sunnypilot v2026.002.000

version: sunnypilot v2026.002.000 (staging)
date: 2026-07-21T07:44:02
master commit: 2c334ede443d7391d27575af8c854a095ba702a8

openpilot/sunnypilot/modeld_v2/tests/conftest.py

@@ -0,0 +1,204 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import pickle
+import pytest
+
+import openpilot.sunnypilot.models.helpers as helpers
+import openpilot.sunnypilot.modeld_v2.modeld as modeld_module
+from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
+from openpilot.sunnypilot.models.split_model_constants import SplitModelConstants
+
+
+class DummyOverride:
+  def __init__(self, key, value):
+    self.key = key
+    self.value = value
+
+
+class DummyArtifact:
+  def __init__(self, file_name):
+    self.fileName = file_name
+
+
+class DummyModelType:
+  def __init__(self, raw):
+    self.raw = raw
+
+
+class DummyModel:
+  def __init__(self, type_str, artifact_file):
+    self.type = DummyModelType(type_str)
+    self.artifact = DummyArtifact(artifact_file)
+
+
+class DummyBundle:
+  def __init__(self, is_20hz=False, models=None, generation=10):
+    self.overrides = [DummyOverride('lat', '.1'), DummyOverride('long', '.3')]
+    self.generation = generation
+    self.is20hz = is_20hz
+    self.models = models or []
+
+
+class Archetype:
+  def __init__(self, name, metadata_structure, model_stubs, is_20hz,
+               expected_model_type, expected_constants_class, expected_parser_module,
+               expected_desire_key):
+    self.name = name
+    self.metadata_structure = metadata_structure
+    self.model_stubs = model_stubs
+    self.is_20hz = is_20hz
+    self.expected_model_type = expected_model_type
+    self.expected_constants_class = expected_constants_class
+    self.expected_parser_module = expected_parser_module
+    self.expected_desire_key = expected_desire_key
+
+
+def _noop_jit(**kwargs):
+  pass
+
+
+def _make_vision_policy_metadata(vision_input_shapes, policy_input_shapes,
+                                 vision_output_slices, policy_output_slices):
+  return {
+    'vision': {'input_shapes': vision_input_shapes, 'output_slices': vision_output_slices},
+    'policy': {'input_shapes': policy_input_shapes, 'output_slices': policy_output_slices},
+  }
+
+
+def _make_multi_policy_metadata(vision_input_shapes, policy_input_shapes,
+                                vision_output_slices, policy_output_slices):
+  return {
+    'vision': {'input_shapes': vision_input_shapes, 'output_slices': vision_output_slices},
+    'offPolicy': {'input_shapes': policy_input_shapes, 'output_slices': policy_output_slices},
+  }
+
+
+def _make_tri_policy_metadata(vision_input_shapes, policy_input_shapes,
+                              vision_output_slices, policy_output_slices):
+  return {
+    'vision': {'input_shapes': vision_input_shapes, 'output_slices': vision_output_slices},
+    'onPolicy': {'input_shapes': policy_input_shapes, 'output_slices': policy_output_slices},
+    'offPolicy': {'input_shapes': policy_input_shapes, 'output_slices': policy_output_slices},
+  }
+
+
+def _make_supercombo_metadata(input_shapes, output_slices):
+  return {'model': {'input_shapes': input_shapes, 'output_slices': output_slices}}
+
+
+SPLIT_VISION_INPUT_SHAPES = {'img': (1, 12, 128, 256), 'big_img': (1, 12, 128, 256)}
+SPLIT_POLICY_INPUT_SHAPES = {'features_buffer': (1, 25, 512), 'desire_pulse': (1, 25, 8), 'traffic_convention': (1, 2)}
+SPLIT_VISION_SLICES = {'hidden_state': slice(0, 512), 'pose': slice(512, 524)}
+SPLIT_POLICY_SLICES = {'plan': slice(0, 495), 'meta': slice(495, 550)}
+
+SUPERCOMBO_INPUT_SHAPES = {
+  'img': (1, 12, 128, 256), 'big_img': (1, 12, 128, 256),
+  'desire': (1, 100, 8), 'features_buffer': (1, 99, 512),
+  'lateral_control_params': (1, 2), 'prev_desired_curv': (1, 100, 1),
+  'traffic_convention': (1, 2),
+}
+SUPERCOMBO_SLICES = {'plan': slice(0, 495), 'hidden_state': slice(495, 1007), 'meta': slice(1007, 1062)}
+
+CAM_W, CAM_H = 1928, 1208
+
+ARCHETYPES = {
+  'vision_policy_split': Archetype(
+    name='vision_policy_split',
+    metadata_structure=_make_vision_policy_metadata(
+      SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES,
+      SPLIT_VISION_SLICES, SPLIT_POLICY_SLICES),
