Merge branch 'master' into policy

* [mici] ui: fix swipe-down in sunnylink and models panels

* update mici replay test

* [mici] ui: push model sub-views as separate widgets for correct back navigation

.github/workflows/build-single-tinygrad-model.yaml

@@ -12,11 +12,11 @@ on:
         required: false
         type: string
       recompiled_dir:
-        description: 'Existing recompiled directory number (e.g. 3 for recompiled3)'
+        description: 'Existing recompiled directory number (e.g. 1 for recompiled1)'
         required: true
         type: string
       json_version:
-        description: 'driving_models version number to update (e.g. 5 for driving_models_v5.json)'
+        description: 'driving_models version number to update (e.g. 18 for driving_models_v18.json)'
         required: true
         type: string
       artifact_suffix:
@@ -63,12 +63,11 @@ on:
         default: 'None'
         options:
           - None
-          - Simple Plan Models
-          - Space Lab Models
-          - TR Models
-          - DTR Models
+          - Master Models
+          - Release Models
+          - 2025 World Models
+          - 2026 World Models
           - Custom Merge Models
-          - FOF series models
           - Other
       custom_model_folder:
         description: 'Custom model folder name (if "Other" selected)'

.github/workflows/prebuilt.yaml

@@ -1,39 +0,0 @@
-name: prebuilt
-on:
-  schedule:
-    - cron: '0 * * * *'
-  workflow_dispatch:
-
-env:
-  DOCKER_LOGIN: docker login ghcr.io -u ${{ github.actor }} -p ${{ secrets.GITHUB_TOKEN }}
-  BUILD: release/ci/docker_build_sp.sh
-
-jobs:
-  build_prebuilt:
-    name: build prebuilt
-    runs-on: ubuntu-latest
-    if: github.repository == 'sunnypilot/sunnypilot'
-    env:
-      PUSH_IMAGE: true
-    permissions:
-      checks: read
-      contents: read
-      packages: write
-    steps:
-    - name: Wait for green check mark
-      if: ${{ github.event_name != 'workflow_dispatch' }}
-      uses: lewagon/wait-on-check-action@ccfb013c15c8afb7bf2b7c028fb74dc5a068cccc
-      with:
-        ref: master
-        wait-interval: 30
-        running-workflow-name: 'build prebuilt'
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        check-regexp: ^((?!.*(build master-ci|create badges).*).)*$
-    - uses: actions/checkout@v6
-      with:
-        submodules: true
-    - run: git lfs pull
-    - name: Build and Push docker image
-      run: |
-        $DOCKER_LOGIN
-        eval "$BUILD"

.github/workflows/sunnypilot-build-model.yaml

@@ -30,6 +30,11 @@ on:
         required: false
         type: string
         default: ''
+      target_hardware:
+        description: 'Hardware target to compile for (qcom or usbgpu)'
+        required: false
+        type: string
+        default: 'qcom'
   workflow_dispatch:
     inputs:
       upstream_branch:
@@ -46,6 +51,14 @@ on:
         required: false
         type: boolean
         default: true
+      target_hardware:
+        description: 'Hardware target to compile for'
+        required: true
+        type: choice
+        options:
+          - qcom
+          - usbgpu
+        default: 'qcom'
 
 
 run-name: Build model [${{ inputs.custom_name || inputs.upstream_branch }}] from ref [${{ inputs.upstream_branch }}]
@@ -161,19 +174,30 @@ jobs:
           name: models-${{ env.REF }}${{ inputs.artifact_suffix }}
           path: ${{ env.MODELS_DIR }}
       - run: |
-          rm -f ${{ env.MODELS_DIR }}/{dmonitoring_model,big_driving_policy,big_driving_vision}.onnx
+          rm -f ${{ env.MODELS_DIR }}/{dmonitoring_model,big_driving_policy,big_driving_vision,big_driving_supercombo}.onnx
 
       - name: Build Model
         run: |
           source /etc/profile
           export UV_PROJECT_ENVIRONMENT=${HOME}/venv
           export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
+          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
           export PYTHONPATH="${PYTHONPATH}:${{ env.TINYGRAD_PATH }}:${{ github.workspace }}"
 
           COMPILE_MODELD="${{ github.workspace }}/openpilot/sunnypilot/modeld_v2/compile_modeld.py"
           MODEL_SIZE=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
           CAMERA_RES=$(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')")
-          TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+
+          if [ "${{ inputs.target_hardware }}" == "usbgpu" ]; then
+            echo "USBGPU build"
+            export USBGPU=1
+            TG_FLAGS="DEV=AMD USBGPU=1 IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+            OUTPUT_PKL="${{ env.MODELS_DIR }}/big_driving_tinygrad.pkl"
+          else
+            echo "QCOM build"
+            TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
+            OUTPUT_PKL="${{ env.MODELS_DIR }}/driving_tinygrad.pkl"
+          fi
 
           # Generate metadata for all ONNX files
           find "${{ env.MODELS_DIR }}" -maxdepth 1 -name '*.onnx' | while IFS= read -r onnx_file; do
@@ -186,7 +210,13 @@ jobs:
           POLICY_ONNX="${{ env.MODELS_DIR }}/driving_policy.onnx"
           OFF_POLICY_ONNX="${{ env.MODELS_DIR }}/driving_off_policy.onnx"
           ON_POLICY_ONNX="${{ env.MODELS_DIR }}/driving_on_policy.onnx"
-          SUPERCOMBO_ONNX="${{ env.MODELS_DIR }}/supercombo.onnx"
+          SUPERCOMBO_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/supercombo.onnx" "${{ env.MODELS_DIR }}/driving_supercombo.onnx"; do
+            if [ -f "$f" ]; then
+              SUPERCOMBO_ONNX="$f"
+              break
+            fi
+          done
 
           MODEL_TYPE="" ONNX_ARGS="" OUTPUT_NAME=""
           if [ -f "$VISION_ONNX" ]; then
@@ -207,24 +237,15 @@ jobs:
           fi
 
           if [ -n "$MODEL_TYPE" ]; then
-            echo "Detected: $MODEL_TYPE -> driving_tinygrad.pkl"
+            echo "Detected: $MODEL_TYPE -> $OUTPUT_PKL"
             env ${TG_FLAGS} python3 "$COMPILE_MODELD" \
               --model-type $MODEL_TYPE \
               --model-size $MODEL_SIZE \
               --camera-resolutions $CAMERA_RES \
               $ONNX_ARGS \
-              --output "${{ env.MODELS_DIR }}/driving_tinygrad.pkl"
+              --output "$OUTPUT_PKL"
           fi
 
-      - name: Validate Model Outputs
-        run: |
-          source /etc/profile
-          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
-          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
-          python3 "${{ github.workspace }}/release/ci/model_generator.py" \
-            --validate-only \
-            --model-dir "${{ env.MODELS_DIR }}"
-
       - name: Prepare Output
         run: |
           sudo rm -rf ${{ env.OUTPUT_DIR }}

docs/AI_POLICY.md

@@ -0,0 +1,44 @@
+# AI policy
+
+## Why this exists
+
+We use AI tools ourselves, so this isn't an anti-AI stance. The problem is people submitting code, issues, or comments they don't actually understand. AI makes that very easy to do, and it creates real work for reviewers who have to figure out what you meant when you can't explain it yourself.
+
+If you're not going to put effort into understanding and verifying your submission, we're not going to put effort into reviewing it.
+
+## The rule
+
+You are responsible for everything you submit: code, PR descriptions, issues, bug reports, comments.
+
+1. Understand what you submit. If a reviewer asks why you did something, you answer from your own understanding, not by re-prompting. If you can't do that, don't submit it.
+
+2. Test your change. AI gets things wrong all the time. Run it, break it, confirm it actually works.
+
+3. Driving fixes need real evidence. Attach a dongle ID, upload logs, and include segments that show the fix working. A route hash by itself proves nothing.
+
+4. No AI-generated media (images, diagrams, videos) in issues or PRs.
+
+## Disclosure
+
+If AI tools helped you write something, say so. Add an `Assisted-by:` line in your commit message:
+
+```
+Assisted-by: GitHub Copilot
+Assisted-by: Claude
+```
+
+Disclosing won't count against your PR. It helps reviewers know where to look. Hiding it and getting caught will.
+
+## How we review
+
+Reviewers are looking at whether you understand your own change. Can you explain it? Can you respond to feedback without re-prompting? Does your PR description say why you made the change, not just list what changed?
+
+Good code from someone who used AI and understands what they wrote is fine. How you got there doesn't matter as long as you can stand behind it.
+
+## What happens
+
+Submissions that don't meet this bar get closed. If it keeps happening, you get blocked.
+
+## Maintainers
+
+Maintainers use AI at their discretion. They've earned that through sustained contribution and they know the codebase.

docs/CONTRIBUTING.md

@@ -1,3 +1,5 @@
+> sunnypilot follows [commaai/openpilot](https://github.com/commaai/openpilot)'s contributing guidelines. The following applies to all contributions here.
+
 # How to contribute
 
 Our software is open source so you can solve your own problems without needing help from others. And if you solve a problem and are so kind, you can upstream it for the rest of the world to use. Check out our [post about externalization](https://blog.comma.ai/a-2020-theme-externalization/).
@@ -35,6 +37,7 @@ All of these are examples of good PRs:
 * **UI design**: we do not have a good review process for this yet
 * **New features**: We believe openpilot is mostly feature-complete, and the rest is a matter of refinement and fixing bugs. As a result of this, most feature PRs will be immediately closed, however the beauty of open source is that forks can and do offer features that upstream openpilot doesn't.
 * **Negative expected value**: This is a class of PRs that makes an improvement, but the risk or validation costs more than the improvement. The risk can be mitigated by first getting a failing test merged.
+* **AI-generated contributions**: see our [AI policy](AI_POLICY.md)
 
 ### First contribution
 

openpilot/cereal/custom.capnp

@@ -69,6 +69,8 @@ struct LeadData {
 struct SelfdriveStateSP @0x81c2f05a394cf4af {
   mads @0 :ModularAssistiveDrivingSystem;
   intelligentCruiseButtonManagement @1 :IntelligentCruiseButtonManagement;
+  buttonsPressed @2 :UInt16;
+  buttonsReleaseToggle @3 :UInt16;
 
   enum AudibleAlert {
     none @0;
@@ -137,10 +139,16 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
     eta @2 :UInt32;
   }
 
