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Merge branch 'archived_ralph/dgr-001-performance-contract' into merge/all-branches-into-master

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# Conflicts:
#	.claude/memory/MEMORY.md
#	.scratch/distributed-gguf-runtime/PRD.md
#	.scratch/distributed-gguf-runtime/RALPH-CONTEXT.md
#	.scratch/distributed-gguf-runtime/README.md
#	.scratch/distributed-gguf-runtime/architecture.md
#	.scratch/distributed-gguf-runtime/evidence/DGR-017/README.md
#	.scratch/distributed-gguf-runtime/implementation-strategy.md
#	.scratch/distributed-gguf-runtime/issues/07-add-isolated-concurrent-local-hot-kv-state.md
#	.scratch/distributed-gguf-runtime/issues/13-harden-failure-cancellation-and-restart-semantics.md
#	.scratch/distributed-gguf-runtime/milestones.md
#	.scratch/distributed-gguf-runtime/prd.json
#	docs/issues/distributed-gguf-runtime/01-lock-the-safetensors-versus-gguf-performance-contract.md
#	docs/issues/distributed-gguf-runtime/02-adopt-the-versioned-grpc-shard-protocol.md
#	docs/issues/distributed-gguf-runtime/03-define-exact-artifact-and-runtime-recipe-identity.md
#	docs/issues/distributed-gguf-runtime/05-implement-dense-llama-range-aware-gguf-ownership.md
#	docs/issues/distributed-gguf-runtime/06-implement-architecture-defined-boundary-input-output.md
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# DGR-001 downstream stop-condition handoff
Status: **DGR-001 is complete; native-track promotion is blocked by the immutable v1 verdict.**
This is no longer an execution-prerequisite blocker. The required real benchmark
ran successfully, every recipe completed at concurrency 1 and 4, artifacts were
verified, and deterministic/full test gates passed.
## Locked result
`contract-evaluation.json` records:
```text
verdict: stop
quality_lane_pass: false
speed_benefit: true
fit_benefit: true
stop_condition_met: true
```
The exact-revision BF16 GGUF quality lane compared every prompt but achieved
`0.3333` exact match and `0.9471` mean similarity against the Transformers BF16
reference. V1 requires `0.90` and `0.97`. Quantized Q4_K_M had substantial speed
and fit benefits, but the contract explicitly forbids speed from redeeming a
failed near-lossless quality lane.
## Scope of this stop
The measured baseline is Qwen2.5-0.5B on CPU using a CPU-only llama.cpp build.
It is not a Radeon, large-model, distributed, or native-shard result. Therefore:
1. Do not silently mark v1 promoted or weaken its thresholds after observing the
data.
2. Do not let DGR-004 or later runtime stories treat DGR-001 completion as a
positive promotion signal.
3. A human may choose one of these explicit paths:
- stop the native GGUF track as v1 directs;
- diagnose and fix the BF16 runtime divergence, then rerun the exact v1 plan;
- authorize a separately versioned GPU/large-model contract whose scope and
workload are locked before its measurements.
All raw evidence, configuration, artifacts, hashes, and reproduction commands
are in this directory and `README.md`.

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# DGR-001 — Safetensors versus GGUF performance contract
Status: **complete; immutable v1 verdict is `stop`.**
DGR-001 successfully produced a controlled local-real CPU baseline. Completion
means the experiment and decision contract are durable and verified; it does
**not** mean the native GGUF track is approved to continue. The locked quality
gate failed, so dependent runtime work requires a human decision or a new,
explicitly versioned experiment/contract rather than silently weakening v1.
## Controlled workload
- Model: `Qwen/Qwen2.5-0.5B-Instruct`
- Exact source revision: `7ae557604adf67be50417f59c2c2f167def9a775`
- Machine: `fedora`, Linux `7.0.14-101.fc43.x86_64`, 32 logical CPUs
- Device: CPU for every recipe; VRAM is therefore correctly reported as zero
- Runtime reference: Transformers `5.13.0`, PyTorch
`2.10.0+rocm7.13.0a20260513`, BF16 safetensors
- GGUF runtime: llama.cpp version 9991, commit
`e920c523e3b8a0163fe498af5bf90df35ff51d25`
- Workload: three fixed short/medium/long prompts, greedy sampling, 32 output
tokens, three repeats, two warmups, concurrency 1 and 4, 16 CPU threads
- Evidence class: `local-real`
All artifacts are beneath `/run/media/popov/DATA/llm/`; no model artifact was
created under `/home`.
## Recipes and exact artifacts
| Recipe | Artifact | SHA-256 |
|---|---|---|
| Transformers BF16 reference | complete mounted Hugging Face snapshot | `e596e9d6205fdc9177569cccd7f8b471b058f66e3630c8e4326d5aad52bd18b6` |
| llama.cpp BF16 quality lane | `Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf` | `e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862` |
| llama.cpp Q4_K_M performance/fit lane | `Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf` | `a88e3f570e2efeaf06b50df9859db2c70d8646aa3a2c94a14e14d5797a2921a5` |
The snapshot digest covers every sorted relative path, resolved size, and file
byte, so tokenizer/config drift is included. The BF16 GGUF was converted
directly from the exact snapshot while preserving BF16 weights. Q4_K_M was
quantized from an exact-revision F16 conversion with the pinned quantizer.
Runtime validation recomputes every declared digest before model loading.
## Real results
All recipes completed every request with zero failures.
| Metric | Transformers BF16 | llama.cpp BF16 | llama.cpp Q4_K_M |
|---|---:|---:|---:|
| Decode tok/s, c=1 | 40.8 | 98.5 | 207.7 |
| Aggregate decode tok/s, c=4 | 46.5 | 222.8 | 195.7 |
| TTFT p50, c=1 | 40.0 ms | 15.1 ms | 21.6 ms |
| Peak resident memory, c=1 | 1.94 GB | 1.11 GB | 0.54 GB |
| Artifact size | 1.00 GB | 0.99 GB | 0.40 GB |
| Failures | 0 | 0 | 0 |
Against the reference, the eligible Q4_K_M lane measured:
- single-request decode speedup: **5.10×**;
- concurrency-4 aggregate throughput speedup: **4.20×**;
- resident-memory ratio: **0.279×**;
- artifact-size ratio: **0.398×**.
The near-lossless BF16 quality lane compared all three prompts but measured:
- exact match: **0.3333** (v1 requires at least `0.90`);
- mean text similarity: **0.9471** (v1 requires at least `0.97`).
Tokenization and stopping were controlled: every runtime saw the same prompt
token counts and reported 31 post-TTFT decode tokens. The v1 mismatch is a
real greedy-output divergence on two prompts, not missing coverage or a
text-length artifact. Its root cause remains undetermined; no post-contract
logit-tie claim is acceptance evidence. Therefore `contract-evaluation.json`
records:
```text
verdict: stop
quality_lane_pass: false
speed_benefit: true
fit_benefit: true
stop_condition_met: true
```
Thresholds were not changed after observing these results.
## Post-contract parity and ROCm diagnostics
`summarize-quality-parity.py` verifies and separates two signed sources. The CPU
v1 row uses CPU kernels and a Transformers BF16 oracle; it remains at `0.3333`
exact match with an unexplained divergence. The ROCm row uses a different plan,
GPU kernels, and a Transformers float32 oracle. In that narrower diagnostic,
the same BF16 GGUF artifact matches all three 32-token sequences exactly (`1.0`
exact match and `1.0` similarity). No conversion corruption was observed in
that three-sequence ROCm sample; this does not prove global conversion
correctness or explain the CPU result.
A separate HIP build at commit `e920c523` was compiled for `gfx1151` and
measured `ROCm0: Radeon 8060S Graphics`; its `llama-server` SHA-256 is
`b6bb4da687dbde86e243ba006cef05919b7b97255cd7e2371e1d451220aca139`.
A signed `gpu-diagnostic` profile measured zero failures:
| GPU metric | Transformers BF16 ROCm | llama.cpp Q4 ROCm | Q4 ratio |
|---|---:|---:|---:|
| Decode tok/s, c=1 | 81.12 | 251.25 | **3.10×** |
| Aggregate decode tok/s, c=4 | 91.24 | 511.33 | **5.60×** |
| TTFT p50, c=1 | 13.77 ms | 11.80 ms | **0.857×** |
The GPU report is signed under the distinct
`run_configured_gpu_diagnostic/v1` producer. The v1 evaluator rejects that
producer even when its signature is valid. llama-server process VRAM remains
unmeasured, so this diagnostic cannot replace or satisfy the immutable v1
contract. Its signed backend detail records the measured `ROCm0: Radeon 8060S
Graphics` device and `25/25` offloaded layers.
## Implementation
- `recipe_benchmark.py` provides the runtime-neutral measurement core, true
concurrency, continuous in-flight peak-memory sampling, percentile/throughput
aggregation, failures, and output drift.
- `recipe_drivers.py` provides opt-in Transformers and llama-server drivers,
mounted-drive confinement, exact artifact/runtime verification, equal
device/thread budgets, greedy-only validation, measured host provenance, a
CPU-only v1 guard until process VRAM can be measured honestly, and a distinct
signed GPU diagnostic profile that the v1 evaluator cannot accept.
- Peak RSS is runtime-scoped: Transformers reports growth above its pre-runtime
Python baseline, while llama.cpp reports its isolated server process tree.
Both are sampled continuously during in-flight requests.
- TTFT uses each runtime's prompt/first-token compute boundary; end-to-end HTTP,
scheduling, and queue overhead remains in latency and `queue_wait_ms`.
- The exact canonical plan SHA-256 locks prompts, model/revision, sampling,
output length, repeats, warmups, and concurrency. The evaluator also requires
equal prompt/decode token counts across recipes.
- llama.cpp's `predicted_n` includes the first token while `predicted_ms` begins
after it; the driver subtracts that token so decode throughput matches the
Transformers inter-token convention.
- `performance_contract.py` rejects wrong plans, unsigned or incorrectly signed
real evidence, wrong config/artifact/runtime/backend/host bindings, missing
recipes/concurrency, mixed model revisions, incomplete quality coverage, and
failed references.
- Every non-synthetic report is Ed25519-signed over the complete canonical JSON,
including raw outcomes and metrics. The contract pins the public key and exact
config SHA-256; the private key remains outside Git at mode `0600`.
- The signer fingerprint is independently anchored outside this evidence
directory in `../../trusted-evidence-signers.json` and checked by tests.
- Quantized drift remains advisory. Only the near-lossless lane can satisfy the
quality gate, and only performance-fit recipes can earn speed/fit benefits.
## Evidence files
- `performance-contract.json` — immutable v1 thresholds and stop condition
- `benchmark-config.json` — exact real-run plan, drivers, artifacts, and hashes
- `results.json` — raw machine-readable per-request and aggregate evidence
- `results.txt` — human-readable benchmark summary
- `baseline.json` — distilled measurements for later comparison
- `contract-evaluation.json` — fail-closed v1 verdict
- `quality-parity-diagnosis.json` / `.md` — run/device-scoped signed-evidence summary
- `summarize-quality-parity.py` — verifies both evidence chains and regenerates it
- `gpu-diagnostic-config.json` — exact ROCm diagnostic artifacts and runtimes
- `gpu-diagnostic-results.json` / `.txt` — signed GPU outcomes and summary
- `commands.txt` — reproducible conversion, benchmark, evaluation, and test commands
- `BLOCKED.md` — downstream stop-condition handoff
- `known-unrelated-failure.md` — clean-base reproduction of the tracker race
- `../../trusted-evidence-signers.json` — repository-reviewed signer fingerprint
## Verification
```text
Targeted: 28 passed (5/5 consecutive focused runs)
Latest full suite: 755 passed, 13 skipped
Earlier full suite: 751 passed, 13 skipped
Current cancellation retry matrix, DGR-001: 4/5 passed
Earlier cancellation retry matrix, clean d904c40: 4/5 passed
compileall: passed
git diff --check: passed
Evidence JSON parse/integrity checks: passed
```
The intermittent tracker cancellation race reproduced at the same rate on the
clean base and is retained in `known-unrelated-failure.md`; the final full suite
completed green. DGR-001 changes no tracker/proxy files.
The earlier Ralph claim that the full suite was blocked by Protobuf 6.33.6 was
invalid: it used Hermes Agent's internal venv. Verification above used the
project `.venv`, which has the DGR-002-compatible runtime. Real inference used
`.venv-rocm` Python 3.12.
## Limitations and dependent-story handoff
- The immutable contract result is a **0.5B CPU baseline**. The separate Radeon
diagnostic is real local GPU evidence, but neither result covers a large
model, distributed execution, network transport, or a native shard worker.
- A separate `GGML_HIP=ON` llama.cpp build exists and produced GPU timings, but
llama-server process VRAM is not measurable by the current driver; GPU
memory/fit claims therefore remain ineligible for v1.
- Absolute timings are developer-machine measurements; locked ratios and raw
artifacts are provided for reproducibility.
- DGR-014 may consume v1 only with the exact plan/evidence requirements enforced
by `performance_contract.py`.
- DGR-004 and later native-runtime work must not treat DGR-001 completion as a
promotion. V1 says `stop`; proceeding requires a human decision backed by a
separately versioned GPU/large-model contract or a diagnosed quality fix.

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{
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}

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{
"artifact_storage_root": "/run/media/popov/DATA/llm",
"evidence_class": "local-real",
"host": {
"benchmark_lane": "cpu-controlled-baseline",
"llama_cpp_commit": "e920c523e3b8a0163fe498af5bf90df35ff51d25",
"llama_cpp_version": "9991",
"llama_server_sha256": "fd8fe612970f23e447f2e717cfa51665be06b8d7315ba60556e010f6bca510dd",
"converter_sha256": "c819f18fb22927b49fabc3b35d1c9e21ee638b3817eccd1bd4efbcc7116eeb4d",
"quantizer_sha256": "bd0cc8c7be6d48aad4755b31062e0e59a887cbadd43dbb8771853d5858bb198f",
"transformers_version": "5.13.0"
},
"plan": {
"plan_id": "dgr-001-controlled-whole-model-baseline-v1",
"model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"prompts": [
{
"id": "short-fact",
"text": "The capital of France is",
"context_class": "short"
},
{
"id": "medium-code",
"text": "Complete this Python function without commentary:\n\ndef fibonacci(n):\n \"\"\"Return the nth Fibonacci number for n >= 0.\"\"\"\n",
"context_class": "medium"
},
{
"id": "long-summary",
"text": "A distributed inference service divides a transformer across consumer machines. The tracker owns admission, routing, cancellation, accounting, and telemetry, while workers own only model execution. Every request carries an immutable model identity and revision. Workers must reject incompatible protocol versions and resource demands before allocating large buffers. Activation tensors are chunked, checksummed, bounded by negotiated limits, and propagated with explicit flow-control credits. A caller may disconnect at any time, so cancellation must release queued work, in-flight transfers, and cache reservations without double billing. Retries can occur after network failures, requiring idempotent request identifiers and deterministic completion accounting. The system keeps the existing safetensors path as a correctness reference while a native GGUF path is measured. Benchmarks compare the same prompts, output lengths, sampling policy, device, and concurrency, and they separate near-lossless quality checks from quantized speed and fit claims. Summarize the design priorities in three concise bullet points.",
"context_class": "long"
}
],
"sampling": {
"temperature": 0.0,
"top_p": 1.0,
"top_k": 1,
"seed": 1234,
"max_output_tokens": 32
},
"concurrency_levels": [1, 4],
"repeats": 3,
"warmup_requests": 2
},
"recipes": [
{
"id": "transformers-safetensors-reference",
"runtime": "transformers-5.13.0",
"weight_format": "safetensors",
"weight_quantization": "bfloat16",
"lane": "quality",
"device": "cpu",
"artifact_path": "/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775",
"artifact_sha256": "e596e9d6205fdc9177569cccd7f8b471b058f66e3630c8e4326d5aad52bd18b6",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": true,
"notes": "artifact_sha256 is the deterministic digest of every snapshot path and file byte",
"driver": {
"type": "transformers",
"model_path": "/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775",
"device": "cpu",
"dtype": "bfloat16",
"threads": 16
}
},
{
"id": "llama-cpp-near-lossless-quality",
"runtime": "llama.cpp-9991-e920c523",
"weight_format": "gguf",
"weight_quantization": "bfloat16",
"lane": "quality",
"device": "cpu",
"artifact_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf",
"artifact_sha256": "e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": false,
"notes": "Converted directly from the exact mounted safetensors revision while preserving BF16 weights with pinned llama.cpp",
"driver": {
"type": "llama-cpp-server",
"binary": "/run/media/popov/d/DEV/llamacpp/llama.cpp/build/bin/llama-server",
"binary_sha256": "fd8fe612970f23e447f2e717cfa51665be06b8d7315ba60556e010f6bca510dd",
"gguf_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf",
"device": "cpu",
"threads": 16,
"n_parallel": 4,
"context_per_slot": 512,
"n_gpu_layers": 0
}
},
{
"id": "llama-cpp-quantized-performance-fit",
"runtime": "llama.cpp-9991-e920c523",
"weight_format": "gguf",
"weight_quantization": "Q4_K_M",
"lane": "performance-fit",
"device": "cpu",
"artifact_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf",
"artifact_sha256": "a88e3f570e2efeaf06b50df9859db2c70d8646aa3a2c94a14e14d5797a2921a5",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": false,
"notes": "Quantized from the exact-revision F16 GGUF with pinned llama-quantize",
"driver": {
"type": "llama-cpp-server",
"binary": "/run/media/popov/d/DEV/llamacpp/llama.cpp/build/bin/llama-server",
"binary_sha256": "fd8fe612970f23e447f2e717cfa51665be06b8d7315ba60556e010f6bca510dd",
"gguf_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf",
"device": "cpu",
"threads": 16,
"n_parallel": 4,
"context_per_slot": 512,
"n_gpu_layers": 0
}
}
]
}

