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feat: DGR-017 - Lock the GLM-5.2 Max target and alpha contract

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Dobromir Popov
2026-07-13 23:39:47 +03:00
parent 9580ed643e
commit e7c780a623
16 changed files with 3588 additions and 2 deletions
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123
packages/node/meshnet_node/glm_alpha/__init__.py Normal file
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"""The locked GLM-5.2 Max alpha target: identity, resource plan, and acceptance contract.
Three files, three jobs:
- :mod:`~meshnet_node.glm_alpha.manifest` — *is this the exact artifact?* Pinned
repository revisions, six shard digests, and the architecture-critical config
snapshot they must agree with.
- :mod:`~meshnet_node.glm_alpha.planner` — *can this route hold it?* Deterministic
memory, KV, and seam arithmetic over the exact artifact bytes, counting unified
memory once.
- :mod:`~meshnet_node.glm_alpha.contract` — *what would have counted as success?*
The acceptance thresholds, locked before the target runs and digest-bound so a
later change cannot be silent.
Nothing here downloads, loads, or executes a model. This package is the contract
DGR-018, DGR-019, and DGR-020 are judged against.
"""
from __future__ import annotations
from .contract import (
ALPHA_CONTRACT_ID,
ALPHA_CONTRACT_SCHEMA_VERSION,
VERDICT_ALPHA,
VERDICT_STOP,
AlphaContract,
AlphaContractError,
compute_contract_digest,
load_alpha_contract,
parse_alpha_contract,
require_contract_target,
seal_contract,
)
from .manifest import (
ALPHA_QUANTIZATION,
ALPHA_SHARD_COUNT,
ArchitectureSnapshot,
GlmTargetError,
Shard,
TargetManifest,
canonical_sha256,
load_architecture_snapshot,
load_target_manifest,
parse_architecture_snapshot,
parse_target_manifest,
require_pinned_target,
)
from .planner import (
AGGREGATE_HARD_FIT_FLOOR_GIB,
ALPHA_CONTEXT_TOKENS,
ALPHA_KV_DTYPE,
MIN_LINK_RATE_GBPS,
PLACEMENT_IMBALANCE_FACTOR,
RECOMMENDED_LINK_RATE_GBPS,
RESERVE_FLOOR_GIB,
RESERVE_FRACTION,
NodeMemory,
ResourcePlanError,
RouteFit,
SeamPlan,
TopologyPlan,
kv_bytes,
plan_all_tiers,
plan_route,
plan_seams,
plan_topology,
)
def load_locked_target() -> tuple[AlphaContract, TargetManifest, ArchitectureSnapshot]:
"""Load and cross-bind the packaged alpha contract, manifest, and snapshot."""
contract = load_alpha_contract()
manifest = load_target_manifest()
snapshot = load_architecture_snapshot()
require_contract_target(contract, manifest, snapshot)
return contract, manifest, snapshot
__all__ = [
"AGGREGATE_HARD_FIT_FLOOR_GIB",
"ALPHA_CONTEXT_TOKENS",
"ALPHA_CONTRACT_ID",
"ALPHA_CONTRACT_SCHEMA_VERSION",
"ALPHA_KV_DTYPE",
"ALPHA_QUANTIZATION",
"ALPHA_SHARD_COUNT",
"MIN_LINK_RATE_GBPS",
"PLACEMENT_IMBALANCE_FACTOR",
"RECOMMENDED_LINK_RATE_GBPS",
"RESERVE_FLOOR_GIB",
"RESERVE_FRACTION",
"VERDICT_ALPHA",
"VERDICT_STOP",
"AlphaContract",
"AlphaContractError",
"ArchitectureSnapshot",
"GlmTargetError",
"NodeMemory",
"ResourcePlanError",
"RouteFit",
"SeamPlan",
"Shard",
"TargetManifest",
"TopologyPlan",
"canonical_sha256",
"compute_contract_digest",
"kv_bytes",
"load_alpha_contract",
"load_architecture_snapshot",
"load_locked_target",
"load_target_manifest",
"parse_alpha_contract",
"parse_architecture_snapshot",
"parse_target_manifest",
"plan_all_tiers",
"plan_route",
"plan_seams",
"plan_topology",
"require_contract_target",
"require_pinned_target",
"seal_contract",
]

332
packages/node/meshnet_node/glm_alpha/contract.py Normal file
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"""The immutable GLM-5.2 Max alpha acceptance contract.
The contract exists to answer one question that cannot be answered honestly after
the fact: *what would have counted as success?*
Its thresholds are locked before the target ever runs (DGR-017), and DGR-020 reads
them back to publish an ``alpha`` or ``stop`` verdict. The whole point is that the
gap between those two moments is where a threshold quietly becomes "0.1 tokens/sec
was always the goal". So the document carries ``contract_sha256`` over its own
canonical content, and :func:`load_alpha_contract` recomputes it on every load. An
agent that edits a threshold and forgets the digest is rejected; an agent that
edits both has left a diff on a file whose whole purpose is to not change.
This is a tamper-*evidence* mechanism, not tamper-proofing. It cannot stop a
determined rewrite of the file and the digest together — nothing in-repo can. What
it does is remove the possibility of a silent one, which is the failure mode that
actually happens: a number nudged mid-run, with no reviewer ever seeing that it
moved.
"""
from __future__ import annotations
import json
import re
from dataclasses import dataclass
from importlib.resources import files
from pathlib import Path
from typing import Any, Mapping
from .manifest import (
ALPHA_QUANTIZATION,
ALPHA_SHARD_COUNT,
ArchitectureSnapshot,
GlmTargetError,
TargetManifest,
canonical_sha256,
)
ALPHA_CONTRACT_SCHEMA_VERSION = 1
ALPHA_CONTRACT_VERSION = 1
ALPHA_CONTRACT_ID = "glm-5.2-max-alpha/v1"
_CONTRACT_RESOURCE = "alpha-contract.json"
DIGEST_FIELD = "contract_sha256"
VERDICT_ALPHA = "alpha"
VERDICT_STOP = "stop"
# Every section the roadmap's acceptance matrix (section 5) locks. A contract that
# omits one is not a weaker contract, it is an unreviewable one.
REQUIRED_SECTIONS: tuple[str, ...] = (
"identity_and_fit",
"semantic_correctness",
"target_run",
"performance",
"reliability",
"storage",
)
class AlphaContractError(GlmTargetError):
"""Raised when the alpha contract is missing, malformed, or has been mutated."""
def contract_signing_payload(document: Mapping[str, Any]) -> dict:
"""The contract content the digest covers: everything except the digest itself."""
unsigned = dict(document)
unsigned.pop(DIGEST_FIELD, None)
return unsigned
def compute_contract_digest(document: Mapping[str, Any]) -> str:
"""SHA-256 over the canonical contract content."""
return canonical_sha256(contract_signing_payload(document))
@dataclass(frozen=True)
class AlphaContract:
"""A locked, digest-bound alpha acceptance contract."""
schema_version: int
contract_version: int
contract_id: str
locked_at: str
locked_by: str
target: Mapping[str, Any]
sections: Mapping[str, Mapping[str, Any]]
verdicts: tuple[str, ...]
amendment_policy: str
digest: str
raw: Mapping[str, Any]
source: str = "<memory>"
def section(self, name: str) -> Mapping[str, Any]:
if name not in self.sections:
raise AlphaContractError(f"contract section {name!r} is missing from {self.source}")
return self.sections[name]
def threshold(self, section: str, key: str) -> Any:
block = self.section(section)
if key not in block:
raise AlphaContractError(
f"threshold {section}.{key} is not locked in {self.source}; an unlocked "
"threshold cannot be used to judge a result"
)
return block[key]
def to_dict(self) -> dict:
return dict(self.raw)
def parse_alpha_contract(data: Any, source: str = "<memory>") -> AlphaContract:
"""Validate a contract document and verify it has not been mutated since locking."""
