mirror of
https://github.com/firestar5683/StarPilot.git
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firestar5683
abb9646499
Keep bounded automatic learning collection through valid live calibration refinements. Retire generations on resets or invalid calibration, preserve live physical transforms and existing controller tunes, and validate fitted coefficient bounds before publication. Exercise the current controller and historical wire paths.
219 lines
10 KiB
Python
219 lines
10 KiB
Python
"""Historical CarParams wire stays readable and cannot masquerade as a fit."""
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from contextlib import contextmanager
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from pathlib import Path
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from tempfile import TemporaryDirectory
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import unittest
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import capnp
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from openpilot.cereal import custom, log, messaging
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from openpilot.starpilot.flm.learning_source import SERVICE
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from tools.ci.custom_schema_contract import describe, validate
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NOW = 400_000_000_000
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# Exact occupied prefix from c2109496a72e85ca702ecef7aff53ff45adcfb8c,
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# cereal/custom.capnp. No learner pointer existed in these recordings.
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HISTORICAL_CAR_PARAMS = """@0xb526ba661d550a59;
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struct StarPilotCarParams @0xaedffd8f31e7b55d {
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alternativeExperience @0 :Int16;
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canUsePedal @1 :Bool;
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canUseSDSU @2 :Bool;
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flags @3 :UInt32;
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isHDA2 @4 :Bool;
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openpilotLongitudinalControlDisabled @5 :Bool;
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safetyConfigs @6 :List(SafetyConfig);
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pcmCruiseSpeed @7 :Bool = true;
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redneckCruiseAvailable @8 :Bool;
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struct SafetyConfig {
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safetyParam @0 :UInt16;
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}
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}
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"""
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# Exact transient c204 payload: incompatible with the older occupied prefix.
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TRANSIENT_FLAT_LEARNER = """@0xb526ba661d550a59;
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struct AutoSteeringLearning @0xaedffd8f31e7b55d {
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version @0 :UInt16;
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status @1 :Status;
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reason @2 :Text;
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identity @3 :Identity;
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session @4 :Text;
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sequence @5 :UInt64;
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observedMonoTime @6 :UInt64; # MONOTONIC, separate from Event BOOTTIME.
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validUntilMonoTime @7 :UInt64;
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sourceWindowStartMonoTime @8 :UInt64;
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sourceWindowEndMonoTime @9 :UInt64;
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family @10 :Family;
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latAccelFactor @11 :Float64;
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acceptancePolicyId @12 :Text;
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trainWindows @13 :UInt32;
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heldoutWindows @14 :UInt32;
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trainBaselineLoss @15 :Float64;
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trainCandidateLoss @16 :Float64;
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heldoutBaselineLoss @17 :Float64;
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heldoutCandidateLoss @18 :Float64;
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enum Status { unavailable @0; collecting @1; candidate @2; rejected @3; qualifiedFit @4; }
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enum Family { none @0; linearFactor @1; }
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struct Identity {
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vehicle @0 :Text;
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brand @1 :Text;
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controller @2 :Text;
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policy @3 :Text;
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profile @4 :Text;
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conversionVersion @5 :Text;
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firmwareSha256 @6 :Text;
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basis @7 :List(Float64);
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baselineSha256 @8 :Text;
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preferenceSha256 @9 :Text;
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resetGeneration @10 :Text;
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controllerRevision @11 :Text;
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}
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}
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"""
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@contextmanager
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def historical_car_params(source=HISTORICAL_CAR_PARAMS):
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with TemporaryDirectory() as directory:
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path = Path(directory) / 'historical.capnp'
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path.write_text(source)
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yield capnp.SchemaParser().load(str(path))
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class TestAutomaticLearningWire(unittest.TestCase):