+  struct Chunk {
+    fileName @0 :Text;
+    sha256 @1 :Text;
+  }
+
   struct Artifact {
     fileName @0 :Text;
     downloadUri @1 :DownloadUri;
     downloadProgress @2 :DownloadProgress;
+    chunks @3 :List(Chunk);
   }
 
   struct Model {
@@ -155,6 +163,7 @@ struct ModelManagerSP @0xaedffd8f31e7b55d {
       policy @3;
       offPolicy @4;
       onPolicy @5;
+      chunked @6;
     }
   }
 

openpilot/common/params_keys.h

@@ -222,6 +222,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"SubaruStopAndGo", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"SubaruStopAndGoManualParkingBrake", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"TeslaCoopSteering", {PERSISTENT | BACKUP, BOOL, "0"}},
+    {"TeslaMadsScreenButton", {PERSISTENT | BACKUP, INT, "0"}},
     {"ToyotaEnforceStockLongitudinal", {PERSISTENT | BACKUP, BOOL, "0"}},
     {"ToyotaStopAndGoHack", {PERSISTENT | BACKUP, BOOL, "0"}},
 

openpilot/selfdrive/controls/lib/longitudinal_planner.py

@@ -158,7 +158,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
   def publish(self, sm, pm):
     plan_send = messaging.new_message('longitudinalPlan')
 
-    plan_send.valid = sm.all_checks(service_list=['carState', 'controlsState', 'selfdriveState', 'radarState'])
+    plan_send.valid = sm.all_checks()
 
     longitudinalPlan = plan_send.longitudinalPlan
     longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']

openpilot/selfdrive/controls/plannerd.py

@@ -29,19 +29,19 @@ def main():
   longitudinal_planner = LongitudinalPlanner(CP, CP_SP)
   pm = messaging.PubMaster(['longitudinalPlan', 'driverAssistance', 'longitudinalPlanSP'])
   sm = messaging.SubMaster(['carControl', 'carState', 'controlsState', 'liveParameters', 'radarState', 'modelV2', 'selfdriveState',
-                            'liveMapDataSP', 'carStateSP', gps_location_service],
-                           poll='carState', ignore_alive=ignore_services, ignore_avg_freq=ignore_services, ignore_valid=ignore_services)
+                            'liveMapDataSP', 'carStateSP', 'selfdriveStateSP', gps_location_service],
+                           poll='modelV2', ignore_alive=ignore_services, ignore_avg_freq=ignore_services, ignore_valid=ignore_services)
 
   while True:
     sm.update()
-    longitudinal_planner.sla.update_car_state(sm['carState'])
+    longitudinal_planner.sla.update_buttons(sm['selfdriveStateSP'].buttonsReleaseToggle)
     if sm.updated['modelV2']:
       longitudinal_planner.update(sm)
       longitudinal_planner.publish(sm, pm)
 
       ldw.update(sm.frame, sm['modelV2'], sm['carState'], sm['carControl'])
       msg = messaging.new_message('driverAssistance')
-      msg.valid = sm.all_checks(['carState', 'carControl', 'modelV2', 'liveParameters'])
+      msg.valid = sm.all_checks()
       msg.driverAssistance.leftLaneDeparture = ldw.left
       msg.driverAssistance.rightLaneDeparture = ldw.right
       pm.send('driverAssistance', msg)

openpilot/selfdrive/selfdrived/selfdrived.py

@@ -30,6 +30,7 @@ from openpilot.sunnypilot import get_sanitize_int_param
 from openpilot.sunnypilot.selfdrive.car.car_specific import CarSpecificEventsSP
 from openpilot.sunnypilot.selfdrive.car.cruise_helpers import CruiseHelper
 from openpilot.sunnypilot.selfdrive.car.intelligent_cruise_button_management.controller import IntelligentCruiseButtonManagement
+from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP
 
 REPLAY = "REPLAY" in os.environ
@@ -177,6 +178,7 @@ class SelfdriveD(CruiseHelper):
     self.car_events_sp = CarSpecificEventsSP(self.CP, self.CP_SP)
 
     CruiseHelper.__init__(self, self.CP)
+    self.button_state_tracker = ButtonStateTracker()
 
   def update_events(self, CS):
     """Compute onroadEvents from carState"""
@@ -597,6 +599,8 @@ class SelfdriveD(CruiseHelper):
     icbm.sendButton = self.icbm.cruise_button
     icbm.vTarget = self.icbm.v_target
 
+    self.button_state_tracker.publish(ss_sp)
+
     self.pm.send('selfdriveStateSP', ss_sp_msg)
 
     # onroadEventsSP - logged every second or on change
@@ -616,6 +620,7 @@ class SelfdriveD(CruiseHelper):
       self.mads.update(CS)
     self.update_alerts(CS)
 
+    self.button_state_tracker.update(CS)
     self.publish_selfdriveState(CS)
 
     self.CS_prev = CS

openpilot/selfdrive/ui/layouts/main.py

@@ -13,6 +13,7 @@ from openpilot.selfdrive.ui.body.layouts.onroad import BodyLayout
 
 if gui_app.sunnypilot_ui():
   from openpilot.selfdrive.ui.sunnypilot.layouts.settings.settings import SettingsLayoutSP as SettingsLayout
+  from openpilot.selfdrive.ui.sunnypilot.layouts.home import HomeLayoutSP as HomeLayout
 
 
 class MainState(IntEnum):

openpilot/selfdrive/ui/mici/layouts/main.py

@@ -13,6 +13,7 @@ from openpilot.system.ui.lib.application import gui_app
 
 if gui_app.sunnypilot_ui():
   from openpilot.selfdrive.ui.sunnypilot.mici.layouts.settings import SettingsLayoutSP as SettingsLayout
+  from openpilot.selfdrive.ui.sunnypilot.mici.layouts.home import MiciHomeLayoutSP as MiciHomeLayout
 
 ONROAD_DELAY = 2.5  # seconds
 

openpilot/selfdrive/ui/sunnypilot/layouts/home.py

@@ -0,0 +1,68 @@
+"""
+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.selfdrive.ui.layouts.home import HomeLayout, HomeLayoutState, HEAD_BUTTON_FONT_SIZE, SPACING
+from openpilot.system.ui.lib.application import gui_app, FontWeight
+from openpilot.system.ui.lib.text_measure import measure_text_cached
+from openpilot.system.ui.lib.multilang import tr, trn
+from openpilot.system.ui.widgets.label import gui_label
+
+BRAND_FONT_SIZE = 48
+BRAND_DESC_SPACING = 12
+
+
+class HomeLayoutSP(HomeLayout):
+  def _render_header(self):
+    font = gui_app.font(FontWeight.MEDIUM)
+
+    version_text_width = self.header_rect.width
+
+    if self.update_available:
+      version_text_width -= self.update_notif_rect.width
+
+      highlight_color = rl.Color(75, 95, 255, 255) if self.current_state == HomeLayoutState.UPDATE else rl.Color(54, 77, 239, 255)
+      rl.draw_rectangle_rounded(self.update_notif_rect, 0.3, 10, highlight_color)
+
+      text = tr("UPDATE")
+      text_size = measure_text_cached(font, text, HEAD_BUTTON_FONT_SIZE)
+      text_x = self.update_notif_rect.x + (self.update_notif_rect.width - text_size.x) // 2
+      text_y = self.update_notif_rect.y + (self.update_notif_rect.height - text_size.y) // 2
+      rl.draw_text_ex(font, text, rl.Vector2(int(text_x), int(text_y)), HEAD_BUTTON_FONT_SIZE, 0, rl.WHITE)
+
+    if self.alert_count > 0:
+      version_text_width -= self.alert_notif_rect.width
+
+      highlight_color = rl.Color(255, 70, 70, 255) if self.current_state == HomeLayoutState.ALERTS else rl.Color(226, 44, 44, 255)
+      rl.draw_rectangle_rounded(self.alert_notif_rect, 0.3, 10, highlight_color)
+
+      alert_text = trn("{} ALERT", "{} ALERTS", self.alert_count).format(self.alert_count)
+      text_size = measure_text_cached(font, alert_text, HEAD_BUTTON_FONT_SIZE)
+      text_x = self.alert_notif_rect.x + (self.alert_notif_rect.width - text_size.x) // 2
+      text_y = self.alert_notif_rect.y + (self.alert_notif_rect.height - text_size.y) // 2
+      rl.draw_text_ex(font, alert_text, rl.Vector2(int(text_x), int(text_y)), HEAD_BUTTON_FONT_SIZE, 0, rl.WHITE)
+
+    if self.update_available or self.alert_count > 0:
+      version_text_width -= SPACING * 1.5
+
+    version_right = self.header_rect.x + self.header_rect.width
+    version_left = version_right - version_text_width
+
+    brand = "sunnypilot"
+    description = self.params.get("UpdaterCurrentDescription") or ""
+
+    desc_width = 0
+    if description:
+      desc_size = measure_text_cached(gui_app.font(FontWeight.NORMAL), description, BRAND_FONT_SIZE)
+      desc_width = desc_size.x
+      desc_rect = rl.Rectangle(version_right - desc_width, self.header_rect.y, desc_width, self.header_rect.height)
+      gui_label(desc_rect, description, BRAND_FONT_SIZE, rl.WHITE, alignment=rl.GuiTextAlignment.TEXT_ALIGN_RIGHT)
+
+    brand_size = measure_text_cached(gui_app.font(FontWeight.AUDIOWIDE), brand, BRAND_FONT_SIZE)
+    spacing = BRAND_DESC_SPACING if description else 0
+    brand_x = version_right - desc_width - spacing - brand_size.x
+    brand_rect = rl.Rectangle(max(version_left, brand_x), self.header_rect.y, brand_size.x, self.header_rect.height)
+    gui_label(brand_rect, brand, BRAND_FONT_SIZE, rl.WHITE, font_weight=FontWeight.AUDIOWIDE)

openpilot/selfdrive/ui/sunnypilot/layouts/onboarding.py

@@ -20,7 +20,7 @@ class SunnylinkConsentPage(Widget):
     self._done_callback = done_callback
     self._step = 0
 
-    self._title = self._child(Label(tr("sunnylink"), font_size=90, font_weight=FontWeight.BOLD, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT))
+    self._title = self._child(Label(tr("sunnylink"), font_size=90, font_weight=FontWeight.AUDIOWIDE, text_alignment=rl.GuiTextAlignment.TEXT_ALIGN_LEFT))
 
     self._content = [
       {

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

@@ -43,7 +43,7 @@ class ModelsLayout(Widget):
     self._initialize_items()
 
     self.clear_cache_item.action_item.set_value(f"{self.calculate_cache_size():.2f} MB")
-    for ctrl, key in [(self.lane_turn_value_control, "LaneTurnValue"), (self.delay_control, "LagdToggleDelay")]:
+    for ctrl, key in [(self.lane_turn_value_control, "LaneTurnValue"), (self.delay_control, "LagdToggleDelay"), (self.camera_offset, "CameraOffset")]:
       ctrl.action_item.set_value(int(float(ui_state.params.get(key, return_default=True)) * 100))
 
     self._scroller = Scroller(self.items, line_separator=True, spacing=0)
@@ -93,9 +93,14 @@ class ModelsLayout(Widget):
 
     self.lagd_toggle = toggle_item_sp(tr("Live Learning Steer Delay"), "", param="LagdToggle")
 
+    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.current_model_item, self.cancel_download_item, self.supercombo_label, self.vision_label,
-                  self.policy_label, self.off_policy_label, self.on_policy_label, self.refresh_item, self.clear_cache_item, self.lane_turn_desire_toggle,
-                  self.lane_turn_value_control, self.lagd_toggle, self.delay_control]
+                  self.policy_label, self.off_policy_label, self.on_policy_label, self.refresh_item, self.clear_cache_item,
+                  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. " +
@@ -232,6 +237,7 @@ class ModelsLayout(Widget):
     advanced_controls: bool = ui_state.params.get_bool("ShowAdvancedControls")
     turn_desire: bool = ui_state.params.get_bool("LaneTurnDesire")
     live_delay: bool = ui_state.params.get_bool("LagdToggle")
+    camera_offset: bool = ui_state.params.get("ModelManager_ActiveBundle") is not None
 
     self.lane_turn_desire_toggle.action_item.set_state(turn_desire)
     self.lane_turn_value_control.set_visible(turn_desire and advanced_controls)
@@ -240,6 +246,7 @@ class ModelsLayout(Widget):
     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:
       self.lane_turn_value_control.action_item.value_change_step = new_step
+    self.camera_offset.set_visible(camera_offset)
 
     self._update_lagd_description(live_delay)
     self.model_manager = ui_state.sm["modelManagerSP"]

openpilot/selfdrive/ui/sunnypilot/layouts/settings/steering_sub_layouts/mads_settings.py

@@ -7,7 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 from collections.abc import Callable
 import pyray as rl
 
-from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
+from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType, TeslaFlagsSP
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.sunnypilot.mads.helpers import MadsSteeringModeOnBrake
 from openpilot.system.ui.lib.multilang import tr, tr_noop
@@ -96,7 +96,10 @@ class MadsSettingsLayout(Widget):
     if brand == "rivian":
       return True
     elif brand == "tesla":
-      return not (ui_state.CP_SP is not None and ui_state.CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS)
+      if ui_state.CP_SP is None or not ui_state.CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS:
+        return True
+      screen_button = int(ui_state.params.get("TeslaMadsScreenButton", return_default=True))
+      return screen_button == MadsScreenButtonType.OFF
     return False
 
   def _update_steering_mode_description(self, button_index: int):

openpilot/selfdrive/ui/sunnypilot/layouts/settings/vehicle/brands/tesla.py

@@ -4,10 +4,11 @@ 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 opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
 from openpilot.selfdrive.ui.sunnypilot.layouts.settings.vehicle.brands.base import BrandSettings
 from openpilot.selfdrive.ui.ui_state import ui_state
 from openpilot.system.ui.lib.multilang import tr
-from openpilot.system.ui.sunnypilot.widgets.list_view import toggle_item_sp
+from openpilot.system.ui.sunnypilot.widgets.list_view import multiple_button_item_sp, toggle_item_sp
 
 COOP_STEERING_MIN_KMH = 23
 OEM_STEERING_MIN_KMH = 48
@@ -18,7 +19,14 @@ class TeslaSettings(BrandSettings):
   def __init__(self):
     super().__init__()
     self.coop_steering_toggle = toggle_item_sp(tr("Cooperative Steering (Beta)"), "", param="TeslaCoopSteering")
-    self.items = [self.coop_steering_toggle]
+    self.mads_screen_button = multiple_button_item_sp(
+      title=lambda: tr("MADS Screen Activation"),
+      description="",
+      buttons=[lambda: tr("Off"), lambda: tr("3-Finger"), lambda: tr("4-Finger"), lambda: tr("5-Finger")],
+      param="TeslaMadsScreenButton",
+      inline=False,
+    )
+    self.items = [self.coop_steering_toggle, self.mads_screen_button]
 
   def update_settings(self):
     is_metric = ui_state.is_metric
@@ -41,3 +49,18 @@ class TeslaSettings(BrandSettings):
 
     self.coop_steering_toggle.set_description(coop_steering_desc)
     self.coop_steering_toggle.action_item.set_enabled(ui_state.is_offroad())
+
+    has_vehicle_bus = ui_state.CP_SP is not None and bool(ui_state.CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS)
+    self.mads_screen_button.set_visible(has_vehicle_bus)
+
+    mads_screen_button_desc = (
+      f"{tr('Use a multi-finger press on the infotainment screen to toggle MADS.')} " +
+      f"{tr('This allows the use of full MADS functionality when enabled.')}<br><br>" +
+      f"{tr('Selecting a higher finger count may reduce accidental activations.')}<br><br>" +
+      f"<b>{tr('Note: Setting this to Off will reset your MADS settings to default.')}</b>"
+    )
+    if not ui_state.is_offroad():
+      mads_screen_button_disabled_msg = tr("Enable \"Always Offroad\" in Device panel, or turn vehicle off to change.")
+      mads_screen_button_desc = f"<b>{mads_screen_button_disabled_msg}</b><br><br>{mads_screen_button_desc}"
+    self.mads_screen_button.set_description(mads_screen_button_desc)
+    self.mads_screen_button.action_item.set_enabled(ui_state.is_offroad())

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

@@ -0,0 +1,15 @@
+"""
+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.selfdrive.ui.mici.layouts.home import MiciHomeLayout
+from openpilot.system.ui.lib.application import FontWeight
+from openpilot.system.ui.widgets.label import UnifiedLabel
+
+
+class MiciHomeLayoutSP(MiciHomeLayout):
+  def __init__(self):
+    super().__init__()
+    self._openpilot_label = UnifiedLabel("sunnypilot", font_size=88, font_weight=FontWeight.AUDIOWIDE, max_width=480, wrap_text=False)

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

@@ -4,7 +4,6 @@ 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 collections.abc import Callable
 import pyray as rl
 
 from openpilot.cereal import custom
@@ -48,10 +47,8 @@ class CurrentModelInfo(Widget):
     self.info_text.render()
 
 class ModelsLayoutMici(NavScroller):
-  def __init__(self, back_callback: Callable):
+  def __init__(self):
     super().__init__()
-    self.set_back_callback(back_callback)
-    self.original_back_callback = back_callback
     self.focused_widget = None
 
     self.current_model_info = CurrentModelInfo()
@@ -85,12 +82,10 @@ class ModelsLayoutMici(NavScroller):
 
     return folders
 
-  def _show_selection_view(self, items, back_callback: Callable):
-    self._scroller._items = items
-    for item in items:
-      item.set_touch_valid_callback(lambda: self._scroller.scroll_panel.is_touch_valid() and self._scroller.enabled)
-    self._scroller.scroll_panel.set_offset(0)
-    self.set_back_callback(back_callback)
+  def _push_selection_view(self, items):
+    scroller = NavScroller()
+    scroller._scroller.add_widgets(items)
+    gui_app.push_widget(scroller)
 
   def _show_folders(self):
     self.focused_widget = self.select_model_btn
@@ -112,15 +107,18 @@ class ModelsLayoutMici(NavScroller):
           folder_buttons.insert(0, btn)
         else:
           folder_buttons.append(btn)
-    self._show_selection_view(folder_buttons, self._reset_main_view)
+    self._push_selection_view(folder_buttons)
+
+  def _pop_to_main(self):
+    gui_app.pop_widgets_to(self)
 
   def _select_model(self, bundle):
     ui_state.params.put("ModelManager_DownloadIndex", bundle.index)
-    self._reset_main_view()
+    self._pop_to_main()
 
   def _select_default(self):
     ui_state.params.remove("ModelManager_ActiveBundle")
-    self._reset_main_view()
+    self._pop_to_main()
 
   def _select_folder(self, folder_name):
     favs = ui_state.params.get("ModelManager_Favs")
@@ -135,13 +133,7 @@ class ModelsLayoutMici(NavScroller):
       btn = BigButton(txt)
       btn.set_click_callback(lambda b=bundle: self._select_model(b))
       btns.append(btn)
-    self._show_selection_view(btns, self._show_folders)
-
-  def _reset_main_view(self):
-    self._scroller._items = self.main_items  # type: ignore[assignment] # ty: ignore[invalid-assignment]
-    self.set_back_callback(self.original_back_callback)
-    self._scroller.scroll_panel.set_offset(0)
-    self._scroller.scroll_to(0)
+    self._push_selection_view(btns)
 
   def hide_event(self):
     super().hide_event()

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

@@ -32,11 +32,11 @@ class SettingsLayoutSP(OP.SettingsLayout):
                                                 BIG_ICON_SIZE)
     self.icon_offroad_slider = gui_app.texture("icons_mici/settings/device/lkas.png", BIG_ICON_SIZE, BIG_ICON_SIZE)
 
-    sunnylink_panel = SunnylinkLayoutMici(back_callback=gui_app.pop_widget)
+    sunnylink_panel = SunnylinkLayoutMici()
     sunnylink_btn = SettingsBigButton(tr("sunnylink"), "", gui_app.texture("icons_mici/settings/developer/ssh.png", 55, 55))
     sunnylink_btn.set_click_callback(lambda: gui_app.push_widget(sunnylink_panel))
 
-    models_panel = ModelsLayoutMici(back_callback=gui_app.pop_widget)
+    models_panel = ModelsLayoutMici()
     models_btn = SettingsBigButton(tr("models"), "", gui_app.texture("../../sunnypilot/selfdrive/assets/offroad/icon_models.png", ICON_SIZE, ICON_SIZE))
     models_btn.set_click_callback(lambda: gui_app.push_widget(models_panel))
 

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

@@ -6,7 +6,6 @@ See the LICENSE.md file in the root directory for more details.
 """
 import pyray as rl
 
-from collections.abc import Callable
 
 from openpilot.cereal import custom
 from openpilot.selfdrive.ui.mici.widgets.button import BigButton, BigToggle
@@ -54,9 +53,8 @@ class SunnylinkInfo(Widget):
     self.sponsor_text.render()
 
 class SunnylinkLayoutMici(NavScroller):
-  def __init__(self, back_callback: Callable):
+  def __init__(self):
     super().__init__()
-    self.set_back_callback(back_callback)
     self._restore_in_progress = False
     self._backup_in_progress = False
     self._sunnylink_enabled = ui_state.params.get("SunnylinkEnabled")

openpilot/selfdrive/ui/tests/diff/replay_script.py

@@ -338,8 +338,11 @@ def build_mici_script(pm: PubMaster, main_layout, script: Script) -> None:
 
   settings_cases: Cases = [
     lambda: scroll_through_cases(toggle_cases),
+    None,  # sunnylink (just open and close)
+    None,  # models (just open and close)
     lambda: scroll_through_cases(network_cases),
     lambda: scroll_through_cases(device_cases),
+    lambda: script.wait(WAIT_SHORT),  # software
     lambda: script.wait(WAIT_SHORT),  # pairing
     lambda: run_actions(lambda: swipe_up(height * 3), lambda: swipe_down(height * 3)),  # firehose (scroll down and back up)
     lambda: scroll_through_cases(developer_cases),

openpilot/sunnypilot/mads/helpers.py

@@ -9,7 +9,7 @@ from openpilot.common.params import Params
 from opendbc.car import structs
 from opendbc.safety import ALTERNATIVE_EXPERIENCE
 from opendbc.sunnypilot.car.hyundai.values import HyundaiFlagsSP, HyundaiSafetyFlagsSP
-from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
+from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType, TeslaFlagsSP
 
 
 MADS_NO_ACC_MAIN_BUTTON = ("rivian", "tesla")
@@ -21,17 +21,20 @@ class MadsSteeringModeOnBrake:
   DISENGAGE = 2
 
 
-def get_mads_limited_brands(CP: structs.CarParams, CP_SP: structs.CarParamsSP) -> bool:
+def get_mads_limited_brands(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params: Params) -> bool:
   if CP.brand == 'rivian':
     return True
   if CP.brand == 'tesla':
-    return not CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS
+    if not CP_SP.flags & TeslaFlagsSP.HAS_VEHICLE_BUS:
+      return True
+    screen_button = int(params.get("TeslaMadsScreenButton", return_default=True))
+    return screen_button == MadsScreenButtonType.OFF
 
   return False
 
 
 def read_steering_mode_param(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params: Params):
-  if get_mads_limited_brands(CP, CP_SP):
+  if get_mads_limited_brands(CP, CP_SP, params):
     return MadsSteeringModeOnBrake.DISENGAGE
 
   return params.get("MadsSteeringMode", return_default=True)
@@ -63,7 +66,7 @@ def set_car_specific_params(CP: structs.CarParams, CP_SP: structs.CarParamsSP, p
   # MADS is currently partially supported for these platforms due to lack of consistent states to engage controls
   # Only MadsSteeringModeOnBrake.DISENGAGE is supported for these platforms
   # TODO-SP: To enable MADS full support for Rivian and most Tesla, identify consistent signals for MADS toggling
-  mads_partial_support = get_mads_limited_brands(CP, CP_SP)
+  mads_partial_support = get_mads_limited_brands(CP, CP_SP, params)
   if mads_partial_support:
     params.put("MadsSteeringMode", 2, block=True)
     params.put_bool("MadsUnifiedEngagementMode", True, block=True)

openpilot/sunnypilot/mads/tests/test_mads_steering_mode.py

@@ -13,7 +13,7 @@ from openpilot.selfdrive.selfdrived.events import Events
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP
 from openpilot.sunnypilot.mads.helpers import MadsSteeringModeOnBrake, read_steering_mode_param
 from openpilot.sunnypilot.mads.mads import ModularAssistiveDrivingSystem
-from opendbc.sunnypilot.car.tesla.values import TeslaFlagsSP
+from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType, TeslaFlagsSP
 
 State = custom.ModularAssistiveDrivingSystem.ModularAssistiveDrivingSystemState
 EventName = log.OnroadEvent.EventName
@@ -38,6 +38,12 @@ def make_panda_state(mocker, controls_allowed_lateral=True):
   return ps
 
 
+def make_params_mock(mocker, values):
+  params = mocker.MagicMock()
+  params.get = mocker.MagicMock(side_effect=lambda k, **kwargs: values[k])
+  return params
+
+
 def make_mads(mocker, steering_mode):
   sd = mocker.MagicMock()
   sd.CP = structs.CarParams()
@@ -223,15 +229,27 @@ class TestBrandSteeringModeRestrictions:
     params = mocker.MagicMock()
     assert read_steering_mode_param(CP, CP_SP, params) == MadsSteeringModeOnBrake.DISENGAGE
 
-  def test_tesla_with_vehicle_bus_uses_param(self, mocker):
+  @pytest.mark.parametrize("screen_button", [MadsScreenButtonType.THREE_FINGER,
+                                             MadsScreenButtonType.FOUR_FINGER,
+                                             MadsScreenButtonType.FIVE_FINGER])
+  def test_tesla_with_vehicle_bus_uses_param(self, mocker, screen_button):
     CP = structs.CarParams()
     CP.brand = "tesla"
     CP_SP = structs.CarParamsSP()
     CP_SP.flags = TeslaFlagsSP.HAS_VEHICLE_BUS
-    params = mocker.MagicMock()
-    params.get = mocker.MagicMock(return_value=MadsSteeringModeOnBrake.REMAIN_ACTIVE)
+    params = make_params_mock(mocker, {"TeslaMadsScreenButton": screen_button,
+                                       "MadsSteeringMode": MadsSteeringModeOnBrake.REMAIN_ACTIVE})
     assert read_steering_mode_param(CP, CP_SP, params) == MadsSteeringModeOnBrake.REMAIN_ACTIVE
 
+  def test_tesla_with_vehicle_bus_screen_button_off_forced_to_disengage(self, mocker):
+    CP = structs.CarParams()
+    CP.brand = "tesla"
+    CP_SP = structs.CarParamsSP()
+    CP_SP.flags = TeslaFlagsSP.HAS_VEHICLE_BUS
+    params = make_params_mock(mocker, {"TeslaMadsScreenButton": MadsScreenButtonType.OFF,
+                                       "MadsSteeringMode": MadsSteeringModeOnBrake.REMAIN_ACTIVE})
+    assert read_steering_mode_param(CP, CP_SP, params) == MadsSteeringModeOnBrake.DISENGAGE
+
   @pytest.mark.parametrize("brand", ["hyundai", "toyota", "honda", "gm"])
   def test_other_brands_use_param(self, mocker, brand):
     CP = structs.CarParams()

openpilot/sunnypilot/modeld_v2/SConscript

@@ -82,18 +82,3 @@ if os.path.isfile(supercombo_onnx):
   compile_combined('supercombo',
                    f'--supercombo-onnx {supercombo_onnx}',
                    'driving_combined_supercombo_tinygrad.pkl')
-
-if PC:
-  inputs = tinygrad_files + [File(Dir("#openpilot/sunnypilot/modeld_v2").File("install_models_pc.py").abspath)]
-  outputs = []
-  model_dir = Dir("models").abspath
-  cmd = f'python3 {Dir("#openpilot/sunnypilot/modeld_v2").abspath}/install_models_pc.py {model_dir}'
-
-  for model_name in ['supercombo', 'driving_vision', 'driving_off_policy', 'driving_on_policy', 'driving_policy']:
-    if File(f"models/{model_name}.onnx").exists():
-        inputs.append(File(f"models/{model_name}.onnx"))
-        inputs.append(File(f"models/{model_name}_tinygrad.pkl"))
-        outputs.append(File(f"models/{model_name}_metadata.pkl"))
-  if outputs:
-    lenv.Command(outputs, inputs, cmd)
-

openpilot/sunnypilot/modeld_v2/compile_modeld.py

@@ -10,471 +10,355 @@ import argparse
 import os
 import pickle
 import time
-from functools import partial
 from collections import defaultdict
-
+from functools import partial
 import numpy as np
-from tinygrad.tensor import Tensor
+os.environ['GMMU'] = '0'
+
+def _patch_tinygrad_fetch_fw():
+  import hashlib
+  import pathlib
+  import zstandard
+  from tinygrad import helpers
+  _orig_fetch_fw = helpers.fetch_fw
+  def fetch_fw(path, name, sha256):
+    p = pathlib.Path(f"/lib/firmware/{path}/{name}.zst")
+    if p.is_file():
+      blob = zstandard.ZstdDecompressor().stream_reader(p.read_bytes()).read()
+      if hashlib.sha256(blob).hexdigest() == sha256:
+        return blob
+    return _orig_fetch_fw(path, name, sha256)
+  helpers.fetch_fw = fetch_fw
+_patch_tinygrad_fetch_fw()
+
+from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, make_frame_prepare, sample_desire, sample_skip, shift_and_sample
+from tinygrad import dtypes
 from tinygrad.device import Device
 from tinygrad.engine.jit import TinyJit
-
-from openpilot.selfdrive.modeld.compile_modeld import (
-  NV12Frame, make_frame_prepare,
-  shift_and_sample, sample_skip, sample_desire,
-)
+from tinygrad.tensor import Tensor
 
 MODEL_TYPES = ('vision_policy', 'supercombo', 'vision_multi_policy')
 
 
-def _detect_desire_key(policy_input_shapes):
-  for k in policy_input_shapes:
-    if k.startswith('desire'):
-      return k
-  return None
+def _detect_desire_key(shapes: dict) -> str | None:
+  return next((key for key in shapes if key.startswith('desire')), None)
 
