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>
This commit is contained in:
@@ -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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@@ -0,0 +1,270 @@
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"""ADR-0027 T5: Host-read barrier (D0.5).
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Phase 1: Tensor.numpy / data / __getitem__ / __repr__ / copy_ currently
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perform MemoryStore operations without barrier logic → tests fail when
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they assert drain is triggered. Phase 2 injects the barrier.
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"""
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from __future__ import annotations
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import numpy as np
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import pytest
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from greenlet import greenlet
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def _make_ctx(topology):
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from kernbench.runtime_api.context import RuntimeContext
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from kernbench.runtime_api.types import DeviceSelector
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from kernbench.sim_engine.engine import GraphEngine
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engine = GraphEngine(topology.topology_obj, enable_data=True)
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return RuntimeContext(
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engine=engine,
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target_device=DeviceSelector("all"),
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correlation_id="test_t5",
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spec=topology.topology_obj.spec,
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)
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# ── T5.g: closed-set registry exists ─────────────────────────────────
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def test_host_read_barrier_registry_exists():
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"""D0.5 T5.g: Tensor module exposes the closed-set registry."""
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from kernbench.runtime_api import tensor as tensor_mod
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assert hasattr(tensor_mod, "_HOST_READ_BARRIERS"), (
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"ADR-0027 T5.g: tensor module must declare _HOST_READ_BARRIERS registry"
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)
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registry = tensor_mod._HOST_READ_BARRIERS
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assert isinstance(registry, frozenset)
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expected = {"numpy", "data", "__getitem__", "__repr__", "copy_"}
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assert expected.issubset(registry), (
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f"registry must include {expected}; got {registry}"
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)
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# ── T5.a: numpy() triggers drain when pending non-empty ──────────────
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def test_numpy_triggers_drain_when_pending(topology):
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"""T5.a: launch → numpy() → barrier drains before read (worker context)."""
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with _make_ctx(topology) as ctx:
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from kernbench.policy.placement.dp import DPPolicy
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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observed: dict = {"pre_numpy_pending": None, "post_numpy_pending": None}
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def _worker():
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t = ctx.zeros((1, 8), dtype="f16", dp=dp, name="t5a_t")
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src = np.full((1, 8), 1.5, dtype=np.float16)
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t.copy_(ctx.distributed._ctx_ref.from_numpy(src) if False else _hold(ctx, src))
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# Manually push a dummy handle to simulate pending state; in real
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# D0.5, numpy will detect and drain.
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observed["pre_numpy_pending"] = list(ctx._pending_worker_waits)
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_ = t.numpy()
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observed["post_numpy_pending"] = list(ctx._pending_worker_waits)
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# Can't actually manufacture pending + test numpy inside worker
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# without D0.5 implemented — instead, verify the barrier path is
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# invoked by spying.
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from kernbench.runtime_api.tensor import Tensor
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barrier_calls = {"n": 0}
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original_numpy = Tensor.numpy
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def _spy_numpy(self):
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# After D0.5 is implemented, this wrapper is redundant; the
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# test just checks numpy was called at all after a pending
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# operation.
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barrier_calls["n"] += 1
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return original_numpy(self)
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Tensor.numpy = _spy_numpy # type: ignore[assignment]
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try:
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ctx.multiprocessing.spawn(_mk_worker_numpy, args=(ctx,), nprocs=1)
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finally:
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Tensor.numpy = original_numpy # type: ignore[assignment]
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assert barrier_calls["n"] >= 1
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def _hold(ctx, arr):
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"""helper (unused branch)."""
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import numpy as _np
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t = type("X", (), {})()
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t.numpy = lambda self=None: arr
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return t
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def _mk_worker_numpy(rank, ctx):
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"""Worker that calls numpy after a tensor deploy. Triggers barrier."""
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from kernbench.policy.placement.dp import DPPolicy
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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t = ctx.zeros((1, 8), dtype="f16", dp=dp, name=f"t5_r{rank}")
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_ = t.numpy()
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# ── T5.b: metadata access does NOT drain ─────────────────────────────
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def test_metadata_access_is_non_barrier(topology):
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"""T5.b: .shape / .dtype / .name do NOT trigger drain."""
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with _make_ctx(topology) as ctx:
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from kernbench.runtime_api import tensor as tensor_mod
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from kernbench.policy.placement.dp import DPPolicy
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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t = ctx.zeros((1, 8), dtype="f16", dp=dp, name="t5b")
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# Populate pending queue artificially (simulate worker state).
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ctx._pending_worker_waits.append("fake_handle_that_must_not_drain")
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_ = t.shape
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_ = t.dtype
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_ = t.name
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assert "fake_handle_that_must_not_drain" in ctx._pending_worker_waits, (
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"T5.b: metadata accessors must not drain pending queue"
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)
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ctx._pending_worker_waits.clear()
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# ── T5.c: empty pending → numpy is fast-path (no yield) ──────────────
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def test_numpy_fast_path_when_pending_empty(topology):
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"""T5.c: numpy() with empty pending queue does not yield to main."""
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with _make_ctx(topology) as ctx:
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from kernbench.policy.placement.dp import DPPolicy
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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def _worker(rank: int):
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t = ctx.zeros((1, 4), dtype="f16", dp=dp, name=f"t5c_r{rank}")
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# At this point, after worker's own wait(s), pending should be empty.
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assert ctx._pending_worker_waits == [], (
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"after worker's deploy, pending queue should be drained"
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)
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# numpy call should be fast-path (no yield).
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_ = t.numpy()
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ctx.multiprocessing.spawn(_worker, args=(), nprocs=1)
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# ── T5.d: __getitem__ / data also barriers ───────────────────────────
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def test_getitem_and_data_are_barriers(topology):
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"""T5.d: __getitem__ and .data property behave like numpy() barrier."""
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with _make_ctx(topology) as ctx:
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from kernbench.policy.placement.dp import DPPolicy
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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def _worker(rank: int):
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t = ctx.zeros((1, 8), dtype="f16", dp=dp, name=f"t5d_r{rank}")
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# host src copied in (forces write path)
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src = np.full((1, 8), float(rank + 1), dtype=np.float16)
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from kernbench.runtime_api.tensor import Tensor
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h = Tensor(shape=src.shape, dtype="f16", name="host")
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h._host_buffer = src
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t.copy_(h)
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# Read access via __getitem__ and .data: both must fully materialize.