if not isinstance(data, Mapping):
raise AlphaContractError(f"contract root in {source} must be a JSON object")
schema_version = data.get("schema_version")
if (
not isinstance(schema_version, int)
or isinstance(schema_version, bool)
or schema_version != ALPHA_CONTRACT_SCHEMA_VERSION
):
raise AlphaContractError(
f"{source} declares alpha-contract schema version {schema_version!r}, but this "
f"node reads version {ALPHA_CONTRACT_SCHEMA_VERSION}"
)
contract_version = data.get("contract_version")
if (
not isinstance(contract_version, int)
or isinstance(contract_version, bool)
or contract_version != ALPHA_CONTRACT_VERSION
):
raise AlphaContractError(
f"{source} declares contract version {contract_version!r}, but this node "
f"reads version {ALPHA_CONTRACT_VERSION}"
)
contract_id = data.get("contract_id")
if contract_id != ALPHA_CONTRACT_ID:
raise AlphaContractError(
f"{source} declares contract_id {contract_id!r}, but this node is locked "
f"to {ALPHA_CONTRACT_ID!r}"
)
for field in ("locked_at", "locked_by"):
value = data.get(field)
if not isinstance(value, str) or not value.strip():
raise AlphaContractError(f"{source} must carry a non-empty {field}")
declared = data.get(DIGEST_FIELD)
if not isinstance(declared, str) or not declared:
raise AlphaContractError(
f"{source} carries no {DIGEST_FIELD}; an unsealed contract cannot prove it "
"predates the results it judges"
)
computed = compute_contract_digest(data)
if computed != declared:
raise AlphaContractError(
f"{source} has been modified since it was locked: its content hashes to "
f"{computed}, but it declares {declared}. Alpha thresholds are locked before "
"target execution and may not be weakened afterwards. To change them, open a "
"new contract_id under human review; do not edit this one."
)
if not data.get("locked_before_target_execution"):
raise AlphaContractError(
f"{source} does not assert locked_before_target_execution; a contract written "
"after the results are known is not a contract"
)
missing = [name for name in REQUIRED_SECTIONS if not isinstance(data.get(name), Mapping)]
if missing:
raise AlphaContractError(
f"{source} is missing locked acceptance section(s) {missing}"
)
verdicts = data.get("verdicts")
if not isinstance(verdicts, list) or sorted(verdicts) != sorted([VERDICT_ALPHA, VERDICT_STOP]):
raise AlphaContractError(
f"{source} must offer exactly the verdicts {[VERDICT_ALPHA, VERDICT_STOP]}; a "
"third outcome is how 'it loaded' becomes a pass"
)
target = data.get("target")
if not isinstance(target, Mapping):
raise AlphaContractError(f"{source} is missing its 'target' block")
for field in (
"source_repo_id",
"source_revision",
"gguf_repo_id",
"gguf_revision",
"quantization",
"target_manifest_sha256",
"architecture_snapshot_sha256",
"reasoning_effort",
):
if not target.get(field):
raise AlphaContractError(f"{source} target block is missing {field!r}")
for field in ("source_revision", "gguf_revision"):
if not re.fullmatch(r"[0-9a-f]{40}", str(target[field])):
raise AlphaContractError(f"{source} target.{field} is not a full commit revision")
for field in ("target_manifest_sha256", "architecture_snapshot_sha256"):
if not re.fullmatch(r"[0-9a-f]{64}", str(target[field])):
raise AlphaContractError(f"{source} target.{field} is not a SHA-256 digest")
if target["quantization"] != ALPHA_QUANTIZATION:
raise AlphaContractError(
f"{source} targets {target['quantization']!r}, not locked alpha quantization "
f"{ALPHA_QUANTIZATION!r}"
)
if target["reasoning_effort"] != "max":
raise AlphaContractError(f"{source} must lock reasoning_effort='max'")
shard_count = target.get("shard_count")
if (
not isinstance(shard_count, int)
or isinstance(shard_count, bool)
or shard_count != ALPHA_SHARD_COUNT
):
raise AlphaContractError(
f"{source} target.shard_count must be exactly {ALPHA_SHARD_COUNT}"
)
total_bytes = target.get("total_bytes")
if not isinstance(total_bytes, int) or isinstance(total_bytes, bool) or total_bytes <= 0:
raise AlphaContractError(f"{source} target.total_bytes must be a positive integer")
amendment_policy = data.get("amendment_policy")
if not isinstance(amendment_policy, str) or not amendment_policy.strip():
raise AlphaContractError(f"{source} must state its amendment policy")
return AlphaContract(
schema_version=schema_version,
contract_version=contract_version,
contract_id=contract_id,
locked_at=str(data["locked_at"]),
locked_by=str(data["locked_by"]),
target=target,
sections={name: data[name] for name in REQUIRED_SECTIONS},
verdicts=tuple(verdicts),
amendment_policy=amendment_policy,
digest=declared,
raw=data,
source=source,
)
def require_contract_target(
contract: AlphaContract,
manifest: TargetManifest,
snapshot: ArchitectureSnapshot,
) -> None:
"""Bind the sealed contract to the exact manifest and architecture snapshot.
Repository revisions alone do not bind shard LFS objects or derived architecture
semantics. Call this before planning, admission, download, or execution.
"""
expected = contract.target
actual = {
"source_repo_id": manifest.source_repo_id,
"source_revision": manifest.source_revision,
"gguf_repo_id": manifest.gguf_repo_id,
"gguf_revision": manifest.gguf_revision,
"quantization": manifest.quantization,
"shard_count": len(manifest.shards),
"total_bytes": manifest.total_bytes,
"target_manifest_sha256": manifest.digest,
"architecture_snapshot_sha256": snapshot.digest,
}
if snapshot.source_repo_id != manifest.source_repo_id:
raise AlphaContractError(
"architecture snapshot repository does not match the target manifest repository"
)
if snapshot.source_revision != manifest.source_revision:
raise AlphaContractError(
"architecture snapshot revision does not match the target manifest revision"
)
mismatches = {
key: (expected.get(key), value)
for key, value in actual.items()
if expected.get(key) != value
}
if mismatches:
details = ", ".join(
f"{key}: locked={locked!r}, actual={value!r}"
for key, (locked, value) in sorted(mismatches.items())
)
raise AlphaContractError(f"target documents do not match the sealed contract: {details}")
def load_alpha_contract(path: Path | None = None) -> AlphaContract:
"""Load the packaged alpha contract, or one at ``path``."""
if path is not None:
source = str(path)
try:
raw = path.read_text(encoding="utf-8")
except OSError as exc:
raise AlphaContractError(f"cannot read {source}: {exc.strerror or exc}") from exc
else:
source = f"packaged {_CONTRACT_RESOURCE}"
try:
raw = (
files("meshnet_node.glm_alpha")
.joinpath("data", _CONTRACT_RESOURCE)
.read_text(encoding="utf-8")
)
except (OSError, FileNotFoundError, ModuleNotFoundError) as exc:
raise AlphaContractError(
f"{source} is missing from this node installation ({type(exc).__name__})"
) from exc
try:
data = json.loads(raw)
except json.JSONDecodeError as exc:
raise AlphaContractError(
f"{source} is not valid JSON: {exc.msg} at line {exc.lineno} column {exc.colno}"
) from exc
return parse_alpha_contract(data, source=source)
def seal_contract(document: Mapping[str, Any]) -> dict:
"""Return the document with a freshly computed digest.
This is the only supported way to produce a contract file. It is deliberately
*not* called at load time: sealing on load would turn every mutation into a
valid contract, which is precisely the property the digest exists to deny.
