sccl: drive allreduce tests via torch.distributed; reorganize into tests/sccl/
Convert the multidevice allreduce correctness + latency/buffer-kind sweeps to run through the real PyTorch-distributed path (init_process_group(backend="ahbm") -> mp.spawn -> dist.all_reduce) instead of direct ctx.launch, and reorganize the CCL/allreduce tests into a tests/sccl/ package split one test per file. Production change (required for the distributed path on non-square SIP grids): - AhbmCCLBackend now reads explicit system.sips.w/h from the spec, with a square-only sqrt fallback that raises on ambiguity, instead of silently guessing round(sqrt(count)). This fixes the 2x3 / 3x2 torus + mesh cases, which previously resolved to a wrong 2x2 grid. Mirrors the test helper's _sip_topo_dims precedence (explicit w/h > square fallback > raise). Test reorganization (tests/sccl/): - _allreduce_helpers.py: shared plumbing (distributed driver, config writers, direct-launch run_allreduce parity reference, sweep/buffer-kind constants, plot aggregators, topology-diagram + FSIM-comparison emitters). - test_allreduce_ring_torus_mesh.py: correctness across ring/torus/mesh. - test_distributed_default_topology.py: full distributed path on topology.yaml. - test_plot_latency_sweep.py / test_plot_buffer_kind_sweep.py: sweep rows. - test_plot_topology_diagram.py / test_plot_comparison_fsim.py: plot emitters. - test_intercube_root_center.py: moved in (ADR-0032 center-root latency guard). Also: - Move the FSIM comparison plot generator out of scripts/ into the sccl suite. - Delete superseded test files (test_allreduce_multidevice, test_distributed_lrab_hierarchical_allreduce, test_allreduce_buffer_kind_sweep) and repoint conftest aggregators + the ipcq buffer-kind importers. - Regenerate the allreduce_latency_plots derived artifacts from the full sweep. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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+12
-12
@@ -1,13 +1,13 @@
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buffer_kind,sip_topology,n_sips,n_elem,bytes_per_pe,latency_ns
|
||||
hbm,torus_2d,6,128,256,2120.0399999999754
|
||||
hbm,torus_2d,6,1024,2048,2716.74499999995
|
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hbm,torus_2d,6,8192,16384,7315.185000000081
|
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hbm,torus_2d,6,32768,65536,23081.265000008738
|
||||
sram,torus_2d,6,128,256,2060.0399999999754
|
||||
sram,torus_2d,6,1024,2048,2908.74499999995
|
||||
sram,torus_2d,6,8192,16384,9523.185000000081
|
||||
sram,torus_2d,6,32768,65536,32201.265000008752
|
||||
tcm,torus_2d,6,128,256,1964.0399999999754
|
||||
tcm,torus_2d,6,1024,2048,2476.74499999995
|
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tcm,torus_2d,6,8192,16384,6403.185000000081
|
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tcm,torus_2d,6,32768,65536,19865.265000008738
|
||||
hbm,torus_2d,6,128,256,2120.040000000012
|
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hbm,torus_2d,6,1024,2048,2717.2783333333473
|
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hbm,torus_2d,6,8192,16384,7315.184999999989
|
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hbm,torus_2d,6,32768,65536,23081.26500000037
|
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sram,torus_2d,6,128,256,2060.040000000012
|
||||
sram,torus_2d,6,1024,2048,2909.2783333333473
|
||||
sram,torus_2d,6,8192,16384,9523.184999999869
|
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sram,torus_2d,6,32768,65536,32201.265000000385
|
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tcm,torus_2d,6,128,256,1964.040000000012
|
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tcm,torus_2d,6,1024,2048,2477.2783333333473
|
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tcm,torus_2d,6,8192,16384,6403.185000000109
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tcm,torus_2d,6,32768,65536,19865.265000000378
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@@ -1,37 +1,37 @@
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algorithm,sip_topology,n_sips,n_elem,bytes_per_pe,bytes_per_sip,latency_ns
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,8,16,256,2666.5524999999725
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,32,64,1024,2747.7399999999725
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,64,128,2048,2855.98999999998
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,128,256,4096,3072.4899999999725
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,512,1024,16384,3336.579999999951
|
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intercube_allreduce,mesh_2d_no_wrap,6,1024,2048,32768,3707.49999999992
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,2048,4096,65536,4449.339999999875
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,4096,8192,131072,5933.020000000055
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,8192,16384,262144,8900.380000000157
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,16384,32768,524288,14835.099999997583
|
||||
intercube_allreduce,mesh_2d_no_wrap,6,32768,65536,1048576,26704.540000017492
|
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intercube_allreduce,mesh_2d_no_wrap,6,49152,98304,1572864,38573.980000026335
|
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intercube_allreduce,ring_1d,6,8,16,256,2365.2558333333036
|
||||
intercube_allreduce,ring_1d,6,32,64,1024,2436.9433333333036
|
||||
intercube_allreduce,ring_1d,6,64,128,2048,2532.526666666643
|
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intercube_allreduce,ring_1d,6,128,256,4096,2723.6933333333036
|
||||
intercube_allreduce,ring_1d,6,512,1024,16384,3042.0349999999544
|
||||
intercube_allreduce,ring_1d,6,1024,2048,32768,3390.201666666597
|
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intercube_allreduce,ring_1d,6,2048,4096,65536,4079.7349999998714
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intercube_allreduce,ring_1d,6,4096,8192,131072,5458.801666666721
|
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intercube_allreduce,ring_1d,6,8192,16384,262144,8216.93500000014
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intercube_allreduce,ring_1d,6,16384,32768,524288,13733.201666664638
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intercube_allreduce,ring_1d,6,32768,65536,1048576,24765.735000014545
|
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intercube_allreduce,ring_1d,6,49152,98304,1572864,35798.268333355256
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intercube_allreduce,torus_2d,6,8,16,256,1700.6024999999754
|
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intercube_allreduce,torus_2d,6,32,64,1024,1753.2899999999754
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intercube_allreduce,torus_2d,6,64,128,2048,1823.539999999979
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intercube_allreduce,torus_2d,6,128,256,4096,1964.0399999999754
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intercube_allreduce,torus_2d,6,512,1024,16384,2196.2849999999653
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intercube_allreduce,torus_2d,6,1024,2048,32768,2476.74499999995
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intercube_allreduce,torus_2d,6,2048,4096,65536,3037.664999999919
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intercube_allreduce,torus_2d,6,4096,8192,131072,4159.50500000003
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intercube_allreduce,torus_2d,6,8192,16384,262144,6403.185000000081
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intercube_allreduce,torus_2d,6,16384,32768,524288,10890.544999998769
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intercube_allreduce,torus_2d,6,32768,65536,1048576,19865.265000008738
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intercube_allreduce,torus_2d,6,49152,98304,1572864,28839.985000013185
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,8,16,256,2666.552500000015
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,32,64,1024,2747.7400000000152
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,64,128,2048,2855.990000000018
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,128,256,4096,3072.490000000019
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,512,1024,16384,3337.1133333333582
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,1024,2048,32768,3708.0333333333692
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,2048,4096,65536,4449.873333333393
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,4096,8192,131072,5933.020000000124
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,8192,16384,262144,8900.379999999863
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,16384,32768,524288,14835.099999999224
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,32768,65536,1048576,26704.540000000765
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lrab_hierarchical_allreduce,mesh_2d_no_wrap,6,49152,98304,1572864,38573.97999999701
|
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lrab_hierarchical_allreduce,ring_1d,6,8,16,256,2365.255833333347
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lrab_hierarchical_allreduce,ring_1d,6,32,64,1024,2436.9433333333473
|
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lrab_hierarchical_allreduce,ring_1d,6,64,128,2048,2532.526666666683
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lrab_hierarchical_allreduce,ring_1d,6,128,256,4096,2723.693333333349
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lrab_hierarchical_allreduce,ring_1d,6,512,1024,16384,3048.635000000021
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lrab_hierarchical_allreduce,ring_1d,6,1024,2048,32768,3393.4016666666957
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lrab_hierarchical_allreduce,ring_1d,6,2048,4096,65536,4082.401666666714
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lrab_hierarchical_allreduce,ring_1d,6,4096,8192,131072,5458.80166666677
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lrab_hierarchical_allreduce,ring_1d,6,8192,16384,262144,8216.934999999943
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lrab_hierarchical_allreduce,ring_1d,6,16384,32768,524288,13733.201666665835
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lrab_hierarchical_allreduce,ring_1d,6,32768,65536,1048576,24765.73500000064
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lrab_hierarchical_allreduce,ring_1d,6,49152,98304,1572864,35798.268333331536
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lrab_hierarchical_allreduce,torus_2d,6,8,16,256,1700.6025000000095
|
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lrab_hierarchical_allreduce,torus_2d,6,32,64,1024,1753.2900000000102
|
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lrab_hierarchical_allreduce,torus_2d,6,64,128,2048,1823.540000000012
|
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lrab_hierarchical_allreduce,torus_2d,6,128,256,4096,1964.040000000012
|
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lrab_hierarchical_allreduce,torus_2d,6,512,1024,16384,2196.8183333333463
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lrab_hierarchical_allreduce,torus_2d,6,1024,2048,32768,2477.2783333333473
|
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lrab_hierarchical_allreduce,torus_2d,6,2048,4096,65536,3038.1983333333583
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lrab_hierarchical_allreduce,torus_2d,6,4096,8192,131072,4159.5050000000665
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lrab_hierarchical_allreduce,torus_2d,6,8192,16384,262144,6403.185000000109
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lrab_hierarchical_allreduce,torus_2d,6,16384,32768,524288,10890.5449999995
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lrab_hierarchical_allreduce,torus_2d,6,32768,65536,1048576,19865.265000000378
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lrab_hierarchical_allreduce,torus_2d,6,49152,98304,1572864,28839.98500000059
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|
@@ -1,176 +0,0 @@
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"""One-shot: render the broken-y-axis allreduce comparison with the FSIM
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single-device reference. Reads docs/diagrams/allreduce_latency_plots/summary.csv
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and writes comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png
|
||||
alongside it.
