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neuron-tai/tests/test_toploc_audit.py
Dobromir Popov 7cf8d9bcf3 test descriptions
2026-07-11 22:25:30 +03:00

280 lines
10 KiB
Python

"""AH-006: validator TOPLOC audit primitive."""
from __future__ import annotations
from collections import namedtuple
from types import SimpleNamespace
from meshnet_validator import ToplocAuditConfig, ValidatorProcess
from meshnet_validator.audit import (
build_activation_proofs,
verify_activation_proofs,
verify_activation_proofs_detailed,
)
class FakeToploc:
def __init__(self) -> None:
self.build_calls: list[dict] = []
self.verify_calls: list[dict] = []
def build_proofs_base64(
self,
activations,
*,
decode_batching_size: int,
topk: int,
skip_prefill: bool,
):
self.build_calls.append({
"decode_batching_size": decode_batching_size,
"topk": topk,
"skip_prefill": skip_prefill,
})
return {
"activation_fingerprint": tuple(tuple(row) for row in activations),
"decode_batching_size": decode_batching_size,
"topk": topk,
"skip_prefill": skip_prefill,
}
def verify_proofs_base64(
self,
activations,
proofs,
*,
decode_batching_size: int,
topk: int,
skip_prefill: bool,
):
self.verify_calls.append({
"decode_batching_size": decode_batching_size,
"topk": topk,
"skip_prefill": skip_prefill,
})
return proofs == {
"activation_fingerprint": tuple(tuple(row) for row in activations),
"decode_batching_size": decode_batching_size,
"topk": topk,
"skip_prefill": skip_prefill,
}
class FakeValidationLog:
def __init__(self, events) -> None:
self._events = events
def list_completed_inferences(self, *, after_index: int = -1):
return [event for event in self._events if event.index > after_index]
class FakeRegistry:
def __init__(self) -> None:
self.slashes: list[dict] = []
self.audit_outcomes: list[dict] = []
def get_wallet(self, wallet_address: str):
return SimpleNamespace(banned=False)
def submit_slash_proof(self, **kwargs):
self.slashes.append(kwargs)
return kwargs
def record_audit_outcome(self, wallet_address: str, *, passed: bool):
self.audit_outcomes.append({"wallet_address": wallet_address, "passed": passed})
class FakeContracts:
def __init__(self, events) -> None:
self.validation = FakeValidationLog(events)
self.registry = FakeRegistry()
class TeacherForcedValidator(ValidatorProcess):
def __init__(self, *args, reference_activations, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.reference_activations = reference_activations
self.teacher_forced_calls: list[dict] = []
def _run_reference(self, messages: list[dict]) -> str:
raise AssertionError("TOPLOC audits must not free-generate reference text")
def _run_teacher_forced_prefill(self, **kwargs):
self.teacher_forced_calls.append(kwargs)
return self.reference_activations
def test_stub_activation_tensors_round_trip_through_toploc_proofs():
"Stub activation tensors round trip through toploc proofs\n\nTags: audit, calibration"
fake_toploc = FakeToploc()
activations = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
config = ToplocAuditConfig(topk=2, decode_batching_size=16)
claim = build_activation_proofs(activations, config=config, backend=fake_toploc)
assert verify_activation_proofs(activations, claim, config=config, backend=fake_toploc) is True
assert fake_toploc.build_calls == [{
"decode_batching_size": 16,
"topk": 2,
"skip_prefill": True,
}]
assert fake_toploc.verify_calls == [{
"decode_batching_size": 16,
"topk": 2,
"skip_prefill": True,
}]
def test_validator_teacher_forces_claimed_tokens_for_toploc_audit():
"Validator teacher forces claimed tokens for toploc audit\n\nTags: audit, calibration"
fake_toploc = FakeToploc()
activations = [[0.25, 0.5], [0.75, 1.0]]
config = ToplocAuditConfig(topk=2, decode_batching_size=16)
claim = build_activation_proofs(activations, config=config, backend=fake_toploc)
event = SimpleNamespace(
index=0,
session_id="session-toploc-ok",
model="Qwen2.5-0.5B-Instruct",
messages=[{"role": "user", "content": "hello"}],
observed_output="honest but hardware-divergent text",
route_nodes=[{"wallet_address": "wallet-good", "shard_end": 31}],
claimed_token_ids=[101, 202, 303],
toploc_proof=claim.as_mapping(),
)
contracts = FakeContracts([event])
validator = TeacherForcedValidator(
contracts=contracts,
reference_node_url="http://reference.invalid",
sample_rate=1.0,
random_seed=7,
toploc_config=config,
toploc_backend=fake_toploc,
reference_activations=activations,
)
assert validator.validate_once() == []
assert contracts.registry.slashes == []
assert validator.teacher_forced_calls == [{
"model": "Qwen2.5-0.5B-Instruct",
"messages": [{"role": "user", "content": "hello"}],
"claimed_token_ids": [101, 202, 303],
