Debugging Gradients in Warp

Gradient bugs in Warp are almost never math bugs. The forward simulation looks perfectly healthy while the backward pass silently reads clobbered values, skips arrays, or double-counts adjoints. Users routinely burn days tuning physics knobs, loss functions, and assets when the real cause is a two-line taping-pattern fix. Your job is to find that fix with evidence, not intuition.

The single most important discipline: measure before hypothesizing. It is cheap for you to run a shrunk reproduction and compare autodiff against finite differences. The way the gradient is wrong (its signature) prunes the hypothesis space far faster than reading code ever will. Do not start proposing fixes from code reading alone — plausible-looking diagnoses of differentiability bugs are very often wrong, and an unverified "fix" that happens to perturb the numbers wastes everyone's time.

When to Use This Skill

Anything trained, optimized, calibrated, or fit through Warp kernels flows through wp.Tape gradients — so when such a workflow misbehaves, gradients are the prime suspect even if the user never says the word. Activate on the symptoms users actually report: training that diverges, NaNs, or does nothing; loss that stalls or plateaus above where it should; fits that converge to a wrong or biased answer or are worse than a reference implementation; pipelines that work at small scale but fail at production scale or fail a QA recheck. Also activate on explicit gradient symptoms — exploding, NaN/inf, zero, or subtly wrong gradients, wp.autograd.gradcheck failures, suspected wp.Tape/backward issues — and when the user asks whether their gradients can be trusted.

Do not activate for forward-only Warp work (kernel authoring, rendering, performance tuning), Warp build or installation problems, autograd questions in other frameworks with no Warp involvement, or pure performance work on a backward pass whose gradients the user has already validated.

The canonical background is Warp's own documentation — consult the relevant section before diagnosing in its territory (online at https://nvidia.github.io/warp/stable/; in a Warp source checkout the same content is under docs/user_guide/; pip installs do not include it):

• The "Differentiability" guide — especially "Array Overwrites", "Debugging

Gradients", "Array Overwrite Tracking", and "Limitations and Workarounds" (in-place math, component assignment, dynamic loops).

• The FAQ, section "Differentiation and Interoperability" — what state a

tape does and does not preserve, and checkpointing.

Prerequisites

Executing this skill assumes all of the following; if one is missing, surface that to the user instead of improvising around it:

• The user's script (or a faithful reproduction) is available in the

workspace, runnable, and modifiable — diagnosis executes it repeatedly and edits it to apply fixes.

• This skill's references/ files (quick-checks.md, verification.md,

custom-gradients.md, case-studies.md) accompany it and are consulted at the steps that cite them.

Instructions

1. Note the user's Warp version first (wp.__version__ or the banner

Warp prints at init). Several verification behaviors changed in Warp 1.17 — copy-adjoint accumulation, overwrite-warning call sites, read-flag lifetime, gradcheck's restore_inputs — and the references mark each with a version caveat. On Warp < 1.17, a whole bug class exists that later versions fixed (quick-checks §1's version caveat), and some tools need workarounds.

1. Reproduce and shrink. Get the user's script running, then cut it

down: fewer particles/elements, fewer time steps, fewer optimizer iterations, CPU device if the sim allows. You need a repro that runs in seconds, because you will run it many times. Keep the structure (number of kernels, the taping pattern, buffer reuse) intact — that is where the bug lives. Shrinking the physics is fine; restructuring the dataflow is not.

If the script cannot be made to run (missing dependencies, broken code), report the blocking issue as the deliverable and stop — do not proceed to verify a program that never ran.

1. Instrument and establish ground truth (details and templates in

references/verification.md):

• Set wp.config.verify_autograd_array_access = True before module load

and rerun under an active tape. Capture every warning. This catches the single most common bug class (write-after-read overwrites) nearly for free. Know its blind spots: it needs a tape, it cannot see arrays stored inside Warp structs, and it disables kernel caching (expect a kernel rebuild — JIT module recompilation only, not a rebuild of the native library). If the tracker runs clean but gradients are still wrong, specifically check for in-place mutations of arrays held inside Warp structs (quick-checks §1 and Limitations) before trusting the clean result.

• Run one end-to-end finite-difference check: wrap the full forward

pass (sim steps + loss) in a Python callable and hand it to wp.autograd.gradcheck with the true optimization inputs — it compares the autodiff gradient against central differences, restoring array inputs between evaluations (Warp 1.17+) so in-place-mutating forwards are checked from pristine state; on older Warp use the manual harness in references/verification.md. The reference is the user's actual objective over the full horizon, compared against the gradient the optimizer actually consumes — never a narrower window (see references/verification.md). This confirms gradients are actually wrong (users are sometimes wrong about this — report "gradients are correct" findings honestly) and yields the error signature. The template in references/verification.md fixes the eps/tolerance choices and the seed-pinning a stochastic forward needs — do not eyeball pass/fail against floating-point or sampling noise. If the backward pass runs out of memory while establishing ground truth, apply the checkpointing pattern from "Edge case: out of memory" below before proceeding.