+    model_stubs=[DummyModel('vision', 'driving_test_tinygrad.pkl'),
+                 DummyModel('policy', 'driving_test_tinygrad.pkl')],
+    is_20hz=True,
+    expected_model_type='split',
+    expected_constants_class=SplitModelConstants,
+    expected_parser_module='parse_model_outputs_split',
+    expected_desire_key='desire',
+  ),
+  'vision_multi_policy': Archetype(
+    name='vision_multi_policy',
+    metadata_structure=_make_multi_policy_metadata(
+      SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES,
+      SPLIT_VISION_SLICES, SPLIT_POLICY_SLICES),
+    model_stubs=[DummyModel('vision', 'driving_test_tinygrad.pkl'),
+                 DummyModel('offPolicy', 'driving_test_tinygrad.pkl')],
+    is_20hz=True,
+    expected_model_type='multi_policy',
+    expected_constants_class=SplitModelConstants,
+    expected_parser_module='parse_model_outputs_split',
+    expected_desire_key='desire',
+  ),
+  'tri_policy': Archetype(
+    name='tri_policy',
+    metadata_structure=_make_tri_policy_metadata(
+      SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES,
+      SPLIT_VISION_SLICES, SPLIT_POLICY_SLICES),
+    model_stubs=[DummyModel('vision', 'driving_test_tinygrad.pkl'),
+                 DummyModel('onPolicy', 'driving_test_tinygrad.pkl'),
+                 DummyModel('offPolicy', 'driving_test_tinygrad.pkl')],
+    is_20hz=True,
+    expected_model_type='multi_policy',
+    expected_constants_class=SplitModelConstants,
+    expected_parser_module='parse_model_outputs_split',
+    expected_desire_key='desire',
+  ),
+  'supercombo_non20hz': Archetype(
+    name='supercombo_non20hz',
+    metadata_structure=_make_supercombo_metadata(SUPERCOMBO_INPUT_SHAPES, SUPERCOMBO_SLICES),
+    model_stubs=[DummyModel('supercombo', 'driving_test_tinygrad.pkl')],
+    is_20hz=False,
+    expected_model_type='supercombo',
+    expected_constants_class=ModelConstants,
+    expected_parser_module='parse_model_outputs',
+    expected_desire_key='desire',
+  ),
+}
+
+
+def make_pkl_data(archetype):
+  return {
+    'metadata': archetype.metadata_structure,
+    (CAM_W, CAM_H): {'run_policy': _noop_jit, 'warp_enqueue': _noop_jit},
+  }
+
+
+def write_pkl(tmp_path, archetype):
+  pkl_path = tmp_path / 'driving_test_tinygrad.pkl'
+  with open(pkl_path, 'wb') as f:
+    pickle.dump(make_pkl_data(archetype), f)
+  return pkl_path
+
+
+def make_bundle(archetype):
+  return DummyBundle(
+    models=archetype.model_stubs,
+    is_20hz=archetype.is_20hz,
+  )
+
+
+@pytest.fixture
+def patch_modeld(monkeypatch):
+  def _patch(bundle):
+    monkeypatch.setattr(helpers, 'get_active_bundle', lambda params=None: bundle, raising=False)
+    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None: bundle, raising=False)
+
+  return _patch
+
+
+@pytest.fixture
+def model_state_factory(tmp_path, monkeypatch, patch_modeld):
+  from openpilot.common.hardware import hw
+
+  def _create(archetype):
+    write_pkl(tmp_path, archetype)
+    bundle = make_bundle(archetype)
+    patch_modeld(bundle)
+    monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
+    return modeld_module.ModelState(cam_w=CAM_W, cam_h=CAM_H)
+
+  return _create

openpilot/sunnypilot/modeld_v2/tests/test_camera_offset_helper.py

@@ -0,0 +1,84 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+import numpy as np
+
+from openpilot.common.transformations.camera import DEVICE_CAMERAS
+from openpilot.common.transformations.model import get_warp_matrix
+from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
+
+
+class MockStruct:
+  def __init__(self, **kwargs):
+    for k, v in kwargs.items():
+      setattr(self, k, v)
+
+  def __getitem__(self, item):
+    return getattr(self, item)
+
+
+class TestCameraOffset:
+  def setup_method(self):
+    self.camera_offset = CameraOffsetHelper()
+    self.dc = DEVICE_CAMERAS[('mici', 'os04c10')]
+
+  def test_smoothing(self):
+    self.camera_offset.set_offset(0.2)
+
+    sm = MockStruct(
+      deviceState=MockStruct(deviceType='mici'),
+      roadCameraState=MockStruct(sensor='os04c10'),
+      liveCalibration=MockStruct(rpyCalib=[0.0, 0.0, 0.0], height=[1.22])
+    )
+
+    intrinsics_main = self.dc.fcam.intrinsics
