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>
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@@ -70,29 +70,14 @@ CASES = [
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# Default fallback — no world_size override → ADR-0024 D1 derives
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# from topology (SIP count = 2). Exercises the new SIP-level TP
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# launcher + cross-SIP ring.
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# XFAIL — architectural blocker (ADR-0024 Phase B, future redesign):
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# Bench workers call torch.zeros / copy_ which internally drive
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# env.run in the WORKER-greenlet context. Any KernelLaunchMsg already
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# pending in the SimPy queue gets stepped inside that worker context,
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# which in turn spawns kernel_runner + kernel greenlet with parent =
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# worker (not main). When the worker later yields / finishes, the
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# kernel greenlet is orphaned; its next switch_to_simpy raises
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# GreenletExit mid-add, producing rank 0 mean=1 (expected 3).
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# Fix requires redesigning worker semantics so env.run only ever
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# drives from main (options: lazy-deploy tensor API, coroutine
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# worker, or setup/verify split). Not a single-PR change — parked
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# until ADR-0027 (Megatron TP) starts, at which point a proper
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# architectural solution lands together with TP use cases.
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# ADR-0027 D0+D1 landed the architectural fix (worker-wait
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# generalization + torch.multiprocessing.spawn scheduler drain), so
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# this case now passes normally. Keeping it as the topology-default
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# smoke.
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pytest.param(
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"ring_allreduce_tcm", "kernbench.ccl.algorithms.ring_allreduce",
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"ring_1d", "tcm", None, 8, 2,
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id="ring_default_ws",
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marks=pytest.mark.xfail(
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reason="ADR-0024 Phase B: worker-greenlet env.run captures "
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"kernel greenlet as child → orphaned on worker yield. "
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"Needs architectural redesign (see test comment).",
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strict=True,
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),
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),
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# Buffer variants at 8-rank (fast — same kernel, different slot space).
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pytest.param(
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