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https://github.com/norandom/OpenUltraSAST.git
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test(detection-core): verify bounded validation cost
This commit is contained in:
Marius Ciepluch
@@ -422,7 +422,7 @@
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- Mutate a semantic identity and verify transitive staleness through all dependent readiness and measurement records.
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- Done when every injected failure is diagnosed at its source and no late admission-only surprise remains.
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- _Requirements: 5.3, 5.4, 5.5, 8.3, 8.4, 8.5, 15.4, 15.5_
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- [ ] 11.3 Verify bounded validation cost and total consumer deadline accounting
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- [x] 11.3 Verify bounded validation cost and total consumer deadline accounting
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- Measure canonicalization and validation across increasing relation, observation, receipt, and case counts.
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- Exercise large transition sets for linear behavior and bounded memory without weakening semantic checks.
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- Include optional model and proof processing in the same measured consumer budget.
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@@ -7,6 +7,7 @@ import math
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import re
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from collections.abc import Sequence
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from dataclasses import dataclass
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from fractions import Fraction
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from openultrasast.core import canonical
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@@ -38,13 +39,16 @@ def _nonnegative_int(value: object, field: str) -> None:
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raise ValueError(f"{field} must be a non-negative integer")
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def _finite_number(value: object, field: str, *, positive: bool = False) -> float:
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if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value):
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raise ValueError(f"{field} must be finite")
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number = float(value)
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if positive and number <= 0:
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raise ValueError(f"{field} must be positive")
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return number
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def _percentile(value: object) -> tuple[float, Fraction]:
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if isinstance(value, bool) or not isinstance(value, (int, float)):
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raise ValueError("percentile must be finite")
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if isinstance(value, float) and not math.isfinite(value):
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raise ValueError("percentile must be finite")
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if value <= 0:
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raise ValueError("percentile must be positive")
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if value > 1:
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raise ValueError("percentile must not exceed 1")
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return float(value), Fraction(str(value))
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@dataclass(frozen=True)
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@@ -241,9 +245,7 @@ def derive_budget_derivation_digest(value: BudgetDerivation) -> str:
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def _validate_derivation(value: BudgetDerivation, *, allow_empty_digest: bool = False) -> None:
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_text(value.workload_class, "workload_class")
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_positive_int(value.sample_count, "sample_count")
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percentile = _finite_number(value.percentile, "percentile", positive=True)
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if percentile > 1:
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raise ValueError("percentile must not exceed 1")
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_percentile(value.percentile)
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if not isinstance(value.reference_workload, ReferenceWorkload):
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raise TypeError("reference_workload must be ReferenceWorkload")
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if not isinstance(value.reference_hardware, ReferenceHardware):
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@@ -257,12 +259,12 @@ def _validate_derivation(value: BudgetDerivation, *, allow_empty_digest: bool =
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raise ValueError("budget derivation digest does not match fields")
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def _quantile(sorted_values: Sequence[float], percentile: float) -> float:
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def _quantile(sorted_values: Sequence[int], percentile: Fraction) -> Fraction:
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position = percentile * (len(sorted_values) - 1)
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lower = math.floor(position)
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lower = position.numerator // position.denominator
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upper = math.ceil(position)
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if lower == upper:
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return sorted_values[lower]
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return Fraction(sorted_values[lower])
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weight = position - lower
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return sorted_values[lower] * (1 - weight) + sorted_values[upper] * weight
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@@ -286,8 +288,8 @@ def derive_budget(
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reference_hardware: ReferenceHardware,
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) -> BudgetDerivation:
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_text(workload_class, "workload_class")
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value = _finite_number(percentile, "percentile", positive=True)
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if value > 1 or not samples:
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value, exact_percentile = _percentile(percentile)
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if not samples:
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raise ValueError("v2 derivation requires samples and percentile in (0, 1]")
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_validate_reference_workload(reference_workload)
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_validate_reference_hardware(reference_hardware)
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@@ -301,7 +303,7 @@ def derive_budget(
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raise ValueError("sample workload class does not match derivation")
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if not _compatible(sample.complexity, reference_workload):
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raise ValueError("sample complexity is outside reference workload")
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budget_ms = math.ceil(_quantile(sorted(float(item.total_reply_ms) for item in measured), value))
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budget_ms = math.ceil(_quantile(sorted(item.total_reply_ms for item in measured), exact_percentile))
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return BudgetDerivation(workload_class, len(measured), value, reference_workload, reference_hardware, budget_ms, "")
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@@ -0,0 +1,211 @@
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"""Complete consumer measurements used by the detection-core load contract."""
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from __future__ import annotations
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import time
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from collections.abc import Callable
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from dataclasses import dataclass
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from typing import TYPE_CHECKING
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from detection_core_fixtures import (
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InMemoryContentResolver,
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InMemoryPopulationCustody,
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custody_contract,
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stored_artifact,
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)
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from detection_core_performance_reporting import nanoseconds_to_milliseconds
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from test_core_consumer_v2 import _proof
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from test_core_validation_v2 import _catalog, _model_validation_case, _review_policy, _trust_policy, _with_fact_set
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from openultrasast.core import budget, canonical, family, provenance, validation
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from openultrasast.core.consumer import PushRequest, consume_records, make_push_request
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from openultrasast.core.proof import ProofAttachment, ProofTrustPolicy
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from openultrasast.core.validation import EvidenceValidationContext, EvidenceValidationInput
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if TYPE_CHECKING:
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from detection_core_performance import Workload
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from openultrasast.core.evidence import EvidenceRecord
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@dataclass(frozen=True)
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class PreparedConsumer:
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value: EvidenceValidationInput
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context: EvidenceValidationContext
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custody: provenance.PopulationCustodyPort
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resolver: InMemoryContentResolver
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request: PushRequest
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attachments: tuple[ProofAttachment, ...]
