533e6992992482eec5d64d74d1d6b8dcd4c0c4d1
4 Commits
| Author | SHA1 | Message | Date | |
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 mukesh
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1d8b9401e5 |
Intercube allreduce: pe0 cube-mesh reduce + multi-SIP ring/torus/mesh
New intercube allreduce kernel replacing the old flat ring algorithms. Reduces across the 4x4 cube mesh within each SIP (pe0-only, same-lane), then inter-SIP exchange on root cube, then broadcast back. Supports ring_1d, torus_2d, and mesh_2d_no_wrap SIP topologies driven by topology.yaml. Integrated with dist.init_process_group / dist.all_reduce. New files: - src/kernbench/ccl/algorithms/intercube_allreduce.py (kernel) - src/kernbench/ccl/sfr_config.py (configure_sfr_intercube_multisip) - tests/test_allreduce_multidevice.py (config-driven, 3 topologies) - tests/test_distributed_intercube_allreduce.py (full distributed path) - tests/test_intercube_sfr_config.py (SFR wiring verification) Modified: - distributed.py: AhbmCCLBackend uses configure_sfr_intercube_multisip - topologies.py: added torus_2d, mesh_2d_no_wrap - install.py: global_E/W/N/S in _OPPOSITE_DIR - topology.yaml: added system.sips.topology - ccl.yaml: single intercube_allreduce algorithm - benches/ccl_allreduce.py: row_wise cube-mesh tensor layout Removed old flat-ring algorithms and their tests. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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 ywkang
|
cfc2d74ec4 |
Refactor ccl_allreduce bench: rank=SIP only, remove rank=PE legacy path
The unified ccl_allreduce bench previously carried two execution models
in one worker with ``if world_size == n_sips:`` branching:
- TP mode (rank = SIP, ADR-0024/0027): proper ProcessGroup semantics.
- Legacy rank = PE mode: single-driver worker allocating one big tensor
distributed across all PEs via _derive_dp, with kernel-level SPMD via
program_id.
The second model is unnecessary — intra-SIP PE-level collectives are
expressed inside the kernel (tl.send/tl.recv with program_id, IPCQ) and
do not need a host-side ProcessGroup. Removing it lets the bench be a
clean reference implementation of the TP launcher.
benches/ccl_allreduce.py:
- Config resolved once in run() via _resolve_cfg -> _BenchCfg dataclass.
- rank != n_sips now raises RuntimeError explicitly.
- _worker / _allocate_rank_tile / _init_with_rank_value / _report each
have one concern; duplicated init + verification paths collapsed.
- _derive_dp and the second verify+print block deleted.
- 166 lines -> 91 lines.
ccl.yaml:
- mesh_allreduce_4 (world_size: 4) and tree_allreduce_7 (world_size: 7)
algorithm entries removed (rank = PE only).
- Algorithm kernel files (kernbench.ccl.algorithms.mesh_allreduce,
tree_allreduce) kept as-is for direct-dispatch future use.
tests/test_ccl_allreduce_matrix.py:
- Matrix shrinks from 7 cases to 3: ring × {tcm, hbm, sram} at ws =
topology SIP count (= 2). mesh_2x2, tree_binary_7, ring_multi_cube,
and the three ring_*_8 cases removed.
tests/test_ccl_performance.py:
- _run_8rank renamed to _run_ring; world_size: 8 override dropped; now
exercises rank = SIP ring all-reduce.
tests/test_mp_spawn.py, tests/test_ccl_ddp_launcher.py:
- Monkeypatch target updated from bench.worker to bench._worker
(signature now takes BenchCfg instead of (rank, world_size)).
555 passed, 1 intentional skip. Tests that directly call
install_ipcq(world_size_override=N) for kernel-level sanity
(test_ccl_hello_world_guide, test_recv_copy_to_dst, test_tl_recv_async,
test_ccl_deadlock_detection) are unchanged — they never went through
the bench and still exercise the kernel-only path.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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|
ywkang
|
10b33b44ba |
Add Tensor indexing + hierarchical 3-level all-reduce kernel
Tensor.__setitem__ / __getitem__: - Shard-aligned slice assignment and read on deployed tensors. - Scalar broadcast and numpy array assignment supported. - Cross-shard slices raise NotImplementedError (use copy_ for that). - 3 new tests: single-PE, multi-PE, cross-shard error case. Hierarchical all-reduce kernel (src/kernbench/ccl/algorithms/): - 3-level reduce: intra-cube (E/W) → inter-cube (N/S) → inter-SIP (parent). - Bidirectional ring reduce at each level: ceil((N-1)/2) rounds. Left half sends via dir_dec, right half via dir_inc (wrap). Representative receives from both sides. - Chain broadcast for reverse path: cube 0 PE 0 → all PE 0s → all PEs. - Registered in ccl.yaml as "hierarchical_allreduce" with topology: none (neighbors() override builds the full 3-level neighbor map). - kernel_args derives pes_per_cube/cubes_per_sip/num_sips from world_size. - Mock-verified at 8/16/32/64/128 ranks. Mock runtime fixes: - Direction pairing: explicit N↔S, E↔W, parent↔parent instead of "first matching reverse". Fixes 2-element rings where N and S both point to the same peer. - Deadlock detection: send-counter based (not just queue-depth-total) to catch chain reductions where send+recv pairs net to zero. - Multi-cube program_id: pes_per_cube parameter enables program_id(axis=0) = PE within cube, program_id(axis=1) = cube id. Legacy single-cube tests unaffected (default = world_size). 504 tests pass in 12s. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> |
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|
ywkang
|
998cc85762 |
Add PE-level IPCQ collective infra + unified ccl_allreduce bench (ADR-0023)
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>
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