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https://github.com/firestar5683/StarPilot.git
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firestar5683
172 lines
6.6 KiB
Python
172 lines
6.6 KiB
Python
"""Actual effect owner lifecycle; profile fixture is not physical fit proof."""
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from dataclasses import replace
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import pytest
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from openpilot.starpilot.flm.adaptive_profile import Profile, ReplayValidation
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from openpilot.starpilot.flm.canonical_surface_adapter import (
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MANIFEST_SHA256,
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NAMES,
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full_original_anchor,
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full_profile_sha256,
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original_anchor,
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parameters_for,
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)
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from openpilot.starpilot.flm.event_objective import EffectObjective
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from openpilot.starpilot.flm.full_surface_profile import FullSurfaceEnvelope
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from openpilot.starpilot.flm.selected_effects import EffectOwner, EffectTrial
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from openpilot.starpilot.flm.torque_adapter import Admission
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NOW = 1_000_000_000
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BASE = 'b' * 64
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SOURCE = 'e' * 64
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def fixture(*, accumulated=False):
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names = ('ff_gain_left',)
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prior = dict.fromkeys(NAMES, 0.0)
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if accumulated:
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prior['center_taper_max'] = 0.1
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proposed = dict(prior, ff_gain_left=0.004)
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envelope = FullSurfaceEnvelope(
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1,
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MANIFEST_SHA256,
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full_original_anchor(BASE),
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original_anchor(BASE, names),
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SOURCE,
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'c' * 64,
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'selected-controller',
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SOURCE,
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'0',
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BASE,
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full_profile_sha256(BASE, prior),
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full_profile_sha256(BASE, proposed),
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tuple(prior.items()),
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tuple(proposed.items()),
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names,
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'f' * 64,
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)
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profile = Profile(
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'a' * 64,
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1,
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NOW,
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NOW + 500_000_000,
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parameters_for(names),
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(0.004,),
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tuple(f'train{i}' for i in range(4)),
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tuple(f'held{i}' for i in range(4)),
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1.0,
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0.8,
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(('held', 0.1),),
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policy='event_effect_coordinate_v2',
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effect_envelope=envelope,
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)
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validation = ReplayValidation(profile, BASE, 'physical-model', True, True, 0.1)
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objective = EffectObjective('ff.left', names, envelope.trial_anchor, SOURCE)
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context = {
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'cp_sha256': envelope.cp_sha256,
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'controller_revision': envelope.controller_revision,
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'source_epoch': SOURCE,
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'reset_generation': '0',
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'selected_baseline_revision': BASE,
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'context_key': SOURCE,
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'persistent_source_epoch': 'f' * 64,
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'current_full_profile_sha256': envelope.prior_full_profile_sha256,
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}
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owner = EffectOwner(
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profile.identity, objective, selected_baseline_revision=BASE, physical_model_revision='physical-model', original_anchor=envelope.replay_anchor()
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)
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return owner, profile, validation, context
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def test_expired_proof_withdraws_all30_not_just_fitted_group():
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owner, p, v, context = fixture(accumulated=True)
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owner.prepare(Admission(p, NOW, True, False, True, v), active=True, current_context=context)
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assert owner.source is not None
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assert 0 < owner.applied_full['center_taper_max'] <= 0.005 * 0.01
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owner.prepare(Admission(p, NOW + 500_000_001, True, False, True, v), active=True, current_context=context)
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assert owner.source is None
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assert all(value == 0 for value in owner.applied_full.values())
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@pytest.mark.parametrize('reason', ['manual', 'off', 'inactive', 'unhealthy'])
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def test_withdrawal_and_recovery_slew_all30(reason):