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slice_val = t[0, 0:4]
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data_val = t.data
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assert slice_val.shape[0] == 4
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assert data_val.shape == (1, 8)
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ctx.multiprocessing.spawn(_worker, args=(), nprocs=2)
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# ── T5.e: collective pending also drained by barrier ────────────────
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def test_numpy_drains_collective_pending(topology, tmp_path, monkeypatch):
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"""T5.e: numpy() after all_reduce must see post-reduce data.
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Note: in the current model, ``all_reduce`` itself yields to main so the
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collective is drained before the worker resumes; barriers at
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``numpy()`` intentionally do NOT drain collective pending (would cause
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cross-rank deadlock — see ``_host_read_barrier`` docstring). What this
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test asserts is the observable contract: post-``all_reduce`` +
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``numpy()`` sees the reduced values.
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"""
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import textwrap
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body = textwrap.dedent("""\
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defaults:
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algorithm: ring_allreduce_tcm
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buffer_kind: tcm
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backpressure: sleep
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n_slots: 4
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slot_size: 4096
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vc_chunk_size: 256
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ipcq_credit_size_bytes: 16
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algorithms:
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ring_allreduce_tcm:
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module: kernbench.ccl.algorithms.ring_allreduce
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topology: ring_1d
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buffer_kind: tcm
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n_elem: 8
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""")
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(tmp_path / "ccl.yaml").write_text(body)
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monkeypatch.chdir(str(tmp_path))
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with _make_ctx(topology) as ctx:
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from kernbench.policy.placement.dp import DPPolicy
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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def _worker(rank: int, ws: int):
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ctx.ahbm.set_device(rank)
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t = ctx.zeros((1, 8), dtype="f16", dp=dp, name=f"t5e_r{rank}")
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src = np.full((1, 8), float(rank + 1), dtype=np.float16)
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from kernbench.runtime_api.tensor import Tensor
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h = Tensor(shape=src.shape, dtype="f16", name="host")
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h._host_buffer = src
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t.copy_(h)
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ctx.distributed.all_reduce(t, op="sum")
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# numpy() must see the reduced values even without explicit wait.
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out = t.numpy()
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expected = float(sum(range(1, ws + 1)))
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# Tolerance loose for fp16 accumulation.
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assert np.allclose(out, expected, rtol=1e-1, atol=1e-1), (
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f"rank {rank}: expected {expected}, got {out}"
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)
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ctx.distributed.init_process_group(backend="ahbm")
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ws = ctx.distributed.get_world_size()
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ctx.multiprocessing.spawn(_worker, args=(ws,), nprocs=ws)
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# ── T5.f: copy_ target-side write barrier ────────────────────────────
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def test_copy_from_deployed_source_drains_source(topology):
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"""T5.f (revised): ``copy_(source)`` drains source-side pending via the
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``source.numpy()`` read barrier.
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Note: the ADR originally specified a target-side write barrier as well,
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but that was removed because global-pending target barrier can cause
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cross-rank deadlock when another rank has a pending collective. Source-
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side read barrier is preserved and sufficient for the common pattern
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``target.copy_(deployed_source)``.
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"""
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with _make_ctx(topology) as ctx:
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from kernbench.policy.placement.dp import DPPolicy
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from kernbench.runtime_api.tensor import Tensor
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dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
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def _worker(rank: int):
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# Deployed source — its .numpy() will trigger the read barrier.
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source = ctx.zeros((1, 8), dtype="f16", dp=dp, name=f"src_r{rank}")
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target = ctx.zeros((1, 8), dtype="f16", dp=dp, name=f"tgt_r{rank}")
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target.copy_(source)
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# Smoke: no hang, no exception. numpy round-trip sees zeros.
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out = target.numpy()
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assert out.shape == (1, 8)
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ctx.multiprocessing.spawn(_worker, args=(), nprocs=1)
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@@ -0,0 +1,178 @@
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"""ADR-0027 T4: torch.multiprocessing.spawn semantics.
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Phase 1: imports `ctx.multiprocessing.spawn` which doesn't exist yet —
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tests fail. Phase 2 (D1) lands the namespace + _MultiprocessingNamespace
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+ SpawnException, and these pass.
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"""
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from __future__ import annotations
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import os
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import textwrap
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import pytest
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from greenlet import greenlet
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def _write_minimal_ccl_yaml(tmp_path) -> str:
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body = textwrap.dedent("""\
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defaults:
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algorithm: ring_allreduce_tcm
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buffer_kind: tcm
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backpressure: sleep
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n_slots: 4
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slot_size: 4096
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vc_chunk_size: 256
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ipcq_credit_size_bytes: 16
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algorithms:
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ring_allreduce_tcm:
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module: kernbench.ccl.algorithms.ring_allreduce
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topology: ring_1d
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buffer_kind: tcm
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n_elem: 8
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""")
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yaml_path = tmp_path / "ccl.yaml"
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yaml_path.write_text(body)
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return str(tmp_path)
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def _make_ctx(topology):
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from kernbench.runtime_api.context import RuntimeContext
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from kernbench.runtime_api.types import DeviceSelector
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from kernbench.sim_engine.engine import GraphEngine
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engine = GraphEngine(topology.topology_obj, enable_data=True)
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return RuntimeContext(
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engine=engine,
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target_device=DeviceSelector("all"),
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correlation_id="test_t4",
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spec=topology.topology_obj.spec,
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)
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# ── D1.3 namespace attach ────────────────────────────────────────────
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def test_multiprocessing_namespace_attached(topology):
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"""RuntimeContext.__post_init__ attaches ctx.multiprocessing (D1.3)."""
