Merge branch 'master' into toyota-bsm

.github/workflows/test_models.yml

@@ -3,62 +3,9 @@ on:
   pull_request:
     paths:
       - 'tinygrad_repo'
-      - 'openpilot/sunnypilot/modeld_v2/**'
   workflow_dispatch:
 
-env:
-  CI: 1
-  GIT_CONFIG_COUNT: 1
-  GIT_CONFIG_KEY_0: lfs.fetchexclude
-  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
-
 jobs:
-  plain-artifact:
-    name: Plain artifact round trip and pickle converter
-    runs-on: ubuntu-24.04
-    env:
-      PYTHONPATH: ${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}
-      TINYGRAD_TESTS: 1
-    steps:
-      - uses: actions/checkout@v7
-      - run: ./tools/op.sh setup
-      - name: Build openpilot
-        run: scons -j$(nproc)
-      - name: Compile, load, cache and fuse a model with the pinned tinygrad
-        env:
-          DEV: CPU
-        run: tools/op.sh test -v openpilot/sunnypilot/modeld_v2/tests/test_plain_artifact.py
-      - name: Install LLVM 20 and ROCm comgr for the amd mock, as tinygrad_repo/.github/actions/setup-tinygrad does
-        run: |
-          . /etc/os-release
-          curl -fsSL https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc > /dev/null
-          echo "deb http://apt.llvm.org/$VERSION_CODENAME/ llvm-toolchain-$VERSION_CODENAME-20 main" | sudo tee /etc/apt/sources.list.d/llvm.list
-          sudo mkdir -p /etc/apt/keyrings
-          curl -fsSL https://repo.radeon.com/rocm/rocm.gpg.key | gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
-          echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/7.1 $VERSION_CODENAME main" | sudo tee /etc/apt/sources.list.d/rocm.list
-          printf 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600\n' | sudo tee /etc/apt/preferences.d/rocm-pin-600
-          sudo apt-get -qq update && sudo apt-get -qq install -y --no-install-recommends libllvm20 clang-20 lld-20 comgr
-          printf '/opt/rocm/lib\n/opt/rocm/lib64\n' | sudo tee /etc/ld.so.conf.d/rocm.conf && sudo ldconfig
-          echo "/usr/lib/llvm-20/bin" >> "$GITHUB_PATH"
-      - name: Convert a pickled USB+AMD jit and replay it under tinygrad's amd mock
-        env:
-          DEV: MOCKUSB+AMD
-          CHECK_OOB: 0
-          GMMU: 0
-        run: tools/op.sh test -v openpilot/sunnypilot/modeld_v2/tests/test_convert_upstream_pkl.py
-      - name: Where tinygrad moved, for the helper or failure point named above
-        if: failure() && github.event_name == 'pull_request'
-        run: |
-          git fetch -q --depth=1 origin "${{ github.base_ref }}"
-          old=$(git rev-parse "origin/${{ github.base_ref }}:tinygrad_repo") new=$(git rev-parse HEAD:tinygrad_repo)
-          git -C tinygrad_repo fetch -q origin "$old" || true
-          echo "tinygrad_repo $old -> $new, in what helpers.py and plain_artifact_compiler.py read:"
-          git -C tinygrad_repo diff "$old" "$new" -- tinygrad/uop/ops.py tinygrad/engine/jit.py tinygrad/engine/realize.py tinygrad/device.py \
-            tinygrad/helpers.py tinygrad/dtype.py tinygrad/renderer/__init__.py
-          echo "and in what convert_upstream_pkl.py decodes:"
-          git -C tinygrad_repo diff --stat "$old" "$new" -- tinygrad/runtime/autogen/am/pm4_nv.py tinygrad/runtime/autogen/amd_gpu.py
-          git -C tinygrad_repo diff "$old" "$new" -- tinygrad/runtime/support/hcq2.py tinygrad/runtime/support/usb.py tinygrad/runtime/ops_amd.py
-
   generate-matrix:
     runs-on: ubuntu-latest
     outputs:

openpilot/cereal/custom.capnp

@@ -468,13 +468,6 @@ struct ModelDataV2SP @0xa1680744031fdb2d {
   laneTurnDirection @0 :TurnDirection;
   leftLaneChangeEdgeBlock @1 :Bool;
   rightLaneChangeEdgeBlock @2 :Bool;
-  blendfulCurvature @3 :Float32;
-  blendfulTrust @4 :Float32;
-  modelCurvature @5 :Float32;
-  blendfulGoingAround @6 :Bool;
-  blendfulLeftProb @7 :Float32;
-  blendfulRightProb @8 :Float32;
-  blendfulLaneWidth @9 :Float32;
 
   enum TurnDirection {
     none @0;

openpilot/cereal/services.py

@@ -94,7 +94,7 @@ _services: dict[str, tuple] = {
   "carControlSP": (True, 100., 10),
   "carStateSP": (True, 100., 10),
   "liveMapDataSP": (True, 1., 1),
-  "modelDataV2SP": (True, 20., 10, QueueSize.BIG),
+  "modelDataV2SP": (True, 20., None, QueueSize.BIG),
   "liveLocationKalman": (True, 20.),
 
   # debug

openpilot/common/params_keys.h

@@ -170,7 +170,6 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"DevUIInfo", {PERSISTENT | BACKUP, INT, "0"}},
     {"EnableCopyparty", {PERSISTENT | BACKUP, BOOL}},
     {"EnableGithubRunner", {PERSISTENT | BACKUP, BOOL}},
-    {"FollowDistanceAccelBoost", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"GreenLightAlert", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"GithubRunnerSufficientVoltage", {CLEAR_ON_MANAGER_START , BOOL}},
     {"HasAcceptedTermsSP", {PERSISTENT, STRING, "0"}},
@@ -258,7 +257,6 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"LagdValueCache", {PERSISTENT, FLOAT, "0.2"}},
     {"LaneTurnDesire", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"LaneTurnValue", {PERSISTENT | BACKUP, FLOAT, "19.0"}},
-    {"Blendful", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"PlanplusControl", {PERSISTENT | BACKUP, FLOAT, "1.0"}},
 
     // mapd

openpilot/selfdrive/controls/lib/longcontrol.py

@@ -9,14 +9,6 @@ CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
 
 LongCtrlState = car.CarControl.Actuators.LongControlState
 
-STOPPING_JERK_MIN = 0.3  # m/s^3
-STOPPING_JERK_MAX = 2.0  # m/s^3
-STOPPING_RELEASE_JERK = 1.5  # m/s^3
-STOPPING_TAPER_SCALE = 0.5
-STOPPING_HOLD_SPEED = 0.05  # m/s, below this ramp into the standstill hold
-STOPPING_HOLD_JERK = 1.0  # m/s^3, command ramp rate for the standstill hold
-STOPPING_EXIT_FRAMES = 15  # 0.15 s at 100 Hz, debounce should_stop at standstill
-
 
 def long_control_state_trans(CP_SP, active, long_control_state,
                              should_stop, brake_pressed, cruise_standstill):
@@ -55,57 +47,28 @@ class LongControl:
     self.pid = PIDController(0.0, (CP.longitudinalTuning.kiBP, CP.longitudinalTuning.kiV),
                              rate=1 / DT_CTRL)
     self.last_output_accel = 0.0
-    self.stopping_jerk = STOPPING_JERK_MIN
-    self.stopping_exit_frames = 0
 
   def reset(self):
     self.pid.reset()
 
-  def _stopping_accel(self, v_ego, standstill):
-    v_ego = max(float(v_ego), 0.0)
-    if standstill or v_ego < STOPPING_HOLD_SPEED:
-      goal = float(self.CP.stopAccel)
-      jerk = STOPPING_HOLD_JERK
-    else:
-      taper = -np.sqrt(2.0 * self.stopping_jerk * STOPPING_TAPER_SCALE * v_ego)
-      goal = max(self.last_output_accel, taper) if self.last_output_accel < 0.0 else taper
-      jerk = STOPPING_RELEASE_JERK
-
-    output_accel = np.clip(min(goal, 0.0), self.last_output_accel - jerk * DT_CTRL,
-                           self.last_output_accel + jerk * DT_CTRL)
-    return float(output_accel)
-
   def update(self, active, CS, a_target, should_stop, accel_limits):
     """Update longitudinal control. This updates the state machine and runs a PID loop"""
     self.pid.neg_limit = accel_limits[0]
     self.pid.pos_limit = accel_limits[1]
 
-    if self.long_control_state == LongCtrlState.stopping and not should_stop:
-      self.stopping_exit_frames += 1
-    else:
-      self.stopping_exit_frames = 0
-    should_stop_trans = should_stop or (self.long_control_state == LongCtrlState.stopping and
-                                        self.stopping_exit_frames < STOPPING_EXIT_FRAMES)
-
-    prev_state = self.long_control_state
     self.long_control_state = long_control_state_trans(self.CP_SP, active, self.long_control_state,
-                                                       should_stop_trans, CS.brakePressed,
+                                                       should_stop, CS.brakePressed,
                                                        CS.cruiseState.standstill)
-
-    if self.long_control_state == LongCtrlState.stopping and prev_state != LongCtrlState.stopping:
-      entry_v = max(float(CS.vEgo), STOPPING_HOLD_SPEED)
-      entry_a = min(float(self.last_output_accel), 0.0)
-      if entry_a == 0.0:
-        entry_a = min(float(a_target), 0.0)
-      self.stopping_jerk = float(np.clip(entry_a ** 2 / (2.0 * entry_v),
-                                         STOPPING_JERK_MIN, STOPPING_JERK_MAX))
-
     if self.long_control_state == LongCtrlState.off:
       self.reset()
       output_accel = 0.
 
     elif self.long_control_state == LongCtrlState.stopping:
-      output_accel = self._stopping_accel(CS.vEgo, CS.standstill)
+      output_accel = self.last_output_accel
+      if output_accel > self.CP.stopAccel:
+        output_accel = min(output_accel, 0.0)
+        # TODO: can we just go straight to stopAccel?
+        output_accel -= 1.0 * DT_CTRL  # m/s^2/s while trying to stop
       self.reset()
 
     else:  # LongCtrlState.pid

openpilot/selfdrive/controls/tests/test_accel_boost.py

@@ -1,101 +0,0 @@
-from types import SimpleNamespace
-
-from openpilot.cereal import log
-from openpilot.common.realtime import DT_MDL
-from openpilot.common.test import OpenpilotTestCase
-from openpilot.selfdrive.controls.lib.accel_boost import (
-  AccelBoost, ACCEL_BOOST_MAX, ACCEL_BOOST_PER_OVERRIDE, ACCEL_BOOST_RATE
-)
-
-E2E_ACCEL = 0.5
-PLANNER_ACCEL = 2.0
-RATE_PER_STEP = ACCEL_BOOST_RATE * DT_MDL
-
-
-class FakeSubMaster:
-  def __init__(self, enabled=True, gas_pressed=False, v_ego=15.0,
-               personality=log.LongitudinalPersonality.standard):
-    self._messages = {
-      'selfdriveState': SimpleNamespace(enabled=enabled, personality=personality),
-      'carState': SimpleNamespace(gasPressed=gas_pressed, vEgo=v_ego),
-    }
-
-  def __getitem__(self, key):
-    return self._messages[key]
-
-
-def run_boost(boost, sm, steps=1):
-  out = E2E_ACCEL
-  for _ in range(steps):
-    out = boost.update(sm, E2E_ACCEL, PLANNER_ACCEL, PLANNER_ACCEL)
-  return out
-
-
-class TestAccelBoost(OpenpilotTestCase):
-  def _boost_to_max(self):
-    boost = AccelBoost()
-    sm = FakeSubMaster()
-    steps = round(ACCEL_BOOST_PER_OVERRIDE / RATE_PER_STEP)
-    # Eligibility is latched on a frame without gas, then applied on the next override.
-    run_boost(boost, sm)
-    for _ in range(10):
-      sm['carState'].gasPressed = True
-      run_boost(boost, sm, steps)
-      sm['carState'].gasPressed = False
-      run_boost(boost, sm)
-    return boost
-
-  def test_no_boost_without_gas(self):
-    boost = AccelBoost()
-    sm = FakeSubMaster(gas_pressed=False)
-    out = run_boost(boost, sm)
-    self.assertEqual(boost.total_boost, 0.0)
-    self.assertAlmostEqual(out, E2E_ACCEL)
-
-  def test_boost_requires_e2e_to_be_limiting(self):
-    boost = AccelBoost()
-    sm = FakeSubMaster(gas_pressed=False)
-    # Eligibility is latched before the override, so keep the e2e plan at the planner limit here.
-    boost.update(sm, E2E_ACCEL, E2E_ACCEL, E2E_ACCEL)
-    sm['carState'].gasPressed = True
-    for _ in range(10):
-      boost.update(sm, E2E_ACCEL, E2E_ACCEL, E2E_ACCEL)
-    self.assertEqual(boost.total_boost, 0.0)
-
-  def test_per_override_cap(self):
-    boost = AccelBoost()
-    sm = FakeSubMaster(gas_pressed=False)
-    run_boost(boost, sm)
-    sm['carState'].gasPressed = True
-    run_boost(boost, sm, 1000)
-    self.assertAlmostEqual(boost.total_boost, ACCEL_BOOST_PER_OVERRIDE)
-
-  def test_max_boost(self):
-    boost = self._boost_to_max()
-    self.assertAlmostEqual(boost.total_boost, ACCEL_BOOST_MAX)
-
-  def test_boost_is_applied_to_e2e_accel(self):
-    boost = self._boost_to_max()
-    sm = FakeSubMaster(gas_pressed=True)
-    out = run_boost(boost, sm)
-    self.assertAlmostEqual(out, E2E_ACCEL + boost.total_boost)
-
-  def test_boost_does_not_depend_on_following_distance(self):
-    totals = []
-    for personality in (log.LongitudinalPersonality.relaxed,
-                        log.LongitudinalPersonality.standard,
-                        log.LongitudinalPersonality.aggressive):
-      boost = AccelBoost()
-      sm = FakeSubMaster(personality=personality)
-      steps = round(ACCEL_BOOST_PER_OVERRIDE / RATE_PER_STEP)
-      run_boost(boost, sm)
-      for _ in range(10):
-        sm['carState'].gasPressed = True
-        run_boost(boost, sm, steps)
-        sm['carState'].gasPressed = False
-        run_boost(boost, sm)
-      totals.append(boost.total_boost)
-
-    self.assertEqual(len(totals), 3)
-    for total in totals:
-      self.assertAlmostEqual(total, ACCEL_BOOST_MAX)

openpilot/selfdrive/controls/tests/test_longcontrol.py

@@ -1,9 +1,6 @@
-from opendbc.car.structs import car
-from openpilot.common.realtime import DT_CTRL
 from openpilot.common.test import OpenpilotTestCase
 from openpilot.cereal import custom
-from openpilot.selfdrive.controls.lib.longcontrol import (LongControl, LongCtrlState, long_control_state_trans,
-                                                           STOPPING_RELEASE_JERK)
+from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState, long_control_state_trans
 