"""
sealed = dict(document)
sealed[DIGEST_FIELD] = compute_contract_digest(document)
return sealed

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packages/node/meshnet_node/glm_alpha/data/alpha-contract.json Normal file
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{
"schema_version": 1,
"contract_version": 1,
"contract_id": "glm-5.2-max-alpha/v1",
"locked_at": "2026-07-13",
"locked_by": "DGR-017",
"locked_before_target_execution": true,
"target": {
"source_repo_id": "zai-org/GLM-5.2",
"source_revision": "b4734de4facf877f85769a911abafc5283eab3d9",
"gguf_repo_id": "unsloth/GLM-5.2-GGUF",
"gguf_revision": "abc55e72527792c6e77069c99b4cb7de16fa9f23",
"quantization": "UD-IQ1_S",
"shard_count": 6,
"total_bytes": 216715360960,
"target_manifest_sha256": "0b6aed04479d204902bb64c0203f1a46cab26a47b378ecccf85237b63f6c1962",
"architecture_snapshot_sha256": "253fbd94b06b42acc4724ec2c7f33914e2d4cc43f54a36dff6af19a80ae6ceb1",
"reasoning_effort": "max"
},
"identity_and_fit": {
"require_exact_revisions": true,
"require_all_shard_sha256": true,
"require_per_node_owned_tensor_report": true,
"require_owned_tensor_union_equals_inventory": true,
"max_unintended_tensor_overlap": 0,
"require_no_single_node_can_admit_complete_recipe": true,
"min_node_reserve_fraction": 0.2,
"min_node_reserve_gib": 8.0,
"require_measured_peak_scratch_inside_reserve": true,
"forbid_swap": true,
"forbid_overcommit": true,
"forbid_mmap_only_fit_claim": true,
"forbid_double_counted_unified_memory": true,
"aggregate_hard_fit_floor_gib": 224.0,
"aggregate_floor_class": "experimental_hard_fit_floor",
"recommended_topologies": [
"5x64GiB",
"3x96GiB",
"3x128GiB"
],
"arithmetic_minimum_requires_measured_placement_evidence": true
},
"semantic_correctness": {
"require_active_moe_routing": true,
"require_active_shared_expert": true,
"require_active_dsa_lightning_indexer": true,
"require_active_sparse_attention": true,
"require_active_indexshare_full_and_shared": true,
"dense_attention_fallback_satisfies_alpha": false,
"require_rendered_reasoning_effort_marker": "<|system|>Reasoning Effort: Max",
"f32_seam_fixture_exact_match_tokens": 32,
"f32_seam_fixture_requires_exact_match": true,
"min_greedy_token_agreement": 0.9,
"min_mean_state_cosine_similarity": 0.999,
"forbid_nonfinite_tensors": true,
"require_fail_closed_on_fingerprint_mismatch": true
},
"target_run": {
"context_tokens": 16384,
"kv_dtype": "Q8_0",
"concurrency": 1,
"prompt_lane_tokens": 4096,
"min_output_tokens": 512,
"min_output_tokens_with_natural_eos": 128,
"require_same_switch_wired_network": true,
"min_link_rate_gbps": 2.5,
"recommended_link_rate_gbps": 10.0,
"require_sentinels": [
"coding",
"structured_tool_call_json",
"multi_step_reasoning"
],
"require_openai_compatible_response_fields": [
"model",
"finish_reason",
"usage"
]
},
"performance": {
"min_median_decode_tokens_per_second": 0.5,
"max_ttft_seconds_at_4096_prompt": 600,
"max_unexplained_stall_seconds": 60,
"warmups": 1,
"require_per_stage_telemetry": [
"compute",
"queue",
"kv",
"seam_bytes",
"seam_latency",
"rss",
"vram",
"backend_timing"
],
"quality_pass_with_speed_fail_verdict": "stop",
"forbid_generalising_results_to_other_hardware": true
},
"reliability": {
"consecutive_clean_cold_starts": 2,
"require_cancellation_releases_buffers_and_kv": true,
"require_worker_loss_aborts_route": true,
"retry_policy": "from_token_zero_on_new_compatible_route",
"forbid_silent_kv_migration": true,
"require_reject_stale_epoch": true,
"require_reject_duplicate_step_id": true,
"synthetic_workers_satisfy_alpha": false,
"layer_reduced_fixtures_satisfy_alpha": false
},
"storage": {
"mounted_storage_only": true,
"forbidden_path_prefixes": [
"/home"
],
"forbid_secrets_in_logs": true,
"forbid_unrestricted_prompt_payloads_in_logs": true
},
"verdicts": [
"alpha",
"stop"
],
"amendment_policy": "Thresholds are locked before target execution. They may not be weakened, moved, or reinterpreted after results are known. A change requires a new contract_id and contract_version under human review, and the superseded contract is retained.",
"contract_sha256": "aab23220280c053a3c14ff559df3cb5c9e1bf7f0f7188c6519e2e9d9ad036ed9"
}

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packages/node/meshnet_node/glm_alpha/data/architecture-snapshot.json Normal file
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{
"schema_version": 1,
"observed_at": "2026-07-13",
"source_repo_id": "zai-org/GLM-5.2",
"source_revision": "b4734de4facf877f85769a911abafc5283eab3d9",
"source_files": [
{
"path": "config.json",
"size_bytes": 3732,
"sha256": "185f93ee6d12548e16a847e279dc0c3c90b1524c970b0866b42fb545747d859a",
"url": "https://huggingface.co/zai-org/GLM-5.2/resolve/b4734de4facf877f85769a911abafc5283eab3d9/config.json"
},
{
"path": "chat_template.jinja",
"size_bytes": 5076,
"sha256": "172dc74a35e1752df75ecfb2b2cf9326d2852bb1379868ebeec9571654489679",
"url": "https://huggingface.co/zai-org/GLM-5.2/resolve/b4734de4facf877f85769a911abafc5283eab3d9/chat_template.jinja"
},
{
"path": "generation_config.json",
"size_bytes": 194,
"sha256": "ac76b43d8683d3b930126870fc8be73d8679308fe752fa1f381096d8354f6a55",
"url": "https://huggingface.co/zai-org/GLM-5.2/resolve/b4734de4facf877f85769a911abafc5283eab3d9/generation_config.json"
},
{
"path": "tokenizer_config.json",
"size_bytes": 761,
"sha256": "98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc",
"url": "https://huggingface.co/zai-org/GLM-5.2/resolve/b4734de4facf877f85769a911abafc5283eab3d9/tokenizer_config.json"
}
],
"architecture": {
"architectures": [
"GlmMoeDsaForCausalLM"
],
"model_type": "glm_moe_dsa",
"num_hidden_layers": 78,
"num_nextn_predict_layers": 1,
"total_artifact_layers": 79,
"first_k_dense_replace": 3,
"dense_layers": 3,
"sparse_moe_layers": 75,
"hidden_size": 6144,
"intermediate_size": 12288,
"moe_intermediate_size": 2048,
"n_routed_experts": 256,
"num_experts_per_tok": 8,
"n_shared_experts": 1,
"scoring_func": "sigmoid",
"topk_method": "noaux_tc",
"norm_topk_prob": true,
"routed_scaling_factor": 2.5,
"num_attention_heads": 64,
"head_dim": 192,
"qk_nope_head_dim": 192,
"qk_rope_head_dim": 64,
"qk_head_dim": 256,
"v_head_dim": 256,
"kv_lora_rank": 512,
"q_lora_rank": 2048,
"mla_cached_values_per_token_per_layer": 576,
"index_topk": 2048,
"index_head_dim": 128,
"index_n_heads": 32,
"index_topk_freq": 4,
"index_skip_topk_offset": 3,
"index_share_for_mtp_iteration": true,
"indexer_full_layers": 21,
"indexer_shared_layers": 57,
"indexer_types_sha256": "ec3b4927af83cf02baf37fb10454c40176ec8bf501ae89334b27a9df5fa17025",
"max_position_embeddings": 1048576,
"vocab_size": 154880,
"rope_theta": 8000000,
"dtype": "bfloat16",
"tie_word_embeddings": false
},
"reasoning_effort": {
"alpha_mode": "max",
"rendered_marker": "<|system|>Reasoning Effort: Max",
"template_rule": "effective_reasoning_effort = 'high' if reasoning_effort == 'high' else 'max'",
"default_is_max": true,
"suppressed_when": "enable_thinking is defined and false",
"note": "The template recognises exactly one non-max level ('high'); every other value, including an absent reasoning_effort, renders Max. Alpha therefore asserts the rendered 'Reasoning Effort: Max' marker, not merely the presence of a request field."
},
"notes": [
"indexer_types has 78 entries: layers 0-2 are 'full', then a repeating [shared, shared, shared, full] pattern, giving 21 Full producer layers and 57 Shared consumer layers.",
"MLA caches kv_lora_rank (512) + qk_rope_head_dim (64) = 576 values per token per backbone layer.",
"num_nextn_predict_layers=1 is the NextN/MTP layer present in the artifact. Alpha does not run MTP; the tensors are loaded or explicitly excluded by a certified recipe and must never be silently reinterpreted as a 79th backbone layer.",
"Values are derived from the pinned config.json. The runtime must re-derive them from the artifact and fail closed on contradictory metadata; marketing names are not compatibility identity."
