feat: make the harness backend-agnostic

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>

_mm_cluster_fix_plugin.py

@@ -105,6 +105,18 @@ def _warmup_load_backend():
 
 def pytest_configure(config):
     from _term_style import tag
+    try:
+        from backends import get_backend
+        if not get_backend().supports_mm_cluster_fix:
+            print(f"{tag('[mm-cluster-fix-plugin]')} skipped: backend "
+                  f"{get_backend().name!r} is not the CUDA Hopper path",
+                  file=sys.stderr, flush=True)
+            return
+    except Exception as exc:
+        print(f"{tag('[mm-cluster-fix-plugin]')} backend probe failed ({exc}); "
+              "leaving cluster_remote_mm_scenario unpatched",
+              file=sys.stderr, flush=True)
+        return
     _patch_loaded()
     _warmup_load_backend()
     _patch_loaded()