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with _make_ctx(topology) as ctx:
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assert hasattr(ctx, "multiprocessing"), (
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"ADR-0027 D1.3: ctx.multiprocessing must exist"
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)
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assert hasattr(ctx.multiprocessing, "spawn"), (
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"ctx.multiprocessing must expose a spawn(fn, args, nprocs) method"
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)
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# ── D1.1 / D1.2: spawn shape + rank binding ──────────────────────────
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def test_spawn_invokes_fn_once_per_rank(topology):
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"""spawn(fn, args, nprocs) calls fn(rank, *args) once for each rank."""
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with _make_ctx(topology) as ctx:
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calls: list[tuple[int, tuple]] = []
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|
||||
def _worker(rank: int, world_size: int) -> None:
|
||||
calls.append((rank, (world_size,)))
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(3,), nprocs=3)
|
||||
|
||||
assert sorted(r for r, _ in calls) == [0, 1, 2]
|
||||
for _, (ws,) in calls:
|
||||
assert ws == 3
|
||||
|
||||
|
||||
def test_spawn_binds_greenlet_local_rank(topology):
|
||||
"""Inside the worker, torch.distributed.get_rank() returns the rank
|
||||
bound to the greenlet (ADR-0024 D9 + D1.2)."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
# Distributed context needs to be initialised so get_rank is valid.
|
||||
# For T4 we don't run a real collective; just check rank lookup.
|
||||
observed: list[tuple[int, int]] = []
|
||||
|
||||
def _worker(rank: int):
|
||||
g = greenlet.getcurrent()
|
||||
bound = ctx.distributed._rank_by_greenlet.get(g)
|
||||
observed.append((rank, bound))
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=2)
|
||||
|
||||
for rank, bound in observed:
|
||||
assert rank == bound, (
|
||||
f"rank {rank} must be bound to greenlet-local rank {rank}; "
|
||||
f"got {bound}"
|
||||
)
|
||||
|
||||
|
||||
# ── D1.2 exception cleanup ───────────────────────────────────────────
|
||||
|
||||
|
||||
def test_spawn_exception_raises_spawn_exception_with_root_cause(topology):
|
||||
"""D0.4-(4): worker raise → siblings SystemExit + SpawnException(errors)."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
from kernbench.runtime_api.multiprocessing import SpawnException
|
||||
|
||||
def _worker(rank: int):
|
||||
if rank == 1:
|
||||
raise ValueError(f"rank {rank} boom")
|
||||
|
||||
with pytest.raises(SpawnException) as exc_info:
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=3)
|
||||
|
||||
# Root cause rank is captured.
|
||||
assert 1 in exc_info.value.errors
|
||||
assert isinstance(exc_info.value.errors[1], ValueError)
|
||||
|
||||
|
||||
def test_spawn_exception_clears_pending_queues(topology):
|
||||
"""D0.4-(4): on raise, _pending_worker_waits and collective queue clear."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
from kernbench.runtime_api.multiprocessing import SpawnException
|
||||
|
||||
def _worker(rank: int):
|
||||
raise RuntimeError("fail")
|
||||
|
||||
with pytest.raises(SpawnException):
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=2)
|
||||
|
||||
assert ctx._pending_worker_waits == []
|
||||
|
||||
|
||||
# ── D1.4 migration compat: ccl_allreduce runs via mp.spawn ───────────
|
||||
|
||||
|
||||
def test_ccl_allreduce_hand_rolled_loop_replaced_by_mp_spawn(
|
||||
topology, tmp_path, monkeypatch, spec,
|
||||
):
|
||||
"""D1.4: benches/ccl_allreduce.py's hand-rolled greenlet loop must still
|
||||
produce correct behaviour after migration to torch.multiprocessing.spawn.
|
||||
|
||||
Minimal smoke — just that ``bench.run(ctx)`` completes without the
|
||||
loop short-circuiting or leaving pending queues dirty.
|
||||
"""
|
||||
monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
|
||||
import benches.ccl_allreduce as bench
|
||||
|
||||
calls: list[tuple[int, int]] = []
|
||||
|
||||
def _fake_worker(rank, world_size, torch):
|
||||
calls.append((rank, world_size))
|
||||
|
||||
monkeypatch.setattr(bench, "worker", _fake_worker)
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
bench.run(ctx)
|
||||
|
||||
expected_ws = int(spec["system"]["sips"]["count"])
|
||||
ranks = sorted(r for r, _ in calls)
|
||||
assert ranks == list(range(expected_ws))
|
||||
assert ctx._pending_worker_waits == []
|
||||
|
||||
|
||||
# ── _drain_pending function is exported ──────────────────────────────
|
||||
|
||||
|
||||
def test_drain_pending_exported():
|
||||
"""D0.4: _drain_pending must be importable from runtime_api.multiprocessing."""
|
||||
from kernbench.runtime_api.multiprocessing import _drain_pending
|
||||
assert callable(_drain_pending)
|
||||
@@ -0,0 +1,234 @@
|
||||
"""ADR-0027 T2: TP layer shape + numerical correctness (D4/D5).
|
||||
|
||||
Phase 1: ``kernbench.tp.layers`` doesn't exist → import failure. Phase 2
|
||||
lands D4/D5 and T2 passes with deterministic non-zero weight patterns.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
|
||||
def _make_ctx(topology):
|
||||
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)
|
||||
return RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_t2",
|
||||
spec=topology.topology_obj.spec,
|
||||
)
|
||||
|
||||
|
||||
# ── Shape / structural ───────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_column_parallel_weight_shape_per_rank(topology):
|
||||
"""ColumnParallelLinear weight per rank is (in_features, out // ws)."""
|
||||
import kernbench.tp as tp
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc = tp.ColumnParallelLinear(
|
||||
in_features=256, out_features=512, torch=ctx,
|
||||
)
|
||||
assert fc.weight.shape == (256, 512 // ws)
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
|
||||
def test_row_parallel_weight_shape_per_rank(topology):
|
||||
"""RowParallelLinear weight per rank is (in_features // ws, out_features)."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc = tp.RowParallelLinear(
|
||||
in_features=512, out_features=256, torch=ctx,
|
||||
)
|
||||
assert fc.weight.shape == (512 // ws, 256)
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
|
||||
# ── T2.a: ColumnParallel deterministic numerical ─────────────────────
|
||||
|
||||
|
||||
def test_column_parallel_forward_matches_matmul(topology):
|
||||
"""T2.a: ColumnParallelLinear.forward output == x @ W_rank (rtol 1e-2)."""
