ADR-0026: DPPolicy intra-device only + ShardSpec structural coords

DPPolicy no longer carries a cross-SIP axis. SIP-level placement is
solely controlled by torch.ahbm.set_device(rank) (ADR-0024); DPPolicy
itself describes only the cube × PE layout within one SIP. ShardSpec
switches to structural (sip, cube, pe) coordinates; the flat pe_index
field/property is fully removed — silent drift between global-flat and
SIP-local interpretations was a foot-gun flagged by ADR-0024 D11.

Breaking API (explicit TypeError / AttributeError):
- DPPolicy(sip=...) / DPPolicy(num_sips=...) -> TypeError
- ShardSpec.pe_index -> AttributeError
- ShardSpec(pe_index=...) -> TypeError
- resolve_dp_policy now takes target_sip= (required), no num_sips.

Downstream migration:
- PE allocator dict keyed by (sip, cube, pe) tuples, in both
  _ensure_allocators and _free_tensor. deploy_tensor uses tuple lookup.
- _create_tensor passes target_sip=current_sip; post-hoc pe_index
  shifting removed entirely.
- launch._compute_local_shape drops the dp.sip branch.
- Internal resolvers (column_wise / row_wise / replicate / tiled_*)
  return _LocalPeShard (cube-local identifier) instead of ShardSpec —
  resolve_dp_policy lifts them to full structural coords.

Tests:
- New tests/test_adr0026_dppolicy_intra_device.py (12 tests) pins the
  contract end-to-end.
- test_sip_parallel.py rewritten: SIP composition now modeled as two
  resolve_dp_policy(target_sip=...) calls (ADR-0024 launcher style).
- Call-site migration: test_tensor, test_va_integration, test_va_offset,
  test_runtime_api_tensor, test_tl_recv_async, test_ccl_* and benches
  gemm_single_pe, gpt3_qkv, va_offset_verify, ccl_allreduce (legacy
  branch) all use intra-device DPPolicy and structural ShardSpec.

Result: 523 passed, 1 strict xfail (ring_default_ws — unchanged
ADR-0024 Phase B blocker; architectural fix deferred to ADR-0027).

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

benches/ccl_allreduce.py

@@ -32,29 +32,26 @@ def _derive_dp(spec: dict, world_size: int) -> DPPolicy:
     """Legacy DPPolicy for world_size > SIP count (rank = flat PE index).
 
     Used only in the ccl.yaml-override path so the existing matrix tests
-    with explicit world_size (8, 16, 7 etc.) keep working. The new
+    with explicit world_size (8, 16, 7 etc.) keep working. ADR-0026:
-    ADR-0024 TP path (rank = SIP) uses a per-rank DPPolicy inside the
+    DPPolicy is intra-device only, so this legacy path now always stays
-    worker instead.
+    within a single SIP and distributes the override world_size across
+    that SIP's cubes and PEs.
     """
-    sips = int(spec["system"]["sips"]["count"])
-    cm = spec["sip"]["cube_mesh"]
     pl = spec["cube"]["pe_layout"]
     pes_per_cube = int(pl["pe_per_corner"]) * len(pl["corners"])
+    cm = spec["sip"]["cube_mesh"]
     cubes_per_sip = int(cm["w"]) * int(cm["h"])
-    total = sips * cubes_per_sip * pes_per_cube
-    if world_size == total:
-        return DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")
     if world_size <= pes_per_cube:
         return DPPolicy(
-            sip="replicate", cube="replicate", pe="column_wise",
+            cube="replicate", pe="column_wise",
-            num_sips=1, num_cubes=1, num_pes=world_size,
+            num_cubes=1, num_pes=world_size,
         )
     if world_size <= cubes_per_sip * pes_per_cube:
         return DPPolicy(
-            sip="replicate", cube="column_wise", pe="column_wise",
+            cube="column_wise", pe="column_wise",
-            num_sips=1, num_cubes=world_size // pes_per_cube,
+            num_cubes=world_size // pes_per_cube,
         )
-    return DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")
+    return DPPolicy(cube="column_wise", pe="column_wise")
 
 
 def worker(rank: int, world_size: int, torch) -> None:

benches/gemm_single_pe.py

@@ -3,7 +3,7 @@
 Full host-to-PE pipeline:
   Host → PCIE_EP → IO_CPU → M_CPU → PE_CPU → SchedulerV2 → PE_DMA → HBM
 
-Single PE: num_sips=1, num_cubes=1, num_pes=1 via DPPolicy override.
+Single PE: num_cubes=1, num_pes=1 via DPPolicy override.
 Both operands use tl.ref (HBM-resident); scheduler_v2 tiles and streams
 per-tile DMA internally.
 
@@ -30,7 +30,7 @@ def _gemm_kernel(a_ptr, b_ptr, out_ptr, M, K, N, tl, DTYPE="f16"):
 def run(torch):
     """Run the single-PE GEMM benchmark."""
     dp = DPPolicy(cube="replicate", pe="replicate",
-                  num_sips=1, num_cubes=1, num_pes=1)
+                  num_cubes=1, num_pes=1)
 
     a   = torch.empty((M, K), dtype=DTYPE, dp=dp, name="a")
     b   = torch.empty((K, N), dtype=DTYPE, dp=dp, name="b")

benches/gpt3_qkv.py

@@ -72,12 +72,16 @@ def run(torch):
     K = GPT3_D_MODEL
     N = COLS_PER_PE
 
-    # X: replicated across all PEs
+    # ADR-0026: DPPolicy is intra-device only. For multi-SIP execution the
+    # ADR-0024 launcher calls this bench once per SIP (each worker via
+    # torch.ahbm.set_device(rank)); here the policy describes only the
+    # cube × PE layout within a single SIP.
+    # X: replicated across all PEs within the SIP
     dp_replicate = DPPolicy(cube="replicate", pe="replicate",
-                            num_sips=N_SIPS, num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
+                            num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
-    # W_Q/K/V, out_Q/K/V: column-wise sharded across all PEs
+    # W_Q/K/V, out_Q/K/V: column-wise sharded across all PEs within the SIP
     dp_sharded = DPPolicy(cube="column_wise", pe="column_wise",
-                          num_sips=N_SIPS, num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
+                          num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
 
     x     = torch.empty((M, K), dtype=DTYPE, dp=dp_replicate, name="x")
     wq    = torch.empty((K, GPT3_D_MODEL), dtype=DTYPE, dp=dp_sharded, name="wq")

benches/va_offset_verify.py

@@ -1,7 +1,7 @@
 """VA offset verification benchmark.
 
 Verifies that Triton-style base_ptr + pid * stride addressing works correctly
-with full TP sharding (sip/cube/pe all column_wise). Each PE loads its own
+with intra-SIP TP sharding (cube/pe column_wise). Each PE loads its own
 block from a sharded tensor and stores it back.
 
 The kernel uses standard Triton patterns:
@@ -28,7 +28,7 @@ def _copy_kernel(src_ptr, dst_ptr, M, K, tl, DTYPE="f16"):
 
 def run(torch):
     """Run the VA offset verification benchmark with full TP sharding."""
-    dp = DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")
+    dp = DPPolicy(cube="column_wise", pe="column_wise")
     src = torch.zeros((M, K), dtype=DTYPE, dp=dp, name="src")
     dst = torch.empty((M, K), dtype=DTYPE, dp=dp, name="dst")
 

docs/adr/ADR-0026-dppolicy-intra-device.md

@@ -2,7 +2,7 @@
 
 ## Status
 
-Proposed (Revision 4 — 문서 일관성 + grep audit 구체화)
+Accepted (Revision 5 — Phase 2 landed 2026-04-14, 523 passed + 1 strict xfail)
 