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def test_historical_car_params_prefix_defaults_and_nested_layout_survive(self):
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with historical_car_params() as historical:
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old_layout = describe(historical, ['StarPilotCarParams'])
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current_layout = describe(custom, ['StarPilotCarParams'])
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self.assertEqual(validate(current_layout, old_layout), [])
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self.assertEqual(set(old_layout['nodes'][str(0xaedffd8f31e7b55d)]['fields']),
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{str(i) for i in range(9)})
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field = log.Event.schema.fields[SERVICE].proto
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self.assertEqual(field.ordinal.explicit, 108)
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self.assertEqual(field.slot.type.struct.typeId, 0xaedffd8f31e7b55d)
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self.assertEqual(custom.StarPilotCarParams.schema.fields['learning'].proto.ordinal.explicit, 9)
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values = {'alternativeExperience': -123, 'canUsePedal': True, 'canUseSDSU': True,
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'flags': 0xa5a51234, 'isHDA2': True, 'openpilotLongitudinalControlDisabled': True,
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'safetyConfigs': [{'safetyParam': 0x5a5a}, {'safetyParam': 0xffff}],
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'pcmCruiseSpeed': False, 'redneckCruiseAvailable': True}
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old = historical.StarPilotCarParams.new_message(**values)
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with custom.StarPilotCarParams.from_bytes(old.to_bytes()) as current:
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self.assertEqual({key: value for key, value in current.to_dict().items() if key != 'learning'}, values)
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self.assertEqual(current.learning.version, 0)
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self.assertEqual(str(current.learning.status), 'unavailable')
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self.assertEqual(current.learning.session, '')
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self.assertEqual(current.learning.latAccelFactor, 0.)
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with custom.StarPilotCarParams.from_bytes(historical.StarPilotCarParams.new_message().to_bytes()) as defaults:
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self.assertTrue(defaults.pcmCruiseSpeed)
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self.assertEqual(defaults.learning.version, 0)
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new = custom.StarPilotCarParams.new_message(**values)
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new.learning.version = 1
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new.learning.status = 'qualifiedFit'
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new.learning.latAccelFactor = 3.25
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with historical.StarPilotCarParams.from_bytes(new.to_bytes()) as prior:
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self.assertEqual(prior.to_dict(), values)
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def test_real_controls_treats_valid_historical_payload_as_absent_learning(self):
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from opendbc.car.car_helpers import interfaces
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from opendbc.car.toyota.values import CAR
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from openpilot.starpilot.flm.tests.test_registered_controls_global import controls_for, frame
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cp = interfaces[CAR.TOYOTA_COROLLA_TSS2].get_non_essential_params(CAR.TOYOTA_COROLLA_TSS2)
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with historical_car_params() as historical, controls_for(cp, 'standard', True) as controls:
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self.assertIsNotNone(controls.global_learning.descriptor)
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cp_before = controls.CP.as_builder().to_bytes()
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native_factor = controls.LaC.torque_params.latAccelFactor
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native_tune = controls.LaC.torque_params.to_dict()
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old = historical.StarPilotCarParams.new_message(
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alternativeExperience=123, flags=0xffffffff, canUsePedal=True,
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safetyConfigs=[{'safetyParam': 65535}], redneckCruiseAvailable=True)
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old_bytes = old.to_bytes()
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readers = []
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for tick in range(100):
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now = NOW + tick * 10_000_000
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if tick % 25 == 0:
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event = messaging.new_message(SERVICE, valid=True)
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with custom.StarPilotCarParams.from_bytes(old_bytes) as envelope:
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event.autoSteeringLearning = envelope
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reader = messaging.log_from_bytes(event.to_bytes())
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readers.append(reader)
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controls.sm.update_msgs(now / 1e9, [reader])
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command, _ = frame(controls, now, tick, collecting=False)
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self.assertTrue(command.latActive)
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self.assertIsNone(controls.torque_host)
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self.assertIsNotNone(controls.global_learning)
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self.assertIsNone(controls.global_learning.profile)
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self.assertIsNone(controls.global_learning.validation)
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self.assertIsNone(controls.global_learning.outcome)
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self.assertEqual(controls.global_learning.applied_coordinates, (0.0, 0.0))