87
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@@ -0,0 +1,87 @@
# Exact source snapshot (already present on mounted storage)
SOURCE=/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775
LLAMA=/run/media/popov/d/DEV/llamacpp/llama.cpp
ROCM_PY=/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv-rocm/bin/python
PROJECT_PY=/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python
OUT=/run/media/popov/DATA/llm/dgr-001
SIGNING_KEY=/home/popov/.config/neuron-tai/keys/dgr-001-evidence-ed25519.pem
# Private signing key is outside Git and must remain owner-only
stat -c '%a %n' "$SIGNING_KEY" # expected: 600
# Converter support check (no writes)
$ROCM_PY $LLAMA/convert_hf_to_gguf.py "$SOURCE" --outtype f16 --outfile "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-F16.gguf" --dry-run
# Exact-revision near-lossless and performance-fit artifacts
$ROCM_PY $LLAMA/convert_hf_to_gguf.py "$SOURCE" --outtype f16 --outfile "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-F16.gguf"
$LLAMA/build/bin/llama-quantize "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-F16.gguf" "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf" Q4_K_M
$ROCM_PY $LLAMA/convert_hf_to_gguf.py "$SOURCE" --outtype bf16 --outfile "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf"
# Runtime and artifact identity
git -C "$LLAMA" rev-parse HEAD
$LLAMA/build/bin/llama-server --version
sha256sum "$LLAMA/build/bin/llama-server" "$LLAMA/convert_hf_to_gguf.py" "$LLAMA/build/bin/llama-quantize"
sha256sum "$SOURCE/model.safetensors" "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf" "$OUT/Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf"
# Deterministic complete-snapshot digest used by benchmark-config.json
PYTHONPATH=packages/node $ROCM_PY - <<'PY'
from pathlib import Path
from meshnet_node.recipe_drivers import _artifact_sha256
print(_artifact_sha256(Path('/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775')))
PY
# Canonical opt-in local-real benchmark
MESHNET_ENABLE_REAL_INFERENCE_TESTS=1 MESHNET_EVIDENCE_SIGNING_KEY="$SIGNING_KEY" \
PYTHONPATH=packages/node $ROCM_PY -m meshnet_node.recipe_benchmark \
--config .scratch/distributed-gguf-runtime/evidence/DGR-001/benchmark-config.json \
--json-out .scratch/distributed-gguf-runtime/evidence/DGR-001/results.json \
--summary-out .scratch/distributed-gguf-runtime/evidence/DGR-001/results.txt
# Distil the baseline and evaluate immutable v1
PYTHONPATH=packages/node $PROJECT_PY - <<'PY'
from pathlib import Path
import json
from meshnet_node.performance_contract import baseline_from_report, evaluate_contract, load_contract
root = Path('.scratch/distributed-gguf-runtime/evidence/DGR-001')
report = json.loads((root / 'results.json').read_text())
contract = load_contract(root / 'performance-contract.json')
(root / 'baseline.json').write_text(json.dumps(baseline_from_report(report), indent=2, sort_keys=True) + '\n')
(root / 'contract-evaluation.json').write_text(json.dumps(evaluate_contract(contract, report).to_dict(), indent=2, sort_keys=True) + '\n')
PY
# Optional ROCm GPU diagnostic (not eligible for immutable v1)
# The version-matched rocm[devel] wheel expands beyond 20 GB; ensure sufficient
# space or relocate its packaged payload before installation.
uv pip install --python "$ROCM_PY" --prerelease=allow \
--index-url https://rocm.nightlies.amd.com/v2/gfx1151/ \
'rocm[devel]==7.13.0a20260513'
ROCM_VENV=/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv-rocm
ROCM_SDK="$ROCM_VENV/bin/rocm-sdk"
ROCM_ROOT="$($ROCM_SDK path --root)"
ROCM_BIN="$($ROCM_SDK path --bin)"
export PATH="$ROCM_VENV/bin:$ROCM_BIN:$PATH"
export ROCM_PATH="$ROCM_ROOT" HIP_PATH="$ROCM_ROOT"
export CMAKE_PREFIX_PATH="$($ROCM_SDK path --cmake):$ROCM_ROOT"
export LD_LIBRARY_PATH="$ROCM_ROOT/lib:$ROCM_ROOT/lib64:${LD_LIBRARY_PATH:-}"
$ROCM_VENV/bin/cmake -S /run/media/popov/d/DEV/llamacpp/llama.cpp \
-B /run/media/popov/d/DEV/llamacpp/llama.cpp/build-hip -G Ninja \
-DGGML_HIP=ON -DGPU_TARGETS=gfx1151 \
-DCMAKE_HIP_COMPILER="$ROCM_VENV/bin/amdclang++" \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
-DLLAMA_BUILD_EXAMPLES=ON -DLLAMA_BUILD_SERVER=ON
$ROCM_VENV/bin/cmake --build /run/media/popov/d/DEV/llamacpp/llama.cpp/build-hip \
--target llama-server llama-cli llama-bench -j 16
MESHNET_ENABLE_REAL_INFERENCE_TESTS=1 MESHNET_EVIDENCE_SIGNING_KEY="$SIGNING_KEY" \
PYTHONPATH=packages/node $ROCM_PY -m meshnet_node.recipe_benchmark \
--profile gpu-diagnostic \
--config .scratch/distributed-gguf-runtime/evidence/DGR-001/gpu-diagnostic-config.json \
--json-out .scratch/distributed-gguf-runtime/evidence/DGR-001/gpu-diagnostic-results.json \
--summary-out .scratch/distributed-gguf-runtime/evidence/DGR-001/gpu-diagnostic-results.txt
PYTHONPATH=packages/node $PROJECT_PY \
.scratch/distributed-gguf-runtime/evidence/DGR-001/summarize-quality-parity.py
# Deterministic verification
PYTHONPATH=packages/node $PROJECT_PY -m pytest -q tests/test_recipe_benchmark.py
PYTHONPATH=packages/node $PROJECT_PY -m pytest -q
PYTHONPATH=packages/node $PROJECT_PY -m compileall -q packages tests
git diff --check

71
.scratch/distributed-gguf-runtime/evidence/DGR-001/contract-evaluation.json Normal file
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{
"contract_version": 1,
"fit_benefit": true,
"plan_id": "dgr-001-controlled-whole-model-baseline-v1",
"quality_lane_pass": false,
"rationale": [
"the near-lossless quality lane failed: the GGUF runtime disagrees with the safetensors reference beyond what near-lossless weights can explain",
"a meaningful speed benefit was measured",
"a meaningful fit benefit was measured"
],
"recipes": [
{
"comparable": true,
"failures": 0,
"fit_benefit": false,
"incomparable_reason": "",
"lane": "quality",
"measurements": {
"aggregate_concurrency": 4,
"aggregate_throughput_speedup": 4.7873,
"artifact_size_ratio": 0.9946,
"artifact_size_win": false,
"compared_prompts": 3,
"decode_speedup": 2.4173,
"exact_match_rate": 0.3333,
"expected_prompts": 3,
"failure_rate": 0.0,
"mean_similarity": 0.9471,
"resident_memory_ratio": 0.5721,
"ttft_ratio": 0.3767
},
"quality_pass": false,
"reasons": [
"single-request decode 2.42x reference (>= 1.25x) at TTFT ratio 0.38",
"aggregate throughput at concurrency 4 is 4.79x reference (>= 1.25x)",
"peak resident memory is 0.57x reference (<= 0.75x)",
"quality lane exact-match 0.33 / similarity 0.947 versus the reference (fail)"
],
"recipe_id": "llama-cpp-near-lossless-quality",
"speed_benefit": false
},
{
"comparable": true,
"failures": 0,
"fit_benefit": true,
"incomparable_reason": "",
"lane": "performance-fit",
"measurements": {
"aggregate_concurrency": 4,
"aggregate_throughput_speedup": 4.2048,
"artifact_size_ratio": 0.398,
"artifact_size_win": true,
"decode_speedup": 5.0967,
"failure_rate": 0.0,
"resident_memory_ratio": 0.2792,
"ttft_ratio": 0.5395
},
"quality_pass": null,
"reasons": [
"single-request decode 5.10x reference (>= 1.25x) at TTFT ratio 0.54",
"aggregate throughput at concurrency 4 is 4.20x reference (>= 1.25x)",
"peak resident memory is 0.28x reference (<= 0.75x)"
],
"recipe_id": "llama-cpp-quantized-performance-fit",
"speed_benefit": true
}
],
"speed_benefit": true,
"stop_condition_met": true,
"verdict": "stop"
}

143
.scratch/distributed-gguf-runtime/evidence/DGR-001/gpu-diagnostic-config.json Normal file
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@@ -0,0 +1,143 @@
{
"artifact_storage_root": "/run/media/popov/DATA/llm",
"evidence_class": "local-real",
"host": {
"benchmark_lane": "rocm-gpu-diagnostic",
"llama_cpp_commit": "e920c523e3b8a0163fe498af5bf90df35ff51d25",
"llama_cpp_version": "9991",
"llama_server_sha256": "b6bb4da687dbde86e243ba006cef05919b7b97255cd7e2371e1d451220aca139",
"converter_sha256": "c819f18fb22927b49fabc3b35d1c9e21ee638b3817eccd1bd4efbcc7116eeb4d",
"quantizer_sha256": "bd0cc8c7be6d48aad4755b31062e0e59a887cbadd43dbb8771853d5858bb198f",
"transformers_version": "5.13.0",
"rocm_target": "gfx1151"
},
"plan": {
"plan_id": "dgr-001-rocm-gpu-diagnostic-v1",
"model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"prompts": [
{
"id": "short-fact",
"text": "The capital of France is",
"context_class": "short"
},
{
"id": "medium-code",
"text": "Complete this Python function without commentary:\n\ndef fibonacci(n):\n \"\"\"Return the nth Fibonacci number for n >= 0.\"\"\"\n",
"context_class": "medium"
},
{
"id": "long-summary",
"text": "A distributed inference service divides a transformer across consumer machines. The tracker owns admission, routing, cancellation, accounting, and telemetry, while workers own only model execution. Every request carries an immutable model identity and revision. Workers must reject incompatible protocol versions and resource demands before allocating large buffers. Activation tensors are chunked, checksummed, bounded by negotiated limits, and propagated with explicit flow-control credits. A caller may disconnect at any time, so cancellation must release queued work, in-flight transfers, and cache reservations without double billing. Retries can occur after network failures, requiring idempotent request identifiers and deterministic completion accounting. The system keeps the existing safetensors path as a correctness reference while a native GGUF path is measured. Benchmarks compare the same prompts, output lengths, sampling policy, device, and concurrency, and they separate near-lossless quality checks from quantized speed and fit claims. Summarize the design priorities in three concise bullet points.",
"context_class": "long"
}
],
"sampling": {
"temperature": 0.0,
"top_p": 1.0,
"top_k": 1,
"seed": 1234,
"max_output_tokens": 32
},
"concurrency_levels": [
1,
4
],
"repeats": 3,
"warmup_requests": 2
},
"recipes": [
{
"id": "transformers-fp32-rocm-quality-oracle",
"runtime": "transformers-5.13.0-rocm-float32",
"weight_format": "safetensors",
"weight_quantization": "bfloat16-weights-float32-accumulation",
"lane": "quality",
"device": "cuda",
"artifact_path": "/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775",
"artifact_sha256": "e596e9d6205fdc9177569cccd7f8b471b058f66e3630c8e4326d5aad52bd18b6",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": true,
"notes": "artifact_sha256 is the deterministic digest of every snapshot path and file byte",
"driver": {
"type": "transformers",
"model_path": "/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775",
"device": "cuda",
"dtype": "float32",
"threads": 16
}
},
{
"id": "llama-cpp-bf16-rocm-quality",
"runtime": "llama.cpp-9991-e920c523-rocm-gfx1151",
"weight_format": "gguf",
"weight_quantization": "bfloat16",
"lane": "quality",
"device": "cuda",
"artifact_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf",
"artifact_sha256": "e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": false,
"notes": "Converted directly from the exact mounted safetensors revision while preserving BF16 weights with pinned llama.cpp",
"driver": {
"type": "llama-cpp-server",
"binary": "/run/media/popov/d/DEV/llamacpp/llama.cpp/build-hip/bin/llama-server",
"binary_sha256": "b6bb4da687dbde86e243ba006cef05919b7b97255cd7e2371e1d451220aca139",
"gguf_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-BF16.gguf",
"device": "cuda",
"threads": 16,
"n_parallel": 4,
"context_per_slot": 512,
"n_gpu_layers": 99
}
},
{
"id": "transformers-bf16-rocm-throughput",
"runtime": "transformers-5.13.0-rocm-bfloat16",
"weight_format": "safetensors",
"weight_quantization": "bfloat16",
"lane": "performance-fit",
"device": "cuda",
"artifact_path": "/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775",
"artifact_sha256": "e596e9d6205fdc9177569cccd7f8b471b058f66e3630c8e4326d5aad52bd18b6",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": false,
"notes": "artifact_sha256 is the deterministic digest of every snapshot path and file byte",
"driver": {
"type": "transformers",
"model_path": "/run/media/popov/DATA/llm/safetensor/models/models--Qwen--Qwen2.5-0.5B-Instruct/snapshots/7ae557604adf67be50417f59c2c2f167def9a775",
"device": "cuda",
"dtype": "bfloat16",
"threads": 16
}
},
{
"id": "llama-cpp-q4-rocm-throughput",
"runtime": "llama.cpp-9991-e920c523-rocm-gfx1151",
"weight_format": "gguf",
"weight_quantization": "Q4_K_M",
"lane": "performance-fit",
"device": "cuda",
"artifact_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf",
"artifact_sha256": "a88e3f570e2efeaf06b50df9859db2c70d8646aa3a2c94a14e14d5797a2921a5",
"source_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"source_model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"is_reference": false,
"notes": "Quantized from the exact-revision F16 GGUF with pinned llama-quantize",
"driver": {
"type": "llama-cpp-server",
"binary": "/run/media/popov/d/DEV/llamacpp/llama.cpp/build-hip/bin/llama-server",
"binary_sha256": "b6bb4da687dbde86e243ba006cef05919b7b97255cd7e2371e1d451220aca139",
"gguf_path": "/run/media/popov/DATA/llm/dgr-001/Qwen2.5-0.5B-Instruct-7ae5576-Q4_K_M.gguf",
"device": "cuda",
"threads": 16,
"n_parallel": 4,
"context_per_slot": 512,
"n_gpu_layers": 99
}
}
]
}

3308
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.scratch/distributed-gguf-runtime/evidence/DGR-001/gpu-diagnostic-results.txt Normal file
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Recipe benchmark dgr-001-rocm-gpu-diagnostic-v1 (local-real)
model Qwen/Qwen2.5-0.5B-Instruct@7ae557604adf67be50417f59c2c2f167def9a775
transformers-fp32-rocm-quality-oracle [quality ] c= 1 ttft p50/p95 11.0/ 35.5 ms; prefill 5746.7 tok/s; decode 35.7 tok/s; aggregate 29.6 tok/s; rss 1.39 GB; vram 2.26 GB; artifact 1.00 GB; failures 0
transformers-fp32-rocm-quality-oracle [quality ] c= 4 ttft p50/p95 27.5/ 80.4 ms; prefill 1985.4 tok/s; decode 9.4 tok/s; aggregate 35.4 tok/s; rss 1.39 GB; vram 2.74 GB; artifact 1.00 GB; failures 0
llama-cpp-bf16-rocm-quality [quality ] c= 1 ttft p50/p95 13.2/ 83.4 ms; prefill 4154.4 tok/s; decode 148.0 tok/s; aggregate 127.4 tok/s; rss 0.84 GB; vram 0.00 GB; artifact 0.99 GB; failures 0
llama-cpp-bf16-rocm-quality [quality ] c= 4 ttft p50/p95 25.1/ 52.1 ms; prefill 2205.4 tok/s; decode 115.1 tok/s; aggregate 337.1 tok/s; rss 0.86 GB; vram 0.00 GB; artifact 0.99 GB; failures 0
transformers-bf16-rocm-throughput [performance-fit ] c= 1 ttft p50/p95 13.8/ 22.2 ms; prefill 4787.3 tok/s; decode 81.1 tok/s; aggregate 73.5 tok/s; rss 0.07 GB; vram 2.74 GB; artifact 1.00 GB; failures 0
transformers-bf16-rocm-throughput [performance-fit ] c= 4 ttft p50/p95 29.7/ 58.5 ms; prefill 2666.5 tok/s; decode 24.4 tok/s; aggregate 91.2 tok/s; rss 0.07 GB; vram 2.74 GB; artifact 1.00 GB; failures 0
llama-cpp-q4-rocm-throughput [performance-fit ] c= 1 ttft p50/p95 11.8/ 37.1 ms; prefill 4219.3 tok/s; decode 251.2 tok/s; aggregate 200.1 tok/s; rss 0.69 GB; vram 0.00 GB; artifact 0.40 GB; failures 0
llama-cpp-q4-rocm-throughput [performance-fit ] c= 4 ttft p50/p95 21.4/ 101.0 ms; prefill 2126.9 tok/s; decode 189.7 tok/s; aggregate 511.3 tok/s; rss 0.72 GB; vram 0.00 GB; artifact 0.40 GB; failures 0
drift llama-cpp-bf16-rocm-quality vs transformers-fp32-rocm-quality-oracle exact 1.00; similarity 1.000 (gated)
drift transformers-bf16-rocm-throughput vs transformers-fp32-rocm-quality-oracle exact 0.33; similarity 0.946 (advisory)
drift llama-cpp-q4-rocm-throughput vs transformers-fp32-rocm-quality-oracle exact 0.00; similarity 0.628 (advisory)

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.scratch/distributed-gguf-runtime/evidence/DGR-001/known-unrelated-failure.md Normal file
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# Observed pre-existing intermittent tracker race
This file records an unrelated timing observation and its repeated reproduction;
it is **not** a DGR-001 benchmark/contract failure.
Test:
```text
tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
```
One earlier full-suite run produced:
```text
1 failed, 745 passed, 13 skipped
```
A five-run isolated retry matrix reproduced the same rate repeatedly:
```text
current DGR-001 branch: 4/5 passed, 1/5 failed
clean d904c40: 4/5 passed, 1/5 failed
```
An earlier full-suite run on the signed-provenance DGR-001 state completed
green:
```text
751 passed, 13 skipped
```
Two full-suite runs after adding the isolated GPU diagnostic profile each hit
the same race and otherwise passed:
```text
1 failed, 750 passed, 13 skipped
```
The latest expanded hardening suite hit the same race and otherwise passed:
```text
1 failed, 754 passed, 13 skipped
```
The final hardened state subsequently completed a full green run:
```text
755 passed, 13 skipped
```
In each failure, the mock upstream's three-second release timeout completed the
stream before the cancel POST, so the request was already absent and the cancel
endpoint returned 404. No tracker/proxy file changed in DGR-001. The race is
therefore timing-sensitive, pre-existing, and unrelated to the benchmark,
provenance, or GPU-diagnostic code.

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{
"schema_version": 1,
"contract_version": 1,
"locked_at": "2026-07-13T00:00:00Z",
"locked_by": "DGR-001",
"plan_id": "dgr-001-controlled-whole-model-baseline-v1",
"thresholds": {
"min_decode_speedup": 1.25,
"max_ttft_ratio": 1.25,
"min_aggregate_throughput_speedup": 1.25,
"max_resident_memory_ratio": 0.75,
"max_artifact_size_ratio": 0.6,
"min_quality_exact_match_rate": 0.9,
"min_quality_mean_similarity": 0.97,
"max_failure_rate": 0.0
},
"baseline": {
"status": "pending-real-evidence",
"required_evidence_class": "local-real",
"required_recipes": [
"transformers-safetensors-reference",
"llama-cpp-near-lossless-quality",
"llama-cpp-quantized-performance-fit"
],
"required_concurrency_levels": [
1,
4
],
"required_controlled_variables": [
"model architecture",
"model revision",
"machine and device",
"formatted prompts and context lengths",
"output length and greedy sampling policy"
],
"required_plan_sha256": "efe24690a9a7164bac6ab3fd0a6b22f078fc08aaefcfb96210ddf154e6050570",
"minimum_prompt_count": 3,
"minimum_repeats": 3,
"minimum_output_tokens": 32,
"required_device": "cpu",
"required_config_sha256": "00b2cce3e2f281bdf92fc5304ba5cac915a178ffccd3b9a25995ce39c00b90d3",
"required_signer_public_key": "zQ/qRMwF/ydazzaxEI24Xvnrl5bZxzw16JYpP0bfRuI=",
"required_artifact_sha256": {
"transformers-safetensors-reference": "e596e9d6205fdc9177569cccd7f8b471b058f66e3630c8e4326d5aad52bd18b6",
"llama-cpp-near-lossless-quality": "e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862",
"llama-cpp-quantized-performance-fit": "a88e3f570e2efeaf06b50df9859db2c70d8646aa3a2c94a14e14d5797a2921a5"
},
"required_recipe_runtime": {
"transformers-safetensors-reference": {
"runtime": "transformers-5.13.0",
"weight_format": "safetensors",
"weight_quantization": "bfloat16",
"device": "cpu"
},
"llama-cpp-near-lossless-quality": {
"runtime": "llama.cpp-9991-e920c523",
"weight_format": "gguf",
"weight_quantization": "bfloat16",
"device": "cpu"
},
"llama-cpp-quantized-performance-fit": {
"runtime": "llama.cpp-9991-e920c523",
"weight_format": "gguf",
"weight_quantization": "Q4_K_M",
"device": "cpu"
}
},
"required_backend_detail": {
"transformers-safetensors-reference": "torch 2.10.0+rocm7.13.0a20260513; dtype bfloat16; device cpu; intra-op threads 16",
"llama-cpp-near-lossless-quality": "version: 9991 (e920c523) | built with GNU 15.2.1 for Linux x86_64; binary sha256 fd8fe612970f23e447f2e717cfa51665be06b8d7315ba60556e010f6bca510dd; threads 16; parallel slots 4; ctx/slot 512; gpu layers 0",
"llama-cpp-quantized-performance-fit": "version: 9991 (e920c523) | built with GNU 15.2.1 for Linux x86_64; binary sha256 fd8fe612970f23e447f2e717cfa51665be06b8d7315ba60556e010f6bca510dd; threads 16; parallel slots 4; ctx/slot 512; gpu layers 0"
},
"required_host_identity": {
"python": "3.12.13",
"torch_version": "2.10.0+rocm7.13.0a20260513",
"transformers_version": "5.13.0",
"llama_server_identities": {
"/run/media/popov/d/DEV/llamacpp/llama.cpp/build/bin/llama-server": {
"sha256": "fd8fe612970f23e447f2e717cfa51665be06b8d7315ba60556e010f6bca510dd",
"version": "version: 9991 (e920c523) | built with GNU 15.2.1 for Linux x86_64"
}
}
}
},
"stop_condition": "Stop the native llama.cpp/GGUF track when, on the same machine and device as the Transformers/safetensors reference and under this plan, no performance-fit GGUF recipe delivers either a meaningful speed benefit (>=25% higher single-request decode tokens/sec without a >25% worse TTFT, or >=25% higher aggregate throughput under concurrency) or a meaningful fit benefit (>=25% lower peak resident memory), or when the near-lossless quality lane fails, which indicates a broken runtime rather than a quantization trade-off.",
"notes": "Quantized performance-fit output drift is reported as advisory only. It is not numerical-equivalence evidence. DGR-014 consumes this immutable v1 contract. Non-synthetic evidence must be Ed25519-signed by the pinned key and match the exact locked config, artifacts, runtimes, backends, and host runtime identity."
}

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{
"conclusion": {
"conversion_corruption_observed_in_rocm_sample": false,
"cpu_bf16_divergence_explained": false,
"recommended_v2_design": "Predeclare a float32 quality oracle separately from the BF16 performance reference, with a larger prompt corpus and immutable thresholds.",
"scope": "The ROCm diagnostic establishes only that the same BF16 GGUF artifact matched the float32 oracle for three GPU sequences; it does not explain the CPU BF16 divergence or prove global conversion correctness.",
"v1_verdict_changed": false
},
"cpu_v1": {
"candidate": "llama.cpp BF16 GGUF",
"candidate_artifact_sha256": "e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862",
"config_sha256": "00b2cce3e2f281bdf92fc5304ba5cac915a178ffccd3b9a25995ce39c00b90d3",
"contract_verdict": "stop",
"device": "cpu",
"exact_match_rate": 0.3333,
"mean_similarity": 0.9471,
"plan_id": "dgr-001-controlled-whole-model-baseline-v1",
"plan_sha256": "efe24690a9a7164bac6ab3fd0a6b22f078fc08aaefcfb96210ddf154e6050570",
"quality_oracle": "Transformers BF16 safetensors",
"report": "results.json",
"report_sha256": "5d99a58806f39821c9206728047b8c5d605027d8a41b88639089b2418da890b5",
"root_cause": "undetermined; no logit-tie claim is acceptance evidence",
"run_id": "e4eedadf-22f6-4907-8990-985456961099"
},
"model_id": "Qwen/Qwen2.5-0.5B-Instruct",
"model_revision": "7ae557604adf67be50417f59c2c2f167def9a775",
"rocm_diagnostic": {
"candidate": "llama.cpp BF16 GGUF",
"candidate_artifact_sha256": "e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862",
"config_sha256": "b0f0c846c818f1307d034cee1f81daa311efc20985c32a4cdbbbd8ffe4153892",
"device": "cuda (ROCm)",
"exact_match_rate": 1.0,
"failures": 0,
"mean_similarity": 1.0,
"measured_backend_detail": "version: 9991 (e920c523) | built with GNU 15.2.1 for Linux x86_64; binary sha256 b6bb4da687dbde86e243ba006cef05919b7b97255cd7e2371e1d451220aca139; threads 16; parallel slots 4; ctx/slot 512; requested gpu layers 99; measured accelerator ROCm0: Radeon 8060S Graphics; measured offload 25/25 layers",
"plan_id": "dgr-001-rocm-gpu-diagnostic-v1",
"plan_sha256": "dae8e40963588f71f5d201fd163d39bd762e392544b5603d483e90d21abee2e8",
"producer": "meshnet_node.recipe_drivers.run_configured_gpu_diagnostic/v1",
"quality_oracle": "Transformers float32 safetensors",
"report": "gpu-diagnostic-results.json",
"report_sha256": "527b33d03627d57d60b30331e6b9119f579a828d6f6acb5c74ca25bab0af5f3d",
"run_id": "31bf44e7-ccd4-4277-84ac-c775dee65411",
"signer_fingerprint": "8baca8742d9b3ed0c3fc54929c23f75ec8c1c739900aaf5334780d598ffa84de",
"v1_eligible": false
},
"schema_version": 2
}

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# DGR-001 quality-parity evidence summary
This summary is generated by `summarize-quality-parity.py` from signed reports.
It contains no independent logit measurements or self-asserted verification flag.
| Source | Device | Quality oracle | BF16 GGUF candidate | Exact | Similarity | Status |
|---|---|---|---|---:|---:|---|
| CPU v1 (`e4eedadf-22f6-4907-8990-985456961099`) | CPU | Transformers BF16 | llama.cpp BF16 | 0.3333 | 0.9471 | immutable `stop` |
| ROCm diagnostic (`31bf44e7-ccd4-4277-84ac-c775dee65411`) | ROCm0 / Radeon 8060S | Transformers float32 | llama.cpp BF16 | 1.0000 | 1.0000 | diagnostic only |
## Interpretation
The CPU and ROCm rows use different plans, devices, kernels, and quality oracles.
The CPU BF16 divergence remains unexplained and v1 remains `stop`. The signed
ROCm report establishes the narrower fact that the same BF16 GGUF artifact
matched the float32 oracle for all three GPU sequences with zero failures.
Its signed backend detail records `ROCm0: Radeon 8060S Graphics` and measured
`25/25` layer offload.
No conversion corruption was observed in that three-sequence ROCm sample. This
does not prove global conversion correctness and does not retroactively change
or explain the CPU result. A future v2 should predeclare a float32 quality oracle
separately from its BF16 performance reference and use a larger corpus.
## Reproduction and bindings
- CPU report SHA-256: `5d99a58806f39821c9206728047b8c5d605027d8a41b88639089b2418da890b5`
- GPU report SHA-256: `527b33d03627d57d60b30331e6b9119f579a828d6f6acb5c74ca25bab0af5f3d`
- BF16 GGUF SHA-256: `e842fdc35d7f00fda95a54e1b51731ba1d196aea45065cc9f46925fdc1d6f862`
- Signer fingerprint: `8baca8742d9b3ed0c3fc54929c23f75ec8c1c739900aaf5334780d598ffa84de`
- Exact verification command: see `commands.txt`.

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Recipe benchmark dgr-001-controlled-whole-model-baseline-v1 (local-real)
model Qwen/Qwen2.5-0.5B-Instruct@7ae557604adf67be50417f59c2c2f167def9a775
transformers-safetensors-reference [quality ] c= 1 ttft p50/p95 40.0/ 195.3 ms; prefill 625.6 tok/s; decode 40.8 tok/s; aggregate 35.5 tok/s; rss 1.94 GB; vram 0.00 GB; artifact 1.00 GB; failures 0
transformers-safetensors-reference [quality ] c= 4 ttft p50/p95 97.0/ 429.1 ms; prefill 264.0 tok/s; decode 13.0 tok/s; aggregate 46.5 tok/s; rss 2.10 GB; vram 0.00 GB; artifact 1.00 GB; failures 0
llama-cpp-near-lossless-quality [quality ] c= 1 ttft p50/p95 15.1/ 63.8 ms; prefill 1717.9 tok/s; decode 98.5 tok/s; aggregate 86.7 tok/s; rss 1.11 GB; vram 0.00 GB; artifact 0.99 GB; failures 0
llama-cpp-near-lossless-quality [quality ] c= 4 ttft p50/p95 32.4/ 218.4 ms; prefill 859.9 tok/s; decode 76.6 tok/s; aggregate 222.8 tok/s; rss 1.14 GB; vram 0.00 GB; artifact 0.99 GB; failures 0
llama-cpp-quantized-performance-fit [performance-fit ] c= 1 ttft p50/p95 21.6/ 147.9 ms; prefill 967.0 tok/s; decode 207.7 tok/s; aggregate 139.3 tok/s; rss 0.54 GB; vram 0.00 GB; artifact 0.40 GB; failures 0
llama-cpp-quantized-performance-fit [performance-fit ] c= 4 ttft p50/p95 48.1/ 416.5 ms; prefill 572.4 tok/s; decode 76.9 tok/s; aggregate 195.7 tok/s; rss 0.57 GB; vram 0.00 GB; artifact 0.40 GB; failures 0
drift llama-cpp-near-lossless-quality vs transformers-safetensors-reference exact 0.33; similarity 0.947 (gated)
drift llama-cpp-quantized-performance-fit vs transformers-safetensors-reference exact 0.00; similarity 0.456 (advisory)

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#!/usr/bin/env python3
"""Build the DGR-001 parity summary from cryptographically verified reports."""
from __future__ import annotations
import base64
import hashlib
import json
from pathlib import Path
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PublicKey
from meshnet_node.performance_contract import (
_canonical_sha256,
evaluate_contract,
load_contract,
report_signing_payload,
)
ROOT = Path(__file__).resolve().parent
def _read(name: str) -> dict:
return json.loads((ROOT / name).read_text(encoding="utf-8"))
def _file_sha256(name: str) -> str:
return hashlib.sha256((ROOT / name).read_bytes()).hexdigest()
def _drift(report: dict, recipe_id: str) -> dict:
return next(item for item in report["drift"] if item["recipe_id"] == recipe_id)
def _recipe(report: dict, recipe_id: str) -> dict:
return next(item for item in report["recipes"] if item["recipe"]["id"] == recipe_id)
def main() -> None:
contract = load_contract(ROOT / "performance-contract.json")
cpu_report = _read("results.json")
gpu_config = _read("gpu-diagnostic-config.json")
gpu_report = _read("gpu-diagnostic-results.json")
cpu_evaluation = evaluate_contract(contract, cpu_report)
if cpu_evaluation.verdict != "stop":
raise RuntimeError("immutable CPU v1 evidence no longer evaluates to stop")
public_key_bytes = base64.b64decode(contract.baseline["required_signer_public_key"])
public_key = Ed25519PublicKey.from_public_bytes(public_key_bytes)
public_key.verify(
base64.b64decode(gpu_report["provenance"]["signature"]),
report_signing_payload(gpu_report),
)
signer_fingerprint = hashlib.sha256(public_key_bytes).hexdigest()
if gpu_report["provenance"]["signer_public_key_sha256"] != signer_fingerprint:
raise RuntimeError("GPU report signer fingerprint does not match the contract trust key")
if gpu_report["provenance"]["config_sha256"] != _canonical_sha256(gpu_config):
raise RuntimeError("GPU report is not bound to gpu-diagnostic-config.json")
if gpu_report.get("schema_version") != 1 or gpu_report.get("evidence_class") != "local-real":
raise RuntimeError("GPU report must be schema-v1 local-real evidence")
expected_producer = "meshnet_node.recipe_drivers.run_configured_gpu_diagnostic/v1"
if gpu_report["provenance"].get("producer") != expected_producer:
raise RuntimeError("GPU report was not emitted by the canonical diagnostic producer")
if gpu_report.get("reference_recipe_id") != "transformers-fp32-rocm-quality-oracle":
raise RuntimeError("GPU report uses the wrong quality reference")
if gpu_report.get("host", {}).get("benchmark_lane") != "rocm-gpu-diagnostic":
raise RuntimeError("GPU report lacks the diagnostic host marker")
trusted = json.loads(
(ROOT.parents[1] / "trusted-evidence-signers.json").read_text(encoding="utf-8")
)
if not any(
signer.get("algorithm") == "ed25519"
and signer.get("fingerprint_sha256") == signer_fingerprint
and signer.get("status") == "active"
for signer in trusted.get("signers", ())
):
raise RuntimeError("GPU signer is not active in the trusted-signers registry")
for field in ("model_id", "model_revision"):
if gpu_report["plan"].get(field) != cpu_report["plan"].get(field):
raise RuntimeError(f"CPU and GPU reports do not share {field}")
if gpu_config["plan"].get(field) != gpu_report["plan"].get(field):
raise RuntimeError(f"GPU config and report do not share {field}")
expected_recipes = {
"transformers-fp32-rocm-quality-oracle": ("quality", "cuda"),
"llama-cpp-bf16-rocm-quality": ("quality", "cuda"),
"transformers-bf16-rocm-throughput": ("performance-fit", "cuda"),
"llama-cpp-q4-rocm-throughput": ("performance-fit", "cuda"),
}
actual_recipes = {
entry["recipe"]["id"]: (entry["recipe"]["lane"], entry["recipe"]["device"])
for entry in gpu_report["recipes"]
}
if actual_recipes != expected_recipes:
raise RuntimeError("GPU report recipe identities, lanes, or devices changed")
gpu_prompt_ids = {prompt["id"] for prompt in gpu_report["plan"]["prompts"]}
levels = {int(level) for level in gpu_report["plan"]["concurrency_levels"]}
repeats = int(gpu_report["plan"]["repeats"])
expected_outcomes = len(gpu_prompt_ids) * repeats * sum(levels)
for entry in gpu_report["recipes"]:
recipe_id = entry["recipe"]["id"]
if not entry.get("available") or len(entry.get("outcomes", ())) != expected_outcomes:
raise RuntimeError(f"GPU recipe {recipe_id!r} lacks complete outcomes")
if any(
not outcome.get("ok")
or outcome.get("recipe_id") != recipe_id
or outcome.get("prompt_id") not in gpu_prompt_ids
or int(outcome.get("concurrency", 0)) not in levels
or not 0 <= int(outcome.get("repeat", -1)) < repeats
for outcome in entry["outcomes"]
):
raise RuntimeError(f"GPU recipe {recipe_id!r} contains failed or invalid outcomes")
if {int(level) for level in entry["concurrency"]} != levels:
raise RuntimeError(f"GPU recipe {recipe_id!r} has wrong concurrency cells")
for prompt_id in gpu_prompt_ids:
for level in levels:
for repeat in range(repeats):
count = sum(
outcome["prompt_id"] == prompt_id
and int(outcome["concurrency"]) == level
and int(outcome["repeat"]) == repeat
for outcome in entry["outcomes"]
)
if count != level:
raise RuntimeError(
f"GPU recipe {recipe_id!r} lacks complete request coverage"
)
if any(
int(cell.get("failures", -1)) != 0
or int(cell.get("requests", -1))
!= len(
[
outcome
for outcome in entry["outcomes"]
if int(outcome["concurrency"]) == int(level)
]
)
for level, cell in entry["concurrency"].items()
):
raise RuntimeError(f"GPU recipe {recipe_id!r} aggregates do not match outcomes")
cpu_quality = _drift(cpu_report, "llama-cpp-near-lossless-quality")
gpu_quality = _drift(gpu_report, "llama-cpp-bf16-rocm-quality")
cpu_recipe = _recipe(cpu_report, "llama-cpp-near-lossless-quality")
gpu_recipe = _recipe(gpu_report, "llama-cpp-bf16-rocm-quality")
gpu_backend = gpu_recipe["load"]["backend_detail"]
if "measured accelerator ROCm0: Radeon 8060S Graphics" not in gpu_backend:
raise RuntimeError("GPU report lacks measured ROCm device evidence")
if "measured offload 25/25 layers" not in gpu_backend:
raise RuntimeError("GPU report lacks measured layer-offload evidence")
if cpu_recipe["recipe"]["artifact_sha256"] != gpu_recipe["recipe"]["artifact_sha256"]:
raise RuntimeError("CPU and GPU diagnostics use different BF16 GGUF artifacts")
if gpu_quality.get("compared_prompts") != len(gpu_prompt_ids):
raise RuntimeError("GPU quality drift lacks complete prompt coverage")
if {item["prompt_id"] for item in gpu_quality.get("per_prompt", ())} != gpu_prompt_ids:
raise RuntimeError("GPU quality drift prompt identities do not match the plan")
summary = {
"schema_version": 2,
"model_id": cpu_report["plan"]["model_id"],
"model_revision": cpu_report["plan"]["model_revision"],
"cpu_v1": {
"report": "results.json",
"report_sha256": _file_sha256("results.json"),
"run_id": cpu_report["provenance"]["run_id"],
"plan_id": cpu_report["plan"]["plan_id"],
"plan_sha256": _canonical_sha256(cpu_report["plan"]),
"config_sha256": cpu_report["provenance"]["config_sha256"],
"device": "cpu",
"quality_oracle": "Transformers BF16 safetensors",
"candidate": "llama.cpp BF16 GGUF",
"candidate_artifact_sha256": cpu_recipe["recipe"]["artifact_sha256"],
"exact_match_rate": cpu_quality["exact_match_rate"],
"mean_similarity": cpu_quality["mean_similarity"],
"contract_verdict": cpu_evaluation.verdict,
"root_cause": "undetermined; no logit-tie claim is acceptance evidence",
},
"rocm_diagnostic": {
"report": "gpu-diagnostic-results.json",
"report_sha256": _file_sha256("gpu-diagnostic-results.json"),
"run_id": gpu_report["provenance"]["run_id"],
"producer": gpu_report["provenance"]["producer"],
"signer_fingerprint": signer_fingerprint,
"plan_id": gpu_report["plan"]["plan_id"],
"plan_sha256": _canonical_sha256(gpu_report["plan"]),
"config_sha256": gpu_report["provenance"]["config_sha256"],
"device": "cuda (ROCm)",
"quality_oracle": "Transformers float32 safetensors",
"candidate": "llama.cpp BF16 GGUF",
"candidate_artifact_sha256": gpu_recipe["recipe"]["artifact_sha256"],
"measured_backend_detail": gpu_backend,
"exact_match_rate": gpu_quality["exact_match_rate"],
"mean_similarity": gpu_quality["mean_similarity"],
"failures": sum(
metrics["failures"]
for entry in gpu_report["recipes"]
for metrics in entry["concurrency"].values()
),
"v1_eligible": False,
},
"conclusion": {
"v1_verdict_changed": False,
"cpu_bf16_divergence_explained": False,
"conversion_corruption_observed_in_rocm_sample": False,
"scope": (
"The ROCm diagnostic establishes only that the same BF16 GGUF artifact "
"matched the float32 oracle for three GPU sequences; it does not explain "
"the CPU BF16 divergence or prove global conversion correctness."
),
"recommended_v2_design": (
"Predeclare a float32 quality oracle separately from the BF16 performance "
"reference, with a larger prompt corpus and immutable thresholds."
),
},
}
(ROOT / "quality-parity-diagnosis.json").write_text(
json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
md = f"""# DGR-001 quality-parity evidence summary
This summary is generated by `summarize-quality-parity.py` from signed reports.
It contains no independent logit measurements or self-asserted verification flag.
| Source | Device | Quality oracle | BF16 GGUF candidate | Exact | Similarity | Status |
|---|---|---|---|---:|---:|---|
| CPU v1 (`{summary['cpu_v1']['run_id']}`) | CPU | Transformers BF16 | llama.cpp BF16 | {summary['cpu_v1']['exact_match_rate']:.4f} | {summary['cpu_v1']['mean_similarity']:.4f} | immutable `stop` |
| ROCm diagnostic (`{summary['rocm_diagnostic']['run_id']}`) | ROCm0 / Radeon 8060S | Transformers float32 | llama.cpp BF16 | {summary['rocm_diagnostic']['exact_match_rate']:.4f} | {summary['rocm_diagnostic']['mean_similarity']:.4f} | diagnostic only |
## Interpretation
The CPU and ROCm rows use different plans, devices, kernels, and quality oracles.
The CPU BF16 divergence remains unexplained and v1 remains `stop`. The signed
ROCm report establishes the narrower fact that the same BF16 GGUF artifact
matched the float32 oracle for all three GPU sequences with zero failures.
Its signed backend detail records `ROCm0: Radeon 8060S Graphics` and measured
`25/25` layer offload.
No conversion corruption was observed in that three-sequence ROCm sample. This
does not prove global conversion correctness and does not retroactively change
or explain the CPU result. A future v2 should predeclare a float32 quality oracle
separately from its BF16 performance reference and use a larger corpus.
## Reproduction and bindings
- CPU report SHA-256: `{summary['cpu_v1']['report_sha256']}`
- GPU report SHA-256: `{summary['rocm_diagnostic']['report_sha256']}`
- BF16 GGUF SHA-256: `{summary['rocm_diagnostic']['candidate_artifact_sha256']}`
- Signer fingerprint: `{signer_fingerprint}`
- Exact verification command: see `commands.txt`.
"""
(ROOT / "quality-parity-diagnosis.md").write_text(md, encoding="utf-8")
if __name__ == "__main__":
main()

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# DGR-002 — Adopt the versioned gRPC Shard protocol
Status: **done**. Every acceptance criterion is met with real command output.
Evidence class: **synthetic/unit** — this story defines a schema and proves both
languages agree on it. No model, GPU, network peer or benchmark is involved, and
none is claimed.
## 1. Summary
`packages/node/native/proto/shard_runtime.proto` is now the semantic contract for
the native Shard data plane: Protocol Buffers over gRPC/HTTP2 (ADR-0020). Python
and C++ both generate from it, and a shared committed conformance vector proves
they encode it identically — byte for byte.
Design decisions worth carrying forward:
- **Everything gRPC gives you is *also* in the schema.** Deadline, cancellation,
identity and flow control are carried as fields, not left to HTTP/2 metadata,
because the existing relay carries these frames as **opaque binary**. A relayed
frame has no HTTP/2 context to inherit a deadline or a channel identity from.
If it is not in the schema, it does not survive the relay.
- **Cancellation is both in-band and out-of-band.** `CancelSignal` rides the
stream; `Cancel` is also a unary RPC. A cancel that can only travel down a
stream that flow control has wedged is not a cancel.
- **Checksums cover the uncompressed payload.** Compression is a per-hop
transport decision (reusing the existing `activation_compression` policies), so
a checksum over the compressed frame would be invalidated by a hop that merely
chose differently.
- **Application-level flow-control credits, not just HTTP/2 windows.** HTTP/2
bounds *bytes in flight*; it does not bound how much *work* a worker has queued,
and a relayed frame gets no window at all. Credits bound queue occupancy and KV
pressure, and negotiation takes the strictest bound of either peer so a sender
cannot talk a worker into unbounded queues.
## 2. Files changed
New:
| Path | What |
|---|---|
| `packages/node/native/proto/shard_runtime.proto` | The schema (sha256 `9e211660…`, see `protocol.json`) |
| `packages/node/native/CMakeLists.txt` | C++ generation + build wiring + ctest |
| `packages/node/native/tests/test_shard_protocol_conformance.cpp` | C++ conformance test |
| `packages/node/native/testdata/*.binpb` | Committed cross-language vectors |
| `packages/node/native/README.md` | How to regenerate and build |
| `packages/node/meshnet_node/native_protocol/__init__.py` | Public Python surface |
| `packages/node/meshnet_node/native_protocol/codec.py` | Bundle encode/decode, fragmentation, CRC32C, chunking, FC negotiation |
| `packages/node/meshnet_node/native_protocol/conformance.py` | Canonical vectors shared by both languages |
| `packages/node/meshnet_node/native_protocol/generated/` | Generated Python stubs (committed) |
| `scripts/generate_native_protocol.py` | Python generation, with `--check` |
| `scripts/generate_protocol_goldens.py` | Vector generation, with `--check` |
| `scripts/bootstrap_native_toolchain.sh` | Builds protobuf C++ from source |
| `tests/test_native_shard_protocol.py` | 45 Python tests |
Modified:
- `packages/node/pyproject.toml` — added runtime floors `grpcio>=1.82.1` and
`protobuf>=7.35.0`, matching the committed generated-code requirements; new
`proto` extra pinning `grpcio-tools==1.82.1`.
- `packages/node/meshnet_node/activation_compression.py` — optional bounded zstd
output for untrusted protocol frames; existing callers remain compatible.
- `packages/node/meshnet_node/native_protocol/__init__.py` — exports negotiated
bound constants and whole-session-message validation.
The canonical PRD marks only DGR-002 passed. `git status` before this story was clean.
## 3. Commands and real results
See `commands.txt` for the exact ordered list. Results:
```
python scripts/generate_native_protocol.py --check -> generated stubs are up to date
python scripts/generate_protocol_goldens.py --check -> conformance vectors are up to date
cmake -S packages/node/native -B build/native -DCMAKE_PREFIX_PATH=/tmp/pbsrc/install
-- gRPC C++ not found: building message types only (sufficient for the conformance test)
cmake --build build/native -j -> Built target shard_protocol_conformance
ctest --test-dir build/native --output-on-failure -> 1/1 Test #1: shard_protocol_conformance ... Passed
100% tests passed out of 1
cmp build/native/cpp_roundtrip.binpb \
packages/node/native/testdata/session_request_golden.binpb -> identical (exit 0)
pytest -q tests/test_native_shard_protocol.py -> 45 passed
pytest -q tests/test_native_shard_protocol.py \
tests/test_activation_compression.py -> 51 passed
pytest -q (final full suite) -> 728 passed, 12 skipped
pytest -q tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
(after an earlier flaky full-suite failure) -> 1 passed, 1 passed, 1 passed
clean minimum-runtime import + codec smoke test -> passed
grpcio==1.82.1, protobuf==7.35.0
compileall -q packages tests -> OK (exit 0)
git diff --check -> clean (exit 0)
```
The C++ lane was rebuilt from scratch by Ralph (`rm -rf build/native`) using only
the documented commands, and reproduced the same result. During controller
review the user explicitly chose not to repeat the destructive build-directory
cleanup, so the independent controller relied on the recorded CMake/CTest run
while reproducing every Python/generation/full-suite gate.
### Controller review corrections
Independent controller review found and fixed two classes of issue before
integration:
1. Generated stubs required gRPC 1.82.1 and Protobuf 7.35.0, while the initial
package metadata allowed much older runtimes that could fail at import time.
2. Flow-control bounds were described but not enforced by the reference decoder.
Tensor declarations, shape rank/dimensions, fragment/tensor counts, fragments,
wire bodies, whole bundles, complete session messages (including envelope
overhead), and zstd window/output expansion are now fail-closed against the
negotiated/default bounds. Unspecified bundle versions, compression and
checksums are rejected rather than interpreted as valid data.
3. Negotiated initial credits could exceed `max_inflight_chunks`; credits are now
capped by the settled in-flight limit.
Controller results: protocol tests `45 passed`; protocol plus shared compression
tests `51 passed`; final full suite `728 passed, 12 skipped`. A clean environment
at the declared minimum gRPC/Protobuf runtime versions imported both generated
stub modules and round-tripped the codec. Generation checks, `compileall`, static
secret scan, and `git diff --check` all passed.
### Full-suite note — a pre-existing flaky test
`tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy`
is **flaky on a clean tree, independent of this story**. Reproduction, run
*before any DGR-002 file existed* (working tree clean, `git status` empty):
```
pytest -q -> 1 failed, 682 passed, 12 skipped
FAILED tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
# same test, three consecutive isolated runs on the same clean tree:
pytest -q tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
-> 1 passed in 1.76s
-> 1 failed in 4.39s
-> 1 passed in 1.10s
```
It is a timing race in proxy cancellation (a 3-second in-flight generation raced
against the cancel assertion), not a deterministic failure, and it touches no code
this story changes. One controller full-suite run reported exactly that one failure
(`1 failed, 719 passed, 12 skipped`); three immediate isolated retries all passed
in 1.11 seconds, and the final exact-code full suite was green (`728 passed,
12 skipped`). It is flagged for whoever owns the tracker cancel path and is **not**
fixed here, since silently touching another story's code is out of scope.
## 4. Acceptance criteria
| Criterion | Where it is proven |
|---|---|
| Schema for capability, health, session stream, release, cancellation | `shard_runtime.proto` `service ShardRuntime`; `test_service_exposes_capability_health_session_release_and_cancel` |
| One long-lived bidi stream per Activation Seam, with deadlines, cancellation, flow control, structured errors | `rpc Session (stream) returns (stream)`; `test_session_is_one_long_lived_bidirectional_stream`; `Envelope.deadline_unix_nanos`, `CancelSignal` + unary `Cancel`, `FlowControl`, `ShardError` |
| Bounded chunking for prefill; small decode fast path | `ChunkInfo` + `plan_prefill_chunks` (128-token bound, ADR-0008); `DecodeStep`; `test_prefill_is_split_into_bounded_token_aligned_chunks`, `test_decode_fast_path_is_much_smaller_than_a_full_envelope_chunk` |
| Envelope carries schema version, work id, session id, epoch, fingerprint, range/effective start, phase, position, idempotency step, cache expectation, compression, checksum | `Envelope` + `NamedTensor`; `test_envelope_carries_every_field_the_protocol_promises` asserts against the **descriptor**, so deleting a field from the `.proto` fails the test |
| Versioned named-tensor bundle: name, shape, dtype, byte order, fragments | `TensorBundle`/`NamedTensor`/`TensorFragment`; `test_named_tensor_bundle_is_versioned_and_fully_described`, `test_bundle_round_trips_multiple_named_tensors` |
| Round-trip + compatibility tests in Python and C++ | 45 Python tests; C++ `ctest` 1/1; cross-language byte equality |
| Targeted pytest passes | 45 passed |
| `compileall packages tests` | exit 0 |
| `git diff --check` | exit 0 |
| Default tests deterministic, download-free, credit-free, GPU-free | Pure in-memory protobuf; no model, no network, no GPU |
| Full deterministic pytest passes, or pre-existing failure recorded | Final exact-code run: 728 passed, 12 skipped; earlier sole flaky failure documented with clean-tree reproduction and 3/3 passing retries |
## 5. How the cross-language claim is actually earned
Two codecs that each round-trip their own output prove only that each is
self-consistent. Instead:
1. Python builds the canonical `SessionRequest` and commits its bytes.
2. The C++ test parses **those** bytes, asserts every field, recomputes the CRC32C
**from the polynomial in independent C++ code**, reassembles the multi-fragment
tensor, and re-serializes to `cpp_roundtrip.binpb`.
3. `test_cpp_and_python_agree_byte_for_byte` asserts that file equals the golden.
Compatibility is tested in both languages: an unknown field from a newer peer
survives a parse/serialize hop (a Shard forwards activations — silently stripping
fields would corrupt a route it is merely a waypoint on), and a sparse message
from an older peer parses to proto3 defaults.
## 6. Limitations and deferred work
- **gRPC C++ was not built or linked.** The C++ lane verifies the *schema* (message
types), not a running gRPC C++ server, because this machine has no gRPC C++ stack
and building it is a large dependency the conformance test does not need.
`CMakeLists.txt` already generates and exports `shard_runtime_grpc` when
`find_package(gRPC)` succeeds. **DGR-008 must install gRPC C++ and extend
`scripts/bootstrap_native_toolchain.sh`.**
- **No wire is exercised.** No client, server, or stream lifecycle exists yet — no
deadline actually fires, no credit is actually consumed. This story defines and
proves the contract; DGR-008/DGR-009 implement it.
- The protobuf C++ toolchain used here was installed to `/tmp/pbsrc/install` (ephemeral).
`scripts/bootstrap_native_toolchain.sh` reproduces it; prefer a durable prefix such
as `build/native-toolchain`.
- `crc32c` has a pure-Python fallback (used here) and picks up `google_crc32c` when
present. The fallback is byte-exact but slow; a worker on the hot path should install
the native package. Not a correctness limitation.
- Compression on the wire is zstd-or-none only, matching the existing seam.
## 7. Compatibility and migration notes
- **This does not change the existing HTTP activation wire.** `X-Meshnet-Wire` stays
at `2` and the legacy `/forward` path is untouched. The native protocol is a
*separate* contract with its own `SchemaVersion`, starting at 1. Nothing in this
story is on any live request path — it is additive.
- Semantics are deliberately preserved from the existing ADRs so the two transports
mean the same thing: `effective_start_layer` (ADR-0012), `CacheMode`/`expected_past_len`
and `ERROR_CODE_CACHE_MISS` mapping to today's HTTP 409 `cache_miss` (ADR-0022),
bfloat16 boundary dtype and 128-token prefill chunks (ADR-0008), fingerprint/recipe
identity mirroring the capability report (ADR-0023).
- `TensorFragment` field 5 (`uncompressed_size`) is **reserved**: it was removed
because `NamedTensor.total_bytes` is the single source of truth. Never recycle it —
a recycled field number is the one schema change peers cannot detect, because the
bytes still parse.
- Committed Python stubs are guarded by `--check` in the test suite, so they cannot
drift from the schema unnoticed.
## 8. Handoff to dependent stories
- **DGR-003 (runtime recipe/fingerprint):** populate `Fingerprint`
(`model_artifact_digest`, `runtime_recipe_digest`, `recipe_id`, `recipe_version`,
`catalogue_version`). The mismatch outcome is already specified:
`ERROR_CODE_FINGERPRINT_MISMATCH`. Do not invent a second identity struct.
- **DGR-005/006 (range loading, architecture boundary):** the boundary payload is a
**named bundle**, not a bare tensor — a boundary needing more than one tensor is
already representable. Execute `[effective_start_layer, end_layer)`, never from
`start_layer`.
- **DGR-007 (concurrent sessions/KV):** isolate on `(route_session_id, route_epoch)`.
`CacheExpectation`/`CacheResult` and `ERROR_CODE_CACHE_MISS` are the contract; a
decode step whose `expected_past_len` does not match **must** miss, never fall back
to a silent stateless forward. `idempotency_step` means a retried step is
acknowledged (`Ack.duplicate`), not re-applied — re-applying advances the KV cache
twice and desynchronises the route.
- **DGR-008 (C++ worker):** link `shard_runtime_grpc` from `CMakeLists.txt`; you must
first install gRPC C++ (see limitations). Honour `FlowControl` credits and the
`max_chunk_bytes` bound. Use `packages/node/meshnet_node/native_protocol/codec.py`
as the reference for fragment reassembly and checksum validation.
- **DGR-009 (Meshnet integration):** the relay may carry these serialized frames as
opaque binary — that is exactly why deadline/cancel/identity are in-band. Do not add
a second control plane.
- **Anyone editing the schema:** run both `--check` scripts; if a vector legitimately
changes, regenerate it and say so, because the C++ test asserts those exact bytes.

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# DGR-002 — exact commands, in order. Run from the repository root.
# Interpreter: <repo>/.venv/bin/python (CPython 3.14.6). Deterministic, GPU-free,
# no model download, no API credits.
# --- toolchain (this machine had no protoc, no cmake, no protobuf C++ headers)
.venv/bin/python -m pip install grpcio-tools==1.82.1 grpcio==1.82.1 cmake==4.4.0
scripts/bootstrap_native_toolchain.sh /tmp/pbsrc/install # protobuf C++ 33.1 + abseil 20250814.1
# --- schema generation (Python stubs; committed)
.venv/bin/python scripts/generate_native_protocol.py
.venv/bin/python scripts/generate_native_protocol.py --check # -> "generated stubs are up to date"
# --- cross-language conformance vectors (committed)
.venv/bin/python scripts/generate_protocol_goldens.py
.venv/bin/python scripts/generate_protocol_goldens.py --check # -> "conformance vectors are up to date"
# --- C++ generation, build and conformance test
cmake -S packages/node/native -B build/native -DCMAKE_PREFIX_PATH=/tmp/pbsrc/install
cmake --build build/native -j"$(nproc)"
ctest --test-dir build/native --output-on-failure # -> 1/1 Passed
cmp build/native/cpp_roundtrip.binpb packages/node/native/testdata/session_request_golden.binpb
# --- Python tests
.venv/bin/python -m pytest -q tests/test_native_shard_protocol.py # -> 29 passed
.venv/bin/python -m pytest -q # full suite
# --- repository gates
.venv/bin/python -m compileall -q packages tests
git diff --check
# --- independent controller review after Ralph
PYTHONPATH=packages/node .venv/bin/python -m pytest -q tests/test_native_shard_protocol.py
# -> 45 passed
PYTHONPATH=packages/node .venv/bin/python -m pytest -q \
tests/test_native_shard_protocol.py tests/test_activation_compression.py
# -> 51 passed
PYTHONPATH=packages/node .venv/bin/python -m pytest -q
# -> final exact-code run: 728 passed, 12 skipped
for i in 1 2 3; do PYTHONPATH=packages/node .venv/bin/python -m pytest -q \
tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy; done
# -> 1 passed, 1 passed, 1 passed
# clean minimum-runtime venv: protobuf==7.35.0 grpcio==1.82.1
# generated pb2 + pb2_grpc imports and one-byte codec round trip -> passed
# The user chose to rely on Ralph's recorded successful C++ CMake/CTest run
# rather than repeat deletion of an isolated generated build directory.

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{
"schema_version": "SCHEMA_VERSION_1",
"bundle_version": 1,
"proto_path": "packages/node/native/proto/shard_runtime.proto",
"proto_sha256": "9e211660b3fcefc88bcdf3851c3571088c00349aacb5adc5ef45083c83d0cce2",
"protoc": "grpc_tools 1.82.1 (python) / protobuf 33.1 (C++)",
"service": {
"GetCapability": {
"client_streaming": false,
"server_streaming": false
},
"Health": {
"client_streaming": false,
"server_streaming": false
},
"Session": {
"client_streaming": true,
"server_streaming": true
},
"Release": {
"client_streaming": false,
"server_streaming": false
},
"Cancel": {
"client_streaming": false,
"server_streaming": false
}
},
"envelope_fields": [
"cache_expectation",
"chunk",
"deadline_unix_nanos",
"fingerprint",
"idempotency_step",
"phase",
"position",
"route_epoch",
"route_session_id",
"schema_version",
"shard_range",
"work_id"
],
"named_tensor_fields": [
"byte_order",
"checksum",
"compression",
"dtype",
"fragments",
"name",
"shape",
"total_bytes"
],
"phases": [
"PHASE_UNSPECIFIED",
"PHASE_PREFILL",
"PHASE_DECODE",
"PHASE_RELEASE",
"PHASE_CANCEL"
],
"error_codes": [
"ERROR_CODE_UNSPECIFIED",
"ERROR_CODE_SCHEMA_UNSUPPORTED",
"ERROR_CODE_FINGERPRINT_MISMATCH",
"ERROR_CODE_EPOCH_STALE",
"ERROR_CODE_SHARD_RANGE_MISMATCH",
"ERROR_CODE_CACHE_MISS",
"ERROR_CODE_RESOURCE_EXHAUSTED",
"ERROR_CODE_PAYLOAD_CORRUPT",
"ERROR_CODE_CANCELLED",
"ERROR_CODE_DEADLINE_EXCEEDED",
"ERROR_CODE_FLOW_CONTROL_VIOLATION",
"ERROR_CODE_INTERNAL"
],
"bounds": {
"max_prefill_chunk_tokens": 128,
"max_chunk_bytes": 4194304,
"max_fragment_bytes": 1048576,
"max_inflight_chunks": 8,
"max_fragments_per_tensor": 64,
"max_tensors_per_bundle": 64,
"max_tensor_rank": 8,
"max_tensor_dimension": 2147483647,
"whole_session_message_enforced": true
},
"golden_vectors": {
"session_request_golden.binpb": "c2c3df8a717ddeae7bd99624d2c7f34c09a518988de990237fe313b75cff0817",
"capability_report_golden.binpb": "71ac5f150775f398515b43a63596a5cbe8d2ad607e7e4de56bd44fbe7987080c"
},
"verification": {
"python_protocol_tests": "45 passed",
"python_protocol_and_compression_tests": "51 passed",
"full_suite": "728 passed, 12 skipped",
"minimum_runtime": "grpcio 1.82.1 / protobuf 7.35.0 passed import and codec smoke"
}
}

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# DGR-003 — exact Artifact and runtime recipe identity
Evidence class: deterministic offline/unit. No model payload, GPU, external API,
network node, or API credit is required or claimed.
## Result — delayed-review repair, 2026-07-14
DGR-003 defines and tests an exact, model-agnostic compatibility identity and
connects it to DGR-002's gRPC `Fingerprint` plus tracker parsing, admission,
route partitioning, and certification. It is **not complete**: the existing
production doctor/backend path still emits the legacy capability report without
constructing a `ShardIdentity` from authoritative loaded artifact/runtime state.
No exact recipe is therefore claimed live or routable from that path; supplied
exact identities remain dark until tracker-owned certification.
A matching digest proves canonical consistency, **not node authenticity or real
execution**. Tracker-owned certification of a fingerprint by a non-synthetic,
complete, multi-node distributed forward is the execution trust boundary.
## Implementation
- `ArtifactIdentity` binds artifact ID/revision, exact content digest,
architecture/config digest, layer count, and optional derivative binding.
- `DerivativeBinding` binds a split artifact to the exact source artifact digest
and its end-exclusive layer range. A Shard cannot advertise outside that range.
- `RuntimeRecipe` keeps these canonical axes separate rather than hiding them in
a backend label:
- weight quantization;
- activation and compute dtypes;
- KV dtype and layout;
- tokenizer revision;
- architecture adapter;
- backend and runtime version;
- boundary and protocol schema versions;
- recipe ID/version and catalogue version.
- `CompatibilityFingerprint` populates the existing DGR-002 Protobuf
`Fingerprint`; `check_session_open()` fails closed on schema, fingerprint,
advertised/effective range, non-empty route session, positive route epoch,
and (when supplied) exact tracker route-session/epoch assignment.
- Node and tracker implementations independently canonicalize the declaration.
This is intentional: the tracker must not trust a digest copied from a node,
and future native/C++ workers also need an independent implementation. Their
behavior is pinned by `tests/data/recipe_fingerprint_vectors.json`.
- Tracker admission cross-checks the exact identity against the capability
proof's model, range, recipe labels, backend, and weight quantization. Any
disagreement fails closed.
- `TrackerServer` owns the sole live certification ledger and passes it through
direct and replicated registration paths. A known exact recipe is
`uncertified` and dark for user traffic until the same exact fingerprint is
certified. Restart fails closed; durable/cluster-wide certification events
require the later real-forward control path and are not claimed here.
- Certification evidence is bound to the promoted fingerprint, requires at
least two distinct nodes, complete layer coverage, generated tokens, and
`synthetic=false`. Unknown or mismatched fingerprints cannot be promoted.
## Files changed
- `packages/node/meshnet_node/runtime_recipe.py`
- `packages/tracker/meshnet_tracker/recipe.py`
- `packages/tracker/meshnet_tracker/capability.py`
- `packages/tracker/meshnet_tracker/server.py`
- `tests/data/recipe_fingerprint_vectors.json`
- `tests/test_runtime_recipe_identity.py`
- this evidence directory, issue state, and DGR-003 PRD state
A late review of dependency DGR-017 also found and fixed two genuine contract
continuity defects during delayed DGR-003 review: v1 now has an independently
trusted digest and recursively immutable parsed state. Those changes and tests
are recorded in DGR-017 evidence rather than claimed as DGR-003 functionality.
## Verification
Exact commands and outcomes are in `commands.txt`.
Observed final results:
- DGR-003 identity + node/tracker capability suites: **126 passed**.
- DGR-017 focused dependency repair suite: **99 passed**.
- Tracker routing suite: **93 passed**.
- First delayed-review integrated run: **898 passed, 13 skipped, 1 failed** on
the pre-existing tracker-cancellation race.
- Final delayed-review integrated rerun: **899 passed, 13 skipped** in
**253.64s**; Hermes controller acceptance rerun: **899 passed, 13 skipped**
in **252.66s**.
- `python -m compileall -q packages tests`: pass.
- `git diff --check`: pass.
- Ruff on the changed identity, capability, contract, and test modules: pass.
- `server.py` has 8 pre-existing Ruff findings at both pushed baseline and the
current tree; DGR-003 added no finding.
The first integrated full-suite run produced **871 passed, 13 skipped, 1 failed**
on the known unrelated
`test_tracker_dashboard_can_cancel_inflight_proxy` timing race. Its fixture
completed after three seconds just before cancellation, so the cancel endpoint
returned 404. In this delayed repair it again produced a 404 after the stream
finished (first integrated run: **898 passed, 13 skipped, 1 failed**); three
immediate isolated repeats passed before a fourth reproduced the same race.
No cancellation-test code was changed. The final complete integrated rerun
passed **899/899** tests.
## Limitations
- Certification state is process-local in this story. The same running tracker
reuses it across registrations, but durable/cluster-wide certification-event
persistence belongs with the later real distributed-forward control path.
Restart or failover therefore returns exact recipes to the safe dark state;
it never makes an unsupported recipe routable.
- The node module has no certification ledger or admission policy; it holds only
identity construction and handshake validation. The Tracker is the sole
promotion authority.
- **Completion blocker:** `doctor._validate_recipe()` calls
`build_capability_report()` without `identity=`, because the legacy
Transformers backend does not expose an immutable artifact-content pin and
full runtime recipe axes authoritative enough to build one. Adding a guessed
identity would weaken this contract. Production emission must be added with
the authoritative native worker/backend loading seam; until then the issue and
PRD deliberately remain incomplete.
- This story proves identity and admission behavior with deterministic fixtures.
It does not claim a real GLM forward or hardware certification.
## Compatibility
- Capability report identity is additive. Legacy reports without the new block
retain ADR-0023's explicit compatibility-policy behavior.
- Reports that opt into exact identity are held to it and fail closed on malformed,
inconsistent, unknown, dark, or mismatched declarations.
- No new wire identity was invented; DGR-002's `Fingerprint` remains the gRPC
representation.
## Handoff
DGR-004 and native workers must build `ShardIdentity` from the actual immutable
artifact pin, patch/runtime pin, tokenizer, numerical recipe, cache layout,
schema versions, and owned range. At `SessionOpen`, compare its
`CompatibilityFingerprint` and return DGR-002's
`ERROR_CODE_FINGERPRINT_MISMATCH` on any mismatch.
A digest match is not certification. Only tracker-recorded evidence from the
same exact fingerprint and a real complete distributed forward can move that
recipe out of dark status.
## Native emission closure — 2026-07-14
Status: **done**. DGR-004/DGR-005's native loaded-artifact seam now reaches the
production capability-report path through `NativeWorkerBackendAdapter`.
### Files changed
- `packages/node/meshnet_node/native_backend.py` — immutable loaded-GGUF report,
immutable artifact and numerical pins, exact identity derivation, and the
SessionOpen boundary.
- `packages/node/meshnet_node/doctor.py` — includes exact identity only for the
native adapter and derives all matching capability-proof fields from it.
- `tests/test_native_identity_emission.py` — deterministic native report,
immutable-pin, SessionOpen, capability emission, legacy-dark, and
tracker-uncertified tests.
- This issue, `prd.json`, and this evidence directory.
### Correctness and trust boundary
The native report carries the end-exclusive owned range, mapped/resident/
registered bytes, GGUF architecture metadata digest, and layer count. The
adapter constructs `ShardIdentity` only from that report plus immutable artifact
pin, tokenizer revision, and numerical recipe inputs. It does not accept a
caller-supplied shard range.
`on_session_open()` calls `check_session_open()` before returning
`SessionAccepted`, preserving fingerprint, schema, range, tracker-session, and
epoch fail-closed behavior. The legacy Transformers backend is deliberately not
an adapter and its doctor report remains identity-free.
The tracker evaluates a self-consistent native report as `uncertified`: digest
equality is canonical consistency, not node authenticity. Only its owned
certification ledger can promote a real distributed forward.
### Verification
- Focused/adversarial DGR-003, node/tracker capability, doctor, and native
dependency suites: **171 passed, 1 skipped**.
- Native protocol CMake configure/build plus CTest: **1/1 passed**.
- `compileall`, Ruff, and `git diff --check`: pass.
- Full deterministic suite: **902 passed, 13 skipped** (255.01s).
No model payload, GPU, external API, network node, or real distributed forward
was run or claimed. The standalone gRPC process remains DGR-008 work; this
story supplies its exact native identity and fail-closed SessionOpen contract.

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# DGR-003 final verification — 2026-07-14
# Native emission closure — 2026-07-14
PYTHONPATH=packages/node:packages/tracker:packages/contracts /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_native_identity_emission.py tests/test_runtime_recipe_identity.py tests/test_node_capability.py tests/test_tracker_capability_admission.py tests/test_node_doctor.py tests/test_llama_cpp_dependency.py
# result: 171 passed, 1 skipped in 7.07s
ruff check packages/node/meshnet_node/native_backend.py packages/node/meshnet_node/doctor.py tests/test_native_identity_emission.py
# result: All checks passed
git diff --check
# result: pass
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m compileall packages tests
# result: pass
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/cmake -S packages/node/native -B build/dgr-003-native-protocol -DCMAKE_PREFIX_PATH=/tmp/pbsrc/install
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/cmake --build build/dgr-003-native-protocol -j2
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/ctest --test-dir build/dgr-003-native-protocol --output-on-failure
# result: configured and built shard_protocol_conformance; 1/1 CTest passed
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q
# result: 902 passed, 13 skipped in 255.01s
PYTHONPATH=packages/node:packages/tracker:packages/contracts /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_runtime_recipe_identity.py tests/test_node_capability.py tests/test_tracker_capability_admission.py
# result: 99 passed in 4.76s
PYTHONPATH=packages/node /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_glm_alpha_target.py
# result: 99 passed in 0.15s
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q
# first integrated result: 871 passed, 13 skipped, 1 failed in 258.18s
# sole failure: tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
# fixture completed at ~3s before cancellation; cancel endpoint returned 404
for i in 1 2 3 4 5; do
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
done
# result: 5/5 passed (1.14s, 1.14s, 1.26s, 1.14s, 1.64s)
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q
# final integrated result: 872 passed, 13 skipped in 253.46s
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m compileall -q packages tests
# result: pass
git diff --check
# result: pass
ruff check packages/node/meshnet_node/glm_alpha/contract.py packages/node/meshnet_node/runtime_recipe.py packages/tracker/meshnet_tracker/recipe.py packages/tracker/meshnet_tracker/capability.py tests/test_glm_alpha_target.py tests/test_runtime_recipe_identity.py
# result: All checks passed!
git show e7c780a:packages/tracker/meshnet_tracker/server.py > /tmp/dgr003-server-base.py
ruff check /tmp/dgr003-server-base.py
ruff check packages/tracker/meshnet_tracker/server.py
# result: both baseline and current server.py report the same 8 pre-existing findings
# ---------------------------------------------------------------------------
# Delayed-review repair continuation — 2026-07-14
# No model payload, GPU, external API, or real inference was run.
# ---------------------------------------------------------------------------
PYTHONPATH=packages/node:packages/tracker:packages/contracts /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_runtime_recipe_identity.py tests/test_node_capability.py tests/test_tracker_capability_admission.py
# result: 126 passed in 4.77s
# includes adversarial certification binding, unknown participant, mutation-atomicity,
# report/identity revision+config, route partition, golden-vector, and SessionOpen tests
PYTHONPATH=packages/node:packages/tracker /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python scripts/gen_recipe_fingerprint_vectors.py --check
# result: tests/data/recipe_fingerprint_vectors.json matches the identity implementation
PYTHONPATH=packages/node /run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_glm_alpha_target.py
# result: 99 passed in 0.11s
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_tracker_routing.py
# result: 93 passed in 46.83s
# there is no separate tests/test_tracker_server.py in this repository
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m compileall -q packages tests
# result: pass
ruff check packages/node/meshnet_node/runtime_recipe.py packages/tracker/meshnet_tracker/recipe.py packages/tracker/meshnet_tracker/capability.py tests/test_runtime_recipe_identity.py scripts/gen_recipe_fingerprint_vectors.py
# result: All checks passed!
git show e7c780a:packages/tracker/meshnet_tracker/server.py > /tmp/dgr003-server-base.py
ruff check /tmp/dgr003-server-base.py
ruff check packages/tracker/meshnet_tracker/server.py
# result: baseline has 8 pre-existing findings; current has 7 because DGR-003 now
# uses the previously unused STATE_ADMITTED import. No new server.py finding.
git diff --check
# result: pass
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q
# result: 898 passed, 13 skipped, 1 failed in 255.43s
# sole failure: tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
# the fixture completed its three-second stream before the cancel request, so cancel returned 404
for i in 1 2 3 4 5; do
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy || exit 1
done
# result: first 3 passed (1.16s, 1.65s, 1.64s); attempt 4 reproduced the same 404 race.
# The test was not modified because it is outside the current DGR-003 P1 repair.
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q
# result: 899 passed, 13 skipped in 253.64s (0:04:13)
# Hermes controller acceptance rerun after agent completion
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python -m pytest -q
# result: 899 passed, 13 skipped in 252.66s (0:04:12)

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# DGR-004 verification blocker — 2026-07-14
## Verified state
The pre-existing DGR-004 boundary is present and its lock data is internally
consistent:
- `scripts/llama_cpp_dependency.py inspect` reports pin
`e920c523e3b8a0163fe498af5bf90df35ff51d25`, one patch, no model downloads,
and no semantic certification.
- The existing clean cached checkout at `build/dgr-004-final/source` is at the
locked commit/tree and contains only the expected staged patch changes.
- The existing `llama-gguf-hash --help` smoke binary runs successfully.
- `python -m compileall -q packages tests`, Ruff on the DGR-004 Python files,
and `git diff --check` pass.
## Blocker
The verification environment no longer contains the `.venv` recorded in
`commands.txt`, nor a `cmake` executable on `PATH`. The available global
pytest environment cannot import the native protocol because its protobuf
runtime is 6.33.6 while the checked-in generated code requires 7.35.0. This
causes both `tests/test_llama_cpp_dependency.py` and the native protocol suite
to fail during the repository-wide autouse fixture setup, before their tests
run.
This prevents the required fresh focused test and native CTest verification.
No DGR-004 completion state, commit, or push is claimed from this worktree.
## Continuation
1. Restore the project test environment used by the prior evidence (including
protobuf >= 7.35.0 and CMake), without changing DGR-004 source files.
2. Run the exact focused test command from `commands.txt` and the clean
`reproduce` command using the local llama.cpp object cache.
3. Re-run compileall, Ruff, diff check, and the deterministic full suite.
4. Only then apply the supervising engine's commit policy and unblock DGR-005.
## Dependency graph
`DGR-004 verification -> DGR-005 range-aware GGUF ownership -> DGR-003 live
ShardIdentity emission`. DGR-005 and DGR-003-emission were not modified.

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# DGR-004 — Reproducible pinned llama.cpp patch stack
Status: **done**. This is reproducible native-build infrastructure evidence, not model execution evidence.
## Delivered boundary
- Pin: `ggml-org/llama.cpp` at `e920c523e3b8a0163fe498af5bf90df35ff51d25` (tree `6c91a11407a3a3fb160f5dac705f9c59718f54f1`).
- Ordered patch: `0001-cmake-reserve-meshnet-patch-stack-abi-marker.patch`, SHA-256 `1454216c019c1cb7f78d1d836fe4054164fff1d498391013bcaf13cc2d328c75`.
- The sole patch adds an interface-library CMake marker. It adds no model execution/loading, networking, Tracker, relay, gRPC, billing, or authentication code.
- `scripts/llama_cpp_dependency.py` makes a fresh checkout, validates commit/tree/baseline blob, validates patch order/digests/context, applies the series, and verifies the exact resulting Git index tree. It rejects stale destinations, upstream drift, changed patches, untracked files, and local edits.
## Build and smoke result
The clean build cloned only the already-present exact Git object cache as a read-only source and did not trust its worktree. CMake 4.4.0 and GCC 15.2.1 built `llama-gguf-hash` with the locked Release/CPU flags in `UPSTREAM_LOCK.json`; `llama-gguf-hash --help` passed with no model download or load.
llama.cpp tests are intentionally off for this small no-model smoke target, so no upstream CTest applies. Meshnet's focused native protocol suite passed independently. Exact results are in `commands.txt` and `results.json`.
## License, compatibility, and handoff
llama.cpp is MIT licensed. The materializer requires upstream `LICENSE`, preserves all upstream notices, and `THIRD_PARTY_NOTICES.md` requires including them in redistribution. No Mesh-LLM code or patch was adopted.
The lock records the patched upstream blob and resulting patched tree. Pin updates must intentionally revise those values, the patch digest/order, toolchain metadata, and evidence.
This stock/native build is **infrastructure evidence only**: not a standalone Meshnet worker (DGR-008), GLM semantic acceptance, DSA/IndexShare proof, numerical equivalence, performance success, model-fit evidence, or route certification. The stock dense-MLA fallback remains explicitly uncertified. DGR-001 CPU v1 remains `stop`; DGR-017 is a separate target contract. DGR-005 may consume this dense-Llama structural boundary; DGR-018/DGR-019 must prove GLM semantics.
## Files changed
- `packages/node/native/llama/*`
- `scripts/llama_cpp_dependency.py`
- `tests/test_llama_cpp_dependency.py`
- this evidence directory, the DGR-004 issue, and `prd.json`

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# DGR-004 commands and real results — 2026-07-14
```text
$ .venv/bin/python -m pytest -q tests/test_llama_cpp_dependency.py tests/test_native_shard_protocol.py
47 passed, 1 skipped in 0.59s
$ .venv/bin/python scripts/llama_cpp_dependency.py reproduce --work-dir build/dgr-004-smoke --source-repository /run/media/popov/d/DEV/llamacpp/llama.cpp
llama-gguf-hash --help -> exit 0; output contains "Hash a GGUF file"
$ touch build/dgr-004-drift/source/DGR-004-local-edit
$ .venv/bin/python scripts/llama_cpp_dependency.py apply --source-dir build/dgr-004-drift/source
DGR-004 dependency error: local edits detected in materialized llama.cpp checkout
exit 2
$ .venv/bin/python -m compileall -q packages tests
exit 0
$ ruff check scripts/llama_cpp_dependency.py tests/test_llama_cpp_dependency.py
All checks passed!
$ git diff --check
exit 0
$ .venv/bin/python -m pytest -q --cache-clear
902 passed, 13 skipped in 255.01s (0:04:15)
```
The source-cache command avoids transient network availability only. The script defaults to the public upstream URL and verifies the exact object/tree, not external worktree state.

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{
"evidence_class": "native build infrastructure",
"llama_cpp": {
"upstream": "https://github.com/ggml-org/llama.cpp.git",
"commit": "e920c523e3b8a0163fe498af5bf90df35ff51d25",
"commit_tree": "6c91a11407a3a3fb160f5dac705f9c59718f54f1",
"patched_tree": "4a37c06fac668834435b803caa59ba272bdace5c",
"patch_sha256": "1454216c019c1cb7f78d1d836fe4054164fff1d498391013bcaf13cc2d328c75"
},
"toolchain": {"cmake": "4.4.0", "cxx": "GCC 15.2.1", "generator": "Unix Makefiles", "target": "llama-gguf-hash", "configure_flags": ["-DCMAKE_BUILD_TYPE=Release", "-DLLAMA_BUILD_TESTS=OFF", "-DLLAMA_BUILD_EXAMPLES=ON", "-DLLAMA_BUILD_SERVER=OFF", "-DLLAMA_BUILD_TOOLS=OFF", "-DLLAMA_BUILD_APP=OFF", "-DLLAMA_CURL=OFF"]},
"checks": {"clean_materialize_apply_build_smoke": "passed", "local_edit_detection": "passed (exit 2)", "focused_pytest": "47 passed, 1 skipped", "compileall": "passed", "ruff": "passed", "git_diff_check": "passed", "full_pytest": "902 passed, 13 skipped"},
"model_downloads": false,
"model_loaded": false,
"inference_run": false,
"glm_semantic_certification": false,
"performance_certification": false,
"route_certification": false
}

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# DGR-005 decomposition — 2026-07-14
## Verified starting point
- The mandated environment is present: project Python 3.14.6, CMake 4.4.0,
and protobuf 7.35.1.
- DGR-003's focused identity/capability tests and DGR-004's dependency tests
pass together: `95 passed`.
- The DGR-004 materialized source at the pinned commit is available for source
inspection. It contains only the DGR-004 CMake-marker patch.
## Why this chain cannot safely claim DGR-005 yet
At the locked llama.cpp revision, `llama_model_base::load_tensors()`:
1. sizes `layers` to `hparams.n_layer_all`;
2. calls every architecture loader, which registers each architecture's layer
tensors; and
3. runs a generic optional-scale pass over the full layer count before creating
mmap/backend buffers.
Filtering names after this point does not meet the ownership contract: it
leaves full-model model/graph assumptions and can make a middle Shard silently
look valid while it lacks the endpoint and boundary semantics needed by the
next story. A generic `blk.N.*` filter alone is also not an architecture
adapter, which violates ADR-0020's fail-closed dense-Llama-first rule.
## Required child slices
1. **DGR-005A — native dense-Llama ownership API and loader**
- Add an explicit end-exclusive owned range to the project-owned native
interface and validate it against immutable GGUF layer metadata.
- Restrict registration, optional scales, allocation and mmap ranges to the
owned `blk.N.*` tensors.
- Record authoritative loaded start/end and mapped/resident byte counters
from the instantiated model, not command-line input.
- Add a deterministic synthetic dense-Llama GGUF fixture plus native tests
for head, middle and tail ranges.
2. **DGR-005B — endpoint ownership and graph guard**
- Load token embeddings only for the head, and final norm/output head only
for the tail, including tied embeddings.
- Make the dense-Llama graph fail closed when an endpoint-required tensor is
absent; do not infer endpoint ownership from an empty pointer.
- Prove that split ranges map fewer bytes than the whole-model fixture and
that the loaded range report matches actual registered tensors.
3. **DGR-003-emission follow-up**
- Expose the resulting immutable native loaded-artifact report to a native
worker/backend adapter.
- Construct `ShardIdentity` only from that report plus the immutable
artifact, tokenizer and numerical-recipe inputs. The legacy Transformers
doctor path must remain identity-free rather than fabricate a pin.
- Wire `check_session_open()` at the worker SessionOpen boundary; current
unit coverage already verifies its fail-closed fingerprint, range,
session and epoch behavior.
## Handoff and non-claims
No DGR-005 source patch, identity-emission code, issue status, or `prd.json`
pass state was changed. No model was loaded, downloaded, benchmarked, or
certified. This document is a supervised-review handoff, not DGR-005 evidence
of completion.

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# DGR-005 — dense-Llama range-aware GGUF ownership
Evidence class: deterministic offline/unit (synthetic fixture) plus
real-model integration (TinyLlama 1.1B, opt-in via MESHNET_ENABLE_REAL_INFERENCE_TESTS=1).
## Result
All six acceptance criteria pass:
1. **Range-aware tensor ownership**: native C++ patch (`0002-dense-llama-owned-range-loader.patch`,
169 lines as merged — DGR-005A's original 365-line version was slimmed by DGR-005B)
adds `llama_model_params.meshnet_owned_layer_start/end`, `llama_meshnet_range_report`,
and restricts `blk.N.*` registration to the owned range.
2. **Head/tail embedding loading**: head loads `token_embd.weight`; tail loads `output_norm`/`output`
(with tied-embedding dedup). Middle shards load zero endpoint tensors.
3. **Mapped/resident memory scales with owned tensors**: proven with TinyLlama 1.1B Q4_K_M.
4. **Targeted pytest tests**: `tests/test_llama_cpp_dependency.py` (3 tests — lock/patch
manifest consistency, offline dependency report, control-plane-code scan; re-verified
2026-07-14: `3 passed, 6 skipped` together with the opt-in integration file), native CTest
(`test-meshnet-range-ownership` synthetic fixture, added by the 0002 patch).
5. **compileall, ruff, git diff --check, full pytest**: all pass.
6. **Integration test**: `tests/test_gguf_distributed_load.py` (6/6, opt-in real model).
## Files changed (vs HEAD at DGR-004)
- `packages/node/native/llama/patches/0002-dense-llama-owned-range-loader.patch` — 169-line native patch (as merged)
- `packages/node/native/llama/patches/SHA256SUMS` — updated hash
- `packages/node/native/llama/patches/series` — added patch to series
- `packages/node/native/llama/UPSTREAM_LOCK.json` — updated patched_tree, serial number
- `scripts/llama_cpp_dependency.py` — `inspect` report for 2-patch stack
- `tests/test_llama_cpp_dependency.py` — patch_count 2
- `packages/node/native/llama/meshnet-range-loader.cpp` — C CLI wrapper
- `tests/test_gguf_distributed_load.py` — real-model integration test
## Commands
```text
# Build patched llama.cpp + range loader
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/cmake \
-S build/dgr-004-final/source -B build/dgr-004-final/build \
-DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_EXAMPLES=ON \
-DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_SERVER=OFF \
-DLLAMA_BUILD_TOOLS=ON -DLLAMA_CURL=OFF
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/cmake \
--build build/dgr-004-final/build --target llama-simple -j$(nproc)
g++ -std=c++17 -Ibuild/dgr-004-final/source -Ibuild/dgr-004-final/source/include \
-Ibuild/dgr-004-final/source/ggml/include -Lbuild/dgr-004-final/build/bin \
packages/node/native/llama/meshnet-range-loader.cpp -lllama \
-Wl,-rpath,build/dgr-004-final/build/bin \
-o build/dgr-004-final/build/bin/meshnet-range-loader
# Focused tests (no model download)
PYTHONPATH=packages/node:packages/tracker:packages/contracts
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python \
-m pytest -q tests/test_llama_cpp_dependency.py
# Real-model integration test (opt-in, downloads ~670 MB)
MESHNET_ENABLE_REAL_INFERENCE_TESTS=1 PYTHONPATH=... \
/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python \
-m pytest -q tests/test_gguf_distributed_load.py
```
## Limitations
- Dense-Llama architecture only (LLM_ARCH_LLAMA). GLM/MoE/MLA is DGR-006+.
- Graph-level endpoint assertions (`has_token_embeddings`, `has_output_head`) were
simplified to only `start_layer`/`end_layer`/`mapped_bytes`/`resident_bytes` in
the patch as merged. Full endpoint tracking is available via the integration test
by observing which tensors are registered per shard.
- Loading the full `llama-simple` CLI requires reconfiguring with `-DLLAMA_BUILD_EXAMPLES=ON`.
The smoke-only build (`llama-gguf-hash`) is sufficient for patch verification.
- TinyLlama 1.1B is a baseline dense-Llama architecture only.
## Commits
- `252d131` feat: DGR-005A dense Llama owned range loader
- `f844ae6` feat: DGR-005B endpoint ownership and graph guard
- `31065c0` feat: distributed GGUF shard load integration test with TinyLlama 1.1B
- `d6b808d` chore: mark DGR-005 passes:true in PRD

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# DGR-006 — architecture-defined boundary input/output
Status: complete deterministic/offline contract and dense-fixture evidence.
## Result
The native protocol now carries a versioned `TensorBundle` on the decode fast
path. It includes explicit architecture and boundary-point metadata. Its legacy
`NamedTensor` field remains a compact one-tensor encoding for certified dense
boundaries; the writer deliberately selects it only for a one-tensor bundle and
new readers wrap that representation into a bundle. The bundle is authoritative
when present, allowing MoE/MLA sidebands without a second transport contract.
`architecture_boundary.py` is the fail-closed adapter boundary. Dense head
Shards accept token IDs and own embedding. Middle/tail Shards accept only a
validated bundle. Dense, MoE, and MLA route through explicit adapters; unknown
architectures are rejected. The dense F32 fixture proves whole-model versus
two-range boundary parity without model downloads or real inference.
Tail output is explicit in the schema: `TailResult` contains either logits or a
sampled token and binds sampling parameters plus request ID, runtime recipe,
chat template/version, reasoning mode, and architecture identity. The adapter
builds and validates the serialized protobuf result before returning it.
## Files changed
- `packages/node/native/proto/shard_runtime.proto`
- `packages/node/meshnet_node/native_protocol/{codec.py,__init__.py,conformance.py,generated/*}`
- `packages/node/native/testdata/decode_step_golden.binpb`
- `packages/node/native/tests/test_shard_protocol_conformance.cpp`
- `packages/node/meshnet_node/architecture_boundary.py`
- `tests/test_architecture_boundary.py`
- `tests/test_native_shard_protocol.py`
- `packages/node/native/README.md`
## Commands and results
All Python commands used `/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python`.
All native commands used `/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/cmake`.
```text
python scripts/generate_native_protocol.py --check -> passed
python scripts/generate_protocol_goldens.py --check -> passed
pytest -q tests/test_architecture_boundary.py \
tests/test_native_shard_protocol.py tests/test_llama_cpp_dependency.py
-> 59 passed
cmake -S packages/node/native -B build/native \
-DCMAKE_PREFIX_PATH=/tmp/pbsrc/install -> configured
cmake --build build/native -j$(nproc) -> built shard_protocol_conformance
ctest --test-dir build/native --output-on-failure -> 1/1 passed
python -m compileall -q packages tests -> passed
git diff --check -> passed
pytest -q -> 917 passed, 18 skipped
```
## Compatibility and limitations
- Existing Nodes that send `DecodeStep.tensor` are accepted. New multi-tensor
Nodes require the versioned bundle and older Nodes safely preserve it as an
unknown field rather than interpreting it as a single tensor.
- The committed C++ conformance vector covers the multi-tensor decode path.
- The dense parity result is a deterministic F32 structural fixture, not real
GGUF inference or GLM certification. No real inference was run.
- MoE and MLA adapters define and validate their sideband contracts but are not
architecture certifications. DGR-019 owns GLM MoE/MLA/DSA/IndexShare semantics.
## Handoff
DGR-007 can key its Hot KV state to the validated decoded bundle. DGR-008 can
translate the generated `TailResult` and decode bundle over gRPC. DGR-019 must
replace the generic MoE/MLA sideband names with exact certified GLM semantics.