+    intrinsics_extra = self.dc.ecam.intrinsics
+    device_from_calib_euler = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+    main_transform = get_warp_matrix(device_from_calib_euler, intrinsics_main, False).astype(np.float32)
+    extra_transform = get_warp_matrix(device_from_calib_euler, intrinsics_extra, True).astype(np.float32)
+
+    self.camera_offset.update(main_transform, extra_transform, sm, False)
+    np.testing.assert_almost_equal(self.camera_offset.actual_camera_offset, 0.02)
+    self.camera_offset.update(main_transform, extra_transform, sm, False)
+    np.testing.assert_almost_equal(self.camera_offset.actual_camera_offset, 0.038)
+
+  def test_camera_offset_(self):
+    intrinsics = self.dc.fcam.intrinsics
+    transform = np.eye(3, dtype=np.float32)
+    height = 1.22
+    offset = 0.1
+
+    cy = intrinsics[1, 2]
+    expected_shear = np.eye(3, dtype=np.float32)
+    expected_shear[0, 1] = offset / height
+    expected_shear[0, 2] = -offset / height * cy
+
+    result = CameraOffsetHelper.apply_camera_offset(transform, intrinsics, height, offset)
+    np.testing.assert_array_almost_equal(result, expected_shear)
+
+  def test_update(self):
+    sm = MockStruct(
+      deviceState=MockStruct(deviceType='mici'),
+      roadCameraState=MockStruct(sensor='os04c10'),
+      liveCalibration=MockStruct(rpyCalib=[0.0, 0.0, 0.0], height=[1.22])
+    )
+    intrinsics_main = self.dc.fcam.intrinsics
+    intrinsics_extra = self.dc.ecam.intrinsics
+    device_from_calib_euler = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+    main_transform = get_warp_matrix(device_from_calib_euler, intrinsics_main, False).astype(np.float32)
+    extra_transform = get_warp_matrix(device_from_calib_euler, intrinsics_extra, True).astype(np.float32)
+
+    self.camera_offset.set_offset(0.0)  # test default offset doesn't change transformation
+    main_out, extra_out = self.camera_offset.update(main_transform, extra_transform, sm, False)
+    np.testing.assert_array_equal(main_out, main_transform)
+    np.testing.assert_array_equal(extra_out, extra_transform)
+
+    self.camera_offset.set_offset(0.2)  # test valid offset changes transformation
+    main_out, extra_out = self.camera_offset.update(main_transform, extra_transform, sm, False)
+    assert not np.array_equal(main_out, main_transform)
+    assert not np.array_equal(extra_out, extra_transform)
+    assert main_out[0, 1] != 0.0
+    assert main_out[0, 2] != 0.0

openpilot/sunnypilot/modeld_v2/tests/test_combined_pkl_loader.py

@@ -0,0 +1,260 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import pytest
+
+import openpilot.sunnypilot.models.helpers as helpers
+import openpilot.sunnypilot.modeld_v2.modeld as modeld_module
+from openpilot.sunnypilot.modeld_v2.modeld import _find_driving_pkl
+from openpilot.sunnypilot.modeld_v2.tests.conftest import DummyModel, DummyBundle, ARCHETYPES, CAM_W, CAM_H, \
+  SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES
+
+ModelState = modeld_module.ModelState
+
+
+# Pkl discovery
+
+class TestFindDrivingPkl:
+  def test_returns_none_when_no_bundle(self):
+    assert _find_driving_pkl(None) is None
+
+  def test_returns_none_when_no_models(self):
+    bundle = DummyBundle(models=[])
+    assert _find_driving_pkl(bundle) is None
+
+  def test_returns_none_when_pkl_not_on_disk(self):
+    bundle = DummyBundle(models=[
+      DummyModel('vision', 'driving_fof_tinygrad.pkl'),
+      DummyModel('policy', 'driving_fof_tinygrad.pkl'),
+    ])
+    assert _find_driving_pkl(bundle) is None
+
+  def test_finds_pkl_by_artifact_name(self, tmp_path, monkeypatch):
+    (tmp_path / 'driving_fof_tinygrad.pkl').write_bytes(b'fake')
+    from openpilot.common.hardware import hw
+    monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
+
+    bundle = DummyBundle(models=[
+      DummyModel('vision', 'driving_fof_tinygrad.pkl'),
+      DummyModel('policy', 'driving_fof_tinygrad.pkl'),
+    ])
+    result = _find_driving_pkl(bundle)
+    assert result is not None
+    assert 'driving_fof_tinygrad.pkl' in result
+
+
+# Init — assertion guard
+
+class TestModelStateCombinedInit:
+  def test_asserts_when_no_pkl(self, monkeypatch):
