Boosting Video Generation Inference: Full Graph Compilation with torch.compile

This article examines the challenges of optimizing video generation model inference, moving from operator-level tweaks to full-graph compilation using torch.compile, and details systematic strategies to eliminate Graph Breaks, handle dynamic shapes, KV-Cache indexing, and Python-side caches, achieving a 47.6% speedup on a 14B model without accuracy loss.

AIInference AccelerationVideo Generation

0 likes · 14 min read

Boosting Video Generation Inference: Full Graph Compilation with torch.compile

DataFunSummit

Jan 13, 2025 · Artificial Intelligence

Deep Learning Approaches for Solving Graph Optimization Problems

This article reviews the use of deep learning, including supervised, reinforcement, and self‑supervised paradigms, to address graph optimization problems such as facility location and balanced graph partitioning, discusses existing research challenges, presents a three‑stage self‑supervised model with graph contrastive pre‑training, and evaluates its performance on synthetic and real‑world datasets.

Deep Learningcombinatorial optimizationexperimental evaluation

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Deep Learning Approaches for Solving Graph Optimization Problems

Alibaba Cloud Developer

Jul 2, 2019 · Artificial Intelligence

How MNN Powers Mobile AI: Inside Alibaba’s Open‑Source Inference Engine

Alibaba’s MNN (Mobile Neural Network) engine, now open‑sourced on GitHub, showcases how a lightweight, end‑side deep‑learning inference framework tackles fragmentation, optimizes model conversion, scheduling, and execution across diverse devices, delivering significant performance gains for mobile and IoT AI applications.

Inference EngineMNNMobile AI

0 likes · 15 min read

How MNN Powers Mobile AI: Inside Alibaba’s Open‑Source Inference Engine