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>
run_batch.py drives run_pytest.sh per operator across GPUs (stable-hash
sharding, per-op subprocess isolation, process-group timeouts, retry with
deterministic-failure cutoff, two-level dtype fallback, .complete resume,
per-op REPLAY_FROM). batch_summary.py aggregates run.log tables into
summary.csv. ops/ holds the curated assets: dual-repo inventories rebuilt
via AST scan + pytest collect verification, shape sets migrated from the
old regression harness and merged with upstream core_shapes class-name
keys (upstream's set_shapes falls back op_name -> MRO class name ->
1-D DEFAULT_SHAPES, so replacing the shape file without class keys
crashes the BLAS family), and a dismiss list where all 76 entries carry
verified reasons. Validated end to end: 1036-op full screen with zero
failures.
Also fix two cudagraph plugin regressions: newer torch appends "enable
device-side assertions" to every CUDA error, so the loose fatal-error
marker disabled the documented do_bench fallback entirely; and an aborted
graph capture can leave the default CUDA RNG generator stuck in capturing
state, poisoning every later torch.randn - captures now run under a
throwaway RNG state. run_pytest.sh gains an optional DTYPES passthrough.
AharrypotterandClaude Opus 5
zhoulin