ADR-0027: Megatron TP API + worker-wait generalization + mp.spawn

Implements ADR-0027 Phase 2 end-to-end. All 559 tests pass (was 523 +
1 xfail; ring_default_ws strict-xfail is now resolved).

D0 — Worker-wait generalization (context.py):
- _pending_worker_waits queue on RuntimeContext.
- ctx.wait(h) in worker context defers to main via g.parent.switch().
  Fast-path for already-completed handles.
- Worker API is unchanged: tensor deploy, launch, etc. still look
  synchronous; they're transparently cooperatively scheduled.
- Solves ADR-0024 Phase B kernel-greenlet orphan bug (env.run now
  only ever drives from main; kernel _parent is always main).

D0.5 — Host-read barrier (tensor.py):
- Explicit _HOST_READ_BARRIERS registry (T5.g closed-set via code
  review, not reflection-magic).
- numpy/data/__getitem__/__repr__ drain pending worker-waits before
  host-observable read.
- copy_: source-side barrier via source.numpy(). Target-side write
  barrier is intentionally NOT applied — global pending target barrier
  prematurely drains cross-rank collectives → deadlock.
- Collective pending is excluded from barrier drain condition
  (collective is cross-rank; its own yield in all_reduce covers the
  invariant naturally).

D1 — torch.multiprocessing.spawn (runtime_api/multiprocessing.py):
- API signature parity with real PyTorch spawn; execution is
  cooperative greenlet scheduler (process isolation etc. are explicit
  non-goals per D1.0).
- _drain_pending drains worker-waits then collectives in one barrier,
  loop-until-empty.
- Round-based exception handling with SystemExit sibling abort +
  SpawnException(errors) wrapping root-cause ranks.
- RuntimeContext attaches ctx.multiprocessing in __post_init__.
- benches/ccl_allreduce.py hand-rolled loop collapses to one
  torch.multiprocessing.spawn call.

D2–D6 — kernbench.tp package:
- parallel_state: initialize_model_parallel, get_*_rank,
  get_*_world_size, with weak active-ctx registry in context.py.
- layers: ColumnParallelLinear, RowParallelLinear (shape-only
  primitives — fp16 gemm via tl.load + tl.dot + tl.store).
- kernels: _gemm_kernel used by TP layers (self-contained; no bench
  dependency).
- primitives / mappings stubs per D6/D8.

Data-path fixes (surfaced by TP gemm + all_reduce sequence):
- sim_engine/op_log.py: dma_write snapshot is skipped for TCM
  sources (PE scratch is repopulated by Phase 2 math/gemm replay —
  capturing Phase-1-time snapshot picked up STALE data from prior
  kernel's output aliased at the same scratch addr, causing the later
  kernel's dma_write to overwrite Phase 2 result with stale value).
- sim_engine/op_log.py + sim_engine/data_executor.py: per-operand
  space recorded on GemmCmd and composite gemm records so HBM-resident
  operands (tl.load output) don't default to TCM during replay.
- runtime_api/context.py: ctx.zeros writes zero-init to MemoryStore
  at VA keys so kernels reading via VA see deterministic init even
  without explicit copy_().

Tests (Phase 1 + Phase 2):
- test_worker_wait_drain (T3): orphan invariant + resume + multi-rank
  drain + idempotency + exception propagation.
- test_mp_spawn (T4): spawn shape + bind + SpawnException scope.
- test_host_read_barrier (T5): barrier contract per entry-point +
  closed-set registry check.
- test_tp_parallel_state (T1): initialize + rank lookup.
- test_tp_layers (T2): shape + deterministic numerical correctness
  (concat-matmul equality for RowParallel, not mean-only).
- test_tp_mlp (T6): full 2-layer MLP with deterministic weight
  numerical match + rank-consistency post all-reduce.
- test_ccl_allreduce_matrix: ring_default_ws xfail removed (T7).

Regression: 523 pre + 35 new + 1 ex-xfail = 559 passed, 1 intentional
skip (T3.e historical failure documentation).

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

benches/ccl_allreduce.py

@@ -19,7 +19,6 @@ Driven entirely by ``ccl.yaml`` + ``topology.yaml``:
 from __future__ import annotations
 
 import numpy as np
-from greenlet import greenlet
 
 from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
 from kernbench.policy.placement.dp import DPPolicy
@@ -153,35 +152,14 @@ def run(torch) -> None:
     n_sips = int(spec.get("system", {}).get("sips", {}).get("count", 1))
 
     if world_size == n_sips:
-        # ADR-0024 D12/D13: one greenlet per rank. After each scheduler
-        # round, the main greenlet drains any pending collective handles
-        # (ADR-0024 D7) — this must happen in the main context, not inside
-        # a worker, so env.run is invoked with main as the current greenlet
-        # and kernel_runner's spawned kernel greenlets correctly get main
-        # as their parent.
-        backend = dist._backend
-        gs: list[greenlet] = []
-        for rank in range(world_size):
-            def _entry(r: int = rank) -> None:
-                worker(r, world_size, torch)
-            g = greenlet(_entry)
-            dist._bind_rank(g, rank)
-            gs.append(g)
-        while True:
-            alive = [g for g in gs if not g.dead]
-            if not alive:
-                break
-            for g in alive:
-                if not g.dead:
-                    g.switch()
-            # Drain pending collective handles. All sibling workers have
-            # either submitted (and yielded) or completed; their kernels
-            # are live in the SimPy queue, ready to exchange via IPCQ.
-            pending = backend._pending_collective_handles
-            if pending:
-                for h, _sip_id, meta in pending:
-                    torch.wait(h, _meta=meta)
-                backend._pending_collective_handles = []
+        # ADR-0027 D1: ``torch.multiprocessing.spawn`` replaces the prior
+        # hand-rolled greenlet loop. The spawn namespace absorbs the
+        # scheduler drain (D0.4) so kernel_runner's spawned kernel greenlets
+        # correctly get main as their parent (ADR-0024 Phase B blocker
+        # resolved via D0 worker-wait generalisation).
+        torch.multiprocessing.spawn(
+            worker, args=(world_size, torch), nprocs=world_size,
+        )
     else:
         # Legacy single-worker path (ccl.yaml world_size override).
         worker(rank=dist.get_rank(), world_size=world_size, torch=torch)