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trust: ProofTrustPolicy | None
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@dataclass(frozen=True)
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class CompleteRun:
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mode: str
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size: int
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measured: budget.MeasuredSample
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resolver: InMemoryContentResolver
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def merge_resolvers(values: tuple[InMemoryContentResolver, ...]) -> InMemoryContentResolver:
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artifacts: dict[str, canonical.ResolvedContent] = {}
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for value in values:
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artifacts.update(value.artifacts)
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return InMemoryContentResolver(artifacts)
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def measurement(changed_bytes: int, artifact_id: str) -> tuple[bytes, canonical.ContentRef, InMemoryContentResolver]:
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content = canonical.canonical_bytes({"schema_version": budget.CHANGED_BYTES_MEASUREMENT_SCHEMA, "changed_bytes": changed_bytes})
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reference, resolver = stored_artifact(content, artifact_id=artifact_id)
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if not content or reference.byte_length != len(content):
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raise AssertionError("changed-byte measurement input was not opened")
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return content, reference, resolver
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def _named_inputs(
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record: EvidenceRecord,
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contract: family.FamilyContract,
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change: bytes,
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) -> tuple[provenance.NamedInputs, InMemoryContentResolver]:
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stamp = record.packet.provenance
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contents = {
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provenance.ProvenanceInputRole.ANALYZER: canonical.canonical_bytes("analyzer"),
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provenance.ProvenanceInputRole.FAMILY_CONTRACT: canonical.canonical_bytes(family._contract_value(contract)),
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provenance.ProvenanceInputRole.FACT_SET: validation.fact_set_bytes(()),
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provenance.ProvenanceInputRole.ACQUISITION: canonical.canonical_bytes("acquisition"),
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provenance.ProvenanceInputRole.CONFIGURATION: canonical.canonical_bytes("configuration"),
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provenance.ProvenanceInputRole.POPULATION: canonical.canonical_bytes("population"),
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provenance.ProvenanceInputRole.CHANGE_INPUT: change,
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}
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digests = {
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provenance.ProvenanceInputRole.ANALYZER: stamp.analyzer_revision,
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provenance.ProvenanceInputRole.FAMILY_CONTRACT: stamp.family_contract_digest,
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provenance.ProvenanceInputRole.FACT_SET: stamp.fact_set_digest,
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provenance.ProvenanceInputRole.ACQUISITION: stamp.acquisition_record_digest,
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provenance.ProvenanceInputRole.CONFIGURATION: stamp.configuration_digest,
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provenance.ProvenanceInputRole.POPULATION: stamp.population_digest,
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provenance.ProvenanceInputRole.CHANGE_INPUT: stamp.change_input_digest,
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}
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items: list[provenance.NamedInput] = []
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resolvers: list[InMemoryContentResolver] = []
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for role in provenance.ProvenanceInputRole:
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reference, resolver = stored_artifact(contents[role], artifact_id=f"performance-{role.value}-{stamp.change_input_digest}")
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if reference.content_digest != digests[role] or reference.byte_length <= 0:
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raise AssertionError(f"performance input {role.value} did not bind its exact bytes")
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items.append(provenance.NamedInput(role, digests[role], reference))
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resolvers.append(resolver)
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selected = tuple(items)
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named = provenance.NamedInputs(selected, provenance.derive_named_inputs_digest(selected))
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return named, merge_resolvers(tuple(resolvers))
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def deterministic_prepared(
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workload: Workload,
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size: int,
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derivation: budget.BudgetDerivation,
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) -> PreparedConsumer:
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from detection_core_performance import _record
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contract, raw, change = _record(workload, size)
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record = _with_fact_set(raw, ())
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named, named_resolver = _named_inputs(record, contract, change)
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initial, custody_policy, _ = custody_contract()
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context = validation.make_evidence_validation_context(
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family_catalog=_catalog(contract),
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fact_packs=(),
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model_programs=(),
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lineages=(),
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fact_review_policy=_review_policy(),
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provenance_trust_policy=_trust_policy(),
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custody_policy=custody_policy,
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)
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measured, reference, measure_resolver = measurement(len(change), f"consumer-changed-bytes-{size}")
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if measured != canonical.canonical_bytes({"schema_version": budget.CHANGED_BYTES_MEASUREMENT_SCHEMA, "changed_bytes": len(change)}):
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raise AssertionError("changed-byte measurement serialization is unstable")
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complexity = budget.ChangeComplexity(
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record.packet.scope.change_digest,
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workload.workload_class,
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len(record.packet.scope.regions),
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len(record.packet.transitions),
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len(change),
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reference,
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)
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request = make_push_request(
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scope=record.packet.scope,
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workload_class=workload.workload_class,
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complexity=complexity,
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budget=derivation,
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)
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value = validation.make_evidence_validation_input(record=record, named_inputs=named)
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resolver = merge_resolvers((named_resolver, measure_resolver))
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custody = InMemoryPopulationCustody(initial, custody_policy)
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return PreparedConsumer(value, context, custody, resolver, request, (), None)
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def optional_prepared(
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workload: Workload,
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derivation: budget.BudgetDerivation,
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artifact_id: str,
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) -> PreparedConsumer:
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value, context, custody, resolver, _ = _model_validation_case()
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attachment, trust, proof_resolver = _proof(value.record)
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selected = next(item for item in value.named_inputs.inputs if item.role is provenance.ProvenanceInputRole.CHANGE_INPUT)
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change = resolver.resolve(selected.content).content
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if (
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not change
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or canonical.bytes_digest(change) != selected.content.content_digest
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or selected.content.content_digest != value.record.packet.provenance.change_input_digest
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):
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raise AssertionError("optional consumer did not open its exact change input")
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_, reference, measure_resolver = measurement(len(change), artifact_id)
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complexity = budget.ChangeComplexity(
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value.record.packet.scope.change_digest,
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workload.workload_class,
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len(value.record.packet.scope.regions),
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len(value.record.packet.transitions),
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len(change),
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reference,
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)
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request = make_push_request(
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scope=value.record.packet.scope,
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workload_class=workload.workload_class,
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complexity=complexity,
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budget=derivation,
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)
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combined = merge_resolvers((resolver, proof_resolver, measure_resolver))
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return PreparedConsumer(value, context, custody, combined, request, (attachment,), trust)
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def measure_complete(
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setup: Callable[[], PreparedConsumer],
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mode: str,
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size: int,
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) -> CompleteRun:
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started = time.monotonic_ns()
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prepared = setup()
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preparation_ms = nanoseconds_to_milliseconds(max(1, time.monotonic_ns() - started))
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verdict = consume_records(
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prepared.request,
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(prepared.value,),
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prepared.context,
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prepared.custody,
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preparation_ms,
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prepared.resolver,
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attachments=prepared.attachments,
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proof_trust=prepared.trust,
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)
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wall_ms = nanoseconds_to_milliseconds(max(1, time.monotonic_ns() - started))
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elapsed = max(wall_ms, verdict.total_reply_ms)
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sample = budget.MeasuredSample(prepared.request.complexity, elapsed)
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return CompleteRun(mode, size, sample, prepared.resolver)
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__all__ = [
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"CompleteRun",
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"PreparedConsumer",
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"deterministic_prepared",
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"measure_complete",
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"measurement",
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"merge_resolvers",
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"optional_prepared",
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]
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@@ -0,0 +1,354 @@
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"""Test-owned measured workload composition over production detection-core validators."""
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from __future__ import annotations
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import gc
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import json
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import time
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import tracemalloc
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from dataclasses import dataclass, replace
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from functools import lru_cache, partial
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from pathlib import Path
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import detection_core_performance_reporting as reporting
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from detection_core_fixtures import InMemoryContentResolver, stored_artifact
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from detection_core_scaling import _integer_median
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from detection_core_work import ValidationWork, count_validation_work
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from test_core_admission_metrics import _setup as admission_setup
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from test_core_family import _contract as family_contract
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from test_core_family import _stamp as family_stamp
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from test_core_readiness import _chain as readiness_chain
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from openultrasast.core import admission_case, budget, canonical, evidence, family, readiness, semantics
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from openultrasast.core.consumer import PushRequest
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from openultrasast.core.proof import ProofAttachment, ProofTrustPolicy
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from openultrasast.core.provenance import DecisionProvenance, PopulationCustodyPort
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from openultrasast.core.validation import EvidenceValidationContext, EvidenceValidationInput
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PERFORMANCE_FIXTURE = Path(__file__).parent / "fixtures/detection_core/performance_workload.json"
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_SCHEMA = "detection-core-performance-workload-v1"
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@dataclass(frozen=True)
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class Workload:
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workload_class: str
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sizes: tuple[int, ...]
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repeats: int
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percentile: float
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changed_regions: int
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changed_bytes: int
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bytes_read: int
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content_digest: str
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@dataclass(frozen=True)
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class PerformancePoint:
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relation_count: int
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observation_count: int
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receipt_count: int
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case_count: int
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work: ValidationWork
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elapsed_ns: int
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elapsed_samples_ns: tuple[int, ...]
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peak_bytes: int
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peak_samples_bytes: tuple[int, ...]
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serialized_bytes: int
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change_digest: str
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changed_bytes: int
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changed_bytes_measurement: bytes
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change_content: bytes
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@property
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def elapsed_ms(self) -> int:
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return reporting.nanoseconds_to_milliseconds(self.elapsed_ns)
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@dataclass(frozen=True)
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class ConsumerMeasurement:
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mode: str
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size: int
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measured: budget.MeasuredSample
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@property
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def total_reply_ms(self) -> int:
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return self.measured.total_reply_ms
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@dataclass(frozen=True)
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class PerformanceProfile:
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workload: Workload
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points: tuple[PerformancePoint, ...]
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consumer_samples: tuple[ConsumerMeasurement, ...]
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samples: tuple[budget.MeasuredSample, ...]