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owner, p, v, context = fixture(accumulated=True)
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owner.prepare(Admission(p, NOW, True, False, True, v), active=True, current_context=context)
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owner.prepare(
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Admission(p, NOW + 10_000_000, reason != 'off', reason == 'manual', reason != 'unhealthy', v), active=reason != 'inactive', current_context=context
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)
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assert owner.source is None and not any(owner.applied_full.values())
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owner.prepare(Admission(p, NOW + 20_000_000, True, False, True, v), active=True, current_context=context)
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assert owner.source is not None
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assert max(abs(value) for value in owner.applied_full.values()) <= 0.005 * 0.01 + 1e-12
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def test_changed_current_context_never_leaves_previous_source_attached():
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owner, p, v, context = fixture()
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owner.prepare(Admission(p, NOW, True, False, True, v), active=True, current_context=context)
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with pytest.raises(ValueError):
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owner.prepare(Admission(p, NOW + 10_000_000, True, False, True, v), active=True, current_context=dict(context, source_epoch='d' * 64))
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assert owner.source is None
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def test_replay_trial_preserves_nonzero_group_and_outside_group_original_prior():
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owner, p, _, _ = fixture(accumulated=True)
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prior = dict(p.effect_envelope.prior_coordinates)
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prior['ff_gain_left'] = 0.1
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envelope = replace(
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p.effect_envelope,
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prior_coordinates=tuple(prior.items()),
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coordinates=tuple(prior.items()),
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prior_full_profile_sha256=full_profile_sha256(BASE, prior),
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full_profile_sha256=full_profile_sha256(BASE, prior),
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)
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trial = EffectTrial(owner.objective, (0.104,), BASE, envelope.replay_anchor())
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source = trial.source()
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assert source.values['ff_gain_left'] == 0.104
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assert source.values['center_taper_max'] == 0.1
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assert source.values['ff_gain_right'] == 0
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def test_trial_group_switch_preserves_full30_state_and_cas_without_step_jump():
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owner, p, v, context = fixture()
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owner.prepare(Admission(p, NOW, True, False, True, v), active=True, current_context=context)
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before = dict(owner.applied_full)
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old = p.effect_envelope
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names = ('turn_in_threshold_reduction_left',)
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prior = dict(old.coordinates)
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proposed = dict(prior, turn_in_threshold_reduction_left=0.004)
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envelope = replace(
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old,
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trial_names=names,
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trial_anchor=original_anchor(BASE, names),
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prior_coordinates=tuple(prior.items()),
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coordinates=tuple(proposed.items()),
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prior_full_profile_sha256=old.full_profile_sha256,
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full_profile_sha256=full_profile_sha256(BASE, proposed),
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)
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p2 = replace(p, sequence=2, parameters=parameters_for(names), coefficients=(0.004,), effect_envelope=envelope)
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v2 = replace(v, profile=p2)
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objective = EffectObjective('friction.left', names, envelope.trial_anchor, SOURCE)
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next_owner = EffectOwner(
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p.identity,
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objective,
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selected_baseline_revision=BASE,
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physical_model_revision='physical-model',
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original_anchor=envelope.replay_anchor(),
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applied_full=before,
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)
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context2 = dict(context, current_full_profile_sha256=old.full_profile_sha256)
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next_owner.prepare(Admission(p2, NOW + 10_000_000, True, False, True, v2), active=True, current_context=context2)
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assert next_owner.trace.before == tuple(before.items())
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assert next_owner.applied_full['ff_gain_left'] >= before['ff_gain_left']
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assert 0 < next_owner.applied_full['turn_in_threshold_reduction_left'] <= 0.005 * 0.01
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assert all(abs(next_owner.applied_full[n] - before[n]) <= 0.005 * 0.01 + 1e-12 for n in NAMES)
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stale = EffectOwner(
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p.identity, objective, selected_baseline_revision=BASE, physical_model_revision='physical-model', original_anchor=envelope.replay_anchor()
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)
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with pytest.raises(ValueError):
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stale.prepare(Admission(p2, NOW + 10_000_000, True, False, True, v2), active=True, current_context=context)
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assert stale.source is None
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