 
 class TestLongControlStateTransition(OpenpilotTestCase):
@@ -45,121 +42,3 @@ class TestLongControlStateTransition(OpenpilotTestCase):
     next_state = long_control_state_trans(CP_SP, active, current_state,
                              should_stop=False, brake_pressed=False, cruise_standstill=False)
     assert next_state == LongCtrlState.pid
-
-
-class TestLongControlStopping(OpenpilotTestCase):
-  ACCEL_LIMITS = (-2.0, 2.0)
-
-  def make_control(self):
-    CP = car.CarParams.new_message()
-    CP.stopAccel = -2.0
-    CP.longitudinalTuning.kiBP = [0.0]
-    CP.longitudinalTuning.kiV = [0.0]
-    CP_SP = custom.CarParamsSP.new_message()
-    return LongControl(CP, CP_SP)
-
-  def make_cs(self, v_ego, a_ego, standstill=False, cruise_standstill=None):
-    CS = car.CarState.new_message()
-    CS.vEgo = float(v_ego)
-    CS.aEgo = float(a_ego)
-    CS.standstill = standstill
-    CS.brakePressed = False
-    CS.cruiseState.standstill = standstill if cruise_standstill is None else cruise_standstill
-    return CS
-
-  def enter_stopping(self, loc, v_ego=0.23, a_ego=-0.55):
-    # Run in pid first so last_output_accel is a realistic decel, then enter stopping.
-    out = 0.0
-    for _ in range(100):
-      out = loc.update(True, self.make_cs(2.0, a_ego), a_ego, False, self.ACCEL_LIMITS)
-    assert loc.long_control_state == LongCtrlState.pid
-    out = loc.update(True, self.make_cs(v_ego, a_ego), a_ego, True, self.ACCEL_LIMITS)
-    assert loc.long_control_state == LongCtrlState.stopping
-    return out
-
-  def test_entry_is_continuous(self):
-    loc = self.make_control()
-    out = self.enter_stopping(loc)
-    assert abs(out - loc.last_output_accel) < 1e-9
-    assert abs(out - (-0.55)) < 0.02
-
-  def test_positive_entry_ramps_down(self):
-    loc = self.make_control()
-    loc.last_output_accel = 0.3
-    loc.stopping_jerk = 0.66
-    out = loc._stopping_accel(0.23, False)
-    assert abs(out - (0.3 - STOPPING_RELEASE_JERK * DT_CTRL)) < 1e-6  # no jump to zero
-
-  def test_taper_never_steps(self):
-    loc = self.make_control()
-    v_ego = 0.23
-    out = self.enter_stopping(loc)
-    prev = out
-    max_step = 0.0
-    for _ in range(400):
-      out = loc.update(True, self.make_cs(v_ego, out), -0.55, True, self.ACCEL_LIMITS)
-      max_step = max(max_step, abs(out - prev))
-      prev = out
-      v_ego = max(0.0, v_ego + out * DT_CTRL)
-
-    # bounded by the release jerk (and the 1.0 m/s^3 hold ramp), never a step to stopAccel
-    assert max_step <= STOPPING_RELEASE_JERK * DT_CTRL + 1e-6
-    assert -2.01 < out < -1.9
-
-  def test_taper_scale_releases_to_shallower_decel(self):
-    loc = self.make_control()
-    loc.last_output_accel = -0.5
-    loc.stopping_jerk = 0.5
-    for _ in range(200):
-      loc.last_output_accel = loc._stopping_accel(0.1, False)
-    # 0.5x taper at 0.1 m/s targets ~-0.22 m/s^2 instead of ~-0.32
-    assert -0.24 < loc.last_output_accel < -0.20
-
-  def test_taper_releases_before_standstill(self):
-    loc = self.make_control()
-    v_ego = 0.23
-    out = self.enter_stopping(loc)
-    while v_ego > 0.11:
-      out = loc.update(True, self.make_cs(v_ego, out), -0.55, True, self.ACCEL_LIMITS)
-      v_ego = max(0.0, v_ego + out * DT_CTRL)
-    assert out > -0.5  # noticeably released before the hold takes over
-
-  def test_hot_entry_still_bounded(self):
-    loc = self.make_control()
-    out = self.enter_stopping(loc, v_ego=0.25, a_ego=-1.2)
-    prev = out
-    assert abs(out - (-1.2)) < 0.021
-    for _ in range(30):
-      out = loc.update(True, self.make_cs(0.25, out), -1.2, True, self.ACCEL_LIMITS)
-      assert abs(out - prev) <= 2.0 * DT_CTRL + 1e-6
-      prev = out
-
-  def test_hold_ramps_instead_of_stepping(self):
-    loc = self.make_control()
-    loc.last_output_accel = -0.3
-    out = loc._stopping_accel(0.0, True)
-    assert out > -0.32  # first hold frame is a ramp, not stopAccel
-    for _ in range(300):
-      prev = out
-      loc.last_output_accel = out
-      out = loc._stopping_accel(0.0, True)
-      assert abs(out - prev) <= 1.0 * DT_CTRL + 1e-6
-    assert -2.01 < out < -1.9
-
-  def test_should_stop_debounce(self):
-    loc = self.make_control()
-    self.enter_stopping(loc)
-    # settle at standstill
-    out = -0.55
-    for _ in range(300):
-      out = loc.update(True, self.make_cs(0.0, 0.0, standstill=True), 0.2, True, self.ACCEL_LIMITS)
-    assert out < -1.9
-    # one dropped should_stop frame must not release the brake. cruiseState.standstill is
-    # false here, matching the observed standstill where should_stop briefly drops.
-    out = loc.update(True, self.make_cs(0.0, 0.0, standstill=True, cruise_standstill=False), 0.2, False, self.ACCEL_LIMITS)
-    assert loc.long_control_state == LongCtrlState.stopping
-    assert out < -1.9
-    # and it must eventually exit when the request to move persists
-    for _ in range(20):
-      out = loc.update(True, self.make_cs(0.0, 0.0, standstill=True, cruise_standstill=False), 0.2, False, self.ACCEL_LIMITS)
-    assert loc.long_control_state == LongCtrlState.pid

openpilot/selfdrive/ui/mici/layouts/settings/toggles.py

@@ -21,9 +21,6 @@ class TogglesLayoutMici(NavScroller):
                                                                "Use the steering wheel distance button on supported cars.")
     self._initial_cruise_speed = InitialCruiseSpeedButton()
     self._initial_cruise_speed.set_visible(lambda: ui_state.CP is not None and ui_state.has_longitudinal_control and not ui_state.CP.pcmCruise)
-    self._accel_boost_toggle = BigParamControl("extra acceleration boost", "FollowDistanceAccelBoost",
-                                               description="Adds extra acceleration on top of the stock gas-override boost.\n" +
-                                                           "The extra boost follows Following Distance: strongest when Aggressive, weakest when Relaxed.")
     is_metric_toggle = BigToggle("use metric units", initial_state=ui_state.params.get_bool("IsMetric"), toggle_callback=self._on_metric_toggle)
     ldw_toggle = BigParamControl("lane departure warnings", "IsLdwEnabled",
                                  description="Warn when you drift across a detected lane line.\n" +
@@ -43,7 +40,6 @@ class TogglesLayoutMici(NavScroller):
 
     self._scroller.add_widgets([
       self._personality_toggle,
-      self._accel_boost_toggle,
       self._initial_cruise_speed,
       is_metric_toggle,
       ldw_toggle,
@@ -55,7 +51,6 @@ class TogglesLayoutMici(NavScroller):
 
     # Toggle lists
     self._refresh_toggles = (
-      ("FollowDistanceAccelBoost", self._accel_boost_toggle),
       ("IsMetric", is_metric_toggle),
       ("IsLdwEnabled", ldw_toggle),
       ("AlwaysOnDM", always_on_dm_toggle),
@@ -97,7 +92,6 @@ class TogglesLayoutMici(NavScroller):
 
     if ui_state.CP is not None:
       self._personality_toggle.set_visible(ui_state.has_longitudinal_control)
-      self._accel_boost_toggle.set_visible(ui_state.has_longitudinal_control)
 
     # Refresh toggles from params to mirror external changes
     for key, item in self._refresh_toggles:

openpilot/selfdrive/ui/mici/onroad/accel_boost_indicator.py

@@ -1,141 +0,0 @@
-"""
-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 pyray as rl
-
-from openpilot.cereal import log
-from openpilot.common.filter_simple import FirstOrderFilter
-from openpilot.selfdrive.ui.mici.onroad import SIDE_PANEL_WIDTH
-from openpilot.selfdrive.ui.ui_state import ui_state
-from openpilot.system.ui.lib.application import FontWeight, gui_app
-from openpilot.system.ui.lib.text_measure import measure_text_cached
-from openpilot.system.ui.widgets import Widget
-from openpilot.sunnypilot.selfdrive.controls.lib.accel_boost_ext import get_accel_boost_max
-
-# The readout is wider than the side panel, so the value is right-aligned and bleeds over the camera view
-BAR_WIDTH = 24
-# Fades to white only near the profile cap, like the stock torque bar fading toward orange at high torque
-CAP_BLEND_START = 0.75
-MAX_BOOST_BLEND = 0.45
-
-COLOR_TRACK = rl.Color(255, 255, 255, 45)
-COLOR_TRACK_EDGE = rl.Color(255, 255, 255, 70)
-COLOR_TEXT = rl.Color(255, 255, 255, 235)
-COLOR_TEXT_SHADOW = rl.Color(0, 0, 0, 150)
-
-# Temporary debug readout next to the bar, flip off once the bar alone reads right
-SHOW_VALUE = True
-VALUE_FONT_SIZE = 24
-
-AGGRESSIVE_COLOR = rl.Color(255, 80, 60, 255)
-STANDARD_COLOR = rl.Color(0, 230, 120, 255)
-RELAXED_COLOR = rl.Color(70, 150, 255, 255)
-
-FADE_THRESHOLD = 0.01
-
-
-def lighten(color: rl.Color, amount: float) -> rl.Color:
-  """Lerp towards white in RGB, so the hue stays put the way HSV blending would not."""
-  amount = min(max(amount, 0.0), 1.0)
-  return rl.Color(int(color.r + (255 - color.r) * amount),
-                  int(color.g + (255 - color.g) * amount),
-                  int(color.b + (255 - color.b) * amount), color.a)
-
-
-def get_boost_fraction(boost: float, max_boost: float) -> float:
-  """How much of the active profile's boost budget is in use, clamped to a full bar."""
-  if max_boost <= 0.0:
-    return 0.0
-  return min(max(boost / max_boost, 0.0), 1.0)
-
-
-def get_boost_style(personality) -> tuple[rl.Color, float]:
-  if personality == log.LongitudinalPersonality.aggressive:
-    color = AGGRESSIVE_COLOR
-  elif personality == log.LongitudinalPersonality.relaxed:
-    color = RELAXED_COLOR
-  else:
-    color = STANDARD_COLOR
-
-  return color, get_accel_boost_max(personality)
-
-
-class AccelBoostIndicator(Widget):
-  def __init__(self):
-    super().__init__()
-    self._boost_filter = FirstOrderFilter(0.0, 0.15, 1 / gui_app.target_fps)
-    self._alpha_filter = FirstOrderFilter(0.0, 0.1, 1 / gui_app.target_fps)
-    # Smoothly rescale the bar when the follow distance profile changes mid-drive
-    self._max_boost_filter = FirstOrderFilter(0.0, 0.25, 1 / gui_app.target_fps)
-    self._font = gui_app.font(FontWeight.BOLD)
-    self._profile_color = STANDARD_COLOR
-    self._max_boost = 0.0
-
-    self.panel_in_use = False
-
-  def _update_state(self):
-    sm = ui_state.sm
-    extra_enabled = ui_state.follow_distance_accel_boost
-    visible = (ui_state.has_longitudinal_control and sm.alive['selfdriveState'] and sm.alive['longitudinalPlan'] and
-               sm['selfdriveState'].enabled)
-    self._alpha_filter.update(1.0 if visible and extra_enabled else 0.0)
-    self._boost_filter.update(float(sm['longitudinalPlan'].accelBoost) if visible else 0.0)
-
-    if sm.alive['selfdriveState']:
-      self._profile_color, self._max_boost = get_boost_style(sm['selfdriveState'].personality)
-    self._max_boost_filter.update(self._max_boost)
-
-    self.panel_in_use = extra_enabled and ui_state.has_longitudinal_control
-
-  def _render(self, _):
-    alpha = self._alpha_filter.x
-    if alpha < FADE_THRESHOLD or self._max_boost <= 0.0:
-      return
-
-    cx = self.rect.x + self.rect.width - SIDE_PANEL_WIDTH / 2
-    bar_x = cx - BAR_WIDTH / 2
-    track_top = self.rect.y + self.rect.height * 0.08
-    track_bottom = self.rect.y + self.rect.height * 0.92
-    track_h = max(1.0, track_bottom - track_top)
-
-    # track, light enough to read on the black side panel
-    rl.draw_rectangle_rounded(rl.Rectangle(bar_x, track_top, BAR_WIDTH, track_h), 0.5, 12,
-                              rl.Color(COLOR_TRACK.r, COLOR_TRACK.g, COLOR_TRACK.b, int(COLOR_TRACK.a * alpha)))
-    rl.draw_rectangle_rounded_lines_ex(rl.Rectangle(bar_x, track_top, BAR_WIDTH, track_h), 0.5, 12, 1,
-                                       rl.Color(COLOR_TRACK_EDGE.r, COLOR_TRACK_EDGE.g, COLOR_TRACK_EDGE.b,
-                                                int(COLOR_TRACK_EDGE.a * alpha)))
-
-    # Scale to the current profile's cap, so a maxed boost fills the bar and a profile change rescales it
-    boost = max(self._boost_filter.x, 0.0)
-    ratio = get_boost_fraction(boost, self._max_boost_filter.x)
-    fill_h = ratio * track_h
-    cap_blend = max(0.0, (ratio - CAP_BLEND_START) / (1.0 - CAP_BLEND_START))
-    fill_color = lighten(self._profile_color, MAX_BOOST_BLEND * cap_blend)
-    if fill_h > 0.0:
-      # soft glow around the tip, like the confidence ball's gradient
-      tip_y = track_bottom - fill_h
-      glow = rl.Color(fill_color.r, fill_color.g, fill_color.b, int(70 * alpha))
-      rl.draw_circle_gradient(rl.Vector2(cx, tip_y), int(BAR_WIDTH * 0.9), glow, rl.BLANK)
-
-      fill = rl.Color(fill_color.r, fill_color.g, fill_color.b, int(255 * alpha))
-      if fill_h < BAR_WIDTH:
-        rl.draw_circle(int(cx), int(track_bottom - fill_h / 2), max(1, int(BAR_WIDTH / 2)), fill)
-      else:
-        rl.draw_rectangle_rounded(rl.Rectangle(bar_x, track_bottom - fill_h, BAR_WIDTH, fill_h), 0.5, 12, fill)
-
-    if SHOW_VALUE:
-      value = f"+{boost:.2f}"
-      value_size = measure_text_cached(self._font, value, VALUE_FONT_SIZE)
-      value_pos = rl.Vector2(bar_x - 8 - value_size.x, (track_top + track_bottom) / 2 - value_size.y / 2)
-      self._draw_text(self._font, value, value_pos, VALUE_FONT_SIZE,
-                      rl.Color(COLOR_TEXT.r, COLOR_TEXT.g, COLOR_TEXT.b, int(COLOR_TEXT.a * alpha)))
-
-  @staticmethod
-  def _draw_text(font, text: str, pos: rl.Vector2, font_size: int, color: rl.Color) -> None:
-    # Drop shadow keeps the readout legible against the camera feed
-    shadow = rl.Color(COLOR_TEXT_SHADOW.r, COLOR_TEXT_SHADOW.g, COLOR_TEXT_SHADOW.b, int(COLOR_TEXT_SHADOW.a * color.a / 255))
-    rl.draw_text_ex(font, text, rl.Vector2(pos.x + 1, pos.y + 1), font_size, 0, shadow)
-    rl.draw_text_ex(font, text, pos, font_size, 0, color)

openpilot/selfdrive/ui/mici/onroad/augmented_road_view.py

@@ -5,7 +5,6 @@ from opendbc.car.structs import car
 from openpilot.cereal.visionipc import VisionStreamType
 from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
 from openpilot.selfdrive.ui.mici.onroad import SIDE_PANEL_WIDTH
-from openpilot.selfdrive.ui.mici.onroad.accel_boost_indicator import AccelBoostIndicator
 from openpilot.selfdrive.ui.mici.onroad.alert_renderer import AlertRenderer
 from openpilot.selfdrive.ui.mici.onroad.driver_state import DriverStateRenderer
 from openpilot.selfdrive.ui.mici.onroad.hud_renderer import HudRenderer
@@ -182,7 +181,6 @@ class AugmentedRoadView(CameraView):
     self._alert_renderer = AlertRenderer()
     self._driver_state_renderer = DriverStateRenderer()
     self._confidence_ball = ConfidenceBall()
-    self._accel_boost_indicator = AccelBoostIndicator()
     self._offroad_label = UnifiedLabel("start the car to\nuse sunnypilot", 54, FontWeight.DISPLAY,
                                        text_color=rl.Color(255, 255, 255, int(255 * 0.9)),
                                        alignment=TextAlignment.CENTER,
@@ -277,10 +275,7 @@ class AugmentedRoadView(CameraView):
     if show_navigation:
       self._navigation.render(rl.Rectangle(self._content_rect.x + self._content_rect.width, self.rect.y, PANEL_WIDTH, self.rect.height))
     else:
-      # The boost bar owns the side panel while the extra boost is on; the ball only shows with the feature off
-      self._accel_boost_indicator.render(self.rect)
-      if not self._accel_boost_indicator.panel_in_use:
-        self._confidence_ball.render(self.rect)
+      self._confidence_ball.render(self.rect)
 
     self._bookmark_icon.render(self.rect)
 

openpilot/selfdrive/ui/mici/onroad/model_renderer.py

@@ -141,7 +141,6 @@ class ModelRenderer(Widget, ModelRendererSP):
     if ui_state.status != UIStatus.DISENGAGED:
       self._draw_lane_lines()
       self._draw_path()
-      self.blendful.draw(self._rect)
 
     if render_lead_indicator:
       self._update_leads(sm)
@@ -220,7 +219,6 @@ class ModelRenderer(Widget, ModelRendererSP):
     # Update road edges using raw points
     for road_edge in self._road_edges:
       road_edge.projected_points = self._map_line_to_polygon(road_edge.raw_points, line_width_factor, 0.0, max_idx)
-    self.blendful.update(self, max_idx)
 