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reference_workload: budget.ReferenceWorkload
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reference_hardware: budget.ReferenceHardware
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budget: budget.BudgetDerivation
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resolver: InMemoryContentResolver
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sample_bytes: int
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@dataclass(frozen=True)
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class OptionalConsumerCase:
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value: EvidenceValidationInput
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context: EvidenceValidationContext
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custody: PopulationCustodyPort
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resolver: InMemoryContentResolver
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request: PushRequest
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attachment: ProofAttachment
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trust: ProofTrustPolicy
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def _strict_object(value: object) -> dict[str, object]:
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if not isinstance(value, dict) or not all(isinstance(key, str) for key in value):
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raise ValueError("performance workload must be an object")
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return value
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def _positive_int(value: object, field: str) -> int:
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if isinstance(value, bool) or not isinstance(value, int) or value <= 0:
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raise ValueError(f"{field} must be a positive integer")
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return value
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def read_workload(path: Path) -> Workload:
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content = path.read_bytes()
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if not content:
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raise ValueError("performance workload input is empty")
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raw = _strict_object(json.loads(content))
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expected = {"schema_version", "workload_class", "sizes", "repeats", "percentile", "changed_regions", "changed_bytes"}
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if set(raw) != expected or raw["schema_version"] != _SCHEMA:
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raise ValueError("performance workload schema is not the reviewed closed schema")
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sizes_raw = raw["sizes"]
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if not isinstance(sizes_raw, list):
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raise ValueError("performance workload sizes must be a list")
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sizes = tuple(_positive_int(item, "workload size") for item in sizes_raw)
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if len(sizes) < 3 or tuple(sorted(set(sizes))) != sizes:
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raise ValueError("performance workload sizes must be unique and increasing")
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workload_class = raw["workload_class"]
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percentile = raw["percentile"]
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if not isinstance(workload_class, str) or not workload_class:
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raise ValueError("workload class must be nonempty")
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||||
if isinstance(percentile, bool) or not isinstance(percentile, (int, float)) or not 0 < percentile <= 1:
|
||||
raise ValueError("percentile must be in (0, 1]")
|
||||
return Workload(
|
||||
workload_class,
|
||||
sizes,
|
||||
_positive_int(raw["repeats"], "repeats"),
|
||||
float(percentile),
|
||||
_positive_int(raw["changed_regions"], "changed regions"),
|
||||
_positive_int(raw["changed_bytes"], "changed bytes"),
|
||||
len(content),
|
||||
canonical.bytes_digest(content),
|
||||
)
|
||||
|
||||
|
||||
def _location(line: int) -> semantics.SourceLocation:
|
||||
return semantics.SourceLocation("src/app.py", line, 0, "handle")
|
||||
|
||||
|
||||
def _provenance() -> semantics.EvidenceProvenance:
|
||||
return semantics.EvidenceProvenance(
|
||||
semantics.ProvenanceKind.PRODUCT_CODE,
|
||||
canonical.content_digest("performance-source"),
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
)
|
||||
|
||||
|
||||
def _relation(index: int) -> semantics.SemanticRelation:
|
||||
line = index * 2 + 2
|
||||
return semantics.make_relation(
|
||||
kind=semantics.RelationKind.CONSTRAINT_EFFECT,
|
||||
role="constraint",
|
||||
source=semantics.SemanticNode(f"operation-{index}", "operation", _location(line)),
|
||||
target=semantics.SemanticNode(f"policy-{index}", "policy", _location(line + 1)),
|
||||
location=_location(line),
|
||||
status=semantics.RelationStatus.COMPLETE,
|
||||
conditions=(f"policy-{index}",),
|
||||
reasons=(),
|
||||
provenance=_provenance(),
|
||||
)
|
||||
|
||||
|
||||
def _observation(index: int) -> semantics.Observation:
|
||||
return semantics.make_observation(
|
||||
role="security_object",
|
||||
location=_location(index * 2 + 2),
|
||||
fields=(semantics.FieldValue("resource", semantics.FieldKind.TEXT, f"document-{index}"),),
|
||||
status=semantics.RelationStatus.COMPLETE,
|
||||
provenance=_provenance(),
|
||||
)
|
||||
|
||||
|
||||
def _change_content(workload: Workload, size: int) -> bytes:
|
||||
return canonical.canonical_bytes(
|
||||
{"workload_input_digest": workload.content_digest, "size": size, "source": "x" * (workload.changed_bytes + size)}
|
||||
)
|
||||
|
||||
|
||||
def _record(workload: Workload, size: int) -> tuple[family.FamilyContract, evidence.EvidenceRecord, bytes]:
|
||||
contract = family_contract()
|
||||
content = _change_content(workload, size)
|
||||
change_digest = canonical.bytes_digest(content)
|
||||
fields = (semantics.FieldValue("operation", semantics.FieldKind.TEXT, "read"),)
|
||||
scope = evidence.AnalyzedScope(
|
||||
"base",
|
||||
"head",
|
||||
change_digest,
|
||||
(evidence.SourceRegion("src/app.py", 1, size * 2 + 4),),
|
||||
)
|
||||
packet = evidence.EvidencePacket(
|
||||
evidence.EVIDENCE_PACKET_SCHEMA,
|
||||
contract.family_id,
|
||||
family.family_contract_digest(contract),
|
||||
family.derive_security_object_id(contract.security_object, fields),
|
||||
"python",
|
||||
scope,
|
||||
tuple(_relation(index) for index in range(size)),
|
||||
tuple(_observation(index) for index in range(size)),
|
||||
fields,
|
||||
(),
|
||||
replace(family_stamp(contract), change_input_digest=change_digest),
|
||||
)
|
||||
decision = evidence.Decision(
|
||||
"blocked",
|
||||
evidence.NeutralOutcome.VIOLATION,
|
||||
evidence.EvidenceBasis.DETERMINISTIC,
|
||||
scope,
|
||||
(),
|
||||
evidence.derive_packet_digest(packet),
|
||||
DecisionProvenance(None, None, None, None, None, None),
|
||||
)
|
||||
return contract, evidence.EvidenceRecord(packet, decision, ""), content
|
||||
|
||||
|
||||
def _validate_work(
|
||||
record: evidence.EvidenceRecord,
|
||||
contract: family.FamilyContract,
|
||||
size: int,
|
||||
receipts: tuple[readiness.ReadinessResult, ...],
|
||||
cases: tuple[admission_case.AdmissionCaseResult, ...],
|
||||
) -> int:
|
||||
content = evidence.evidence_record_bytes(record)
|
||||
family.validate_record(contract, record)
|
||||
for index in range(size):
|
||||
readiness.validate_readiness_result(receipts[index % len(receipts)])
|
||||
for index in range(size):
|
||||
admission_case.validate_admission_case_shape(cases[index % len(cases)])
|
||||
return len(content)
|
||||
|
||||
|
||||
def _measure_point(
|
||||
workload: Workload,
|
||||
size: int,
|
||||
receipts: tuple[readiness.ReadinessResult, ...],
|
||||
cases: tuple[admission_case.AdmissionCaseResult, ...],
|
||||
) -> PerformancePoint:
|
||||
contract, record, content = _record(workload, size)
|
||||
elapsed: list[int] = []
|
||||
peaks: list[int] = []
|
||||
_validate_work(record, contract, size, receipts, cases)
|
||||
for _ in range(workload.repeats):