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||||
|
||||
This is a derived-artifact generator (per CLAUDE.md): plotting only, no production
|
||||
or test logic touched.
|
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"""
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from __future__ import annotations
|
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|
||||
import csv
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from pathlib import Path
|
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import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as mticker
|
||||
|
||||
ROOT = Path(__file__).resolve().parent.parent
|
||||
PLOT_DIR = ROOT / "docs" / "diagrams" / "allreduce_latency_plots"
|
||||
CSV_PATH = PLOT_DIR / "summary.csv"
|
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|
||||
EXT_LABEL = "FSIM (single device): 366 µs"
|
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EXT_LATENCY_NS = 366_000.0
|
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|
||||
COLORS = {
|
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"ring_1d": "tab:blue",
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"torus_2d": "tab:orange",
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"mesh_2d_no_wrap": "tab:green",
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||||
}
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# Display labels (data keys above stay as the summary.csv sip_topology
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# values; these are only the human-readable legend strings). All non-FSIM
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||||
# runs use 6 devices; the grid differs per topology.
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||||
DISPLAY = {
|
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"ring_1d": "Ring 1x6 (6 devices)",
|
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"torus_2d": "2D Torus 2x3 (6 devices)",
|
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"mesh_2d_no_wrap": "2D Mesh 2x3 (6 devices)",
|
||||
}
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|
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# Hand-derived theoretical model for torus_2d (6 SIPs). Mirrors
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# _aggregate_sweep_plots in tests/test_allreduce_multidevice.py.
|
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NOC_PACKET_BYTES = 128
|
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PES_PER_CUBE = 8
|
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T_STARTUP_NS = 1346.0
|
||||
TAU_NS = (8741.0 - 1346.0) / (6144 - 1)
|
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|
||||
|
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def _theoretical_torus_2d_ns(bytes_per_pe: int) -> float:
|
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bytes_per_cube = int(bytes_per_pe) * PES_PER_CUBE
|
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n_packets = max(1, -(-bytes_per_cube // NOC_PACKET_BYTES))
|
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return T_STARTUP_NS + (n_packets - 1) * TAU_NS
|
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|
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|
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def _plot_theoretical(ax, records):
|
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torus_rs = sorted(
|
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[r for r in records if r["sip_topology"] == "torus_2d"],
|
||||
key=lambda r: r["bytes_per_pe"],
|
||||
)
|
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if not torus_rs:
|
||||
return
|
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ax.plot(
|
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[r["bytes_per_pe"] for r in torus_rs],
|
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[_theoretical_torus_2d_ns(r["bytes_per_pe"]) for r in torus_rs],
|
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color="tab:red", linestyle="--", linewidth=1.6, marker="x",
|
||||
label="Theoretical 2D Torus 2x3",
|
||||
)
|
||||
|
||||
|
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def _bytes_fmt(x, _pos):
|
||||
if x >= 1024 * 1024:
|
||||
return f"{x / (1024 * 1024):.0f}M"
|
||||
if x >= 1024:
|
||||
return f"{x / 1024:.0f}K"
|
||||
return f"{int(x)}"
|
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|
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|
||||
def _load_records():
|
||||
rows = []
|
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with open(CSV_PATH, newline="") as f:
|
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r = csv.DictReader(f)
|
||||
for row in r:
|
||||
rows.append({
|
||||
"sip_topology": row["sip_topology"],
|
||||
"bytes_per_pe": int(row["bytes_per_pe"]),
|
||||
"latency_ns": float(row["latency_ns"]),
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def _ext_x(records):
|
||||
"""Anchor the external reference at the largest payload (96 KB / PE)."""
|
||||
return max(r["bytes_per_pe"] for r in records)
|
||||
|
||||
|
||||
def _plot_curves(ax, records, topologies):
|
||||
for topo in topologies:
|
||||
rs = sorted([r for r in records if r["sip_topology"] == topo],
|
||||
key=lambda r: r["bytes_per_pe"])
|
||||
if not rs:
|
||||
continue
|
||||
ax.plot(
|
||||
[r["bytes_per_pe"] for r in rs],
|
||||
[r["latency_ns"] for r in rs],
|
||||
marker="o",
|
||||
label=DISPLAY.get(topo, topo),
|
||||
color=COLORS.get(topo),
|
||||
)
|
||||
|
||||
|
||||
def emit_broken(records):
|
||||
topologies = sorted({r["sip_topology"] for r in records})
|
||||
max_local = max(r["latency_ns"] for r in records)
|
||||
|
||||
fig, (ax_top, ax_bot) = plt.subplots(
|
||||
2, 1, sharex=True,
|
||||
gridspec_kw={"height_ratios": [1, 4], "hspace": 0.05},
|
||||
figsize=(9, 6.5),
|
||||
)
|
||||
|
||||
# Bottom panel: today's three curves + theoretical, linear y.
|
||||
_plot_curves(ax_bot, records, topologies)
|
||||
_plot_theoretical(ax_bot, records)
|
||||
ax_bot.set_ylim(0, max_local * 1.10)
|
||||
|
||||
# Top panel: only the external reference marker, linear y around 366 µs.
|
||||
ax_top.scatter(
|
||||
[_ext_x(records)], [EXT_LATENCY_NS],
|
||||
marker="*", s=240, color="tab:red", zorder=5,
|
||||
label=EXT_LABEL,
|
||||
)
|
||||
ax_top.set_ylim(EXT_LATENCY_NS * 0.93, EXT_LATENCY_NS * 1.05)
|
||||
|
||||
# Hide the spine between the two panels and draw diagonal "break" ticks.
|
||||
ax_top.spines["bottom"].set_visible(False)
|
||||
ax_bot.spines["top"].set_visible(False)
|
||||
ax_top.tick_params(labeltop=False, bottom=False)
|
||||
ax_bot.xaxis.tick_bottom()
|
||||
|
||||
d = 0.012 # diagonal-tick size, in axis-fraction
|
||||
kw = dict(transform=ax_top.transAxes, color="k", clip_on=False, lw=1)
|
||||
ax_top.plot((-d, +d), (-d, +d), **kw)
|
||||
ax_top.plot((1 - d, 1 + d), (-d, +d), **kw)
|
||||
kw.update(transform=ax_bot.transAxes)
|
||||
ax_bot.plot((-d, +d), (1 - d * 4, 1 + d * 4), **kw)
|
||||
ax_bot.plot((1 - d, 1 + d), (1 - d * 4, 1 + d * 4), **kw)
|
||||
|
||||
ax_bot.set_xscale("log", base=2)
|
||||
ax_bot.set_xlabel("Bytes per PE (log scale)")
|
||||
ax_bot.set_ylabel("Time (ns)")
|
||||
ax_top.set_ylabel("Time (ns)")
|
||||
ax_bot.grid(True, alpha=0.3)
|
||||
ax_top.grid(True, alpha=0.3)
|
||||
ax_bot.xaxis.set_major_formatter(mticker.FuncFormatter(_bytes_fmt))
|
||||
|
||||
# One legend covering both axes.
|
||||
handles_bot, labels_bot = ax_bot.get_legend_handles_labels()
|
||||
handles_top, labels_top = ax_top.get_legend_handles_labels()
|
||||
ax_bot.legend(handles_bot + handles_top, labels_bot + labels_top,
|
||||
loc="upper left")
|
||||
|
||||
fig.suptitle("Multidevice allreduce (ring, Mesh, 2DTorus) vs FSIM latency")
|
||||
fig.tight_layout()
|
||||
out = PLOT_DIR / "comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png"
|
||||
fig.savefig(out, dpi=120)
|
||||
plt.close(fig)
|
||||
print(f"wrote {out}")
|
||||
|
||||
|
||||
def main():
|
||||
records = _load_records()
|
||||
if not records:
|
||||
raise SystemExit(f"no rows in {CSV_PATH}")
|
||||
emit_broken(records)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -59,10 +59,23 @@ class AhbmCCLBackend:
|
||||
self._sip_topo_kind = topo_map.get(self._sip_topo, 0)
|
||||
else:
|
||||
self._sip_topo_kind = 0
|
||||
sips = spec.get("system", {}).get("sips", {})
|
||||
if self._sip_topo == "ring_1d":
|
||||
self._sip_topo_w, self._sip_topo_h = 0, 0
|
||||
elif sips.get("w") is not None and sips.get("h") is not None:
|
||||
w, h = int(sips["w"]), int(sips["h"])
|
||||
if w * h != self._n_sips:
|
||||
raise ValueError(
|
||||
f"sip layout {w}x{h} != sips.count ({self._n_sips})"
|
||||
)
|
||||
self._sip_topo_w, self._sip_topo_h = w, h
|
||||
else:
|
||||
side = int(round(math.sqrt(self._n_sips)))
|
||||
if side * side != self._n_sips:
|
||||
raise ValueError(
|
||||
f"SIP topology '{self._sip_topo}' requires square "
|
||||
f"sips.count or explicit sips.w/h, got {self._n_sips}"
|
||||
)
|
||||
self._sip_topo_w, self._sip_topo_h = side, side
|
||||
|
||||
# IPCQ install: wire all pe0s across all cubes and SIPs
|
||||
|
||||
+2
-2
@@ -46,8 +46,8 @@ def pytest_sessionfinish(session, exitstatus):
|
||||
except Exception as e:
|
||||
print(f"[conftest] aggregator {attr}() in {name} failed: {e}")
|
||||
|
||||
_exec("test_allreduce_multidevice.py", "_aggregate_sweep_plots")
|
||||
_exec("test_allreduce_buffer_kind_sweep.py", "aggregate_buffer_kind_plot")
|
||||
_exec("sccl/_allreduce_helpers.py", "_aggregate_sweep_plots")
|
||||
_exec("sccl/_allreduce_helpers.py", "aggregate_buffer_kind_plot")
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
|
||||
@@ -1,25 +1,193 @@
|
||||
"""Config-driven multi-device allreduce test application.
|
||||
"""Shared plumbing for the sccl allreduce tests.