1. Match the signature against the table below to rank hypotheses.

1. Scan the code against the known-pattern checklist

(references/quick-checks.md). This is fast for you — do it in the same pass, but let the signature decide which findings are plausible causes versus incidental smells.

1. Localize if still ambiguous. Binary-search the pipeline: truncate to K

steps and find where FD and autodiff first diverge; run wp.autograd.gradcheck_tape to test each recorded launch in isolation. Remember gradcheck_tape validates kernels individually — it is structurally blind to inter-kernel overwrites, so a clean per-kernel pass plus a wrong end-to-end gradient points at the taping pattern, not the kernels. It also silently skips kernels compiled with enable_backward=False (see Limitations) — if any kernel in the pipeline sets that, a clean pass says nothing about it; verify it separately.

1. Fix minimally, then re-verify with the exact same FD harness that

established the failure. A gradient fix without a before/after FD comparison is not a fix. Verify the exact program you are shipping — the fixed file as it stands, every line included — never a re-implementation of it in a diagnostic script: a rebuilt pipeline silently drops whatever you believed was irrelevant, and if that belief is wrong the verification passes while the shipped code stays broken. Mechanically: the harness must import the fixed module (or execute the fixed file) and call into it — the only code that may live outside the shipped program is the FD driver itself. Also rerun the overwrite tracker to confirm the warnings are gone. "Minimally" applies to the code diff, not the diagnosis: when the root cause is structural (e.g., accidental gradient truncation, quick-checks §8), the minimal correct fix is the restructure — do not substitute a smaller change that only silences the surface symptom.

1. Close the loop on the user's original complaint. Rerun their actual

workflow (their script, their printed metrics). The job is done when the symptom they reported is resolved — an optimization that was "exploding" should now demonstrably improve its objective, not merely avoid NaN. If gradients verify correct at the full horizon but training still fails, that is a new signature-table entry, not a victory; keep diagnosing (or report the verified gradients and the remaining non-gradient cause, e.g. learning rate).

Failure signatures

| Signature | Leading hypotheses | |---|---| | Gradients exactly zero | Missing requires_grad=True somewhere in the chain (note wp.zeros defaults to False; zeros_like/clone inherit from source); enable_backward=False at module/kernel level; loss array not connected to the tape; grads read after tape.zero(); a piecewise-constant op (round/floor/sign/cast/threshold) in the chain — there zero is correct and the fix is a surrogate gradient such as a straight-through estimator, not a bug hunt (quick-checks §9c); on Warp < 1.17, a tape-recorded copy/clone whose source has other downstream readers (see the version caveat in references/quick-checks.md) | | Gradients grow without bound across optimizer iterations | Missing tape.zero()/tape.reset() between iterations; state-object aliasing that carries an in-tape overwrite across frames (case study 1) | | Off by an exact small factor (2x, Nx) | Double accumulation: a duplicate launch recorded on the tape — note that since Warp 1.13 the store adjoint consumes the output gradient on first use, so a bare duplicate is inert unless the rewritten array has retain_grad=True (quick-checks §7) or the Warp version is older; overlapping tape scopes taping the same work twice. Also: a backward seed that does not match the stated objective — seeding a per-element loss adjoint with ones backpropagates the sum, exactly N× the mean objective's gradient | | NaN or inf | Non-differentiable point evaluated in the backward pass (wp.sqrt(0), wp.length(0), wp.normalize(0), division) — needs a custom gradient (references/custom-gradients.md) or, better, a stable reformulation; an overflow evaluated in the unselected branch of wp.where (a select, not a branch — quick-checks §9b); dynamic-loop local not recomputed during replay (documented to produce inf) | | Subtly wrong, often worse with more steps/iterations | Write-after-read overwrite: wp.copy onto an already-read array, ping-pong buffers within one tape, Python rebinding that aliases two "different" states (case studies); in-place *=//=; vector/matrix component reassignment; dynamic-loop intermediates; on Warp < 1.17, a recorded copy/clone that is not the last consumer of its source (version caveat in references/quick-checks.md) | | Per-window FD agrees but full-horizon FD disagrees; or gradients "verified" yet the optimizer stalls or worsens the loss | Accidental gradient truncation: a tape-per-step loop with backward inside it and state carried between tapes optimizes a different objective than the one being reported (see quick-checks §8). The structural fix is one tape over the whole horizon with total_steps + 1 distinct state buffers. The solver-space analog: a partially converged iterative solve inside the tape makes FD and autodiff agree on the wrong program — converge it outside the tape and warm-start the taped iterations (quick-checks §8) | | Gradients disagree (vs a reference implementation or run-to-run) only on a sparse, data-dependent subset; forward outputs match to float precision | Under-determined forward choice at a non-smooth point (quick-checks §9): both answers can be valid subgradients, and FD cannot adjudicate at a kink. Check whether the discrete choice differs at exactly the mismatching elements before hunting corruption | | FD and autodiff agree at the full horizon but optimization still fails | Not a gradient bug. Say so. Look at learning rate, loss landscape, physics stability — and report the verified-correct gradients as the finding |

