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/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)
-    # W_Q/K/V, out_Q/K/V: column-wise sharded across all PEs
+                            num_cubes=N_CUBES, num_pes=N_PE_PER_CUBE)
+    # 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")