|
||||
started = time.perf_counter_ns()
|
||||
_validate_work(record, contract, size, receipts, cases)
|
||||
elapsed.append(max(1, time.perf_counter_ns() - started))
|
||||
tracemalloc.start()
|
||||
_validate_work(record, contract, size, receipts, cases)
|
||||
tracemalloc.stop()
|
||||
for _ in range(workload.repeats):
|
||||
gc.collect()
|
||||
tracemalloc.start()
|
||||
_validate_work(record, contract, size, receipts, cases)
|
||||
_, peak = tracemalloc.get_traced_memory()
|
||||
tracemalloc.stop()
|
||||
peaks.append(peak)
|
||||
serialized, work = count_validation_work(lambda: _validate_work(record, contract, size, receipts, cases))
|
||||
measurement, _, _ = reporting.measurement(size, len(content))
|
||||
return PerformancePoint(
|
||||
size,
|
||||
size,
|
||||
size,
|
||||
size,
|
||||
work,
|
||||
_integer_median(tuple(elapsed)),
|
||||
tuple(elapsed),
|
||||
_integer_median(tuple(peaks)),
|
||||
tuple(peaks),
|
||||
serialized,
|
||||
record.packet.scope.change_digest,
|
||||
len(content),
|
||||
measurement,
|
||||
content,
|
||||
)
|
||||
|
||||
|
||||
@lru_cache(maxsize=1)
|
||||
def measured_profile(workload: Workload) -> PerformanceProfile:
|
||||
import detection_core_consumer_performance as consumer_performance
|
||||
|
||||
cases = admission_setup().cases
|
||||
receipts = readiness_chain()
|
||||
points = tuple(_measure_point(workload, size, receipts, cases) for size in workload.sizes)
|
||||
hardware = reporting.hardware()
|
||||
seed = reporting.seed_budget(workload, points, hardware)
|
||||
complete = []
|
||||
for point in points:
|
||||
for _ in range(workload.repeats):
|
||||
setup = partial(consumer_performance.deterministic_prepared, workload, point.relation_count, seed)
|
||||
complete.append(consumer_performance.measure_complete(setup, "deterministic", point.relation_count))
|
||||
for repeat in range(workload.repeats):
|
||||
setup = partial(consumer_performance.optional_prepared, workload, seed, f"optional-measurement-{repeat}")
|
||||
complete.append(consumer_performance.measure_complete(setup, "model_and_proof", 1))
|
||||
consumers = tuple(ConsumerMeasurement(item.mode, item.size, item.measured) for item in complete)
|
||||
report = reporting.sample_report(workload, points, consumers)
|
||||
report_ref, report_resolver = stored_artifact(report, artifact_id="performance-complete-samples")
|
||||
changed = tuple(item.measured.complexity.changed_bytes for item in complete)
|
||||
reference = reporting.reference(workload, report_ref, (min(changed), max(changed)))
|
||||
samples = tuple(item.measured for item in complete)
|
||||
derived = budget.derive_budget(samples, workload.workload_class, workload.percentile, reference, hardware)
|
||||
return PerformanceProfile(
|
||||
workload,
|
||||
points,
|
||||
consumers,
|
||||
samples,
|
||||
reference,
|
||||
hardware,
|
||||
derived,
|
||||
consumer_performance.merge_resolvers((report_resolver, *(item.resolver for item in complete))),
|
||||
len(report),
|
||||
)
|
||||
|
||||
|
||||
def assert_large_set_envelope(profile: PerformanceProfile) -> None:
|
||||
from detection_core_scaling import assert_incremental_envelope
|
||||
|
||||
assert_incremental_envelope(profile.points)
|
||||
|
||||
|
||||
def reject_invalid_large_set(workload: Workload, size: int) -> None:
|
||||
contract, record, _ = _record(workload, size)
|
||||
invalid = replace(record.packet.transitions[-1], role="undeclared-role", relation_id="")
|
||||
packet = replace(record.packet, transitions=(*record.packet.transitions[:-1], invalid))
|
||||
decision = replace(record.decision, packet_digest=evidence.derive_packet_digest(packet))
|
||||
candidate = evidence.EvidenceRecord(packet, decision, "")
|
||||
try:
|
||||
family.validate_record(contract, candidate)
|
||||
except family.FamilyContractError:
|
||||
return
|
||||
raise AssertionError("large invalid semantic set was accepted")
|
||||
|
||||
|
||||
def optional_consumer_case(profile: PerformanceProfile) -> OptionalConsumerCase:
|
||||
from detection_core_consumer_performance import optional_prepared
|
||||
|
||||
prepared = optional_prepared(profile.workload, profile.budget, "optional-final-measurement")
|
||||
return OptionalConsumerCase(
|
||||
prepared.value,
|
||||
prepared.context,
|
||||
prepared.custody,
|
||||
prepared.resolver,
|
||||
prepared.request,
|
||||
prepared.attachments[0],
|
||||
prepared.trust,
|
||||
)
|
||||
@@ -0,0 +1,139 @@
|
||||
"""Measurement artifact and budget composition for detection-core load tests."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from dataclasses import replace
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from detection_core_fixtures import InMemoryContentResolver, stored_artifact
|
||||
|
||||
from openultrasast.core import budget, canonical
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from detection_core_performance import ConsumerMeasurement, PerformancePoint, Workload
|
||||
|
||||
_SAMPLE_SCHEMA = "detection-core-performance-samples-v1"
|
||||
|
||||
|
||||
def nanoseconds_to_milliseconds(value: object) -> int:
|
||||
if type(value) is not int or value <= 0:
|
||||
raise ValueError("elapsed nanoseconds must be an exact positive integer")
|
||||
return (value + 999_999) // 1_000_000
|
||||
|
||||
|
||||
def hardware() -> budget.ReferenceHardware:
|
||||
pages = os.sysconf("SC_PHYS_PAGES")
|
||||
page_size = os.sysconf("SC_PAGE_SIZE")
|
||||
value = budget.ReferenceHardware(
|
||||
platform.processor() or platform.machine() or "unreported-cpu",
|
||||
os.cpu_count() or 1,
|
||||
int(pages * page_size),
|
||||
platform.platform(),
|
||||
sys.version.split()[0],
|
||||
"",
|
||||
)
|
||||
return replace(value, record_digest=budget.derive_reference_hardware_digest(value))
|
||||
|
||||
|
||||
def sample_report(
|
||||
workload: Workload,
|
||||
points: tuple[PerformancePoint, ...],
|
||||
consumers: tuple[ConsumerMeasurement, ...],
|
||||
) -> bytes:
|
||||
return canonical.canonical_bytes(
|
||||
{
|
||||
"schema_version": _SAMPLE_SCHEMA,
|
||||
"workload_class": workload.workload_class,
|
||||
"workload_input_digest": workload.content_digest,
|
||||
"repeats": workload.repeats,
|
||||
"points": tuple(_point_value(point) for point in points),
|
||||
"consumer_samples": tuple(_consumer_value(item) for item in consumers),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
def _point_value(point: PerformancePoint) -> dict[str, canonical.JsonValue]:
|
||||
return {
|
||||
"relations": point.relation_count,
|
||||
"observations": point.observation_count,
|
||||
"receipts": point.receipt_count,
|
||||
"cases": point.case_count,
|
||||
"work": {
|
||||
"canonical_relations": point.work.canonical_relations,
|
||||
"canonical_observations": point.work.canonical_observations,
|
||||
"validated_relations": point.work.validated_relations,
|
||||
"validated_observations": point.work.validated_observations,
|
||||
"readiness_receipts": point.work.readiness_receipts,
|
||||
"admission_cases": point.work.admission_cases,
|
||||
"total_operations": point.work.total_operations,
|
||||
},
|
||||
"elapsed_samples_ns": point.elapsed_samples_ns,
|
||||
"peak_samples_bytes": point.peak_samples_bytes,
|
||||
"serialized_bytes": point.serialized_bytes,
|
||||
"change_digest": point.change_digest,
|
||||
"changed_bytes": point.changed_bytes,
|
||||
}
|
||||
|
||||
|
||||
def _consumer_value(item: ConsumerMeasurement) -> dict[str, canonical.JsonValue]:
|
||||
return {
|
||||
"mode": item.mode,
|
||||
"size": item.size,
|
||||
"change_id": item.measured.complexity.change_id,
|
||||
"changed_regions": item.measured.complexity.changed_regions,
|
||||
"transition_count": item.measured.complexity.transition_count,
|
||||
"changed_bytes": item.measured.complexity.changed_bytes,
|
||||
"total_reply_ms": item.total_reply_ms,
|
||||
}
|
||||
|
||||
|
||||
def measurement(size: int, changed_bytes: int) -> tuple[bytes, canonical.ContentRef, InMemoryContentResolver]:
|
||||
content = canonical.canonical_bytes({"schema_version": budget.CHANGED_BYTES_MEASUREMENT_SCHEMA, "changed_bytes": changed_bytes})
|
||||
reference, resolver = stored_artifact(content, artifact_id=f"performance-changed-bytes-{size}")
|
||||
return content, reference, resolver
|
||||
|
||||
|
||||
def reference(
|
||||
workload: Workload,
|
||||
report_ref: canonical.ContentRef,
|
||||
changed_range: tuple[int, int],
|
||||
) -> budget.ReferenceWorkload:
|
||||
value = budget.ReferenceWorkload(
|
||||
"measured-detection-core-consumer",
|
||||