115
.scratch/distributed-gguf-runtime/evidence/DGR-017/commands.txt Normal file
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@@ -0,0 +1,115 @@
# DGR-017 — exact commands and real results (2026-07-13)
# Project venv is used explicitly. NOTE: bare `pytest` on this machine resolves to
# Hermes Agent's internal venv (/home/popov/.hermes/...), which DGR-001 already
# recorded as the cause of a bogus "suite is blocked" claim. Always use $VP.
VP=/run/media/popov/d/DEV/repos/d-popov.com/AI/.venv/bin/python # Python 3.14.6
# ---------------------------------------------------------------------------
# 1. Resolve the target from upstream metadata ONLY. No weight payload downloaded.
# Sizes and SHA-256 come from the HF LFS pointer metadata (paths-info), not the blobs.
# ---------------------------------------------------------------------------
curl -sS "https://huggingface.co/api/models/zai-org/GLM-5.2"
-> sha b4734de4facf877f85769a911abafc5283eab3d9 (matches the roadmap pin)
-> license mit, lastModified 2026-07-02T08:08:14.000Z
curl -sS "https://huggingface.co/api/models/unsloth/GLM-5.2-GGUF"
-> sha abc55e72527792c6e77069c99b4cb7de16fa9f23 (matches the roadmap pin)
-> license mit, lastModified 2026-06-23T15:18:23.000Z
-> six UD-IQ1_S shards present
curl -sS -X POST -d '{"paths": [<6 UD-IQ1_S shards>]}' \
"https://huggingface.co/api/models/unsloth/GLM-5.2-GGUF/paths-info/abc55e7..."
-> all six shards resolved with exact size + LFS oid (sha256)
-> sum = 216,715,360,960 bytes = 201.832 GiB = 216.715 GB
-> matches the roadmap's published byte total EXACTLY
-> UD-IQ1_M fallback = 228,492,966,624 bytes = 212.801 GiB (also matches)
curl -sS ".../resolve/b4734de4.../{config.json,chat_template.jinja,
generation_config.json,tokenizer_config.json}"
-> config.json 3732 B sha256 185f93ee6d12548e16a847e279dc0c3c90b1524c970b0866b42fb545747d859a
-> chat_template.jinja 5076 B sha256 172dc74a35e1752df75ecfb2b2cf9326d2852bb1379868ebeec9571654489679
-> generation_config.json 194 B sha256 ac76b43d8683d3b930126870fc8be73d8679308fe752fa1f381096d8354f6a55
-> tokenizer_config.json 761 B sha256 98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc
# ---------------------------------------------------------------------------
# 2. Verify the checked-in pins still match live upstream (reproducible, no weights)
# ---------------------------------------------------------------------------
$VP scripts/refresh_glm_target_manifest.py --check
-> "target manifest and architecture snapshot match upstream"
-> exit 0
# ---------------------------------------------------------------------------
# 3. Upstream llama.cpp / donor status refresh (GitHub REST API, read-only)
# ---------------------------------------------------------------------------
curl -sS "https://api.github.com/repos/ggml-org/llama.cpp/issues/{24730,24770,25407,24231}"
-> #24730 issue OPEN "Feature Request: Support for GLM 5.2"
-> #24770 PR MERGED 2026-06-20 dense-MLA compatibility loader (DSA tensors optional)
-> #24231 PR MERGED 2026-07-11 generic GGML_OP_LIGHTNING_INDEXER [CHANGED since roadmap]
-> #25407 PR OPEN updated 2026-07-13, non-draft, 12 files, +414/-7 GLM 5.2 Indexer support
curl -sS "https://api.github.com/repos/Mesh-LLM/mesh-llm{,/branches/feat%2Fjianyang-glm-52}"
-> Apache-2.0, 2048 stars, branch head 9bd18f1509dff7fac21578635084035b3ba90a38 (2026-07-12)
-> recorded as donor only; nothing forked, nothing adopted
# ---------------------------------------------------------------------------
# 4. Seal the alpha contract (digest over its own canonical content)
# ---------------------------------------------------------------------------
$VP -c "seal_contract(...)" -> contract_sha256 aab23220280c053a3c14ff559df3cb5c9e1bf7f0f7188c6519e2e9d9ad036ed9
# ---------------------------------------------------------------------------
# 5. Generate the machine-readable resource plan from the pinned artifact
# ---------------------------------------------------------------------------
PYTHONPATH=packages/node $VP <generate resource-plan.json>
-> manifest_sha256 0b6aed04479d204902bb64c0203f1a46cab26a47b378ecccf85237b63f6c1962
-> architecture_sha256 253fbd94b06b42acc4724ec2c7f33914e2d4cc43f54a36dff6af19a80ae6ceb1
-> alpha_contract_sha256 aab23220280c053a3c14ff559df3cb5c9e1bf7f0f7188c6519e2e9d9ad036ed9
-> tier arithmetic minimum 32:9 48:6 64:4 96:3 128:2 (reproduces the roadmap table)
-> tier recommended 32:10 48:6 64:5 96:3 128:3 (reproduces the roadmap table)
-> 5x64 GiB unified fits, +53.28 GiB headroom
-> 3x96 GiB unified fits, +27.68 GiB headroom
-> 2x128 / 4x64 (fit probes) fit with only +2.08 GiB headroom across the WHOLE route
-> 2x112 GiB (= 224 GiB, the hard-fit floor) DOES NOT FIT: -23.52 GiB
-> 3x64 GiB does not fit: -49.12 GiB
# ---------------------------------------------------------------------------
# 6. Quality gates (project .venv, deterministic, offline, GPU-free)
# ---------------------------------------------------------------------------
$VP -m pytest -q tests/test_glm_alpha_target.py
-> 97 passed in 0.12s
-> includes coordinated shard/config substitution, malformed telemetry, and
contract-ID reseal rejection tests added during controller review
$VP -m pip wheel --no-deps packages/node -w /tmp/dgr017-wheel
$VP -m pip install --no-deps --target /tmp/dgr017-install /tmp/dgr017-wheel/*.whl
$VP -I -c "... from meshnet_node.glm_alpha import load_locked_target ..."
-> INSTALLED_WHEEL_PASS
-> packaged alpha-contract.json, target-manifest.json, and architecture-snapshot.json
load and cross-bind successfully outside the source tree
$VP -m compileall -q packages tests
-> exit 0
git diff --check
-> exit 0
$VP -m pytest -q # full deterministic suite
-> first final run: 1 failed, 851 passed, 13 skipped; only the tracker cancellation
race already documented by DGR-001/DGR-002 failed
$VP -m pytest -q tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
-> 1 passed, repeated 5/5 in isolation
$VP -m pytest -q # integrated rerun
-> 852 passed, 13 skipped in 253.30s (0:04:13)
# ---------------------------------------------------------------------------
# 7. Late independent-review repair (2026-07-14)
# ---------------------------------------------------------------------------
PYTHONPATH=packages/node $VP -m pytest -q tests/test_glm_alpha_target.py
-> 99 passed in 0.15s
-> adds trusted-v1-digest rejection after coordinated mutation + reseal
-> adds nested parsed-state immutability and isolated to_dict() coverage
$VP -m pytest -q # after DGR-003 integration and DGR-017 repair
-> first run: 871 passed, 13 skipped, 1 known cancellation-race failure
$VP -m pytest -q tests/test_tracker_routing.py::test_tracker_dashboard_can_cancel_inflight_proxy
-> 5/5 passed in isolation
$VP -m pytest -q # integrated rerun
-> 872 passed, 13 skipped in 253.46s (0:04:13)

255
.scratch/distributed-gguf-runtime/evidence/DGR-017/resource-plan.json Normal file
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@@ -0,0 +1,255 @@
{
"generated_by": "DGR-017 meshnet_node.glm_alpha.planner",
"target": {
"gguf_repo_id": "unsloth/GLM-5.2-GGUF",
"gguf_revision": "abc55e72527792c6e77069c99b4cb7de16fa9f23",
"quantization": "UD-IQ1_S",
"total_bytes": 216715360960,
"total_gib": 201.832,
"total_gb": 216.715
},
"manifest_sha256": "0b6aed04479d204902bb64c0203f1a46cab26a47b378ecccf85237b63f6c1962",
"architecture_snapshot_sha256": "253fbd94b06b42acc4724ec2c7f33914e2d4cc43f54a36dff6af19a80ae6ceb1",
"alpha_contract_sha256": "aab23220280c053a3c14ff559df3cb5c9e1bf7f0f7188c6519e2e9d9ad036ed9",
"kv_assumptions": {
"dtype": "Q8_0",
"bytes_per_value": 1.0625,
"context_tokens": 16384,
"concurrency": 1,
"indexer_layout": "conservative",
"mla_values_per_token_per_layer": 576,
"backbone_layers": 78,
"indexer_full_layers": 21,
"note": "Alpha budgets indexer keys across all 78 layers (current experimental DSA layout), not only the 21 Full layers."
},
"kv_table_gib": {
"16384": {
"mla_only_q8_gib": 0.73,
"optimized_dsa_q8_gib": 0.77,
"conservative_dsa_q8_gib": 0.89,
"conservative_dsa_f16_gib": 1.68
},
"131072": {
"mla_only_q8_gib": 5.83,
"optimized_dsa_q8_gib": 6.18,
"conservative_dsa_q8_gib": 7.12,
"conservative_dsa_f16_gib": 13.41
},
"1048576": {
"mla_only_q8_gib": 46.62,
"optimized_dsa_q8_gib": 49.41,
"conservative_dsa_q8_gib": 56.98,
"conservative_dsa_f16_gib": 107.25
}
},
"aggregate_hard_fit_floor_gib": 224.0,
"aggregate_floor_class": "experimental_hard_fit_floor",
"placement_imbalance_factor": 1.1,
"tier_table": {
"32": {
"physical_usable_gib": 32.0,
"reserve_gib": 8.0,
"placement_budget_gib": 24.0,
"weight_gib": 201.832,
"kv_gib": 0.89,
"total_placement_gib": 202.722,
"arithmetic_minimum_nodes": 9,
"recommended_nodes": 10,
"imbalance_factor": 1.1
},
"48": {
"physical_usable_gib": 48.0,
"reserve_gib": 9.6,
"placement_budget_gib": 38.4,
"weight_gib": 201.832,
"kv_gib": 0.89,
"total_placement_gib": 202.722,
"arithmetic_minimum_nodes": 6,
"recommended_nodes": 6,
"imbalance_factor": 1.1
},
"64": {
"physical_usable_gib": 64.0,
"reserve_gib": 12.8,
"placement_budget_gib": 51.2,
"weight_gib": 201.832,
"kv_gib": 0.89,
"total_placement_gib": 202.722,
"arithmetic_minimum_nodes": 4,
"recommended_nodes": 5,
"imbalance_factor": 1.1
},
"96": {
"physical_usable_gib": 96.0,
"reserve_gib": 19.2,
"placement_budget_gib": 76.8,
"weight_gib": 201.832,
"kv_gib": 0.89,
"total_placement_gib": 202.722,
"arithmetic_minimum_nodes": 3,
"recommended_nodes": 3,
"imbalance_factor": 1.1
},
"128": {
"physical_usable_gib": 128.0,
"reserve_gib": 25.6,
"placement_budget_gib": 102.4,
"weight_gib": 201.832,
"kv_gib": 0.89,
"total_placement_gib": 202.722,
"arithmetic_minimum_nodes": 2,
"recommended_nodes": 3,
"imbalance_factor": 1.1
}
},
"routes": {
"recommended_5x64_unified": {
"node_count": 5,
"aggregate_usable_gib": 320.0,
"aggregate_placement_budget_gib": 256.0,
"required_placement_gib": 202.722,
"fits": true,
"meets_hard_fit_floor": true,
"no_single_node_can_admit_target": true,
"headroom_gib": 53.278,
"reasons": []
},
"recommended_3x96_unified": {
"node_count": 3,
"aggregate_usable_gib": 288.0,
"aggregate_placement_budget_gib": 230.4,
"required_placement_gib": 202.722,
"fits": true,
"meets_hard_fit_floor": true,
"no_single_node_can_admit_target": true,
"headroom_gib": 27.678,
"reasons": []
},
"recommended_3x128_unified": {
"node_count": 3,
"aggregate_usable_gib": 384.0,
"aggregate_placement_budget_gib": 307.2,
"required_placement_gib": 202.722,
"fits": true,
"meets_hard_fit_floor": true,
"no_single_node_can_admit_target": true,
"headroom_gib": 104.478,
"reasons": []
},
"fit_probe_2x128_unified": {
"node_count": 2,
"aggregate_usable_gib": 256.0,
"aggregate_placement_budget_gib": 204.8,
"required_placement_gib": 202.722,
"fits": true,
"meets_hard_fit_floor": true,
"no_single_node_can_admit_target": true,
"headroom_gib": 2.078,
"reasons": []
},
"fit_probe_4x64_unified": {
"node_count": 4,
"aggregate_usable_gib": 256.0,
"aggregate_placement_budget_gib": 204.8,
"required_placement_gib": 202.722,
"fits": true,
"meets_hard_fit_floor": true,
"no_single_node_can_admit_target": true,
"headroom_gib": 2.078,
"reasons": []
},
"hard_fit_floor_2x112_unified": {
"node_count": 2,
"aggregate_usable_gib": 224.0,
"aggregate_placement_budget_gib": 179.2,
"required_placement_gib": 202.722,
"fits": false,
"meets_hard_fit_floor": true,
"no_single_node_can_admit_target": true,
"headroom_gib": -23.522,
"reasons": [
"aggregate placement budget 179.2 GiB is below the 202.7 GiB the target needs after each node's reserve"
]
},
"insufficient_3x64_unified": {
"node_count": 3,
"aggregate_usable_gib": 192.0,
"aggregate_placement_budget_gib": 153.6,
"required_placement_gib": 202.722,
"fits": false,
"meets_hard_fit_floor": false,
"no_single_node_can_admit_target": true,
"headroom_gib": -49.122,
"reasons": [
"aggregate placement budget 153.6 GiB is below the 202.7 GiB the target needs after each node's reserve",
"aggregate usable memory 192.0 GiB is below the 224 GiB experimental hard-fit floor"
]
}
},
"seams": {
"3_nodes_2.5gbe": {
"node_count": 3,
"seam_count": 2,
"hidden_size": 6144,
"bytes_per_token_per_seam": 12288,
"prefill_bytes_per_seam": 201326592,
"decode_bytes_per_seam_per_token": 12288,
"dsa_sideband_bytes_per_query": 8192,
"link_rate_gbps": 2.5,
"meets_alpha_minimum": true,
"is_recommended_link": false,
"decode_serialization_ms_per_token": 0.0786,
"decode_latency_ms_per_token": 1.0,
"decode_bandwidth_share_ms_per_token": 0.0786,
"prefill_serialization_ms": 1288.49
},
"3_nodes_10.0gbe": {
"node_count": 3,
"seam_count": 2,
"hidden_size": 6144,
"bytes_per_token_per_seam": 12288,
"prefill_bytes_per_seam": 201326592,
"decode_bytes_per_seam_per_token": 12288,
"dsa_sideband_bytes_per_query": 8192,
"link_rate_gbps": 10.0,
"meets_alpha_minimum": true,
"is_recommended_link": true,
"decode_serialization_ms_per_token": 0.0197,
"decode_latency_ms_per_token": 1.0,
"decode_bandwidth_share_ms_per_token": 0.0197,
"prefill_serialization_ms": 322.123
},
"5_nodes_2.5gbe": {
"node_count": 5,
"seam_count": 4,
"hidden_size": 6144,
"bytes_per_token_per_seam": 12288,
"prefill_bytes_per_seam": 201326592,
"decode_bytes_per_seam_per_token": 12288,
"dsa_sideband_bytes_per_query": 8192,
"link_rate_gbps": 2.5,
"meets_alpha_minimum": true,
"is_recommended_link": false,
"decode_serialization_ms_per_token": 0.1573,
"decode_latency_ms_per_token": 2.0,
"decode_bandwidth_share_ms_per_token": 0.1573,
"prefill_serialization_ms": 2576.98
},
"5_nodes_10.0gbe": {
"node_count": 5,
"seam_count": 4,
"hidden_size": 6144,
"bytes_per_token_per_seam": 12288,
"prefill_bytes_per_seam": 201326592,
"decode_bytes_per_seam_per_token": 12288,
"dsa_sideband_bytes_per_query": 8192,
"link_rate_gbps": 10.0,
"meets_alpha_minimum": true,
"is_recommended_link": true,
"decode_serialization_ms_per_token": 0.0393,
"decode_latency_ms_per_token": 2.0,
"decode_bandwidth_share_ms_per_token": 0.0393,
"prefill_serialization_ms": 644.245
}
}
}

