Numerics Debugging (PyTorch)

---
name: numerics_debugging
description: Capture and compare per-op activations between two TorchTitan runs to spot numerics divergence (eager vs aot_fx_trace, FSDP vs no-FSDP, before vs after a refactor). Use when the user wants to debug bitwise / numeric drift in training, or invokes /numerics_debugging.
metadata:
  origin: PyTorch
---

# Numerics Debugging (DebugMode-based)

Per-op activation capture + comparison toolkit. Captures activations on a
designated step via `torch.utils._debug_mode.DebugMode`, then diffs two
captures into an HTML report to surface numerics divergence between runs
that should agree (bitwise, or within float32 reduction-order noise).

Two pieces live in `scripts/`:

- `activation_tracer.py` — runtime capture, driven from `Profiler` by
  `ActivationCaptureProfiler`. Output:
  `{dump_folder}/numerics/rank_{N}_activations.log`.
- `compare_numerics.py` — diffs two logs, produces an HTML report.
  Standard library only, no torch import.

They sit **outside** the torchtitan package on purpose. Nothing in core
torchtitan or `graph_trainer` references them; an agent must edit torchtitan
to wire the tracer in before a capture run, and revert the edits when done
(they don't belong on `main`). Because `.claude` is not a valid Python
package name, `activation_tracer` is imported by putting `scripts/` on
`sys.path` rather than by dotted module path — see
[references/patching.md](references/patching.md).

> The two runs being compared **must use the same dtype and seed**. The
> matcher keys on shape + float64 L1 norm; a precision change (bf16 vs
> fp32) makes every row diverge and the matcher degrades to the
> structural-only `stats` pass.

## Workflow

1. **Patch torchtitan** to wire the capture into `Profiler` (and
   `graph_trainer` if you're capturing the traced path). Full patch set:
   [references/patching.md](references/patching.md).
2. **Define capture recipes** for the two runs. Each recipe should derive from
   the configuration under test and set the capture controls in Python:
   ```python
   def numerics_run_a() -> Trainer.Config:
       config = base_config_a()
       config.training.steps = 2
       config.profiler.dump_numerics = True
       config.profiler.profile_freq = 2
       config.debug.seed = 42
       config.debug.deterministic = True
       config.training.mixed_precision_param = "float32"
       return config
   ```
   Then capture once per recipe:
   ```bash
   MODULE=my_numerics_configs CONFIG=numerics_run_a ./run_train.sh \
       --output-dir ./outputs/run_A
   ```
   Repeat with `numerics_run_b` and `./outputs/run_B`. The capture step is
   `profile_freq`. With `profile_freq=2` and `training.steps=2`, step 1 warms
   up and step 2 is the snapshot. Capture
   adds ~10–40% memory only on the capture step (stats are computed inline
   in float64; tensors aren't held).
3. **Diff** the two logs:
   ```bash
   python .claude/skills/numerics_debugging/scripts/compare_numerics.py \
       outputs/run_A/numerics/rank_0_activations.log \
       outputs/run_B/numerics/rank_0_activations.log \
       --name1 run_A --name2 run_B \
       -o diff.html
   ```
   Open `diff.html`. Each row pairs one op from each run; cells turn red
   when a stat diverges; the "Match method" chip shows which of the four
   matching passes (override / exact key / fuzzy key / stats) paired the
   row.

## Customizing

Excluded ops, numel / dtype filter, hash function, the manual-override
file format, and HTML appearance are all tunable. See
[references/customization.md](references/customization.md), which also
catalogs the common eager-vs-traced mismatch patterns (AC-recompute FQN
drift, per-layer counter shifts, collective renaming) you'll see in the
diff and how to express them as overrides.