Examples

A representative session, end to end. A user reports "my cloth sim trains for a while, then the loss creeps back up — tuning the learning rate doesn't help." No mention of gradients; the leap is made because the workflow optimizes through Warp kernels.

1. Their script runs 512 particles for 200 steps per iteration. Shrink to 16

particles, 10 steps, CPU — repro now runs in ~2 s and shows the same creep.

1. wp.config.verify_autograd_array_access = True under the tape prints:

array ... was read from kernel integrate and is now being written to by kernel integrate — a write-after-read overwrite.

1. End-to-end wp.autograd.gradcheck on the shrunk repro: max relative error

0.4 against finite differences. Gradients are confirmed wrong, with the "subtly wrong, worse with more steps" signature.

1. The signature row plus quick-checks §1 point at buffer reuse inside one

tape: the sim steps state_a → state_b → state_a, ping-ponging two buffers, so the backward pass reads clobbered states.

1. Minimal fix: allocate num_steps + 1 distinct state buffers recorded on

the tape (physics untouched; only the dataflow changes).

1. Re-verify: same gradcheck harness now passes (max relative error 3e-4);

the overwrite warning is gone; the user's full-size training run now decreases monotonically.

Report: root cause (in-tape buffer reuse), the evidence chain (warning + before/after FD numbers), the two-line diff, and a pointer to the "Array Overwrites" section of the Differentiability guide.

Reporting

Lead with the root cause and the evidence chain: the FD-vs-autodiff numbers that established the failure, the warning or localization step that found the cause, the minimal diff, and the FD numbers after the fix. Name the documentation section that covers the pattern so the user can read the canonical explanation. If you checked patterns that came up clean (e.g., the overwrite tracker found nothing), say so — it tells the user what has been ruled out.

If the user is only asking whether their gradients are trustworthy, stop after verification and report; apply fixes when they ask for fixes.

Preserve the evidence: leave the diagnostic scripts (FD harness, shrunk repro) in the workspace and list them in the report instead of deleting them — they are the reproducible half of the evidence chain, and the user or a reviewer should be able to rerun the exact verification that justified the fix. Never delete files you did not create.

Limitations

The verification tooling has blind spots — a clean pass through any one tool is not a clean bill of health (details in references/verification.md):

• The overwrite tracker requires an active tape, cannot see arrays stored

inside Warp structs, and disables kernel caching while enabled.

• wp.autograd.gradcheck does not accept struct inputs; wrap the forward

in a callable over the underlying arrays. On Warp < 1.17 it does not restore mutated array inputs between evaluations (use the manual harness).

• wp.autograd.gradcheck_tape validates each recorded launch in

isolation — it is structurally blind to inter-kernel overwrite bugs and silently skips kernels compiled with enable_backward=False.

• The *=//= non-differentiability warning is emitted only at codegen

time under wp.LOG_DEBUG, so its absence from a normal run means nothing.

• Warp has no built-in gradient checkpointing; long-horizon memory

pressure needs the application-level pattern below.

• At non-smooth points (ties, kinks, argmin selections), finite

differences cannot adjudicate between valid subgradients — FD-vs-AD disagreement there is not automatically a bug (quick-checks §9).

Edge case: out of memory

If the backward pass fails to allocate (long simulations keep every intermediate state alive on the tape), the fix is gradient checkpointing: save periodic states, replay the segments between them during backward. Warp has no built-in utility — applications implement it themselves. Use warp/examples/optim/example_fluid_checkpoint.py as the reference pattern, and see the FAQ's "Differentiation and Interoperability" section.

Reference files

• references/quick-checks.md — the known-bug-pattern checklist with doc

pointers and the caveats that make each pattern easy to miss.

• references/verification.md — tooling details: overwrite tracker setup and

blind spots, end-to-end FD harness template, wp.autograd gradcheck/jacobian usage and caveats, tape visualization, bisection.

• references/custom-gradients.md — @wp.func_grad, @wp.func_replay,

@wp.func_native: when they are required and how they are misused.

• references/case-studies.md — two real debugging sagas (state aliasing;

differentiable-copy overwrite) showing how subtle the surface symptoms are. Read these when the checklist comes up clean — they calibrate what "subtle" means here.