     # Update path using raw points
     if lead and lead.present:

openpilot/selfdrive/ui/mici/tests/test_accel_boost_indicator.py

@@ -1,53 +0,0 @@
-import unittest
-
-from openpilot.cereal import log
-from openpilot.selfdrive.controls.lib.accel_boost import ACCEL_BOOST_MAX
-from openpilot.selfdrive.ui.mici.onroad.accel_boost_indicator import (get_boost_style, get_boost_fraction, lighten,
-                                                                     AGGRESSIVE_COLOR, RELAXED_COLOR, STANDARD_COLOR)
-from openpilot.sunnypilot.selfdrive.controls.lib.accel_boost_ext import (SCALE_AGGRESSIVE, SCALE_RELAXED, SCALE_STANDARD,
-                                                                        get_accel_boost_max)
-
-
-class TestAccelBoostIndicator(unittest.TestCase):
-  def test_bar_scales_with_following_distance(self):
-    for personality, color, scale in (
-        (log.LongitudinalPersonality.relaxed, RELAXED_COLOR, SCALE_RELAXED),
-        (log.LongitudinalPersonality.standard, STANDARD_COLOR, SCALE_STANDARD),
-        (log.LongitudinalPersonality.aggressive, AGGRESSIVE_COLOR, SCALE_AGGRESSIVE)):
-      bar_color, full_scale = get_boost_style(personality)
-      self.assertEqual(bar_color, color)
-      self.assertAlmostEqual(full_scale, ACCEL_BOOST_MAX * (1.0 + scale))
-
-  def test_bar_fills_at_the_active_profile_cap(self):
-    caps = [get_accel_boost_max(p) for p in (log.LongitudinalPersonality.relaxed,
-                                             log.LongitudinalPersonality.standard,
-                                             log.LongitudinalPersonality.aggressive)]
-    self.assertEqual(caps, sorted(caps))
-
-    for cap in caps:
-      self.assertAlmostEqual(get_boost_fraction(cap, cap), 1.0)
-      self.assertAlmostEqual(get_boost_fraction(0.0, cap), 0.0)
-      self.assertAlmostEqual(get_boost_fraction(cap * 0.5, cap), 0.5)
-
-    # A boost beyond the current cap (e.g. after switching to a shorter profile) still fills the bar
-    self.assertAlmostEqual(get_boost_fraction(caps[2], caps[0]), 1.0)
-    self.assertAlmostEqual(get_boost_fraction(0.0, 0.0), 0.0)
-
-  def test_cap_lighten_keeps_the_profile_hue(self):
-    # An HSV blend towards white sweeps relaxed blue through pink, so the fill lightens in RGB instead
-    relaxed = lighten(RELAXED_COLOR, 0.45)
-    aggressive = lighten(AGGRESSIVE_COLOR, 0.45)
-    self.assertGreater(relaxed.b, relaxed.r)
-    self.assertGreaterEqual(relaxed.b, relaxed.g)
-    self.assertGreater(aggressive.r, aggressive.b)
-    # lighter in RGB means the dimmest channel came up towards white
-    self.assertGreater(relaxed.r, RELAXED_COLOR.r)
-    self.assertGreater(aggressive.g, AGGRESSIVE_COLOR.g)
-
-  def test_wire_personality_picks_the_right_profile(self):
-    # Over the wire personality is a capnp DynamicEnum, which compares equal to the cereal enum but hashes differently
-    msg = log.SelfdriveState.new_message()
-    msg.personality = log.LongitudinalPersonality.aggressive
-    color, full_scale = get_boost_style(msg.personality)
-    self.assertEqual(color, AGGRESSIVE_COLOR)
-    self.assertAlmostEqual(full_scale, get_accel_boost_max(log.LongitudinalPersonality.aggressive))

openpilot/selfdrive/ui/onroad/model_renderer.py

@@ -123,8 +123,6 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
     # Draw elements
     self._draw_lane_lines()
     self._draw_path()
-    if self._lateral_active:
-      self.blendful.draw(self._rect)
 
     if render_lead_indicator and radar_state:
       self._draw_lead_indicator()
@@ -174,7 +172,6 @@ class ModelRenderer(Widget, ChevronMetrics, ModelRendererSP):
     # Update road edges using raw points
     for road_edge in self._road_edges:
       road_edge.projected_points = self._map_line_to_polygon(road_edge.raw_points, 0.025, 0.0, max_idx, max_distance)
-    self.blendful.update(self, max_idx, max_distance)
 
     # Update path using raw points
     if lead and lead.present:

openpilot/selfdrive/ui/sunnypilot/layouts/settings/cruise.py

@@ -30,11 +30,6 @@ ACC_NOLONG_DESCRIPTION = tr_noop("This feature can only be used with sunnypilot
 ACC_PCMCRUISE_DISABLED_DESCRIPTION = tr_noop("This feature is not supported on this platform due to vehicle limitations.")
 ONROAD_ONLY_DESCRIPTION = tr_noop("Start the vehicle to check vehicle compatibility.")
 
-ACCEL_BOOST_EXT_DESCRIPTION = tr_noop("Adds extra acceleration on top of the stock gas-override boost when you hold the gas pedal " +
-                                       "while sunnypilot longitudinal control is active. The extra boost follows the Following Distance setting: " +
-                                       "strongest when Aggressive, weakest when Relaxed. The stock boost remains active when this is off.")
-ACCEL_BOOST_EXT_NOLONG_DESCRIPTION = tr_noop("This feature requires sunnypilot longitudinal control to be available.")
-
 
 class CruiseLayout(Widget):
   def __init__(self):
@@ -81,11 +76,6 @@ class CruiseLayout(Widget):
       min_value=1, max_value=3, value_change_step=1,
       inline=True)
 
-    self.accel_boost_toggle = toggle_item_sp(
-      title=tr("Extra Acceleration Boost by Following Distance"),
-      description="",
-      param="FollowDistanceAccelBoost")
-
     self.sla_settings_button = simple_button_item_sp(
       button_text=lambda: tr("Speed Limit"),
       button_width=800,
@@ -96,7 +86,6 @@ class CruiseLayout(Widget):
       self.icbm_toggle,
       self.scc_v_toggle,
       self.scc_m_toggle,
-      self.accel_boost_toggle,
       self.custom_acc_toggle,
       self.custom_acc_short_increment,
       self.custom_acc_long_increment,
@@ -115,7 +104,6 @@ class CruiseLayout(Widget):
     self._scroller.show_event()
     self.icbm_toggle.show_description(True)
     self.custom_acc_toggle.show_description(True)
-    self.accel_boost_toggle.show_description(True)
 
   def _set_current_panel(self, panel: PanelType):
     self._current_panel = panel
@@ -160,17 +148,10 @@ class CruiseLayout(Widget):
         self.scc_v_toggle.action_item.set_enabled(False)
         self.scc_m_toggle.action_item.set_enabled(False)
 
-      self.accel_boost_toggle.action_item.set_enabled(has_long)
-      boost_desc = tr(ACCEL_BOOST_EXT_DESCRIPTION) if has_long else tr(ACCEL_BOOST_EXT_NOLONG_DESCRIPTION)
-      if self.accel_boost_toggle.description != boost_desc:
-        self.accel_boost_toggle.set_description(boost_desc)
-        self.accel_boost_toggle.show_description(True)
-
     else:
       has_icbm = has_long = False
       self.icbm_toggle.action_item.set_enabled(False)
       self.icbm_toggle.set_description(tr(ONROAD_ONLY_DESCRIPTION))
-      self.accel_boost_toggle.action_item.set_enabled(False)
 
     show_custom_acc_desc = False
 

openpilot/selfdrive/ui/sunnypilot/layouts/settings/models.py

@@ -102,18 +102,13 @@ class ModelsLayout(Widget):
 
     self.lagd_toggle = toggle_item_sp(tr("Live Learning Steer Delay"), "", param="LagdToggle")
 
-    self.blendful_toggle = toggle_item_sp(tr("Blendful"),
-                                          tr("Nudges the car toward the lane center when the lane lines are clear. Drives laneless when they are not."),
-                                          param="Blendful")
-
     self.camera_offset = option_item_sp(tr("Adjust Camera Offset"), "CameraOffset", -35, 35,
                                         tr("Virtually shift camera's perspective to move model's center to Left(+ values) or Right (- values)"),
                                         1, None, True, "", style.BUTTON_ACTION_WIDTH, None, True,
                                         lambda v: f"{v / 100:.2f} m")
 
     self.items = [self.small_model_item, self.big_model_item, self.cancel_download_item, self.download_item, self.refresh_item, self.clear_cache_item,
-                  self.blendful_toggle, self.lane_turn_desire_toggle, self.lane_turn_value_control, self.lagd_toggle, self.delay_control,
-                  self.camera_offset]
+                  self.lane_turn_desire_toggle, self.lane_turn_value_control, self.lagd_toggle, self.delay_control, self.camera_offset]
 
   def _update_lagd_description(self, lagd_toggle: bool):
     desc = tr("Enable this for the car to learn and adapt its steering response time. Disable to use a fixed steering response time. " +
@@ -248,9 +243,7 @@ class ModelsLayout(Widget):
     if ds.verifying in statuses:
       return {"name": name, "downloading": True, "progress": progress, "status_text": tr("verifying")}
     if ds.downloading in statuses:
-      speed = max((p.speed for p in progresses), default=0.0)
-      status_text = f"{speed / 1024 ** 2:.1f} MB/s" if speed > 0 else "--.- MB/s"
-      return {"name": name, "downloading": True, "progress": progress, "status_text": status_text}
+      return {"name": name, "downloading": True, "progress": progress}
     if statuses <= {ds.downloaded, ds.cached}:
       return {"name": name, "text_color": ON_COLOR, "icon": "icons/checkmark.png"}
     # circled_slash is authored grey; tinting it again only darkens it
@@ -327,7 +320,6 @@ class ModelsLayout(Widget):
     self.lane_turn_desire_toggle.action_item.set_state(turn_desire)
     self.lane_turn_value_control.set_visible(turn_desire and advanced_controls)
     self.lagd_toggle.action_item.set_state(live_delay)
-    self.blendful_toggle.action_item.set_state(ui_state.params.get_bool("Blendful"))
     self.delay_control.set_visible(not live_delay and advanced_controls)
     new_step = int(round(100 / CV.MPH_TO_KPH)) if ui_state.is_metric else 100
     if self.lane_turn_value_control.action_item is not None and self.lane_turn_value_control.action_item.value_change_step != new_step:

openpilot/selfdrive/ui/sunnypilot/mici/layouts/models.py

@@ -11,7 +11,7 @@ import pyray as rl
 from openpilot.cereal import custom
 from openpilot.selfdrive.ui.mici.widgets.dialog import BigConfirmationDialog, BigDialog
 from openpilot.sunnypilot.models.helpers import ACTIVE_BUNDLE_KEYS, get_selected_bundle
-from openpilot.selfdrive.ui.mici.widgets.button import BigButton, BigParamControl
+from openpilot.selfdrive.ui.mici.widgets.button import BigButton
 from openpilot.selfdrive.ui.ui_state import ui_state, device
 from openpilot.selfdrive.ui.sunnypilot.model_info import (active_source, big_model_state, bundles_for_source, carrying_model,
                                                            default_model_name, model_cache_size_mb, model_info, queued_name,
@@ -95,12 +95,7 @@ class ModelsLayoutMici(NavScroller):
     self.clear_cache_btn.set_click_callback(self._confirm_clear_cache)
     self._cache_size_time = 0.0
 
-    self.blendful_toggle = BigParamControl("blendful", "Blendful",
-                                           description="Nudges the car toward the lane center when the lane lines are clear.\n" +
-                                                       "Drives laneless when they are not.")
-
-    self.main_items = [self.current_model_info, self.select_model_btn, self.cancel_download_btn, self.refresh_btn, self.clear_cache_btn,
-                       self.blendful_toggle]
+    self.main_items = [self.current_model_info, self.select_model_btn, self.cancel_download_btn, self.refresh_btn, self.clear_cache_btn]
     self._scroller.add_widgets(self.main_items)
 
   @property
@@ -210,10 +205,6 @@ class ModelsLayoutMici(NavScroller):
       device.set_override_interactive_timeout(None)
       self._was_downloading = False
 
-  def show_event(self):
-    super().show_event()
-    self.blendful_toggle.refresh()
-
   def _update_state(self):
     super()._update_state()
 
@@ -266,7 +257,6 @@ class ModelsLayoutMici(NavScroller):
       count = 0
       verifying = False
       canceling = False
-      speed = 0.0
       for model in manager.selectedBundle.models:
         count += 1
         p = model.artifact.downloadProgress
@@ -275,7 +265,6 @@ class ModelsLayoutMici(NavScroller):
           progress += p.progress
           verifying = verifying or p.status == custom.ModelManagerSP.DownloadStatus.verifying
           canceling = canceling or p.canceling
-          speed = max(speed, p.speed)  # only one file downloads at a time, so the non-zero speed is the live one
         elif p.status in (custom.ModelManagerSP.DownloadStatus.downloaded,
                           custom.ModelManagerSP.DownloadStatus.cached):
           progress += 100.0
@@ -290,10 +279,7 @@ class ModelsLayoutMici(NavScroller):
       self.current_model_info.current_model_text.set_text(name_text)
       self.current_model_info.info_header.set_text(tr("progress") + self._download_progress)
       self.current_model_info.info_header._shimmer = True
-      progress_text = f"{progress/count:.2f}%"
-      if not verifying and not canceling:
-        progress_text += f"  |  {speed / 1024 ** 2:.1f} MB/s" if speed > 0 else "  |  --.- MB/s"
-      self.current_model_info.info_text.set_text(progress_text)
+      self.current_model_info.info_text.set_text(f"{progress/count:.2f}%")
 
     elif manager.selectedBundle and manager.selectedBundle.status == custom.ModelManagerSP.DownloadStatus.downloaded:
       self.current_model_info.info_header.set_text(tr("downloaded"))

openpilot/selfdrive/ui/sunnypilot/mici/onroad/model_renderer.py

@@ -6,7 +6,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 import pyray as rl
 from openpilot.selfdrive.ui.ui_state import UIStatus
-from openpilot.selfdrive.ui.sunnypilot.onroad.blendful import BlendfulRenderer
 from openpilot.selfdrive.ui.sunnypilot.onroad.rainbow_path import RainbowPath
 
