adr: add ADR-0045 (bench module contract — registration, dispatch, authoring)
Documents src/kernbench/benches/: how @bench registration + audit work, how the CLI dispatches via run_bench/RuntimeContext, and the contract a new bench module must satisfy. Nine decisions (D1-D9) cover: - @bench name/description rules and duplicate detection - Module-file convention (_-prefixed helpers vs bench modules) - def run(torch) signature; torch = RuntimeContext - Minimum-one-submit rule (else NO_REQUESTS) - Single-device convention + multi-SIP CCL exception (ADR-0024/0027) - resolve() name/index decision tree; indices are not a stable API - Exact RuntimeContext surface exposed to benches - Env-var parameterization (matmul_composite / gemm_sweep.py pattern) Four alternatives rejected with documented reasons (manifest YAML, decorator entry= arg, @multi_device_bench split, stable indices). Verifier (tools/verify_adr_lang_pairs.py) passes for EN/KO pair. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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# ADR-0045: Bench Module Contract — registration, dispatch, and authoring
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## Status
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Accepted (2026-05-21).
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Unifies the `src/kernbench/benches/` registration mechanism (@bench), the
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CLI dispatch path (`kernbench run/list`), and the contract a new bench
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module must follow. ADR-0010 (CLI surface) specifies the `kernbench
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list/run` interface, but **how benches are registered and what signature
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they must follow** had no ADR-level coverage.
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## First action
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When `kernbench.benches` is imported, `__init__.py` immediately calls
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`_eager_import_and_audit(__path__, __name__)`. Its first action is to
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enumerate every sibling module in the package directory via
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`pkgutil.iter_modules(__path__)` and **eagerly import** each one via
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`importlib.import_module(...)` — except modules matching either:
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- name `registry` (the infrastructure module itself), or
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- name starting with `_` (helper modules).
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At import time, each `@bench(name=..., description=...)` decorator inside
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the imported module runs, appending `(name, description, fn)` to
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`_PENDING` and adding `fn.__module__` to `_REGISTERED_MODULES`.
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Once imports finish, `_audit_modules(imported, _REGISTERED_MODULES)`
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runs; if any imported module did not invoke `@bench` at least once, it
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raises `RuntimeError("Bench module(s) missing @bench decorator: ...")`
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immediately. At this point indices are still unassigned — the first call
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to `list_all()` / `resolve(...)` triggers `_finalize()`, which sorts
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`_PENDING` alphabetically by name and assigns 1-based indices.
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In short, **the bench infrastructure's first act is "eagerly import
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every non-helper module in the package and audit that each one
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registered at least one bench"**.
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## Context
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`src/kernbench/benches/` currently holds 8 bench modules (`ccl_allreduce`,
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`gemm_single_pe`, `gpt3_qkv`, `ipcq_allreduce`, `matmul_composite`,
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`qkv_gemm`, `qkv_gemm_multi_pe`, `va_offset_verify`). Every bench follows
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the same unified flow:
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```
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kernbench run --topology <T> --bench <N>
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↓
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cli/main.py::cmd_run
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↓ resolve_topology(T) + resolve(N) + resolve_device(device_arg)
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↓
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runtime_api/bench_runner.py::run_bench(topology, bench_fn, device, engine_factory)
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↓ engine_factory(topology, device) → GraphEngine
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↓ RuntimeContext(engine, target_device, correlation_id, spec)
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↓
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bench_fn(ctx) ← invokes the bench's run(torch)
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↓ ctx.empty/zeros/from_numpy/launch/distributed.* etc. submit work
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↓
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ctx.wait_all() ← drains any outstanding handles
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↓
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BenchResult(completion, correlation_id, trace, traces, engine)
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```
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ADR-0010 covers only the CLI surface (`run/list/probe/web`); ADR-0007
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covers only the runtime API ↔ sim_engine boundary. The question "what
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shape must a new bench file take?" had to be answered by grepping the
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codebase. As a result:
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- The @bench decorator contract (kebab-case name, non-empty description)
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lived only in the source.
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- The bench function signature (`def run(torch)`) was a de-facto
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convention enforced by the CLI dispatcher calling `spec.run`.
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- New bench authors learned the "helpers must use `_` prefix" rule only
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after seeing the audit's RuntimeError.
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- The single-device convention (CLAUDE.md Part 2 CLI Semantics) and its
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interaction with multi-SIP CCL benches was ambiguous for bench
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authors.
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This ADR consolidates all of it in one place.
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## Decision
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### D1. @bench decorator contract
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```python
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from kernbench.benches.registry import bench
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@bench(name="my-bench", description="Short, complete-sentence description.")
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def run(torch):
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...