+    bundle = DummyBundle(models=[], is_20hz=True)
+    monkeypatch.setattr(helpers, 'get_active_bundle', lambda params=None: bundle, raising=False)
+    monkeypatch.setattr(modeld_module, 'get_active_bundle', lambda params=None: bundle, raising=False)
+    with pytest.raises(AssertionError, match="No driving pkl found"):
+      ModelState(cam_w=CAM_W, cam_h=CAM_H)
+
+
+class TestStockEquivalence:
+
+  def test_split_queue_keys_match_stock(self, model_state_factory):
+    from openpilot.selfdrive.modeld.compile_modeld import make_input_queues
+    from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip
+
+    state = model_state_factory(ARCHETYPES['vision_policy_split'])
+
+    frame_skip = derive_frame_skip(SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES)
+    # action_t is a deep-model prerequisite the SP loader doesn't provide yet; see skip_keys below
+    stock_shapes = {**SPLIT_VISION_INPUT_SHAPES, **SPLIT_POLICY_INPUT_SHAPES, 'action_t': (1, 2)}
+    stock_queues, stock_npy = make_input_queues(stock_shapes, frame_skip, device='NPY')
+
+    # TODO-SP: remove action_t skip once SP adds prerequisite for deep models (action_t input queue)
+    # prev_feat is a stock QCOM corruption workaround handled inside the SP loader's JIT path
+    skip_keys = {'action_t', 'prev_feat'}
+    # stock packs the per-key policy inputs into packed_npy_inputs; the npy views carry the individual keys
+    stock_queue_keys = set(stock_queues.keys())
+    if 'packed_npy_inputs' in stock_queue_keys:
+      stock_queue_keys.remove('packed_npy_inputs')
+      stock_queue_keys |= set(stock_npy.keys())
+    assert set(state.input_queues.keys()) == stock_queue_keys - skip_keys, \
+      f"Queue keys differ: v2={set(state.input_queues.keys())}, stock={stock_queue_keys}"
+    assert set(state.numpy_inputs.keys()) == set(stock_npy.keys()) - skip_keys, \
+      f"Npy keys differ: v2={set(state.numpy_inputs.keys())}, stock={set(stock_npy.keys())}"
+
+  def test_split_queue_keys_work_with_desire_key(self, model_state_factory):
+    from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
+
+    policy_shapes_desire = {'features_buffer': (1, 25, 512), 'desire': (1, 25, 8), 'traffic_convention': (1, 2)}
+    frame_skip = derive_frame_skip(SPLIT_VISION_INPUT_SHAPES, policy_shapes_desire)
+    queues, npy = make_split_input_queues(SPLIT_VISION_INPUT_SHAPES, policy_shapes_desire, frame_skip, device='NPY')
+
+    assert 'desire_q' in queues
+    assert 'desire' in npy
+    assert 'img_q' in queues
+    assert 'feat_q' in queues
+
+  def test_split_vision_input_names_match_stock(self, model_state_factory):
+    state = model_state_factory(ARCHETYPES['vision_policy_split'])
+    assert state.vision_input_names == ['img', 'big_img']
+
+  def test_split_output_slices_preserved(self, model_state_factory):
+    arch = ARCHETYPES['vision_policy_split']
+    state = model_state_factory(arch)
+    assert state.vision_output_slices == arch.metadata_structure['vision']['output_slices']
+    assert state.policy_output_slices == arch.metadata_structure['policy']['output_slices']
+
+
+ARCHETYPE_NAMES = list(ARCHETYPES.keys())
+
+
+class TestModelTypeDetection:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_combined_model_type(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert state._combined_model_type == arch.expected_model_type, \
+      f"{arch.name}: got {state._combined_model_type}, expected {arch.expected_model_type}"
+
+
+class TestConstantsSelection:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_constants_class(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert type(state.constants) is arch.expected_constants_class, \
+      f"{arch.name}: got {type(state.constants).__name__}, expected {arch.expected_constants_class.__name__}"
+
+
+class TestParserSelection:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_parser_module(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    parser_module = type(state.parser).__module__
+    assert parser_module.endswith(arch.expected_parser_module), \
+      f"{arch.name}: parser from {parser_module}, expected module ending with {arch.expected_parser_module}"
+
+
