feat: expose the upstream benchmark level as BENCH_LEVEL

run_pytest.sh hardcoded --level core, so the only shapes ever reachable were
each operator's core_shapes entry. FlagGems merges a subclass's
set_more_shapes() on top of that at --level comprehensive, and for BLAS that
addition is where the large GEMMs live: mm goes from 5 shapes to 29, taking in
four large-K tuples plus models_shapes/*.yaml, with N/K up to 152064. The
comprehensive path also yields each shape a second time (transposed, for BLAS),
so the case count doubles. None of that was reachable without editing the driver.

BENCH_LEVEL defaults to core, so existing runs are unchanged, and rejects
anything other than core/comprehensive rather than passing it through to pytest.
The chosen level is echoed in the run header, written into the archived
shapes.yaml, and recorded in run_identity.json, because an A/B whose two sides
used different levels is not comparable and is otherwise invisible afterwards.

Sweep cost scales with the shape count, so comprehensive on a BLAS operator
wants PARALLEL_WARMUP_GPUS.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

_run_identity.py

@@ -70,6 +70,7 @@ def main(argv=None) -> int:
     p.add_argument("--device", default=os.environ.get("ZL_DEVICE", ""))
     p.add_argument("--test-file", default=os.environ.get("TEST_FILE", ""))
     p.add_argument("--shape-mode", default=os.environ.get("SHAPE_MODE", ""))
+    p.add_argument("--bench-level", default=os.environ.get("BENCH_LEVEL", "core"))
     args = p.parse_args(argv)
     init_identity(
         args.out_dir,
@@ -81,6 +82,7 @@ def main(argv=None) -> int:
         device=args.device,
         test_file=args.test_file,
         shape_mode=args.shape_mode,
+        bench_level=args.bench_level,
     )
     return 0