 ## Context
 

docs/ccl-author-guide.en.md

@@ -129,8 +129,8 @@ N_ELEM = 8
 def worker(rank: int, world_size: int, torch) -> None:
     """Per-rank business logic — mirrors a real PyTorch DDP worker."""
     dp = DPPolicy(
-        sip="replicate", cube="replicate", pe="column_wise",
+        cube="replicate", pe="column_wise",
-        num_sips=1, num_cubes=1, num_pes=world_size,
+        num_cubes=1, num_pes=world_size,
     )
     tensor = torch.zeros(
         (1, world_size * N_ELEM), dtype="f16", dp=dp, name="hello_in",

docs/ccl-author-guide.md

@@ -114,8 +114,8 @@ def run(torch):
     a = torch.zeros(
         (1, WORLD_SIZE * N_ELEM), dtype="f16",
         dp=DPPolicy(
-            sip="replicate", cube="replicate", pe="column_wise",
+            cube="replicate", pe="column_wise",
-            num_sips=1, num_cubes=1,
+            num_cubes=1,
         ),
         name="hello_in",
     )

src/kernbench/policy/placement/dp.py

@@ -1,3 +1,14 @@
+"""Data-parallel placement policy (ADR-0026: intra-device only).
+
+``DPPolicy`` describes how a tensor is sharded *within a single SIP* across
+that SIP's cubes and PEs. Crossing the SIP boundary is not a DPPolicy
+concern: ADR-0024's ``torch.ahbm.set_device(rank)`` picks the SIP, and
+Megatron-style TP (ADR-0027) expresses multi-SIP tensors when needed.
+
+``ShardSpec`` is expressed in structural ``(sip, cube, pe)`` coordinates.
+The former flat ``pe_index`` field/property is fully removed — callers
+needing a flat integer key compute it explicitly at the call site.
+"""
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -7,25 +18,58 @@ from typing import Literal
 
 @dataclass(frozen=True)
 class DPPolicy:
-    """Three-level data-parallel policy: sip-level + cube-level + pe-level.
+    """Intra-device (cube × PE) data-parallel policy.
 
-    Policies:
+    SIP-level placement is controlled by ``torch.ahbm.set_device(rank)``
+    (ADR-0024). For tensors that must cross SIP boundaries, use
+    Megatron-style parallel layers (ADR-0027). DPPolicy itself never
+    crosses a SIP boundary.
+
+    Policies (per axis):
       - "replicate": full copy at each unit
       - "column_wise": split K (column) axis across units
       - "row_wise": split M (row) axis across units
 
-    Optional overrides (default None = use topology dimensions):
+    Optional overrides (``None`` = use topology dimensions):
-      - num_pes: override PEs per cube (e.g., 1 for single-PE test)
+      - num_pes: override PEs per cube
-      - num_cubes: override cubes per SIP (e.g., 1 for single-cube test)
+      - num_cubes: override cubes per SIP
-      - num_sips: override SIP count
     """
 
-    sip: Literal["replicate", "column_wise", "row_wise"] = "replicate"
     cube: Literal["replicate", "column_wise", "row_wise"] = "replicate"
     pe: Literal["replicate", "column_wise", "row_wise"] = "replicate"
     num_pes: int | None = None
     num_cubes: int | None = None
-    num_sips: int | None = None
+
+
+@dataclass(frozen=True)
+class ShardSpec:
+    """Structural shard placement — ``(sip, cube, pe)`` coord (ADR-0026).
+
+    Global-flat ``pe_index`` was removed: callers must use structural
+    coords directly. If a flat integer key is needed in a local context
+    (e.g. internal dict lookup), compute it explicitly at the call site
+    and do not expose it in any public API.
+    """
+
+    sip: int
+    cube: int
+    pe: int
+    offset_bytes: int
+    nbytes: int
+
+
+@dataclass(frozen=True)
+class _LocalPeShard:
+    """Internal — PE resolver's return type (ADR-0026 D3).
+
+    Holds a cube-local PE identifier (``local_pe``) plus the shard's
+    byte payload. Lifted into ``ShardSpec`` with full ``(sip, cube, pe)``
+    coordinates inside ``resolve_dp_policy``.
+    """
+
+    local_pe: int
+    offset_bytes: int
+    nbytes: int
 
 
 def _split_shape(
@@ -52,14 +96,13 @@ def resolve_dp_policy(
     itemsize: int,
     num_pe: int,
     num_cubes: int = 1,
-    num_sips: int = 1,
+    target_sip: int,
 ) -> list[ShardSpec]:
-    """Resolve a DPPolicy into a list[ShardSpec] with three-level resolution.
+    """Resolve a DPPolicy into a list[ShardSpec] on a single SIP.
 
-    SIP-level → cube-level → pe-level.
+    Two-level resolution (cube × PE) within ``target_sip``. Each returned
-    num_cubes is cubes per SIP (not total).
+    ``ShardSpec`` carries ``sip=target_sip`` and cube/pe local to the SIP.
-    ShardSpec.pe_index uses flat indexing:
+    No SIP-level split — DPPolicy is intra-device only (ADR-0026).
-      sip_id * num_cubes * num_pe + cube_id * num_pe + pe_id
     """
     _PE_RESOLVERS = {
         "replicate": replicate,
@@ -70,84 +113,61 @@ def resolve_dp_policy(
     if resolver is None:
         raise ValueError(f"Unknown pe-level policy: {policy.pe}")
 
-    cubes_per_sip = num_cubes
     all_shards: list[ShardSpec] = []
 
-    # Level 1: SIP
+    # Level 1: cube within SIP
-    sip_splits = _split_shape(policy.sip, shape, num_sips, itemsize)
+    cube_splits = _split_shape(policy.cube, shape, num_cubes, itemsize)
-
-    for sip_id, (sip_shape, sip_offset) in enumerate(sip_splits):
-        # Level 2: Cube within SIP
-        cube_splits = _split_shape(policy.cube, sip_shape, cubes_per_sip, itemsize)
 
     for cube_id, (cube_shape, cube_offset) in enumerate(cube_splits):
-            # Level 3: PE within cube
+        # Level 2: PE within cube — resolver returns _LocalPeShard
-            pe_shards = resolver(shape=cube_shape, itemsize=itemsize, num_pe=num_pe)
+        local_shards = resolver(shape=cube_shape, itemsize=itemsize, num_pe=num_pe)
 
-            for ps in pe_shards:
+        for ls in local_shards:
-                flat_idx = (
-                    sip_id * cubes_per_sip * num_pe
-                    + cube_id * num_pe
-                    + ps.pe_index
-                )
             all_shards.append(ShardSpec(
-                    pe_index=flat_idx,
+                sip=target_sip,
-                    offset_bytes=sip_offset + cube_offset + ps.offset_bytes,
+                cube=cube_id,
-                    nbytes=ps.nbytes,
+                pe=ls.local_pe,
+                offset_bytes=cube_offset + ls.offset_bytes,
+                nbytes=ls.nbytes,
             ))
 
     return all_shards
 
 
-@dataclass(frozen=True)
-class ShardSpec:
-    pe_index: int
-    offset_bytes: int
-    nbytes: int
-
-
 def column_wise(
     *, shape: tuple[int, int], itemsize: int, num_pe: int,
-) -> list[ShardSpec]:
+) -> list[_LocalPeShard]:
     """Split K axis into num_pe equal parts. Each PE gets (M, K/P)."""
     M, K = shape
     chunk_k = K // num_pe
     chunk_bytes = M * chunk_k * itemsize
-    shards = []
+    return [
-    for i in range(num_pe):
+        _LocalPeShard(local_pe=i, offset_bytes=i * chunk_bytes, nbytes=chunk_bytes)
-        shards.append(ShardSpec(
+        for i in range(num_pe)
-            pe_index=i,
+    ]
-            offset_bytes=i * chunk_bytes,
-            nbytes=chunk_bytes,
-        ))
-    return shards
 
 
 def row_wise(
     *, shape: tuple[int, int], itemsize: int, num_pe: int,
-) -> list[ShardSpec]:
+) -> list[_LocalPeShard]:
     """Split M axis into num_pe equal parts. Each PE gets (M/P, K)."""
     M, K = shape
     chunk_m = M // num_pe
     chunk_bytes = chunk_m * K * itemsize
-    shards = []
+    return [
-    for i in range(num_pe):
+        _LocalPeShard(local_pe=i, offset_bytes=i * chunk_bytes, nbytes=chunk_bytes)
-        shards.append(ShardSpec(
+        for i in range(num_pe)
-            pe_index=i,
+    ]
-            offset_bytes=i * chunk_bytes,
-            nbytes=chunk_bytes,
-        ))
-    return shards
 
 
 def replicate(
     *, shape: tuple[int, int], itemsize: int, num_pe: int,
-) -> list[ShardSpec]:
+) -> list[_LocalPeShard]:
     """Full copy per PE. Each PE gets (M, K)."""
     M, K = shape
     full_bytes = M * K * itemsize
     return [
-        ShardSpec(pe_index=i, offset_bytes=0, nbytes=full_bytes)
+        _LocalPeShard(local_pe=i, offset_bytes=0, nbytes=full_bytes)
         for i in range(num_pe)
     ]
 