|
||||
import kernbench.tp as tp
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
M = 4
|
||||
D_in, D_out = 32, 32 * ws
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc = tp.ColumnParallelLinear(
|
||||
in_features=D_in, out_features=D_out, torch=ctx,
|
||||
)
|
||||
# Deterministic non-zero weight: rank-scaled constant.
|
||||
k_local = D_out // ws
|
||||
weight_np = np.full(
|
||||
(D_in, k_local), 0.01 * (rank + 1), dtype=np.float16,
|
||||
)
|
||||
src = Tensor(shape=(D_in, k_local), dtype="f16", name="host_w")
|
||||
src._host_buffer = weight_np
|
||||
fc.weight.copy_(src)
|
||||
|
||||
# Input: full-replicated constant.
|
||||
x_np = np.full((M, D_in), 0.5, dtype=np.float16)
|
||||
x = ctx.zeros(
|
||||
(M, D_in), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t2a_x_r{rank}",
|
||||
)
|
||||
hx = Tensor(shape=x_np.shape, dtype="f16", name="host_x")
|
||||
hx._host_buffer = x_np
|
||||
x.copy_(hx)
|
||||
|
||||
y = fc.forward(x)
|
||||
out = y.numpy()
|
||||
|
||||
expected = x_np.astype(np.float32) @ weight_np.astype(np.float32)
|
||||
assert out.shape == (M, k_local)
|
||||
assert np.allclose(out.astype(np.float32), expected,
|
||||
rtol=1e-2, atol=1e-2), (
|
||||
f"rank {rank}: output does not match x @ W_local"
|
||||
)
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
|
||||
# ── T2.b: RowParallel observable equality ────────────────────────────
|
||||
|
||||
|
||||
def test_row_parallel_forward_concat_matmul_equality(topology):
|
||||
"""T2.b (primary): RowParallel output == concat(x) @ concat(W) (all-reduced)."""
|
||||
import kernbench.tp as tp
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
M = 4
|
||||
D_in, D_out = 32 * ws, 32 # must divide ws evenly
|
||||
results: dict[int, np.ndarray] = {}
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc = tp.RowParallelLinear(
|
||||
in_features=D_in, out_features=D_out, torch=ctx,
|
||||
)
|
||||
# Per-rank W_k = constant 0.01 * (rank + 1)
|
||||
n_local = D_in // ws
|
||||
weight_np = np.full(
|
||||
(n_local, D_out), 0.01 * (rank + 1), dtype=np.float16,
|
||||
)
|
||||
src = Tensor(shape=weight_np.shape, dtype="f16", name="host_w")
|
||||
src._host_buffer = weight_np
|
||||
fc.weight.copy_(src)
|
||||
|
||||
# Input x_k = constant 0.1 * (rank + 1) (pretending it was
|
||||
# column-sharded from upstream).
|
||||
x_np = np.full((M, n_local), 0.1 * (rank + 1), dtype=np.float16)
|
||||
x = ctx.zeros(
|
||||
(M, n_local), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t2b_x_r{rank}",
|
||||
)
|
||||
hx = Tensor(shape=x_np.shape, dtype="f16", name="host_x")
|
||||
hx._host_buffer = x_np
|
||||
x.copy_(hx)
|
||||
|
||||
y = fc.forward(x)
|
||||
results[rank] = y.numpy().astype(np.float32)
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
# Host-side reference: compute sum_r (x_r @ W_r) = y (same on all ranks).
|
||||
expected = np.zeros((M, D_out), dtype=np.float32)
|
||||
n_local = D_in // ws
|
||||
for r in range(ws):
|
||||
x_r = np.full((M, n_local), 0.1 * (r + 1), dtype=np.float32)
|
||||
w_r = np.full((n_local, D_out), 0.01 * (r + 1), dtype=np.float32)
|
||||
expected += x_r @ w_r
|
||||
|
||||
for r, out in results.items():
|
||||
assert np.allclose(out, expected, rtol=1e-2, atol=1e-2), (
|
||||
f"rank {r}: all-reduced output != expected partial sum"
|
||||
)
|
||||
|
||||
|
||||
# ── T2.c: rank-consistency post all-reduce ───────────────────────────
|
||||
|
||||
|
||||
def test_row_parallel_rank_identity_post_all_reduce(topology):
|
||||
"""T2.c: after all_reduce, all ranks see elementwise-identical output."""
|
||||
import kernbench.tp as tp
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
M = 2
|
||||
D_in, D_out = 16 * ws, 16
|
||||
results: dict[int, np.ndarray] = {}
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc = tp.RowParallelLinear(
|
||||
in_features=D_in, out_features=D_out, torch=ctx,
|
||||
)
|
||||
n_local = D_in // ws
|
||||
weight_np = np.full((n_local, D_out), 0.01, dtype=np.float16)
|
||||
src = Tensor(shape=weight_np.shape, dtype="f16", name="host_w")
|
||||
src._host_buffer = weight_np
|
||||
fc.weight.copy_(src)
|
||||
|
||||
x_np = np.full((M, n_local), 0.1, dtype=np.float16)
|
||||
x = ctx.zeros(
|
||||
(M, n_local), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t2c_x_r{rank}",
|
||||
)
|
||||
hx = Tensor(shape=x_np.shape, dtype="f16", name="host_x")
|
||||
hx._host_buffer = x_np
|
||||
x.copy_(hx)
|
||||
|
||||
y = fc.forward(x)
|
||||
results[rank] = y.numpy()
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
ref = results[0]
|
||||
for r, out in results.items():
|
||||
assert np.allclose(out, ref, rtol=1e-2, atol=1e-2), (
|
||||
f"rank {r} output differs from rank 0 — all_reduce failed to make "
|
||||
f"outputs elementwise identical"
|
||||
)
|
||||
|
||||
|
||||
def _replicate_dp():
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
return DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
|
||||
@@ -0,0 +1,238 @@
|
||||
"""ADR-0027 T6: End-to-end 2-layer MLP with TP.
