Add 2D grid program_id semantics (ADR-0022)

tl.program_id(axis=0) returns local PE id within cube,
tl.program_id(axis=1) returns cube id. Enables cube-aware
sharding in benchmark kernels.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/kernbench/components/builtin/pe_cpu.py

@@ -42,6 +42,9 @@ class PeCpuComponent(ComponentBase):
             self._cube_idx = int(parts[1].replace("cube", ""))
         except (IndexError, ValueError):
             self._cube_idx = 0
+        # num_cubes from spec (for tl.program_id(axis=1))
+        spec = ctx.spec if ctx else {}
+        self._num_cubes = spec.get("system", {}).get("sips", {}).get("cubes_per_sip", 1)
 
     def _find_shard(self, shards: tuple) -> Any:
         """Find shard matching this PE's (sip, cube, pe). Fallback to positional index."""
@@ -139,6 +142,7 @@ class PeCpuComponent(ComponentBase):
             pe_idx=self._pe_idx,
             sip_idx=self._sip_idx,
             cube_idx=self._cube_idx,
+            num_cubes=self._num_cubes,
             scheduler_id=scheduler_id,
             out_ports=self.out_ports,
             store=store,
@@ -155,7 +159,11 @@ class PeCpuComponent(ComponentBase):
         )
         from kernbench.triton_emu.tl_context import TLContext, run_kernel
 
-        tl = TLContext(pe_id=self._pe_idx, num_programs=num_programs, dispatch_cycles=0)
+        tl = TLContext(
+            pe_id=self._pe_idx, num_programs=num_programs,
+            cube_id=self._cube_idx, num_cubes=self._num_cubes,
+            dispatch_cycles=0,
+        )
         run_kernel(kernel_fn, tl, *kernel_args)
         commands = tl.commands
 

src/kernbench/triton_emu/kernel_runner.py

@@ -50,11 +50,13 @@ class KernelRunner:
         scheduler_id: str,
         out_ports: dict[str, simpy.Store],
         store: MemoryStore | None = None,
+        num_cubes: int = 1,
     ) -> None:
         self._pe_prefix = pe_prefix
         self._pe_idx = pe_idx
         self._sip_idx = sip_idx
         self._cube_idx = cube_idx
+        self._num_cubes = num_cubes
         self._scheduler_id = scheduler_id
         self._out_ports = out_ports
         self._store = store
@@ -83,6 +85,8 @@ class KernelRunner:
         tl = TLContext(
             pe_id=self._pe_idx,
             num_programs=num_programs,
+            cube_id=self._cube_idx,
+            num_cubes=self._num_cubes,
             dispatch_cycles=0,
             runner=self,
         )

src/kernbench/triton_emu/tl_context.py

@@ -53,9 +53,13 @@ class TLContext:
         num_programs: int = 1,
         dispatch_cycles: int = 1,
         runner: Any = None,
+        cube_id: int = 0,
+        num_cubes: int = 1,
     ) -> None:
         self._pe_id = pe_id
         self._num_programs = num_programs
+        self._cube_id = cube_id
+        self._num_cubes = num_cubes
         self._dispatch_cycles = dispatch_cycles
         self._commands: list[PeCommand] = []
         self._handle_counter = 0
@@ -234,11 +238,23 @@ class TLContext:
     # ── Index / Scalar (PE_CPU, no engine) ────────────────────────
 
     def program_id(self, axis: int = 0) -> int:
-        """Return program instance index."""
+        """Return program instance index.
+
+        axis=0: local PE id within cube.
+        axis=1: cube id.
+        """
+        if axis == 1:
+            return self._cube_id
         return self._pe_id
 
     def num_programs(self, axis: int = 0) -> int:
-        """Return total number of program instances."""
+        """Return total number of program instances.
+
+        axis=0: num PEs per cube.
+        axis=1: num cubes.
+        """
+        if axis == 1:
+            return self._num_cubes
         return self._num_programs
 
     def arange(self, start: int, end: int, dtype: str = "i32") -> TensorHandle: