Date: 2026-07-24 America/Toronto
This authorizes one component-only probe of the runtime graph substrate. It does not load the Laguna target or DFlash draft, start an endpoint, generate a token, time endpoint throughput, stage a payload, use the network, or submit to LocalMaxxing.
The approved record remains unchanged:
33.89498511171744 tok/s;cmrx6p5dv001bo4017hb7sixz;8936aac144929190c1e53f8b8624ca397ce16f5b; andb6076ce1249ffee0e30bee528f4cd15c3bffb234.The experimental target-only integration is separately committed at vLLM
e09f34a008c31cb4c691697215a6eff3aa2eb5be. It is default-off, keeps the
DFlash drafter eager, forces every target call except the true single-request
DFlash-7 M=8 verifier eager before graph padding/dispatch, pins the graph
output, and rejects any replay-time tensor pointer/offset/shape/stride/dtype or
device drift. Its focused CPU gate passed 17/17 and an independent re-audit
found no remaining dynamic-input blocker.
Can the installed XPU graph and XCCL stack directly record and replay the incumbent Laguna collective pattern without stale data or changed BF16 bytes?
The probe records one graph containing exactly:
all_gather_into_tensor calls with per-rank shape
[1, 8, 3072] and gathered shape [4, 8, 3072];It then performs 32 changing-input epochs with four unique replays per epoch. Every replay checks the raw uint8 view of all 97 gathered tensors, all 97 fixed-rank sums, and the final all-reduce. This is 24,960 raw comparisons per rank and 99,840 across the four-card process group. Source and output digests must change on all 127 transitions.
The final all-reduce fixture uses small integral BF16 values so its expected sum is order-independent. The 97 rank sums use the literal incumbent rank-ordered BF16 boundaries.
d9e88ff060c9b98dd169547d6aeb9ae31f55c629;experiments/laguna-s-2.1-xpu-b70/tools/probe_laguna_m8_xccl_graph.py;1797ae4df5f61f6583fa0ba8942cac7e212f4dfbdc36a28e971734d4f89f264f;/home/steve/.venvs/deepseek-v4-xpu/bin/python,
SHA-256
202c17d1671602a4ef1d43e9b2fdbef0769443f37bf5e51f6b603e0b2c27d9d8;/home/steve/.venvs/deepseek-v4-xpu/lib/libccl.so,
SHA-256
ace144a390a53720b2743844decf127661c942b56f3b414900b9d8c11461acc3;26fa68779adb03b200a8c3001cf81e59fc9a3d63e0f38627ec0005ffce574e7a;0fe232b18985ae078dd546b57bc6d11bacf1030834c0544f7e3feb53ed71c1d0;0b7f98a5-e50a-46a5-81ea-15938b55317a;7.0.0-28-generic; and/mnt/fast-ai/llm-optimization-artifacts/laguna-s-2.1/runs/laguna-m8-xccl-direct-graph-d9e88ff06-20260724T141124Z.The tool rejects any output outside the internal-NVMe artifact subtree, requires a new root, and verifies its backing mount is ext4 on an NVMe block device. The external Corsair/NTFS volume is forbidden.
After a read-only four-card discovery/process check confirms no foreign XPU work:
env \
PYTHONDONTWRITEBYTECODE=1 \
PYTHONNOUSERSITE=1 \
ONEAPI_DEVICE_SELECTOR=level_zero:0,1,2,3 \
ZE_AFFINITY_MASK=0,1,2,3 \
CCL_ATL_TRANSPORT=ofi \
CCL_TOPO_P2P_ACCESS=1 \
FI_TCP_IFACE=eno1 \
CCL_KVS_IFACE=eno1 \
LD_LIBRARY_PATH=/home/steve/.venvs/deepseek-v4-xpu/lib:/opt/intel/oneapi/umf/1.1/lib:/opt/intel/oneapi/compiler/2026.0/lib:/opt/intel/oneapi/compiler/2026.0/opt/compiler/lib \
timeout --signal=TERM --kill-after=15s 240s \
/home/steve/.venvs/deepseek-v4-xpu/bin/python \
-m torch.distributed.run \
--standalone \
--nproc_per_node=4 \
/home/steve/llm-optimizations/experiments/laguna-s-2.1-xpu-b70/tools/probe_laguna_m8_xccl_graph.py \
--output-root /mnt/fast-ai/llm-optimization-artifacts/laguna-s-2.1/runs/laguna-m8-xccl-direct-graph-d9e88ff06-20260724T141124Z \
--epochs 32 \
--replays-per-epoch 4
pass, all 99,840 raw comparisons match, every freshness
transition changes, and there is no timeout, fallback, device loss, or
runtime error.