+class TestDesireKeyDetection:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_desire_key(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert state.desire_key == arch.expected_desire_key, \
+      f"{arch.name}: got {state.desire_key}, expected {arch.expected_desire_key}"
+
+
+class TestVisionInputNames:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_vision_names_contain_img(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert len(state.vision_input_names) >= 1
+    for name in state.vision_input_names:
+      assert 'img' in name, f"{arch.name}: vision input name '{name}' missing 'img'"
+
+
+class TestOutputSlices:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_vision_slices_populated(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert len(state.vision_output_slices) > 0, f"{arch.name}: vision_output_slices empty"
+
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_policy_slices_match_type(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    if arch.expected_model_type == 'supercombo':
+      assert state.policy_output_slices == {}, f"{arch.name}: supercombo should have empty policy slices"
+    else:
+      assert len(state.policy_output_slices) > 0, f"{arch.name}: split/multi should have policy slices"
+
+
+class TestInputQueueCreation:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_queues_not_empty(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert len(state.input_queues) > 0, f"{arch.name}: input_queues empty"
+
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_npy_contains_transforms(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert 'tfm' in state.numpy_inputs, f"{arch.name}: 'tfm' missing from npy"
+    assert 'big_tfm' in state.numpy_inputs, f"{arch.name}: 'big_tfm' missing from npy"
+    assert state.numpy_inputs['tfm'].shape == (3, 3)
+    assert state.numpy_inputs['big_tfm'].shape == (3, 3)
+
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_npy_contains_desire(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert arch.expected_desire_key in state.numpy_inputs, \
+      f"{arch.name}: '{arch.expected_desire_key}' missing from npy"
+
+
+class TestFrameBufferParams:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_frame_buf_params_per_vision_input(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    for name in state.vision_input_names:
+      assert name in state.frame_buf_params, f"{arch.name}: frame_buf_params missing '{name}'"
+      nv12_info = state.frame_buf_params[name]
+      assert len(nv12_info) >= 4, f"{arch.name}: nv12_info for '{name}' too short"
+
+
+class TestBundleOverrides:
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_smoothing_params_from_overrides(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert state.LAT_SMOOTH_SECONDS == 0.1
+    assert state.LONG_SMOOTH_SECONDS == 0.3
+
+  @pytest.mark.parametrize("archetype_name", ARCHETYPE_NAMES)
+  def test_generation_from_bundle(self, archetype_name, model_state_factory):
+    arch = ARCHETYPES[archetype_name]
+    state = model_state_factory(arch)
+    assert state.generation == 10
+
+
+class TestMlsimProperty:
+  def test_mlsim_false_for_gen10(self, model_state_factory):
+    state = model_state_factory(ARCHETYPES['supercombo_non20hz'])
+    assert state.mlsim is False
+
+  def test_mlsim_true_for_gen11(self, tmp_path, monkeypatch, patch_modeld):
+    from openpilot.common.hardware import hw
+    from openpilot.sunnypilot.modeld_v2.tests.conftest import write_pkl, ARCHETYPES as A
+
+    arch = A['supercombo_non20hz']
+    write_pkl(tmp_path, arch)
+    bundle = DummyBundle(models=arch.model_stubs, is_20hz=arch.is_20hz, generation=11)
+    patch_modeld(bundle)
+    monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
+
+    state = ModelState(cam_w=CAM_W, cam_h=CAM_H)
+    assert state.mlsim is True
+
+
+class TestCrossArchetypeMismatch:
+  def test_wrong_is_20hz_changes_constants(self, tmp_path, monkeypatch, patch_modeld):
+    from openpilot.common.hardware import hw
+    from openpilot.sunnypilot.modeld_v2.tests.conftest import write_pkl
+    from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
+
+    arch = ARCHETYPES['vision_policy_split']
+    write_pkl(tmp_path, arch)
