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>
289 lines
9.1 KiB
Python
289 lines
9.1 KiB
Python
import pytest
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from kernbench.policy.address.allocator import AddressConfig, AllocationError, PEMemAllocator
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from kernbench.policy.placement.dp import (
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DPPolicy,
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ShardSpec,
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column_wise,
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replicate,
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resolve_dp_policy,
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row_wise,
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tiled_column_major,
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tiled_row_major,
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)
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from kernbench.runtime_api.kernel import (
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KernelLaunchMsg,
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KernelRef,
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MemoryReadMsg,
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MemoryWriteMsg,
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ScalarArg,
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TensorArg,
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TensorArgShard,
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)
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from kernbench.runtime_api.tensor import (
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TensorHandle,
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TensorShard,
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deploy_tensor,
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dtype_itemsize,
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)
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_MB = 1 << 20
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_GB = 1 << 30
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_CFG = AddressConfig(
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sip_count=2,
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cubes_per_sip=16,
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pes_per_cube=8,
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hbm_bytes_per_cube=48 * _GB,
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hbm_slices_per_cube=8,
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tcm_bytes_per_pe=16 * _MB,
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tcm_scheduler_reserved_bytes=4 * _MB,
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sram_bytes_per_cube=32 * _MB,
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)
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def _make_allocators(num_pe: int = 8) -> dict[tuple[int, int, int], PEMemAllocator]:
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return {
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(0, 0, i): PEMemAllocator(rack_id=0, sip_id=0, cube_id=0, pe_id=i, cfg=_CFG)
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for i in range(num_pe)
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}
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# ── Tensor types ─────────────────────────────────────────────────────
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def test_tensor_shard_immutable():
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ts = TensorShard(sip=0, cube=0, pe=0, pa=0x1000, nbytes=4096, offset_bytes=0)
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with pytest.raises(AttributeError):
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ts.pa = 0x2000 # type: ignore[misc]
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# hashable
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{ts}
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def test_tensor_handle_nbytes():
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th = TensorHandle(
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name="A",
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shape=(1024, 512),
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dtype="fp16",
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itemsize=2,
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shards=(),
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)
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assert th.nbytes == 1024 * 512 * 2 # 1 MB
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# ── Message types (ADR-0012) ─────────────────────────────────────────
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def test_memory_write_msg_fields():
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msg = MemoryWriteMsg(
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correlation_id="c0",
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request_id="r0",
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dst_sip=0,
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dst_cube=3,
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dst_pe=5,
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dst_pa=0xDEAD,
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nbytes=4096,
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pattern="zero",
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)
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assert msg.msg_type == "memory_write"
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assert msg.src_kind == "pattern"
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assert msg.dst_pa == 0xDEAD
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assert msg.pattern == "zero"
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with pytest.raises(AttributeError):
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msg.nbytes = 0 # type: ignore[misc]
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def test_memory_read_msg_fields():
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msg = MemoryReadMsg(
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correlation_id="c0",
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request_id="r1",
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src_sip=1,
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src_cube=2,
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src_pe=7,
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src_pa=0xBEEF,
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nbytes=2048,
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)
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assert msg.msg_type == "memory_read"
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assert msg.src_pa == 0xBEEF
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assert msg.nbytes == 2048
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def test_kernel_launch_msg_fields():
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shard = TensorArgShard(sip=0, cube=0, pe=0, pa=0x100, nbytes=1024, offset_bytes=0)
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targ = TensorArg(shards=(shard,))
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sarg = ScalarArg(dtype="fp32", value=1.0)
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kref = KernelRef(name="gemm", kind="builtin")
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msg = KernelLaunchMsg(
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correlation_id="c0",
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request_id="r2",
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kernel_ref=kref,
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args=(targ, sarg),
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)
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assert msg.msg_type == "kernel_launch"
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assert msg.kernel_ref.name == "gemm"
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assert len(msg.args) == 2
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assert msg.args[0].arg_kind == "tensor"
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assert msg.args[1].arg_kind == "scalar"
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# ── Placement: column_wise ───────────────────────────────────────────
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def test_column_wise_placement():
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"""(1024, 512) fp16 across 8 PEs → K axis split → 8 shards, each (1024, 64) = 128KB"""
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shards = column_wise(shape=(1024, 512), itemsize=2, num_pe=8)
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assert len(shards) == 8
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expected_nbytes = 1024 * 64 * 2 # 128 KB
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for i, s in enumerate(shards):
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assert s.local_pe == i
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assert s.nbytes == expected_nbytes
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# offsets are contiguous
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assert shards[0].offset_bytes == 0
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assert shards[1].offset_bytes == expected_nbytes
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# total coverage
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assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
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# ── Placement: row_wise ──────────────────────────────────────────────
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def test_row_wise_placement():
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"""(1024, 512) fp16 across 8 PEs → M axis split → 8 shards, each (128, 512) = 128KB"""
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shards = row_wise(shape=(1024, 512), itemsize=2, num_pe=8)