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```
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- `name`: kebab-case string matching `^[a-z][a-z0-9]*(-[a-z0-9]+)*$`.
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Lowercase letters, digits, and dashes only; underscores forbidden;
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must start with a letter.
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- `description`: non-empty string (stripped length > 0). Displayed
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verbatim by `kernbench list`.
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- The decorator **returns the function unchanged** — direct invocation
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is fine. Its only side effect is appending to `_PENDING`.
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Violations of the first two rules raise `ValueError` at decoration time.
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Duplicate names are caught at `_finalize()` with
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`RuntimeError("duplicate bench name: ...")`.
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### D2. Module-file convention
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Every `src/kernbench/benches/<slug>.py` must be one of:
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- **A bench module**: at top-level import, `@bench(...)` runs at least
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once to register at least one bench.
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- **A helper module**: the filename starts with `_` (e.g.,
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`_shared_helpers.py`). `iter_modules` skips it.
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The audit (`_audit_modules`) rejects any non-helper that fails to call
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`@bench`. Intended consequence: dropping a new file into `benches/`
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automatically registers its benches, and helper modules are clearly
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flagged by their filename prefix alone.
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### D3. The bench function signature is `def run(torch)`
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The decorator does not enforce a function name, but **CLI dispatch calls
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`spec_entry.run`** (the decorated callable). The convention is therefore:
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- Function name: `run`. Other names work, but always use `run` for
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readability and grep-ability.
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- Argument: a single positional `torch`. In practice this is a
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`RuntimeContext` instance exposing PyTorch-style namespaces
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(zeros/empty/launch/distributed/...) — see ADR-0024 D3.
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- Return value: any (`Any`). `run_bench` ignores it and tracks
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completion via `ctx.handles()` / `engine.get_completion()`.
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The `torch` name imitates a PyTorch-compatible idiom; the actual PyTorch
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module is not passed in (aligned with ADR-0024's "rank = SIP" launcher
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convention).
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### D4. A bench must submit at least once
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If `ctx.handles()` is empty after the bench returns, `run_bench` reports
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`BenchResult.completion = ok=False, error_code="NO_REQUESTS"`. So a
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meaningful bench must invoke at least one of:
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- Tensor-creation APIs: `torch.zeros(...)`, `torch.empty(...)` — these
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internally submit `MmuMapMsg` and (for zeros) `MemoryWriteMsg`.
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- Kernel-launch API: `torch.launch(name, fn, *args)` — submits per-SIP
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`KernelLaunchMsg`.
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- (Exception) Empty placeholder benches: e.g.,
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`ipcq_allreduce.py`'s `print(...)`-only stub will receive a
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NO_REQUESTS result. CI is expected to recognize and handle placeholder
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benches specially.
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### D5. Single-device convention + multi-SIP exception (ADR-0024/0027)
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CLAUDE.md Part 2 CLI Semantics' **"benchmarks MUST remain
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single-device"** rule is interpreted as follows:
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- **Standard bench (single-SIP use)**: define tensor placement with
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`dp = DPPolicy(...)` and launch with `torch.launch(...)`. The SIP
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index is chosen by `--device` (CLI's responsibility).
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- **CCL bench (multi-SIP use)**: as an exception, use
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`torch.distributed.init_process_group(backend="ahbm")` plus
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`torch.multiprocessing.spawn(_worker, ..., nprocs=ws)` for the
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rank = SIP pattern (ADR-0024 D3). `--device` is ignored (or treated
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as `all`); each spawned worker calls `torch.ahbm.set_device(rank)` to
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bind to its SIP.
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Multi-device patterns outside these two (e.g., one bench function
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launching across multiple SIPs in the same process) are forbidden by
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this ADR. Even with `--device all`, the bench runs once; multi-SIP use
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inside that single run must follow D5's second pattern.
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### D6. Name/index resolution (`resolve`)
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`resolve(identifier: str)` returns a BenchSpec via:
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1. If `identifier.isdigit()`: convert to int and find the spec where
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`index ==` that value. If none, `ValueError("No bench with index
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...")`.
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2. If `identifier in _REGISTRY`: direct lookup.
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3. Otherwise: `ValueError("Unknown bench ...")`.
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Empty or whitespace-only identifiers raise `ValueError("bench
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identifier must be a non-empty string.")`.
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The CLI passes `--bench` directly to `resolve`, so users can use either
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`kernbench run --bench gemm-single-pe` or `kernbench run --bench 2`.
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### D7. Indices are not a stable API
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`_finalize()` sorts `_PENDING` alphabetically by name and assigns
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1-based indices. Adding a new bench can shift existing benches'
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indices. Therefore:
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- Human-interactive use: indices are fine.
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- Scripts / CI automation: always use the name.