 
-def _detect_vision_keys(vision_input_shapes):
-  img_keys = sorted([k for k in vision_input_shapes if 'img' in k])
-  road_key = next((k for k in img_keys if 'big' not in k), None)
-  wide_key = next((k for k in img_keys if 'big' in k), None)
-  if road_key is None or wide_key is None:
-    raise ValueError(f"Cannot determine road/wide image keys from {list(vision_input_shapes.keys())}")
-  return road_key, wide_key
+def _detect_vision_keys(shapes: dict) -> tuple[str | None, str | None]:
+  img_keys = sorted(key for key in shapes if 'img' in key)
+  return (
+    next((key for key in img_keys if 'big' not in key), None),
+    next((key for key in img_keys if 'big' in key), None)
+  )
 
 
-def make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, device):
-  road_key, _ = _detect_vision_keys(vision_input_shapes)
-  img = vision_input_shapes[road_key]
-  n_frames = img[1] // 6
-  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
-
-  fb = policy_input_shapes['features_buffer']
-  desire_key = _detect_desire_key(policy_input_shapes)
-  dp = policy_input_shapes[desire_key]
-  tc = policy_input_shapes.get('traffic_convention', (1, 2))
-
-  npy = {
-    'desire': np.zeros(dp[2], dtype=np.float32),
-    'traffic_convention': np.zeros(tc, dtype=np.float32),
-    'tfm': np.zeros((3, 3), dtype=np.float32),
-    'big_tfm': np.zeros((3, 3), dtype=np.float32),
-  }
-
-  handled = {'features_buffer', desire_key, 'traffic_convention'}
-  for key, shape in policy_input_shapes.items():
-    if key in handled:
-      continue
-    npy[key] = np.zeros(shape, dtype=np.float32)
-
-  input_queues = {
-    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'feat_q': Tensor(np.zeros((frame_skip * (fb[1] - 1) + 1, fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),
-    'desire_q': Tensor(np.zeros((frame_skip * dp[1], dp[0], dp[2]), dtype=np.float32), device=device).contiguous().realize(),
-    **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
-  }
-  return input_queues, npy
+def derive_frame_skip(vision_input_shapes: dict, policy_input_shapes: dict) -> int:
+  features_buffer = policy_input_shapes.get('features_buffer')
+  return 1 if not features_buffer or features_buffer[1] >= 99 else 4
 
 
-def make_run_split_policy(vision_runner, policy_runner, nv12: NV12Frame, model_w, model_h,
-                          vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                          vision_road_key, vision_wide_key, prepare_only=False):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
-  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
-  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+def get_policy_npy_shapes(input_shapes: dict, is_supercombo: bool = False) -> tuple[dict, list[int]]:
+  desire_key = _detect_desire_key(input_shapes)
+  shapes = {}
+  if desire_key:
+    shapes['desire'] = (input_shapes[desire_key][2],)
 
-  def run_policy(img_q, big_img_q, feat_q, desire_q, desire, traffic_convention, tfm, big_tfm, frame, big_frame, **extra):
-    npy_tensors = [tfm.to(Device.DEFAULT), big_tfm.to(Device.DEFAULT),
-                   desire.to(Device.DEFAULT), traffic_convention.to(Device.DEFAULT)]
-    extra_device = {k: extra[k].to(Device.DEFAULT) for k in extra_policy_keys}
-    Tensor.realize(*npy_tensors, *extra_device.values())
-    tfm, big_tfm, desire, traffic_convention = npy_tensors
+  if is_supercombo and 'features_buffer' in input_shapes:
+    fb = input_shapes['features_buffer']
+    shapes['prev_feat'] = (fb[0], fb[2])
 
-    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
-    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+  for key, shape in input_shapes.items():
+    if key not in (desire_key, 'features_buffer') and 'img' not in key:
+      shapes[key] = tuple(shape)
 
-    if prepare_only:
-      return img, big_img
-
-    vision_out = next(iter(vision_runner({vision_road_key: img, vision_wide_key: big_img}).values())).cast('float32')
-
-    new_feat = vision_out[:, vision_features_slice].reshape(1, -1).unsqueeze(0)
-    feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn)
-    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
-
-    inputs = {'features_buffer': feat_buf, desire_key: desire_buf, 'traffic_convention': traffic_convention, **extra_device}
-    policy_out = next(iter(policy_runner(inputs).values())).cast('float32')
-
-    return vision_out, policy_out
-  return run_policy
+  sizes = [int(np.prod(size)) for size in shapes.values()]
+  return shapes, sizes
 
 
-def compile_split_policy(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
-                         vision_runner, policy_runner, vision_metadata, policy_metadata):
-  print(f"Compiling combined policy JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
-
-  vision_features_slice = vision_metadata['output_slices']['hidden_state']
-  vision_input_shapes = vision_metadata['input_shapes']
-  policy_input_shapes = policy_metadata['input_shapes']
-  desire_key = _detect_desire_key(policy_input_shapes)
-  extra_policy_keys = [k for k in policy_input_shapes if k not in ('features_buffer', desire_key, 'traffic_convention')]
-  vision_road_key, vision_wide_key = _detect_vision_keys(vision_input_shapes)
-
-  _run = make_run_split_policy(vision_runner, policy_runner, nv12, model_w, model_h,
-                               vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                               vision_road_key, vision_wide_key, prepare_only)
-  run_policy_jit = TinyJit(_run, prune=True)
-
-  SEED = 42
-
-  def random_inputs_run_fn(fn, seed, test_val=None, test_buffers=None, expect_match=True):
-    input_queues, npy = make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, Device.DEFAULT)
-    rng = np.random.default_rng(seed)
-    Tensor.manual_seed(seed)
-
-    testing = test_val is not None or test_buffers is not None
-    n_runs = 1 if testing else 3
-
-    for i in range(n_runs):
-      frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-      big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-      for v in npy.values():
-        v[:] = rng.standard_normal(v.shape).astype(v.dtype)
-      Device.default.synchronize()
-      st = time.perf_counter()
-      outs = fn(**input_queues, frame=frame, big_frame=big_frame)
-      mt = time.perf_counter()
-      Device.default.synchronize()
-      et = time.perf_counter()
-      print(f"  [{i+1}/{n_runs}] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
-
-      if i == 0:
-        val = [np.copy(v.numpy()) for v in outs]
-        buffers = [np.copy(v.numpy().copy()) for v in input_queues.values()]
-
-    if test_val is not None:
-      match = all(np.array_equal(a, b) for a, b in zip(val, test_val, strict=True))
-      assert match == expect_match, f"outputs {'differ from' if expect_match else 'match'} baseline (seed={seed})"
-    if test_buffers is not None:
-      match = all(np.array_equal(a, b) for a, b in zip(buffers, test_buffers, strict=True))
-      assert match == expect_match, f"buffers {'differ from' if expect_match else 'match'} baseline (seed={seed})"
-    return fn, val, buffers
-
-  print('capture + replay')
-  run_policy_jit, test_val, test_buffers = random_inputs_run_fn(run_policy_jit, SEED)
-
-  print('pickle round trip')
-  run_policy_jit = pickle.loads(pickle.dumps(run_policy_jit))
-  random_inputs_run_fn(run_policy_jit, SEED, test_val, test_buffers, expect_match=True)
-  random_inputs_run_fn(run_policy_jit, SEED+1, test_val, test_buffers, expect_match=False)
-  return run_policy_jit
-
-
-def derive_frame_skip(vision_input_shapes, policy_input_shapes):
-  fb = policy_input_shapes.get('features_buffer')
-  if fb is None:
-    return 1
-  fb_history = fb[1]
-  if fb_history >= 99:
-    return 1
-  return 4
-
-
-def make_supercombo_input_queues(input_shapes, frame_skip, device):
-  img_shape = input_shapes.get('img', input_shapes.get('input_imgs'))
-  if img_shape is None:
-    raise ValueError("No img input found in model shapes")
+def generate_queues_and_npy(input_shapes: dict, frame_skip: int, device: str = Device.DEFAULT,
+                            is_supercombo: bool = False, use_packed: bool = True) -> tuple[dict, dict]:
+  road_key, _ = _detect_vision_keys(input_shapes)
+  if not road_key:
+    raise ValueError("Vision road key missing from input shapes.")
 
+  img_shape = input_shapes[road_key]
   n_frames = img_shape[1] // 6
   img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img_shape[2], img_shape[3])
 
-  numpy_keys = {}
-  queue_keys = {}
+  desire_key = _detect_desire_key(input_shapes)
+  if not desire_key:
+    raise ValueError("Desire key missing from input shapes.")
 
-  for key, shape in input_shapes.items():
-    if 'img' in key:
-      continue
-    if len(shape) == 3 and shape[1] > 1:
-      if key.startswith('desire'):
-        numpy_keys[key] = np.zeros(shape[2], dtype=np.float32)
-        queue_keys[f'{key}_q'] = Tensor(
-          np.zeros((frame_skip * shape[1], shape[0], shape[2]), dtype=np.float32),
-          device=device).contiguous().realize()
-      elif key == 'features_buffer':
-        queue_keys['feat_q'] = Tensor(
-          np.zeros((frame_skip * (shape[1] - 1) + 1, shape[0], shape[2]), dtype=np.float32),
-          device=device).contiguous().realize()
-      else:
-        numpy_keys[key] = np.zeros(shape, dtype=np.float32)
-    elif len(shape) == 2:
-      numpy_keys[key] = np.zeros(shape, dtype=np.float32)
+  desire_shape = input_shapes[desire_key]
+  features_buffer = input_shapes.get('features_buffer')
 
-  if 'traffic_convention' not in numpy_keys:
-    tc_shape = input_shapes.get('traffic_convention', (1, 2))
-    numpy_keys['traffic_convention'] = np.zeros(tc_shape, dtype=np.float32)
+  if use_packed:  # remove packed detection block after all models are recompiled
+    npy_arrays = {
+      'tfm': np.zeros((3, 3), dtype=np.float32),
+      'big_tfm': np.zeros((3, 3), dtype=np.float32)
+    }
 
-  numpy_keys['tfm'] = np.zeros((3, 3), dtype=np.float32)
-  numpy_keys['big_tfm'] = np.zeros((3, 3), dtype=np.float32)
+    shapes, sizes = get_policy_npy_shapes(input_shapes, is_supercombo=is_supercombo)
+    packed_npy_inputs = np.zeros(sum(sizes), dtype=np.float32)
 