"complete deterministic and model+proof consumer runs; scaled relations, observations, receipts, and cases",
|
||||
(workload.changed_regions, workload.changed_regions),
|
||||
(1, workload.sizes[-1]),
|
||||
changed_range,
|
||||
report_ref,
|
||||
"",
|
||||
)
|
||||
return replace(value, record_digest=budget.derive_reference_workload_digest(value))
|
||||
|
||||
|
||||
def seed_budget(
|
||||
workload: Workload,
|
||||
points: tuple[PerformancePoint, ...],
|
||||
reference_hardware: budget.ReferenceHardware,
|
||||
) -> budget.BudgetDerivation:
|
||||
report = sample_report(workload, points, ())
|
||||
report_ref, _ = stored_artifact(report, artifact_id="performance-calibration-seed")
|
||||
reference_workload = reference(workload, report_ref, (1, max(point.changed_bytes for point in points)))
|
||||
samples = []
|
||||
for point in points:
|
||||
_, measured_bytes, _ = measurement(point.relation_count, point.changed_bytes)
|
||||
complexity = budget.ChangeComplexity(
|
||||
point.change_digest,
|
||||
workload.workload_class,
|
||||
workload.changed_regions,
|
||||
point.relation_count,
|
||||
point.changed_bytes,
|
||||
measured_bytes,
|
||||
)
|
||||
samples.extend(budget.MeasuredSample(complexity, nanoseconds_to_milliseconds(value)) for value in point.elapsed_samples_ns)
|
||||
return budget.derive_budget(tuple(samples), workload.workload_class, 1.0, reference_workload, reference_hardware)
|
||||
|
||||
|
||||
__all__ = ["hardware", "measurement", "reference", "sample_report", "seed_budget"]
|
||||
@@ -0,0 +1,127 @@
|
||||
"""Deterministic work and isolated-memory scaling checks."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from fractions import Fraction
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from detection_core_performance import PerformancePoint
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class _SlopeInterval:
|
||||
lower: Fraction
|
||||
upper: Fraction
|
||||
|
||||
|
||||
def _work_units(point: PerformancePoint) -> int:
|
||||
return point.relation_count + point.observation_count + point.receipt_count + point.case_count
|
||||
|
||||
|
||||
def _positive_integer(value: object, name: str) -> int:
|
||||
if type(value) is not int or value <= 0:
|
||||
raise AssertionError(f"incremental validation {name} requires exact positive integer values")
|
||||
return value
|
||||
|
||||
|
||||
def _integer_median(values: tuple[int, ...]) -> int:
|
||||
ordered = tuple(sorted(values))
|
||||
midpoint = len(ordered) // 2
|
||||
if len(ordered) % 2:
|
||||
return ordered[midpoint]
|
||||
return (ordered[midpoint - 1] + ordered[midpoint]) // 2
|
||||
|
||||
|
||||
def _series(
|
||||
points: tuple[PerformancePoint, ...],
|
||||
sample_field: str,
|
||||
summary_field: str,
|
||||
name: str,
|
||||
) -> tuple[tuple[int, ...], tuple[int, ...]]:
|
||||
samples = tuple(tuple(getattr(point, sample_field)) for point in points)
|
||||
if len({len(values) for values in samples}) != 1:
|
||||
raise AssertionError("incremental validation measurements require equal repeat counts")
|
||||
if any(len(values) < 2 for values in samples):
|
||||
raise AssertionError("incremental validation measurements require repeated positive integer samples")
|
||||
for values in samples:
|
||||
for value in values:
|
||||
_positive_integer(value, name)
|
||||
medians = tuple(_integer_median(values) for values in samples)
|
||||
summaries = tuple(_positive_integer(getattr(point, summary_field), name) for point in points)
|
||||
if medians != summaries:
|
||||
raise AssertionError(f"incremental validation {name} summary does not match repeated median")
|
||||
spreads = tuple(max(values) - min(values) for values in samples)
|
||||
return medians, spreads
|
||||
|
||||
|
||||
def _slope_intervals(
|
||||
units: tuple[int, ...],
|
||||
medians: tuple[int, ...],
|
||||
spreads: tuple[int, ...],
|
||||
) -> tuple[_SlopeInterval, ...]:
|
||||
result = []
|
||||
pairs = zip(units[:-1], units[1:], medians[:-1], medians[1:], spreads[:-1], spreads[1:], strict=True)
|
||||
for old_units, new_units, old_value, new_value, old_spread, new_spread in pairs:
|
||||
width = new_units - old_units
|
||||
increment = new_value - old_value
|
||||
uncertainty = Fraction(old_spread + new_spread, 2)
|
||||
lower = (increment - uncertainty) / width
|
||||
upper = (increment + uncertainty) / width
|
||||
result.append(_SlopeInterval(lower, upper))
|
||||
return tuple(result)
|
||||
|
||||
|
||||
def _assert_memory(points: tuple[PerformancePoint, ...], units: tuple[int, ...]) -> None:
|
||||
medians, spreads = _series(points, "peak_samples_bytes", "peak_bytes", "memory")
|
||||
for median, spread in zip(medians, spreads, strict=True):
|
||||
if spread > max(1_024, median // 10):
|
||||
raise AssertionError("incremental validation memory is obscured by point-local variability")
|
||||
increments = tuple(new - old for old, new in zip(medians[:-1], medians[1:], strict=True))
|
||||
if any(increment <= 0 for increment in increments):
|
||||
raise AssertionError("incremental validation memory requires monotonic measured peaks")
|
||||
slopes = _slope_intervals(units, medians, spreads)
|
||||
for previous, current in zip(slopes[:-1], slopes[1:], strict=True):
|
||||
if current.lower > previous.upper * Fraction(51, 50):
|
||||
raise AssertionError(
|
||||
f"incremental validation memory slope increased beyond point-local uncertainty: {current.lower}>{previous.upper}"
|
||||
)
|
||||
|
||||
|
||||
def _assert_work(points: tuple[PerformancePoint, ...]) -> None:
|
||||
pairs = (
|
||||
("canonical_relations", "relation_count"),
|
||||
("validated_relations", "relation_count"),
|
||||
("canonical_observations", "observation_count"),
|
||||
("validated_observations", "observation_count"),
|
||||
("readiness_receipts", "receipt_count"),
|
||||
("admission_cases", "case_count"),
|
||||
)
|
||||
for point in points:
|
||||
expected_total = 0
|
||||
for work_field, point_field in pairs:
|
||||
measured = _positive_integer(getattr(point.work, work_field), "work")
|
||||
expected = _positive_integer(getattr(point, point_field), "work")
|
||||
if measured != expected:
|
||||
raise AssertionError(f"incremental validation work missed {work_field}: {measured}!={expected}")
|
||||
expected_total += expected
|
||||
total = _positive_integer(point.work.total_operations, "work")
|
||||
if total != expected_total:
|
||||
raise AssertionError(f"incremental validation work total disagrees: {total}!={expected_total}")
|
||||
|
||||
|
||||
def assert_incremental_envelope(points: tuple[PerformancePoint, ...]) -> None:
|
||||
units = tuple(_work_units(point) for point in points)
|
||||
dimensions = tuple(
|
||||
tuple(getattr(point, field) for point in points) for field in ("relation_count", "observation_count", "receipt_count", "case_count")
|
||||
)
|
||||
increasing = all(all(new > old for old, new in zip(values[:-1], values[1:], strict=True)) for values in dimensions)
|
||||
if len(units) < 3 or not increasing:
|
||||
raise AssertionError("incremental validation requires increasing workload points")
|
||||
_series(points, "elapsed_samples_ns", "elapsed_ns", "elapsed")
|
||||
_assert_work(points)
|
||||
_assert_memory(points, units)
|
||||
|
||||
|
||||
__all__ = ["assert_incremental_envelope"]
|
||||
@@ -0,0 +1,65 @@
|
||||
"""Test-owned counters around actual detection-core validation boundaries."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from dataclasses import dataclass
|
||||
from types import ModuleType
|
||||
from typing import Any
|
||||
from unittest.mock import patch
|
||||
|
||||
from openultrasast.core import admission_case, evidence, family, readiness
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ValidationWork:
|
||||
canonical_relations: int
|
||||
canonical_observations: int
|
||||
validated_relations: int
|
||||
validated_observations: int
|
||||
readiness_receipts: int
|
||||
admission_cases: int
|
||||
total_operations: int
|
||||
|
||||
|
||||
def _counted(
|
||||
module: ModuleType,
|
||||
function_name: str,
|
||||
counter: dict[str, int],
|
||||
counter_name: str,
|
||||
) -> Any:
|
||||
original = getattr(module, function_name)
|
||||
|
||||
def wrapped(*args: object, **kwargs: object) -> Any:
|
||||
result = original(*args, **kwargs)
|
||||
counter[counter_name] += 1
|
||||
return result
|
||||
|
||||
return patch.object(module, function_name, wrapped)
|
||||
|
||||
|
||||
def count_validation_work(run: Callable[[], int]) -> tuple[int, ValidationWork]:
|
||||
"""Run real production validators and count each successful scaled boundary."""
