Add-Model Trace

Manual Invocation

Load this skill when /add-model Phase 6 component parity has failed and the root cause requires layer-by-layer divergence analysis. This skill is not auto-fired. The calling subagent (DiT, VAE, encoder, or generic port skill) loads it when its standard parity-debug loop hits a wall and cannot isolate the divergence from end-to-end tensor comparisons alone.

Do not load this skill for first-pass parity failures. Try weight-diff and end-to-end tensor comparison first. Load this skill only when those do not isolate the cause.

Goal

Find the first numerical divergence point between FastVideo's port and the official reference, layer by layer, by instrumenting both sides at matching tensor boundaries. The investigation must leave zero source residue in production code when it closes.

When To Run

After a component parity test FAILS at a bf16-noise-realistic tolerance AND the calling subagent's first-pass debug (weight-diff, end-to-end tensor compare) does not isolate the cause.

Required inputs before starting:

• A working FastVideo loader for the component under investigation.
• A working official loader, typically via tests/local_tests/helpers/<family>_upstream.py::load_upstream_<component>.
• Shared deterministic test inputs (same tensors on both sides).
• The component parity test file path and its current failure output.

Primary Path: FastVideo Activation Trace

Use FastVideo's first-class activation trace before writing custom hooks: fastvideo/hooks/activation_trace.py, documented in docs/contributing/activation_trace.md.

Pipeline runs attach trace to the transformer during pipeline initialization. Component-only parity harnesses may call attach_activation_trace(model) from local test/debug code; do not add trace calls to production model code.

Prefix the failing parity command with a tight layer regex:

FASTVIDEO_TRACE_ACTIVATIONS=1 \
FASTVIDEO_TRACE_LAYERS="^block\.layers\.[0-9]+$" \
FASTVIDEO_TRACE_STATS="abs_mean,sum,max,shape" \
FASTVIDEO_TRACE_STEPS="0" \
FASTVIDEO_TRACE_OUTPUT="/tmp/opencode/fv_trace.jsonl" \
pytest tests/local_tests -k "parity" -v -s

Match the layer regex to the actual model.named_modules() names. Empty or broad regexes are expensive; prefer block-level names first, then narrow to submodules after the first divergent block is known.

Trace Compare Contract

One JSONL file per side. FastVideo output should use FASTVIDEO_TRACE_OUTPUT; the upstream harness should emit the same JSONL shape:

{"module":"block.layers.0","tensor":"out","step":0,"abs_mean":0.0123,"sum":1.0,"max":0.5,"shape":[1,16,32]}

Compare rows by (module, step, tensor). The first row whose shape, abs_mean, or max diverges beyond the component tolerance is the first broken boundary. Keep FASTVIDEO_TRACE_LAYERS, FASTVIDEO_TRACE_STATS, and FASTVIDEO_TRACE_STEPS identical between sides; if row order differs, sort or normalize before diffing.

Drill-Down Loop

Initial run: trace every top-level block (^block\.layers\.[0-9]+$ or the family's equivalent). Identify the first block index where abs_mean or max drifts beyond tolerance while earlier blocks match.

Drill run: tighten FASTVIDEO_TRACE_LAYERS to submodules inside the first divergent block: attention output, MLP projections, norm outputs, modality adapters, or other named boundaries exposed by named_modules().

Iterate: if the first divergent operation is a free function or tensor op not visible as an nn.Module, use the fallback instrumentation hierarchy below.

The loop ends when the first divergent submodule or operation is identified with a file:line citation in the official source.

Fallback Instrumentation Hierarchy

Use these only when activation trace cannot observe the needed boundary or statistic.

(1) Custom forward hooks

module.register_forward_hook(...) and register_forward_pre_hook(...). Always within try/finally with handle.remove(). Zero source residue.

(2) Runtime monkey-patch

module.attr = wrapped_func or cls.method = wrapped_method, restored via try/finally (save original first). Use for free functions and non-Module sites such as activation functions (swiglu, apply_rotary_emb).