|
||||
|
||||
Reads ``ccl.yaml`` + ``topology.yaml``, dynamically loads the kernel
|
||||
module from ``ccl.yaml → module``, and picks the inter-SIP exchange
|
||||
pattern from ``topology.yaml → system.sips.topology``.
|
||||
|
||||
Run directly::
|
||||
|
||||
python -m pytest tests/allreduce_app.py -v -s
|
||||
Not a test module (no ``test_`` prefix → pytest does not collect it).
|
||||
Holds the distributed driver, the direct-launch parity reference, the
|
||||
config writers, the sweep/buffer-kind constants, the plot aggregators
|
||||
(called from ``conftest.pytest_sessionfinish``), and the topology-diagram
|
||||
emitter. The per-test files under ``tests/sccl/`` import from here, as do
|
||||
the external buffer-kind / root-center tests under ``tests/``.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib
|
||||
import math
|
||||
import textwrap
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from kernbench.ccl.install import load_ccl_config, resolve_algorithm_config
|
||||
from kernbench.ccl.sfr_config import configure_sfr_intercube_multisip
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
TOPOLOGY_PATH = Path(__file__).parent.parent.parent / "topology.yaml"
|
||||
|
||||
DEFAULT_N_ELEM = 8
|
||||
|
||||
|
||||
# ── config writers ────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _write_ccl_yaml(tmp_path) -> str:
|
||||
body = textwrap.dedent("""\
|
||||
defaults:
|
||||
algorithm: lrab_hierarchical_allreduce
|
||||
buffer_kind: tcm
|
||||
backpressure: sleep
|
||||
n_slots: 4
|
||||
slot_size: 4096
|
||||
vc_chunk_size: 256
|
||||
ipcq_credit_size_bytes: 16
|
||||
|
||||
algorithms:
|
||||
lrab_hierarchical_allreduce:
|
||||
module: kernbench.ccl.algorithms.lrab_hierarchical_allreduce
|
||||
topology: none
|
||||
buffer_kind: tcm
|
||||
n_elem: 8
|
||||
root_cube: 15
|
||||
""")
|
||||
(tmp_path / "ccl.yaml").write_text(body)
|
||||
return str(tmp_path)
|
||||
|
||||
|
||||
def _write_temp_configs(
|
||||
tmp_path, sip_topology, n_sips, algorithm, n_elem_override=None,
|
||||
sip_w=None, sip_h=None,
|
||||
):
|
||||
"""Write temp topology.yaml and ccl.yaml with the given overrides."""
|
||||
with open(TOPOLOGY_PATH) as f:
|
||||
topo_cfg = yaml.safe_load(f)
|
||||
topo_cfg["system"]["sips"]["count"] = n_sips
|
||||
topo_cfg["system"]["sips"]["topology"] = sip_topology
|
||||
if sip_w is not None and sip_h is not None:
|
||||
topo_cfg["system"]["sips"]["w"] = int(sip_w)
|
||||
topo_cfg["system"]["sips"]["h"] = int(sip_h)
|
||||
else:
|
||||
topo_cfg["system"]["sips"].pop("w", None)
|
||||
topo_cfg["system"]["sips"].pop("h", None)
|
||||
topo_path = tmp_path / "topology.yaml"
|
||||
with open(topo_path, "w") as f:
|
||||
yaml.dump(topo_cfg, f, default_flow_style=False)
|
||||
|
||||
ccl_path = Path(__file__).parent.parent.parent / "ccl.yaml"
|
||||
with open(ccl_path) as f:
|
||||
ccl_cfg = yaml.safe_load(f)
|
||||
ccl_cfg["defaults"]["algorithm"] = algorithm
|
||||
if n_elem_override is not None:
|
||||
ccl_cfg.setdefault("algorithms", {}).setdefault(
|
||||
algorithm, {},
|
||||
)["n_elem"] = int(n_elem_override)
|
||||
# Ensure IPCQ slot is big enough for the per-message payload.
|
||||
per_msg_bytes = int(n_elem_override) * 2 # f16
|
||||
default_slot = int(ccl_cfg["defaults"].get("slot_size", 4096))
|
||||
if per_msg_bytes > default_slot:
|
||||
ccl_cfg["defaults"]["slot_size"] = per_msg_bytes
|
||||
tmp_ccl = tmp_path / "ccl.yaml"
|
||||
with open(tmp_ccl, "w") as f:
|
||||
yaml.dump(ccl_cfg, f, default_flow_style=False)
|
||||
|
||||
return str(topo_path), str(tmp_ccl)
|
||||
|
||||
|
||||
# ── distributed driver (init_process_group → mp.spawn → all_reduce) ────
|
||||
|
||||
|
||||
def _worker(rank: int, n_cubes: int, n_elem: int, n_sips: int, torch) -> None:
|
||||
"""Per-SIP worker: allocate, fill, all_reduce, verify."""
|
||||
torch.ahbm.set_device(rank)
|
||||
|
||||
dp = DPPolicy(
|
||||
cube="row_wise", pe="replicate",
|
||||
num_pes=1, num_cubes=n_cubes,
|
||||
)
|
||||
tensor = torch.zeros(
|
||||
(n_cubes, n_elem), dtype="f16", dp=dp,
|
||||
name=f"sip{rank}",
|
||||
)
|
||||
tensor.copy_(torch.from_numpy(
|
||||
np.full((n_cubes, n_elem), float(rank + 1), dtype=np.float16)
|
||||
))
|
||||
|
||||
torch.distributed.all_reduce(tensor, op="sum")
|
||||
|
||||
arr = tensor.numpy()
|
||||
expected = float(n_cubes * sum(range(1, n_sips + 1)))
|
||||
for cube_id in range(n_cubes):
|
||||
assert np.allclose(arr[cube_id], expected, rtol=1e-1, atol=1e-1), (
|
||||
f"SIP{rank} cube {cube_id}: "
|
||||
f"got {arr[cube_id][:4]}, expected {expected}"
|
||||
)
|
||||
|
||||
if rank == 0:
|
||||
print(f"\n lrab_hierarchical_allreduce (ws={n_sips}): "
|
||||
f"{n_sips * n_cubes} OK")
|
||||
|
||||
|
||||
def _crit_ns(engine) -> float:
|
||||
"""Critical-path latency = max per-result pe_exec_ns over engine results."""
|
||||
vals = [
|
||||
float(tr.get("pe_exec_ns", 0.0) or 0.0)
|
||||
for _, (_, tr) in engine._results.items()
|
||||
if isinstance(tr, dict)
|
||||
]
|
||||
return max(vals) if vals else 0.0
|
||||
|
||||
|
||||
def _run_distributed(tmp_path, monkeypatch, topo_path, correlation_id, n_elem):
|
||||
"""Build engine + run the collective via the full distributed path.
|
||||
|
||||
Returns ``(engine, n_cubes)``. ``monkeypatch.chdir`` points the backend's
|
||||
``load_ccl_config()`` (cwd lookup) at the temp ``ccl.yaml``.
|
||||
"""
|
||||
monkeypatch.chdir(tmp_path)
|
||||
topo = resolve_topology(topo_path)
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
spec = topo.topology_obj.spec
|
||||
n_sips = int(spec["system"]["sips"]["count"])
|
||||
cm = spec["sip"]["cube_mesh"]
|
||||
n_cubes = int(cm["w"]) * int(cm["h"])
|
||||
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id=correlation_id,
|
||||
spec=spec,
|
||||
) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
assert ctx.distributed.get_world_size() == n_sips
|
||||
ctx.multiprocessing.spawn(
|
||||
_worker, args=(n_cubes, n_elem, n_sips, ctx), nprocs=n_sips,
|
||||
)
|
||||
return engine, n_cubes
|
||||
|
||||
|
||||
# ── correctness config matrix (used by test_allreduce) ─────────────────
|
||||
|
||||
CONFIGS = [
|
||||
pytest.param(
|
||||
"lrab_hierarchical_allreduce", "ring_1d", 6, None, None,
|
||||
id="ring_6sip",
|
||||
),
|
||||
pytest.param(
|
||||
"lrab_hierarchical_allreduce", "torus_2d", 6, 2, 3,
|
||||
id="torus_6sip_2x3",
|
||||
),
|
||||
pytest.param(
|
||||
"lrab_hierarchical_allreduce", "mesh_2d_no_wrap", 6, 2, 3,
|
||||
id="mesh_6sip_2x3",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
# ── direct-launch helper (parity reference only) ───────────────────────
|
||||
|
||||
|
||||
def _sip_topo_dims(
|
||||
@@ -51,14 +219,14 @@ def run_allreduce(
|
||||
algorithm: str | None = None,
|
||||
ccl_yaml: str | None = None,
|
||||
) -> dict:
|
||||
"""Config-driven allreduce: read yaml, load kernel, run.
|
||||
"""Config-driven allreduce via direct ctx.launch (no distributed wrapper).
|
||||
|
||||
Everything is resolved from config — no hardcoded kernel imports.
|
||||
Retained as the parity reference for the distributed path and reused by
|
||||
the external buffer-kind / root-center micro-tests.