|
||||
counts = {
|
||||
"canonical_relations": 0,
|
||||
"canonical_observations": 0,
|
||||
"validated_relations": 0,
|
||||
"validated_observations": 0,
|
||||
"readiness_receipts": 0,
|
||||
"admission_cases": 0,
|
||||
}
|
||||
hooks = (
|
||||
_counted(evidence, "_relation_value", counts, "canonical_relations"),
|
||||
_counted(evidence, "_observation_value", counts, "canonical_observations"),
|
||||
_counted(family, "_validate_relation", counts, "validated_relations"),
|
||||
_counted(family, "_validate_observation", counts, "validated_observations"),
|
||||
_counted(readiness, "validate_readiness_result", counts, "readiness_receipts"),
|
||||
_counted(admission_case, "validate_admission_case_shape", counts, "admission_cases"),
|
||||
)
|
||||
with hooks[0], hooks[1], hooks[2], hooks[3], hooks[4], hooks[5]:
|
||||
serialized_bytes = run()
|
||||
values = tuple(counts.values())
|
||||
return serialized_bytes, ValidationWork(*values, sum(values))
|
||||
|
||||
|
||||
__all__ = ["ValidationWork", "count_validation_work"]
|
||||
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"schema_version": "detection-core-performance-workload-v1",
|
||||
"workload_class": "small",
|
||||
"sizes": [8, 32, 128, 256],
|
||||
"repeats": 3,
|
||||
"percentile": 1.0,
|
||||
"changed_regions": 1,
|
||||
"changed_bytes": 120
|
||||
}
|
||||
@@ -161,6 +161,7 @@ def test_derivation_is_deterministic_and_discloses_measurement_context() -> None
|
||||
assert first.sample_count == 5
|
||||
assert first.percentile == 0.8
|
||||
assert first.budget_ms == 144
|
||||
assert first.derivation_digest == "89149c1b78b413f543cc3c5950f9ee1b55379c11feb7499d9d49e972f5427b93"
|
||||
assert len(first.derivation_digest) == 64
|
||||
|
||||
|
||||
@@ -193,6 +194,58 @@ def test_derivation_refuses_missing_or_nonfinite_inputs(
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"percentile",
|
||||
(10**400, -(10**400), True, "0.8", object(), math.inf, -math.inf, math.nan, 0, -1, 1.1),
|
||||
)
|
||||
def test_derivation_refuses_oversized_or_malformed_percentile_without_overflow(percentile: object) -> None:
|
||||
with pytest.raises((TypeError, ValueError)) as caught:
|
||||
budget_v2.derive_budget(
|
||||
_samples(),
|
||||
workload_class="small",
|
||||
percentile=percentile, # type: ignore[arg-type]
|
||||
reference_workload=_reference_workload(),
|
||||
reference_hardware=_hardware(),
|
||||
)
|
||||
assert not isinstance(caught.value, OverflowError)
|
||||
|
||||
|
||||
def test_huge_reply_times_derive_check_and_excess_remain_exact() -> None:
|
||||
huge = 10**400
|
||||
complexity, resolver = _complexity()
|
||||
samples = (
|
||||
budget_v2.MeasuredSample(complexity, huge),
|
||||
budget_v2.MeasuredSample(complexity, huge + 2),
|
||||
)
|
||||
derived = budget_v2.derive_budget(
|
||||
samples,
|
||||
workload_class="small",
|
||||
percentile=0.5,
|
||||
reference_workload=_reference_workload(),
|
||||
reference_hardware=_hardware(),
|
||||
)
|
||||
|
||||
assert derived.budget_ms == huge + 1
|
||||
assert derived == replace(derived, derivation_digest="")
|
||||
encoded = canonical.canonical_bytes({"budget_ms": derived.budget_ms})
|
||||
assert encoded == canonical.canonical_bytes({"budget_ms": huge + 1})
|
||||
assert str(huge + 1).encode() in encoded
|
||||
budget_v2.check_budget(complexity, huge + 1, derived, resolver)
|
||||
with pytest.raises(budget_v2.BudgetGateFailure, match="budget:") as caught:
|
||||
budget_v2.check_budget(complexity, huge + 2, derived, resolver)
|
||||
assert f"{huge + 2}ms>{huge + 1}ms" in str(caught.value)
|
||||
|
||||
|
||||
def test_budget_derivation_refuses_oversized_percentile_after_tampering() -> None:
|
||||
derived = _derivation()
|
||||
object.__setattr__(derived, "percentile", 10**400)
|
||||
complexity, resolver = _complexity()
|
||||
|
||||
with pytest.raises(ValueError, match="percentile must not exceed 1") as caught:
|
||||
budget_v2.check_budget(complexity, 100, derived, resolver)
|
||||
assert not isinstance(caught.value, OverflowError)
|
||||
|
||||
|
||||
def test_derivation_refuses_mixed_classes_and_incompatible_complexity() -> None:
|
||||
wrong_class, _ = _complexity(workload_class="large")
|
||||
too_large, _ = _complexity(changed_bytes=501)
|
||||
|
||||
@@ -0,0 +1,379 @@
|
||||
"""Measured performance and total consumer deadline validation for detection-core."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import math
|
||||
from dataclasses import replace
|
||||
|
||||
import pytest
|
||||
from detection_core_performance import (
|
||||
PERFORMANCE_FIXTURE,
|
||||
PerformanceProfile,
|
||||
assert_large_set_envelope,
|
||||
measured_profile,
|
||||
optional_consumer_case,
|
||||
read_workload,
|
||||
reject_invalid_large_set,
|
||||
)
|
||||
from detection_core_scaling import _series
|
||||
|
||||
import openultrasast.core.consumer as consumer_module
|
||||
from openultrasast.core import budget, canonical
|
||||
from openultrasast.core.consumer import ConsumerContractError, consume_records, make_push_request
|
||||
|
||||
|
||||
def _instrument_optional_path(
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
profile: PerformanceProfile,
|
||||
) -> tuple[list[str], list[int]]:
|
||||
calls: list[str] = []
|
||||
clock = [0]
|
||||
original_validate = consumer_module.validate_evidence_input
|
||||
original_attach = consumer_module.attach_differential_proof
|
||||
original_outputs = consumer_module._derive_outputs
|
||||
|
||||
def measured_validate(*args: object, **kwargs: object) -> object:
|
||||
result = original_validate(*args, **kwargs) # type: ignore[arg-type]
|
||||
calls.append("model")
|
||||
return result
|
||||
|
||||
def measured_attach(*args: object, **kwargs: object) -> object:
|
||||
result = original_attach(*args, **kwargs) # type: ignore[arg-type]
|
||||
calls.append("proof")
|
||||
return result
|
||||
|
||||
def delayed_outputs(*args: object, **kwargs: object) -> object:
|
||||
result = original_outputs(*args, **kwargs) # type: ignore[arg-type]
|
||||
clock[0] += (profile.budget.budget_ms + 1) * 1_000_000
|
||||
return result
|
||||
|
||||
monkeypatch.setattr(consumer_module, "validate_evidence_input", measured_validate)
|
||||
monkeypatch.setattr(consumer_module, "attach_differential_proof", measured_attach)
|
||||
monkeypatch.setattr(consumer_module, "_derive_outputs", delayed_outputs)
|
||||
monkeypatch.setattr(consumer_module, "_monotonic_ns", lambda: clock[0])
|
||||
return calls, clock
|
||||
|
||||
|
||||
def test_measured_profile_discloses_increasing_workload_and_fixed_budget_contract() -> None:
|
||||
workload = read_workload(PERFORMANCE_FIXTURE)
|
||||
profile = measured_profile(workload)
|
||||
|
||||
assert workload.bytes_read > 0
|
||||
assert len(workload.content_digest) == 64
|
||||
assert tuple(point.relation_count for point in profile.points) == workload.sizes
|
||||
assert tuple(point.observation_count for point in profile.points) == workload.sizes
|
||||
assert tuple(point.receipt_count for point in profile.points) == workload.sizes
|
||||
assert tuple(point.case_count for point in profile.points) == workload.sizes