-  input_queues = {
-    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
-    **queue_keys,
-    **{k: Tensor(v, device='NPY').realize() for k, v in numpy_keys.items()},
-  }
-  return input_queues, numpy_keys
+    split_indices = np.cumsum(sizes[:-1]) if len(sizes) > 1 else []
+    split_views = np.split(packed_npy_inputs, split_indices) if len(sizes) > 0 else []
+    for (k, s), v in zip(shapes.items(), split_views, strict=True):
+      npy_arrays[k] = v.reshape(s)
+
+    queues = {
+      'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'desire_q': Tensor(np.zeros((frame_skip * desire_shape[1], desire_shape[0], desire_shape[2]),
+                    dtype=np.float32), device=device).contiguous().realize(),
+      'packed_npy_inputs': Tensor(packed_npy_inputs, device='NPY').realize(),
+    }
+
+    if features_buffer:
+      queues['feat_q'] = Tensor(np.zeros((frame_skip * (features_buffer[1] - 1) + 1, features_buffer[0], features_buffer[2]),
+                         dtype=np.float32), device=device).contiguous().realize()
+
+    queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items() if key in ('tfm', 'big_tfm')})
+  else:
+    # TODO-SP: Remove legacy queuing fallback else block after all models are recompiled
+    npy_arrays = {
+      'desire': np.zeros(desire_shape[2], dtype=np.float32),
+      'tfm': np.zeros((3, 3), dtype=np.float32),
+      'big_tfm': np.zeros((3, 3), dtype=np.float32)
+    }
+
+    for key, shape in input_shapes.items():
+      if key not in npy_arrays and 'img' not in key and key not in ('features_buffer', desire_key):
+        npy_arrays[key] = np.zeros(shape, dtype=np.float32)
+
+    queues = {
+      'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+      'desire_q': Tensor(np.zeros((frame_skip * desire_shape[1], desire_shape[0], desire_shape[2]),
+                    dtype=np.float32), device=device).contiguous().realize()
+    }
+
+    if features_buffer:
+      queues['feat_q'] = Tensor(np.zeros((frame_skip * (features_buffer[1] - 1) + 1, features_buffer[0], features_buffer[2]),
+                         dtype=np.float32), device=device).contiguous().realize()
+
+    queues.update({key: Tensor(value, device='NPY').realize() for key, value in npy_arrays.items()})
+
+  return queues, npy_arrays
 
 
-def make_run_supercombo(model_runner, nv12: NV12Frame, model_w, model_h,
-                        features_slice, frame_skip, input_shapes, prepare_only=False):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
+def make_split_input_queues(vision_input_shapes: dict, policy_input_shapes: dict,
+                            frame_skip: int, device: str = Device.DEFAULT, use_packed: bool = True) -> tuple[dict, dict]:
+  return generate_queues_and_npy({**vision_input_shapes, **policy_input_shapes}, frame_skip, device, is_supercombo=False, use_packed=use_packed)
+
+
+def make_supercombo_input_queues(input_shapes: dict, frame_skip: int,
+                                 device: str = Device.DEFAULT, use_packed: bool = True) -> tuple[dict, dict]:
+  return generate_queues_and_npy(input_shapes, frame_skip, device, is_supercombo=True, use_packed=use_packed)
+
+
+def create_jit_runner(vision_runner, policy_runners: list, nv12: NV12Frame, model_size: tuple[int, int],
+            features_slice: slice, frame_skip: int, input_shapes: dict, prepare_only: bool):
+  frame_prepare = make_frame_prepare(nv12, *model_size)
   sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
   sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
 
   desire_key = _detect_desire_key(input_shapes)
-  if desire_key is None:
-    raise ValueError(f"No desire* key found in input_shapes: {list(input_shapes.keys())}")
-  road_img_key, wide_img_key = _detect_vision_keys(input_shapes)
-  extra_policy_keys = [k for k in input_shapes
-                       if k not in (desire_key, 'features_buffer', 'traffic_convention')
-                       and 'img' not in k]
+  road_key, wide_key = _detect_vision_keys(input_shapes)
 
-  def run_supercombo(img_q, big_img_q, feat_q, desire_q,
-                     frame, big_frame, **kwargs):
-    desire = kwargs.get(desire_key)
-    traffic_convention = kwargs.get('traffic_convention')
-    tfm = kwargs['tfm']
-    big_tfm = kwargs['big_tfm']
+  if not desire_key or not road_key or not wide_key:
+    raise ValueError("Missing required vision or desire keys in input shapes.")
 
-    tfm = tfm.to(Device.DEFAULT)
-    big_tfm = big_tfm.to(Device.DEFAULT)
-    desire = desire.to(Device.DEFAULT)
-    traffic_convention = traffic_convention.to(Device.DEFAULT)
-    Tensor.realize(tfm, big_tfm, desire, traffic_convention)
+  is_supercombo = vision_runner is None
+  npy_shapes, npy_sizes = get_policy_npy_shapes(input_shapes, is_supercombo=is_supercombo)
 
-    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
-    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+  def runner(img_q, big_img_q, feat_q, packed_npy_inputs, frame, big_frame, tfm, big_tfm, **kwargs):
+    desire_q = kwargs['desire_q']
+
+    packed_npy_inputs_dev = packed_npy_inputs.to(Device.DEFAULT)
+    tfm_dev = tfm.to(Device.DEFAULT)
+    big_tfm_dev = big_tfm.to(Device.DEFAULT)
+
+    Tensor.realize(packed_npy_inputs_dev, tfm_dev, big_tfm_dev)
+
+    img = shift_and_sample(img_q, frame_prepare(frame, tfm_dev).unsqueeze(0), sample_skip_fn).realize()
+    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm_dev).unsqueeze(0), sample_skip_fn).realize()
 
     if prepare_only:
       return img, big_img
 
-    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
-    feat_buf = sample_skip_fn(feat_q)
+    unpacked_tensors = [tensor.reshape(shape) for tensor, shape in zip(packed_npy_inputs_dev.split(npy_sizes), npy_shapes.values(), strict=True)]
+    unpacked_dict = dict(zip(npy_shapes.keys(), unpacked_tensors, strict=True))
 
-    inputs = {road_img_key: img, wide_img_key: big_img,
-              desire_key: desire_buf, 'features_buffer': feat_buf,
-              'traffic_convention': traffic_convention}
-    for k in extra_policy_keys:
-      if k in kwargs:
-        inputs[k] = kwargs[k].to(Device.DEFAULT)
+    desire_dev = unpacked_dict['desire']
+    desire_buf = shift_and_sample(desire_q, desire_dev.reshape(1, 1, -1), sample_desire_fn).realize()
 
-    model_out = next(iter(model_runner(inputs).values())).cast('float32')
+    inputs = {desire_key: desire_buf}
+    for key, tensor_val in unpacked_dict.items():
+      if key not in ('desire', 'prev_feat'):
+        inputs[key] = tensor_val
 
-    new_feat = model_out[:, features_slice].reshape(1, -1).unsqueeze(0)
-    shift_and_sample(feat_q, new_feat, sample_skip_fn)
+    if 'prev_feat' in unpacked_dict:
+      prev_feat_dev = unpacked_dict['prev_feat']
+      inputs['features_buffer'] = shift_and_sample(feat_q, prev_feat_dev.reshape(1, 1, -1), sample_skip_fn).realize()
 
-    return model_out
+    if vision_runner:
+      vision_out_cast = next(iter(vision_runner({road_key: img, wide_key: big_img}).values())).cast('float32').realize()
+      if 'features_buffer' not in inputs:
+        new_feat = vision_out_cast[:, features_slice].reshape(1, -1).unsqueeze(0)
+        inputs['features_buffer'] = shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
+      policy_outs = [next(iter(pol_runner(inputs).values())).cast('float32').realize() for pol_runner in policy_runners]
+      return (vision_out_cast, *policy_outs) if len(policy_outs) > 1 else (vision_out_cast, policy_outs[0])
 
-  return run_supercombo
+    inputs.update({road_key: img, wide_key: big_img})
+    if 'features_buffer' not in inputs:
+      inputs['features_buffer'] = sample_skip_fn(feat_q)
+
+    policy_out = next(iter(policy_runners[0](inputs).values())).cast('float32').realize()
+    if 'features_buffer' not in inputs and features_slice is not None:
+      new_feat = policy_out[:, features_slice].reshape(1, -1).unsqueeze(0)
+      shift_and_sample(feat_q, new_feat, sample_skip_fn).realize()
+    return policy_out
+
+  return runner
 
 
-def make_run_vision_multi_policy(vision_runner, policy_runners, nv12: NV12Frame, model_w, model_h,
-                                 vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                                 vision_road_key, vision_wide_key, prepare_only=False):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
-  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
-  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+def compile_and_warmup(nv12: NV12Frame, model_size: tuple[int, int], prepare_only: bool, frame_skip: int, vision_runner, policy_runners: list, metadata: dict):
+  print(f"Compiling combined JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
 
-  def run_multi_policy(img_q, big_img_q, feat_q, desire_q, desire,
-                       traffic_convention, tfm, big_tfm, frame, big_frame, **extra):
-    npy_tensors = [tfm.to(Device.DEFAULT), big_tfm.to(Device.DEFAULT),
-                   desire.to(Device.DEFAULT), traffic_convention.to(Device.DEFAULT)]
-    extra_device = {k: extra[k].to(Device.DEFAULT) for k in extra_policy_keys}
-    Tensor.realize(*npy_tensors, *extra_device.values())
-    tfm, big_tfm, desire, traffic_convention = npy_tensors
+  all_shapes = {key: value for meta in metadata.values() for key, value in meta['input_shapes'].items()}
 
-    img = shift_and_sample(img_q, frame_prepare(frame, tfm).unsqueeze(0), sample_skip_fn)
-    big_img = shift_and_sample(big_img_q, frame_prepare(big_frame, big_tfm).unsqueeze(0), sample_skip_fn)
+  feat_meta = metadata.get('vision') or metadata.get('model') or metadata.get('policy')
+  if not feat_meta:
+    raise ValueError("Could not find vision, model, or policy metadata.")
 
