillions of temporal motif matches even with a graph with only millions of vertices. Both the motifs and the input graphs can have multiple edges between two vertices, leading to a combinatorial explosion problem. It is not feasible for state-of-the-art algorithms to exactly count temporal motifs involving just four vertices with trillions of matches. We design an algorithm, TEACUPS, that addresses this problem using a novel technique of temporal path sampling. We combine a path sampling method with carefully designed temporal data structures, to propose an efficient approximate algorithm for temporal motif counting. TEACUPS is an unbiased estimator with provable concentration behavior, which can be used to bound the estimation error. For a Bitcoin graph with hundreds of millions of edges, TEACUPS runs in less than 1 minute, while the exact counting algorithm takes more than a day. We empirically demonstrate the accuracy of TEACUPS on large datasets, showing an average of 30 x speedup (up to 2000 x speedup) compared to existing GPU-based exact counting methods while preserving high count estimation accuracy.

Convolutional Neural Networks (CNNs) have demonstrated remarkable performance across a wide range of machine learning tasks. However, the high accuracy usually comes at the cost of substantial computation and energy consumption, making it difficult to be deployed on mobile and embedded devices. In CNNs, the compute-intensive convolutional layers are usually followed by a ReLU activation layer, which clamps negative outputs to zeros, resulting in large activation sparsity. By exploiting such sparsity in CNN models, we propose a software-hardware co-design BitSET, that aggressively saves energy during CNN inference. The bit-serial BitSET accelerator adopts a prediction-based bit-level early termination technique that terminates the ineffectual computation of negative outputs early. To assist the algorithm, we propose a novel weight encoding that allows more accurate predictions with fewer bits. BitSET leverages the bit-level computation reduction both in the predictive early termination algorithm and in the non-predictive, energy-efficient bit-serial architecture. Compared to UNPU, an energy-efficient bit-serial CNN accelerator, BitSET yields an average 1.5× speedup and 1.4× energy efficiency improvement with no accuracy loss due to a 48% reduction in bit-level computations. Relaxing the allowed accuracy loss to 1% increases the gains to an average of 1.6× speedup and 1.4× energy efficiency improvement.