|
||||
assert all(
|
||||
type(value) is int and value > 0
|
||||
for point in profile.points
|
||||
for value in (point.elapsed_ns, *point.elapsed_samples_ns, point.peak_bytes, *point.peak_samples_bytes)
|
||||
)
|
||||
assert profile.sample_bytes > 0
|
||||
expected_samples = (len(workload.sizes) + 1) * workload.repeats
|
||||
assert profile.budget.sample_count == len(profile.samples) == expected_samples
|
||||
assert profile.budget.percentile == workload.percentile
|
||||
assert profile.budget.reference_workload == profile.reference_workload
|
||||
assert profile.budget.reference_hardware == profile.reference_hardware
|
||||
assert profile.budget.budget_ms == max(sample.total_reply_ms for sample in profile.samples)
|
||||
assert len(profile.budget.derivation_digest) == 64
|
||||
|
||||
|
||||
def test_large_transition_validation_is_proportional_bounded_and_semantically_strict() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
|
||||
assert_large_set_envelope(profile)
|
||||
reject_invalid_large_set(profile.workload, profile.workload.sizes[-1])
|
||||
|
||||
|
||||
def _adversarial_values(profile: PerformanceProfile, shape: str) -> tuple[int, ...]:
|
||||
sizes = tuple(point.relation_count for point in profile.points)
|
||||
if shape == "quadratic":
|
||||
return tuple(size**2 for size in sizes)
|
||||
if shape == "fixed-overhead-quadratic":
|
||||
return tuple(100_000 + size**2 for size in sizes)
|
||||
if shape == "n^1.1":
|
||||
return tuple(round(10_000 * size**1.1) for size in sizes)
|
||||
if shape == "n^1.25":
|
||||
return tuple(round(10_000 * size**1.25) for size in sizes)
|
||||
if shape == "mild-superlinear":
|
||||
return tuple(round(10_000 * size**1.05) for size in sizes)
|
||||
return (1_000, 4_000, 500_000, 502_000)
|
||||
|
||||
|
||||
def _valid_memory_points(profile: PerformanceProfile) -> tuple[object, ...]:
|
||||
return tuple(
|
||||
replace(
|
||||
point,
|
||||
peak_bytes=100_000 + point.relation_count * 1_000,
|
||||
peak_samples_bytes=(100_000 + point.relation_count * 1_000,) * profile.workload.repeats,
|
||||
)
|
||||
for point in profile.points
|
||||
)
|
||||
|
||||
|
||||
def _adversarial_points(
|
||||
profile: PerformanceProfile,
|
||||
target: str,
|
||||
values: tuple[int, ...],
|
||||
) -> tuple[object, ...]:
|
||||
source = _valid_memory_points(profile) if target == "work" else profile.points
|
||||
if target == "work":
|
||||
return tuple(replace(point, work=replace(point.work, total_operations=value)) for point, value in zip(source, values, strict=True))
|
||||
return tuple(
|
||||
replace(point, peak_bytes=value, peak_samples_bytes=(value,) * profile.workload.repeats)
|
||||
for point, value in zip(source, values, strict=True)
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("target", ("work", "memory"))
|
||||
@pytest.mark.parametrize(
|
||||
"shape",
|
||||
("quadratic", "fixed-overhead-quadratic", "n^1.1", "n^1.25", "mild-superlinear", "penultimate-spike"),
|
||||
)
|
||||
def test_adversarial_superlinear_profiles_are_rejected(target: str, shape: str) -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
values = _adversarial_values(profile, shape)
|
||||
points = _adversarial_points(profile, target, values)
|
||||
with pytest.raises(AssertionError, match=f"incremental validation {target}"):
|
||||
assert_large_set_envelope(replace(profile, points=points))
|
||||
|
||||
|
||||
def test_elapsed_samples_are_published_but_not_used_as_deterministic_scaling_oracle() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
values = (1_000, 4_000, 500_000, 502_000)
|
||||
points = tuple(
|
||||
replace(point, elapsed_ns=value, elapsed_samples_ns=(value,) * profile.workload.repeats)
|
||||
for point, value in zip(_valid_memory_points(profile), values, strict=True)
|
||||
)
|
||||
|
||||
assert_large_set_envelope(replace(profile, points=points))
|
||||
|
||||
|
||||
@pytest.mark.parametrize("metric", ("elapsed", "memory"))
|
||||
@pytest.mark.parametrize("invalid", (math.inf, math.nan, 1.5, True, 0, -1))
|
||||
def test_measurement_series_refuse_non_integer_nonpositive_or_nonfinite_values(metric: str, invalid: object) -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
point = profile.points[-1]
|
||||
if metric == "elapsed":
|
||||
changed = replace(point, elapsed_samples_ns=(*point.elapsed_samples_ns[:-1], invalid))
|
||||
else:
|
||||
changed = replace(point, peak_samples_bytes=(*point.peak_samples_bytes[:-1], invalid))
|
||||
with pytest.raises(AssertionError, match="positive integer"):
|
||||
assert_large_set_envelope(replace(profile, points=(*profile.points[:-1], changed)))
|
||||
|
||||
|
||||
@pytest.mark.parametrize("metric", ("elapsed", "memory"))
|
||||
def test_measurement_series_refuse_unequal_repeats_and_mismatched_summaries(metric: str) -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
point = profile.points[-1]
|
||||
if metric == "elapsed":
|
||||
unequal = replace(point, elapsed_samples_ns=point.elapsed_samples_ns[:-1])
|
||||
mismatch = replace(point, elapsed_ns=point.elapsed_ns + 1)
|
||||
else:
|
||||
unequal = replace(point, peak_samples_bytes=point.peak_samples_bytes[:-1])
|
||||
mismatch = replace(point, peak_bytes=point.peak_bytes + 1)
|
||||
with pytest.raises(AssertionError, match="equal repeat counts"):
|
||||
assert_large_set_envelope(replace(profile, points=(*profile.points[:-1], unequal)))
|
||||
with pytest.raises(AssertionError, match="summary does not match repeated median"):
|
||||
assert_large_set_envelope(replace(profile, points=(*profile.points[:-1], mismatch)))
|
||||
|
||||
|
||||
def test_inflated_repeat_variance_cannot_hide_superlinear_memory() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
values = _adversarial_values(profile, "n^1.25")
|
||||
points = tuple(
|
||||
replace(
|
||||
point,
|
||||
peak_bytes=value,
|
||||
peak_samples_bytes=(max(1, value // 100), value, value * 100),
|
||||
)
|
||||
for point, value in zip(profile.points, values, strict=True)
|
||||
)
|
||||
with pytest.raises(AssertionError, match="incremental validation memory"):
|
||||
assert_large_set_envelope(replace(profile, points=points))
|
||||
|
||||
|
||||
def test_huge_work_disagreement_is_a_named_domain_failure() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
point = profile.points[-1]
|
||||
changed = replace(point, work=replace(point.work, total_operations=10**400))
|
||||
|
||||
with pytest.raises(AssertionError, match="incremental validation work total disagrees"):
|
||||
assert_large_set_envelope(replace(profile, points=(*profile.points[:-1], changed)))
|
||||
|
||||
|
||||
def test_huge_superlinear_memory_is_a_named_domain_failure() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
huge = 10**400
|
||||
values = (huge, huge * 2, huge * 8, huge * 64)
|
||||
points = tuple(
|
||||
replace(point, peak_bytes=value, peak_samples_bytes=(value,) * profile.workload.repeats)
|
||||
for point, value in zip(profile.points, values, strict=True)
|
||||
)
|
||||
|
||||
with pytest.raises(AssertionError, match="incremental validation memory slope increased"):
|