]
}

90
packages/node/meshnet_node/glm_alpha/data/target-manifest.json Normal file
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{
"schema_version": 1,
"manifest_version": 1,
"observed_at": "2026-07-13",
"observed_by": "DGR-017",
"alpha_quantization": "UD-IQ1_S",
"source_model": {
"repo_id": "zai-org/GLM-5.2",
"revision": "b4734de4facf877f85769a911abafc5283eab3d9",
"last_modified": "2026-07-02T08:08:14.000Z",
"weight_license": "mit",
"code_documentation_license": "apache-2.0",
"url": "https://huggingface.co/zai-org/GLM-5.2",
"revision_url": "https://huggingface.co/zai-org/GLM-5.2/tree/b4734de4facf877f85769a911abafc5283eab3d9",
"api_url": "https://huggingface.co/api/models/zai-org/GLM-5.2"
},
"gguf_artifact": {
"repo_id": "unsloth/GLM-5.2-GGUF",
"revision": "abc55e72527792c6e77069c99b4cb7de16fa9f23",
"last_modified": "2026-06-23T15:18:23.000Z",
"license": "mit",
"url": "https://huggingface.co/unsloth/GLM-5.2-GGUF",
"revision_url": "https://huggingface.co/unsloth/GLM-5.2-GGUF/tree/abc55e72527792c6e77069c99b4cb7de16fa9f23",
"api_url": "https://huggingface.co/api/models/unsloth/GLM-5.2-GGUF",
"quantization": "UD-IQ1_S",
"shard_count": 6,
"total_bytes": 216715360960,
"total_gib": 201.832,
"total_gb": 216.715,
"shards": [
{
"index": 1,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00001-of-00006.gguf",
"size_bytes": 9423744,
"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"
},
{
"index": 2,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00002-of-00006.gguf",
"size_bytes": 49208128256,
"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"
},
{
"index": 3,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00003-of-00006.gguf",
"size_bytes": 49684417024,
"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"
},
{
"index": 4,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00004-of-00006.gguf",
"size_bytes": 49396052864,
"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"
},
{
"index": 5,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00005-of-00006.gguf",
"size_bytes": 49246275936,
"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"
},
{
"index": 6,
"path": "UD-IQ1_S/GLM-5.2-UD-IQ1_S-00006-of-00006.gguf",
"size_bytes": 19171063136,
"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"
}
]
},
"diagnostic_fallback": {
"quantization": "UD-IQ1_M",
"shard_count": 6,
"total_bytes": 228492966624,
"total_gib": 212.801,
"total_gb": 228.493,
"policy": "First diagnostic fallback only if UD-IQ1_S exposes a runtime or quality defect. It does not satisfy the alpha 'lowest published quantization' target unless human review changes the target contract."
},
"storage": {
"mounted_storage_only": true,
"forbidden_path_prefixes": [
"/home"
],
"note": "Model artifacts resolve through the machine-specific .env.<hostname> mounted-drive configuration. A path under /home fails admission closed."
}
}

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"""The pinned GLM-5.2 Max target manifest and architecture snapshot.
This module is the *identity* half of the alpha target contract. It answers one
question: is the artifact on this disk the exact artifact alpha was locked
against?
Identity is pinned by repository revision **and** by every shard's LFS SHA-256.
A revision alone is not enough — a repository can be force-pushed, and a tag can
be moved — and a size alone is not enough, because two different quantizations of
the same model land within a rounding error of each other. The manifest therefore
carries both, plus the aggregate byte total, and cross-checks the aggregate
against the sum of the shards. A manifest whose declared total disagrees with its
own shards is rejected rather than trusted, because that is the exact shape a
hand-edited "it fits now" manifest takes.
Nothing here downloads a weight payload. Sizes and hashes come from the Hugging
Face metadata API (see ``scripts/refresh_glm_target_manifest.py``); verification
against a local file is DGR-018's job, using the digests locked here.
"""
from __future__ import annotations
import hashlib
import json
import re
from dataclasses import dataclass
from importlib.resources import files
from pathlib import Path
from typing import Any, Mapping
TARGET_MANIFEST_SCHEMA_VERSION = 1
ARCHITECTURE_SNAPSHOT_SCHEMA_VERSION = 1
ALPHA_QUANTIZATION = "UD-IQ1_S"
ALPHA_SHARD_COUNT = 6
_SHA256_RE = re.compile(r"\A[0-9a-f]{64}\Z")
_MANIFEST_RESOURCE = "target-manifest.json"
_ARCHITECTURE_RESOURCE = "architecture-snapshot.json"
GIB = 1024**3
GB = 1000**3
class GlmTargetError(ValueError):
"""Raised when the target manifest or architecture snapshot is not the pinned target."""
def canonical_sha256(value: Any) -> str:
"""SHA-256 over canonical JSON — the repository's digest convention."""
payload = json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False)
return hashlib.sha256(payload.encode("utf-8")).hexdigest()
def _require_mapping(value: Any, what: str) -> Mapping[str, Any]:
if not isinstance(value, Mapping):
raise GlmTargetError(f"{what} must be a JSON object, got {type(value).__name__}")
return value
def _require_text(value: Any, what: str) -> str:
if not isinstance(value, str) or not value.strip():
raise GlmTargetError(f"{what} must be a non-empty string")
return value
def _require_int(value: Any, what: str) -> int:
if not isinstance(value, int) or isinstance(value, bool):
raise GlmTargetError(f"{what} must be an integer, got {type(value).__name__}")
return value
def _require_sha256(value: Any, what: str) -> str:
text = _require_text(value, what)
if not _SHA256_RE.match(text):
raise GlmTargetError(
f"{what} must be a lowercase 64-character hex SHA-256, got {text!r}"
)
return text
def _require_revision(value: Any, what: str) -> str:
text = _require_text(value, what)
if not re.fullmatch(r"[0-9a-f]{40}", text):
raise GlmTargetError(
f"{what} must be a full 40-character commit revision, got {text!r}; "
"a branch name or short SHA is not an immutable pin"
)
return text
@dataclass(frozen=True)
class Shard:
"""One GGUF shard of the alpha artifact."""
index: int
path: str
size_bytes: int
sha256: str
url: str
def to_dict(self) -> dict:
return {
"index": self.index,
"path": self.path,
"size_bytes": self.size_bytes,
"sha256": self.sha256,
"url": self.url,
}
@dataclass(frozen=True)
class TargetManifest:
"""The pinned, self-consistent GLM-5.2 ``UD-IQ1_S`` target."""
schema_version: int
manifest_version: int
observed_at: str
quantization: str
source_repo_id: str
source_revision: str
source_license: str
gguf_repo_id: str
gguf_revision: str
gguf_license: str
total_bytes: int
shards: tuple[Shard, ...]
raw: Mapping[str, Any]
source: str = "<memory>"
@property
def total_gib(self) -> float:
return self.total_bytes / GIB
@property
def total_gb(self) -> float:
return self.total_bytes / GB
def shard(self, index: int) -> Shard:
for shard in self.shards:
if shard.index == index:
return shard
raise GlmTargetError(f"shard {index} is not in {self.source}")
@property
def digest(self) -> str:
"""Stable identity of this manifest, for the DGR-003 runtime recipe."""