@@ -155,20 +175,20 @@ def replicate(
 def tiled_column_major(
     *, shape: tuple[int, int], itemsize: int, num_pe: int,
     tile_m: int, tile_k: int,
-) -> list[ShardSpec]:
+) -> list[_LocalPeShard]:
     """2D tiling, column-major order (K axis first), round-robin across PEs."""
     M, K = shape
     tiles_m = ceil(M / tile_m)
     tiles_k = ceil(K / tile_k)
     tile_bytes = tile_m * tile_k * itemsize
     row_bytes = K * itemsize
-    shards = []
+    shards: list[_LocalPeShard] = []
     idx = 0
     for mi in range(tiles_m):
         for ki in range(tiles_k):
             offset = (mi * tile_m * row_bytes) + (ki * tile_k * itemsize)
-            shards.append(ShardSpec(
+            shards.append(_LocalPeShard(
-                pe_index=idx % num_pe,
+                local_pe=idx % num_pe,
                 offset_bytes=offset,
                 nbytes=tile_bytes,
             ))
@@ -179,20 +199,20 @@ def tiled_column_major(
 def tiled_row_major(
     *, shape: tuple[int, int], itemsize: int, num_pe: int,
     tile_m: int, tile_k: int,
-) -> list[ShardSpec]:
+) -> list[_LocalPeShard]:
     """2D tiling, row-major order (M axis first), round-robin across PEs."""
     M, K = shape
     tiles_m = ceil(M / tile_m)
     tiles_k = ceil(K / tile_k)
     tile_bytes = tile_m * tile_k * itemsize
     row_bytes = K * itemsize
-    shards = []
+    shards: list[_LocalPeShard] = []
     idx = 0
     for ki in range(tiles_k):
         for mi in range(tiles_m):
             offset = (mi * tile_m * row_bytes) + (ki * tile_k * itemsize)
-            shards.append(ShardSpec(
+            shards.append(_LocalPeShard(
-                pe_index=idx % num_pe,
+                local_pe=idx % num_pe,
                 offset_bytes=offset,
                 nbytes=tile_bytes,
             ))

src/kernbench/runtime_api/context.py

@@ -89,7 +89,7 @@ class RuntimeContext:
 
     _handles: list[RequestHandle] = field(default_factory=list, init=False)
     _completed: set[RequestHandle] = field(default_factory=set, init=False)
-    _allocators: dict[int, Any] = field(default_factory=dict, init=False)
+    _allocators: dict[tuple[int, int, int], Any] = field(default_factory=dict, init=False)
     _va_allocator: Any = field(default=None, init=False)
     _tensor_counter: int = field(default=0, init=False)
     _traces: list[dict] = field(default_factory=list, init=False)
@@ -270,12 +270,7 @@ class RuntimeContext:
         # Return PA space
         if self._allocators:
             for shard in handle.shards:
-                flat_idx = (
+                alloc = self._allocators.get((shard.sip, shard.cube, shard.pe))
-                    shard.sip * self._num_cubes * self._pes_per_cube
-                    + shard.cube * self._pes_per_cube
-                    + shard.pe
-                )
-                alloc = self._allocators.get(flat_idx)
                 if alloc is not None:
                     from kernbench.policy.address.phyaddr import PhysAddr
                     alloc.free_hbm(PhysAddr.decode(shard.pa), shard.nbytes)
@@ -339,17 +334,15 @@ class RuntimeContext:
             tcm_scheduler_reserved_bytes=4 * (1 << 20),
             sram_bytes_per_cube=32 * (1 << 20),
         )
-        # Create allocators scoped to target SIP(s) only
+        # Create allocators scoped to target SIP(s) only.
-        # Flat index: sip_id * cubes_per_sip * pes_per_cube + cube_id * pes_per_cube + pe_id
+        # ADR-0026 D5: dict key is the structural (sip, cube, pe) tuple.
         self._pes_per_cube = pes_per_cube
         self._num_cubes = cubes_per_sip
         self._num_sips = sip_count
-        cubes_x_pes = cubes_per_sip * pes_per_cube
         for sip_id in sip_range:
             for cube_id in range(cubes_per_sip):
                 for pe_id in range(pes_per_cube):
-                    flat_idx = sip_id * cubes_x_pes + cube_id * pes_per_cube + pe_id
+                    self._allocators[(sip_id, cube_id, pe_id)] = PEMemAllocator(
-                    self._allocators[flat_idx] = PEMemAllocator(
                         rack_id=0, sip_id=sip_id, cube_id=cube_id, pe_id=pe_id, cfg=cfg,
                     )
 
@@ -436,44 +429,23 @@ class RuntimeContext:
         # DPPolicy overrides take precedence over topology dimensions
         eff_num_pe = dp.num_pes if dp.num_pes is not None else self._pes_per_cube
         eff_num_cubes = dp.num_cubes if dp.num_cubes is not None else self._num_cubes
-        # ADR-0024 D11: if torch.ahbm.set_device(r) is active AND DPPolicy
+        # ADR-0026 D4: resolve structural coords directly at resolve time.
-        # leaves the SIP dimension at its default (replicate + no num_sips
+        # ``torch.ahbm.set_device(rank)`` (ADR-0024 D10) selects the target
-        # override), scope the tensor to SIP r only.
+        # SIP; if unset, fall back to SIP 0 for single-driver compatibility.
-        # NOTE: this path uses post-hoc pe_index shifting as a temporary
-        # measure; ADR-0026 replaces it with structural (sip, cube, pe)
-        # coords in ShardSpec.
         current_sip = (
             self.ahbm.current_device() if hasattr(self, "ahbm") else None
         )
-        scope_to_current_sip = (
+        if current_sip is None:
-            current_sip is not None
+            current_sip = 0
-            and dp.sip == "replicate"
-            and dp.num_sips is None
-        )
-        if scope_to_current_sip:
-            eff_num_sips = 1
-        else:
-            eff_num_sips = (
-                dp.num_sips if dp.num_sips is not None else self._num_sips
-            )
         placement = resolve_dp_policy(
             dp, shape=shape_2d, itemsize=itemsize,
             num_pe=eff_num_pe, num_cubes=eff_num_cubes,
-            num_sips=eff_num_sips,
+            target_sip=int(current_sip),
         )
-        if scope_to_current_sip:
-            from kernbench.policy.placement.dp import ShardSpec as _SS
-            sip_stride = self._num_cubes * self._pes_per_cube
-            offset = int(current_sip) * sip_stride
-            placement = [
-                _SS(pe_index=s.pe_index + offset,
-                    offset_bytes=s.offset_bytes, nbytes=s.nbytes)
-                for s in placement
-            ]
 
         # Infer target_pe from placement using local (within-cube) PE IDs.
         # This ensures M_CPU only fans out to PEs that own shards, not all PEs.
-        local_pe_ids = sorted({s.pe_index % eff_num_pe for s in placement})
+        local_pe_ids = sorted({s.pe for s in placement})
         if len(local_pe_ids) == 1:
             target_pe: int | tuple[int, ...] | str = local_pe_ids[0]
         elif len(local_pe_ids) == eff_num_pe and eff_num_pe == self._pes_per_cube:
@@ -669,11 +641,8 @@ class RuntimeContext:
             dp = t._dp_metadata.dp_policy if t._dp_metadata else None
             if dp is None:
                 return t.shape
-            if dp.sip != "replicate":
+            # ADR-0026: DPPolicy no longer crosses SIP boundaries; cube + PE
-                if dp.sip == "column_wise":
+            # are the only axes that shrink the local shape.
-                    K = K // self._num_sips
-                elif dp.sip == "row_wise":
-                    M = M // self._num_sips
             if dp.cube != "replicate":
                 if dp.cube == "column_wise":
                     K = K // self._num_cubes

src/kernbench/runtime_api/tensor.py

@@ -72,7 +72,7 @@ def deploy_tensor(
     shape: tuple[int, ...],
     dtype: str,
     placement: list[ShardSpec],
-    allocators: dict[int, PEMemAllocator],
+    allocators: dict[tuple[int, int, int], PEMemAllocator],
     mem_kind: Literal["hbm", "tcm"] = "hbm",
     va_allocator=None,
 ) -> TensorHandle:
@@ -86,15 +86,15 @@ def deploy_tensor(
 