+    bundle = DummyBundle(models=arch.model_stubs, is_20hz=False)
+    patch_modeld(bundle)
+    monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
+
+    state = ModelState(cam_w=CAM_W, cam_h=CAM_H)
+    assert type(state.constants) is ModelConstants, \
+      "Wrong is_20hz should produce wrong constants class"

openpilot/sunnypilot/modeld_v2/tests/test_compile_modeld.py

@@ -0,0 +1,161 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import numpy as np
+import pytest
+
+from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, _detect_desire_key
+
+
+class TestDeriveFrameSkip:
+  def test_non20hz_supercombo(self):
+    vision = {}
+    policy = {'features_buffer': (1, 99, 512), 'desire': (1, 100, 8)}
+    assert derive_frame_skip(vision, policy) == 1
+
+  def test_20hz_supercombo(self):
+    vision = {}
+    policy = {'features_buffer': (1, 24, 512), 'desire': (1, 25, 8)}
+    assert derive_frame_skip(vision, policy) == 4
+
+  def test_split_vision_policy(self):
+    vision = {'img': (1, 12, 128, 256)}
+    policy = {'features_buffer': (1, 25, 512), 'desire_pulse': (1, 25, 8)}
+    assert derive_frame_skip(vision, policy) == 4
+
+  def test_no_features_buffer(self):
+    assert derive_frame_skip({}, {}) == 1
+
+
+class TestFrameSkipBufferLengthEquivalence:
+  @pytest.mark.parametrize("frame_skip,expected_buffer_length", [
+    (1, 2),
+    (4, 5),
+  ])
+  def test_img_buffer_size_matches_warp_buffer_length(self, frame_skip, expected_buffer_length):
+    n_frames = 2
+    img_buf_dim0 = frame_skip * (n_frames - 1) + 1
+    assert img_buf_dim0 == expected_buffer_length, \
+      f"frame_skip={frame_skip}: img_buf[0]={img_buf_dim0}, expected {expected_buffer_length}"
+
+  @pytest.mark.parametrize("is_20hz,expected_frame_skip,expected_buffer_length", [
+    (False, 1, 2),
+    (True, 4, 5),
+  ])
+  def test_is_20hz_to_frame_skip_to_buffer_length(self, is_20hz, expected_frame_skip, expected_buffer_length):
+    if is_20hz:
+      policy_shapes = {'features_buffer': (1, 24, 512)}
+    else:
+      policy_shapes = {'features_buffer': (1, 99, 512)}
+    frame_skip = derive_frame_skip({}, policy_shapes)
+    assert frame_skip == expected_frame_skip
+
+    n_frames = 2
+    img_buf_dim0 = frame_skip * (n_frames - 1) + 1
+    assert img_buf_dim0 == expected_buffer_length
+
+
+class TestTemporalSamplingEquivalence:
+  def test_non20hz_desire_sampling_identity(self):
+    buf = np.random.randn(100, 1, 8).astype(np.float32)
+    frame_skip = 1
+    sampled = buf[::frame_skip].reshape(-1, 8)
+    assert sampled.shape == (100, 8)
+    np.testing.assert_array_equal(sampled, buf[:, 0, :])
+
+  def test_20hz_desire_sampling_max(self):
+    buf = np.zeros((100, 1, 8), dtype=np.float32)
+    buf[99, 0, 3] = 1.0
+    frame_skip = 4
+    reshaped = buf.reshape(-1, frame_skip, 1, 8).max(axis=1)
+    sampled = reshaped.reshape(-1, 8)
+    assert sampled.shape == (25, 8)
+    assert sampled[24, 3] == 1.0
+    assert sampled[23, 3] == 0.0
+
+  def test_split_features_buffer_sampling_skip(self):
+    buf = np.arange(100 * 512, dtype=np.float32).reshape(100, 1, 512)
+    frame_skip = 4
+    sampled = buf[::frame_skip].reshape(-1, 512)
+    assert sampled.shape == (25, 512)
+    np.testing.assert_array_equal(sampled[0], buf[0, 0])
+    np.testing.assert_array_equal(sampled[1], buf[4, 0])
+    np.testing.assert_array_equal(sampled[24], buf[96, 0])
+
+  def test_non20hz_features_buffer_sampling_identity(self):
+    buf = np.arange(99 * 512, dtype=np.float32).reshape(99, 1, 512)
+    frame_skip = 1
+    sampled = buf[::frame_skip].reshape(-1, 512)
+    assert sampled.shape == (99, 512)
+    np.testing.assert_array_equal(sampled, buf[:, 0, :])
+
+
+class TestTemporalIdxEquivalence:
+  @pytest.mark.parametrize("mode,desire_shape,fb_shape,frame_skip", [
+    ('non20hz', (1, 100, 8), (1, 99, 512), 1),
+    ('20hz', (1, 25, 8), (1, 24, 512), 4),
+    ('split', (1, 25, 8), (1, 25, 512), 4),
+  ])
+  def test_features_buffer_idx_equivalence(self, mode, desire_shape, fb_shape, frame_skip):
+    history = fb_shape[1]
+
+    if mode == 'non20hz':
+      modelstate_idxs = np.arange(history)
+      buf_len = history
+    elif mode == '20hz':
+      buf_len = (history + 1) * 4
