Design and benchmark optimized inference pipelines
Overview
This question evaluates knowledge of ML system design and model inference optimization, specifically familiarity with PyTorch's compilation stack (TorchDynamo, TorchInductor and external backends), common acceleration techniques such as quantization, operator fusion, CUDA graphs, batching and parallelism, and the competency to design fair, reproducible performance benchmarks. It is commonly asked to assess reasoning about performance trade-offs, measurement methodology and reproducibility when optimizing latency, throughput, GPU/SM utilization and memory, and it tests both conceptual understanding of compilation and optimization strategies and practical application in benchmark design and reporting.
Accelerating PyTorch Inference: TorchDynamo, Techniques, and Benchmark Design
Context
You are asked to explain how PyTorch's compilation stack accelerates inference and to design a fair, reproducible benchmark for measuring improvements over a vanilla PyTorch baseline.
Tasks
A) TorchDynamo (aka PyTorch Dynamo) for Inference
Describe:
- What TorchDynamo does for accelerating inference.
- How it captures and compiles graphs (graph breaks, guards, shape specialization).
- How it relates to TorchInductor and to external backends (e.g., TensorRT).
B) Techniques to Speed Up Inference (briefly list and define)
Include, at minimum:
- Data/model/pipeline parallelism
- Effective batching
- Operator fusion
- Quantization
- Kernel autotuning
- CUDA Graphs
- Overlapping compute and data transfer (pinned memory, streams)
- Sparsity
- Caching (e.g., KV-cache, allocator)
- Graph-level compilers
C) Design a Fair Inference Benchmark
Specify:
- Metrics: latency (p50/p95/p99), throughput, GPU/SM utilization, memory.
- Test setup: GPU model, software versions, precision, batch size, sequence length, warmup and iteration counts, concurrency.
- Baselines: vanilla PyTorch eager.
- Reporting: absolute values and percentage improvements.
Solution
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