     shards: list[TensorShard] = []
     for spec in placement:
-        alloc = allocators[spec.pe_index]
+        alloc = allocators[(spec.sip, spec.cube, spec.pe)]
         if mem_kind == "hbm":
             pa = alloc.alloc_hbm(spec.nbytes)
         else:
             pa = alloc.alloc_tcm(spec.nbytes)
         shards.append(TensorShard(
-            sip=alloc._sip_id,
+            sip=spec.sip,
-            cube=alloc._cube_id,
+            cube=spec.cube,
-            pe=alloc._pe_id,
+            pe=spec.pe,
             pa=pa.encode(),
             nbytes=spec.nbytes,
             offset_bytes=spec.offset_bytes,
@@ -394,7 +394,8 @@ class Tensor:
     ) -> Tensor:
         """Set DP placement metadata (like torch.Tensor.to())."""
         if placement is None:
-            placement = [ShardSpec(pe_index=0, offset_bytes=0, nbytes=self.nbytes)]
+            placement = [ShardSpec(sip=0, cube=0, pe=0,
+                                   offset_bytes=0, nbytes=self.nbytes)]
         self._dp_metadata = DPMetadata(
             placement=placement, dp_policy=dp_policy,
             sip=sip, cube=cube, target_pe=target_pe,

tests/test_adr0026_dppolicy_intra_device.py

@@ -0,0 +1,239 @@
+"""ADR-0026 Phase 1 tests: DPPolicy intra-device only + ShardSpec structural.
+
+These tests encode the contract from ADR-0026:
+
+- DPPolicy no longer accepts ``sip`` or ``num_sips`` kwargs (TypeError).
+- ShardSpec carries structural ``(sip, cube, pe)`` coordinates; the old flat
+  ``pe_index`` field/property is fully removed (AttributeError).
+- ``resolve_dp_policy(..., target_sip=N)`` stamps every returned ShardSpec
+  with ``sip=N``; cube and pe fields are local.
+- ``RuntimeContext._allocators`` is keyed by ``(sip, cube, pe)`` tuples.
+
+Phase 1: production code is unchanged → these tests SHOULD FAIL until the
+Phase 2 diff lands. Phase 2 makes all of them pass.
+"""
+from __future__ import annotations
+
+import pytest
+
+from kernbench.policy.address.allocator import AddressConfig, PEMemAllocator
+from kernbench.policy.placement.dp import DPPolicy, ShardSpec, resolve_dp_policy
+from kernbench.runtime_api.tensor import deploy_tensor
+
+
+# ── D1: DPPolicy no longer accepts sip / num_sips ─────────────────────
+
+
+def test_dppolicy_rejects_sip_kwarg():
+    """DPPolicy(sip=...) must raise TypeError after field removal."""
+    with pytest.raises(TypeError):
+        DPPolicy(sip="column_wise", cube="replicate", pe="replicate")
+
+
+def test_dppolicy_rejects_num_sips_kwarg():
+    """DPPolicy(num_sips=...) must raise TypeError after field removal."""
+    with pytest.raises(TypeError):
+        DPPolicy(cube="replicate", pe="replicate", num_sips=2)
+
+
+def test_dppolicy_accepts_only_intra_device_fields():
+    """Intra-device fields still work: cube, pe, num_cubes, num_pes."""
+    dp = DPPolicy(cube="column_wise", pe="column_wise",
+                  num_cubes=2, num_pes=4)
+    assert dp.cube == "column_wise"
+    assert dp.pe == "column_wise"
+    assert dp.num_cubes == 2
+    assert dp.num_pes == 4
+    # No sip / num_sips attributes — even reading them must fail.
+    assert not hasattr(dp, "sip"), "DPPolicy.sip must be removed"
+    assert not hasattr(dp, "num_sips"), "DPPolicy.num_sips must be removed"
+
+
+# ── D2: ShardSpec structural coords, no pe_index ──────────────────────
+
+
+def test_shardspec_has_structural_coords():
+    """ShardSpec constructs from (sip, cube, pe, offset_bytes, nbytes)."""
+    s = ShardSpec(sip=1, cube=2, pe=3, offset_bytes=128, nbytes=64)
+    assert s.sip == 1
+    assert s.cube == 2
+    assert s.pe == 3
+    assert s.offset_bytes == 128
+    assert s.nbytes == 64
+
+
+def test_shardspec_has_no_pe_index_attr():
+    """Flat pe_index must be fully removed — no field, no property."""
+    s = ShardSpec(sip=0, cube=0, pe=0, offset_bytes=0, nbytes=8)
+    with pytest.raises(AttributeError):
+        _ = s.pe_index  # noqa: F841
+
+
+def test_shardspec_rejects_pe_index_kwarg():
+    """ShardSpec(pe_index=...) must raise TypeError."""
+    with pytest.raises(TypeError):
+        ShardSpec(pe_index=0, offset_bytes=0, nbytes=8)  # type: ignore[call-arg]
+
+
+# ── D3: resolve_dp_policy(target_sip=...) structural semantics ────────
+
+
+def test_resolve_dp_policy_target_sip_stamps_shards():
+    """All returned shards must carry sip == target_sip; cube/pe local."""
+    dp = DPPolicy(cube="column_wise", pe="column_wise")
+    shards = resolve_dp_policy(
+        dp, shape=(4, 32), itemsize=2,
+        num_pe=4, num_cubes=2, target_sip=1,
+    )
+    assert len(shards) == 2 * 4
+    assert all(s.sip == 1 for s in shards)
+    assert all(0 <= s.cube < 2 for s in shards)
+    assert all(0 <= s.pe < 4 for s in shards)
+
+
+def test_resolve_dp_policy_target_sip_differ_only_in_sip():
+    """Same policy + dims on two SIPs → shards identical except .sip."""
+    dp = DPPolicy(cube="replicate", pe="column_wise")
+    shards_0 = resolve_dp_policy(
+        dp, shape=(4, 32), itemsize=2,
+        num_pe=4, num_cubes=1, target_sip=0,
+    )
+    shards_1 = resolve_dp_policy(
+        dp, shape=(4, 32), itemsize=2,
+        num_pe=4, num_cubes=1, target_sip=1,
+    )
+    assert len(shards_0) == len(shards_1)
+    for a, b in zip(shards_0, shards_1):
+        assert a.sip == 0 and b.sip == 1
+        assert a.cube == b.cube
+        assert a.pe == b.pe
+        assert a.offset_bytes == b.offset_bytes
+        assert a.nbytes == b.nbytes
+
+
+def test_resolve_dp_policy_no_num_sips_param():
+    """resolve_dp_policy must not accept num_sips anymore.
+
+    Post-Phase-2 signature drops ``num_sips`` (DPPolicy no longer crosses
+    SIP boundaries) and adds required ``target_sip``. Calling with
+    ``num_sips=...`` must raise TypeError (unexpected keyword argument).
+    """
+    dp = DPPolicy(cube="replicate", pe="replicate")
+    with pytest.raises(TypeError, match="num_sips"):
+        resolve_dp_policy(
+            dp, shape=(4, 8), itemsize=2,
+            num_pe=1, num_cubes=1, num_sips=2,  # type: ignore[call-arg]
+        )
+
+
+# ── D5: Allocator dict keyed by (sip, cube, pe) tuples ────────────────
+
+
+_MB = 1 << 20
+_GB = 1 << 30
+
+_CFG = AddressConfig(
+    sip_count=2,
+    cubes_per_sip=2,
+    pes_per_cube=4,
+    hbm_bytes_per_cube=_GB,
+    hbm_slices_per_cube=4,
+    tcm_bytes_per_pe=_MB,
+    tcm_scheduler_reserved_bytes=0,
+    sram_bytes_per_cube=_MB,
+)
+
+
+def _make_tuple_allocators(
+    num_sips: int = 1, num_cubes: int = 1, num_pe: int = 4,
+) -> dict[tuple[int, int, int], PEMemAllocator]:
+    return {
+        (s, c, p): PEMemAllocator(
+            rack_id=0, sip_id=s, cube_id=c, pe_id=p, cfg=_CFG,
+        )
+        for s in range(num_sips)
+        for c in range(num_cubes)
+        for p in range(num_pe)
+    }
+
+
+def test_deploy_tensor_uses_tuple_lookup():
+    """deploy_tensor(allocators={(sip,cube,pe): alloc, ...}) succeeds."""
+    dp = DPPolicy(cube="replicate", pe="column_wise")
+    placement = resolve_dp_policy(
+        dp, shape=(4, 16), itemsize=2,
+        num_pe=4, num_cubes=1, target_sip=0,
+    )
+    allocators = _make_tuple_allocators(num_sips=1, num_cubes=1, num_pe=4)
+    handle = deploy_tensor(
+        name="t", shape=(4, 16), dtype="f16",
+        placement=placement, allocators=allocators,
+    )
+    assert len(handle.shards) == 4
+    # Each shard's TensorShard carries structural coords; those coords
+    # must match the shard's ShardSpec (sip, cube, pe).
+    for spec, shard in zip(placement, handle.shards):
+        assert shard.sip == spec.sip
+        assert shard.cube == spec.cube
+        assert shard.pe == spec.pe
+
+
+def test_runtime_context_allocator_keys_are_tuples(topology):
+    """After ctx tensor op, ctx._allocators keys are (sip, cube, pe) tuples.
+
+    Ensures D5 migration landed (allocator population + lookup).
+    """
+    from kernbench.runtime_api.context import RuntimeContext
+    from kernbench.runtime_api.types import DeviceSelector
+    from kernbench.sim_engine.engine import GraphEngine
+
+    engine = GraphEngine(topology.topology_obj, enable_data=True)
+    ctx = RuntimeContext(
+        engine=engine,
+        target_device=DeviceSelector("sip:0"),
+        correlation_id="test_adr0026_tuple_keys",
+        spec=topology.topology_obj.spec,
+    )
+    dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
+    _ = ctx.zeros((1, 16), dtype="f16", dp=dp)
+
+    assert ctx._allocators, "allocators dict should be populated"
+    keys = list(ctx._allocators.keys())
+    assert all(isinstance(k, tuple) and len(k) == 3 for k in keys), (
+        f"_allocators keys must be (sip, cube, pe) tuples; got {keys[:5]}"
+    )
+
+
+# ── D4 (via regression): no SIP-crossing tensor without set_device ────
+
+
+def test_create_tensor_on_target_sip_via_set_device(topology):
+    """torch.ahbm.set_device(1) + DPPolicy(cube=replicate, pe=replicate)
+    → all shards land on SIP 1 structurally (no post-hoc shifting needed)."""
+    from kernbench.runtime_api.context import RuntimeContext
+    from kernbench.runtime_api.types import DeviceSelector
+    from kernbench.sim_engine.engine import GraphEngine
+
+    # Skip the test if topology has only 1 SIP (nothing to verify).
+    n_sips = int(
+        topology.topology_obj.spec.get("system", {})
+        .get("sips", {}).get("count", 1)
+    )
+    if n_sips < 2:
+        pytest.skip("topology has <2 SIPs; set_device(1) not meaningful")
+
+    engine = GraphEngine(topology.topology_obj, enable_data=True)
+    ctx = RuntimeContext(
+        engine=engine,
+        target_device=DeviceSelector("sip:1"),
+        correlation_id="test_adr0026_set_device",
+        spec=topology.topology_obj.spec,
+    )
+    ctx.ahbm.set_device(1)
+    dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
+    t = ctx.zeros((1, 16), dtype="f16", dp=dp)
+
+    assert t._handle is not None
+    assert all(s.sip == 1 for s in t._handle.shards), (
+        f"expected all shards on SIP 1; got {[s.sip for s in t._handle.shards]}"
+    )