+      step = int(-buf_len / history)
+      modelstate_idxs = np.arange(step, step * (history + 1), step)[::-1]
+    elif mode == 'split':
+      buf_len = history * 4
+      skip = buf_len // history
+      modelstate_idxs = np.arange(buf_len)[-1 - (skip * (history - 1))::skip]
+
+    assert len(modelstate_idxs) == fb_shape[1], \
+      f"{mode}: ModelState idx count {len(modelstate_idxs)} != input shape {fb_shape[1]}"
+
+  @pytest.mark.parametrize("mode,desire_shape,fb_shape,frame_skip", [
+    ('non20hz', (1, 100, 8), (1, 99, 512), 1),
+    ('20hz', (1, 25, 8), (1, 24, 512), 4),
+    ('split', (1, 25, 8), (1, 25, 512), 4),
+  ])
+  def test_desire_idx_equivalence(self, mode, desire_shape, fb_shape, frame_skip):
+    history = desire_shape[1]
+
+    compile_desire_buf_len = frame_skip * history if mode != 'non20hz' else history
+    compile_sampled_count = compile_desire_buf_len // frame_skip if frame_skip > 1 else compile_desire_buf_len
+    assert compile_sampled_count == history, \
+      f"{mode}: compile desire samples {compile_sampled_count} != model input {history}"
+
+
+class TestDetectDesireKey:
+  def test_finds_desire(self):
+    shapes = {'features_buffer': (1, 99, 512), 'desire': (1, 100, 8), 'traffic_convention': (1, 2)}
+    assert _detect_desire_key(shapes) == 'desire'
+
+  def test_finds_desire_pulse(self):
+    shapes = {'features_buffer': (1, 25, 512), 'desire_pulse': (1, 25, 8), 'traffic_convention': (1, 2)}
+    assert _detect_desire_key(shapes) == 'desire_pulse'
+
+  def test_returns_none_when_no_desire(self):
+    shapes = {'features_buffer': (1, 99, 512), 'traffic_convention': (1, 2)}
+    assert _detect_desire_key(shapes) is None
+
+
+class TestOutputSlicePreservation:
+  def test_vision_hidden_state_slice_used_for_features(self):
+    mock_slices = {'hidden_state': slice(0, 512), 'plan': slice(512, 1024)}
+    features_slice = mock_slices['hidden_state']
+    fake_output = np.random.randn(1, 1024).astype(np.float32)
+    features = fake_output[:, features_slice]
+    assert features.shape == (1, 512)
+
+  def test_policy_output_slices_independent(self):
+    vision_slices = {'hidden_state': slice(0, 512)}
+    policy_slices = {'plan': slice(0, 495), 'meta': slice(495, 550)}
+    assert set(vision_slices.keys()) & set(policy_slices.keys()) == set(), \
+      "vision and policy slices should not overlap in keys"

openpilot/sunnypilot/modeld_v2/tests/test_recovery_power.py

@@ -0,0 +1,70 @@
+import numpy as np
+
+from openpilot.cereal import log
+
+from openpilot.sunnypilot.modeld_v2.constants import Plan
+from openpilot.sunnypilot.modeld_v2.modeld import ModelState
+import openpilot.sunnypilot.modeld_v2.modeld as modeld
+
+
+class MockStruct:
+  def __init__(self, **kwargs):
+    for k, v in kwargs.items():
+      setattr(self, k, v)
+
+
+def test_recovery_power_scaling():
+  state = MockStruct(
+    PLANPLUS_CONTROL=0.75,
+    LONG_SMOOTH_SECONDS=0.3,
+    LAT_SMOOTH_SECONDS=0.1,
+    MIN_LAT_CONTROL_SPEED=0.3,
+    mlsim=True,
+    generation=12,
+    constants=MockStruct(T_IDXS=np.arange(100), DESIRE_LEN=8)
+  )
+  prev_action = log.ModelDataV2.Action()
+  recorded_vel: list = []
+  recorded_curv_plans: list = []
+
+  def mock_accel(plan_vel, plan_accel, t_idxs, action_t=0.0):
+    recorded_vel.append(plan_vel.copy())
+    return 0.0, False
+
+  def mock_curvature(output, plan, vego, lat_action_t, mlsim):
+    recorded_curv_plans.append(plan.copy())
+    return 0.0
+
+  modeld.get_accel_from_plan = mock_accel
+  modeld.get_curvature_from_output = mock_curvature
+  plan = np.random.rand(1, 100, 15).astype(np.float32)
+  planplus = np.random.rand(1, 100, 15).astype(np.float32)
+  merged_plan = plan + planplus
+
+  model_output: dict = {
+    'plan': merged_plan.copy(),
+    'planplus': planplus.copy()
+  }
+
+  test_cases: list = [
+    # (control, v_ego)
+    (0.55, 20.0),
+    (1.0, 25.0),
+    (1.5, 25.1),
+    (2.0, 20.0),
+    (0.75, 19.0),
+    (0.8, 25.1),
+  ]
+
+  for control, v_ego in test_cases:
+    state.PLANPLUS_CONTROL = control
+    recorded_vel.clear()
+    recorded_curv_plans.clear()
+    ModelState.get_action_from_model(state, model_output, prev_action, 0.0, 0.0, v_ego)