return canonical_sha256(self.raw)
def to_dict(self) -> dict:
return dict(self.raw)
def _parse_shards(raw: Any, expected_count: int, expected_total: int) -> tuple[Shard, ...]:
if not isinstance(raw, list):
raise GlmTargetError("gguf_artifact.shards must be a JSON array")
if len(raw) != expected_count:
raise GlmTargetError(
f"the alpha artifact has exactly {expected_count} shards, "
f"but the manifest lists {len(raw)}"
)
shards: list[Shard] = []
seen_index: set[int] = set()
seen_sha: set[str] = set()
for position, entry in enumerate(raw):
item = _require_mapping(entry, f"shards[{position}]")
index = _require_int(item.get("index"), f"shards[{position}].index")
if index in seen_index:
raise GlmTargetError(f"duplicate shard index {index} in the manifest")
seen_index.add(index)
size_bytes = _require_int(item.get("size_bytes"), f"shards[{index}].size_bytes")
if size_bytes <= 0:
raise GlmTargetError(f"shards[{index}].size_bytes must be positive")
sha256 = _require_sha256(item.get("sha256"), f"shards[{index}].sha256")
if sha256 in seen_sha:
raise GlmTargetError(
f"shard {index} repeats SHA-256 {sha256}; two distinct shards cannot "
"have the same content digest"
)
seen_sha.add(sha256)
shards.append(
Shard(
index=index,
path=_require_text(item.get("path"), f"shards[{index}].path"),
size_bytes=size_bytes,
sha256=sha256,
url=_require_text(item.get("url"), f"shards[{index}].url"),
)
)
expected_indices = set(range(1, expected_count + 1))
if seen_index != expected_indices:
missing = sorted(expected_indices - seen_index)
raise GlmTargetError(
f"the manifest is missing shard(s) {missing}; all {expected_count} "
"shards of the alpha artifact must be pinned"
)
summed = sum(shard.size_bytes for shard in shards)
if summed != expected_total:
raise GlmTargetError(
f"declared total_bytes {expected_total} does not equal the sum of the "
f"shard sizes {summed}; the manifest is not self-consistent"
)
return tuple(sorted(shards, key=lambda shard: shard.index))
def parse_target_manifest(data: Any, source: str = "<memory>") -> TargetManifest:
"""Validate an already-decoded target manifest, failing closed."""
doc = _require_mapping(data, f"manifest root in {source}")
schema_version = _require_int(doc.get("schema_version"), f"'schema_version' in {source}")
if schema_version != TARGET_MANIFEST_SCHEMA_VERSION:
raise GlmTargetError(
f"{source} declares target-manifest schema version {schema_version}, "
f"but this node reads version {TARGET_MANIFEST_SCHEMA_VERSION}"
)
quantization = _require_text(doc.get("alpha_quantization"), f"'alpha_quantization' in {source}")
if quantization != ALPHA_QUANTIZATION:
raise GlmTargetError(
f"{source} pins quantization {quantization!r}, but the locked alpha "
f"quantization is {ALPHA_QUANTIZATION!r}; a different quantization is a "
"different target and requires a human contract change"
)
source_model = _require_mapping(doc.get("source_model"), f"'source_model' in {source}")
gguf = _require_mapping(doc.get("gguf_artifact"), f"'gguf_artifact' in {source}")
gguf_quant = _require_text(gguf.get("quantization"), f"gguf_artifact.quantization in {source}")
if gguf_quant != quantization:
raise GlmTargetError(
f"{source} declares alpha_quantization {quantization!r} but the GGUF "
f"artifact block says {gguf_quant!r}"
)
shard_count = _require_int(gguf.get("shard_count"), f"gguf_artifact.shard_count in {source}")
if shard_count != ALPHA_SHARD_COUNT:
raise GlmTargetError(
f"{source} declares {shard_count} shards; the pinned alpha artifact has "
f"exactly {ALPHA_SHARD_COUNT}"
)
total_bytes = _require_int(gguf.get("total_bytes"), f"gguf_artifact.total_bytes in {source}")
shards = _parse_shards(gguf.get("shards"), shard_count, total_bytes)
return TargetManifest(
schema_version=schema_version,
manifest_version=_require_int(doc.get("manifest_version"), f"'manifest_version' in {source}"),
observed_at=_require_text(doc.get("observed_at"), f"'observed_at' in {source}"),
quantization=quantization,
source_repo_id=_require_text(source_model.get("repo_id"), "source_model.repo_id"),
source_revision=_require_revision(source_model.get("revision"), "source_model.revision"),
source_license=_require_text(source_model.get("weight_license"), "source_model.weight_license"),
gguf_repo_id=_require_text(gguf.get("repo_id"), "gguf_artifact.repo_id"),
gguf_revision=_require_revision(gguf.get("revision"), "gguf_artifact.revision"),
gguf_license=_require_text(gguf.get("license"), "gguf_artifact.license"),
total_bytes=total_bytes,
shards=shards,
raw=doc,
source=source,
)
@dataclass(frozen=True)
class ArchitectureSnapshot:
"""Architecture-critical metadata derived from the pinned ``config.json``."""
schema_version: int
source_repo_id: str
source_revision: str
architecture: Mapping[str, Any]
reasoning_effort: Mapping[str, Any]
source_files: Mapping[str, Mapping[str, Any]]
raw: Mapping[str, Any]
source: str = "<memory>"
def __getitem__(self, key: str) -> Any:
if key not in self.architecture:
raise GlmTargetError(f"architecture field {key!r} is missing from {self.source}")
return self.architecture[key]
@property
def digest(self) -> str:
return canonical_sha256(self.raw)
def file_sha256(self, path: str) -> str:
entry = self.source_files.get(path)
if entry is None:
raise GlmTargetError(f"{path!r} is not pinned in {self.source}")
return str(entry["sha256"])
def to_dict(self) -> dict:
return dict(self.raw)
# Fields the distributed runtime cannot plan or shard without. Absent or
# contradictory values fail closed rather than defaulting.
REQUIRED_ARCHITECTURE_FIELDS: tuple[str, ...] = (
"model_type",
"num_hidden_layers",
"num_nextn_predict_layers",
"total_artifact_layers",
"first_k_dense_replace",
"dense_layers",
"sparse_moe_layers",
"hidden_size",
"n_routed_experts",
"num_experts_per_tok",
"n_shared_experts",
"kv_lora_rank",
"qk_rope_head_dim",
"mla_cached_values_per_token_per_layer",
"index_topk",
"index_head_dim",
"indexer_full_layers",
"indexer_shared_layers",
"max_position_embeddings",
"vocab_size",
)
def parse_architecture_snapshot(data: Any, source: str = "<memory>") -> ArchitectureSnapshot:
"""Validate an architecture snapshot and its internal arithmetic."""