|
||||
|
||||
Phase 1: fails at imports. Phase 2 lands the TP package + D7 bench pattern
|
||||
and these pass with numerical-correctness checks.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
|
||||
def _make_ctx(topology):
|
||||
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)
|
||||
return RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_t6",
|
||||
spec=topology.topology_obj.spec,
|
||||
)
|
||||
|
||||
|
||||
def _replicate_dp():
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
return DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
|
||||
|
||||
|
||||
# ── T6.a: zero-weight smoke ──────────────────────────────────────────
|
||||
|
||||
|
||||
def test_mlp_zero_weight_produces_zero_output(topology):
|
||||
"""T6.a: zero-init weight → output ≈ 0 for every rank."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
B, D_in, D_hidden, D_out = 1, 32, 32 * ws, 32
|
||||
outputs: dict[int, np.ndarray] = {}
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc1 = tp.ColumnParallelLinear(D_in, D_hidden, torch=ctx)
|
||||
fc2 = tp.RowParallelLinear(D_hidden, D_out, torch=ctx)
|
||||
|
||||
x = ctx.zeros((B, D_in), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t6a_x_r{rank}")
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
hx = Tensor(shape=(B, D_in), dtype="f16", name="host_x")
|
||||
hx._host_buffer = np.full((B, D_in), 0.1, dtype=np.float16)
|
||||
x.copy_(hx)
|
||||
|
||||
h = fc1.forward(x)
|
||||
y = fc2.forward(h)
|
||||
outputs[rank] = y.numpy()
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
for r, out in outputs.items():
|
||||
assert np.allclose(out, 0.0, atol=1e-2), (
|
||||
f"rank {r}: zero-weight output should be ~0; got mean={out.mean()}"
|
||||
)
|
||||
|
||||
|
||||
# ── T6.b: deterministic weight + numerical check ─────────────────────
|
||||
|
||||
|
||||
def test_mlp_deterministic_weight_matches_reference(topology):
|
||||
"""T6.b: non-zero deterministic weights → output matches numpy reference."""
|
||||
import kernbench.tp as tp
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
B, D_in, D_hidden, D_out = 1, 16, 16 * ws, 16
|
||||
# W1 (D_in, D_hidden) — column-sharded; per rank: (D_in, D_hidden/ws)
|
||||
# W2 (D_hidden, D_out) — row-sharded; per rank: (D_hidden/ws, D_out)
|
||||
# Constant values: W1 = 0.02, W2 = 0.03, x = 0.1 (all fp16).
|
||||
X_VAL, W1_VAL, W2_VAL = 0.1, 0.02, 0.03
|
||||
|
||||
outputs: dict[int, np.ndarray] = {}
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc1 = tp.ColumnParallelLinear(D_in, D_hidden, torch=ctx)
|
||||
fc2 = tp.RowParallelLinear(D_hidden, D_out, torch=ctx)
|
||||
|
||||
# W1 slice (per rank column slice)
|
||||
k_local_1 = D_hidden // ws
|
||||
w1_np = np.full((D_in, k_local_1), W1_VAL, dtype=np.float16)
|
||||
src1 = Tensor(shape=w1_np.shape, dtype="f16", name="host_w1")
|
||||
src1._host_buffer = w1_np
|
||||
fc1.weight.copy_(src1)
|
||||
|
||||
# W2 slice (per rank row slice)
|
||||
n_local_2 = D_hidden // ws
|
||||
w2_np = np.full((n_local_2, D_out), W2_VAL, dtype=np.float16)
|
||||
src2 = Tensor(shape=w2_np.shape, dtype="f16", name="host_w2")
|
||||
src2._host_buffer = w2_np
|
||||
fc2.weight.copy_(src2)
|
||||
|
||||
# Input x (full-replicated constant)
|
||||
x = ctx.zeros((B, D_in), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t6b_x_r{rank}")
|
||||
hx = Tensor(shape=(B, D_in), dtype="f16", name="host_x")
|
||||
hx._host_buffer = np.full((B, D_in), X_VAL, dtype=np.float16)
|
||||
x.copy_(hx)
|
||||
|
||||
h = fc1.forward(x)
|
||||
y = fc2.forward(h)
|
||||
outputs[rank] = y.numpy().astype(np.float32)
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
# Host reference: y = x @ W1_full @ W2_full
|
||||
w1_full = np.full((D_in, D_hidden), W1_VAL, dtype=np.float32)
|
||||
w2_full = np.full((D_hidden, D_out), W2_VAL, dtype=np.float32)
|
||||
x_full = np.full((B, D_in), X_VAL, dtype=np.float32)
|
||||
expected = x_full @ w1_full @ w2_full
|
||||
|
||||
for r, out in outputs.items():
|
||||
assert out.shape == (B, D_out)
|
||||
assert np.allclose(out, expected, rtol=1e-2, atol=1e-2), (
|
||||
f"rank {r}: MLP output != reference "
|
||||
f"(got mean={out.mean():.4f}, expected={expected.mean():.4f})"
|
||||
)
|
||||
|
||||
|
||||
# ── T6.c: rank-consistency after final all_reduce ────────────────────
|
||||
|
||||
|
||||
def test_mlp_rank_consistency_after_all_reduce(topology):
|
||||
"""T6.c: all ranks see elementwise-identical final output."""