(3) Source edits in FastVideo's own code

Only when (1) and (2) are insufficient. Track all edits within a single named git stash boundary OR a temporary branch. Run git diff before closing the investigation to confirm cleanup.

(4) Source edits in official repo source

Allowed only when hook and monkey-patch approaches cannot capture the site. For git-tracked or editable official clones, use git diff in the clone path to verify cleanup. For non-editable site-packages, back up the target file before editing and restore it before handoff.

Hypothesis Toggles

Use env-var-gated monkey-patches to A/B test suspect implementations without source edits. Pattern: <FAMILY>_DEBUG_PATCH_<HYPOTHESIS>=1.

Example from the magi-human investigation:

MAGI_DEBUG_PATCH_LINEAR=1

This patched PackedExpertLinear.forward to mirror upstream's _BF16ComputeLinear explicit-cast pattern, isolating a dtype-cast difference as the root cause.

Document all toggles in the script docstring. Each toggle must:

• save the original before patching;
• restore the original in a try/finally block;
• print a [debug] Patched <ClassName>.<method> line to stdout when active.

Cleanup Gate

The calling agent MUST report [cleanup-gate] PASS on all five items before handoff. Do not hand off with any item unresolved.

1. git diff in the FastVideo repo: empty. No stray prints, hooks, or monkey-patches in production code.
2. git diff in the official-repo clone (if used): empty. For non-editable site-packages installs: diff original.py original.py.trace-backup is empty OR pip install --force-reinstall <pkg> succeeded and the installed file matches the original.
3. git stash list: only the named investigation stash (or empty). No unnamed stashes left from this session.
4. No new untracked files outside /tmp/opencode/ (logs) and the existing debug script directory (tests/local_tests/transformers/ or equivalent).
5. mypy clean on any production files touched during the investigation.

Escape Hatches

Escalate to the calling bucket skill when:

• A forward hook on an official module raises because of a custom forward signature or varlen handler args that the hook closure cannot satisfy. The bucket skill has component-specific knowledge to work around this.
• The first divergent layer is block[0], meaning the divergence is in the adapter, modality dispatcher, coordinate embedding, or packing step before any block runs. Check those sites first; the bug is not in attention or MLP.
• Per-block drift is never zero anywhere across all blocks. This usually means the inputs are not bit-identical between sides. Verify with a state-dict compare (weight-diff script) AND confirm the input tensors are the same object or have identical values before the forward call.

Handoff

Return to the calling subagent with:

• FastVideo trace JSONL path and upstream trace JSONL path.
• Trace settings used: FASTVIDEO_TRACE_LAYERS, FASTVIDEO_TRACE_STATS, and FASTVIDEO_TRACE_STEPS.
• The first divergent (module, step, tensor) row and observed drift.
• The upstream file:line citation where the divergence originates.
• Fallback hook/patch verdict if activation trace could not observe the boundary.
• Hypothesis verdict if an A/B toggle was used, for example PATCH_LINEAR=1.
• Cleanup-gate status: [cleanup-gate] PASS or a list of unresolved items.

The calling agent uses this to scope the production fix in the FastVideo component file.

References

• docs/contributing/activation_trace.md for canonical activation-trace env vars, JSONL output, cost model, and troubleshooting.
• fastvideo/hooks/activation_trace.py for the implementation and attach_activation_trace(model) entry point.
• templates/block_trace_debug.py in this skill directory: fallback custom-hook template when activation trace cannot observe the needed boundary or stat.
• tests/local_tests/transformers/_debug_magi_human_block_parity.py in the FastVideo3 repo: historical worked example for custom hook/patch debugging.
• add-model/SKILL.md Phase 6: the calling context for this skill.
• add-model-03-port-dit/SKILL.md, add-model-04-port-vae/SKILL.md, add-model-05-port-encoder/SKILL.md, add-model-06-port-generic/SKILL.md: bucket-specific debug language and component-specific escape-hatch knowledge.

Changelog