doc = _require_mapping(data, f"snapshot root in {source}")
schema_version = _require_int(doc.get("schema_version"), f"'schema_version' in {source}")
if schema_version != ARCHITECTURE_SNAPSHOT_SCHEMA_VERSION:
raise GlmTargetError(
f"{source} declares architecture-snapshot schema version {schema_version}, "
f"but this node reads version {ARCHITECTURE_SNAPSHOT_SCHEMA_VERSION}"
)
arch = _require_mapping(doc.get("architecture"), f"'architecture' in {source}")
missing = [field for field in REQUIRED_ARCHITECTURE_FIELDS if field not in arch]
if missing:
raise GlmTargetError(
f"{source} is missing architecture-critical field(s) {missing}; the "
"runtime cannot shard or plan an architecture it cannot fully describe"
)
layers = _require_int(arch["num_hidden_layers"], "num_hidden_layers")
nextn = _require_int(arch["num_nextn_predict_layers"], "num_nextn_predict_layers")
total_layers = _require_int(arch["total_artifact_layers"], "total_artifact_layers")
if total_layers != layers + nextn:
raise GlmTargetError(
f"total_artifact_layers {total_layers} != num_hidden_layers {layers} + "
f"num_nextn_predict_layers {nextn}; the NextN layer must be counted "
"explicitly, never folded into the backbone"
)
dense = _require_int(arch["dense_layers"], "dense_layers")
sparse = _require_int(arch["sparse_moe_layers"], "sparse_moe_layers")
if dense != _require_int(arch["first_k_dense_replace"], "first_k_dense_replace"):
raise GlmTargetError("dense_layers must equal first_k_dense_replace")
if dense + sparse != layers:
raise GlmTargetError(
f"dense_layers {dense} + sparse_moe_layers {sparse} != num_hidden_layers {layers}"
)
full = _require_int(arch["indexer_full_layers"], "indexer_full_layers")
shared = _require_int(arch["indexer_shared_layers"], "indexer_shared_layers")
if full + shared != layers:
raise GlmTargetError(
f"indexer_full_layers {full} + indexer_shared_layers {shared} != "
f"num_hidden_layers {layers}; every layer holds exactly one IndexShare role"
)
if full <= 0:
raise GlmTargetError(
"indexer_full_layers must be positive; a route with no Full producer layer "
"has no index for its Shared consumers to reuse"
)
mla = _require_int(
arch["mla_cached_values_per_token_per_layer"], "mla_cached_values_per_token_per_layer"
)
expected_mla = _require_int(arch["kv_lora_rank"], "kv_lora_rank") + _require_int(
arch["qk_rope_head_dim"], "qk_rope_head_dim"
)
if mla != expected_mla:
raise GlmTargetError(
f"mla_cached_values_per_token_per_layer {mla} != kv_lora_rank + "
f"qk_rope_head_dim ({expected_mla})"
)
reasoning = _require_mapping(doc.get("reasoning_effort"), f"'reasoning_effort' in {source}")
if reasoning.get("alpha_mode") != "max":
raise GlmTargetError(
f"{source} does not lock reasoning_effort=max; alpha is defined as the "
"Max reasoning mode of this exact checkpoint"
)
_require_text(reasoning.get("rendered_marker"), "reasoning_effort.rendered_marker")
files_raw = doc.get("source_files")
if not isinstance(files_raw, list) or not files_raw:
raise GlmTargetError(f"'source_files' in {source} must be a non-empty JSON array")
source_files: dict[str, Mapping[str, Any]] = {}
for position, entry in enumerate(files_raw):
item = _require_mapping(entry, f"source_files[{position}]")
path = _require_text(item.get("path"), f"source_files[{position}].path")
_require_sha256(item.get("sha256"), f"source_files[{path}].sha256")
source_files[path] = item
for required in ("config.json", "chat_template.jinja"):
if required not in source_files:
raise GlmTargetError(
f"{source} does not pin {required!r}; config and chat-template drift "
"silently changes runtime semantics"
)
return ArchitectureSnapshot(
schema_version=schema_version,
source_repo_id=_require_text(doc.get("source_repo_id"), "source_repo_id"),
source_revision=_require_revision(doc.get("source_revision"), "source_revision"),
architecture=arch,
reasoning_effort=reasoning,
source_files=source_files,
raw=doc,
source=source,
)
def _read_resource(resource: str, path: Path | None) -> tuple[str, str]:
if path is not None:
try:
return str(path), path.read_text(encoding="utf-8")
except OSError as exc:
raise GlmTargetError(f"cannot read {path}: {exc.strerror or exc}") from exc
source = f"packaged {resource}"
try:
raw = files("meshnet_node.glm_alpha").joinpath("data", resource).read_text(encoding="utf-8")
except (OSError, FileNotFoundError, ModuleNotFoundError) as exc:
raise GlmTargetError(
f"{source} is missing from this node installation ({type(exc).__name__})"
) from exc
return source, raw
def _load_json(resource: str, path: Path | None) -> tuple[str, Any]:
source, raw = _read_resource(resource, path)
try:
return source, json.loads(raw)
except json.JSONDecodeError as exc:
raise GlmTargetError(
f"{source} is not valid JSON: {exc.msg} at line {exc.lineno} column {exc.colno}"
) from exc
def load_target_manifest(path: Path | None = None) -> TargetManifest:
"""Load the packaged target manifest, or one at ``path``."""
source, data = _load_json(_MANIFEST_RESOURCE, path)
return parse_target_manifest(data, source=source)
def load_architecture_snapshot(path: Path | None = None) -> ArchitectureSnapshot:
"""Load the packaged architecture snapshot, or one at ``path``."""
source, data = _load_json(_ARCHITECTURE_RESOURCE, path)
return parse_architecture_snapshot(data, source=source)
def require_pinned_target(
manifest: TargetManifest,
snapshot: ArchitectureSnapshot,
*,
expected_source_revision: str,
expected_gguf_revision: str,
) -> None:
"""Reject any target whose revisions are not the ones alpha was locked against.
Callers pass the revisions from the locked alpha contract, so a swapped
manifest cannot quietly re-point the target at a different upstream commit.
"""
if manifest.source_revision != expected_source_revision:
raise GlmTargetError(
f"source revision {manifest.source_revision} does not match the locked "
f"alpha revision {expected_source_revision}"
)
if manifest.gguf_revision != expected_gguf_revision:
raise GlmTargetError(
f"GGUF revision {manifest.gguf_revision} does not match the locked alpha "
f"revision {expected_gguf_revision}"
)
if snapshot.source_revision != manifest.source_revision:
raise GlmTargetError(
f"the architecture snapshot was taken at {snapshot.source_revision} but the "
f"manifest pins {manifest.source_revision}; config metadata and weights must "
"come from one revision"
)

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packages/node/meshnet_node/glm_alpha/planner.py Normal file
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"""Deterministic memory, KV, and network planner for the GLM-5.2 Max alpha route.
Everything here is arithmetic over the exact pinned artifact bytes and the exact
pinned architecture. There is no measurement, no probing, and no heuristic tuned
to a result — the planner is written *before* the target runs so that a later
story cannot discover a topology that "works" and then rationalise it.
Three ideas do the real work.
**Unified memory is one pool.** On an integrated-GPU machine the "VRAM" the driver
reports is carved out of the same physical DRAM the OS is already counting. Adding
them produces a node that appears to hold twice what it holds, and the failure mode
is not a clean admission rejection — it is an OOM or a swap-thrash halfway through
a 200 GiB load. :class:`NodeMemory` therefore refuses to be constructed from an
additive claim about one shared pool.
**The reserve is not optional headroom.** Weights plus KV are not the whole
resident cost: backend workspaces, quantization scratch, the graph plan, the
process, and the OS all live outside them, and the largest of those scale with the
backend rather than with the shard. Alpha reserves ``max(20% of physically usable
memory, 8 GiB)`` per node, and the *remainder* is the placement budget.
**Equal layer counts are not equal bytes.** Embeddings and the output head are
endpoint-only; three layers are dense and 75 are MoE; shared experts, indexer
tensors, and quant block alignment all skew the per-node share. Until DGR-018/019
report measured per-tensor placement, the planner carries an explicit
:data:`PLACEMENT_IMBALANCE_FACTOR` and reports the arithmetic minimum and the
recommended count as two separate numbers. The arithmetic minimum is a fit probe;
it is admissible only with exact measured placement evidence behind it.
"""
from __future__ import annotations
import math
from dataclasses import dataclass
from typing import Literal
from .manifest import GIB, ArchitectureSnapshot, TargetManifest
# Q8_0 stores 32 int8 quants plus one fp16 scale per block: 34 bytes / 32 values.
Q8_0_BYTES_PER_VALUE = 34 / 32
F16_BYTES_PER_VALUE = 2.0
KV_DTYPES: dict[str, float] = {
"Q8_0": Q8_0_BYTES_PER_VALUE,
"F16": F16_BYTES_PER_VALUE,
}
# The alpha KV configuration, locked by the roadmap.
ALPHA_KV_DTYPE = "Q8_0"
ALPHA_CONTEXT_TOKENS = 16384
ALPHA_CONCURRENCY = 1
# The reserve every node holds outside its weight-plus-KV placement budget.
RESERVE_FRACTION = 0.20
RESERVE_FLOOR_GIB = 8.0
# The aggregate runtime-accessible memory at which the artifact *just* fits.
# This is an experimental hard-fit floor, not an operational envelope: it has no
# room for a backend that allocates more scratch than another, and none for the
# imbalance below.
AGGREGATE_HARD_FIT_FLOOR_GIB = 224.0
# How much more than an equal share the worst-placed node is expected to hold.
# 1.10 is the roadmap's recommended-topology column expressed as arithmetic: it
# reproduces 10 / 6 / 5 / 3 / 3 nodes for the 32 / 48 / 64 / 96 / 128 GiB tiers.
# DGR-019 must replace it with measured per-tensor placement.
PLACEMENT_IMBALANCE_FACTOR = 1.10
# Alpha network floor. A link rate is a bandwidth claim, never a speed claim.
MIN_LINK_RATE_GBPS = 2.5
RECOMMENDED_LINK_RATE_GBPS = 10.0
BF16_BYTES = 2
DSA_SIDEBAND_INT32_BYTES = 4
IndexerLayout = Literal["optimized", "conservative"]
class ResourcePlanError(ValueError):
"""Raised when a node or route cannot be accounted for honestly."""
@dataclass(frozen=True)
class NodeMemory:
"""One node's physically usable memory, counted once.