 LANE_LINE_COLORS_SP = {
@@ -18,4 +17,3 @@ LANE_LINE_COLORS_SP = {
 class ModelRendererSP:
   def __init__(self):
     self.rainbow_path = RainbowPath()
-    self.blendful = BlendfulRenderer()

openpilot/selfdrive/ui/sunnypilot/onroad/blendful.py

@@ -1,90 +0,0 @@
-"""
-Copyright (c) 2021-, 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 pyray as rl
-from openpilot.common.filter_simple import FirstOrderFilter
-from openpilot.common.realtime import DT_MDL
-from openpilot.selfdrive.ui.ui_state import ui_state
-from openpilot.sunnypilot.modeld_v2.blendful import LANE_WIDTH_CLIP, LOOKAHEAD_TIME, MIN_LOOKAHEAD
-from openpilot.system.ui.lib.application import gui_app, FontWeight
-from openpilot.system.ui.lib.shader_polygon import draw_polygon
-from openpilot.system.ui.lib.text_measure import measure_text_cached
-
-BLENDFUL_COLOR = (0, 200, 255)
-FULL_BAR = 1.0
-
-
-class BlendfulRenderer:
-  def __init__(self, readout: bool = True):
-    self._readout = readout
-    self._center_points = np.empty((0, 2), dtype=np.float32)
-    self._trust = 0.0
-    self._font = gui_app.font(FontWeight.BOLD)
-    self._pull_display = FirstOrderFilter(0.0, 0.5, DT_MDL)
-    self._trust_display = FirstOrderFilter(0.0, 0.5, DT_MDL)
-    self._alpha = FirstOrderFilter(0.0, 0.3, DT_MDL)
-
-  def update(self, renderer, max_idx: int, *map_args) -> None:
-    blendful = ui_state.sm['modelDataV2SP']
-    if ui_state.sm.updated['modelDataV2SP']:
-      self._alpha.update(float(blendful.blendfulTrust > 0.0))
-      if blendful.blendfulTrust > 0.0:
-        self._trust = blendful.blendfulTrust
-      if self._readout:
-        lookahead = max(ui_state.sm['carState'].vEgo * LOOKAHEAD_TIME, MIN_LOOKAHEAD)
-        self._pull_display.update(blendful.blendfulCurvature * lookahead ** 2 / 2)
-        self._trust_display.update(self._trust)
-
-    left, right = renderer._lane_lines[1].raw_points, renderer._lane_lines[2].raw_points
-    stds = list(ui_state.sm['modelV2'].laneLineStds)
-    if self._alpha.x < 0.01 or len(left) == 0 or len(right) != len(left) or len(stds) < 3:
-      self._center_points = np.empty((0, 2), dtype=np.float32)
-      return
-
-    if blendful.blendfulTrust > 0.0:
-      l_prob, r_prob = blendful.blendfulLeftProb, blendful.blendfulRightProb
-      half_width = np.clip(right[:, 1] - left[:, 1], *LANE_WIDTH_CLIP) / 2 if l_prob * r_prob > 0.5 else blendful.blendfulLaneWidth / 2
-      l_var = stds[1] ** 2 / max(l_prob, 1e-4)
-      r_var = stds[2] ** 2 / max(r_prob, 1e-4)
-      center = left.copy()
-      center[:, 1] = (r_var * (left[:, 1] + half_width) + l_var * (right[:, 1] - half_width)) / (l_var + r_var)
-      self._center_points = renderer._map_line_to_polygon(center, 0.15, 0.0, max_idx, *map_args)
-
-  def _color(self, rgb, alpha: float) -> rl.Color:
-    return rl.Color(*rgb, int(alpha * self._alpha.x))
-
-  def draw(self, rect: rl.Rectangle) -> None:
-    if self._center_points.size and self._trust > 0.05:
-      draw_polygon(rect, self._center_points, self._color(BLENDFUL_COLOR, np.clip(self._trust, 0.2, 1.0) * 255))
-    if self._center_points.size and self._readout:
-      self._draw_readout(rect)
-
-  def _draw_readout(self, rect: rl.Rectangle) -> None:
-    pull, trust = self._pull_display.x, self._trust_display.x
-    w, h = 190, 76
-    x, y = rect.x + (rect.width - w) / 2, rect.y + 10
-    cx, cy = x + w / 2, y + 30
-    rl.draw_rectangle_rounded(rl.Rectangle(x, y, w, h), 0.5, 12, self._color((0, 0, 0), 170))
-
-    text = f"{abs(pull) * 100:.0f}"
-    text_w = measure_text_cached(self._font, text, 44, 0).x
-    rl.draw_text_ex(self._font, text, rl.Vector2(cx - text_w / 2, cy - 24), 44, 0, self._color((255, 255, 255), 255))
-
-    active = self._color(BLENDFUL_COLOR, np.clip(abs(pull) / 0.1, 0.0, 1.0) * 255)
-    idle = self._color((255, 255, 255), 40)
-    lx, rx = x + 18, x + w - 18
-    rl.draw_triangle(rl.Vector2(lx, cy), rl.Vector2(lx + 16, cy + 12), rl.Vector2(lx + 16, cy - 12), active if pull < 0 else idle)
-    rl.draw_triangle(rl.Vector2(rx - 16, cy - 12), rl.Vector2(rx - 16, cy + 12), rl.Vector2(rx, cy), active if pull > 0 else idle)
-
-    bar = rl.Rectangle(x + 24, y + h - 16, w - 48, 6)
-    rl.draw_rectangle_rounded(bar, 1.0, 6, self._color((255, 255, 255), 40))
-    rl.draw_rectangle(int(cx - 1), int(bar.y - 2), 2, int(bar.height + 4), self._color((255, 255, 255), 120))
-    fill = bar.width / 2 * float(np.sqrt(np.clip(abs(pull) / FULL_BAR, 0.0, 1.0)))
-    if fill > 1:
-      fill_x = cx if pull > 0 else cx - fill
-      rl.draw_rectangle_rounded(rl.Rectangle(fill_x, bar.y, fill, bar.height), 1.0, 6,
-                                self._color(BLENDFUL_COLOR, np.clip(0.3 + 0.7 * trust, 0.0, 1.0) * 255))

openpilot/selfdrive/ui/sunnypilot/onroad/model_renderer.py

@@ -6,7 +6,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.selfdrive.ui.ui_state import ui_state, UIStatus
-from openpilot.selfdrive.ui.sunnypilot.onroad.blendful import BlendfulRenderer
 from openpilot.selfdrive.ui.sunnypilot.onroad.chevron_metrics import ChevronMetrics
 from openpilot.selfdrive.ui.sunnypilot.onroad.rainbow_path import RainbowPath
 from openpilot.selfdrive.ui.sunnypilot.ui_state import MADSState
@@ -17,7 +16,6 @@ class ModelRendererSP:
   def __init__(self):
     self.rainbow_path = RainbowPath()
     self.chevron_metrics = ChevronMetrics()
-    self.blendful = BlendfulRenderer(readout=False)
     self._width_filter = FirstOrderFilter(0.9, 0.1, 1 / gui_app.target_fps)
 
   @property

openpilot/selfdrive/ui/sunnypilot/ui_state.py

@@ -36,7 +36,7 @@ class UIStateSP:
     self.is_sp_release: bool = self.params.get_bool("IsReleaseSpBranch")
     self.sm_services_ext = [
       "modelManagerSP", "selfdriveStateSP", "longitudinalPlanSP", "backupManagerSP",
-      "lateralTorqueParameters", "carStateSP", "liveMapDataSP", "carParamsSP", "lateralDelay", "modelDataV2SP"
+      "lateralTorqueParameters", "carStateSP", "liveMapDataSP", "carParamsSP", "lateralDelay"
     ]
 
     self.sunnylink_state = SunnylinkState()
@@ -64,7 +64,6 @@ class UIStateSP:
     self.enforce_torque_control: bool = False
     self.custom_torque_params: bool = False
     self.torque_override_enabled: bool = False
-    self.follow_distance_accel_boost: bool = False
     self._sp_initialized: bool = False
 
   def update(self) -> None:
@@ -178,7 +177,6 @@ class UIStateSP:
     self.enforce_torque_control = self.params.get_bool("EnforceTorqueControl")
     self.custom_torque_params = self.params.get_bool("CustomTorqueParams")
     self.torque_override_enabled = self.params.get_bool("TorqueParamsOverrideEnabled")
-    self.follow_distance_accel_boost = self.params.get_bool("FollowDistanceAccelBoost")
     self.torque_override_lat_accel_factor = float(self.params.get("TorqueParamsOverrideLatAccelFactor", return_default=True))
     self.torque_override_friction = float(self.params.get("TorqueParamsOverrideFriction", return_default=True))
     self.true_v_ego_ui = self.params.get_bool("TrueVEgoUI")

openpilot/sunnypilot/modeld_v2/blendful.py

@@ -1,126 +0,0 @@
-"""
-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.filter_simple import FirstOrderFilter
-from openpilot.common.realtime import DT_MDL
-from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
-
-LANE_WIDTH_DEFAULT = 3.6  # m (reseeded to model's lane width upon initialization)
-LANE_WIDTH_CLIP = (2.8, 4.5)  # m
-LOOKAHEAD_TIME = 1.5  # s
-MIN_LOOKAHEAD = 10.0  # m
-MIN_SPEED = 1.0  # m/s
-LINE_STD = (0.2, 0.6)
-POINT_STD = 0.1
-YIELD = (0.2, 0.45)
-X_IDXS = np.array(ModelConstants.X_IDXS)
-
-
-class Blendful:
-  def __init__(self):
-    self.lane_width = FirstOrderFilter(LANE_WIDTH_DEFAULT, 10.0, DT_MDL, initialized=False)
-    self.line_gone = FirstOrderFilter(np.zeros(2), 2.0, DT_MDL)
-    self.line_probs_slow = FirstOrderFilter(np.zeros(2), 3.0, DT_MDL)
-    self.line_probs = FirstOrderFilter(np.zeros(2), 0.5, DT_MDL)
-    self.usual_offset = FirstOrderFilter(0.0, 10.0, DT_MDL, initialized=False)
-    self.going_around = False
-    self.since_turning = 0.0
-    self.trust = 0.0
-    self.l_prob = 0.0
-    self.r_prob = 0.0
-    self.model_curvature = 0.0
-    self.smooth = 0.0 # smoothens lanelines below .53 lat accel
-    self.curvature = 0.0
-
-  def update(self, model_output: dict[str, np.ndarray], v_ego: float, active: bool, model_curvature: float) -> float:
-    self.l_prob = self.r_prob = 0.0
-    fading = not active or v_ego < MIN_SPEED
-    if fading:
-      self.trust = 0.0
-      self.usual_offset.initialized = False
-      self.lane_width.initialized = False
-      self.going_around = False
-    self.model_curvature = model_curvature
-    target = 0.0 if fading else self._target(model_output, v_ego, model_curvature)
-    target = self.smooth = self.smooth + (target - self.smooth) * DT_MDL / (DT_MDL + np.interp(abs(model_curvature) * v_ego ** 2, [0.18, 0.53], [0.25, 0.0]))
-    build = 0.5 / max(v_ego, 1.0) ** 2 * DT_MDL  # jerk over 1 second
-    release = (0.5 if fading else 2.0) / max(v_ego, 1.0) ** 2 * DT_MDL  # lets go fast, but past zero it builds like any other pull
-    c = self.curvature
-    self.curvature = float(np.clip(target, max(c - release, -build) if c > 0 else c - build, min(c + release, build) if c < 0 else c + build))
-    return self.curvature
-
-  def _target(self, model_output: dict[str, np.ndarray], v_ego: float, model_curvature: float) -> float:
-    lookahead = max(v_ego * LOOKAHEAD_TIME, MIN_LOOKAHEAD)
-    lines = model_output['lane_lines'][0, :, :, 0]
-    stds = model_output['lane_lines_stds'][0, :, :, 0]
-
-    probs = self.line_probs.update(model_output['lane_lines_prob'][0, 1::2][1:3])
-    gone = self.line_gone.update((model_output['lane_lines_prob'][0, 1::2][1:3] < 0.3).astype(float))
-    dropping = bool(np.any(probs < 0.6 * self.line_probs_slow.update(model_output['lane_lines_prob'][0, 1::2][1:3])))
-    l_prob = float(probs[0] * np.interp(np.interp(0.5 * lookahead, X_IDXS, stds[1]), LINE_STD, [1.0, 0.0]))
-    r_prob = float(probs[1] * np.interp(np.interp(0.5 * lookahead, X_IDXS, stds[2]), LINE_STD, [1.0, 0.0]))
-    self.l_prob, self.r_prob = l_prob, r_prob
-    # one line steadily on its own still centers at half say conf. a line that is disappearing gives control back to laneless
-    if gone[1] > 0.8 and l_prob > 0.5:
-      lane_trust = l_prob / 2
-    elif gone[0] > 0.8 and r_prob > 0.5:
-      lane_trust = r_prob / 2
-    elif dropping:
-      lane_trust = min(l_prob, r_prob)
-    else:
-      lane_trust = l_prob + r_prob - l_prob * r_prob
-    if lane_trust <= 0.0:
-      self.trust = 0.0
-      return 0.0
-
-    win = (X_IDXS >= 0.5 * lookahead) & (X_IDXS <= 1.5 * lookahead)
-    x = X_IDXS[win]
-    half_width = self.lane_width.x / 2
-    if l_prob * r_prob > 0.5:
-      width = np.interp(lookahead, X_IDXS, lines[2]) - np.interp(lookahead, X_IDXS, lines[1])
-      self.lane_width.update(float(np.clip(width, *LANE_WIDTH_CLIP)))
-      half_width = np.clip(lines[2, win] - lines[1, win], *LANE_WIDTH_CLIP) / 2
-    l_var = stds[1, win] ** 2 / max(l_prob, 1e-4)
-    r_var = stds[2, win] ** 2 / max(r_prob, 1e-4)
-    center = (r_var * (lines[1, win] + half_width) + l_var * (lines[2, win] - half_width)) / (l_var + r_var)
-    weight = 1.0 / (l_var * r_var / (l_var + r_var) + POINT_STD ** 2)
-
-    # Fit curvature to place the plant in center: e ~ dk x^2 / 2
-    plan_x = np.interp(x, model_output['plan'][0, :, 0], model_output['plan'][0, :, 1])
-    plan_share = np.interp(abs(model_curvature) * lookahead ** 2 / 2, [0.5, 2.0], [0.3, 0.5]) # blending up to 50% plan with action during sharp curvature
-    error = np.clip(center - ((1 - plan_share) * model_curvature * x ** 2 / 2 + plan_share * plan_x), -1.0, 1.0)
-    dk = 2.0 * np.sum(weight * error * x ** 2) / np.sum(weight * x ** 4)
-
-    # A model planning to end up further from its usual spot in the lane than the plant is now is likely going around something
-    hw_now = np.clip(lines[2, 0] - lines[1, 0], *LANE_WIDTH_CLIP) / 2 if l_prob * r_prob > 0.5 else self.lane_width.x / 2
-    center_now = (l_prob * (lines[1, 0] + hw_now) + r_prob * (lines[2, 0] - hw_now)) / (l_prob + r_prob)
-    plan_y = np.interp(lookahead, model_output['plan'][0, :, 0], model_output['plan'][0, :, 1])
-    offset = plan_y - np.interp(lookahead, x, center)
-    if min(gone) > 0.5:  # both lines gone: turns, intersections, relearn ideal model position
-      self.usual_offset.initialized = False
-    usual = self.usual_offset.x if self.usual_offset.initialized else offset
-    swerve = abs(offset - usual) - 0.25 * abs(model_curvature) * lookahead ** 2 / 2
-    away = swerve - abs(-center_now - usual)
-    lane_curvature = 2.0 * np.polyfit(x, center, 2)[0]
-    inside_curve = abs(lane_curvature) * lookahead ** 2 / 2 > 0.1 and (offset - usual) * lane_curvature > 0
-    self.since_turning = 0.0 if abs(model_curvature) * lookahead ** 2 / 2 > 0.5 else self.since_turning + DT_MDL
-    if inside_curve:
-      self.going_around = False
-    elif away > YIELD[0] and self.since_turning > 2.0:
-      self.going_around = True
-    elif swerve < YIELD[0] / 2:
-      self.going_around = False
-    self.usual_offset.update_alpha(20.0 if self.going_around else 10.0)
-    self.usual_offset.update(offset)
-    if inside_curve:
-      lane_trust *= np.interp(abs(offset) - abs(center_now) - 0.25 * abs(model_curvature) * lookahead ** 2 / 2, (0.5, 1.0), [1.0, 0.0])
-    else:
-      lane_trust *= np.interp(swerve, (YIELD[0] / 2, YIELD[0]), [1.0, 0.0]) if self.going_around else np.interp(away, YIELD, [1.0, 0.0])
-
-    self.trust = lane_trust if lane_trust < self.trust else self.trust + (lane_trust - self.trust) * DT_MDL / (1.5 + DT_MDL)
-    return float(self.trust * dk)

openpilot/sunnypilot/modeld_v2/convert_upstream_pkl.py

@@ -7,7 +7,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 import argparse
 import dataclasses
-import inspect
 import itertools
 import mmap
 import re
@@ -46,11 +45,6 @@ def _address(v):
   return _root(v.src[0]), add + start, size
 
