ywkang
998cc85762
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>
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
"""Tests for CCL backend install (ADR-0023 D10/D11)."""
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from __future__ import annotations
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from kernbench.ccl.install import (
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install_ipcq,
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linear_rank_to_pe,
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load_ccl_config,
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resolve_algorithm_config,
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)
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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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def _engine():
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topo = resolve_topology("topology.yaml").topology_obj
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return GraphEngine(topo, enable_data=True), topo
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def test_load_ccl_config():
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cfg = load_ccl_config()
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assert "defaults" in cfg
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assert "algorithms" in cfg
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def test_resolve_algorithm_config_default():
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cfg = load_ccl_config()
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merged = resolve_algorithm_config(cfg)
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assert merged["algorithm"] == cfg["defaults"]["algorithm"]
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# ccl.yaml no longer carries defaults.world_size — backend derives
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# it from topology.yaml at install time. Just check the field is
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# absent here (verified per-test where install_ipcq is called).
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assert "world_size" not in merged or merged["world_size"] >= 1
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def test_resolve_algorithm_config_override():
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cfg = load_ccl_config()
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merged = resolve_algorithm_config(cfg, name="ring_allreduce_hbm")
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assert merged["algorithm"] == "ring_allreduce_hbm"
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assert merged["buffer_kind"] == "hbm" # algo override
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# defaults still apply
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assert merged["n_slots"] == cfg["defaults"]["n_slots"]
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def test_linear_rank_to_pe():
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engine, topo = _engine()
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spec = topo.spec
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# Cube 0 of SIP 0
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assert linear_rank_to_pe(0, spec) == (0, 0, 0)
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assert linear_rank_to_pe(7, spec) == (0, 0, 7)
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# Should not exceed total PE count
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pes_per_sip = (
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spec["sip"]["cube_mesh"]["w"] * spec["sip"]["cube_mesh"]["h"]
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* spec["cube"]["pe_layout"]["pe_per_corner"]
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* len(spec["cube"]["pe_layout"]["corners"])
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)
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sips = spec["system"]["sips"]["count"]
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total = sips * pes_per_sip
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assert total >= 8
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def test_install_ipcq_neighbors_correct():
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engine, topo = _engine()
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cfg = load_ccl_config()
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merged = resolve_algorithm_config(cfg, name="ring_allreduce_tcm")
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# Force a single-cube 8-rank install for the assertions below.
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merged["world_size"] = 8
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plan = install_ipcq(engine, topo.spec, merged)
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assert plan["world_size"] == 8
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assert plan["buffer_kind"] == "tcm"
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# Each rank should have E and W entries
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for r, nbrs in plan["neighbor_table"].items():
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assert "E" in nbrs
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assert "W" in nbrs
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# Inspect installed PE_IPCQ for rank 0
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ipcq = engine._components["sip0.cube0.pe0.pe_ipcq"]
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qp_e = ipcq.queue_pairs["E"]
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qp_w = ipcq.queue_pairs["W"]
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assert qp_e["peer"].pe == 1 # rank 0's E neighbor is rank 1
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assert qp_w["peer"].pe == 7 # rank 0's W neighbor is rank 7
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# rx_base addresses should be unique
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assert qp_e["my_rx_base_pa"] != qp_w["my_rx_base_pa"]
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def test_install_ipcq_credit_stores_wired():
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engine, topo = _engine()
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cfg = load_ccl_config()
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merged = resolve_algorithm_config(cfg, name="ring_allreduce_tcm")
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merged["world_size"] = 8
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install_ipcq(engine, topo.spec, merged)
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# rank 0 (pe0) sending E goes to rank 1 (pe1)
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# rank 0's peer_credit_store on E direction should equal rank 1's credit_inbox
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pe0 = engine._components["sip0.cube0.pe0.pe_ipcq"]
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pe1 = engine._components["sip0.cube0.pe1.pe_ipcq"]
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qp_e = pe0.queue_pairs["E"]
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assert qp_e["peer_credit_store"] is pe1.credit_inbox
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