89
.scratch/distributed-gguf-runtime/evidence/DGR-017/upstream-status.json Normal file
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@@ -0,0 +1,89 @@
{
"observed_at": "2026-07-13",
"observed_by": "DGR-017",
"method": "GitHub REST API (api.github.com), read-only; no fork, no clone, no patch adopted",
"refresh_note": "The roadmap's 2026-07-13 observations were re-verified against live upstream. One item changed: PR #24231 is now MERGED (2026-07-11), which the roadmap already anticipated as 'generic CPU lightning-indexer support is merged'.",
"llama_cpp": {
"repo": "ggml-org/llama.cpp",
"items": [
{
"ref": "issue #24730",
"url": "https://github.com/ggml-org/llama.cpp/issues/24730",
"title": "Feature Request: Support for GLM 5.2",
"type": "issue",
"state": "open",
"updated_at": "2026-07-03T22:02:15Z",
"meaning": "The umbrella GLM-5.2 support request is still open. GLM-5.2 is not fully supported upstream."
},
{
"ref": "PR #24770",
"url": "https://github.com/ggml-org/llama.cpp/pull/24770",
"title": "model : glm-dsa load DSA indexer tensors as optional",
"type": "pull_request",
"state": "closed",
"merged_at": "2026-06-20T10:48:24Z",
"meaning": "MERGED. GLM-5.2 loads, but through a dense-MLA compatibility path with DSA indexer tensors treated as optional. This is the fallback the alpha contract explicitly refuses: it can produce text without performing DSA/IndexShare computation."
},
{
"ref": "PR #24231",
"url": "https://github.com/ggml-org/llama.cpp/pull/24231",
"title": "New GGML_OP_LIGHTNING_INDEXER that implements DeepSeek V3.2/V4 lightning indexer",
"type": "pull_request",
"state": "closed",
"merged_at": "2026-07-11T09:39:07Z",
"meaning": "MERGED since the roadmap was written. A generic lightning-indexer op now exists in GGML. Backend coverage beyond CPU remains uneven and must be verified per backend by DGR-018, not assumed."
},
{
"ref": "PR #25407",
"url": "https://github.com/ggml-org/llama.cpp/pull/25407",
"title": "GLM 5.2 Indexer support",
"type": "pull_request",
"state": "open",
"draft": false,
"mergeable_state": "unstable",
"head_sha": "8dedd06415f36f10fc6091241a39b23c1bf0ee11",
"base": "master",
"commits": 6,
"changed_files": 12,
"additions": 414,
"deletions": 7,
"updated_at": "2026-07-13T15:28:51Z",
"meaning": "OPEN and actively moving (updated today). This is the real DSA/IndexShare implementation alpha needs. It is narrow — 12 files, +414/-7 — which is the single most important finding for donor policy: the semantics alpha requires are reviewable and trackable upstream, not a 261-patch fork."
}
]
},
"capability_status_for_alpha": {
"gguf_load_of_UD-IQ1_S": "expected via merged #24770, unverified by this project; DGR-018 must prove it against the exact pinned artifact",
"moe_routing_and_shared_expert": "expected supported; unverified here",
"compressed_mla_kv": "supported via the dense-MLA compatibility path",
"dsa_lightning_indexer": "generic GGML op merged (#24231); GLM-5.2 wiring still open (#25407)",
"indexshare_full_shared_roles": "NOT upstream; only in open PR #25407",
"mtp_nextn": "not required for alpha; NextN tensors must be explicitly loaded or excluded, never silently reinterpreted",
"conclusion": "As of 2026-07-13 no released upstream llama.cpp performs native GLM-5.2 DSA + IndexShare. A stock pin today would satisfy 'it emits text' via the dense fallback and would FAIL the alpha semantic-correctness contract. This is the gating technical risk for DGR-004 and DGR-018."
},
"donor": {
"repo": "Mesh-LLM/mesh-llm",
"url": "https://github.com/Mesh-LLM/mesh-llm",
"license": "Apache-2.0",
"stars_observed": 2048,
"pushed_at": "2026-07-13T06:45:51Z",
"glm_branch": "feat/jianyang-glm-52",
"glm_branch_head": "9bd18f1509dff7fac21578635084035b3ba90a38",
"glm_branch_head_date": "2026-07-12T06:37:43Z",
"policy": "TEST AND PATCH DONOR ONLY. Do not adopt the fork, its scheduler, discovery, routing, public mesh, or package manager. Meshnet remains the sole control plane (RALPH-CONTEXT runtime decision, ADR-0020).",
"focused_candidates": [
"GLM DSA graph semantics",
"lightning indexer and sparse-attention tests",
"IndexShare metadata and Full/Shared role validation",
"top-k sideband shape and lifecycle",
"stage-local KV filtering",
"target parity and performance fixtures"
],
"adoption_state": "none adopted in DGR-017. This story reads and records upstream state; it takes no patch and forks nothing."
},
"recommendation_for_dgr_004_and_dgr_018": [
"Track upstream PR #25407 rather than forking Mesh-LLM. At 12 files and +414/-7 it is small enough to review, reproduce, and carry as a numbered patch in the project's own pinned stack.",
"Any pin chosen before #25407 merges will load GLM-5.2 through the dense-MLA compatibility path. DGR-018 must therefore prove DSA/IndexShare are ACTIVE, not merely that the model emits text — the alpha contract already forbids the fallback.",
"Verify lightning-indexer backend coverage (#24231) on the specific backend the route will use. CPU support being merged says nothing about ROCm/HIP."
]
}

53
.scratch/distributed-gguf-runtime/evidence/DGR-018/BLOCKED.md Normal file
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@@ -0,0 +1,53 @@
# DGR-018 — BLOCKED: no 256-GiB-class oracle host
Recorded: 2026-07-14 (MAINT-003). Preflight scripts preserved at commit
`a0f28b5` ("chore: preserve DGR-018 preflight scripts (postponed)").
## Blocker
DGR-018 requires a 256-GiB-class host with at least **224 GiB
runtime-accessible memory** (the DGR-017 experimental hard-fit floor for the
whole-model `UD-IQ1_S` oracle) and **250 GB free storage** on one filesystem
outside `/home` (216.715 GB artifact plus resume/temp headroom). The available
development host fails both gates, so the whole-model oracle cannot be
established. Per the issue's finish contract, no smaller model may be
substituted.
DGR-019 (needs the DGR-018 oracle for parity certification) and DGR-020
(needs DGR-018 and DGR-019, plus enough physical consumer nodes that no single
node admits the whole recipe) are blocked transitively.
## Exact preflight output
Command (offline; resolves everything from the pinned target manifest and
never contacts the network):
```
$ python scripts/glm_whole_model_preflight.py
target: UD-IQ1_S 216.715 GB, 6 shards @ abc55e725277
[FAIL] storage: need >= 250 GB free on one filesystem outside ['/home']; observed no eligible filesystem
[FAIL] memory: need >= 224 GiB runtime-accessible memory (DGR-017 experimental hard-fit floor); observed 124.9 GiB MemTotal
destination: NONE — no filesystem outside ['/home'] has 250 GB free
- /run/media/popov/DATA (ext4): 74.2 GB free
- / (ext4): 51.1 GB free
- /run/media/popov/Windows (fuseblk): 26.0 GB free
- /run/media/popov/d (fuseblk): 5.1 GB free
verdict: fail
$ echo $?
1
```
Host: Linux 7.0.14-101.fc43.x86_64 x86_64, `MemTotal: 130997376 kB`
(124.9 GiB). The full machine-readable report (including the ordered
download/verify plan against revision `abc55e72527792c6e77069c99b4cb7de16fa9f23`,
manifest SHA-256 `0b6aed04479d204902bb64c0203f1a46cab26a47b378ecccf85237b63f6c1962`)
is in [preflight.json](preflight.json).
## How to resume
1. On a qualifying host, run `python scripts/glm_whole_model_preflight.py`
(optionally `--dest DIR`); it must exit 0 with `verdict: pass`.
2. Download shards in the preflight's ordered plan; verify each with
`python scripts/verify_glm_shards.py` before the next transfer starts.
3. Proceed with the DGR-018 issue
(`.scratch/distributed-gguf-runtime/issues/18-certify-whole-model-glm-5-2-runtime-semantics.md`).

140
.scratch/distributed-gguf-runtime/evidence/DGR-018/preflight.json Normal file
View File

@@ -0,0 +1,140 @@
{
"generated_by": "scripts/glm_whole_model_preflight.py",
"target": {
"gguf_repo_id": "unsloth/GLM-5.2-GGUF",
"gguf_revision": "abc55e72527792c6e77069c99b4cb7de16fa9f23",
"quantization": "UD-IQ1_S",
"shard_count": 6,
"total_bytes": 216715360960,
"total_gb": 216.715,
"manifest_sha256": "0b6aed04479d204902bb64c0203f1a46cab26a47b378ecccf85237b63f6c1962"
},
"forbidden_path_prefixes": [
"/home"
],
"mounts": [
{
"mountpoint": "/run/media/popov/DATA",
"fstype": "ext4",
"total_gb": 1208.8,
"free_gb": 74.2,
"free_bytes": 74201321472,
"forbidden": false,
"eligible": false
},
{
"mountpoint": "/",
"fstype": "ext4",
"total_gb": 217.7,
"free_gb": 51.1,
"free_bytes": 51073683456,
"forbidden": false,
"eligible": false
},
{
"mountpoint": "/run/media/popov/Windows",
"fstype": "fuseblk",
"total_gb": 434.9,
"free_gb": 26.0,
"free_bytes": 25964466176,
"forbidden": false,
"eligible": false
},
{
"mountpoint": "/run/media/popov/d",
"fstype": "fuseblk",
"total_gb": 161.1,
"free_gb": 5.1,
"free_bytes": 5148332032,
"forbidden": false,
"eligible": false
}
],
"chosen_destination": null,
"checks": [
{
"check": "storage",
"requirement": ">= 250 GB free on one filesystem outside ['/home']",
"observed": "no eligible filesystem",
"passes": false
},
{
"check": "memory",
"requirement": ">= 224 GiB runtime-accessible memory (DGR-017 experimental hard-fit floor)",
"observed": "124.9 GiB MemTotal",
"passes": false,
"waived": false
}
],
"download_authorized": false,
"storage_only": false,
"download_plan": [
{
"step": 1,
"shard_index": 1,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00001-of-00006.gguf",
"size_bytes": 9423744,
"size_gb": 0.009,
"sha256": "46b6148389219ae45167cb8124fbb18ef7d432daf619b4faf9e06ea80d3f4777",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00001-of-00006.gguf",
"download_command": "curl -L -C - --fail -o \"$GLM_DEST/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00001-of-00006.gguf\" \"https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00001-of-00006.gguf\"",
"verify_command": "python scripts/verify_glm_shards.py --model-dir \"$GLM_DEST\" --shard 1"
},
{
"step": 2,
"shard_index": 6,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00006-of-00006.gguf",
"size_bytes": 19171063136,
"size_gb": 19.171,
"sha256": "3b767f55df64e0432d52fcf1a14eb47a1ef3bbc91339e2ae220f38602237d7d7",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00006-of-00006.gguf",
"download_command": "curl -L -C - --fail -o \"$GLM_DEST/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00006-of-00006.gguf\" \"https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00006-of-00006.gguf\"",
"verify_command": "python scripts/verify_glm_shards.py --model-dir \"$GLM_DEST\" --shard 6"
},
{
"step": 3,
"shard_index": 2,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00002-of-00006.gguf",
"size_bytes": 49208128256,
"size_gb": 49.208,
"sha256": "f2180207285e04fcaa5b8c53ba6e77ad5cc58666b6e7c6b04a5eded3fe8bef09",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00002-of-00006.gguf",
"download_command": "curl -L -C - --fail -o \"$GLM_DEST/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00002-of-00006.gguf\" \"https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00002-of-00006.gguf\"",
"verify_command": "python scripts/verify_glm_shards.py --model-dir \"$GLM_DEST\" --shard 2"
},
{
"step": 4,
"shard_index": 3,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00003-of-00006.gguf",
"size_bytes": 49684417024,
"size_gb": 49.684,
"sha256": "b1c0c5a302cc8d5d9ea0bcd4467c01db72c26839f820f7e882079582ea0a8d2b",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00003-of-00006.gguf",
"download_command": "curl -L -C - --fail -o \"$GLM_DEST/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00003-of-00006.gguf\" \"https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00003-of-00006.gguf\"",
"verify_command": "python scripts/verify_glm_shards.py --model-dir \"$GLM_DEST\" --shard 3"
},
{
"step": 5,
"shard_index": 4,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00004-of-00006.gguf",
"size_bytes": 49396052864,
"size_gb": 49.396,
"sha256": "a6a42da6975e29f89866dcde2956e9e50e6ea26635fb5063b74f3973f4f863b6",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00004-of-00006.gguf",
"download_command": "curl -L -C - --fail -o \"$GLM_DEST/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00004-of-00006.gguf\" \"https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00004-of-00006.gguf\"",
"verify_command": "python scripts/verify_glm_shards.py --model-dir \"$GLM_DEST\" --shard 4"
},
{
"step": 6,
"shard_index": 5,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00005-of-00006.gguf",
"size_bytes": 49246275936,
"size_gb": 49.246,
"sha256": "a4a9851a50db533f21ef824e5d8038f04e6782e7d602d18e5fdd6643f68ccccb",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00005-of-00006.gguf",
"download_command": "curl -L -C - --fail -o \"$GLM_DEST/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00005-of-00006.gguf\" \"https://huggingface.co/unsloth/GLM-5.2-GGUF/resolve/abc55e72527792c6e77069c99b4cb7de16fa9f23/UD-IQ1_S/GLM-5.2-UD-IQ1_S-00005-of-00006.gguf\"",
"verify_command": "python scripts/verify_glm_shards.py --model-dir \"$GLM_DEST\" --shard 5"
}
],
"verdict": "fail"
}

62
.scratch/distributed-gguf-runtime/evidence/MAINTENANCE-HANDOFF.md Normal file
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@@ -0,0 +1,62 @@
# Maintenance review handoff — distributed GGUF runtime
Date: 2026-07-14
Scope: close the maintenance review, preserve the hard blockers, and hand off the remaining implementation work to the next model.
## What is complete
- Completed stories are now recorded in `docs/issues/distributed-gguf-runtime/`.
- The PRD and milestone docs were updated to reflect the closed set and the blocked set.
- The DGR-018 preflight scripts were preserved at commit `a0f28b5`.
- The current feature line has delivered DGR-001 through DGR-006 and DGR-017.
## Hard / unsolved issues for later
### 1) DGR-018 requires hardware we do not have
DGR-018 is blocked because the whole-model GLM-5.2 UD-IQ1_S oracle requires:
- a **256-GiB-class host**,
- at least **224 GiB runtime-accessible memory**,
- at least **250 GB free storage on one filesystem outside `/home`**.
The current development host reports only **124.9 GiB MemTotal** and has no eligible filesystem with 250 GB free.
The authoritative blocker evidence is in `evidence/DGR-018/BLOCKED.md` and `evidence/DGR-018/preflight.json`.
### 2) DGR-019 and DGR-020 are transitively blocked
- **DGR-019** needs the DGR-018 oracle for parity certification.
- **DGR-020** needs DGR-018 and DGR-019, plus enough physical consumer nodes that no single node can admit the whole recipe.
No smaller model may be substituted for these stories.
### 3) The remainder of the graph stays blocked unless replanned
The current graph makes **DGR-007 depend on DGR-019**, which means:
- DGR-007 through DGR-016 are also blocked transitively.
- Unblocking the dense pipeline without the 256-GiB host would require an explicit replanning decision to relax the DGR-007 → DGR-019 dependency.
- That replanning decision has **not** been made.
### 4) Maintenance-only tasks should stay separate from feature implementation
The review uncovered that the codebase now has a clean closed-story split, but further work should avoid mixing:
- maintenance cleanup,
- blocked-hardware preparation,
- and actual distributed GLM implementation.
The next model should treat the maintenance pass as closed and only pick up real implementation work that is not hardware-blocked.
## Recommended next move
Use the next model to continue on the **non-blocked implementation queue** only.
Priority candidates are whatever is still actionable without the GLM oracle host; if a story depends on DGR-018, keep it deferred.
## Reference files
- `docs/issues/distributed-gguf-runtime/README.md`
- `.scratch/distributed-gguf-runtime/PRD.md`
- `.scratch/distributed-gguf-runtime/milestones.md`
- `.scratch/distributed-gguf-runtime/evidence/DGR-018/BLOCKED.md`
- `.scratch/distributed-gguf-runtime/evidence/DGR-018/preflight.json`