``physical_usable_gib`` is what the node can actually place bytes into after
firmware and fixed carve-outs — not the marketing capacity, and not a sum of
two views of the same DRAM.
"""
name: str
physical_usable_gib: float
unified: bool
def __post_init__(self) -> None:
if not isinstance(self.name, str) or not self.name.strip():
raise ResourcePlanError("node name must be a non-empty physical-host identity")
if (
isinstance(self.physical_usable_gib, bool)
or not isinstance(self.physical_usable_gib, (int, float))
or not math.isfinite(self.physical_usable_gib)
or self.physical_usable_gib <= 0
):
raise ResourcePlanError(
f"node {self.name!r} must declare finite positive usable memory"
)
@classmethod
def from_host(
cls,
name: str,
*,
system_ram_gib: float,
gpu_memory_gib: float = 0.0,
unified: bool,
) -> "NodeMemory":
"""Build a node from a host's reported RAM and GPU memory.
On a unified machine the GPU memory *is* system RAM, so it is counted once
and never added. Passing a non-zero ``gpu_memory_gib`` alongside
``unified=True`` is the double-count this project has already decided is a
bug (RALPH-CONTEXT runtime decision 16), so it is rejected rather than
silently discarded: a caller who believes an integrated GPU adds memory has
a wrong model of the machine, and quietly ignoring the argument would let
that belief survive.
"""
if not isinstance(unified, bool):
raise ResourcePlanError(f"node {name!r} unified flag must be boolean")
for value, label, allow_zero in (
(system_ram_gib, "system RAM", False),
(gpu_memory_gib, "GPU memory", True),
):
if (
isinstance(value, bool)
or not isinstance(value, (int, float))
or not math.isfinite(value)
or value < 0
or (not allow_zero and value == 0)
):
qualifier = "finite non-negative" if allow_zero else "finite positive"
raise ResourcePlanError(f"node {name!r} must declare {qualifier} {label}")
if unified:
if gpu_memory_gib:
raise ResourcePlanError(
f"node {name!r} declares unified memory and {gpu_memory_gib} GiB of "
"separate GPU memory. Integrated-GPU memory is carved out of the same "
"physical DRAM as system RAM; adding them double-counts one pool. "
"Pass unified=True with system_ram_gib only."
)
usable = system_ram_gib
else:
usable = system_ram_gib + gpu_memory_gib
return cls(name=name, physical_usable_gib=usable, unified=unified)
@property
def reserve_gib(self) -> float:
"""``max(20% of physically usable memory, 8 GiB)``."""
return max(RESERVE_FRACTION * self.physical_usable_gib, RESERVE_FLOOR_GIB)
@property
def placement_budget_gib(self) -> float:
"""What remains for weights plus KV after the reserve."""
return self.physical_usable_gib - self.reserve_gib
def kv_bytes(
snapshot: ArchitectureSnapshot,
*,
context_tokens: int = ALPHA_CONTEXT_TOKENS,
concurrency: int = ALPHA_CONCURRENCY,
dtype: str = ALPHA_KV_DTYPE,
indexer_layout: IndexerLayout = "conservative",
include_indexer: bool = True,
) -> int:
"""Bytes of MLA (and DSA indexer) KV cache for the whole model.
``indexer_layout`` is the honest part. Correct DSA only needs indexer keys for
the Full producer layers, but the current experimental implementation may
allocate them across every backbone layer. Alpha budgets ``conservative``
(all 78) so that a route admitted by this planner cannot be surprised by the
implementation it actually gets.
"""
if (
not isinstance(context_tokens, int)
or isinstance(context_tokens, bool)
or context_tokens <= 0
or not isinstance(concurrency, int)
or isinstance(concurrency, bool)
or concurrency <= 0
):
raise ResourcePlanError("context_tokens and concurrency must be positive integers")
if dtype not in KV_DTYPES:
raise ResourcePlanError(
f"unsupported KV dtype {dtype!r}; alpha locks {ALPHA_KV_DTYPE} "
f"(known: {', '.join(sorted(KV_DTYPES))})"
)
bytes_per_value = KV_DTYPES[dtype]
layers = int(snapshot["num_hidden_layers"])
mla_values = int(snapshot["mla_cached_values_per_token_per_layer"])
total_values = mla_values * layers
if include_indexer:
if indexer_layout == "optimized":
indexer_layers = int(snapshot["indexer_full_layers"])
elif indexer_layout == "conservative":
indexer_layers = layers
else: # pragma: no cover - Literal keeps this unreachable from typed callers
raise ResourcePlanError(f"unknown indexer_layout {indexer_layout!r}")
total_values += int(snapshot["index_head_dim"]) * indexer_layers
return int(total_values * context_tokens * concurrency * bytes_per_value)
@dataclass(frozen=True)
class TopologyPlan:
"""The node count a homogeneous tier needs, and how it was reached."""
physical_usable_gib: float
reserve_gib: float
placement_budget_gib: float
weight_gib: float
kv_gib: float
total_placement_gib: float
arithmetic_minimum_nodes: int
recommended_nodes: int
imbalance_factor: float
@property
def is_arithmetic_minimum_topology(self) -> bool:
"""True when the recommendation offers no imbalance headroom at all."""
return self.recommended_nodes == self.arithmetic_minimum_nodes
def to_dict(self) -> dict:
return {
"physical_usable_gib": round(self.physical_usable_gib, 3),
"reserve_gib": round(self.reserve_gib, 3),
"placement_budget_gib": round(self.placement_budget_gib, 3),
"weight_gib": round(self.weight_gib, 3),
"kv_gib": round(self.kv_gib, 3),
"total_placement_gib": round(self.total_placement_gib, 3),
"arithmetic_minimum_nodes": self.arithmetic_minimum_nodes,
"recommended_nodes": self.recommended_nodes,
"imbalance_factor": self.imbalance_factor,
}
def plan_topology(
manifest: TargetManifest,
snapshot: ArchitectureSnapshot,
*,
physical_usable_gib: float,
context_tokens: int = ALPHA_CONTEXT_TOKENS,
concurrency: int = ALPHA_CONCURRENCY,
kv_dtype: str = ALPHA_KV_DTYPE,
indexer_layout: IndexerLayout = "conservative",
imbalance_factor: float = PLACEMENT_IMBALANCE_FACTOR,
) -> TopologyPlan:
"""Minimum and recommended node count for a homogeneous tier of this size."""
if (
isinstance(imbalance_factor, bool)
or not isinstance(imbalance_factor, (int, float))
or not math.isfinite(imbalance_factor)
or imbalance_factor < 1.0
):
raise ResourcePlanError(
"imbalance_factor must be finite and at least 1.0; a lower value would "
"assume the worst-placed node holds less than an equal share"
)
node = NodeMemory(
name=f"{physical_usable_gib:g}GiB-tier",
physical_usable_gib=physical_usable_gib,
unified=False,
)
budget = node.placement_budget_gib
if budget <= 0:
raise ResourcePlanError(
f"a {physical_usable_gib:g} GiB node has no placement budget after its "
f"{node.reserve_gib:.1f} GiB reserve"
)
weight_gib = manifest.total_bytes / GIB
kv_gib = (
kv_bytes(
snapshot,
context_tokens=context_tokens,
concurrency=concurrency,
dtype=kv_dtype,
indexer_layout=indexer_layout,
)
/ GIB
)
total = weight_gib + kv_gib
return TopologyPlan(
physical_usable_gib=physical_usable_gib,
reserve_gib=node.reserve_gib,
placement_budget_gib=budget,
weight_gib=weight_gib,
kv_gib=kv_gib,
total_placement_gib=total,
arithmetic_minimum_nodes=math.ceil(total / budget),
recommended_nodes=math.ceil(total * imbalance_factor / budget),
imbalance_factor=imbalance_factor,
)
@dataclass(frozen=True)
class RouteFit:
"""Whether a concrete, possibly heterogeneous set of nodes can hold the target."""
node_count: int
aggregate_usable_gib: float
aggregate_placement_budget_gib: float
required_placement_gib: float
fits: bool
meets_hard_fit_floor: bool
no_single_node_can_admit_target: bool
headroom_gib: float
reasons: tuple[str, ...]