+
+    expected_accel_plan_vel = plan[0, :, Plan.VELOCITY][:, 0] + planplus[0, :, Plan.VELOCITY][:, 0]
+    np.testing.assert_allclose(recorded_vel[0], expected_accel_plan_vel, rtol=1e-5, atol=1e-6)
+
+    # For the below, yes, I know this isn't the same slicing as fillmodlmsg. This is to show that the values are only scaled on curv
+    expected_curv_plan_vel = plan[0, :, Plan.VELOCITY][:, 0] + control * planplus[0, :, Plan.VELOCITY][:, 0]
+    np.testing.assert_allclose(recorded_curv_plans[0][:, Plan.VELOCITY][:, 0], expected_curv_plan_vel, rtol=1e-5, atol=1e-6)

openpilot/sunnypilot/modeld_v2/tests/test_warp.py

@@ -0,0 +1,103 @@
+import os
+os.environ['DEV'] = 'CPU'
+import pytest
+import numpy as np
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.sunnypilot.modeld_v2.warp import CAMERA_CONFIGS
+from openpilot.sunnypilot.modeld_v2.warp import Warp, MODEL_W, MODEL_H
+
+VISION_NAME_PAIRS = [  # needed to account for supercombos input_imgs
+  ('img', 'big_img'),
+  ('input_imgs', 'big_input_imgs'),
+]
+
+
+class MockVisionBuf:
+  def __init__(self, w, h):
+    self.width = w
+    self.height = h
+    _, _, _, yuv_size = get_nv12_info(w, h)
+    self.data = np.zeros(yuv_size, dtype=np.uint8)
+
+
+@pytest.mark.parametrize("buffer_length", [2, 5])
+def test_warp_initialization(buffer_length):
+  warp = Warp(buffer_length)
+  assert warp.buffer_length == buffer_length
+  assert warp.img_buffer_shape == (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
+
+
+@pytest.mark.parametrize("buffer_length", [2, 5])
+@pytest.mark.parametrize("cam_w, cam_h", CAMERA_CONFIGS)
+@pytest.mark.parametrize("road, wide", VISION_NAME_PAIRS)
+def test_warp_process(buffer_length, cam_w, cam_h, road, wide):
+  warp = Warp(buffer_length)
+  mock_buf = MockVisionBuf(cam_w, cam_h)
+  transform = np.eye(3, dtype=np.float32).flatten()
+  bufs = {road: mock_buf, wide: mock_buf}
+  transforms = {road: transform, wide: transform}
+
+  out = warp.process(bufs, transforms)
+  assert isinstance(out, dict)
+  assert road in out and wide in out
+  assert out[road].shape == (1, 12, MODEL_H // 2, MODEL_W // 2)
+  assert out[wide].shape == (1, 12, MODEL_H // 2, MODEL_W // 2)
+
+  key = (cam_w, cam_h)
+  assert key in warp.jit_cache
+
+  out2 = warp.process(bufs, transforms)
+  assert out2[road].shape == out[road].shape
+
+
+@pytest.mark.parametrize("road, wide", VISION_NAME_PAIRS)
+def test_warp_buffer_shift(road, wide):
+  warp = Warp(2)
+  cam_w, cam_h = CAMERA_CONFIGS[1]
+  transform = np.eye(3, dtype=np.float32).flatten()
+
+  buf1 = MockVisionBuf(cam_w, cam_h)
+  buf1.data[0] = 255
+  bufs1 = {road: buf1, wide: buf1}
+  transforms = {road: transform, wide: transform}
+  out1 = warp.process(bufs1, transforms)
+  road1 = out1[road].numpy().copy()
+
+  buf2 = MockVisionBuf(cam_w, cam_h)
+  buf2.data[0] = 128
+  bufs2 = {road: buf2, wide: buf2}
+  out2 = warp.process(bufs2, transforms)
+  assert not np.array_equal(road1, out2[road].numpy())
+
+
+@pytest.mark.parametrize("buffer_length", [2, 5])
+@pytest.mark.parametrize("road, wide", VISION_NAME_PAIRS)
+def test_warp_buffer_accumulation(buffer_length, road, wide):
+  warp = Warp(buffer_length)
+  cam_w, cam_h = CAMERA_CONFIGS[0]
+  transform = np.eye(3, dtype=np.float32).flatten()
+  transforms = {road: transform, wide: transform}
+  outputs = []
+
+  for i in range(buffer_length + 1):
+    buf = MockVisionBuf(cam_w, cam_h)
+    buf.data[:] = i * 10
+    out = warp.process({road: buf, wide: buf}, transforms)
+    outputs.append(out[road].numpy().copy())
+
+  assert warp.full_buffers['img'].shape == (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
+  for i in range(1, len(outputs)):
+    assert not np.array_equal(outputs[i - 1], outputs[i])
+
+
+def test_warp_different_cameras_same_instance():
+  warp = Warp(2)
+  transform = np.eye(3, dtype=np.float32).flatten()
+
+  buf1 = MockVisionBuf(*CAMERA_CONFIGS[0])
+  warp.process({'img': buf1, 'big_img': buf1}, {'img': transform, 'big_img': transform})
+  assert len(warp.jit_cache) == 1
+
+  buf2 = MockVisionBuf(*CAMERA_CONFIGS[1])
+  warp.process({'img': buf2, 'big_img': buf2}, {'img': transform, 'big_img': transform})
+  assert len(warp.jit_cache) == 2