Use when something is wrong: Search() hangs, all evaluations return INVALID_SCORE, scores aren't improving, every config returns the same number, ptxas errors fill the log, CV% is too high, or a winning ACF candidate needs NCU profiling to explain. Symptom-indexed table on top. Triggers on "compileiq hang", "socket timeout", "INVALID_SCORE", "not converging", "every score is the same", "TypeError fromhex", "ncu profile", "register spill", "ptxas error", "not in expected format", "high cv".
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
The command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
File Explorer
2 files
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview
name
compileiq-debug
description
Use when something is wrong: Search() hangs, all evaluations return INVALID_SCORE, scores aren't improving, every config returns the same number, ptxas errors fill the log, CV% is too high, or a winning ACF candidate needs NCU profiling to explain. Symptom-indexed table on top. Triggers on "compileiq hang", "socket timeout", "INVALID_SCORE", "not converging", "every score is the same", "TypeError fromhex", "ncu profile", "register spill", "ptxas error", "not in expected format", "high cv".
when_to_use
- User reports any unexpected behavior from Search() or its results.
- User wants to NCU-profile a winning ACF to understand WHY it helps.
- High CV% on validation runs that won't drop with longer warmup.
Don't use when:
- It's actually an environment problem (use compileiq-bootstrap first).
- The search ran fine and the user just wants to validate (use
compileiq-validate-result).
A symptom-indexed cheat sheet. Find the row that matches what the user is
seeing, follow the first action, then dig into the matching detail section.
Symptom table
Symptom
Most-likely cause
First action
Search hangs on first eval; socket timeout
Search space too large for default CIQ_SOCKET_TIMEOUT=20; OR release fetch slow/blocked; OR forkserver issue
Raise CIQ_SOCKET_TIMEOUT=120; if still hangs, CIQ_SEARCH_SPACES_DIR=<local mirror>; if still hangs, CIQ_PROCESS_MODE=spawn. Do NOT symlink BLAS — that fix is obsolete
Every eval returns INVALID_SCORE
Return-type mismatch (tuple vs scalar) / correctness gate / task_timeout too tight
Search.sample(1) + call objective by hand; check num_objectives vs return shape; raise task_timeout
Every eval returns the same score
ACF not reaching the compiler — framework cache is hiding it
Apply Debug-pack O0 ACF by hand; if the score doesn't regress, fix cache-bust (TRITON_ALWAYS_COMPILE=1, HELION_SKIP_CACHE=1, fresh TRITON_CACHE_DIR, drop FlashInfer cubin packages)
TypeError: fromhex() … not dict
Legacy bytes.fromhex(config_blob) in objective
Replace with save_compiler_config(acf_path, config) — see compileiq-author-objective
"not in expected format"
Objective returned wrong shape
num_objectives must equal len(return_tuple); scalar return only when num_objectives=1
Convergence stalled (best score flat)
Pool too small for space; mutate_rate too low; or kernel near-optimal
Raise pool_size; raise mutate_rate; sample diversity with Search.sample(20)
Increasing invalid rate over generations
Mutation arm spreading; compiler version drift mid-run
Not BLAS. Do not send users to symlink libblas.so.
Current shipped binaries link only libm/libc/libstdc++/libgcc_s and
do not require BLAS/LAPACK.
Real causes today, in order of frequency:
CIQ_SOCKET_TIMEOUT too low. Default is 20 seconds, which is fine for
small search spaces but fails on big ones. Raise to 120 first; raise to
300+ for very large spaces.
Release-backed search-space fetch is slow or blocked. First call to
PtxasSearchSpace().retrieve() downloads from github.com. On a corporate
firewall this can stall. Pre-stage the mirror:
num_objectives=1 but objective returns a tuple (latency,). Drop the
trailing comma.
num_objectives=2 but objective returns a scalar.
Correctness gate is rejecting everything because the reference call itself
is wrong.
task_timeout is shorter than a clean compile takes; raise it.