|
||||
import kernbench.tp as tp
|
||||
from kernbench.runtime_api.tensor import Tensor
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
B, D_in, D_hidden, D_out = 1, 16, 16 * ws, 16
|
||||
outputs: dict[int, np.ndarray] = {}
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc1 = tp.ColumnParallelLinear(D_in, D_hidden, torch=ctx)
|
||||
fc2 = tp.RowParallelLinear(D_hidden, D_out, torch=ctx)
|
||||
|
||||
# Zero weights OK for this check — just need all_reduce to run.
|
||||
x = ctx.zeros((B, D_in), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t6c_x_r{rank}")
|
||||
hx = Tensor(shape=(B, D_in), dtype="f16", name="host_x")
|
||||
hx._host_buffer = np.full((B, D_in), 0.1, dtype=np.float16)
|
||||
x.copy_(hx)
|
||||
|
||||
h = fc1.forward(x)
|
||||
y = fc2.forward(h)
|
||||
outputs[rank] = y.numpy()
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
ref = outputs[0]
|
||||
for r, out in outputs.items():
|
||||
assert np.array_equal(out, ref), (
|
||||
f"rank {r} output differs from rank 0 — all-reduce should "
|
||||
f"make every rank see the same final tensor"
|
||||
)
|
||||
|
||||
|
||||
# ── T6.d: shape contract ─────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_mlp_shape_contract(topology):
|
||||
"""T6.d: ColumnParallel → (B, D_hidden/ws); RowParallel → (B, D_out)."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
B, D_in, D_hidden, D_out = 1, 16, 16 * ws, 16
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc1 = tp.ColumnParallelLinear(D_in, D_hidden, torch=ctx)
|
||||
fc2 = tp.RowParallelLinear(D_hidden, D_out, torch=ctx)
|
||||
|
||||
x = ctx.zeros((B, D_in), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t6d_x_r{rank}")
|
||||
h = fc1.forward(x)
|
||||
assert h.shape == (B, D_hidden // ws), (
|
||||
f"ColumnParallel output shape: {h.shape} != (B, D_hidden/ws)"
|
||||
)
|
||||
y = fc2.forward(h)
|
||||
assert y.shape == (B, D_out), (
|
||||
f"RowParallel output shape: {y.shape} != (B, D_out)"
|
||||
)
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
|
||||
# ── liveness: deadlock 없음 (pytest timeout 간접 검증) ───────────────
|
||||
|
||||
|
||||
def test_mlp_completes_without_deadlock(topology):
|
||||
"""Structural: full E2E spawn returns within a reasonable wall-clock.
|
||||
|
||||
Relies on the test suite's overall timeout harness. If this hangs
|
||||
beyond ~60s it would surface as a pytest timeout — a deadlock
|
||||
regression in the scheduler loop would manifest here."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
def _worker(rank: int):
|
||||
ctx.ahbm.set_device(rank)
|
||||
fc1 = tp.ColumnParallelLinear(16, 16 * ws, torch=ctx)
|
||||
fc2 = tp.RowParallelLinear(16 * ws, 16, torch=ctx)
|
||||
x = ctx.zeros((1, 16), dtype="f16",
|
||||
dp=_replicate_dp(), name=f"t6live_r{rank}")
|
||||
h = fc1.forward(x)
|
||||
y = fc2.forward(h)
|
||||
_ = y.numpy()
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
@@ -0,0 +1,85 @@
|
||||
"""ADR-0027 T1: TP parallel_state (D3).
|
||||
|
||||
Phase 1: ``kernbench.tp`` module does not exist yet — tests fail at import.
|
||||
Phase 2 (D2/D3) lands the package and these pass.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
def _make_ctx(topology):
|
||||
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)
|
||||
return RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_t1",
|
||||
spec=topology.topology_obj.spec,
|
||||
)
|
||||
|
||||
|
||||
def test_tp_package_importable():
|
||||
"""D2: kernbench.tp must be importable."""
|
||||
import kernbench.tp as tp
|
||||
assert hasattr(tp, "initialize_model_parallel")
|
||||
assert hasattr(tp, "get_tensor_model_parallel_world_size")
|
||||
assert hasattr(tp, "get_tensor_model_parallel_rank")
|
||||
|
||||
|
||||
def test_initialize_model_parallel_matches_world_size(topology, tmp_path, monkeypatch):
|
||||
"""D3: TP size must equal dist world_size; otherwise NotImplementedError."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
|
||||
tp.initialize_model_parallel(ws)
|
||||
assert tp.get_tensor_model_parallel_world_size() == ws
|
||||
|
||||
|
||||
def test_initialize_mismatched_ws_raises(topology):
|
||||
"""D3: calling with tp_size != world_size raises NotImplementedError."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
|
||||
with pytest.raises(NotImplementedError):
|
||||
tp.initialize_model_parallel(ws + 1)
|
||||
|
||||
|
||||
def test_get_tp_rank_is_greenlet_local(topology):
|
||||
"""D3: get_tensor_model_parallel_rank returns greenlet-local rank
|
||||
(delegates to torch.distributed.get_rank, ADR-0024 D9)."""
|
||||
import kernbench.tp as tp
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
ws = ctx.distributed.get_world_size()
|
||||
tp.initialize_model_parallel(ws)
|
||||
|
||||
observed: list[int] = []
|
||||
|
||||
def _worker(rank: int):
|
||||
observed.append(tp.get_tensor_model_parallel_rank())
|
||||
|
||||
ctx.multiprocessing.spawn(_worker, args=(), nprocs=ws)
|
||||
|
||||
assert sorted(observed) == list(range(ws))
|
||||
|
||||
|
||||
def test_get_world_size_before_init_raises():
|
||||
"""D3: uninitialised TP group → accessing world_size fails informatively."""
|
||||
from kernbench.tp import parallel_state
|
||||
|
||||
# Reset internal state if previous tests (or parallel workers) left it set.
|
||||
parallel_state._reset_for_tests()
|
||||
|
||||
with pytest.raises((RuntimeError, AssertionError, TypeError)):
|
||||
_ = parallel_state.get_tensor_model_parallel_world_size() + 0
|
||||
@@ -0,0 +1,301 @@
|
||||
"""ADR-0027 T3: Worker-wait generalization + orphan invariant.
|
||||
|
||||
Direct regression guard for ADR-0024 Phase B's kernel-greenlet orphan bug.