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self.assertEqual(controls.LaC.torque_params.latAccelFactor, native_factor)
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self.assertEqual(controls.LaC.torque_params.to_dict(), native_tune)
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self.assertTrue(controls.sm.all_checks([SERVICE]))
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self.assertFalse(controls.sm.simulation)
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self.assertEqual(len(readers), 4)
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self.assertEqual(controls.global_learning.session, '')
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self.assertEqual(controls.CP.as_builder().to_bytes(), cp_before)
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def test_transient_flat_learner_bytes_cannot_be_admitted_as_nested_fit(self):
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from opendbc.car.car_helpers import interfaces
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from opendbc.car.toyota.values import CAR
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from openpilot.starpilot.flm.learning_source import AutomaticLearningConsumer, identity_for
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from openpilot.starpilot.flm.tests.test_registered_controls_global import controls_for, frame
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cp = interfaces[CAR.TOYOTA_COROLLA_TSS2].get_non_essential_params(CAR.TOYOTA_COROLLA_TSS2)
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with historical_car_params(TRANSIENT_FLAT_LEARNER) as historical, controls_for(cp, 'standard', True) as controls:
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self.assertIsNotNone(controls.global_learning.descriptor)
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cp_before = controls.CP.as_builder().to_bytes()
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native_factor = controls.LaC.torque_params.latAccelFactor
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native_tune = controls.LaC.torque_params.to_dict()
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legacy = AutomaticLearningConsumer(controls.CP, interfaces[cp.carFingerprint](controls.CP), 'standard', controls.params)
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self.assertTrue(legacy.supported)
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identity, enabled, manual = identity_for(controls.CP, 'standard', controls.params, legacy.conversion)
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self.assertTrue(enabled)
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self.assertFalse(manual)
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self.assertIsNotNone(identity)
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old = historical.AutoSteeringLearning.new_message(
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version=1, status='qualifiedFit', identity=identity, session='transient-c204', sequence=1,
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observedMonoTime=NOW, validUntilMonoTime=NOW + 500_000_000,
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sourceWindowStartMonoTime=NOW - 10_000_000_000,
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sourceWindowEndMonoTime=NOW - 1_000_000_000,
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family='linearFactor', latAccelFactor=native_factor * 1.05,
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acceptancePolicyId=next(iter(legacy.policies)),
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trainWindows=10, heldoutWindows=8, trainBaselineLoss=.1, trainCandidateLoss=.09,
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heldoutBaselineLoss=.1, heldoutCandidateLoss=.09)
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# Use the genuinely admitted policy, so only the changed wire layout denies the fit.
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self.assertEqual(str(old.status), 'qualifiedFit')
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self.assertEqual(old.identity.to_dict(), identity)
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old_bytes = old.to_bytes()
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readers = []
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for tick in range(50):
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now = NOW + tick * 10_000_000
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if tick % 25 == 0:
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event = messaging.new_message(SERVICE, valid=True)
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with custom.StarPilotCarParams.from_bytes(old_bytes) as envelope:
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self.assertEqual(envelope.learning.version, 0)
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event.autoSteeringLearning = envelope
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reader = messaging.log_from_bytes(event.to_bytes())
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readers.append(reader)
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controls.sm.update_msgs(now / 1e9, [reader])
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command, _ = frame(controls, now, tick, collecting=False)
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self.assertTrue(command.latActive)
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self.assertIsNone(controls.torque_host)
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self.assertIsNotNone(controls.global_learning)
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self.assertIsNone(controls.global_learning.profile)
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self.assertIsNone(controls.global_learning.validation)
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self.assertIsNone(controls.global_learning.outcome)
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self.assertEqual(controls.global_learning.applied_coordinates, (0.0, 0.0))
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self.assertEqual(controls.LaC.torque_params.latAccelFactor, native_factor)
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self.assertEqual(controls.LaC.torque_params.to_dict(), native_tune)
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self.assertTrue(controls.sm.all_checks([SERVICE]))
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self.assertEqual(len(readers), 2)
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self.assertEqual(controls.global_learning.session, '')
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self.assertEqual(controls.CP.as_builder().to_bytes(), cp_before)
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