|
||||
"""
|
||||
cfg_all = load_ccl_config(ccl_yaml)
|
||||
cfg = resolve_algorithm_config(cfg_all, algorithm)
|
||||
|
||||
# Dynamic import from ccl.yaml → module
|
||||
algo_module = importlib.import_module(cfg["module"])
|
||||
kernel_fn = algo_module.kernel
|
||||
topo_name_to_kind = algo_module.TOPO_NAME_TO_KIND
|
||||
@@ -83,15 +251,6 @@ def run_allreduce(
|
||||
)
|
||||
|
||||
algo_name = cfg.get("algorithm", "allreduce")
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"algorithm: {algo_name}")
|
||||
print(f"module: {cfg['module']}")
|
||||
print(f"sip_topology: {sip_topo}")
|
||||
print(f"kernel: {kernel_fn.__name__}")
|
||||
print(f"n_sips: {n_sips}")
|
||||
print(f"n_cubes: {n_cubes}")
|
||||
print(f"n_elem: {n_elem}")
|
||||
print(f"{'=' * 60}")
|
||||
|
||||
configure_sfr_intercube_multisip(engine, spec, cfg)
|
||||
|
||||
@@ -112,11 +271,6 @@ def run_allreduce(
|
||||
))
|
||||
tensors.append(t)
|
||||
|
||||
for sip in range(n_sips):
|
||||
arr = tensors[sip].numpy()
|
||||
print(f"[SIP {sip}] input cube0[:4] = {arr[0][:4].tolist()} "
|
||||
f"cube{n_cubes - 1}[:4] = {arr[-1][:4].tolist()}")
|
||||
|
||||
t_start = engine._env.now
|
||||
|
||||
all_pending = []
|
||||
@@ -129,31 +283,14 @@ def run_allreduce(
|
||||
)
|
||||
all_pending.extend(pending)
|
||||
|
||||
for h, sip_id, meta in all_pending:
|
||||
for h, _sip_id, meta in all_pending:
|
||||
ctx.wait(h, _meta=meta)
|
||||
|
||||
t_end = engine._env.now
|
||||
latency_ns = t_end - t_start
|
||||
print(f"\n[{algo_name} ws={n_sips}] sim latency = "
|
||||
f"{latency_ns:.1f} ns ({latency_ns / 1000:.3f} us)")
|
||||
|
||||
for key, (_, trace) in engine._results.items():
|
||||
if not isinstance(trace, dict):
|
||||
continue
|
||||
total = trace.get("total_ns", 0.0)
|
||||
pe_exec = trace.get("pe_exec_ns", 0.0) or 0.0
|
||||
network = total - pe_exec
|
||||
print(f" [{key}] total={total:.1f} ns "
|
||||
f"pe_exec={pe_exec:.1f} ns network={network:.1f} ns")
|
||||
|
||||
expected = float(n_cubes * sum(range(1, n_sips + 1)))
|
||||
|
||||
print()
|
||||
for sip in range(n_sips):
|
||||
arr = tensors[sip].numpy()
|
||||
print(f"[SIP {sip}] output cube0[:4] = {arr[0][:4].tolist()}")
|
||||
print(f"[SIP {sip}] output cube{n_cubes - 1}[:4] = {arr[-1][:4].tolist()}")
|
||||
|
||||
ok_cubes = 0
|
||||
for sip in range(n_sips):
|
||||
arr = tensors[sip].numpy()
|
||||
@@ -166,8 +303,6 @@ def run_allreduce(
|
||||
)
|
||||
ok_cubes += 1
|
||||
|
||||
print(f"\n {algo_name} (ws={n_sips}): {ok_cubes} OK")
|
||||
|
||||
return {
|
||||
"expected": expected,
|
||||
"latency_ns": latency_ns,
|
||||
@@ -175,101 +310,7 @@ def run_allreduce(
|
||||
}
|
||||
|
||||
|
||||
# ── pytest entry point ───────────────────────────────────────────────
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
TOPOLOGY_PATH = Path(__file__).parent.parent / "topology.yaml"
|
||||
|
||||
CONFIGS = [
|
||||
pytest.param(
|
||||
"lrab_hierarchical_allreduce", "ring_1d", 6, None, None,
|
||||
id="ring_6sip",
|
||||
),
|
||||
pytest.param(
|
||||
"lrab_hierarchical_allreduce", "torus_2d", 6, 2, 3,
|
||||
id="torus_6sip_2x3",
|
||||
),
|
||||
pytest.param(
|
||||
"lrab_hierarchical_allreduce", "mesh_2d_no_wrap", 6, 2, 3,
|
||||
id="mesh_6sip_2x3",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
def _write_temp_configs(
|
||||
tmp_path, sip_topology, n_sips, algorithm, n_elem_override=None,
|
||||
sip_w=None, sip_h=None,
|
||||
):
|
||||
"""Write temp topology.yaml and ccl.yaml with the given overrides."""
|
||||
with open(TOPOLOGY_PATH) as f:
|
||||
topo_cfg = yaml.safe_load(f)
|
||||
topo_cfg["system"]["sips"]["count"] = n_sips
|
||||
topo_cfg["system"]["sips"]["topology"] = sip_topology
|
||||
if sip_w is not None and sip_h is not None:
|
||||
topo_cfg["system"]["sips"]["w"] = int(sip_w)
|
||||
topo_cfg["system"]["sips"]["h"] = int(sip_h)
|
||||
else:
|
||||
topo_cfg["system"]["sips"].pop("w", None)
|
||||
topo_cfg["system"]["sips"].pop("h", None)
|
||||
topo_path = tmp_path / "topology.yaml"
|
||||
with open(topo_path, "w") as f:
|
||||
yaml.dump(topo_cfg, f, default_flow_style=False)
|
||||
|
||||
ccl_path = Path(__file__).parent.parent / "ccl.yaml"
|
||||
with open(ccl_path) as f:
|
||||
ccl_cfg = yaml.safe_load(f)
|
||||
ccl_cfg["defaults"]["algorithm"] = algorithm
|
||||
if n_elem_override is not None:
|
||||
ccl_cfg.setdefault("algorithms", {}).setdefault(
|
||||
algorithm, {},
|
||||
)["n_elem"] = int(n_elem_override)
|
||||
# Ensure IPCQ slot is big enough for the per-message payload.
|
||||
per_msg_bytes = int(n_elem_override) * 2 # f16
|
||||
default_slot = int(ccl_cfg["defaults"].get("slot_size", 4096))
|
||||
if per_msg_bytes > default_slot:
|
||||
ccl_cfg["defaults"]["slot_size"] = per_msg_bytes
|
||||
tmp_ccl = tmp_path / "ccl.yaml"
|
||||
with open(tmp_ccl, "w") as f:
|
||||
yaml.dump(ccl_cfg, f, default_flow_style=False)
|
||||
|
||||
return str(topo_path), str(tmp_ccl)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"algorithm,sip_topology,n_sips,sip_w,sip_h", CONFIGS,
|
||||
)
|
||||
def test_allreduce(
|
||||
tmp_path, algorithm, sip_topology, n_sips, sip_w, sip_h,
|
||||
):
|
||||
topo_path, ccl_path = _write_temp_configs(
|
||||
tmp_path, sip_topology, n_sips, algorithm,
|
||||
sip_w=sip_w, sip_h=sip_h,
|
||||
)
|
||||
topo = resolve_topology(topo_path)
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
spec = topo.topology_obj.spec
|
||||
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id=f"test_{algorithm}_{sip_topology}",
|
||||
spec=spec,
|
||||
) as ctx:
|
||||
result = run_allreduce(
|
||||
ctx, engine, spec,
|
||||
algorithm=algorithm, ccl_yaml=ccl_path,
|
||||
)
|
||||
assert result["ok_cubes"] > 0
|
||||
|
||||
|
||||
# ── Latency sweep (parametrized + xdist-friendly) ─────────────────────
|
||||
# ── Latency sweep constants + aggregator ──────────────────────────────
|
||||
|
||||
# avoid 16 (== n_cubes, dim_map collision). Goes up to 96 KB per PE:
|
||||
# bytes_per_pe = n_elem * 2 (f16). 49152 elem * 2 = 96 KB / PE.
|
||||
@@ -289,7 +330,7 @@ _SWEEP_TOPOLOGIES = [
|
||||
# parametrized invocation writes one JSON file here; the aggregator
|
||||
# (run from conftest.pytest_sessionfinish) reads them and emits the
|
||||
# combined CSV + PNG plots.
|
||||
_SWEEP_OUT_DIR = (Path(__file__).parent.parent / "docs" / "diagrams"
|
||||
_SWEEP_OUT_DIR = (Path(__file__).parent.parent.parent / "docs" / "diagrams"
|
||||
/ "allreduce_latency_plots")
|
||||
_SWEEP_ROWS_DIR = _SWEEP_OUT_DIR / "_rows"
|
||||
|
||||
@@ -305,69 +346,6 @@ def _sweep_params():
|
||||
return out
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"algorithm,sip_topology,n_sips,sip_w,sip_h,n_elem", _sweep_params(),
|
||||
)
|
||||
def test_allreduce_latency_one(
|
||||
tmp_path, algorithm, sip_topology, n_sips, sip_w, sip_h, n_elem,
|
||||
):
|
||||
"""One config of the latency sweep. xdist parallelizes across params.
|
||||
|
||||
Writes a single JSON row to ``_SWEEP_ROWS_DIR``. The conftest
|
||||
sessionfinish hook aggregates rows into CSV + plots after all
|
||||
parametrized cases finish.
|
||||
"""
|
||||
import json
|
||||
|
||||
topo_path, ccl_path = _write_temp_configs(
|
||||
tmp_path, sip_topology, n_sips, algorithm,
|
||||
sip_w=sip_w, sip_h=sip_h,
|
||||
n_elem_override=n_elem,
|
||||
)
|
||||
topo = resolve_topology(topo_path)
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
spec = topo.topology_obj.spec
|
||||
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id=f"sweep_{algorithm}_{sip_topology}_{n_elem}",
|
||||
spec=spec,
|
||||
) as ctx:
|
||||
result = run_allreduce(
|
||||
ctx, engine, spec,
|
||||
algorithm=algorithm, ccl_yaml=ccl_path,
|
||||
)
|
||||
assert result["ok_cubes"] > 0
|
||||
|
||||
pe_exec_vals = [
|
||||
float(tr.get("pe_exec_ns", 0.0) or 0.0)
|
||||
for _, (_, tr) in engine._results.items()
|
||||
if isinstance(tr, dict)
|
||||
]
|
||||
crit_ns = max(pe_exec_vals) if pe_exec_vals else 0.0
|
||||
|
||||
cm = spec["sip"]["cube_mesh"]
|
||||
n_cubes = int(cm["w"]) * int(cm["h"])
|
||||
bytes_per_sip = n_cubes * n_elem * _ELEM_BYTES_F16
|
||||
bytes_per_pe = n_elem * _ELEM_BYTES_F16
|
||||
|
||||
record = {
|
||||
"algorithm": algorithm,
|
||||
"sip_topology": sip_topology,
|
||||
"n_sips": n_sips,
|
||||
"n_elem": n_elem,
|
||||
"bytes_per_pe": bytes_per_pe,
|
||||
"bytes_per_sip": bytes_per_sip,
|
||||
"latency_ns": crit_ns,
|
||||
}
|
||||
|
||||
_SWEEP_ROWS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
row_path = _SWEEP_ROWS_DIR / f"{sip_topology}_{n_elem}.json"
|
||||
with open(row_path, "w", encoding="utf-8") as f:
|
||||
json.dump(record, f)
|
||||
|
||||
|
||||
def _aggregate_sweep_plots() -> bool:
|
||||
"""Read all per-config rows and emit CSV + PNG plots.