 
-def _buffer(u):
-  from tinygrad.uop.ops import Ops
-  return u.buffer if u.op is Ops.BUFFER else getattr(u.arg, "buffer", None)
-
-
 def _patched(buf, topo) -> tuple[bytearray, dict]:
   from tinygrad.uop.ops import Ops
   blob, rows = bytearray(buf.max_numel() * buf.dtype.itemsize), {}
@@ -78,7 +72,7 @@ def _evaluate(u, env):
     table, index = _root(u.src[0].src[0]), _evaluate(u.src[0].src[1], env)
     return struct.unpack_from(u.dtype.fmt, table.arg, index * u.dtype.itemsize)[0] if table.op is Ops.BINARY and index is not None else None
   if u.op in GroupOp.ALU and None not in (values := [_evaluate(x, env) for x in u.src]):
-    return exec_alu(u.op, u.dtype, values, *([False] if "truncate_output" in inspect.signature(exec_alu).parameters else []))
+    return exec_alu(u.op, u.dtype, values, False)
   return None
 
 
@@ -118,7 +112,7 @@ def convert(jit, kwargs: dict, frame: dict, target) -> tuple[dict, memoryview]:
   from tinygrad.uop.ops import Ops
   submit = jit.captured._linear.src[0].without_after
   kernels, topo = submit.arg.aux.kernels, list(jit.captured._linear.toposort(enter_calls=True))
-  allocs = {str(name): u for u in topo if u.op is Ops.ALLOC and (name := getattr(u, "tag", None) or u.arg.name) is not None}
+  allocs = {str(u.tag): u for u in topo if u.op is Ops.ALLOC and u.tag is not None}
   (commands, patched), kernargs = _patched(allocs["cmdbuf_compute_0"], topo), allocs["kernargs_compute_0"]
   argument_words, pokes = _patched(kernargs, topo)[1], _input_pokes(submit, kernargs)
   sh = {name: getattr(amd_gpu, f"reg{name}") + amd_gpu.GC_BASE__INST0_SEG0 - pm4.PACKET3_SET_SH_REG_START
@@ -164,7 +158,7 @@ def convert(jit, kwargs: dict, frame: dict, target) -> tuple[dict, memoryview]:
         args.append({"input": pokes[slot][0]})
       elif slot in argument_words:
         base, at, nbytes = _address(argument_words[slot])
-        args.append({"buffer": buffers.setdefault(base, len(buffers)), "offset": at, "nbytes": nbytes or base.max_numel() * base.dtype.itemsize, "dtype": u8})
+        args.append({"buffer": buffers.setdefault(base, len(buffers)), "offset": at, "nbytes": nbytes or base.arg.buffer.nbytes, "dtype": u8})
     outs, ins = kernel[6]
     calls.append({"kind": "program", "name": re.sub(r"\x1b\[[0-9;]*m", "", kernel[1]), "lib": programs[image_alloc], "target": dataclasses.asdict(target),
                   "signature": [[f"data{i}", i, u8, [a.get("nbytes", 1)]] for i, a in enumerate(args)], "global_size": grid, "local_size": local,
@@ -172,13 +166,12 @@ def convert(jit, kwargs: dict, frame: dict, target) -> tuple[dict, memoryview]:
     needs_init |= {args[i]["buffer"] for i in ins if "buffer" in args[i] and args[i]["buffer"] not in written}
     written |= {args[i]["buffer"] for i in outs if "buffer" in args[i]}
 
-  assert all(_buffer(b) is not None for b, i in buffers.items() if i in needs_init), "a kernel reads a scratch before any kernel writes it"
   names = list(jit.captured.expected_names)
   inputs = [{"slot": slot, "name": names[slot] if slot < len(names) else None, "dtype": repr(u.dtype), "numel": int(u.max_numel()), "device": u.device}
             for slot, u in enumerate(_prepare_jit_inputs((), kwargs)[0])]
   header = {"input_names": names, "calls": calls, "returns": {"kind": "NoneType", "tensors": []}, "inputs": inputs,
-            "buffers": [{"nbytes": bb.nbytes if (bb := _buffer(b)) else b.max_numel() * b.dtype.itemsize, "device": bb.device if bb else b.device, "dtype": u8}
-                        | ({"init": blob.add(bb.ensure_allocated().as_memoryview())} if i in needs_init else {}) for b, i in buffers.items()],
+            "buffers": [{"nbytes": b.arg.buffer.nbytes, "device": b.arg.buffer.device, "dtype": u8}
+                        | ({"init": blob.add(b.arg.buffer.ensure_allocated().as_memoryview())} if i in needs_init else {}) for b, i in buffers.items()],
             "check": {"inputs": [[path, store(array), device] for path, array, device in frame["inputs"]],
                       "outputs": [store(array) for array in frame["outputs"]], "output_container": frame["output_container"]}}
   with sink:

openpilot/sunnypilot/modeld_v2/helpers.py

@@ -250,12 +250,6 @@ def _allocate_buffers(header: dict, blob: memoryview, device_map: dict | None, s
   return buffers
 
 
-def _fields(what, /, **values):  # a tinygrad dataclass built or replaced with the fields it has today: a dropped field is fine, a new required one is not
-  known = {field.name for field in dataclasses.fields(what)}
-  values = {name: value for name, value in values.items() if name in known}
-  return what(**values) if isinstance(what, type) else dataclasses.replace(what, **values)
-
-
 def _program_uop(call: dict, lib: bytes):
   from tinygrad.helpers import Target
   from tinygrad.uop.ops import KernelInfo, Ops, ProgramInfo, UOp
@@ -263,9 +257,9 @@ def _program_uop(call: dict, lib: bytes):
   for name, compact_slot, dtype, shape in call["signature"]:
     parameter = UOp.param(call["globals"][compact_slot], _dtype(dtype), math.prod(shape))
     image = (shape[0], shape[1]) if len(shape) == 3 and shape[2] == 4 else None
-    parameters.append(parameter.replace(arg=_fields(parameter.arg, name=name, image=image)))
-  info = _fields(ProgramInfo, global_size=tuple(call["global_size"]), local_size=tuple(call["local_size"]), globals=tuple(call["globals"]),
-                 outs=tuple(call["outs"]), ins=tuple(call["ins"]), target=_fields(Target, **call["target"]))
+    parameters.append(parameter.replace(arg=dataclasses.replace(parameter.arg, name=name, image=image)))
+  info = ProgramInfo(global_size=tuple(call["global_size"]), local_size=tuple(call["local_size"]), vars=(), globals=tuple(call["globals"]),
+                     outs=tuple(call["outs"]), ins=tuple(call["ins"]), target=Target(**call["target"]))
   return UOp(Ops.PROGRAM, src=(UOp(Ops.SINK, arg=KernelInfo(name=call["name"])), UOp(Ops.LINEAR, src=tuple(parameters)),
                                UOp(Ops.SOURCE, arg=""), UOp(Ops.BINARY, arg=lib)), arg=info)
 

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -7,7 +7,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 from collections.abc import Callable
-from functools import partial
 import gc
 import os
 os.environ['GMMU'] = '0'
@@ -40,7 +39,6 @@ from openpilot.selfdrive.modeld.modeld import ChestnutGpuState
 
 from openpilot.sunnypilot.modeld_v2.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState, get_curvature_from_output
 from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser
-from openpilot.sunnypilot.modeld_v2.blendful import Blendful
 from openpilot.sunnypilot.modeld_v2.constants import ModelConstants, Plan
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
@@ -230,8 +228,7 @@ class ModelState(ModelStateBase):
     return outputs
 
   def get_action_from_model(self, model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
-                            lat_action_t: float, long_action_t: float, v_ego: float,
-                            blendful: Callable[[float], float] | None = None) -> log.ModelDataV2.Action:
+                            lat_action_t: float, long_action_t: float, v_ego: float) -> log.ModelDataV2.Action:
     if 'action' not in model_output:
       plan = model_output['plan'][0]
       desired_accel = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
@@ -243,8 +240,6 @@ class ModelState(ModelStateBase):
     else:
       desired_accel = model_output['action'][0, 1]
       desired_curvature = model_output['action'][0, 0] / (max(1.0, v_ego))**2
-    if blendful is not None:
-      desired_curvature += blendful(desired_curvature)
 
     stop = v_ego < 0.3 and desired_accel < 0.1
     desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, self.LONG_SMOOTH_SECONDS)
@@ -371,8 +366,6 @@ def main(demo=False):
   DH = DesireHelper()
   meta_constants = load_meta_constants()
   RELC = RoadEdgeLaneChangeController()
-  blendful = Blendful()
-  blendful_enabled = False
 
   while True:
     # Keep receiving frames until we are at least 1 frame ahead of previous extra frame
@@ -416,7 +409,6 @@ def main(demo=False):
       model.lat_delay = get_lat_delay(params, sm["lateralDelay"].lateralDelay)
       model.PLANPLUS_CONTROL = params.get("PlanplusControl", return_default=True)
       camera_offset_helper.set_offset(params.get("CameraOffset", return_default=True))
-      blendful_enabled = params.get_bool("Blendful")
     lat_delay = model.lat_delay + model.LAT_SMOOTH_SECONDS
     if sm.updated["extrinsicsCalibration"] and sm.seen['narrowRoadCameraState'] and sm.seen['deviceState']:
       device_from_calib_euler = np.array(sm["extrinsicsCalibration"].rpyCalib, dtype=np.float32)
@@ -490,9 +482,7 @@ def main(demo=False):
 
       if not sm['carControl'].latActive:
         prev_action.desiredCurvature = sm['controlsState'].curvature
-      blendful_active = blendful_enabled and desire == log.Desire.none and DH.lane_change_state == log.LaneChangeState.off
-      action = model.get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego,
-                                           partial(blendful.update, model_output, v_ego, blendful_active))
+      action = model.get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego)
       prev_action = action
       fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
                      publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
@@ -509,13 +499,6 @@ def main(demo=False):
       modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
       mdv2sp_send.valid = modelv2_send.valid
       mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction
-      mdv2sp_send.modelDataV2SP.blendfulCurvature = blendful.curvature
-      mdv2sp_send.modelDataV2SP.blendfulTrust = float(blendful.trust)
-      mdv2sp_send.modelDataV2SP.modelCurvature = float(blendful.model_curvature)
-      mdv2sp_send.modelDataV2SP.blendfulGoingAround = bool(blendful.going_around)
-      mdv2sp_send.modelDataV2SP.blendfulLeftProb = blendful.l_prob
-      mdv2sp_send.modelDataV2SP.blendfulRightProb = blendful.r_prob
-      mdv2sp_send.modelDataV2SP.blendfulLaneWidth = float(blendful.lane_width.x)
       drivingdata_send.drivingModelData.meta.laneChangeState = DH.lane_change_state
       drivingdata_send.drivingModelData.meta.laneChangeDirection = DH.lane_change_direction
 

openpilot/sunnypilot/modeld_v2/plain_artifact_compiler.py

@@ -23,7 +23,7 @@ def record_kernel_linear():
   recorded, hcq_compile, compile_linear = [], realize.hcq_compile, jit.compile_linear
 
   def recording_compile_linear(*args, **kwargs):
-    with mock.patch.object(realize, "hcq_compile", lambda linear, *a, **k: (linear.src and recorded.append(linear), hcq_compile(linear, *a, **k))[1]):
+    with mock.patch.object(realize, "hcq_compile", lambda linear, *a, **k: (recorded.append(linear), hcq_compile(linear, *a, **k))[1]):
       return compile_linear(*args, **kwargs)
 
   with mock.patch.object(jit, "compile_linear", recording_compile_linear):
@@ -38,18 +38,6 @@ def capture_frame(jit, kwargs: dict, output_container: str | None = None):
   return {"inputs": inputs, "outputs": [tensor.numpy().copy() for tensor in outputs], "output_container": output_container}, result
 