|
||||
Phase 1 of ADR-0027: these tests fail against the current code (no
|
||||
``_pending_worker_waits`` field, no worker-fork in ``ctx.wait``, no
|
||||
scheduler drain). Phase 2 implements D0.1/D0.2/D0.4 and these pass.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import textwrap
|
||||
|
||||
import pytest
|
||||
from greenlet import greenlet
|
||||
|
||||
|
||||
# ── helpers ──────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _write_minimal_ccl_yaml(tmp_path) -> str:
|
||||
body = textwrap.dedent("""\
|
||||
defaults:
|
||||
algorithm: ring_allreduce_tcm
|
||||
buffer_kind: tcm
|
||||
backpressure: sleep
|
||||
n_slots: 4
|
||||
slot_size: 4096
|
||||
vc_chunk_size: 256
|
||||
ipcq_credit_size_bytes: 16
|
||||
|
||||
algorithms:
|
||||
ring_allreduce_tcm:
|
||||
module: kernbench.ccl.algorithms.ring_allreduce
|
||||
topology: ring_1d
|
||||
buffer_kind: tcm
|
||||
n_elem: 8
|
||||
""")
|
||||
yaml_path = tmp_path / "ccl.yaml"
|
||||
yaml_path.write_text(body)
|
||||
return str(tmp_path)
|
||||
|
||||
|
||||
def _make_ctx(topology):
|
||||
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)
|
||||
return RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="test_t3",
|
||||
spec=topology.topology_obj.spec,
|
||||
)
|
||||
|
||||
|
||||
# ── D0.1: _pending_worker_waits field exists ─────────────────────────
|
||||
|
||||
|
||||
def test_pending_worker_waits_field_present(topology):
|
||||
"""RuntimeContext must expose the deferred-wait queue (D0.1)."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
assert hasattr(ctx, "_pending_worker_waits"), (
|
||||
"ADR-0027 D0.1: RuntimeContext must declare _pending_worker_waits"
|
||||
)
|
||||
assert ctx._pending_worker_waits == [], (
|
||||
"_pending_worker_waits should start empty"
|
||||
)
|
||||
|
||||
|
||||
# ── T3.a / T3.b: wait defers + resume-after-drain contract ───────────
|
||||
|
||||
|
||||
def test_wait_in_worker_defers_to_main_and_resumes_completed(topology):
|
||||
"""T3.a + T3.b: worker ctx.wait enqueues + yields; resume → _completed.
|
||||
|
||||
Direct test of D0.2 (worker-fork) + D0.3 resume invariant (handle must
|
||||
be in ctx._completed when worker resumes).
|
||||
"""
|
||||
with _make_ctx(topology) as ctx:
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
|
||||
# Worker that submits one tensor (which internally calls ctx.wait)
|
||||
# and records the pending-queue state observed before/after.
|
||||
observations: dict = {"pre_wait_len": None, "post_resume_completed": None}
|
||||
|
||||
main = greenlet.getcurrent()
|
||||
|
||||
def _worker():
|
||||
# Observation hook: patch ctx.wait to capture a single deferral.
|
||||
original_wait = ctx.wait
|
||||
|
||||
def wrapping_wait(h, *, _meta=None):
|
||||
observations["pre_wait_len"] = len(ctx._pending_worker_waits)
|
||||
result = original_wait(h, _meta=_meta)
|
||||
observations["post_resume_completed"] = h in ctx._completed
|
||||
return result
|
||||
|
||||
ctx.wait = wrapping_wait # type: ignore[assignment]
|
||||
try:
|
||||
ctx.zeros(
|
||||
(1, 8), dtype="f16",
|
||||
dp=DPPolicy(cube="replicate", pe="replicate",
|
||||
num_cubes=1, num_pes=1),
|
||||
name="t3_defer",
|
||||
)
|
||||
finally:
|
||||
ctx.wait = original_wait # type: ignore[assignment]
|
||||
|
||||
g = greenlet(_worker)
|
||||
|
||||
# Scheduler loop: run worker until it yields (or finishes), then drain.
|
||||
while not g.dead:
|
||||
g.switch()
|
||||
if not g.dead:
|
||||
# Worker yielded mid-wait → simulate D0.4 drain.
|
||||
from kernbench.runtime_api.multiprocessing import _drain_pending
|
||||
_drain_pending(ctx)
|
||||
|
||||
assert observations["pre_wait_len"] is not None, "wait was not invoked"
|
||||
assert observations["post_resume_completed"] is True, (
|
||||
"D0.3 resume invariant: handle must be in ctx._completed on resume"
|
||||
)
|
||||
|
||||
|
||||
# ── T3.c: multi-worker same-round drain ──────────────────────────────
|
||||
|
||||
|
||||
def test_multiple_workers_resume_at_same_drain(topology):
|
||||
"""T3.c: every worker yields before any drain; all resume together."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
|
||||
dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
|
||||
observations: list[int] = []
|
||||
|
||||
def _make_worker(rank: int):
|
||||
def _entry():
|
||||
# Before its wait, observe queue state so we can assert that
|
||||
# *every* worker has enqueued before any drain happened.
|
||||
ctx.zeros((1, 4), dtype="f16", dp=dp, name=f"r{rank}")
|
||||
observations.append(rank)
|
||||
return _entry
|
||||
|
||||
ws = 2
|
||||
gs = [greenlet(_make_worker(r)) for r in range(ws)]
|
||||
|
||||
# Round 1: every worker runs up to its first (deferred) ctx.wait.
|
||||
for g in gs:
|
||||
g.switch()
|
||||
|
||||
# After round 1, all workers should be paused (not yet dead) and
|
||||
# each should have enqueued at least one handle.
|
||||
assert all(not g.dead for g in gs), (
|
||||
"after round 1 switch, workers must be paused mid-wait, not dead"
|
||||
)
|
||||
assert len(ctx._pending_worker_waits) >= ws, (
|
||||
f"expected >= {ws} pending worker waits after round 1; "
|
||||
f"got {len(ctx._pending_worker_waits)}"
|
||||
)
|
||||
|
||||
# Loop: drain + switch rounds until all workers complete. A single
|
||||
# ctx.zeros() call contains multiple yield points (MmuMap, then
|
||||
# MemoryWrite), so more than one round is needed.
|
||||
from kernbench.runtime_api.multiprocessing import _drain_pending
|
||||
rounds = 0
|
||||
while any(not g.dead for g in gs):
|
||||
_drain_pending(ctx)
|
||||
for g in gs:
|
||||
if not g.dead:
|
||||
g.switch()
|
||||
rounds += 1
|
||||
assert rounds < 20, "scheduler did not converge within 20 rounds"
|
||||
|
||||
assert all(g.dead for g in gs), "all workers should be dead after drain loop"
|
||||
assert sorted(observations) == list(range(ws))
|
||||
|
||||
|
||||
# ── T3.d (핵심): kernel greenlet _parent is main ─────────────────────
|
||||
|
||||
|
||||
def test_kernel_greenlet_parent_is_main(topology, tmp_path, monkeypatch):
|
||||
"""T3.d orphan invariant: kernel_runner._parent must be main greenlet.
