benches: package as kernbench.benches, add @bench registry + list subcommand

Move benches/ -> src/kernbench/benches/ and src/kernbench/cli/probe.py ->
src/kernbench/probes/probe.py. Each bench self-registers via
@bench(name=..., description=...); kernbench list enumerates benches
with auto-assigned indices, --bench accepts kebab-case name or numeric
index. Audit at package-import time fails if any non-underscore module
forgets the decorator. ADR-0010 (EN + KO) updated to reflect the new
resolver path, list subcommand, and probes package separation.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-05-20 14:42:10 -07:00
parent 168b0c89f0
commit 049e3d8bb3
28 changed files with 398 additions and 79 deletions
+44
View File
@@ -0,0 +1,44 @@
"""Single-PE GEMM benchmark via scheduler_v2 (pe_accel).
Full host-to-PE pipeline:
Host → PCIE_EP → IO_CPU → M_CPU → PE_CPU → SchedulerV2 → PE_DMA → HBM
Single PE: num_cubes=1, num_pes=1 via DPPolicy override.
Both operands use tl.ref (HBM-resident); scheduler_v2 tiles and streams
per-tile DMA internally.
Run:
kernbench run gemm_single_pe
"""
from kernbench.benches.registry import bench
from kernbench.policy.placement.dp import DPPolicy
# GEMM dimensions: (M, K) x (K, N) → (M, N)
M, K, N = 32, 128, 32
DTYPE = "f16"
def _gemm_kernel(a_ptr, b_ptr, out_ptr, M, K, N, tl, DTYPE="f16"):
"""Single-PE GEMM: out = a @ b. Both operands streamed from HBM by scheduler."""
M, K, N = int(M), int(K), int(N)
a = tl.ref(int(a_ptr), shape=(M, K), dtype=DTYPE)
b = tl.ref(int(b_ptr), shape=(K, N), dtype=DTYPE)
h = tl.composite(op="gemm", a=a, b=b, out_ptr=int(out_ptr))
tl.wait(h)
@bench(
name="gemm-single-pe",
description="Single-PE GEMM via scheduler_v2 (pe_accel).",
)
def run(torch):
"""Run the single-PE GEMM benchmark."""
dp = DPPolicy(cube="replicate", pe="replicate",
num_cubes=1, num_pes=1)
a = torch.empty((M, K), dtype=DTYPE, dp=dp, name="a")
b = torch.empty((K, N), dtype=DTYPE, dp=dp, name="b")
out = torch.empty((M, N), dtype=DTYPE, dp=dp, name="out")
torch.launch("gemm_single_pe", _gemm_kernel, a, b, out, M, K, N)