-    if prepare_only:
-      return img, big_img
+  features_slice = feat_meta['output_slices']['hidden_state']
+  WARP_DEV = 'CPU' if "USBGPU" in os.environ else Device.DEFAULT
 
-    vision_out = next(iter(vision_runner({vision_road_key: img, vision_wide_key: big_img}).values())).cast('float32')
+  is_supercombo = vision_runner is None
+  run_func = create_jit_runner(vision_runner, policy_runners, nv12, model_size, features_slice, frame_skip, all_shapes, prepare_only)
+  run_jit = TinyJit(run_func, prune=True)
+  queues, npy_arrays = generate_queues_and_npy(all_shapes, frame_skip, Device.DEFAULT, is_supercombo=is_supercombo)
 
-    new_feat = vision_out[:, vision_features_slice].reshape(1, -1).unsqueeze(0)
-    feat_buf = shift_and_sample(feat_q, new_feat, sample_skip_fn)
-    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
-
-    inputs = {'features_buffer': feat_buf, desire_key: desire_buf, 'traffic_convention': traffic_convention, **extra_device}
-
-    policy_outputs = []
-    for runner in policy_runners:
-      policy_out = next(iter(runner(inputs).values())).cast('float32')
-      policy_outputs.append(policy_out)
-
-    return (vision_out, *policy_outputs)
-
-  return run_multi_policy
-
-
-def _warmup_and_serialize(run_jit, input_queues, npy, nv12):
   for i in range(3):
     rng = np.random.default_rng(42 + i)
-    frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-    big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
-    for v in npy.values():
-      v[:] = rng.standard_normal(v.shape).astype(v.dtype)
+    frame = Tensor.randint(nv12.size, low=0, high=256, dtype=dtypes.uint8, device=WARP_DEV).realize()
+    big_frame = Tensor.randint(nv12.size, low=0, high=256, dtype=dtypes.uint8, device=WARP_DEV).realize()
+    for arr in npy_arrays.values():
+      arr[:] = rng.standard_normal(arr.shape).astype(arr.dtype)
+
     Device.default.synchronize()
-    st = time.perf_counter()
-    run_jit(**input_queues, frame=frame, big_frame=big_frame)
-    mt = time.perf_counter()
+    start_time = time.perf_counter()
+    run_jit(**queues, frame=frame, big_frame=big_frame)
+    mid_time = time.perf_counter()
     Device.default.synchronize()
-    et = time.perf_counter()
-    print(f"  [{i + 1}/3] enqueue {(mt - st) * 1e3:6.2f} ms -- total {(et - st) * 1e3:6.2f} ms")
-  return pickle.loads(pickle.dumps(run_jit))
+    print(f"  [{i + 1}/3] enqueue {(mid_time - start_time) * 1e3:6.2f} ms -- total {(time.perf_counter() - start_time) * 1e3:6.2f} ms")
+
+  # TODO-SP: switch to dump_oob/load_oob on next full recompile of all models
+  return pickle.loads(pickle.dumps(run_jit)) if not prepare_only else run_jit
 
 
-def compile_supercombo(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
-                       model_runner, metadata):
-  print(f"Compiling combined supercombo JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
-
-  features_slice = metadata['output_slices']['hidden_state']
-  input_shapes = metadata['input_shapes']
-
-  _run = make_run_supercombo(model_runner, nv12, model_w, model_h,
-                             features_slice, frame_skip, input_shapes, prepare_only)
-  run_jit = TinyJit(_run, prune=True)
-
-  input_queues, npy = make_supercombo_input_queues(input_shapes, frame_skip, Device.DEFAULT)
-
-  run_jit = _warmup_and_serialize(run_jit, input_queues, npy, nv12)
-  return run_jit
+def _parse_size(size_str: str) -> tuple[int, int]:
+  width, height = size_str.lower().split('x')
+  return int(width), int(height)
 
 
-def compile_multi_policy(nv12: NV12Frame, model_w, model_h, prepare_only, frame_skip,
-                         vision_runner, policy_runners, vision_metadata, policy_metadata):
-  print(f"Compiling combined multi-policy JIT for {nv12.width}x{nv12.height} (prepare_only={prepare_only})...")
-
-  vision_features_slice = vision_metadata['output_slices']['hidden_state']
-  vision_input_shapes = vision_metadata['input_shapes']
-  policy_input_shapes = policy_metadata['input_shapes']
-  desire_key = _detect_desire_key(policy_input_shapes)
-  extra_policy_keys = [k for k in policy_input_shapes if k not in ('features_buffer', desire_key, 'traffic_convention')]
-  vision_road_key, vision_wide_key = _detect_vision_keys(vision_input_shapes)
-
-  _run = make_run_vision_multi_policy(vision_runner, policy_runners, nv12, model_w, model_h,
-                                      vision_features_slice, frame_skip, desire_key, extra_policy_keys,
-                                      vision_road_key, vision_wide_key, prepare_only)
-  run_jit = TinyJit(_run, prune=True)
-
-  input_queues, npy = make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, Device.DEFAULT)
-
-  run_jit = _warmup_and_serialize(run_jit, input_queues, npy, nv12)
-  return run_jit
+def read_file_chunked_to_shm(path):
+  if not path:
+    return None
+  import atexit
+  import shutil
+  from openpilot.common.file_chunker import open_file_chunked
+  from openpilot.common.hardware.hw import Paths
+  shm_path = os.path.join(Paths.shm_path(), os.path.basename(path))
+  atexit.register(lambda: os.path.exists(shm_path) and os.remove(shm_path))
+  with open(shm_path, 'wb') as dst, open_file_chunked(path) as src:
+    shutil.copyfileobj(src, dst)
+  return shm_path
 
 
-def _parse_size(s):
-  w, h = s.lower().split('x')
-  return int(w), int(h)
+def _compile_for_resolutions(camera_resolutions: list, model_size: tuple[int, int], frame_skip: int,
+               vision_runner, policy_runners: list, metadata: dict) -> dict:
+  from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+  return {
+    (cam_w, cam_h): {
+      name: compile_and_warmup(NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h)), model_size, prepare_only,
+                   frame_skip, vision_runner, policy_runners, metadata)
+      for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
+    }
+    for cam_w, cam_h in camera_resolutions
+  }
+
+
+def _load_policy_runners(args: argparse.Namespace) -> tuple[list, list]:
+  runners, keys = [], []
+  for name, onnx_arg in [('policy', args.policy_onnx), ('off_policy', args.off_policy_onnx), ('on_policy', args.on_policy_onnx)]:
+    if onnx_arg:
+      runners.append(OnnxRunner(onnx_arg))
+      keys.append(name)
+  return runners, keys
 
 
 if __name__ == "__main__":
-  from tinygrad.nn.onnx import OnnxRunner
-  from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
   from openpilot.selfdrive.modeld.get_model_metadata import make_metadata_dict
+  from tinygrad.nn.onnx import OnnxRunner
 
-  p = argparse.ArgumentParser(description="Compile combined JIT pkl for sunnypilot modeld_v2")
-  p.add_argument('--model-type', choices=MODEL_TYPES, required=True)
-  p.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
-  p.add_argument('--camera-resolutions', type=_parse_size, nargs='+', required=True)
-  p.add_argument('--frame-skip', type=int, default=None, help='frame skip value (auto-derived if not provided)')
-  p.add_argument('--output', required=True)
+  parser = argparse.ArgumentParser(description="Compile combined JIT pkl for sunnypilot modeld_v2")
+  parser.add_argument('--model-type', choices=MODEL_TYPES, required=True)
+  parser.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
+  parser.add_argument('--camera-resolutions', type=_parse_size, nargs='+', required=True)
+  parser.add_argument('--frame-skip', type=int, default=None, help='frame skip value (auto-derived if not provided)')
+  parser.add_argument('--output', required=True)
 
-  p.add_argument('--vision-onnx', help='vision ONNX (for split models)')
-  p.add_argument('--policy-onnx', help='policy ONNX (for vision_policy)')
-  p.add_argument('--off-policy-onnx', help='off-policy ONNX (for vision_multi_policy)')
-  p.add_argument('--on-policy-onnx', help='on-policy ONNX (for vision_multi_policy)')
-  p.add_argument('--supercombo-onnx', help='supercombo ONNX (for supercombo)')
+  parser.add_argument('--vision-onnx', help='vision ONNX (for split models)')
+  parser.add_argument('--policy-onnx', help='policy ONNX (for vision_policy)')
+  parser.add_argument('--off-policy-onnx', help='off-policy ONNX (for vision_multi_policy)')
+  parser.add_argument('--on-policy-onnx', help='on-policy ONNX (for vision_multi_policy)')
+  parser.add_argument('--supercombo-onnx', help='supercombo ONNX (for supercombo)')
 
-  args = p.parse_args()
-  out = defaultdict(dict)
+  args = parser.parse_args()
+  output_data = defaultdict(dict)
+
+  args.vision_onnx = read_file_chunked_to_shm(args.vision_onnx)
+  args.policy_onnx = read_file_chunked_to_shm(args.policy_onnx)
+  args.off_policy_onnx = read_file_chunked_to_shm(args.off_policy_onnx)
+  args.on_policy_onnx = read_file_chunked_to_shm(args.on_policy_onnx)
+  args.supercombo_onnx = read_file_chunked_to_shm(args.supercombo_onnx)
+
+  vision_runner = OnnxRunner(args.vision_onnx) if args.vision_onnx else None
 