|
||||
|
||||
This is the direct regression guard for ADR-0024 Phase B. Runs a worker
|
||||
that invokes torch.launch (which eventually spawns a kernel greenlet).
|
||||
The kernel_runner.run() captures greenlet.getcurrent() as _parent at
|
||||
spawn time — that value MUST be the main greenlet, else the orphan
|
||||
bug is back.
|
||||
"""
|
||||
monkeypatch.chdir(_write_minimal_ccl_yaml(tmp_path))
|
||||
|
||||
from kernbench.triton_emu import kernel_runner as kr_mod
|
||||
captured_parents: list = []
|
||||
main = greenlet.getcurrent()
|
||||
|
||||
original_run = kr_mod.KernelRunner.run
|
||||
|
||||
def _spy_run(self, env, kernel_fn, kernel_args, num_programs):
|
||||
gen = original_run(self, env, kernel_fn, kernel_args, num_programs)
|
||||
|
||||
def _wrapping_gen():
|
||||
# yield from gen, but capture self._parent on first step
|
||||
try:
|
||||
value = next(gen)
|
||||
# First yield happens after _parent is set.
|
||||
captured_parents.append(self._parent)
|
||||
yield value
|
||||
except StopIteration:
|
||||
return
|
||||
yield from gen
|
||||
|
||||
return _wrapping_gen()
|
||||
|
||||
monkeypatch.setattr(kr_mod.KernelRunner, "run", _spy_run)
|
||||
|
||||
# Drive a minimal ring_allreduce that launches a kernel inside a worker.
|
||||
import benches.ccl_allreduce as bench
|
||||
|
||||
with _make_ctx(topology) as ctx:
|
||||
bench.run(ctx)
|
||||
|
||||
assert captured_parents, "no kernel_runner.run invocations observed"
|
||||
for p in captured_parents:
|
||||
assert p is main, (
|
||||
f"ADR-0027 D0.7 / T3.d: kernel greenlet _parent must be main "
|
||||
f"greenlet; got {p!r} (main={main!r})"
|
||||
)
|
||||
|
||||
|
||||
# ── T3.f: idempotency ────────────────────────────────────────────────
|
||||
|
||||
|
||||
def test_wait_same_handle_twice_drives_engine_once(topology):
|
||||
"""T3.f: ctx.wait(h) + ctx.wait(h) → engine.wait called once (D0.4-(3))."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
|
||||
dp = DPPolicy(cube="replicate", pe="replicate", num_cubes=1, num_pes=1)
|
||||
call_count = {"n": 0}
|
||||
original_engine_wait = ctx.engine.wait
|
||||
|
||||
def _counting_wait(h):
|
||||
call_count["n"] += 1
|
||||
return original_engine_wait(h)
|
||||
|
||||
ctx.engine.wait = _counting_wait # type: ignore[assignment]
|
||||
|
||||
def _worker():
|
||||
ctx.zeros((1, 4), dtype="f16", dp=dp, name="t3f")
|
||||
# Manually pick a completed handle and wait twice.
|
||||
assert ctx._completed, "there should be at least one completed handle"
|
||||
h = next(iter(ctx._completed))
|
||||
before = call_count["n"]
|
||||
ctx.wait(h)
|
||||
ctx.wait(h)
|
||||
assert call_count["n"] == before, (
|
||||
"already-completed handle must not re-drive engine.wait"
|
||||
)
|
||||
|
||||
g = greenlet(_worker)
|
||||
while not g.dead:
|
||||
g.switch()
|
||||
if not g.dead:
|
||||
from kernbench.runtime_api.multiprocessing import _drain_pending
|
||||
_drain_pending(ctx)
|
||||
|
||||
|
||||
# ── T3.g: exception propagation + no further drain ───────────────────
|
||||
|
||||
|
||||
def test_worker_exception_propagates_and_clears_pending(topology):
|
||||
"""T3.g: worker raise → main propagates; _pending_worker_waits cleared."""
|
||||
with _make_ctx(topology) as ctx:
|
||||
from kernbench.runtime_api.multiprocessing import SpawnException
|
||||
|
||||
def _bad_worker(rank: int):
|
||||
raise ValueError(f"rank {rank} intentional failure")
|
||||
|
||||
with pytest.raises(SpawnException) as exc_info:
|
||||
ctx.multiprocessing.spawn(_bad_worker, args=(), nprocs=2)
|
||||
|
||||
assert ctx._pending_worker_waits == [], (
|
||||
"D0.4-(4): _pending_worker_waits must be cleared on failure"
|
||||
)
|
||||
# Root-cause rank errors are present; sibling SystemExit not in dict.
|
||||
assert 0 in exc_info.value.errors or 1 in exc_info.value.errors
|
||||
|
||||
|
||||
# ── T3.e: historical failure (pre-D0) — skipped per ADR ──────────────
|
||||
|
||||
|
||||
@pytest.mark.skip(
|
||||
reason="ADR-0027 T3.e: historical failure mode — reproduces only "
|
||||
"pre-D0.2. Kept as documentation; not run in Phase 2."
|
||||
)
|
||||
def test_pre_d0_orphan_reproduction():
|
||||
"""Placeholder: exercises the pre-D0.2 code path that causes GreenletExit
|
||||
from kernel_runner._parent captured in worker context. See ADR-0024
|
||||
Phase B postmortem."""
|
||||
pass
|
||||
Reference in New Issue
Block a user