compileiq-author-objective

compileiq-author-objective

The objective function is where ~80% of CompileIQ user errors happen. This skill tells you the exact shape it must have for current CompileIQ, how to inject --apply-controls for each supported compile path, and how to verify the whole pipeline works before paying for a full search.

For paste-ready full-file templates per framework, see references/templates.md.

When

• Writing a brand-new objective function.
• Migrating an older objective off the legacy bytes.fromhex(config_blob) pattern.
• Diagnosing "every config returns the same score" or "TypeError: fromhex".

The two legal signatures

Mixed-space results keep the same list shape in best["params"]. Unpack it before saving the ACF, for example: user_space, ptxas_config = best["params"]. (Pattern reference: examples/compilers/triton_example/mixed_triton.py:123-146.)

For multi-objective, return tuple[float, ...] of length num_objectives.

Canonical imports

from compileiq.types import INVALID_SCORE, BASELINE_CONFIG
from compileiq.utils.helpers import save_compiler_config

INVALID_SCORE is CompileIQ's sentinel — return it on any failure (compile, hang, wrong answer, exception). Do not redefine it as float('inf').

BASELINE_CONFIG is the empty-dict sentinel CompileIQ passes when a knockout knocks out every parameter (typically with normalize=True).

save_compiler_config(path, hex_str) writes the binary blob to disk; it handles the bytes.fromhex internally (compileiq/utils/helpers.py:128-137). Users never need to touch fromhex themselves.

Self-contained for IsoMultiProcessWorker and Ray

Heavy library imports (torch, triton, helion, cute) go inside the function so the process IsoMultiProcessWorker spawns — or the remote Ray task — can re-import them in a clean state. Cheap module-level constants (paths, regexes) are fine.

Per-eval cache busting (non-negotiable)

import os, tempfile
env = os.environ.copy()
env["TRITON_ALWAYS_COMPILE"] = "1"
env["HELION_SKIP_CACHE"]     = "1"
env["TRITON_CACHE_DIR"]      = tempfile.mkdtemp(prefix="ciq_triton_")

For FlashInfer, additionally confirm the prebuilt cubin cache packages are absent — flashinfer_cubin and flashinfer_jit_cache. See docs/flashinfer_booster.md:56-64 for the import-time check.

Per-framework --apply-controls injection

Baseline knockout branch

def objective(config):
    if isinstance(config, dict) and not config:   # config == BASELINE_CONFIG
        return measure_without_acf()              # establish baseline run
    # config is a hex string (or list with hex tail) — apply ACF
    ...

Correctness-before-timing (mandatory)

The optimizer rewards whatever you measure. If you only measure latency, the algorithm will happily reward configs that compile faster by producing wrong answers. Always verify against a reference first:

if not torch.allclose(actual, reference, atol=1e-2, rtol=0):
    return INVALID_SCORE
return triton.testing.do_bench(lambda: kernel(...), warmup=100, rep=1000, return_mode="mean")

(Pattern from examples/compilers/triton_example/mixed_triton.py:141-146.)

Catch everything → return INVALID_SCORE

try:
    ...
except (subprocess.TimeoutExpired, RuntimeError, FileNotFoundError, ValueError, OSError) as e:
    return INVALID_SCORE

When in doubt, catch broadly. CompileIQ expects INVALID_SCORE as the "this config is broken" signal — re-raising means the entire search fails.

Pre-search canary (mandatory before tuner.start())

Two cheap calls that catch ~90% of "every score is the same" bugs:

# Shape check — does the objective even run?
sample = tuner.sample(1)[0]
score = objective(sample)
print(f"sample run: {score}")
assert isinstance(score, (int, float)) and score == score   # not NaN

# ACF-injection canary using the Debug pack (downloaded once)
from compileiq.utils.helpers import load_compiler_config
O0_HEX = load_compiler_config("debug-pack/O0.acf")
O3_HEX = load_compiler_config("debug-pack/O3.acf")

baseline = objective({})                  # BASELINE_CONFIG path
score_O0 = objective(O0_HEX)
score_O3 = objective(O3_HEX)

assert score_O0 > baseline * 1.05, (
    f"O0 should regress (got {score_O0} vs baseline {baseline}). "
    "ACF is NOT reaching PTXAS — fix the cache-bust."
)
assert abs(score_O3 - baseline) / baseline < 0.05, (
    f"O3 should match baseline (got {score_O3} vs {baseline})."
)
print("ACF injection canary PASSED — safe to start the search.")

If either assertion fails, stop and fix the cache-bust before launching the search. Otherwise every generation's score is measurement noise on a stale binary.

Self-test

A 3-line "smoke" objective inside the SKILL author's repo, used to verify the scaffolding before plugging in a real kernel:

def smoke_objective(config):
    return 1.0   # constant; useful to verify Search() shape, not measurement

Drop it into the Search(...) call and run 2 generations; if that completes and results.get_best_result() returns a dict, your scaffold is correct.

Gotchas

• PTXAS_OPTIONS is not the canonical Triton injection. It still works for raw subprocess invocations, but Triton 3.x prefers the ptx_options= kernel kwarg. See the table above.
• Mixed search spaces require list unpacking. If you pass search_space=[user_dict, PtxasSearchSpace()], your objective must accept a list, not a string. Results keep that list in best["params"]; unpack it before saving the compiler config.
• Don't redefine INVALID_SCORE. Import it from compileiq.types. If you redefine it locally as float('inf'), the value happens to work today but is not guaranteed to in future releases.
• config_blob is no longer a parameter name. The old skill set used def objective(config_blob) and called bytes.fromhex(config_blob). Both are stale. Use def objective(config) and save_compiler_config(path, config).

Next

• Sizing SearchConfiguration and picking a Worker: compileiq-run-search.
• After the search: compileiq-validate-result.
• If something's wrong: compileiq-debug.