Lab 10 - End-to-End Inference Trace

Overview

Lab 10: End-to-End Inference Trace

Annotated code reading lab. Running code is optional.

Related handbook section

End-to-End Inference Trace

This lab maps directly to the handbook section. Read the related handbook section first, then use the lab page and starter file to connect the concept to concrete variables, shapes, APIs, and interview-ready explanations.

Open handbook section Open starter file

Concept Goal

End-to-End Inference Trace

Read the whole pipeline as a trace from config to output media.

Mental Model

Mechanism to keep in mind

`load` creates modules.
`prepare` creates conditions and latents.
`loop` updates latents.
`decode` emits output.

Annotated Code Preview

Starter preview

Excerpt from code/lab-10-end-to-end-inference-trace/inference_trace.md. The linked starter file is the source of truth.

Open starter file

Inference systems separate prefill, decode, batching, KV Cache capacity, and tail latency. The useful explanation names the bottleneck first, then connects it to memory bandwidth, scheduling, and measured serving metrics.

Line-by-line Explanation

What each block is doing

Setup / contract: `load` creates modules.
Main transition: `prepare` creates conditions and latents.
Interview hook: `loop` updates latents.

What to Notice

Reading checkpoints

A trace exposes hidden wrappers.
Shape/device/dtype should be tracked at every boundary.
This trace is a pedagogical abstraction; exact pipeline classes, config fields, acceleration flags and supported pipelines are version-sensitive.

Common Misunderstandings

What this lab prevents

Do not explain only the model family name.
Do not skip scheduler and VAE when tracing output.

Interview Explanation

How to say it out loud

Read the whole pipeline as a trace from config to output media. Then explain the code by naming the state being transformed, the axis or shape that matters, and the tradeoff that would appear in a real system.

External intuition notes

Additional intuition

Use official docs and papers for API behavior and factual claims; use blogs only to improve the mental picture.
If support matrices, performance behavior or backend choices are version-sensitive, check current docs before repeating them.
A strong interview answer names the state object, the shape or axis it changes, and the tradeoff it creates.

InfraLens