def to_dict(self) -> dict:
return {
"node_count": self.node_count,
"aggregate_usable_gib": round(self.aggregate_usable_gib, 3),
"aggregate_placement_budget_gib": round(self.aggregate_placement_budget_gib, 3),
"required_placement_gib": round(self.required_placement_gib, 3),
"fits": self.fits,
"meets_hard_fit_floor": self.meets_hard_fit_floor,
"no_single_node_can_admit_target": self.no_single_node_can_admit_target,
"headroom_gib": round(self.headroom_gib, 3),
"reasons": list(self.reasons),
}
def plan_route(
manifest: TargetManifest,
snapshot: ArchitectureSnapshot,
nodes: list[NodeMemory],
*,
context_tokens: int = ALPHA_CONTEXT_TOKENS,
concurrency: int = ALPHA_CONCURRENCY,
kv_dtype: str = ALPHA_KV_DTYPE,
indexer_layout: IndexerLayout = "conservative",
) -> RouteFit:
"""Evaluate a concrete route. Every node's memory is already counted once."""
if len(nodes) < 2:
raise ResourcePlanError(
"the alpha target is distributed by definition; a route needs at least two "
"physical nodes"
)
names = [node.name for node in nodes]
if len(set(names)) != len(names):
raise ResourcePlanError(
"duplicate node names in the route; one physical machine counted twice is "
"the same double-count as adding integrated-GPU memory to system RAM"
)
weight_gib = manifest.total_bytes / GIB
kv_gib = (
kv_bytes(
snapshot,
context_tokens=context_tokens,
concurrency=concurrency,
dtype=kv_dtype,
indexer_layout=indexer_layout,
)
/ GIB
)
required = weight_gib + kv_gib
aggregate_usable = sum(node.physical_usable_gib for node in nodes)
aggregate_budget = sum(node.placement_budget_gib for node in nodes)
fits = aggregate_budget >= required
largest_budget = max(node.placement_budget_gib for node in nodes)
no_single_node = largest_budget < required
reasons: list[str] = []
if not fits:
reasons.append(
f"aggregate placement budget {aggregate_budget:.1f} GiB is below the "
f"{required:.1f} GiB the target needs after each node's reserve"
)
if not no_single_node:
reasons.append(
"at least one node could admit the complete target alone; that is a "
"single-host run, not distributed alpha"
)
if aggregate_usable < AGGREGATE_HARD_FIT_FLOOR_GIB:
reasons.append(
f"aggregate usable memory {aggregate_usable:.1f} GiB is below the "
f"{AGGREGATE_HARD_FIT_FLOOR_GIB:g} GiB experimental hard-fit floor"
)
return RouteFit(
node_count=len(nodes),
aggregate_usable_gib=aggregate_usable,
aggregate_placement_budget_gib=aggregate_budget,
required_placement_gib=required,
fits=fits,
meets_hard_fit_floor=aggregate_usable >= AGGREGATE_HARD_FIT_FLOOR_GIB,
no_single_node_can_admit_target=no_single_node,
headroom_gib=aggregate_budget - required,
reasons=tuple(reasons),
)
@dataclass(frozen=True)
class SeamPlan:
"""Bytes and latency across the activation seams of a route.
Bandwidth and latency are reported separately on purpose. Decode moves almost
nothing — 12 KiB per token per seam — so a faster link barely helps it. What
decode pays is *serial*: every generated token crosses every seam in order, so
the cost that matters is ``seams x per-hop latency``. A route that claims to be
fast because it is on 10 GbE has confused the two.
"""
node_count: int
seam_count: int
hidden_size: int
bytes_per_token_per_seam: int
prefill_bytes_per_seam: int
decode_bytes_per_seam_per_token: int
dsa_sideband_bytes_per_query: int
link_rate_gbps: float
meets_alpha_minimum: bool
is_recommended_link: bool
decode_serialization_ms_per_token: float
decode_latency_ms_per_token: float
decode_bandwidth_share_ms_per_token: float
prefill_serialization_ms: float
def to_dict(self) -> dict:
return {
"node_count": self.node_count,
"seam_count": self.seam_count,
"hidden_size": self.hidden_size,
"bytes_per_token_per_seam": self.bytes_per_token_per_seam,
"prefill_bytes_per_seam": self.prefill_bytes_per_seam,
"decode_bytes_per_seam_per_token": self.decode_bytes_per_seam_per_token,
"dsa_sideband_bytes_per_query": self.dsa_sideband_bytes_per_query,
"link_rate_gbps": self.link_rate_gbps,
"meets_alpha_minimum": self.meets_alpha_minimum,
"is_recommended_link": self.is_recommended_link,
"decode_serialization_ms_per_token": round(self.decode_serialization_ms_per_token, 4),
"decode_latency_ms_per_token": round(self.decode_latency_ms_per_token, 4),
"decode_bandwidth_share_ms_per_token": round(
self.decode_bandwidth_share_ms_per_token, 4
),
"prefill_serialization_ms": round(self.prefill_serialization_ms, 3),
}
def plan_seams(
snapshot: ArchitectureSnapshot,
*,
node_count: int,
context_tokens: int = ALPHA_CONTEXT_TOKENS,
link_rate_gbps: float = MIN_LINK_RATE_GBPS,
per_hop_latency_ms: float = 0.5,
) -> SeamPlan:
"""Model seam bytes, wire serialization, and serial per-hop latency separately."""
if not isinstance(node_count, int) or isinstance(node_count, bool) or node_count < 2:
raise ResourcePlanError("a seam exists only between two nodes")
if not isinstance(context_tokens, int) or isinstance(context_tokens, bool) or context_tokens <= 0:
raise ResourcePlanError("context_tokens must be a positive integer")
if (
isinstance(link_rate_gbps, bool)
or not isinstance(link_rate_gbps, (int, float))
or not math.isfinite(link_rate_gbps)
or link_rate_gbps <= 0
):
raise ResourcePlanError("link_rate_gbps must be finite and positive")
if (
isinstance(per_hop_latency_ms, bool)
or not isinstance(per_hop_latency_ms, (int, float))
or not math.isfinite(per_hop_latency_ms)
or per_hop_latency_ms < 0
):
raise ResourcePlanError("per_hop_latency_ms must be finite and non-negative")
hidden = int(snapshot["hidden_size"])
bytes_per_token = hidden * BF16_BYTES
seams = node_count - 1
bits_per_ms = link_rate_gbps * 1e9 / 1e3
decode_serialization_ms = (bytes_per_token * 8) / bits_per_ms
prefill_serialization_ms = (bytes_per_token * context_tokens * 8) / bits_per_ms
return SeamPlan(
node_count=node_count,
seam_count=seams,
hidden_size=hidden,
bytes_per_token_per_seam=bytes_per_token,
prefill_bytes_per_seam=bytes_per_token * context_tokens,
decode_bytes_per_seam_per_token=bytes_per_token,
dsa_sideband_bytes_per_query=int(snapshot["index_topk"]) * DSA_SIDEBAND_INT32_BYTES,
link_rate_gbps=link_rate_gbps,
meets_alpha_minimum=link_rate_gbps >= MIN_LINK_RATE_GBPS,
is_recommended_link=link_rate_gbps >= RECOMMENDED_LINK_RATE_GBPS,
decode_serialization_ms_per_token=decode_serialization_ms * seams,
decode_latency_ms_per_token=per_hop_latency_ms * seams,
decode_bandwidth_share_ms_per_token=decode_serialization_ms * seams,
prefill_serialization_ms=prefill_serialization_ms * seams,
)
ALPHA_TIERS_GIB: tuple[float, ...] = (32.0, 48.0, 64.0, 96.0, 128.0)
def plan_all_tiers(
manifest: TargetManifest, snapshot: ArchitectureSnapshot
) -> dict[str, TopologyPlan]:
"""The alpha tier table, recomputed from the pinned artifact and architecture."""
return {
f"{tier:g}": plan_topology(manifest, snapshot, physical_usable_gib=tier)
for tier in ALPHA_TIERS_GIB
}

1
packages/node/pyproject.toml
View File

@@ -42,3 +42,4 @@ include = ["meshnet_node*"]
[tool.setuptools.package-data]
meshnet_node = ["*.json"]
"meshnet_node.glm_alpha" = ["data/*.json"]