tests/test_ccl_deadlock_detection.py

@@ -108,8 +108,8 @@ def test_deadlock_detection_recv_without_send():
             (1, 8 * 8),
             dtype="f16",
             dp=DPPolicy(
-                sip="replicate", cube="replicate", pe="column_wise",
+                cube="replicate", pe="column_wise",
-                num_sips=1, num_cubes=1,
+                num_cubes=1,
             ),
             name="dl_in",
         )

tests/test_ccl_hello_world_guide.py

@@ -51,8 +51,8 @@ def test_hello_send_via_simpy_runner():
         a = torch.zeros(
             (1, world_size * n_elem), dtype="f16",
             dp=DPPolicy(
-                sip="replicate", cube="replicate", pe="column_wise",
+                cube="replicate", pe="column_wise",
-                num_sips=1, num_cubes=1,
+                num_cubes=1,
             ),
             name="hello_in",
         )

tests/test_recv_copy_to_dst.py

@@ -48,8 +48,8 @@ def test_recv_copy_to_dst_via_simpy_runner():
             (1, 8 * 8),
             dtype="f16",
             dp=DPPolicy(
-                sip="replicate", cube="replicate", pe="column_wise",
+                cube="replicate", pe="column_wise",
-                num_sips=1, num_cubes=1,
+                num_cubes=1,
             ),
             name="copy_in",
         )

tests/test_runtime_api_tensor.py

@@ -48,8 +48,8 @@ def test_from_numpy_creates_host_tensor():
         assert h._handle is None
         # Submit a no-op so run_bench has at least one handle.
         torch.zeros((1, 8), dtype="f16",
-                    dp=DPPolicy(sip="replicate", cube="replicate", pe="replicate",
+                    dp=DPPolicy(cube="replicate", pe="replicate",
-                                num_sips=1, num_cubes=1, num_pes=1),
+                                num_cubes=1, num_pes=1),
                     name="dummy")
 
     _run_with(body)
@@ -63,8 +63,8 @@ def test_copy_and_numpy_single_pe():
     a single-PE (no real sharding) tensor."""
 
     def body(torch):
-        dp = DPPolicy(sip="replicate", cube="replicate", pe="replicate",
+        dp = DPPolicy(cube="replicate", pe="replicate",
-                      num_sips=1, num_cubes=1, num_pes=1)
+                      num_cubes=1, num_pes=1)
         t = torch.zeros((1, 16), dtype="f16", dp=dp, name="t")
         src = np.arange(16, dtype=np.float16).reshape(1, 16)
         t.copy_(torch.from_numpy(src))
@@ -83,8 +83,8 @@ def test_copy_and_numpy_multi_pe_column_wise():
 
     def body(torch):
         n_pe = 8
-        dp = DPPolicy(sip="replicate", cube="replicate", pe="column_wise",
+        dp = DPPolicy(cube="replicate", pe="column_wise",
-                      num_sips=1, num_cubes=1, num_pes=n_pe)
+                      num_cubes=1, num_pes=n_pe)
         t = torch.zeros((1, n_pe * 4), dtype="f16", dp=dp, name="t")
         src = np.arange(n_pe * 4, dtype=np.float16).reshape(1, n_pe * 4)
         t.copy_(torch.from_numpy(src))
@@ -107,8 +107,8 @@ def test_copy_and_numpy_multi_cube():
         n_pe_per_cube = 8
         n_cubes = 2
         total = n_cubes * n_pe_per_cube  # 16
-        dp = DPPolicy(sip="replicate", cube="column_wise", pe="column_wise",
+        dp = DPPolicy(cube="column_wise", pe="column_wise",
-                      num_sips=1, num_cubes=n_cubes)
+                      num_cubes=n_cubes)
         t = torch.zeros((1, total * 4), dtype="f16", dp=dp, name="t")
         src = np.arange(total * 4, dtype=np.float16).reshape(1, total * 4)
         t.copy_(torch.from_numpy(src))
@@ -126,8 +126,8 @@ def test_copy_shape_mismatch_raises():
     """copy_ with mismatched shapes raises ValueError."""
 
     def body(torch):
-        dp = DPPolicy(sip="replicate", cube="replicate", pe="replicate",
+        dp = DPPolicy(cube="replicate", pe="replicate",
-                      num_sips=1, num_cubes=1, num_pes=1)
+                      num_cubes=1, num_pes=1)
         t = torch.zeros((1, 8), dtype="f16", dp=dp, name="t")
         src = np.zeros((1, 16), dtype=np.float16)
         with pytest.raises(ValueError, match="copy_ shape mismatch"):
@@ -143,8 +143,8 @@ def test_setitem_getitem_single_pe():
     """Scalar and slice assignment on a single-PE tensor round-trips."""
 
     def body(torch):
-        dp = DPPolicy(sip="replicate", cube="replicate", pe="replicate",
+        dp = DPPolicy(cube="replicate", pe="replicate",
-                      num_sips=1, num_cubes=1, num_pes=1)
+                      num_cubes=1, num_pes=1)
         t = torch.zeros((1, 8), dtype="f16", dp=dp, name="t")
 
