ywkang
4ba0a83e71
Implement ADR-0024 Phase A: SIP-level TP launcher MVP
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Scope (Phase A):
- D1: world_size fallback = SIP count (rank = SIP, TP boundary)
- D9: greenlet-local get_rank + _bind_rank (single-driver fallback = 0)
- D10: torch.ahbm.set_device + torch.accelerator.set_device_index alias
- D11: tensor placement scoped to current-device SIP (post-hoc pe_index
shift — ADR-0026 replaces with structural coords)
- D12/D13: multi-greenlet run() with simple round-robin scheduler;
hybrid dispatch (ws == SIP count → multi-greenlet, else legacy
single-worker for ccl.yaml override compat)
- D7 partial: backend.all_reduce submit + yield + wait via launch()'s
new _defer_wait flag; parent-less greenlets skip yield
- Relaxed shard-count check (len(shards) > 0 instead of == world_size)
- rank_to_pe = SIP-representative [(r, 0, 0)] when ws <= n_sips
Deferred to Phase B:
- Engine-routed install (D2) — keeps sideband
- install_plan.py module (D6) — keeps install.py
- Epoch barrier (D7 full) — simple yield is sufficient for ring ws=2 mock
- Validator registry (D8)
- Cross-SIP multi-greenlet + real kernel integration — matrix
ring_default_ws hangs in SimPy drain despite ADR-0025 direction fix;
marked xfail(run=False) pending Phase B diagnosis (suspected per-rank
kernel_args / program_id mismatch)
Tests:
- test_ccl_ddp_launcher.py (6 new tests) — D1/D9/D10/D11/D12/D13
- test_ccl_allreduce_matrix.py — ring_default_ws xfail'd, override
cases (ring_tcm_8 / hbm_8 / sram_8 / multi_cube / mesh_2x2 /
tree_binary_7) all pass via legacy path
514 tests pass, 1 xfail.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-14 09:00:28 -07:00
ywkang
998cc85762
Add PE-level IPCQ collective infra + unified ccl_allreduce bench (ADR-0023)
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Major changes:
PE-level IPCQ infrastructure:
- New PE_IPCQ component: ring-buffer control plane with 4-direction
neighbor mapping, head/tail pointers, backpressure (poll/sleep).
- PE_DMA extended with vc_comm channel for IPCQ outbound/inbound DMA,
including in-flight data snapshot (D9) and op_log recording at
outbound time for Phase 2 replay correctness.
- IpcqDmaToken piggyback model: data + metadata travel together,
atomic visibility at receiver (invariant I6).
- Credit return fast path: bottleneck-BW latency, no fabric vc_comm.
Phase 2 data execution (ADR-0020 integration):
- op_log extended: DmaWriteCmd now captures src_space/src_addr for
Phase 2 dma_write copy; ipcq_copy ops recorded at outbound time.
- DataExecutor replays dma_write + ipcq_copy in t_start order.
- Engine._flush_data_phase: incremental cursor-based replay after
each engine.wait() so host reads see post-Phase-2 data.
- KernelRunner Phase 1 writes disabled when op_log is active to
prevent stale data from corrupting the MemoryStore snapshot.
TLContext / kernel API:
- tl.send(dir, src=TensorHandle), tl.recv(dir, shape, dtype),
tl.recv_async, tl.wait(RecvFuture), copy_to_dst mode.
- TensorHandle operator overloading (add/sub/mul/div) via thread-local
active TLContext → MathCmd dispatch through PE_MATH.
- PE-local scratch allocator for math output handles.
- tl.load returns space="hbm" handles for correct Phase 2 addressing.
- Additional math functions: maximum, minimum, fma, clamp, softmax, cdiv.
Unified ccl_allreduce bench (PyTorch-compat host code):
- Single benches/ccl_allreduce.py with run() + worker(rank, ws, torch)
split matching real PyTorch DDP worker pattern.
- torch.distributed facade: init_process_group, get_world_size,
get_rank, get_backend, all_reduce, barrier — only real PyTorch names.
- AhbmCCLBackend: eager install_ipcq at init, all_reduce dispatches
kernel via tensor shard metadata (n_elem from shards[0].nbytes).
- world_size derived from topology spec (sips × cubes × pes_per_cube)
with optional algorithm-level override in ccl.yaml.
Tensor API (PyTorch-compat surface):
- Tensor.numpy(): gather-aware (all shards via VA-based addressing).
- Tensor.copy_(source): scatter from host tensor into sharded target.
- RuntimeContext.from_numpy(arr): host-side staging tensor.
- Tensor.data property fixed to use numpy() (was shards[0]-only).
Algorithm modules moved to src/kernbench/ccl/algorithms/:
- ring_allreduce, mesh_allreduce, tree_allreduce, hello_send.
- Each module exports kernel_args(world_size, n_elem) helper.
- ccl.yaml module paths updated to kernbench.ccl.algorithms.*.
Dead code removed:
- 7 per-variant bench files (ccl_allreduce_{tcm,hbm,sram}, etc.).
- _run_ccl_bench greenlet-per-SIP scheduler.
- benches.loader.is_ccl_bench + run_rank detection.
- benches/ccl/ directory.
Tests:
- New test_ccl_allreduce_matrix.py: 7 parametrized cases
(ring×3 buffers, ring 8/16, mesh 4, tree 7).
- New test_runtime_api_tensor.py: copy_/numpy/from_numpy unit tests.
- Existing tests updated for new import paths + world_size_override.
Docs:
- Korean ccl-author-guide.md and ADR-0023 paths updated.
- New English versions: ccl-author-guide.en.md, ADR-0023.en.md.
502 tests pass.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com >
2026-04-12 19:36:59 -07:00
ywkang