 
-def kernel_signature(ast, elf) -> list:
-  from tinygrad.dtype import AddrSpace
-  if hasattr(ast, "kernel_params"):
-    if any(p.arg.addrspace is AddrSpace.ALU for p in ast.kernel_params):
-      raise ValueError(f"{elf.name} takes a variable, which plain artifacts don't support")
-    compact = {slot: i for i, slot in enumerate(ast.arg.globals)}
-    return [[p.arg.name, compact[p.arg.slot], repr(p.dtype), [int(x) for x in p._shape]] for p in ast.kernel_params]
-  if not all(isinstance(slot, int) for _, slot, _, _ in elf.signature):
-    raise ValueError("TinyELF.signature lost its slots and this tinygrad has no UOp.kernel_params: update kernel_signature")
-  return [[name, slot, repr(dtype), [int(x) for x in shape]] for name, slot, dtype, shape in elf.signature]
-
-
 def serialize_jit(jit, kernel_linear, check: dict | None = None) -> tuple[dict, memoryview]:
   from tinygrad.uop.ops import Ops
   sink = tempfile.TemporaryFile(dir=".")
@@ -63,12 +51,6 @@ def serialize_jit(jit, kernel_linear, check: dict | None = None) -> tuple[dict,
     if uop.op is Ops.PARAM:
       input_specs.setdefault(uop.arg.slot, {"dtype": repr(uop.dtype), "numel": int(uop.max_numel()), "device": uop.device})
       return {"input": uop.arg.slot}
-    root, offset = uop, 0
-    while root.op in (Ops.AFTER, Ops.BITCAST, Ops.SHRINK):
-      offset, root = offset + (root.src[1].arg * root.src[0].dtype.itemsize if root.op is Ops.SHRINK else 0), root.src[0]
-    if root.op is Ops.ALLOC:
-      base = bases.setdefault(id(root), (len(bases), root, root.dtype))
-      return {"buffer": base[0], "offset": offset, "nbytes": int(uop.max_numel()) * uop.dtype.itemsize, "dtype": repr(uop.dtype)}
     buffer = uop.buffer
     root = buffer._base if buffer._base is not None else buffer
     base = bases.setdefault(id(root), (len(bases), root, uop.base.dtype if buffer._base is None else uop.dtype))
@@ -81,7 +63,7 @@ def serialize_jit(jit, kernel_linear, check: dict | None = None) -> tuple[dict,
       elf, info = ast.to_elf(), ast.arg
       global_size, local_size = info.launch_dims({})
       calls.append({"kind": "program", "name": elf.name, "lib": blob.add(bytes(elf.lib)), "target": dataclasses.asdict(elf.target),
-                    "signature": kernel_signature(ast, elf),
+                    "signature": [[name, slot, repr(dtype), [int(x) for x in shape]] for name, slot, dtype, shape in elf.signature],
                     "global_size": [int(x) for x in global_size], "local_size": [int(x) for x in local_size],
                     "globals": list(info.globals), "outs": list(info.outs), "ins": list(info.ins), "args": arguments})
       outs, ins = info.outs, info.ins
@@ -95,9 +77,7 @@ def serialize_jit(jit, kernel_linear, check: dict | None = None) -> tuple[dict,
 
   ret = jit.captured.ret
   returns = {"kind": type(ret).__name__, "tensors": [describe(t.uop.base) | {"shape": [int(x) for x in t.shape]} for t in flatten(ret)]}
-  from tinygrad.device import Buffer
-  assert not [base for index, base, _ in bases.values() if index in needs_init and not isinstance(base, Buffer)], "a kernel reads a scratch before writing it"
-  buffers = [{"nbytes": base.nbytes if isinstance(base, Buffer) else int(base.max_numel()) * dtype.itemsize, "device": base.device, "dtype": repr(dtype)}
+  buffers = [{"nbytes": base.nbytes, "device": base.device, "dtype": repr(dtype)}
              | ({"init": blob.add(base.as_memoryview())} if index in needs_init else {}) for index, base, dtype in bases.values()]
   names = list(jit.captured.expected_names)
   if check is not None:

openpilot/sunnypilot/modeld_v2/tests/test_convert_upstream_pkl.py

@@ -1,121 +0,0 @@
-"""
-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 os
-import re
-import sys
-import tempfile
-import unittest
-from unittest import mock
-import numpy as np
-
-os.environ.setdefault("CHECK_OOB", "0")
-os.environ.setdefault("GMMU", "0")
-if sys.platform == "darwin" and os.environ.get("DEV", "").startswith("MOCKUSB"):  # the mock's VRAM is a memfd, which macOS lacks: an unlinked file is the same
-  from tinygrad.runtime.autogen import libc
-
-  def _memfd_create(name, flags):
-    fd, path = tempfile.mkstemp()
-    os.unlink(path)
-    return fd
-  libc.memfd_create = _memfd_create
-
-from openpilot.common.test import OpenpilotTestCase
-from openpilot.sunnypilot.modeld_v2 import convert_upstream_pkl
-from openpilot.sunnypilot.modeld_v2.convert_upstream_pkl import convert
-from openpilot.sunnypilot.modeld_v2.helpers import PlainJit, load_model
-from openpilot.sunnypilot.modeld_v2.plain_artifact_compiler import capture_frame
-
-IMG, DESIRE, STATE, OUT = (6, 8, 8), (8,), (4, 16), (1, 32)
-
-
-class TestConvertUpstreamPkl(OpenpilotTestCase):
-  @classmethod
-  def setUpClass(cls):
-    super().setUpClass()
-    if not os.environ.get("TINYGRAD_TESTS"):
-      raise unittest.SkipTest("TINYGRAD_TESTS not set. skipping converter test")
-    from tinygrad import Device, Tensor
-    assert Device.DEFAULT == "AMD" and getattr(Device["AMD"], "is_usb", False), f"{Device.DEFAULT} is not an AMD device behind the USB bridge"
-    assert (Tensor([1, 2, 3]) * 2).tolist() == [2, 4, 6]
-    cls.tmp = tempfile.TemporaryDirectory()
-    cls.rng = np.random.default_rng(0)
-    cls.weights = Tensor(cls.rng.standard_normal((16, 32)).astype(np.float32)).realize()
-    cls.bias = Tensor(cls.rng.standard_normal((1, 32)).astype(np.float32)).realize()
-    cls.input_specs = {"input_imgs": (IMG, "uint8", Device.DEFAULT), "desire": (DESIRE, "float32", Device.DEFAULT),
-                       "features": (STATE, "float32", Device.DEFAULT)}
-    cls.output_specs = {"outputs": (OUT, "float32", Device.DEFAULT), "next_features": (STATE, "float32", Device.DEFAULT)}
-
-  @classmethod
-  def tearDownClass(cls):
-    cls.tmp.cleanup()
-    super().tearDownClass()
-
-  def model(self, input_imgs, desire, features, output_buffers):
-    from tinygrad import Tensor
-    feat = features.mean(axis=0).reshape(1, 16) + input_imgs.float().mean() + desire.sum()
-    Tensor.realize(output_buffers["outputs"].assign((feat @ self.weights + self.bias).relu()),
-                   output_buffers["next_features"].assign(features[1:].cat(feat * 2)))
-
-  def inputs(self, seed: int) -> dict:
-    from examples.openpilot.helpers import allocate_inputs
-    rng = np.random.default_rng(seed)
-
-    def initialize(arrays: dict):
-      for value in arrays.values():
-        value[...] = rng.standard_normal(value.shape) if value.dtype.kind == "f" else rng.integers(0, 256, value.shape)
-    return allocate_inputs(self.input_specs, initialize) | {"output_buffers": allocate_inputs(self.output_specs)}
-
-  def jit(self):
-    from tinygrad import TinyJit
-    run = TinyJit(self.model, prune=True)
-    kwargs = self.inputs(1)
-    run(**kwargs)
-    run(**kwargs)
-    return run
-
-  def assert_matches_eager(self, plain, seed: int):
-    fresh, expected = self.inputs(seed), self.inputs(seed)
-    plain(**fresh)
-    self.model(**expected)
-    for name in self.output_specs:
-      assert np.array_equal(fresh["output_buffers"][name].numpy(), expected["output_buffers"][name].numpy()), name
-
-  def convert(self, jit) -> tuple[dict, memoryview]:
-    from tinygrad import Device
-    kwargs = self.inputs(2)
-    frame, _ = capture_frame(jit, kwargs, "output_buffers")
-    header, blob = convert(jit, kwargs, frame, Device[Device.DEFAULT].renderer.target)
-    kernels = jit.captured._linear.src[0].without_after.arg.aux.kernels
-    assert [call["name"] for call in header["calls"]] == [re.sub(r"\x1b\[[0-9;]*m", "", kernel[1]) for kernel in kernels]
-    assert all(re.fullmatch(r"\w+", call["name"]) for call in header["calls"]), "kernel names: failure point 1"
-    assert [(tuple(call["outs"]), tuple(call["ins"])) for call in header["calls"]] == [tuple(kernel[6]) for kernel in kernels], "outs/ins: failure point 1"
-    bound = {argument["input"] for call in header["calls"] for argument in call["args"] if "input" in argument}
-    assert bound == {spec["slot"] for spec in header["inputs"]}, f"input slots bound by pokes {sorted(bound)}: failure point 4"
-    assert [spec["name"] for spec in header["inputs"]][:len(self.input_specs)] == sorted(self.input_specs)
-    return header, blob
-
-  def test_converted_jit_matches_eager(self):  # the submit decoded into plain calls, replayed against its own frame
-    jit = self.jit()
-    plain = PlainJit(*self.convert(jit))
-    self.assert_matches_eager(plain, 3)
-    self.assert_matches_eager(plain, 4)
-
-  def test_pickle_round_trip_through_main(self):
-    from examples.openpilot.helpers import dump_pickle, load_pickle
-    from tinygrad.engine.realize import lower_and_compile
-    pkl, out = os.path.join(self.tmp.name, "tiny_tinygrad.pkl"), os.path.join(self.tmp.name, "tiny_plain.pkl")
-    dump_pickle({"run": self.jit(), "input_specs": self.input_specs, "output_specs": self.output_specs, "metadata": {"name": "tiny"}}, pkl)
-
-    artifact = load_pickle(pkl, out_of_band=True)
-    artifact["run"].captured._linear = lower_and_compile(artifact["run"].captured._linear)
-    self.assert_matches_eager(PlainJit(*self.convert(artifact["run"])), 5)
-
-    with mock.patch.object(sys, "argv", ["convert_upstream_pkl", pkl, out]):
-      convert_upstream_pkl.main()
-    bundle = load_model(out, cache=False, verify=True)
-    assert bundle["metadata"] == {"name": "tiny"} and bundle["input_specs"] == self.input_specs and bundle["output_specs"] == self.output_specs
-    self.assert_matches_eager(bundle["run"], 6)

openpilot/sunnypilot/modeld_v2/tests/test_plain_artifact.py

@@ -1,162 +0,0 @@
-"""
-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 dataclasses
-import functools
-import inspect
-import os
-import sys
-import tempfile
-from unittest import mock
-import numpy as np
-import unittest
-
-import openpilot.sunnypilot.modeld_v2.helpers as helpers
-from openpilot.common.test import OpenpilotTestCase
-from openpilot.sunnypilot.modeld_v2 import compile_warp
-from openpilot.sunnypilot.modeld_v2.frame_fusion import frame_part, pack_layout
-from openpilot.sunnypilot.modeld_v2.helpers import PlainJit, cache_is_fresh, load_model, read_parts, write_bundle
-from openpilot.sunnypilot.modeld_v2.plain_artifact_compiler import capture_frame, record_kernel_linear, serialize_jit
-
-CAM_W, CAM_H, MODEL_W, MODEL_H = 32, 16, 16, 8
-FRAME = f"{CAM_W},{CAM_H},{CAM_W},{CAM_H},{CAM_H // 2},{CAM_W * CAM_H * 3 // 2}"
-FRAME_SIZE = CAM_W * CAM_H * 3 // 2
-HOST = {"desire": (8,), "traffic_convention": (2,)}
-STATE, OUT = (4, 16), (1, 32)
-
-
-def compile_jit(fn, inputs: dict, output_container: str | None = None) -> tuple[dict, memoryview]:
-  from tinygrad import TinyJit
-  run = TinyJit(fn, prune=True)
-  with record_kernel_linear() as recorded:
-    run(**inputs)
-    run(**inputs)
-    check, _ = capture_frame(run, inputs, output_container)
-  assert len(recorded) == 1, f"expected one captured kernel list, got {len(recorded)}"
-  return serialize_jit(run, recorded[0], check)
-
-
-class TestPlainArtifact(OpenpilotTestCase):
-  @classmethod
-  def setUpClass(cls):
-    super().setUpClass()
-    if not os.environ.get("TINYGRAD_TESTS"):
-      raise unittest.SkipTest("TINYGRAD_TESTS env var not set, skipping artifact test.")
-    from tinygrad import Tensor
-    cls.tmp = tempfile.TemporaryDirectory()
-    cls.rng = np.random.default_rng(0)
-    cls.weights = Tensor(cls.rng.standard_normal((16, 32)).astype(np.float32)).realize()
-    cls.bias = Tensor(cls.rng.standard_normal((1, 32)).astype(np.float32)).realize()
-
-  @classmethod
-  def tearDownClass(cls):
-    cls.tmp.cleanup()
-    super().tearDownClass()
-
-  def model(self, x, h):  # a two input, one weight model: the shape of a policy without the size
-    return ((x @ self.weights + self.bias).relu() + h.mean()).contiguous()
-
-  def tensors(self, seed: int):
-    from tinygrad import Tensor
-    rng = np.random.default_rng(seed)
-    return {"x": Tensor(rng.standard_normal((1, 16)).astype(np.float32)).realize(), "h": Tensor(rng.standard_normal((4, 16)).astype(np.float32)).realize()}
-
-  def artifact(self, name: str = "model") -> str:
-    path = os.path.join(self.tmp.name, f"{name}_tinygrad.pkl")
-    if not os.path.exists(path):
-      write_bundle(path, {"run": compile_jit(self.model, self.tensors(1))}, {"metadata": {"name": name}})
-    return path
-
-  def test_round_trip_matches_jit(self):  # compiler -> bundle -> PlainJit, replay verified, then fresh inputs bit exact with eager tinygrad
-    bundle = load_model(self.artifact(), cache=False, verify=True)
-    assert bundle["metadata"] == {"name": "model"} and isinstance(bundle["run"], PlainJit)
-    fresh = self.tensors(2)
-    assert np.array_equal(bundle["run"](**fresh).numpy(), self.model(**fresh).numpy())
-
-  def test_replay_check_catches_a_changed_kernel(self):
-    parts, extras = read_parts(self.artifact())
-    header, blob = parts["run"]
-    poisoned = dict(header, check=dict(header["check"], outputs=[header["check"]["inputs"][0][1]]))
-    with self.assertRaises(helpers.ArtifactMismatch):
-      PlainJit(poisoned, blob)
-
-  def test_cache_round_trip_holds_no_weights_and_is_rebuilt_when_stale(self):
-    path = self.artifact("cached")
-    expected = load_model(path, cache=False, verify=False)["run"](**self.tensors(3)).numpy()
-    load_model(path)
-    assert cache_is_fresh(path)
-    with open(f"{path}.cache", "rb") as file:
-      cache = file.read()
-    assert self.weights.numpy().tobytes() not in cache and len(cache) < os.path.getsize(path)
-    with mock.patch.object(helpers, "PlainJit", side_effect=AssertionError("a fresh cache must not rebuild")):
-      cached = load_model(path)
-    assert np.array_equal(cached["run"](**self.tensors(3)).numpy(), expected)
-    with mock.patch.object(helpers, "tinygrad_identity", return_value="another tinygrad"):
-      assert not cache_is_fresh(path)
-      assert isinstance(load_model(path)["run"], PlainJit)
-      assert cache_is_fresh(path)
-    assert not cache_is_fresh(path)  # keyed on the tinygrad that wrote it, so the next boot after a bump rebuilds once
-    os.utime(path)
-    with mock.patch.object(helpers, "tinygrad_identity", return_value="another tinygrad"):
-      assert not cache_is_fresh(path)
-
-  def test_fused_with_warp_matches_warp_then_model(self):
-    from tinygrad import Tensor
-    warp_path = os.path.join(self.tmp.name, "warp_tinygrad.pkl")
-    with mock.patch.object(sys, "argv", ["compile_warp", "--frame", FRAME, "--warp-to", f"{MODEL_W}x{MODEL_H}", "--output", warp_path]):
-      compile_warp.main()
-    warp = load_model(warp_path, cache=False)["run"]
-
-    def native(new_img, desire, traffic_convention, state_feat_q, output_buffers):
-      feat = state_feat_q.mean(axis=0).reshape(1, 16) + new_img.float().mean() + desire.sum() + traffic_convention[0]
-      Tensor.realize(output_buffers["outputs"].assign((feat @ self.weights + self.bias).relu()),
-                     output_buffers["next_state_feat_q"].assign(state_feat_q[1:].cat(feat * 2)))
-
-    def model_inputs(new_img):
-      return {"new_img": new_img} | {name: Tensor(np.ones(shape, np.float32) * 0.5).realize() for name, shape in HOST.items()} | \
-             {"state_feat_q": Tensor(self.rng.standard_normal(STATE).astype(np.float32)).realize(),
-              "output_buffers": {"outputs": Tensor.zeros(OUT).contiguous().realize(), "next_state_feat_q": Tensor.zeros(STATE).contiguous().realize()}}
-    frames = Tensor(self.rng.integers(0, 256, (2, FRAME_SIZE), dtype=np.uint8)).realize()
-    tfm = Tensor(np.tile(np.eye(3, dtype=np.float32), (2, 1, 1))).realize()
-    path = os.path.join(self.tmp.name, "native_tinygrad.pkl")
-    part = compile_jit(native, model_inputs(warp(input_frame=frames, M_inv=tfm).realize()), "output_buffers")
-    extras = {"input_specs": {"new_img": ((2, 6, MODEL_H // 2, MODEL_W // 2), "|u1", "CPU"), "state_feat_q": (STATE, "<f4", "CPU")}
-              | {name: (shape, "<f4", "CPU") for name, shape in HOST.items()},
-              "output_specs": {"outputs": (OUT, "<f4", "CPU"), "next_state_feat_q": (STATE, "<f4", "CPU")},
-              "metadata": {"output_shapes": {"outputs": OUT, "next_state_feat_q": STATE}}}
-    write_bundle(path, {"run": part}, extras)
-
-    fused = load_model(path, cache=False, extend=functools.partial(frame_part, warp_path))
-    assert fused["run"].input_names == ["host", "state_feat_q"]
-    offsets, packed = pack_layout(HOST, FRAME_SIZE)
-    host = np.zeros(packed, np.uint8)
-    inputs = model_inputs(None)
-    for name, shape in ({"tfm": (2, 3, 3)} | HOST).items():
-      np.ndarray(shape, np.float32, host, offsets[name])[...] = tfm.numpy() if name == "tfm" else inputs[name].numpy()
-    host[offsets["frames"]:] = frames.numpy().ravel()
-    fused["run"](host=Tensor(host, device="NPY"), state_feat_q=inputs["state_feat_q"], output_buffers=inputs["output_buffers"])
-
-    expected = model_inputs(warp(input_frame=frames, M_inv=tfm).realize()) | {k: inputs[k] for k in ("state_feat_q",)}
-    native(**expected)
-    for name in ("outputs", "next_state_feat_q"):
-      assert np.array_equal(inputs["output_buffers"][name].numpy(), expected["output_buffers"][name].numpy()), name
-
-  def test_tinygrad_api_the_loader_reads(self):
-    import tinygrad.engine.jit as jit
-    from tinygrad.device import Buffer, TinyELF
-    from tinygrad.helpers import Target
-    from tinygrad.uop.ops import ParamArg, ProgramInfo, UOp
-    fields = {cls: {f.name for f in dataclasses.fields(cls)} for cls in (ProgramInfo, ParamArg, Target)}
-    assert fields[ProgramInfo] >= {"global_size", "local_size", "globals", "outs", "ins", "target"}, "ProgramInfo changed: update helpers._program_uop"
-    assert fields[ParamArg] >= {"slot", "dtype", "name", "image"}, "ParamArg changed: update helpers._program_uop"
-    assert fields[Target] >= {"device", "renderer", "arch"}, "Target changed: update the 'target' key in plain_artifact_compiler.serialize_jit"
-    assert "signature" in getattr(TinyELF, "__annotations__", {}) or hasattr(UOp, "kernel_params"), \
-      "kernel signatures moved: update plain_artifact_compiler.serialize_jit"
-    assert callable(getattr(UOp, "param", None)) and callable(getattr(UOp, "from_buffer", None)), "UOp.param/from_buffer: update helpers.PlainJit"
-    assert callable(getattr(UOp, "store_call", None)) and callable(getattr(UOp, "call", None)), "UOp.call/store_call: update helpers.PlainJit"
-    assert callable(getattr(Buffer, "view", None)) and callable(getattr(Buffer, "ensure_allocated", None)), "Buffer.view: update helpers._upload"
-    assert callable(getattr(jit, "compile_linear", None)) and callable(getattr(jit, "_prepare_jit_inputs", None)), "engine.jit: update helpers._Jit"
-    assert "expected_names" in inspect.signature(jit.CapturedJit).parameters, "CapturedJit: update helpers.PlainJit and plain_artifact_compiler.serialize_jit"