|
||||
|
||||
@@ -469,7 +447,7 @@ def _aggregate_sweep_plots() -> bool:
|
||||
plt.close(fig)
|
||||
|
||||
# Combined overview.png is no longer emitted — the broken-y-axis
|
||||
# comparison (scripts/emit_overview_with_external_ref.py →
|
||||
# comparison (emit_comparison_fsim_plot() below →
|
||||
# comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png)
|
||||
# supersedes it. Per-topology plots above and summary.csv are still
|
||||
# produced.
|
||||
@@ -491,6 +469,118 @@ def _aggregate_sweep_plots() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
# ── Buffer-kind sweep constants + aggregator ──────────────────────────
|
||||
#
|
||||
# Parametrized over (buffer_kind, n_elem) on torus_2d 6 SIPs (3×2). Pre
|
||||
# slot-latency modeling the three lines overlap exactly (slot access is
|
||||
# latency-free today); they spread out once tcm/sram/hbm carry distinct
|
||||
# access costs.
|
||||
|
||||
_BUFFER_KINDS = ["tcm", "sram", "hbm"]
|
||||
_BK_N_ELEM_GRID = [128, 1024, 8192, 32768] # 256 B → 64 KB per slot
|
||||
_BK_ROWS_DIR = _SWEEP_OUT_DIR / "_buffer_kind_rows"
|
||||
# Descriptive output stem (shared by the .png and .csv).
|
||||
_BK_OUT_STEM = "AllReduce_LRAB_2Dtorus_6SiP_2x3_with_TCM_SRAM_HBM"
|
||||
|
||||
|
||||
def _bk_params():
|
||||
out = []
|
||||
for bk in _BUFFER_KINDS:
|
||||
for n_elem in _BK_N_ELEM_GRID:
|
||||
out.append(pytest.param(bk, n_elem, id=f"{bk}-n_elem{n_elem}"))
|
||||
return out
|
||||
|
||||
|
||||
def aggregate_buffer_kind_plot() -> bool:
|
||||
"""Read per-config rows and emit the descriptive .png + .csv (_BK_OUT_STEM).
|
||||
|
||||
Called from conftest.pytest_sessionfinish (controller-only).
|
||||
Returns True if rows were aggregated.
|
||||
"""
|
||||
import csv
|
||||
import json
|
||||
|
||||
if not _BK_ROWS_DIR.exists():
|
||||
return False
|
||||
row_files = sorted(_BK_ROWS_DIR.glob("*.json"))
|
||||
if not row_files:
|
||||
return False
|
||||
|
||||
records = []
|
||||
for p in row_files:
|
||||
with open(p, encoding="utf-8") as f:
|
||||
records.append(json.load(f))
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.ticker import FuncFormatter
|
||||
|
||||
def _fmt_bytes(x, _pos):
|
||||
if x <= 0:
|
||||
return "0"
|
||||
if x >= 1024 * 1024:
|
||||
return f"{x / (1024 * 1024):.0f} MB"
|
||||
if x >= 1024:
|
||||
return f"{x / 1024:.0f} KB"
|
||||
return f"{x:.0f} B"
|
||||
|
||||
_bytes_fmt = FuncFormatter(_fmt_bytes)
|
||||
|
||||
_SWEEP_OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
with open(_SWEEP_OUT_DIR / f"{_BK_OUT_STEM}.csv", "w",
|
||||
newline="", encoding="utf-8") as f:
|
||||
w = csv.DictWriter(f, fieldnames=[
|
||||
"buffer_kind", "sip_topology", "n_sips", "n_elem",
|
||||
"bytes_per_pe", "latency_ns",
|
||||
])
|
||||
w.writeheader()
|
||||
for r in sorted(records, key=lambda r: (
|
||||
r["buffer_kind"], r["bytes_per_pe"],
|
||||
)):
|
||||
w.writerow(r)
|
||||
|
||||
colors = {"tcm": "tab:blue", "sram": "tab:orange", "hbm": "tab:red"}
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
for bk in ["tcm", "sram", "hbm"]:
|
||||
rs = sorted(
|
||||
[r for r in records if r["buffer_kind"] == bk],
|
||||
key=lambda r: r["bytes_per_pe"],
|
||||
)
|
||||
if not rs:
|
||||
continue
|
||||
ax.plot(
|
||||
[r["bytes_per_pe"] for r in rs],
|
||||
[r["latency_ns"] for r in rs],
|
||||
marker="o", lw=2.0,
|
||||
color=colors[bk], label=f"buffer_kind = {bk}",
|
||||
)
|
||||
ax.set_xscale("log", base=2)
|
||||
ax.set_xlabel("Bytes per PE (log scale)")
|
||||
ax.set_ylabel("Time (ns)")
|
||||
ax.set_title(
|
||||
"AllReduce_LRAB_2Dtorus_6SiP(2x3) — IPCQ memory (SRAM, TCM, HBM)"
|
||||
)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.legend()
|
||||
ax.xaxis.set_major_formatter(_bytes_fmt)
|
||||
fig.tight_layout()
|
||||
fig.savefig(_SWEEP_OUT_DIR / f"{_BK_OUT_STEM}.png", dpi=130)
|
||||
plt.close(fig)
|
||||
|
||||
for p in row_files:
|
||||
try:
|
||||
p.unlink()
|
||||
except OSError:
|
||||
pass
|
||||
try:
|
||||
_BK_ROWS_DIR.rmdir()
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
print(f"\nWrote {_SWEEP_OUT_DIR / f'{_BK_OUT_STEM}.png'} "
|
||||
f"from {len(records)} rows")
|
||||
return True
|
||||
|
||||
|
||||
# ── Topology diagram (device-level + cube-level reduction) ────────────
|
||||
|
||||
# Convention: "rows × cols" everywhere, row-major rank assignment
|
||||
@@ -781,7 +871,143 @@ def emit_topology_diagram() -> str:
|
||||
return str(out_path)
|
||||
|
||||
|
||||
def test_emit_topology_diagram():
|
||||
"""Emit topology.png alongside the sweep plots. Pure plotting; no sim."""
|
||||
out = emit_topology_diagram()
|
||||
assert Path(out).exists()
|
||||
# ── Comparison vs FSIM (broken-y-axis) ────────────────────────────────
|
||||
#
|
||||
# Post-processes summary.csv: today's three model curves + a hand-derived
|
||||
# theoretical torus_2d line in the bottom panel, and a single external FSIM
|
||||
# single-device reference marker in the top panel (hardcoded 366 µs; no
|
||||
# external data file). Reads summary.csv written by _aggregate_sweep_plots.
|
||||
|
||||
_FSIM_EXT_LABEL = "FSIM (single device): 366 µs"
|
||||
_FSIM_EXT_LATENCY_NS = 366_000.0
|
||||
_CMP_COLORS = {
|
||||
"ring_1d": "tab:blue",
|
||||
"torus_2d": "tab:orange",
|
||||
"mesh_2d_no_wrap": "tab:green",
|
||||
}
|
||||
_CMP_DISPLAY = {
|
||||
"ring_1d": "Ring 1x6 (6 devices)",
|
||||
"torus_2d": "2D Torus 2x3 (6 devices)",
|
||||
"mesh_2d_no_wrap": "2D Mesh 2x3 (6 devices)",
|
||||
}
|
||||
# Hand-derived theoretical model for torus_2d (6 SIPs): per-PE NOC-packet
|
||||
# count fit to the simulated startup + per-packet tau.
|
||||
_CMP_NOC_PACKET_BYTES = 128
|
||||
_CMP_PES_PER_CUBE = 8
|
||||
_CMP_T_STARTUP_NS = 1346.0
|
||||
_CMP_TAU_NS = (8741.0 - 1346.0) / (6144 - 1)
|
||||
|
||||
|
||||
def emit_comparison_fsim_plot() -> str | None:
|
||||
"""Render comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png.
|
||||
|
||||
Reads ``summary.csv`` (written by ``_aggregate_sweep_plots``). Returns the
|
||||
output path, or ``None`` if summary.csv is absent / empty.