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This caveat is documented in `registry.py`'s module docstring.
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### D8. Surface RuntimeContext exposes to benches
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A bench's `torch` parameter may legitimately use:
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- **Tensor creation**: `torch.empty(shape, dtype=..., dp=DPPolicy(...),
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name=...)`, `torch.zeros(...)`, `torch.from_numpy(arr)`. All submit
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host-side metadata plus device deployment (`MmuMapMsg` +
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`MemoryWriteMsg`).
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- **Kernel launch**: `torch.launch(kernel_name, kernel_fn, *args)` —
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converts `(Tensor, int, float)` positional args to `TensorArg` /
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`ScalarArg`, submits per-SIP `KernelLaunchMsg`, and drains.
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- **Synchronization**: `torch.wait(handle)`, `torch.wait_all()`
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(`run_bench` calls the latter automatically).
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- **Distributed**: `torch.distributed.init_process_group(backend="ahbm")`,
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`torch.distributed.get_world_size()`,
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`torch.distributed.all_reduce(t, op=...)` (ADR-0024/0027).
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- **Multi-process (rank = SIP)**:
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`torch.multiprocessing.spawn(_worker, ..., nprocs=ws)` (ADR-0024 D3 /
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ADR-0027).
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- **Device binding**: `torch.ahbm.set_device(rank)` or
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`torch.accelerator.set_device_index(rank)` (both point to the same
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namespace).
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- **IPCQ install**: `torch.install_ipcq(algorithm=..., ccl_yaml=...)`
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(ADR-0023 D10).
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- **Spec lookup**: `torch.spec` — the dict produced by the topology
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builder (system / cube_mesh / HBM parameters etc.). Use it so the
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bench does not hardcode topology.yaml values.
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Benches must not access RuntimeContext private members (`_handles`,
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`_traces`, `_allocators`, etc.) directly. This aligns with ADR-0007's
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layer-boundary spirit: bench → runtime API → sim_engine flows in one
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direction.
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### D9. Environment-variable parameterization is allowed
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Benches may parameterize themselves via `os.environ.get(...)`, as
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`matmul_composite.py` does for `MATMUL_M`, `MATMUL_K`, `MATMUL_N`,
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`MATMUL_DTYPE`, `MATMUL_VARIANT`. Rationale:
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- The bench function signature is fixed by D3 to `def run(torch)`, so
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positional/keyword arguments cannot carry parameters.
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- The env-var pattern is a natural hook for operational sweeps (e.g.,
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`MATMUL_VARIANT`).
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- External drivers such as `scripts/gemm_sweep.py` (ADR-0044) consume
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this hook (it sets `MATMUL_M/K/N/VARIANT` at
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`scripts/gemm_sweep.py:115-118`).
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When environment variables alter bench behavior, the module docstring
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must list every variable used (`matmul_composite.py` is the canonical
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example).
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## Alternatives Considered
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### A1. An explicit manifest file (YAML) listing benches
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Rejected. The `@bench` + audit pattern guarantees "drop in file → auto-
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register", concentrating cognitive cost in one place (the file itself).
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A separate manifest is prone to drift, and helper separation is already
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clear via the `_` prefix.
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### A2. Allowing the bench's entry-point name in the decorator
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(`@bench(name=..., entry="run_xxx")`)
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Rejected. Breaks the simplicity of dispatch (`spec.run` is a single
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callable). The `run` convention is sufficient; variants can register
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multiple `@bench`-decorated functions in the same module.
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### A3. A separate `@multi_device_bench` decorator for CCL
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Rejected. The two patterns named in D5 (single + ADR-0024 multi-SIP)
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cover all 8 current benches. A separate decorator would force dispatch
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to branch and add complexity; the multi-SIP intent is already obvious
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from the bench's `init_process_group(...)` call.
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### A4. Make indices a stable API (registration order or explicit
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`index=` argument)
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Rejected. D7's trade-off favors user-friendliness — alphabetically
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sorted 1-based indices read naturally in the `list` output. Scripts can
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use names.
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## Consequences
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- "How to add a bench" is consolidated in one ADR — new authors only
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need to read D1-D3 and D8 without grepping source.
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- The `_`-prefixed helper-module pattern is legitimized at ADR level,
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so future `benches/_*.py` shared helpers are free to be added.
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- The CLI's single-device convention and CCL's multi-SIP exception are
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shown to be consistent (D5) — they are orthogonal.
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- The rationale for ADR-0044's GEMM eval harness using env-var hooks
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(D9) is now ADR-pinned.
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- Indices are explicitly unstable (D7), so any CI code calling
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`kernbench run --bench 3` is flagged for review after this ADR is
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accepted.
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