         # Scalar broadcast
@@ -169,8 +169,8 @@ def test_setitem_getitem_multi_pe_shard_aligned():
     def body(torch):
         n_pe = 8
         n_elem = 4  # per shard
-        dp = DPPolicy(sip="replicate", cube="replicate", pe="column_wise",
+        dp = DPPolicy(cube="replicate", pe="column_wise",
-                      num_sips=1, num_cubes=1, num_pes=n_pe)
+                      num_cubes=1, num_pes=n_pe)
         t = torch.zeros((1, n_pe * n_elem), dtype="f16", dp=dp, name="t")
 
         # Write each shard with its rank value
@@ -197,8 +197,8 @@ def test_setitem_cross_shard_raises():
     def body(torch):
         n_pe = 4
         n_elem = 4
-        dp = DPPolicy(sip="replicate", cube="replicate", pe="column_wise",
+        dp = DPPolicy(cube="replicate", pe="column_wise",
-                      num_sips=1, num_cubes=1, num_pes=n_pe)
+                      num_cubes=1, num_pes=n_pe)
         t = torch.zeros((1, n_pe * n_elem), dtype="f16", dp=dp, name="t")
         with pytest.raises(NotImplementedError, match="spans multiple shards"):
             t[0, 2:6] = 1.0  # crosses shard 0 (0:4) and shard 1 (4:8)

tests/test_sip_parallel.py

@@ -1,157 +1,120 @@
-"""Tests for SIP-level tensor parallelism.
+"""Tests for SIP-level tensor parallelism — ADR-0026 structural model.
 
-Validates:
+DPPolicy no longer carries a ``sip`` axis (ADR-0026 D1). SIP placement is
-  SP1. DPPolicy accepts sip field (default "replicate", backward compat)
+now expressed structurally: each call to ``resolve_dp_policy(target_sip=N)``
-  SP2. sip="column_wise": tensor K-axis split across SIPs, each SIP gets K//num_sips
+emits shards pinned to SIP N. Multi-SIP parallelism is composed by calling
-  SP3. sip="row_wise": tensor M-axis split across SIPs
+the resolver once per SIP (typically driven by the ADR-0024 launcher, one
-  SP4. 3-level resolve: sip × cube × pe produces correct flat indices and offsets
+worker greenlet per rank, each worker using ``torch.ahbm.set_device(rank)``).
-  SP5. sip="replicate": all SIPs get full copy (existing behavior)
+
-  SP6. PE_CPU sets num_programs from shard count per cube
+Covered here:
-  SP7. End-to-end: TP kernel with sip="column_wise" completes on multi-SIP topology
+  SP1. ``target_sip`` stamps every shard.
+  SP2. Two-SIP placement: union of two resolver calls covers the whole
+       tensor K-axis when the combined bench treats them as column-split.
+  SP3. Same for row-wise.
+  SP4. Cube + PE sharding within a SIP remains correct across SIPs.
+  SP5. PE_CPU num_programs contract (unchanged by ADR-0026).
 """
-import pytest
+from __future__ import annotations
-from pathlib import Path
 
-from kernbench.policy.placement.dp import DPPolicy, ShardSpec, resolve_dp_policy
+from kernbench.policy.placement.dp import DPPolicy, resolve_dp_policy
 
 
-# ── SP1. DPPolicy sip field ──────────────────────────────────────────
+# ── SP1. target_sip stamps shards ────────────────────────────────────
 
 
-def test_dp_policy_sip_default_replicate():
+def test_target_sip_stamps_all_shards():
-    """DPPolicy without sip= defaults to 'replicate'."""
     dp = DPPolicy(cube="replicate", pe="column_wise")
-    assert dp.sip == "replicate"
-
-
-def test_dp_policy_sip_column_wise():
-    """DPPolicy accepts sip='column_wise'."""
-    dp = DPPolicy(sip="column_wise", cube="replicate", pe="column_wise")
-    assert dp.sip == "column_wise"
-
-
-# ── SP2. sip="column_wise" ──────────────────────────────────────────────
-
-
-def test_sip_column_wise_splits_across_sips():
-    """sip='column_wise' with 2 SIPs: each SIP gets K//2 columns."""
-    dp = DPPolicy(sip="column_wise", cube="replicate", pe="column_wise")
     shards = resolve_dp_policy(
         dp, shape=(128, 256), itemsize=2,
-        num_pe=8, num_cubes=1, num_sips=2,
+        num_pe=8, num_cubes=1, target_sip=3,
     )
-    # 2 SIPs × 1 cube × 8 PEs = 16 shards
+    assert all(s.sip == 3 for s in shards)
-    assert len(shards) == 16
+    assert all(0 <= s.pe < 8 for s in shards)
-
+    assert all(s.cube == 0 for s in shards)
-    # SIP0 shards: first half of K (0 to K//2)
-    # SIP1 shards: second half of K (K//2 to K)
-    total_bytes = 128 * 256 * 2  # 64KB
-    sip0_shards = [s for s in shards if s.pe_index < 8]
-    sip1_shards = [s for s in shards if s.pe_index >= 8]
-
-    # SIP0 offsets start at 0
-    assert sip0_shards[0].offset_bytes == 0
-    # SIP1 offsets start at half
-    assert sip1_shards[0].offset_bytes == total_bytes // 2
-
-    # Total coverage
-    assert sum(s.nbytes for s in sip0_shards) == total_bytes // 2
-    assert sum(s.nbytes for s in sip1_shards) == total_bytes // 2
 
 
-# ── SP3. sip="row_wise" ──────────────────────────────────────────────
+# ── SP2. column-wise placement composed across two SIPs ─────────────
 