|
||||
"""
|
||||
import csv
|
||||
|
||||
csv_path = _SWEEP_OUT_DIR / "summary.csv"
|
||||
if not csv_path.exists():
|
||||
return None
|
||||
records = []
|
||||
with open(csv_path, newline="", encoding="utf-8") as f:
|
||||
for row in csv.DictReader(f):
|
||||
records.append({
|
||||
"sip_topology": row["sip_topology"],
|
||||
"bytes_per_pe": int(row["bytes_per_pe"]),
|
||||
"latency_ns": float(row["latency_ns"]),
|
||||
})
|
||||
if not records:
|
||||
return None
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as mticker
|
||||
|
||||
def _theoretical_torus_2d_ns(bytes_per_pe: int) -> float:
|
||||
bytes_per_cube = int(bytes_per_pe) * _CMP_PES_PER_CUBE
|
||||
n_packets = max(1, -(-bytes_per_cube // _CMP_NOC_PACKET_BYTES))
|
||||
return _CMP_T_STARTUP_NS + (n_packets - 1) * _CMP_TAU_NS
|
||||
|
||||
def _bytes_fmt(x, _pos):
|
||||
if x >= 1024 * 1024:
|
||||
return f"{x / (1024 * 1024):.0f}M"
|
||||
if x >= 1024:
|
||||
return f"{x / 1024:.0f}K"
|
||||
return f"{int(x)}"
|
||||
|
||||
topologies = sorted({r["sip_topology"] for r in records})
|
||||
max_local = max(r["latency_ns"] for r in records)
|
||||
ext_x = max(r["bytes_per_pe"] for r in records)
|
||||
|
||||
fig, (ax_top, ax_bot) = plt.subplots(
|
||||
2, 1, sharex=True,
|
||||
gridspec_kw={"height_ratios": [1, 4], "hspace": 0.05},
|
||||
figsize=(9, 6.5),
|
||||
)
|
||||
|
||||
# Bottom panel: model curves + theoretical torus, linear y.
|
||||
for topo in topologies:
|
||||
rs = sorted([r for r in records if r["sip_topology"] == topo],
|
||||
key=lambda r: r["bytes_per_pe"])
|
||||
if not rs:
|
||||
continue
|
||||
ax_bot.plot(
|
||||
[r["bytes_per_pe"] for r in rs],
|
||||
[r["latency_ns"] for r in rs],
|
||||
marker="o", label=_CMP_DISPLAY.get(topo, topo),
|
||||
color=_CMP_COLORS.get(topo),
|
||||
)
|
||||
torus_rs = sorted(
|
||||
[r for r in records if r["sip_topology"] == "torus_2d"],
|
||||
key=lambda r: r["bytes_per_pe"],
|
||||
)
|
||||
if torus_rs:
|
||||
ax_bot.plot(
|
||||
[r["bytes_per_pe"] for r in torus_rs],
|
||||
[_theoretical_torus_2d_ns(r["bytes_per_pe"]) for r in torus_rs],
|
||||
color="tab:red", linestyle="--", linewidth=1.6, marker="x",
|
||||
label="Theoretical 2D Torus 2x3",
|
||||
)
|
||||
ax_bot.set_ylim(0, max_local * 1.10)
|
||||
|
||||
# Top panel: external FSIM single-device reference marker.
|
||||
ax_top.scatter(
|
||||
[ext_x], [_FSIM_EXT_LATENCY_NS],
|
||||
marker="*", s=240, color="tab:red", zorder=5,
|
||||
label=_FSIM_EXT_LABEL,
|
||||
)
|
||||
ax_top.set_ylim(_FSIM_EXT_LATENCY_NS * 0.93, _FSIM_EXT_LATENCY_NS * 1.05)
|
||||
|
||||
# Hide spine between panels; draw diagonal break ticks.
|
||||
ax_top.spines["bottom"].set_visible(False)
|
||||
ax_bot.spines["top"].set_visible(False)
|
||||
ax_top.tick_params(labeltop=False, bottom=False)
|
||||
ax_bot.xaxis.tick_bottom()
|
||||
d = 0.012
|
||||
kw = dict(transform=ax_top.transAxes, color="k", clip_on=False, lw=1)
|
||||
ax_top.plot((-d, +d), (-d, +d), **kw)
|
||||
ax_top.plot((1 - d, 1 + d), (-d, +d), **kw)
|
||||
kw.update(transform=ax_bot.transAxes)
|
||||
ax_bot.plot((-d, +d), (1 - d * 4, 1 + d * 4), **kw)
|
||||
ax_bot.plot((1 - d, 1 + d), (1 - d * 4, 1 + d * 4), **kw)
|
||||
|
||||
ax_bot.set_xscale("log", base=2)
|
||||
ax_bot.set_xlabel("Bytes per PE (log scale)")
|
||||
ax_bot.set_ylabel("Time (ns)")
|
||||
ax_top.set_ylabel("Time (ns)")
|
||||
ax_bot.grid(True, alpha=0.3)
|
||||
ax_top.grid(True, alpha=0.3)
|
||||
ax_bot.xaxis.set_major_formatter(mticker.FuncFormatter(_bytes_fmt))
|
||||
|
||||
handles_bot, labels_bot = ax_bot.get_legend_handles_labels()
|
||||
handles_top, labels_top = ax_top.get_legend_handles_labels()
|
||||
ax_bot.legend(handles_bot + handles_top, labels_bot + labels_top,
|
||||
loc="upper left")
|
||||
|
||||
fig.suptitle("Multidevice allreduce (ring, Mesh, 2DTorus) vs FSIM latency")
|
||||
fig.tight_layout()
|
||||
out = (_SWEEP_OUT_DIR
|
||||
/ "comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png")
|
||||
fig.savefig(out, dpi=120)
|
||||
plt.close(fig)
|
||||
return str(out)
|
||||
@@ -0,0 +1,35 @@
|
||||
"""Correctness of intercube allreduce across SIP topologies (distributed path).
|
||||
|
||||
Routes through init_process_group → mp.spawn → dist.all_reduce for ring_1d,
|
||||
torus_2d (2×3), and mesh_2d_no_wrap (2×3). Per-rank correctness is asserted
|
||||
inside the worker; spawn raises on failure.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
CONFIGS,
|
||||
DEFAULT_N_ELEM,
|
||||
_crit_ns,
|
||||
_run_distributed,
|
||||
_write_temp_configs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"algorithm,sip_topology,n_sips,sip_w,sip_h", CONFIGS,
|
||||
)
|
||||
def test_allreduce(
|
||||
tmp_path, monkeypatch, algorithm, sip_topology, n_sips, sip_w, sip_h,
|
||||
):
|
||||
topo_path, _ = _write_temp_configs(
|
||||
tmp_path, sip_topology, n_sips, algorithm,
|
||||
sip_w=sip_w, sip_h=sip_h,
|
||||
)
|
||||
engine, _n_cubes = _run_distributed(
|
||||
tmp_path, monkeypatch, topo_path,
|
||||
f"test_{algorithm}_{sip_topology}", DEFAULT_N_ELEM,
|
||||
)
|
||||
# A positive critical path confirms the kernel actually ran.
|
||||
assert _crit_ns(engine) > 0.0
|
||||
@@ -0,0 +1,47 @@
|
||||
"""Full distributed path against topology.yaml as-is (no overrides).
|
||||
|
||||
The same flow a real DDP training script would use:
|
||||
init_process_group(backend="ahbm") → mp.spawn → dist.all_reduce.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
DEFAULT_N_ELEM,
|
||||
TOPOLOGY_PATH,
|
||||
_worker,
|
||||
_write_ccl_yaml,
|
||||
)
|
||||
|
||||
|
||||
def test_distributed_lrab_hierarchical_allreduce(tmp_path, monkeypatch):
|
||||
monkeypatch.chdir(_write_ccl_yaml(tmp_path))
|
||||
|
||||
topo = resolve_topology(str(TOPOLOGY_PATH))
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
spec = topo.topology_obj.spec
|
||||
n_sips = int(spec["system"]["sips"]["count"])
|
||||
cm = spec["sip"]["cube_mesh"]
|
||||
n_cubes = int(cm["w"]) * int(cm["h"])
|
||||
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="dist_intercube_ar",
|
||||
spec=spec,
|
||||
) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
assert ctx.distributed.get_world_size() == n_sips
|
||||
|
||||
t_start = engine._env.now
|
||||
ctx.multiprocessing.spawn(
|
||||
_worker, args=(n_cubes, DEFAULT_N_ELEM, n_sips, ctx),
|
||||
nprocs=n_sips,
|
||||
)
|
||||
t_end = engine._env.now
|
||||
print(f"\n[distributed] sim latency = "
|
||||
f"{t_end - t_start:.1f} ns ({(t_end - t_start) / 1000:.3f} us)")
|
||||
@@ -40,7 +40,7 @@ from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
from tests.test_allreduce_multidevice import (
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
_write_temp_configs,
|
||||
run_allreduce,
|
||||
)
|
||||
@@ -0,0 +1,66 @@
|
||||
"""Buffer-kind sweep (TCM / SRAM / HBM) on torus_2d 6 SIPs (3×2), distributed.
|
||||
|
||||
Each parametrized case writes one JSON row; the conftest sessionfinish hook
|
||||
calls ``aggregate_buffer_kind_plot`` to emit the comparison PNG + csv. Pre
|
||||
slot-latency modeling the three lines overlap exactly (slot access is
|
||||
latency-free today).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
_BK_ROWS_DIR,
|
||||
_ELEM_BYTES_F16,
|
||||
_bk_params,
|
||||
_crit_ns,
|
||||
_run_distributed,
|
||||
_write_temp_configs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("buffer_kind,n_elem", _bk_params())
|
||||
def test_buffer_kind_allreduce_one(tmp_path, monkeypatch, buffer_kind, n_elem):
|
||||
sub = tmp_path / f"{buffer_kind}_{n_elem}"
|
||||
sub.mkdir()
|
||||
topo_path, ccl_path = _write_temp_configs(
|
||||
sub,
|
||||
sip_topology="torus_2d",
|
||||
n_sips=6,
|
||||
algorithm="lrab_hierarchical_allreduce",
|
||||
sip_w=3, sip_h=2,
|
||||
n_elem_override=n_elem,
|
||||
)
|
||||
# Override buffer_kind in the temp ccl.yaml (read by the ahbm backend
|
||||
# at init_process_group time via load_ccl_config()).
|
||||
with open(ccl_path) as f:
|
||||
ccl_cfg = yaml.safe_load(f)
|
||||
ccl_cfg.setdefault("defaults", {})["buffer_kind"] = buffer_kind
|
||||
ccl_cfg.setdefault("algorithms", {}).setdefault(
|
||||
"lrab_hierarchical_allreduce", {},
|
||||
)["buffer_kind"] = buffer_kind
|
||||
with open(ccl_path, "w") as f:
|
||||
yaml.dump(ccl_cfg, f, default_flow_style=False)
|
||||
|
||||
engine, _ = _run_distributed(
|
||||
sub, monkeypatch, topo_path,
|
||||
f"bk_sweep_{buffer_kind}_{n_elem}", n_elem,
|
||||
)
|
||||
crit_ns = _crit_ns(engine)
|
||||
|
||||
bytes_per_pe = n_elem * _ELEM_BYTES_F16
|
||||
record = {
|
||||
"buffer_kind": buffer_kind,
|
||||
"sip_topology": "torus_2d",
|
||||
"n_sips": 6,
|
||||
"n_elem": n_elem,
|
||||
"bytes_per_pe": bytes_per_pe,
|
||||
"latency_ns": crit_ns,
|
||||
}
|
||||
_BK_ROWS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
row_path = _BK_ROWS_DIR / f"{buffer_kind}_{n_elem}.json"
|
||||
with open(row_path, "w", encoding="utf-8") as f:
|
||||
json.dump(record, f)
|
||||
@@ -0,0 +1,23 @@
|
||||
"""Emit the broken-y-axis allreduce-vs-FSIM comparison plot.
