Edge-LLM accuracy debugging

Start an accuracy investigation with edge-llm-few-layer-test. It compares the reduced PyTorch and Edge-LLM executions and identifies the failing layer, logits, cache, or recurrent state. Do not weaken its gate to make a mismatch pass.

If the few-layer test fails and the first incorrect operation is still unknown, use TensorRT debug-tensor instrumentation to capture intermediate values from that layer. The full-intermediate guide and assets/debugTensorDumper.{h,cpp} describe the diagnostic path; the standalone comparator can compare a compatible dump with model-provider PyTorch captures. Match checkpoint bytes, inputs, preprocessing, dtype, and prefill/decode step before comparing values. An exported ONNX graph is not the golden reference.

The source-injection guide provides a way to instrument the builder and runtime with IDebugListener. Check the bundled patch against the current code before applying it in an isolated worktree; the technique is active, but the patch is revision-specific. Do not reset unrelated changes. After fixing the first incorrect operation, rerun the few-layer test and the original end-to-end workload.