Principle:Ggml org Llama cpp Tensor Debugging
| Knowledge Sources | |
|---|---|
| Domains | Debugging, Tensor |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Tensor Debugging is the principle of inspecting and validating intermediate tensor values during model inference for correctness verification.
Description
This principle covers the debugging infrastructure that allows developers to inspect tensor values at intermediate points during model forward passes. It provides mechanisms to dump tensor data, compute summary statistics (mean, variance, min, max, NaN detection), compare tensors between different implementations, and visualize tensor contents. This is essential for diagnosing numerical issues, verifying model conversions, and debugging new architecture implementations.
Usage
Apply this principle when debugging inference correctness issues, verifying that a new model architecture produces expected intermediate values, or when tracking down numerical instability (NaN/Inf values) in the computation graph.
Theoretical Basis
Tensor debugging operates by inserting inspection hooks into the computation graph evaluation pipeline. These hooks can capture tensor data after specific operations and compute diagnostic metrics. Key diagnostic checks include NaN/Inf detection (indicating numerical instability), statistical summaries (to verify values are in expected ranges), and element-wise comparison between reference and test implementations (to verify conversion or optimization correctness). The debugging example demonstrates practical usage patterns for these diagnostic capabilities.