|
||||
|
||||
Post-processes summary.csv (written by the latency sweep) into
|
||||
comparison_mesh_vs_ring_vs_2DTorus_vs_theoretical_vs_fsim.png. Pure
|
||||
plotting; reads the on-disk summary.csv (skips if the sweep has never run).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
_SWEEP_OUT_DIR,
|
||||
emit_comparison_fsim_plot,
|
||||
)
|
||||
|
||||
|
||||
def test_emit_comparison_fsim_plot():
|
||||
if not (_SWEEP_OUT_DIR / "summary.csv").exists():
|
||||
pytest.skip("summary.csv absent; run the latency sweep first")
|
||||
out = emit_comparison_fsim_plot()
|
||||
assert out is not None and Path(out).exists()
|
||||
@@ -0,0 +1,58 @@
|
||||
"""Allreduce latency sweep (distributed path), xdist-friendly.
|
||||
|
||||
Each parametrized case writes one JSON row to the shared staging dir; the
|
||||
conftest sessionfinish hook calls ``_aggregate_sweep_plots`` to emit the
|
||||
per-topology PNGs + summary.csv after all cases finish.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
|
||||
import pytest
|
||||
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
_ELEM_BYTES_F16,
|
||||
_SWEEP_ROWS_DIR,
|
||||
_crit_ns,
|
||||
_run_distributed,
|
||||
_sweep_params,
|
||||
_write_temp_configs,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"algorithm,sip_topology,n_sips,sip_w,sip_h,n_elem", _sweep_params(),
|
||||
)
|
||||
def test_allreduce_latency_one(
|
||||
tmp_path, monkeypatch, algorithm, sip_topology, n_sips, sip_w, sip_h,
|
||||
n_elem,
|
||||
):
|
||||
topo_path, _ = _write_temp_configs(
|
||||
tmp_path, sip_topology, n_sips, algorithm,
|
||||
sip_w=sip_w, sip_h=sip_h,
|
||||
n_elem_override=n_elem,
|
||||
)
|
||||
engine, n_cubes = _run_distributed(
|
||||
tmp_path, monkeypatch, topo_path,
|
||||
f"sweep_{algorithm}_{sip_topology}_{n_elem}", n_elem,
|
||||
)
|
||||
|
||||
crit_ns = _crit_ns(engine)
|
||||
|
||||
bytes_per_sip = n_cubes * n_elem * _ELEM_BYTES_F16
|
||||
bytes_per_pe = n_elem * _ELEM_BYTES_F16
|
||||
|
||||
record = {
|
||||
"algorithm": algorithm,
|
||||
"sip_topology": sip_topology,
|
||||
"n_sips": n_sips,
|
||||
"n_elem": n_elem,
|
||||
"bytes_per_pe": bytes_per_pe,
|
||||
"bytes_per_sip": bytes_per_sip,
|
||||
"latency_ns": crit_ns,
|
||||
}
|
||||
|
||||
_SWEEP_ROWS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
row_path = _SWEEP_ROWS_DIR / f"{sip_topology}_{n_elem}.json"
|
||||
with open(row_path, "w", encoding="utf-8") as f:
|
||||
json.dump(record, f)
|
||||
@@ -0,0 +1,11 @@
|
||||
"""Emit topology.png (device-level + cube-level reduction). Pure plotting; no sim."""
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from tests.sccl._allreduce_helpers import emit_topology_diagram
|
||||
|
||||
|
||||
def test_emit_topology_diagram():
|
||||
out = emit_topology_diagram()
|
||||
assert Path(out).exists()
|
||||
@@ -1,199 +0,0 @@
|
||||
"""Phase 1 buffer-kind allreduce sweep — torus_2d 6 SIPs.
|
||||
|
||||
Parametrized over (buffer_kind, n_elem). Each case runs the standard
|
||||
config-driven allreduce app and writes a JSON row to a shared staging
|
||||
dir; the conftest sessionfinish hook (added in Phase 1) aggregates
|
||||
rows into ``docs/diagrams/allreduce_latency_plots/
|
||||
AllReduce_LRAB_2Dtorus_6SiP_2x3_with_TCM_SRAM_HBM.png``.
|
||||
|
||||
Pre-Phase-2: the three buffer-kind lines overlap exactly because slot
|
||||
access is latency-free today. Post-Phase-2 they spread out (tcm
|
||||
fastest, hbm slowest).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
import yaml
|
||||
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
# Reuse the allreduce app helpers.
|
||||
from tests.test_allreduce_multidevice import (
|
||||
_write_temp_configs,
|
||||
run_allreduce,
|
||||
)
|
||||
|
||||
|
||||
_BUFFER_KINDS = ["tcm", "sram", "hbm"]
|
||||
_N_ELEM_GRID = [128, 1024, 8192, 32768] # 256 B → 64 KB per slot
|
||||
_ELEM_BYTES_F16 = 2
|
||||
|
||||
_OUT_DIR = (Path(__file__).parent.parent / "docs" / "diagrams"
|
||||
/ "allreduce_latency_plots")
|
||||
_ROWS_DIR = _OUT_DIR / "_buffer_kind_rows"
|
||||
# Descriptive output stem (shared by the .png and .csv).
|
||||
_OUT_STEM = "AllReduce_LRAB_2Dtorus_6SiP_2x3_with_TCM_SRAM_HBM"
|
||||
|
||||
|
||||
def _bk_params():
|
||||
out = []
|
||||
for bk in _BUFFER_KINDS:
|
||||
for n_elem in _N_ELEM_GRID:
|
||||
out.append(pytest.param(bk, n_elem, id=f"{bk}-n_elem{n_elem}"))
|
||||
return out
|
||||
|
||||
|
||||
@pytest.mark.parametrize("buffer_kind,n_elem", _bk_params())
|
||||
def test_buffer_kind_allreduce_one(tmp_path, buffer_kind, n_elem):
|
||||
"""One config of the buffer-kind sweep. xdist parallelizes."""
|
||||
sub = tmp_path / f"{buffer_kind}_{n_elem}"
|
||||
sub.mkdir()
|
||||
topo_path, ccl_path = _write_temp_configs(
|
||||
sub,
|
||||
sip_topology="torus_2d",
|
||||
n_sips=6,
|
||||
algorithm="lrab_hierarchical_allreduce",
|
||||
sip_w=3, sip_h=2,
|
||||
n_elem_override=n_elem,
|
||||
)
|
||||
# Override buffer_kind in the temp ccl.yaml.
|
||||
with open(ccl_path) as f:
|
||||
ccl_cfg = yaml.safe_load(f)
|
||||
ccl_cfg.setdefault("defaults", {})["buffer_kind"] = buffer_kind
|
||||
ccl_cfg.setdefault("algorithms", {}).setdefault(
|
||||
"lrab_hierarchical_allreduce", {},
|
||||
)["buffer_kind"] = buffer_kind
|
||||
with open(ccl_path, "w") as f:
|
||||
yaml.dump(ccl_cfg, f, default_flow_style=False)
|
||||
|
||||
topo = resolve_topology(topo_path)
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
spec = topo.topology_obj.spec
|
||||
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id=f"bk_sweep_{buffer_kind}_{n_elem}",
|
||||
spec=spec,
|
||||
) as ctx:
|
||||
result = run_allreduce(
|
||||
ctx, engine, spec,
|
||||
algorithm="lrab_hierarchical_allreduce", ccl_yaml=ccl_path,
|
||||
)
|
||||
assert result["ok_cubes"] > 0
|
||||
|
||||
pe_exec_vals = [
|
||||
float(tr.get("pe_exec_ns", 0.0) or 0.0)
|
||||
for _, (_, tr) in engine._results.items()
|
||||
if isinstance(tr, dict)
|
||||
]
|
||||
crit_ns = max(pe_exec_vals) if pe_exec_vals else 0.0
|
||||
|
||||
bytes_per_pe = n_elem * _ELEM_BYTES_F16
|
||||
record = {
|
||||
"buffer_kind": buffer_kind,
|
||||
"sip_topology": "torus_2d",
|
||||
"n_sips": 6,
|
||||
"n_elem": n_elem,
|
||||
"bytes_per_pe": bytes_per_pe,
|
||||
"latency_ns": crit_ns,
|
||||
}
|
||||
_ROWS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
row_path = _ROWS_DIR / f"{buffer_kind}_{n_elem}.json"
|
||||
with open(row_path, "w", encoding="utf-8") as f:
|
||||
json.dump(record, f)
|
||||
|
||||
|
||||
def aggregate_buffer_kind_plot() -> bool:
|
||||
"""Read per-config rows and emit the descriptive .png + .csv (_OUT_STEM).
|
||||
|
||||
Called from conftest.pytest_sessionfinish (controller-only).
|
||||
Returns True if rows were aggregated.
