Everything about which accelerator a run targets was previously spelled CUDA:
device discovery went through nvidia-smi, pinning through CUDA_VISIBLE_DEVICES,
the vendor guard only recognised NVIDIA, and the -p plugin list was hardcoded in
run_pytest.sh with no way to drop the CUDA-only members. Running the same
operator corpus on another vendor's device therefore meant editing the drivers.
The selection now hangs off one ZL_BACKEND variable (cuda/musa/rocm/ascend, with
the usual aliases):
- backends/ owns per-backend device enumeration, visibility env keys and
capability flags, so nothing above it names a vendor;
- plugin_plan.py derives the -p list from those capabilities instead of a
hardcoded array, which is what lets a non-CUDA run omit the graph-capture and
cluster-fix plugins rather than watch them fail;
- _timing_plugin selects the timer from the same capabilities: CUDA keeps
graph capture with its per-shape fallback, other backends stay on upstream
do_bench and record timer=direct, because capture-then-fallback on a device
without graph support only produces noise;
- _device_setup_plugin replaces the NVIDIA-only vendor guard;
- providers/flaggems.py resolves the pytest node, so a caller may pass TEST_NODE
directly instead of the OP/OP_FILE pair, and SHAPE_MODE=upstream can defer to
FlagGems' own core_shapes;
- _run_identity.py writes run_identity.json next to each run, recording backend,
device, timer, autotune mode and the resolved test node. Two runs whose
calibers differ are otherwise indistinguishable after the fact.
run_batch gains --backend and --devices (--gpus kept as an alias) and carries
backend/device/timer into ops_status.csv. test_backends.py covers the registry,
alias resolution, visibility env and plugin planning.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
For shape-heavy ops the autotune sweep dominates wall time, not the
measurement. _parallel_warmup_plugin shards the sweep across N GPUs and
hands the merged configs to the normal single-GPU serial measurement, so
timing stays comparable. Activated by PARALLEL_WARMUP_GPUS=N (silent
no-op when unset, so it can stay in the plugin list); output layout,
REPLAY_FROM usage and the latency table are unchanged.
Measurement is deliberately not parallelized: N processes saturating one
box couple through the power/thermal budget, so per-card latency gets
dragged by its neighbours by an amount that does not reproduce. Config
keys whose winner differs across shards are counted and reported as a
WARNING -- that count is how much to trust the run.
Fixes found while auditing:
- Shards no longer overwrite CUDA_VISIBLE_DEVICES with a bare shard
index, which a caller who had selected idle cards (e.g. 6,7) would see
re-interpreted as absolute ids 0,1 -- silently benchmarking on the busy
cards they were avoiding.
- Interrupting the sweep no longer leaves N subprocesses holding GPUs;
children are terminated and reaped before the exception propagates
(BaseException, since KeyboardInterrupt is not an Exception).
- run_pytest.sh claims its output dir with a bare mkdir and retreats to
a -2/-3 suffix on collision. The second-resolution timestamp meant two
concurrent runs shared one directory and overwrote each other.
- Replay eviction failures now emit a distinct compile_*_no_evict marker.
LibTuner.cache is a sqlite-backed ConfigCache with no __delitem__, so
the bad config could not be dropped and the retry re-read it, while the
log still claimed a clean fallback to live autotune.
Also trims comment density in both shell scripts and reworks the README:
promotes the parallel and cudagraph sections out from under the A/B flow,
groups the env table by purpose, documents that two record-mode runs are
not comparable, and marks ab_fold_test.sh as a caliber example whose
switch upstream has already removed.
AharrypotterandClaude Opus 5
zhoulin