||||
assert_large_set_envelope(replace(profile, points=points))
|
||||
|
||||
|
||||
def test_huge_even_repeat_median_and_elapsed_conversion_remain_exact() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
huge = 10**400
|
||||
point = replace(
|
||||
profile.points[-1],
|
||||
elapsed_ns=huge + 1,
|
||||
elapsed_samples_ns=(huge, huge + 2, huge, huge + 2),
|
||||
)
|
||||
medians, _ = _series((point,), "elapsed_samples_ns", "elapsed_ns", "elapsed")
|
||||
assert medians == (huge + 1,)
|
||||
|
||||
point = replace(point, elapsed_ns=huge + 999_999)
|
||||
assert point.elapsed_ms == (huge + 1_999_998) // 1_000_000
|
||||
|
||||
|
||||
def test_huge_elapsed_samples_publish_and_derive_without_float_narrowing() -> None:
|
||||
from detection_core_performance_reporting import hardware, sample_report, seed_budget
|
||||
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
huge = 10**400
|
||||
points = tuple(
|
||||
replace(
|
||||
point,
|
||||
elapsed_ns=huge + index,
|
||||
elapsed_samples_ns=(huge + index,) * profile.workload.repeats,
|
||||
)
|
||||
for index, point in enumerate(profile.points)
|
||||
)
|
||||
|
||||
report = sample_report(profile.workload, points, ())
|
||||
assert str(huge).encode() in report
|
||||
derived = seed_budget(profile.workload, points, hardware())
|
||||
assert derived.budget_ms == (huge + len(points) - 1 + 999_999) // 1_000_000
|
||||
|
||||
|
||||
@pytest.mark.parametrize("invalid", (math.inf, math.nan, 1.5, True, 0, -1, "1"))
|
||||
def test_elapsed_conversion_refuses_non_exact_or_nonpositive_values(invalid: object) -> None:
|
||||
from detection_core_performance_reporting import nanoseconds_to_milliseconds
|
||||
|
||||
with pytest.raises(ValueError, match="exact positive integer") as caught:
|
||||
nanoseconds_to_milliseconds(invalid)
|
||||
assert not isinstance(caught.value, OverflowError)
|
||||
|
||||
|
||||
def test_deterministic_work_counts_cover_every_production_validation_boundary() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
|
||||
for point in profile.points:
|
||||
assert point.work.canonical_relations == point.relation_count
|
||||
assert point.work.validated_relations == point.relation_count
|
||||
assert point.work.canonical_observations == point.observation_count
|
||||
assert point.work.validated_observations == point.observation_count
|
||||
assert point.work.readiness_receipts == point.receipt_count
|
||||
assert point.work.admission_cases == point.case_count
|
||||
assert point.work.total_operations == (
|
||||
2 * point.relation_count + 2 * point.observation_count + point.receipt_count + point.case_count
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"field",
|
||||
(
|
||||
"canonical_relations",
|
||||
"validated_relations",
|
||||
"canonical_observations",
|
||||
"validated_observations",
|
||||
"readiness_receipts",
|
||||
"admission_cases",
|
||||
),
|
||||
)
|
||||
def test_deterministic_work_refuses_disagreement_or_a_missing_dimension(field: str) -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
point = profile.points[-1]
|
||||
bad_work = replace(point.work, **{field: 0})
|
||||
changed = replace(point, work=bad_work)
|
||||
|
||||
with pytest.raises(AssertionError, match="incremental validation work"):
|
||||
assert_large_set_envelope(replace(profile, points=(*profile.points[:-1], changed)))
|
||||
|
||||
|
||||
def test_every_sample_binds_exact_scope_and_measured_change_bytes() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
by_size = {point.relation_count: point for point in profile.points}
|
||||
deterministic = (item for item in profile.consumer_samples if item.mode == "deterministic")
|
||||
for item in deterministic:
|
||||
sample = item.measured
|
||||
point = by_size[item.size]
|
||||
assert sample.complexity.change_id == point.change_digest == canonical.bytes_digest(point.change_content)
|
||||
assert sample.complexity.changed_bytes == point.changed_bytes == len(point.change_content)
|
||||
resolved = profile.resolver.resolve(sample.complexity.measurement)
|
||||
assert resolved.content == point.changed_bytes_measurement
|
||||
assert point.change_content == canonical.canonical_bytes(json.loads(point.change_content))
|
||||
for sample in profile.samples:
|
||||
parsed = json.loads(profile.resolver.resolve(sample.complexity.measurement).content)
|
||||
assert parsed["changed_bytes"] == sample.complexity.changed_bytes
|
||||
|
||||
|
||||
def test_budget_samples_are_complete_consumer_runs_with_optional_work() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
|
||||
assert {sample.mode for sample in profile.consumer_samples} == {"deterministic", "model_and_proof"}
|
||||
assert all(sample.total_reply_ms > 0 for sample in profile.consumer_samples)
|
||||
assert tuple(item.measured for item in profile.consumer_samples) == profile.samples
|
||||
|
||||
|
||||
def test_measured_samples_fit_derived_envelope_and_excess_names_gate() -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
for sample in profile.samples:
|
||||
budget.check_budget(sample.complexity, sample.total_reply_ms, profile.budget, profile.resolver)
|
||||
|
||||
selected = profile.samples[-1].complexity
|
||||
with pytest.raises(budget.BudgetGateFailure) as caught:
|
||||
budget.check_budget(selected, profile.budget.budget_ms + 1, profile.budget, profile.resolver)
|
||||
message = str(caught.value)
|
||||
assert selected.change_id in message
|
||||
assert f"{profile.budget.budget_ms + 1}ms>{profile.budget.budget_ms}ms" in message
|
||||
assert profile.budget.workload_class in message
|
||||
assert profile.budget.derivation_digest in message
|
||||
|
||||
|
||||
def test_optional_model_and_proof_processing_share_total_consumer_budget(
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
profile = measured_profile(read_workload(PERFORMANCE_FIXTURE))
|
||||
case = optional_consumer_case(profile)
|
||||
accepted = consume_records(
|
||||
case.request,
|
||||
(case.value,),
|
||||
case.context,
|
||||
case.custody,
|
||||
0,
|
||||
case.resolver,
|
||||
attachments=(case.attachment,),
|
||||
proof_trust=case.trust,
|
||||
)
|
||||
assert 0 < accepted.total_reply_ms <= profile.budget.budget_ms
|
||||
calls, _ = _instrument_optional_path(monkeypatch, profile)
|
||||
request = make_push_request(
|
||||
scope=case.request.scope,
|
||||
workload_class=profile.budget.workload_class,
|
||||
complexity=case.request.complexity,
|
||||
budget=profile.budget,
|
||||
)
|
||||
|
||||
with pytest.raises(ConsumerContractError, match="measured budget gate failed") as caught:
|
||||
consume_records(
|
||||
request,
|
||||
(case.value,),
|
||||
case.context,
|
||||
case.custody,
|
||||
0,
|
||||
case.resolver,
|
||||
attachments=(case.attachment,),
|
||||
proof_trust=case.trust,
|
||||
)
|
||||
|
||||
assert calls == ["model", "proof"]
|
||||
assert request.complexity.change_id in str(caught.value)
|
||||
assert profile.budget.derivation_digest in str(caught.value)
|
||||
assert replace(request.budget, derivation_digest="").derivation_digest == request.budget.derivation_digest
|
||||
Reference in New Issue
Block a user