|
||||
"""
|
||||
import csv
|
||||
|
||||
if not _ROWS_DIR.exists():
|
||||
return False
|
||||
row_files = sorted(_ROWS_DIR.glob("*.json"))
|
||||
if not row_files:
|
||||
return False
|
||||
|
||||
records = []
|
||||
for p in row_files:
|
||||
with open(p, encoding="utf-8") as f:
|
||||
records.append(json.load(f))
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.ticker import FuncFormatter
|
||||
|
||||
def _fmt_bytes(x, _pos):
|
||||
if x <= 0:
|
||||
return "0"
|
||||
if x >= 1024 * 1024:
|
||||
return f"{x / (1024 * 1024):.0f} MB"
|
||||
if x >= 1024:
|
||||
return f"{x / 1024:.0f} KB"
|
||||
return f"{x:.0f} B"
|
||||
|
||||
_bytes_fmt = FuncFormatter(_fmt_bytes)
|
||||
|
||||
_OUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
with open(_OUT_DIR / f"{_OUT_STEM}.csv", "w",
|
||||
newline="", encoding="utf-8") as f:
|
||||
w = csv.DictWriter(f, fieldnames=[
|
||||
"buffer_kind", "sip_topology", "n_sips", "n_elem",
|
||||
"bytes_per_pe", "latency_ns",
|
||||
])
|
||||
w.writeheader()
|
||||
for r in sorted(records, key=lambda r: (
|
||||
r["buffer_kind"], r["bytes_per_pe"],
|
||||
)):
|
||||
w.writerow(r)
|
||||
|
||||
colors = {"tcm": "tab:blue", "sram": "tab:orange", "hbm": "tab:red"}
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
for bk in ["tcm", "sram", "hbm"]:
|
||||
rs = sorted(
|
||||
[r for r in records if r["buffer_kind"] == bk],
|
||||
key=lambda r: r["bytes_per_pe"],
|
||||
)
|
||||
if not rs:
|
||||
continue
|
||||
ax.plot(
|
||||
[r["bytes_per_pe"] for r in rs],
|
||||
[r["latency_ns"] for r in rs],
|
||||
marker="o", lw=2.0,
|
||||
color=colors[bk], label=f"buffer_kind = {bk}",
|
||||
)
|
||||
ax.set_xscale("log", base=2)
|
||||
ax.set_xlabel("Bytes per PE (log scale)")
|
||||
ax.set_ylabel("Time (ns)")
|
||||
ax.set_title(
|
||||
"AllReduce_LRAB_2Dtorus_6SiP(2x3) — IPCQ memory (SRAM, TCM, HBM)"
|
||||
)
|
||||
ax.grid(True, alpha=0.3)
|
||||
ax.legend()
|
||||
ax.xaxis.set_major_formatter(_bytes_fmt)
|
||||
fig.tight_layout()
|
||||
fig.savefig(_OUT_DIR / f"{_OUT_STEM}.png", dpi=130)
|
||||
plt.close(fig)
|
||||
|
||||
for p in row_files:
|
||||
try:
|
||||
p.unlink()
|
||||
except OSError:
|
||||
pass
|
||||
try:
|
||||
_ROWS_DIR.rmdir()
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
print(f"\nWrote {_OUT_DIR / f'{_OUT_STEM}.png'} "
|
||||
f"from {len(records)} rows")
|
||||
return True
|
||||
@@ -1,119 +0,0 @@
|
||||
"""End-to-end distributed test for intercube allreduce.
|
||||
|
||||
Exercises the full process-group path:
|
||||
dist.init_process_group(backend="ahbm")
|
||||
→ mp.spawn(nprocs=n_sips)
|
||||
→ each worker: set_device → allocate → fill → dist.all_reduce → verify
|
||||
|
||||
This is the same flow a real DDP training script would use.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import textwrap
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
TOPOLOGY_PATH = Path(__file__).parent.parent / "topology.yaml"
|
||||
|
||||
N_CUBES = 16
|
||||
N_ELEM = 8
|
||||
|
||||
|
||||
def _write_ccl_yaml(tmp_path) -> str:
|
||||
body = textwrap.dedent("""\
|
||||
defaults:
|
||||
algorithm: lrab_hierarchical_allreduce
|
||||
buffer_kind: tcm
|
||||
backpressure: sleep
|
||||
n_slots: 4
|
||||
slot_size: 4096
|
||||
vc_chunk_size: 256
|
||||
ipcq_credit_size_bytes: 16
|
||||
|
||||
algorithms:
|
||||
lrab_hierarchical_allreduce:
|
||||
module: kernbench.ccl.algorithms.lrab_hierarchical_allreduce
|
||||
topology: none
|
||||
buffer_kind: tcm
|
||||
n_elem: 8
|
||||
root_cube: 15
|
||||
""")
|
||||
(tmp_path / "ccl.yaml").write_text(body)
|
||||
return str(tmp_path)
|
||||
|
||||
|
||||
def _worker(rank: int, n_sips: int, torch) -> None:
|
||||
"""Per-SIP worker: allocate, fill, all_reduce, verify."""
|
||||
from kernbench.policy.placement.dp import DPPolicy
|
||||
|
||||
torch.ahbm.set_device(rank)
|
||||
|
||||
dp = DPPolicy(
|
||||
cube="row_wise", pe="replicate",
|
||||
num_pes=1, num_cubes=N_CUBES,
|
||||
)
|
||||
tensor = torch.zeros(
|
||||
(N_CUBES, N_ELEM), dtype="f16", dp=dp,
|
||||
name=f"sip{rank}",
|
||||
)
|
||||
|
||||
init_arr = np.full((N_CUBES, N_ELEM), float(rank + 1), dtype=np.float16)
|
||||
tensor.copy_(torch.from_numpy(init_arr))
|
||||
|
||||
print(f"[SIP {rank}] input cube0[:4] = {tensor.numpy()[0][:4].tolist()}")
|
||||
|
||||
torch.distributed.all_reduce(tensor, op="sum")
|
||||
|
||||
arr = tensor.numpy()
|
||||
expected = float(N_CUBES * sum(range(1, n_sips + 1)))
|
||||
|
||||
print(f"[SIP {rank}] output cube0[:4] = {arr[0][:4].tolist()}")
|
||||
print(f"[SIP {rank}] output cube15[:4] = {arr[15][:4].tolist()}")
|
||||
|
||||
for cube_id in range(N_CUBES):
|
||||
assert np.allclose(arr[cube_id], expected, rtol=1e-1, atol=1e-1), (
|
||||
f"SIP{rank} cube {cube_id}: "
|
||||
f"got {arr[cube_id][:4]}, expected {expected}"
|
||||
)
|
||||
|
||||
if rank == 0:
|
||||
print(f"\n lrab_hierarchical_allreduce (ws={n_sips}): "
|
||||
f"{n_sips * N_CUBES} OK")
|
||||
|
||||
|
||||
def test_distributed_lrab_hierarchical_allreduce(tmp_path, monkeypatch):
|
||||
"""Full distributed path: init_process_group → mp.spawn → all_reduce."""
|
||||
from kernbench.runtime_api.context import RuntimeContext
|
||||
from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
monkeypatch.chdir(_write_ccl_yaml(tmp_path))
|
||||
|
||||
topo = resolve_topology(str(TOPOLOGY_PATH))
|
||||
engine = GraphEngine(topo.topology_obj, enable_data=True)
|
||||
spec = topo.topology_obj.spec
|
||||
n_sips = int(spec["system"]["sips"]["count"])
|
||||
|
||||
with RuntimeContext(
|
||||
engine=engine,
|
||||
target_device=DeviceSelector("all"),
|
||||
correlation_id="dist_intercube_ar",
|
||||
spec=spec,
|
||||
) as ctx:
|
||||
ctx.distributed.init_process_group(backend="ahbm")
|
||||
|
||||
assert ctx.distributed.get_world_size() == n_sips
|
||||
|
||||
t_start = engine._env.now
|
||||
|
||||
ctx.multiprocessing.spawn(
|
||||
_worker, args=(n_sips, ctx), nprocs=n_sips,
|
||||
)
|
||||
|
||||
t_end = engine._env.now
|
||||
print(f"\n[distributed] sim latency = "
|
||||
f"{t_end - t_start:.1f} ns ({(t_end - t_start) / 1000:.3f} us)")
|
||||
@@ -20,7 +20,7 @@ Reference (Phase 2 will edit these):
|
||||
- ccl.yaml — algorithm.buffer_kind
|
||||
|
||||
The tests reuse the existing config-driven allreduce app
|
||||
(``run_allreduce`` in tests/test_allreduce_multidevice.py) with a 2-SIP
|
||||
(``run_allreduce`` in tests/sccl/_allreduce_helpers.py) with a 2-SIP
|
||||
ring topology and a SMALL n_elem so they finish fast (~3-5 s each).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
@@ -37,7 +37,7 @@ from kernbench.topology.builder import resolve_topology
|
||||
|
||||
# Reuse the test app's helpers so this micro-test file does not
|
||||
# duplicate the run-allreduce + write-temp-configs plumbing.
|
||||
from tests.test_allreduce_multidevice import (
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
_write_temp_configs,
|
||||
run_allreduce,
|
||||
)
|
||||
|
||||
@@ -47,7 +47,7 @@ from kernbench.runtime_api.types import DeviceSelector
|
||||
from kernbench.sim_engine.engine import GraphEngine
|
||||
from kernbench.topology.builder import resolve_topology
|
||||
|
||||
from tests.test_allreduce_multidevice import (
|
||||
from tests.sccl._allreduce_helpers import (
|
||||
_write_temp_configs,
|
||||
run_allreduce,
|
||||
)
|
||||
@@ -59,8 +59,9 @@ def _run_allreduce_with_buffer_kind(
|
||||
"""Run one torus_2d 6-SIP allreduce with the given buffer_kind and
|
||||
return critical-path pe_exec_ns (max across all PEs).
|
||||
|
||||
Mirrors the sweep harness in test_allreduce_buffer_kind_sweep.py
|
||||
so the assertions below compare apples-to-apples against that PNG.
|
||||
Mirrors the buffer-kind sweep harness in
|
||||
tests/sccl/test_plot_buffer_kind_sweep.py so the assertions
|
||||
below compare apples-to-apples against that PNG.
|
||||
"""
|
||||
sub = tmp_path / f"{buffer_kind}_{n_elem}"
|
||||
sub.mkdir()
|
||||
|
||||
Reference in New Issue
Block a user