openpilot/sunnypilot/models/manager.py

@@ -34,8 +34,6 @@ TRANSIENT_HTTP_STATUS = frozenset({408, 429, 500, 502, 503, 504})
 REPORT_INTERVAL = 0.5  # seconds between progress publications
 # byte ranges count encoded bytes but iter_content yields decoded ones, so ask for an unencoded body
 NO_COMPRESSION = {"Accept-Encoding": "identity"}
-SPEED_SMOOTHING = 0.7  # weight of the previous speed sample in the published speed
-SPEED_FLOOR = 0.05 * 1024 ** 2  # bytes/s; anything slower would only display as 0.0 MB/s, so it reads as no rate
 
 
 class DownloadCancelled(Exception):
@@ -156,7 +154,6 @@ class ModelManagerSP:
         artifact.downloadProgress.status = source_artifact.downloadProgress.status
         artifact.downloadProgress.progress = source_artifact.downloadProgress.progress
         artifact.downloadProgress.eta = source_artifact.downloadProgress.eta
-        artifact.downloadProgress.speed = source_artifact.downloadProgress.speed
 
   def _calculate_eta(self, filename: str, progress: float) -> int:
     if filename not in self._download_start_times or progress <= 0:
@@ -170,43 +167,28 @@ class ModelManagerSP:
     eta = total_estimated_time - elapsed_time
     return max(1, int(eta))  # Return at least 1 second if download is ongoing
 
-  def _set_progress(self, artifact, status, progress: float, eta: int = 0, speed: float = 0.0) -> None:
+  def _set_progress(self, artifact, status, progress: float, eta: int = 0) -> None:
     artifact.downloadProgress.status = status
     artifact.downloadProgress.progress = progress
     artifact.downloadProgress.eta = eta
-    artifact.downloadProgress.speed = speed
     self._sync_artifact_progress(artifact)
     self._report_status()
 
-  def _publish_progress(self, artifact, done_bytes: int, total: int, speed: float) -> None:
+  def _publish_progress(self, artifact, done_bytes: int, total: int) -> None:
     # held at 99 until the file passes its hash check
     progress = min(99.0, done_bytes / total * 100) if total > 0 else 0.0
-    if speed > 0 and total > 0:
-      eta = max(1, int((total - done_bytes) / speed))
-    else:
-      eta = self._calculate_eta(artifact.fileName, progress)
-    self._set_progress(artifact, custom.ModelManagerSP.DownloadStatus.downloading, progress, eta, speed)
+    eta = self._calculate_eta(artifact.fileName, progress)
+    self._set_progress(artifact, custom.ModelManagerSP.DownloadStatus.downloading, progress, eta)
 
-  async def _report_until_done(self, tasks: list[asyncio.Future], artifact, progress: _Progress, total: int,
-                               started: tuple[int, float]) -> None:
+  async def _report_until_done(self, tasks: list[asyncio.Future], artifact, progress: _Progress, total: int) -> None:
     pending = set(tasks)
-    last_bytes, last_time = started
-    speed = 0.0
     while pending:
       done, pending = await asyncio.wait(pending, timeout=REPORT_INTERVAL)
       for task in done:
         task.result()  # re-raises a worker failure
       if self._download_interrupted():
         raise DownloadCancelled("Download cancelled")
-      now = time.monotonic()
-      done_bytes = progress.snapshot()
-      if (dt := now - last_time) > 0:
-        instant = max(0.0, (done_bytes - last_bytes) / dt)  # bytes/s this tick; clamped since retried pieces can lower the byte count
-        speed = instant if speed == 0 else SPEED_SMOOTHING * speed + (1 - SPEED_SMOOTHING) * instant
-        if speed < SPEED_FLOOR:  # a stall decays toward 0 without reaching it, and a near-0 speed overflows the eta
-          speed = 0.0
-      last_time, last_bytes = now, done_bytes
-      self._publish_progress(artifact, done_bytes, total, speed)
+      self._publish_progress(artifact, progress.snapshot(), total)
 
   async def _download_file(self, url: str, path: str, artifact) -> None:
     self._download_start_times[artifact.fileName] = time.monotonic()
@@ -217,13 +199,12 @@ class ModelManagerSP:
     await loop.run_in_executor(None, _prepare_target, path, total)
     progress = _Progress()
     cancel = threading.Event()
-    started = progress.snapshot(), time.monotonic()  # before any worker can land bytes
 
     with ThreadPoolExecutor(max_workers=MAX_CONCURRENT_PIECES) as pool:
       tasks = [loop.run_in_executor(pool, _fetch_piece, url, path, i, start, end, progress, cancel, self._block_size)
                for i, (start, end) in enumerate(pieces)]
       try:
-        await self._report_until_done(tasks, artifact, progress, total, started)
+        await self._report_until_done(tasks, artifact, progress, total)
       except BaseException as e:
         cancel.set()
         if isinstance(e, DownloadCancelled):
@@ -266,7 +247,6 @@ class ModelManagerSP:
       self._download_start_times.pop(artifact.fileName, None)
       artifact.downloadProgress.status = status.failed
       artifact.downloadProgress.eta = 0
-      artifact.downloadProgress.speed = 0
       self._sync_artifact_progress(artifact)
       if self.selected_bundle:
         self.selected_bundle.status = status.failed
@@ -279,7 +259,6 @@ class ModelManagerSP:
         os.remove(full_path)
       artifact.downloadProgress.status = status.failed
       artifact.downloadProgress.eta = 0
-      artifact.downloadProgress.speed = 0
       self._sync_artifact_progress(artifact)
       if self.selected_bundle:
         self.selected_bundle.status = status.failed
@@ -322,7 +301,6 @@ class ModelManagerSP:
           artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
           artifact.downloadProgress.progress = 100
           artifact.downloadProgress.eta = 0
-          artifact.downloadProgress.speed = 0
         else:
           seen_artifacts.add(artifact.fileName)
           await self._process_artifact(artifact, destination_path)

openpilot/sunnypilot/models/tests/test_manager_download.py

@@ -6,7 +6,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 import asyncio
-import concurrent.futures
 import contextlib
 import dataclasses
 import hashlib
@@ -130,13 +129,6 @@ def threaded_server(handler):
     thread.join()
 
 
-class InlineExecutor(concurrent.futures.Executor):
-  def submit(self, fn, /, *args, **kwargs):
-    future: concurrent.futures.Future = concurrent.futures.Future()
-    future.set_result(fn(*args, **kwargs))  # the piece lands before the reporter starts
-    return future
-
-
 class ManagerDownloadTestBase(OpenpilotTestCase):
   def setUp(self):
     super().setUp()
@@ -147,7 +139,7 @@ class ManagerDownloadTestBase(OpenpilotTestCase):
     self.addCleanup(self._tmp.cleanup)
     self.dest = self._tmp.name
 
-    for name, value in (('PIECE_SIZE', PIECE_SIZE), ('REPORT_INTERVAL', 0.05), ('PIECE_BACKOFF', 0.01), ('SPEED_FLOOR', 100)):
+    for name, value in (('PIECE_SIZE', PIECE_SIZE), ('REPORT_INTERVAL', 0.05), ('PIECE_BACKOFF', 0.01)):
       self.enterContext(mock.patch.object(manager_module, name, value))
 
     self.reported: list[float] = []
@@ -237,31 +229,6 @@ class TestManagerDownload(ManagerDownloadTestBase):
     assert self.reported and self.reported == sorted(self.reported), "progress only rises"
     assert max(self.reported) <= 99.0, "progress holds below 100 until the hash check"
     assert artifact.fileName not in self.manager._download_start_times
-    assert artifact.downloadProgress.speed > 0, "a running download shows its speed"
-
-  def test_speed_counts_bytes_landed_before_the_first_report(self):
-    self.enterContext(mock.patch.object(manager_module, 'ThreadPoolExecutor', lambda max_workers: InlineExecutor()))
-    artifact = self.download_file()
-    assert artifact.downloadProgress.speed > 0
-
-  def test_eta_comes_from_speed(self):
-    artifact = self.make_artifact()
-    self.manager._publish_progress(artifact, done_bytes=400, total=1000, speed=100)
-    assert artifact.downloadProgress.eta == 6
-
-  def test_stalled_speed_drops_to_zero(self):
-    stall_event = self.stall(0)
-    seen_speed = threading.Event()
-
-    def release_once_zero(*args):
-      if self.artifact.downloadProgress.speed > 0:
-        seen_speed.set()
-      elif seen_speed.is_set():
-        stall_event.set()
-    self.pm.send.side_effect = release_once_zero
-    self.download_file()
-    assert self.server.stall_released_by_event is True, "the stalled speed never reached exactly 0"
-    assert self.artifact.downloadProgress.speed > 0, "the speed must recover once data flows again"
 
   def test_pieces_download_in_parallel(self):
     stall_event = self.stall(0)
@@ -336,8 +303,7 @@ class TestProcessArtifact(ManagerDownloadTestBase):
         self.download_bundle(source)
         assert ACTIVE_BUNDLE_KEYS[source] in store and ACTIVE_BUNDLE_KEYS[other] not in store
         assert self.manager.selected_bundle.status == ds.downloaded
-        assert self.artifact.downloadProgress.progress == 100
-        assert self.artifact.downloadProgress.eta == 0 and self.artifact.downloadProgress.speed == 0
+        assert self.artifact.downloadProgress.progress == 100 and self.artifact.downloadProgress.eta == 0
         assert self.read_path() == WHOLE_BODY
         os.remove(self.path)
 

openpilot/sunnypilot/selfdrive/controls/lib/accel_boost_ext.py

@@ -1,72 +0,0 @@
-"""
-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.
-"""
-from openpilot.cereal import log
-from openpilot.common.params import Params
-from openpilot.common.realtime import DT_MDL
-from openpilot.selfdrive.controls.lib.accel_boost import AccelBoost, ACCEL_BOOST_MAX
-from openpilot.sunnypilot import PARAMS_UPDATE_PERIOD
-
-SCALE_RELAXED = 0.3
-SCALE_STANDARD = 0.5
-SCALE_AGGRESSIVE = 0.7
-
-
-def get_accel_boost_scale(personality) -> float:
-  if personality == log.LongitudinalPersonality.aggressive:
-    return SCALE_AGGRESSIVE
-  if personality == log.LongitudinalPersonality.relaxed:
-    return SCALE_RELAXED
-  return SCALE_STANDARD
-
-
-def get_accel_boost_max(personality, extra_enabled: bool = True) -> float:
-  if not extra_enabled:
-    return ACCEL_BOOST_MAX
-  return ACCEL_BOOST_MAX * (1.0 + get_accel_boost_scale(personality))
-
-
-class AccelBoostExt:
-  def __init__(self, stock: AccelBoost | None = None):
-    self._stock = stock if stock is not None else AccelBoost()
-    self._extra = AccelBoost()
-    self.params = Params()
-    self.frame = -1
-    self.enabled = self.params.get_bool("FollowDistanceAccelBoost")
-
-    self.stock_boost = 0.0
-    self.extra_boost = 0.0
-
-  @property
-  def total_boost(self) -> float:
-    return self._stock.total_boost + self.extra_boost
-
-  def _update_params(self) -> None:
-    if self.frame % int(PARAMS_UPDATE_PERIOD / DT_MDL) == 0:
-      self.enabled = self.params.get_bool("FollowDistanceAccelBoost")
-
-  def update(self, sm, output_a_target_e2e, output_a_target_mpc, a_cruise):
-    self.frame += 1
-    self._update_params()
-
-    if self.enabled and sm['selfdriveState'].enabled:
-      scale = get_accel_boost_scale(sm['selfdriveState'].personality)
-      extra_output = self._extra.update(sm, output_a_target_e2e, output_a_target_mpc, a_cruise)
-      self.extra_boost = (extra_output - output_a_target_e2e) * scale
-    else:
-      self.extra_boost = 0.0
-      self._extra.total_boost = 0.0
-      self._extra.boost_this_override = 0.0
-      self._extra.boost_eligible = False
-
-      output = self._stock.update(sm, output_a_target_e2e, output_a_target_mpc, a_cruise)
-      self.stock_boost = output - output_a_target_e2e
-      return output
-
-    target = output_a_target_e2e + self.extra_boost
-    output = self._stock.update(sm, target, output_a_target_mpc, a_cruise)
-    self.stock_boost = output - target
-    return output