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assert len(shards) == 8
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expected_nbytes = 128 * 512 * 2 # 128 KB
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for i, s in enumerate(shards):
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assert s.local_pe == i
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assert s.nbytes == expected_nbytes
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assert shards[0].offset_bytes == 0
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assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
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# ── Placement: replicate ─────────────────────────────────────────────
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def test_replicate_placement():
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"""(1024, 512) fp16 across 8 PEs → each PE gets full copy = 1MB"""
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shards = replicate(shape=(1024, 512), itemsize=2, num_pe=8)
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assert len(shards) == 8
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full_nbytes = 1024 * 512 * 2 # 1 MB
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for i, s in enumerate(shards):
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assert s.local_pe == i
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assert s.nbytes == full_nbytes
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assert s.offset_bytes == 0 # each is a full copy
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# ── Placement: tiled_column_major ─────────────────────────────────────
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def test_tiled_column_major():
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"""(1024, 512) tile=(256, 128) → 4×4=16 tiles, column-major → round-robin 8 PEs"""
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shards = tiled_column_major(
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shape=(1024, 512), itemsize=2, num_pe=8, tile_m=256, tile_k=128,
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)
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# 4 tiles along M, 4 tiles along K → 16 tiles total
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assert len(shards) == 16
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tile_bytes = 256 * 128 * 2 # 64 KB per tile
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for s in shards:
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assert s.nbytes == tile_bytes
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# column-major: iterate K first, then M
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# tile (m=0,k=0) → PE0, tile (m=0,k=1) → PE1, ..., (m=0,k=3) → PE3
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# tile (m=1,k=0) → PE4, tile (m=1,k=1) → PE5, ..., (m=1,k=3) → PE7
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# tile (m=2,k=0) → PE0, ...
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assert shards[0].local_pe == 0
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assert shards[1].local_pe == 1
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assert shards[7].local_pe == 7
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assert shards[8].local_pe == 0 # wraps around
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# total coverage
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assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
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# ── Placement: tiled_row_major ────────────────────────────────────────
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def test_tiled_row_major():
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"""(1024, 512) tile=(256, 128) → 4×4=16 tiles, row-major → round-robin 8 PEs"""
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shards = tiled_row_major(
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shape=(1024, 512), itemsize=2, num_pe=8, tile_m=256, tile_k=128,
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)
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assert len(shards) == 16
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tile_bytes = 256 * 128 * 2
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for s in shards:
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assert s.nbytes == tile_bytes
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# row-major: iterate M first, then K
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# tile (m=0,k=0) → PE0, tile (m=1,k=0) → PE1, ..., (m=3,k=0) → PE3
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# tile (m=0,k=1) → PE4, tile (m=1,k=1) → PE5, ..., (m=3,k=1) → PE7
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# tile (m=0,k=2) → PE0, ...
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assert shards[0].local_pe == 0
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assert shards[1].local_pe == 1
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assert shards[7].local_pe == 7
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assert shards[8].local_pe == 0 # wraps around
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# total coverage
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assert sum(s.nbytes for s in shards) == 1024 * 512 * 2
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# ── deploy_tensor ────────────────────────────────────────────────────
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def test_deploy_tensor_hbm():
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"""Deploy with column_wise placement → TensorHandle with valid PA shards."""
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allocs = _make_allocators()
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placement = resolve_dp_policy(
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DPPolicy(cube="replicate", pe="column_wise"),
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shape=(1024, 512), itemsize=2,
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num_pe=8, num_cubes=1, target_sip=0,
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)
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th = deploy_tensor(
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name="W",
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shape=(1024, 512),
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dtype="fp16",
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placement=placement,
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allocators=allocs,
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mem_kind="hbm",
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)
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assert th.name == "W"
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assert th.shape == (1024, 512)
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assert th.dtype == "fp16"
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assert th.itemsize == 2
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assert len(th.shards) == 8
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# each shard has a distinct PA
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pas = [s.pa for s in th.shards]
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assert len(set(pas)) == 8
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# each shard placed on correct PE
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for i, s in enumerate(th.shards):
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assert s.pe == i
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assert s.sip == 0
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assert s.cube == 0
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def test_deploy_tensor_tcm():
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"""Deploy with TCM → uses pe_tcm_addr allocation."""
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allocs = _make_allocators()
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placement = [ShardSpec(sip=0, cube=0, pe=0, offset_bytes=0, nbytes=256)]
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th = deploy_tensor(
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name="small",
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shape=(128,),
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dtype="fp16",
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placement=placement,
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allocators=allocs,
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mem_kind="tcm",
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)
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assert len(th.shards) == 1
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assert th.shards[0].pe == 0
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assert th.shards[0].nbytes == 256
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def test_deploy_tensor_overflow():
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"""Allocation exceeding PE HBM capacity raises AllocationError."""
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allocs = _make_allocators()
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# 6 GB per PE slice, try to allocate 7 GB
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big_shard = ShardSpec(sip=0, cube=0, pe=0, offset_bytes=0, nbytes=7 * _GB)
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with pytest.raises(AllocationError):
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deploy_tensor(
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name="toobig",
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shape=(1,),
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dtype="int8",
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placement=[big_shard],
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allocators=allocs,
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)
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