 
-def test_sip_row_wise_splits_across_sips():
+def test_compose_two_sips_column_wise_covers_tensor():
-    """sip='row_wise' with 2 SIPs: each SIP gets M//2 rows."""
+    """Bench splits K-axis across 2 SIPs by calling resolve twice and
-    dp = DPPolicy(sip="row_wise", cube="replicate", pe="column_wise")
+    giving each SIP half of the tensor (half-shape + offset). Shards
-    shards = resolve_dp_policy(
+    from both SIPs together cover the whole K axis."""
+    full_shape = (128, 256)
+    itemsize = 2
+    # Per-SIP half-shape (K split across SIPs).
+    half_shape = (128, 128)
+    dp = DPPolicy(cube="replicate", pe="column_wise")
+
+    shards_sip0 = resolve_dp_policy(
+        dp, shape=half_shape, itemsize=itemsize,
+        num_pe=8, num_cubes=1, target_sip=0,
+    )
+    shards_sip1 = resolve_dp_policy(
+        dp, shape=half_shape, itemsize=itemsize,
+        num_pe=8, num_cubes=1, target_sip=1,
+    )
+
+    total_bytes = full_shape[0] * full_shape[1] * itemsize
+    sip0_bytes = sum(s.nbytes for s in shards_sip0)
+    sip1_bytes = sum(s.nbytes for s in shards_sip1)
+    assert sip0_bytes + sip1_bytes == total_bytes
+    assert all(s.sip == 0 for s in shards_sip0)
+    assert all(s.sip == 1 for s in shards_sip1)
+
+
+# ── SP3. row-wise placement composed across two SIPs ────────────────
+
+
+def test_compose_two_sips_row_wise_covers_tensor():
+    full_shape = (128, 256)
+    itemsize = 2
+    half_shape = (64, 256)  # per-SIP half of M
+    dp = DPPolicy(cube="replicate", pe="column_wise")
+
+    shards_sip0 = resolve_dp_policy(
+        dp, shape=half_shape, itemsize=itemsize,
+        num_pe=8, num_cubes=1, target_sip=0,
+    )
+    shards_sip1 = resolve_dp_policy(
+        dp, shape=half_shape, itemsize=itemsize,
+        num_pe=8, num_cubes=1, target_sip=1,
+    )
+
+    total_bytes = full_shape[0] * full_shape[1] * itemsize
+    assert sum(s.nbytes for s in shards_sip0) + sum(s.nbytes for s in shards_sip1) == total_bytes
+
+
+# ── SP4. cube × PE sharding is independent per SIP ──────────────────
+
+
+def test_cube_pe_sharding_independent_per_sip():
+    """Intra-SIP cube + PE layout matches across SIPs; only sip field differs."""
+    dp = DPPolicy(cube="column_wise", pe="column_wise")
+    s0 = resolve_dp_policy(
         dp, shape=(128, 256), itemsize=2,
-        num_pe=8, num_cubes=1, num_sips=2,
+        num_pe=4, num_cubes=2, target_sip=0,
     )
-    assert len(shards) == 16
+    s1 = resolve_dp_policy(
-
-    sip0_shards = [s for s in shards if s.pe_index < 8]
-    sip1_shards = [s for s in shards if s.pe_index >= 8]
-
-    # SIP0: rows 0..63, SIP1: rows 64..127
-    total_bytes = 128 * 256 * 2
-    assert sip0_shards[0].offset_bytes == 0
-    assert sip1_shards[0].offset_bytes == total_bytes // 2
-
-
-# ── SP4. 3-level resolve ─────────────────────────────────────────────
-
-
-def test_3level_resolve_flat_index():
-    """3-level: sip × cube × pe produces correct flat indices."""
-    dp = DPPolicy(sip="column_wise", cube="replicate", pe="column_wise")
-    shards = resolve_dp_policy(
         dp, shape=(128, 256), itemsize=2,
-        num_pe=8, num_cubes=2, num_sips=2,
+        num_pe=4, num_cubes=2, target_sip=1,
     )
-    # 2 SIPs × 2 cubes × 8 PEs = 32 shards
+    assert len(s0) == len(s1) == 2 * 4
-    assert len(shards) == 32
+    for a, b in zip(s0, s1):
-
+        assert a.sip == 0 and b.sip == 1
-    # Flat index: sip_id * cubes_per_sip * num_pe + cube_id * num_pe + pe_id
+        assert (a.cube, a.pe, a.offset_bytes, a.nbytes) == (
-    indices = [s.pe_index for s in shards]
+            b.cube, b.pe, b.offset_bytes, b.nbytes
-    # SIP0: 0..15, SIP1: 16..31
-    assert min(indices) == 0
-    assert max(indices) == 31
-    assert len(set(indices)) == 32  # all unique
-
-
-def test_3level_offsets_cover_full_tensor():
-    """3-level sharding covers the entire tensor with no gaps."""
-    dp = DPPolicy(sip="column_wise", cube="replicate", pe="column_wise")
-    shards = resolve_dp_policy(
-        dp, shape=(128, 256), itemsize=2,
-        num_pe=4, num_cubes=1, num_sips=2,
         )
-    # 2 SIPs × 1 cube × 4 PEs = 8 shards
-    # sip="column_wise": K=128 per SIP, pe="column_wise": 32 cols per PE
-    total = 128 * 256 * 2
-    # For non-replicate, total shard bytes == tensor bytes
-    # (replicate within cube means cube shards overlap, but sip shards don't)
-    sip0_bytes = sum(s.nbytes for s in shards if s.pe_index < 4)
-    sip1_bytes = sum(s.nbytes for s in shards if s.pe_index >= 4)
-    assert sip0_bytes + sip1_bytes == total
 
 
-# ── SP5. sip="replicate" backward compat ─────────────────────────────
+# ── SP5. PE_CPU num_programs (contract unchanged) ───────────────────
-
-
-def test_sip_replicate_backward_compat():
-    """sip='replicate' produces same result as before (2-level)."""
-    dp_old = DPPolicy(cube="replicate", pe="column_wise")
-    dp_new = DPPolicy(sip="replicate", cube="replicate", pe="column_wise")
-
-    shards_old = resolve_dp_policy(
-        dp_old, shape=(128, 256), itemsize=2,
-        num_pe=8, num_cubes=2, num_sips=2,
-    )
-    shards_new = resolve_dp_policy(
-        dp_new, shape=(128, 256), itemsize=2,
-        num_pe=8, num_cubes=2, num_sips=2,
-    )
-    assert len(shards_old) == len(shards_new)
-    for a, b in zip(shards_old, shards_new):
-        assert a.pe_index == b.pe_index
-        assert a.offset_bytes == b.offset_bytes
-        assert a.nbytes == b.nbytes
-
-
-# ── SP6. PE_CPU num_programs ──────────────────────────────────────────
 
 
 def test_pe_cpu_sets_num_programs():
-    """PE_CPU should create TLContext with num_programs = PEs per cube."""
+    """TLContext reports num_programs from its initializer — used by PE_CPU
-    # This test validates the interface contract.
+    when it launches a kernel on behalf of its shards."""
-    # After implementation, PE_CPU should derive num_programs from the
-    # number of PE shards in the kernel launch's target cube.
     from kernbench.triton_emu.tl_context import TLContext
 
-    # With 8 PEs per cube, num_programs should be 8
     tl = TLContext(pe_id=3, num_programs=8)
     assert tl.program_id(0) == 3
     assert tl.num_programs(0) == 8

tests/test_tensor.py

@@ -2,11 +2,13 @@ import pytest
 
 from kernbench.policy.address.allocator import AddressConfig, AllocationError, PEMemAllocator
 from kernbench.policy.placement.dp import (
+    DPPolicy,
     ShardSpec,
     column_wise,
-    tiled_column_major,
     replicate,
+    resolve_dp_policy,
     row_wise,
+    tiled_column_major,
     tiled_row_major,
 )
 from kernbench.runtime_api.kernel import (
@@ -40,9 +42,9 @@ _CFG = AddressConfig(
 )
 
 
-def _make_allocators(num_pe: int = 8) -> dict[int, PEMemAllocator]:
+def _make_allocators(num_pe: int = 8) -> dict[tuple[int, int, int], PEMemAllocator]:
     return {
-        i: PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=_CFG)
+        (0, 0, i): PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=_CFG)
         for i in range(num_pe)
     }
 
@@ -133,7 +135,7 @@ def test_column_wise_placement():
     assert len(shards) == 8
     expected_nbytes = 1024 * 64 * 2  # 128 KB
     for i, s in enumerate(shards):
-        assert s.pe_index == i
+        assert s.local_pe == i
         assert s.nbytes == expected_nbytes
     # offsets are contiguous
     assert shards[0].offset_bytes == 0
@@ -151,7 +153,7 @@ def test_row_wise_placement():
     assert len(shards) == 8
     expected_nbytes = 128 * 512 * 2  # 128 KB
     for i, s in enumerate(shards):
-        assert s.pe_index == i
+        assert s.local_pe == i
         assert s.nbytes == expected_nbytes
     assert shards[0].offset_bytes == 0
     assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
@@ -166,7 +168,7 @@ def test_replicate_placement():
     assert len(shards) == 8
     full_nbytes = 1024 * 512 * 2  # 1 MB
     for i, s in enumerate(shards):
-        assert s.pe_index == i
+        assert s.local_pe == i
         assert s.nbytes == full_nbytes
         assert s.offset_bytes == 0  # each is a full copy
 
@@ -188,10 +190,10 @@ def test_tiled_column_major():
     # tile (m=0,k=0) → PE0, tile (m=0,k=1) → PE1, ..., (m=0,k=3) → PE3
     # tile (m=1,k=0) → PE4, tile (m=1,k=1) → PE5, ..., (m=1,k=3) → PE7
     # tile (m=2,k=0) → PE0, ...
-    assert shards[0].pe_index == 0
+    assert shards[0].local_pe == 0
-    assert shards[1].pe_index == 1
+    assert shards[1].local_pe == 1
-    assert shards[7].pe_index == 7
+    assert shards[7].local_pe == 7
-    assert shards[8].pe_index == 0  # wraps around
+    assert shards[8].local_pe == 0  # wraps around
     # total coverage
     assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
 
@@ -212,10 +214,10 @@ def test_tiled_row_major():
     # tile (m=0,k=0) → PE0, tile (m=1,k=0) → PE1, ..., (m=3,k=0) → PE3
     # tile (m=0,k=1) → PE4, tile (m=1,k=1) → PE5, ..., (m=3,k=1) → PE7
     # tile (m=0,k=2) → PE0, ...
-    assert shards[0].pe_index == 0
+    assert shards[0].local_pe == 0
-    assert shards[1].pe_index == 1
+    assert shards[1].local_pe == 1
-    assert shards[7].pe_index == 7
+    assert shards[7].local_pe == 7
-    assert shards[8].pe_index == 0  # wraps around
+    assert shards[8].local_pe == 0  # wraps around
     # total coverage
     assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
 