openpilot/sunnypilot/selfdrive/controls/lib/longitudinal_planner.py

@@ -9,7 +9,6 @@ from openpilot.cereal import messaging, custom
 from opendbc.car import structs
 from openpilot.common.constants import CV
 from openpilot.selfdrive.car.cruise import V_CRUISE_MAX
-from openpilot.sunnypilot.selfdrive.controls.lib.accel_boost_ext import AccelBoostExt
 from openpilot.sunnypilot.selfdrive.controls.lib.e2e_alerts_helper import E2EAlertsHelper
 from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.smart_cruise_control import SmartCruiseControl
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.speed_limit_assist import SpeedLimitAssist
@@ -33,16 +32,6 @@ class LongitudinalPlannerSP:
 
     self.output_v_target = 0.
     self.output_a_target = 0.
-    self._accel_boost: AccelBoostExt | None = None
-
-  @property
-  def accel_boost(self) -> AccelBoostExt:
-    assert self._accel_boost is not None
-    return self._accel_boost
-
-  @accel_boost.setter
-  def accel_boost(self, controller) -> None:
-    self._accel_boost = AccelBoostExt(controller)
 
   def update_targets(self, sm: messaging.SubMaster, v_ego: float, a_ego: float, v_cruise: float) -> tuple[float, float]:
     CS = sm['carState']

openpilot/sunnypilot/selfdrive/controls/lib/tests/test_accel_boost_ext.py

@@ -1,237 +0,0 @@
-from types import SimpleNamespace
-from unittest import mock
-
-from openpilot.cereal import log
-from openpilot.common.realtime import DT_MDL
-from openpilot.common.test import OpenpilotTestCase
-from openpilot.selfdrive.controls.lib.accel_boost import AccelBoost, ACCEL_BOOST_MAX, ACCEL_BOOST_PER_OVERRIDE
-from openpilot.sunnypilot import PARAMS_UPDATE_PERIOD
-from openpilot.sunnypilot.selfdrive.controls.lib.accel_boost_ext import AccelBoostExt, get_accel_boost_max
-from openpilot.sunnypilot.selfdrive.controls.lib.longitudinal_planner import LongitudinalPlannerSP
-
-E2E_ACCEL = 0.5
-PLANNER_ACCEL = 2.0
-STEPS_PER_OVERRIDE = round(ACCEL_BOOST_PER_OVERRIDE / (0.025 * DT_MDL))
-EXPECTED_SCALE = {
-  log.LongitudinalPersonality.relaxed: 0.3,
-  log.LongitudinalPersonality.standard: 0.5,
-  log.LongitudinalPersonality.aggressive: 0.7,
-}
-
-
-class FakeParams:
-  def __init__(self, enabled=True):
-    self.enabled = enabled
-
-  def get_bool(self, key):
-    return self.enabled
-
-
-class FakeSubMaster:
-  def __init__(self, enabled=True, gas_pressed=False, v_ego=15.0,
-               personality=log.LongitudinalPersonality.standard):
-    self._messages = {
-      'selfdriveState': SimpleNamespace(enabled=enabled, personality=personality),
-      'carState': SimpleNamespace(gasPressed=gas_pressed, vEgo=v_ego),
-    }
-
-  def __getitem__(self, key):
-    return self._messages[key]
-
-
-def update(boost, sm, e2e=E2E_ACCEL):
-  return boost.update(sm, e2e, PLANNER_ACCEL, PLANNER_ACCEL)
-
-
-class PlannerStub(LongitudinalPlannerSP):
-  def __init__(self):
-    self._accel_boost = None
-
-
-class TestAccelBoostExt(OpenpilotTestCase):
-  def setUp(self):
-    super().setUp()
-    self.params = FakeParams()
-    patcher = mock.patch("openpilot.sunnypilot.selfdrive.controls.lib.accel_boost_ext.Params", return_value=self.params)
-    patcher.start()
-    self.addCleanup(patcher.stop)
-
-  def _boost_to_max(self, personality):
-    boost = AccelBoostExt()
-    sm = FakeSubMaster(personality=personality)
-    # Eligibility is latched on a frame without gas, then applied on the next override.
-    update(boost, sm)
-    for _ in range(10):
-      sm['carState'].gasPressed = True
-      for _ in range(STEPS_PER_OVERRIDE):
-        update(boost, sm)
-      sm['carState'].gasPressed = False
-      update(boost, sm)
-    return boost, sm
-
-  def _applied_extra(self, boost, sm):
-    update(boost, sm)
-    return boost.extra_boost
-
-  def test_off_matches_stock_boost_exactly(self):
-    self.params.enabled = False
-    sm = FakeSubMaster()
-    stock = AccelBoost()
-    ext = AccelBoostExt()
-
-    stock_out = update(stock, sm)
-    ext_out = update(ext, sm)
-    self.assertAlmostEqual(ext_out, stock_out)
-
-    for _ in range(10):
-      sm['carState'].gasPressed = True
-      for _ in range(STEPS_PER_OVERRIDE):
-        stock_out = update(stock, sm)
-        ext_out = update(ext, sm)
-        self.assertAlmostEqual(ext_out, stock_out)
-      sm['carState'].gasPressed = False
-      stock_out = update(stock, sm)
-      ext_out = update(ext, sm)
-      self.assertAlmostEqual(ext_out, stock_out)
-
-    self.assertAlmostEqual(ext.total_boost, stock.total_boost)
-    self.assertAlmostEqual(ext.stock_boost, stock_out - E2E_ACCEL)
-    self.assertEqual(ext.extra_boost, 0.0)
-    self.assertEqual(ext._extra.total_boost, 0.0)
-
-  def test_no_boost_without_gas(self):
-    ext = AccelBoostExt()
-    sm = FakeSubMaster(gas_pressed=False)
-    out = update(ext, sm)
-    self.assertEqual(ext.total_boost, 0.0)
-    self.assertEqual(ext._extra.total_boost, 0.0)
-    self.assertAlmostEqual(out, E2E_ACCEL)
-
-  def test_boost_requires_e2e_to_be_limiting(self):
-    ext = AccelBoostExt()
-    sm = FakeSubMaster(gas_pressed=False)
-    ext.update(sm, E2E_ACCEL, E2E_ACCEL, E2E_ACCEL)
-    sm['carState'].gasPressed = True
-    for _ in range(10):
-      ext.update(sm, E2E_ACCEL, E2E_ACCEL, E2E_ACCEL)
-    self.assertEqual(ext.total_boost, 0.0)
-    self.assertEqual(ext.stock_boost, 0.0)
-    self.assertEqual(ext.extra_boost, 0.0)
-    self.assertEqual(ext._extra.total_boost, 0.0)
-
-  def test_extra_layer_per_override_cap(self):
-    ext = AccelBoostExt()
-    sm = FakeSubMaster(gas_pressed=False)
-    update(ext, sm)
-    sm['carState'].gasPressed = True
-    for _ in range(1000):
-      update(ext, sm)
-
-    self.assertAlmostEqual(ext._extra.total_boost, ACCEL_BOOST_PER_OVERRIDE)
-    applied = self._applied_extra(ext, sm)
-    expected = ACCEL_BOOST_PER_OVERRIDE * EXPECTED_SCALE[log.LongitudinalPersonality.standard]
-    self.assertAlmostEqual(applied, expected)
-
-  def test_extra_scales_with_following_distance(self):
-    applied = {}
-    for personality in (log.LongitudinalPersonality.relaxed,
-                        log.LongitudinalPersonality.standard,
-                        log.LongitudinalPersonality.aggressive):
-      ext, sm = self._boost_to_max(personality)
-      sm['carState'].gasPressed = True
-      applied[personality] = self._applied_extra(ext, sm)
-
-    for personality, value in applied.items():
-      self.assertAlmostEqual(value, ACCEL_BOOST_MAX * EXPECTED_SCALE[personality])
-
-    self.assertLess(applied[log.LongitudinalPersonality.relaxed], applied[log.LongitudinalPersonality.standard])
-    self.assertLess(applied[log.LongitudinalPersonality.standard], applied[log.LongitudinalPersonality.aggressive])
-
-  def test_capnp_dynamic_enum_personality_scales(self):
-    # Real SubMaster messages expose personality as a capnp DynamicEnum; equality still matches the enum value.
-    msg = log.SelfdriveState.new_message()
-    msg.personality = log.LongitudinalPersonality.aggressive
-    ext, sm = self._boost_to_max(msg.personality)
-    sm['carState'].gasPressed = True
-    applied = self._applied_extra(ext, sm)
-    self.assertAlmostEqual(applied, ACCEL_BOOST_MAX * EXPECTED_SCALE[log.LongitudinalPersonality.aggressive])
-
-  def test_extra_feeds_into_stock_last(self):
-    ext, sm = self._boost_to_max(log.LongitudinalPersonality.standard)
-    sm['carState'].gasPressed = True
-    inputs = []
-    original_update = AccelBoost.update
-
-    def spy(boost, sm_, e2e, mpc, cruise):
-      inputs.append(e2e)
-      return original_update(boost, sm_, e2e, mpc, cruise)
-
-    with mock.patch.object(AccelBoost, "update", spy):
-      out = update(ext, sm)
-
-    # First call is our extra layer on the raw target, last call is stock on the boosted target.
-    self.assertAlmostEqual(inputs[0], E2E_ACCEL)
-    self.assertAlmostEqual(inputs[-1], E2E_ACCEL + ext.extra_boost)
-    self.assertAlmostEqual(out, inputs[-1] + ext.stock_boost)
-
-  def test_extra_is_added_on_top_of_stock_boost(self):
-    ext, sm = self._boost_to_max(log.LongitudinalPersonality.aggressive)
-    sm['carState'].gasPressed = True
-    out = update(ext, sm)
-    extra = out - E2E_ACCEL - ext.stock_boost
-    self.assertAlmostEqual(ext._stock.total_boost, ACCEL_BOOST_MAX)
-    self.assertAlmostEqual(ext.total_boost, ACCEL_BOOST_MAX + ext.extra_boost)
-    self.assertAlmostEqual(ext.stock_boost, ACCEL_BOOST_MAX)
-    self.assertAlmostEqual(ext.extra_boost, ACCEL_BOOST_MAX * EXPECTED_SCALE[log.LongitudinalPersonality.aggressive])
-    self.assertAlmostEqual(extra, ext.extra_boost)
-
-  def test_sp_planner_wraps_assigned_stock_controller(self):
-    planner = PlannerStub()
-    stock = AccelBoost()
-    planner.accel_boost = stock
-
-    self.assertIsInstance(planner.accel_boost, AccelBoostExt)
-    self.assertIs(planner.accel_boost._stock, stock)
-
-  def test_wraps_existing_stock_controller(self):
-    stock = AccelBoost()
-    sm = FakeSubMaster()
-    update(stock, sm)
-    sm['carState'].gasPressed = True
-    update(stock, sm)
-
-    ext = AccelBoostExt(stock)
-    self.assertIs(ext._stock, stock)
-    self.assertAlmostEqual(ext.total_boost, stock.total_boost)
-
-  def test_boost_bar_max_matches_profile_cap(self):
-    for personality, scale in EXPECTED_SCALE.items():
-      self.assertAlmostEqual(get_accel_boost_max(personality), ACCEL_BOOST_MAX * (1.0 + scale))
-      self.assertAlmostEqual(get_accel_boost_max(personality, extra_enabled=False), ACCEL_BOOST_MAX)
-
-  def test_total_boost_is_stock_accumulated_plus_extra_applied(self):
-    ext, sm = self._boost_to_max(log.LongitudinalPersonality.aggressive)
-    sm['carState'].gasPressed = True
-    update(ext, sm)
-    self.assertAlmostEqual(ext.total_boost, ext._stock.total_boost + ext.extra_boost)
-
-  def test_turning_toggle_off_restores_stock_only(self):
-    ext, sm = self._boost_to_max(log.LongitudinalPersonality.aggressive)
-    self.assertGreater(ext._extra.total_boost, 0.0)
-
-    self.params.enabled = False
-    sm['carState'].gasPressed = True
-    # Params are re-read every PARAMS_UPDATE_PERIOD; the extra layer must clear on the next poll.
-    for _ in range(int(PARAMS_UPDATE_PERIOD / DT_MDL) + 1):
-      out = update(ext, sm)
-
-    self.assertEqual(ext._extra.total_boost, 0.0)
-    self.assertEqual(ext.extra_boost, 0.0)
-    self.assertAlmostEqual(out, E2E_ACCEL + ext.stock_boost)
-
-  def test_relaxing_following_distance_applies_immediately(self):
-    ext, sm = self._boost_to_max(log.LongitudinalPersonality.aggressive)
-    sm['selfdriveState'].personality = log.LongitudinalPersonality.relaxed
-    sm['carState'].gasPressed = True
-    applied = self._applied_extra(ext, sm)
-    self.assertAlmostEqual(applied, ACCEL_BOOST_MAX * EXPECTED_SCALE[log.LongitudinalPersonality.relaxed])

openpilot/sunnypilot/sunnylink/settings_ui.json

@@ -620,19 +620,6 @@
                 }
               ]
             },
-            {
-              "key": "FollowDistanceAccelBoost",
-              "widget": "toggle",
-              "title": "Extra Acceleration Boost by Following Distance",
-              "description": "Adds extra acceleration on top of the stock gas-override boost. The extra boost follows the Following Distance setting, strongest when Aggressive and weakest when Relaxed. The stock boost remains active when this is off.",
-              "enablement": [
-                {
-                  "type": "capability",
-                  "field": "has_longitudinal_control",
-                  "equals": true
-                }
-              ]
-            },
             {
               "key": "IntelligentCruiseButtonManagement",
               "widget": "toggle",
@@ -2034,14 +2021,8 @@
         {
           "id": "model_behavior",
           "title": "Model Behavior",
-          "description": "Lane desire, Blendful lane centering, and lead-vehicle awareness tuning",
+          "description": "Lane desire and lead-vehicle awareness tuning",
           "items": [
-            {
-              "key": "Blendful",
-              "widget": "toggle",
-              "title": "Blendful Mode",
-              "description": "Nudges the car toward the lane center when the lane lines are clear. Drives laneless when they are not."
-            },
             {
               "key": "LaneTurnDesire",
               "widget": "toggle",

openpilot/sunnypilot/sunnylink/settings_ui_src/pages/cruise.yaml

@@ -30,14 +30,6 @@ sections:
       label: Relaxed
     enablement:
     - $ref: '#/macros/longitudinal'
-  - key: FollowDistanceAccelBoost
-    widget: toggle
-    title: Extra Acceleration Boost by Following Distance
-    description: Adds extra acceleration on top of the stock gas-override boost. The extra boost follows
-      the Following Distance setting, strongest when Aggressive and weakest when Relaxed. The stock boost
-      remains active when this is off.
-    enablement:
-    - $ref: '#/macros/longitudinal'
   - key: IntelligentCruiseButtonManagement
     widget: toggle
     title: Intelligent Cruise Button Management (ICBM) (Alpha)

openpilot/sunnypilot/sunnylink/settings_ui_src/pages/models.yaml

@@ -9,12 +9,8 @@ description: Driving model behavior and camera calibration
 sections:
 - id: model_behavior
   title: Model Behavior
-  description: Lane desire, Blendful lane centering, and lead-vehicle awareness tuning
+  description: Lane desire and lead-vehicle awareness tuning
   items:
-  - key: Blendful
-    widget: toggle
-    title: Blendful Mode
-    description: Nudges the car toward the lane center when the lane lines are clear. Drives laneless when they are not.
   - key: LaneTurnDesire
     widget: toggle
     title: Use Lane Turn Desires