ywkang
357cab525b
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>
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
"""Tests for tl.recv_async + tl.wait (ADR-0023 D4)."""
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from __future__ import annotations
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import numpy as np
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from kernbench.ccl.testing import run_kernel_in_mock
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def kernel_async_recv(t_ptr, n_elem, tl):
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"""Each PE issues recv_async first, then send, then wait — this exercises
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the non-blocking path. Uses TensorHandle math (PE_MATH) for accumulation
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so Phase 2 produces correct final HBM contents."""
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rank = tl.program_id(axis=0)
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world_size = tl.num_programs(axis=0)
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nbytes = n_elem * 2
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pe_addr = t_ptr + rank * nbytes
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acc = tl.load(pe_addr, shape=(n_elem,), dtype="f16")
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current = acc
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for _step in range(world_size - 1):
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future = tl.recv_async(dir="W", shape=(n_elem,), dtype="f16")
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tl.send(dir="E", src=current)
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recv = tl.wait(future)
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acc = acc + recv
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current = recv # forward W's tile to E next round
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tl.store(pe_addr, acc)
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def test_recv_async_mock_runtime():
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n_elem = 8
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inputs = [
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np.full((n_elem,), float(r + 1), dtype=np.float16)
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for r in range(4)
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]
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expected = sum(inputs)
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outputs = run_kernel_in_mock(
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kernel_fn=kernel_async_recv,
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world_size=4,
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topology="ring_1d",
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inputs=inputs,
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kernel_args=(n_elem,),
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)
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for r in range(4):
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assert np.allclose(outputs[r], expected)
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def test_recv_async_simpy_runner():
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"""Run the async kernel through the real SimPy stack via the
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install_ipcq + launch path.
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"""
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import importlib
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from kernbench.runtime_api.bench_runner import run_bench
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from kernbench.runtime_api.types import resolve_device
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from kernbench.sim_engine.engine import GraphEngine
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from kernbench.topology.builder import resolve_topology
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# Re-use the standard 8-PE bench skeleton but swap in the async kernel.
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topo = resolve_topology("topology.yaml")
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# Build a tiny inline bench module
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import types
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mod = types.ModuleType("inline_bench_async")
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from kernbench.policy.placement.dp import DPPolicy
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def run(torch):
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plan = torch.install_ipcq(
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algorithm="ring_allreduce_tcm", world_size_override=8,
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)
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a = torch.zeros(
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(1, 8 * 8),
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dtype="f16",
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dp=DPPolicy(
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cube="replicate", pe="column_wise",
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num_cubes=1,
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),
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name="async_in",
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)
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store = torch.engine.memory_store
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base = a._handle.va_base or a._handle.shards[0].pa
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nbytes = 8 * 2
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for r in range(8):
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store.write("hbm", base + r * nbytes,
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np.full((8,), float(r + 1), dtype=np.float16))
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torch.launch("ring_allreduce_tcm", kernel_async_recv, a, 8)
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for r in range(8):
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result = store.read("hbm", base + r * nbytes, shape=(8,), dtype="f16")
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expected = float(sum(range(1, 9))) # 36
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assert np.allclose(result, expected, rtol=1e-2, atol=1e-2), \
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f"rank {r}: got {result}, expected {expected}"
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mod.run = run
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result = run_bench(
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topology=topo, bench_fn=mod.run,
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device=resolve_device("all"),
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engine_factory=lambda t, d: GraphEngine(
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getattr(t, "topology_obj", t), enable_data=True
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),
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)
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assert result.completion.ok
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