Every eval returns the SAME score
Almost always a framework cache serving a stale binary. Run the O0/O3 canary
from the Debug pack to confirm — see compileiq-booster-pack for the exact
test. If O0 doesn't regress vs baseline, the ACF is not reaching PTXAS. Fix:
Framework
Cache-bust
Triton
TRITON_ALWAYS_COMPILE=1 + unique TRITON_CACHE_DIR per eval
Helion
HELION_SKIP_CACHE=1
FlashInfer
Confirm flashinfer_cubin and flashinfer_jit_cache packages are absent (docs/flashinfer_booster.md:56-64)
Raw nvcc
Clean the build dir between candidates
TypeError around fromhex
Legacy pattern from the pre-2026 skill set:
# OLD — DO NOT USEdefobjective(config_blob):
withopen(tmp_path, "wb") as f:
f.write(bytes.fromhex(config_blob))
...
Replace with:
from compileiq.utils.helpers import save_compiler_config
defobjective(config: str):
save_compiler_config(tmp_path, config)
...
save_compiler_config does the bytes.fromhex internally. See
compileiq-author-objective for the full pattern.
"Not in expected format"
The objective returned a shape CompileIQ's core doesn't expect. Rules:
num_objectives=1: objective must return a single scalar (int | float).
Not a 1-tuple, not a list.
num_objectives>=2: objective must return a tuple or list of that length.
result = objective(sample)
assert (
(search_config.num_objectives == 1andisinstance(result, (int, float)))
or (search_config.num_objectives > 1andlen(result) == search_config.num_objectives)
), f"shape mismatch: {result!r} vs num_objectives={search_config.num_objectives}"
Convergence stalled
Three causes, in order:
Pool too small for the space.pool_size = max(2 * num_objectives + 1, 32) is the auto-derived floor — for spaces with >1k design points, raise to 64-128.
Mutation rate too low. Default mutate_rate=0.25. Raise to 0.3-0.5 if the search is converging on the first generation.
The kernel is genuinely near-optimal. Verify by running Search.sample(20) and timing each sample by hand — if the spread is <5%, the search space is shallow.
Increasing invalid rate
Probably a mutation arm spreading a structurally-bad config across the
population. Re-run with CIQ_KEEP_CACHE=1 so the failing configs are
preserved at ~/.cache/compileiq/, then replay them by hand to identify the
common factor.
High CV% on validation
If cv = std/mean > 10%, validation can't tell the signal from the noise.
Fixes, in order:
Lock GPU and memory clocks (see compileiq-run-search for the
nvidia-smi --lock-*-clocks snippet).
Pin CUDA_VISIBLE_DEVICES=<gpu> so the validation has the GPU to itself.
If the ACF moved any of these meaningfully, that's the mechanism. If none of
them moved but the win is real, look at lower-level metrics (warp stalls,
issue slot utilization) — those are harder to interpret but often the answer.
Reports Used N registers, X bytes stack frame, Y bytes spill stores, Z bytes spill loads. If Y + Z goes up vs baseline, the ACF traded
register pressure for memory traffic — sometimes a real win, sometimes not.
Investigate before shipping.
Self-test
python scripts/diagnose_csv.py --self-test
Synthesizes a small results.csv covering each pathology (clean convergence,
rising invalid rate, stalled best-score) and asserts the heuristic
classifications match.
Explicitly dropped from this skill (vs old /compileiq-debug + /compileiq-profile)
BLAS symlink section — obsolete; current binaries don't link BLAS
(verified by ldd). Carrying it forward sends users on a wild goose chase.
Verbose COMMON_PTXAS_ERRORS dict mapping individual ptxas error
strings to fixes — users get INVALID_SCORE instead, no need to recognize
specific messages.
Duplicate CUDA 13.3+ check — lives in compileiq-bootstrap.
validate_objective_function introspection helper — replaced by
Search.sample(1) + the Debug-pack O0/O3 canary.
Next
For the canary itself: compileiq-booster-pack or
compileiq-author-objective.