"claim": claim,
}]
def test_validator_rejects_swapped_precision_toploc_claim():
"Validator rejects swapped precision toploc claim\n\nTags: audit, calibration"
fake_toploc = FakeToploc()
activations = [[0.25, 0.5], [0.75, 1.0]]
canonical = ToplocAuditConfig(
dtype="bfloat16",
quantization="bfloat16",
topk=2,
decode_batching_size=16,
)
swapped = ToplocAuditConfig(
dtype="bfloat16",
quantization="int8",
topk=2,
decode_batching_size=16,
)
claim = build_activation_proofs(activations, config=swapped, backend=fake_toploc)
event = SimpleNamespace(
index=0,
session_id="session-toploc-bad",
model="Qwen2.5-0.5B-Instruct",
messages=[{"role": "user", "content": "hello"}],
observed_output="looks plausible",
route_nodes=[{"wallet_address": "wallet-bad", "shard_end": 31}],
audit={
"claimed_token_ids": [101, 202, 303],
"toploc_proof": claim.as_mapping(),
},
)
contracts = FakeContracts([event])
validator = TeacherForcedValidator(
contracts=contracts,
reference_node_url="http://reference.invalid",
sample_rate=1.0,
random_seed=7,
toploc_config=canonical,
toploc_backend=fake_toploc,
reference_activations=activations,
)
receipts = validator.validate_once()
assert len(receipts) == 1
assert contracts.registry.slashes[0]["wallet_address"] == "wallet-bad"
assert "TOPLOC activation proof mismatch" in contracts.registry.slashes[0]["reason"]
# AH-021: verify_activation_proofs_detailed surfaces the raw divergence
# metric a calibration corpus needs, instead of only a pass/fail bool.
ChunkResult = namedtuple("ChunkResult", ["exp_intersections", "mant_err_mean", "mant_err_median"])
class FakeToplocWithChunkResults:
"""Mimics the real `toploc` library: verify returns per-chunk results,
not a bool, so `bool(result)` alone (the AH-021 gap #1 bug) is always
true for any non-empty response regardless of divergence."""
def build_proofs_base64(self, activations, *, decode_batching_size, topk, skip_prefill):
return {"activations": activations}
def verify_proofs_base64(self, activations, proofs, *, decode_batching_size, topk, skip_prefill):
return [
ChunkResult(exp_intersections=8, mant_err_mean=0.01, mant_err_median=0.008),
ChunkResult(exp_intersections=6, mant_err_mean=0.03, mant_err_median=0.02),
]
def test_verify_activation_proofs_detailed_aggregates_per_chunk_divergence():
"Verify activation proofs detailed aggregates per chunk divergence\n\nTags: audit, calibration"
fake_toploc = FakeToplocWithChunkResults()
activations = [[1.0, 2.0], [3.0, 4.0]]
config = ToplocAuditConfig(topk=2, decode_batching_size=16)
claim = build_activation_proofs(activations, config=config, backend=fake_toploc)
result = verify_activation_proofs_detailed(activations, claim, config=config, backend=fake_toploc)
assert result.passed is True # non-empty list is truthy, same as legacy behavior
assert result.chunk_count == 2
assert result.exp_intersections == 6 # worst-case (min) across chunks
assert result.mant_err_mean == 0.02 # mean of per-chunk means
assert result.mant_err_median == 0.014 # mean of per-chunk medians
# verify_activation_proofs still returns just the bool for existing callers.
assert verify_activation_proofs(activations, claim, config=config, backend=fake_toploc) is True
def test_verify_activation_proofs_detailed_no_metric_from_plain_bool_backend():
"Verify activation proofs detailed no metric from plain bool backend\n\nTags: audit, calibration"
fake_toploc = FakeToploc()
activations = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
config = ToplocAuditConfig(topk=2, decode_batching_size=16)
claim = build_activation_proofs(activations, config=config, backend=fake_toploc)
result = verify_activation_proofs_detailed(activations, claim, config=config, backend=fake_toploc)
assert result.passed is True
assert result.chunk_count == 0
assert result.exp_intersections is None
assert result.mant_err_mean is None
assert result.mant_err_median is None
def test_verify_activation_proofs_detailed_rejects_config_mismatch_without_calling_backend():
"Verify activation proofs detailed rejects config mismatch without calling backend\n\nTags: audit, calibration"
fake_toploc = FakeToplocWithChunkResults()
activations = [[1.0, 2.0]]
canonical = ToplocAuditConfig(dtype="bfloat16", quantization="bfloat16", topk=2, decode_batching_size=16)
swapped = ToplocAuditConfig(dtype="bfloat16", quantization="int8", topk=2, decode_batching_size=16)
claim = build_activation_proofs(activations, config=swapped, backend=fake_toploc)
result = verify_activation_proofs_detailed(activations, claim, config=canonical, backend=fake_toploc)
assert result.passed is False
assert result.chunk_count == 0