@@ -226,7 +228,11 @@ def test_tiled_row_major():
 def test_deploy_tensor_hbm():
     """Deploy with column_wise placement → TensorHandle with valid PA shards."""
     allocs = _make_allocators()
-    placement = column_wise(shape=(1024, 512), itemsize=2, num_pe=8)
+    placement = resolve_dp_policy(
+        DPPolicy(cube="replicate", pe="column_wise"),
+        shape=(1024, 512), itemsize=2,
+        num_pe=8, num_cubes=1, target_sip=0,
+    )
     th = deploy_tensor(
         name="W",
         shape=(1024, 512),
@@ -253,7 +259,7 @@ def test_deploy_tensor_hbm():
 def test_deploy_tensor_tcm():
     """Deploy with TCM → uses pe_tcm_addr allocation."""
     allocs = _make_allocators()
-    placement = [ShardSpec(pe_index=0, offset_bytes=0, nbytes=256)]
+    placement = [ShardSpec(sip=0, cube=0, pe=0, offset_bytes=0, nbytes=256)]
     th = deploy_tensor(
         name="small",
         shape=(128,),
@@ -271,7 +277,7 @@ def test_deploy_tensor_overflow():
     """Allocation exceeding PE HBM capacity raises AllocationError."""
     allocs = _make_allocators()
     # 6 GB per PE slice, try to allocate 7 GB
-    big_shard = ShardSpec(pe_index=0, offset_bytes=0, nbytes=7 * _GB)
+    big_shard = ShardSpec(sip=0, cube=0, pe=0, offset_bytes=0, nbytes=7 * _GB)
     with pytest.raises(AllocationError):
         deploy_tensor(
             name="toobig",

tests/test_tl_recv_async.py

@@ -75,8 +75,8 @@ def test_recv_async_simpy_runner():
             (1, 8 * 8),
             dtype="f16",
             dp=DPPolicy(
-                sip="replicate", cube="replicate", pe="column_wise",
+                cube="replicate", pe="column_wise",
-                num_sips=1, num_cubes=1,
+                num_cubes=1,
             ),
             name="async_in",
         )

tests/test_va_integration.py

@@ -12,7 +12,7 @@ import pytest
 from kernbench.policy.address.allocator import AddressConfig, PEMemAllocator
 from kernbench.policy.address.pe_mmu import PeMMU
 from kernbench.policy.address.va_allocator import VirtualAllocator
-from kernbench.policy.placement.dp import column_wise, ShardSpec
+from kernbench.policy.placement.dp import DPPolicy, ShardSpec, resolve_dp_policy
 from kernbench.runtime_api.tensor import (
     TensorHandle,
     TensorShard,
@@ -37,9 +37,9 @@ _CFG = AddressConfig(
 )
 
 
-def _make_allocators(num_pe: int = 8) -> dict[int, PEMemAllocator]:
+def _make_allocators(num_pe: int = 8) -> dict[tuple[int, int, int], PEMemAllocator]:
     return {
-        i: PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=_CFG)
+        (0, 0, i): PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=_CFG)
         for i in range(num_pe)
     }
 
@@ -88,7 +88,11 @@ def test_deploy_tensor_assigns_va_base():
     """deploy_tensor with VA allocator assigns va_base to TensorHandle."""
     allocs = _make_allocators()
     va_alloc = _make_va_allocator()
-    placement = column_wise(shape=(1024, 512), itemsize=2, num_pe=8)
+    placement = resolve_dp_policy(
+        DPPolicy(cube="replicate", pe="column_wise"),
+        shape=(1024, 512), itemsize=2,
+        num_pe=8, num_cubes=1, target_sip=0,
+    )
 
     th = deploy_tensor(
         name="W",
@@ -107,7 +111,11 @@ def test_deploy_tensor_va_covers_all_shards():
     """VA allocation covers the entire tensor; each shard is at va_base + offset."""
     allocs = _make_allocators()
     va_alloc = _make_va_allocator()
-    placement = column_wise(shape=(1024, 512), itemsize=2, num_pe=8)
+    placement = resolve_dp_policy(
+        DPPolicy(cube="replicate", pe="column_wise"),
+        shape=(1024, 512), itemsize=2,
+        num_pe=8, num_cubes=1, target_sip=0,
+    )
 
     th = deploy_tensor(
         name="W",
@@ -128,7 +136,11 @@ def test_deploy_tensor_does_not_install_mmu_mappings():
     allocs = _make_allocators()
     va_alloc = _make_va_allocator()
     mmus = _make_mmus()
-    placement = column_wise(shape=(1024, 512), itemsize=2, num_pe=8)
+    placement = resolve_dp_policy(
+        DPPolicy(cube="replicate", pe="column_wise"),
+        shape=(1024, 512), itemsize=2,
+        num_pe=8, num_cubes=1, target_sip=0,
+    )
 
     deploy_tensor(
         name="W",
@@ -153,7 +165,7 @@ def test_tensor_va_property():
 
     allocs = _make_allocators(1)
     va_alloc = _make_va_allocator()
-    placement = [ShardSpec(pe_index=0, offset_bytes=0, nbytes=4096)]
+    placement = [ShardSpec(sip=0, cube=0, pe=0, offset_bytes=0, nbytes=4096)]
 
     t = Tensor(shape=(2048,), dtype="f16", name="test")
     t._handle = deploy_tensor(

tests/test_va_offset.py

@@ -20,7 +20,7 @@ from kernbench.policy.address.allocator import AddressConfig, PEMemAllocator
 from kernbench.policy.address.pe_mmu import PeMMU
 from kernbench.policy.address.phyaddr import PhysAddr
 from kernbench.policy.address.va_allocator import VirtualAllocator
-from kernbench.policy.placement.dp import DPPolicy, column_wise
+from kernbench.policy.placement.dp import DPPolicy, resolve_dp_policy
 from kernbench.runtime_api.tensor import deploy_tensor
 from kernbench.sim_engine.engine import GraphEngine
 from kernbench.runtime_api.context import RuntimeContext
@@ -70,7 +70,7 @@ def _make_standalone(shape, num_pe=NUM_PE):
         sram_bytes_per_cube=32 * _MB,
     )
     allocators = {
-        i: PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=cfg)
+        (0, 0, i): PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=cfg)
         for i in range(num_pe)
     }
     va_alloc = VirtualAllocator(va_base=0x1_0000_0000, va_size=64 * _GB, page_size=4096)
@@ -110,7 +110,11 @@ def test_2d_va_translates_to_local_hbm():
     cols_per_pe = K // NUM_PE
     block_bytes = M * cols_per_pe * ELEM_BYTES
 
-    placement = column_wise(shape=(M, K), itemsize=ELEM_BYTES, num_pe=NUM_PE)
+    placement = resolve_dp_policy(
+        DPPolicy(cube="replicate", pe="column_wise"),
+        shape=(M, K), itemsize=ELEM_BYTES,
+        num_pe=NUM_PE, num_cubes=1, target_sip=0,
+    )
     handle = deploy_tensor(
         name="src", shape=(M, K), dtype="fp16",
         placement=placement, allocators=allocators, va_allocator=va_alloc,
@@ -178,7 +182,11 @@ def test_1d_va_translates_to_local_hbm():
     elems_per_pe = N_1D // NUM_PE
     block_bytes = elems_per_pe * ELEM_BYTES
 
-    placement = column_wise(shape=(1, N_1D), itemsize=ELEM_BYTES, num_pe=NUM_PE)
+    placement = resolve_dp_policy(
+        DPPolicy(cube="replicate", pe="column_wise"),
+        shape=(1, N_1D), itemsize=ELEM_BYTES,
+        num_pe=NUM_PE, num_cubes=1, target_sip=0,
+    )
     handle = deploy_tensor(
         name="src_1d", shape=(N_1D,), dtype="fp16",
         placement=placement, allocators=allocators, va_allocator=va_alloc,
@@ -207,7 +215,9 @@ def test_1d_e2e_completes():
         correlation_id="vo6", spec=graph.spec,
     )
 
-    dp = DPPolicy(sip="column_wise", cube="column_wise", pe="column_wise")
+    # ADR-0026: DPPolicy is intra-device only; SIP scoping comes from the
+    # RuntimeContext's target_device. This 1D e2e runs on a single SIP.
+    dp = DPPolicy(cube="column_wise", pe="column_wise")
     src = ctx.zeros((N_1D,), dtype=DTYPE, dp=dp, name="src_1d")
     dst = ctx.empty((N_1D,), dtype=DTYPE, dp=dp, name="dst_1d")