   if args.model_type == 'vision_policy':
-    assert args.vision_onnx and args.policy_onnx
-    vision_runner = OnnxRunner(args.vision_onnx)
-    policy_runner = OnnxRunner(args.policy_onnx)
-    out['metadata']['vision'] = make_metadata_dict(args.vision_onnx)
-    out['metadata']['policy'] = make_metadata_dict(args.policy_onnx)
-
-    frame_skip = args.frame_skip if args.frame_skip is not None else derive_frame_skip(out['metadata']['vision']['input_shapes'],
-                                                                                       out['metadata']['policy']['input_shapes'])
-
-    for cam_w, cam_h in args.camera_resolutions:
-      nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-      model_w, model_h = args.model_size
-      out[(cam_w, cam_h)] = {
-        name: compile_split_policy(nv12, model_w, model_h, prepare_only, frame_skip,
-                                   vision_runner, policy_runner,
-                                   out['metadata']['vision'], out['metadata']['policy'])
-        for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
-      }
-
+    assert vision_runner and args.policy_onnx
+    policy_runners = [OnnxRunner(args.policy_onnx)]
+    output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx), 'policy': make_metadata_dict(args.policy_onnx)}
   elif args.model_type == 'supercombo':
     assert args.supercombo_onnx
-    model_runner = OnnxRunner(args.supercombo_onnx)
-    out['metadata']['model'] = make_metadata_dict(args.supercombo_onnx)
-
-    frame_skip = args.frame_skip if args.frame_skip is not None else derive_frame_skip({}, out['metadata']['model']['input_shapes'])
-
-    for cam_w, cam_h in args.camera_resolutions:
-      nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-      model_w, model_h = args.model_size
-      out[(cam_w, cam_h)] = {
-        name: compile_supercombo(nv12, model_w, model_h, prepare_only, frame_skip,
-                                 model_runner, out['metadata']['model'])
-        for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
-      }
-
+    policy_runners = [OnnxRunner(args.supercombo_onnx)]
+    output_data['metadata'] = {'model': make_metadata_dict(args.supercombo_onnx)}
   elif args.model_type == 'vision_multi_policy':
-    assert args.vision_onnx
-    vision_runner = OnnxRunner(args.vision_onnx)
-    out['metadata']['vision'] = make_metadata_dict(args.vision_onnx)
+    assert vision_runner
+    policy_runners, policy_names = _load_policy_runners(args)
+    output_data['metadata'] = {'vision': make_metadata_dict(args.vision_onnx)}
+    for name in policy_names:
+      runner_arg = getattr(args, f"{name}_onnx")
+      output_data['metadata'][name] = make_metadata_dict(runner_arg)
 
-    policy_runners = []
-    policy_onnxes = []
-    if args.policy_onnx:
-      policy_onnxes.append(('policy', args.policy_onnx))
-    if args.off_policy_onnx:
-      policy_onnxes.append(('off_policy', args.off_policy_onnx))
-    if args.on_policy_onnx:
-      policy_onnxes.append(('on_policy', args.on_policy_onnx))
+  policy_keys = [key for key in output_data['metadata'].keys() if key != 'vision']
+  first_policy_meta = output_data['metadata'][policy_keys[0]] if policy_keys else {}
+  vision_meta = output_data['metadata'].get('vision', {})
 
-    for name, onnx_path in policy_onnxes:
-      runner = OnnxRunner(onnx_path)
-      policy_runners.append(runner)
-      out['metadata'][name] = make_metadata_dict(onnx_path)
+  derived_frame_skip = args.frame_skip or derive_frame_skip(vision_meta.get('input_shapes', {}), first_policy_meta.get('input_shapes', {}))
+  output_data.update(_compile_for_resolutions(args.camera_resolutions, args.model_size, derived_frame_skip,
+                        vision_runner, policy_runners, output_data['metadata']))
 
-    first_policy_key = policy_onnxes[0][0]
-    frame_skip = args.frame_skip if args.frame_skip is not None else derive_frame_skip(out['metadata']['vision']['input_shapes'],
-                                                                                       out['metadata'][first_policy_key]['input_shapes'])
+  with open(args.output, "wb") as file:
+    # TODO-SP: switch to dump_oob from openpilot/selfdrive/helpers on next full recompile of all models
+    pickle.dump(output_data, file)
 
-    for cam_w, cam_h in args.camera_resolutions:
-      nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-      model_w, model_h = args.model_size
-      out[(cam_w, cam_h)] = {
-        name: compile_multi_policy(nv12, model_w, model_h, prepare_only, frame_skip,
-                                   vision_runner, policy_runners,
-                                   out['metadata']['vision'], out['metadata'][first_policy_key])
-        for name, prepare_only in [('warp_enqueue', True), ('run_policy', False)]
-      }
-
-  with open(args.output, "wb") as f:
-    pickle.dump(out, f)
   pkl_size = os.path.getsize(args.output)
   print(f"Saved combined JIT to {args.output} ({pkl_size / 1e6:.2f} MB)")
 
   from openpilot.common.file_chunker import chunk_file, get_chunk_targets
   chunk_targets = get_chunk_targets(args.output, pkl_size)
   chunk_file(args.output, chunk_targets)
-  num_chunks = len(chunk_targets) - 1
-  print(f"Chunked into {num_chunks} file(s)")
+  print(f"Chunked into {len(chunk_targets) - 1} file(s)")

openpilot/sunnypilot/modeld_v2/install_models_pc.py

@@ -1,75 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import shutil
-import pickle
-import codecs
-from pathlib import Path
-
-from openpilot.common.hardware.hw import Paths
-from openpilot.sunnypilot.modeld_v2.get_model_metadata import MetadataOnnxPBParser, get_name_and_shape, get_metadata_value_by_name
-
-
-def generate_metadata_pkl(model_path, output_path):
-  try:
-    model = MetadataOnnxPBParser(model_path).parse()
-    output_slices = get_metadata_value_by_name(model, 'output_slices')
-    if not output_slices:
-      return False
-    metadata = {
-      'model_checkpoint': get_metadata_value_by_name(model, 'model_checkpoint'),
-      'output_slices': pickle.loads(codecs.decode(output_slices.encode(), "base64")),
-      'input_shapes': dict(get_name_and_shape(x) for x in model["graph"]["input"]),
-      'output_shapes': dict(get_name_and_shape(x) for x in model["graph"]["output"]),
-    }
-    with open(output_path, 'wb') as f:
-      pickle.dump(metadata, f)
-    return True
-  except Exception:
-    return False
-
-
-def install_models(model_dir):
-  model_dir = Path(model_dir)
-  models = ["driving_off_policy", "driving_on_policy", "driving_vision"]
-  found_models = []
-
-  for model in models:
-    if (model_dir / f"{model}.onnx").exists():
-      found_models.append(model)
-
-  if not found_models:
-    return
-
-  try:
-    custom_name = input(f"Found models ({', '.join(found_models)}). Enter model short name (e.g. wmiv4): ").strip()
-  except EOFError:
-    return
-
-  if not custom_name:
-    print("No name provided, skipping installation.")
-    return
-
-  dest_dir = Path(Paths.model_root())
-  dest_dir.mkdir(parents=True, exist_ok=True)
-
-  for model in found_models:
-    onnx_path = model_dir / f"{model}.onnx"
-    tinygrad_pkl = model_dir / f"{model}_tinygrad.pkl"
-    metadata_pkl = model_dir / f"{model}_metadata.pkl"
-
-    if not metadata_pkl.exists():
-      generate_metadata_pkl(onnx_path, metadata_pkl)
-
-    dest_tinygrad = dest_dir / f"{model}_{custom_name}_tinygrad.pkl"
-    dest_metadata = dest_dir / f"{model}_{custom_name}_metadata.pkl"
-
-    if tinygrad_pkl.exists():
-      shutil.move(str(tinygrad_pkl), str(dest_tinygrad))
-    if metadata_pkl.exists():
-      shutil.move(str(metadata_pkl), str(dest_metadata))
-
-if __name__ == "__main__":
-  if len(sys.argv) < 2:
-    print("Usage: install_models_pc.py <model_dir>")
-    sys.exit(1)
-  install_models(sys.argv[1])

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -7,6 +7,7 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 import os
+os.environ['GMMU'] = '0'
 from openpilot.common.hardware import TICI
 os.environ['DEV'] = 'QCOM' if TICI else 'CPU'
 USBGPU = "USBGPU" in os.environ
@@ -23,6 +24,11 @@ from setproctitle import setproctitle
 from openpilot.cereal.messaging import PubMaster, SubMaster
 from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
 from opendbc.car.car_helpers import get_demo_car_params
+
+from tinygrad.tensor import Tensor
+from tinygrad.device import Device
+
+from openpilot.common.file_chunker import open_file_chunked
 from openpilot.common.swaglog import cloudlog
 from openpilot.common.params import Params
 from openpilot.common.filter_simple import FirstOrderFilter
@@ -30,6 +36,7 @@ from openpilot.common.realtime import config_realtime_process, DT_MDL
 from openpilot.common.transformations.camera import DEVICE_CAMERAS
 from openpilot.common.transformations.model import get_warp_matrix
 from openpilot.system import sentry
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
 from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan, smooth_value
 
@@ -37,6 +44,7 @@ from openpilot.sunnypilot.modeld_v2.fill_model_msg import fill_model_msg, fill_p
 from openpilot.sunnypilot.modeld_v2.constants import Plan
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
+from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
 
 from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
 from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
@@ -99,29 +107,37 @@ class ModelState(ModelStateBase):
     self._init_combined(pkl_path, cam_w, cam_h, model_bundle)
 
   def _init_combined(self, pkl_path, cam_w, cam_h, bundle):
-    from tinygrad.tensor import Tensor
-    from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-    from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
-    from tinygrad.device import Device
-
-    from openpilot.common.file_chunker import open_file_chunked
-
     cloudlog.warning(f"loading combined pkl: {pkl_path}")
+    # TODO-SP: switch to load_oob from openpilot/selfdrive/helpers on next full recompile of all models
     jits = pickle.load(open_file_chunked(pkl_path))
 
     self.DEV = Device.DEFAULT
+    self.WARP_DEV = 'CPU' if USBGPU else self.DEV
+    self.QUEUE_DEV = self.DEV
 
     metadata = jits['metadata']
+
+    self._run_policy = jits[(cam_w, cam_h)]['run_policy']
+    self._warp_enqueue = jits[(cam_w, cam_h)]['warp_enqueue']
+
+    # TODO-SP: Remove legacy use_packed detection block after all models are recompiled
+    captured = getattr(self._run_policy, 'captured', None)
+    if captured is not None:
+      use_packed = 'packed_npy_inputs' in getattr(captured, 'expected_names', [])
+    else:
+      use_packed = True
+
     if 'model' in metadata:
       model_metadata = metadata['model']
       self.vision_output_slices = model_metadata['output_slices']
       self.policy_output_slices = {}
       self._policy_slices_list = []
       self._combined_model_type = 'supercombo'
-      self._vision_input_names = [k for k in model_metadata['input_shapes'] if 'img' in k]
+      self._vision_input_names = [key for key in model_metadata['input_shapes'] if 'img' in key]
       from openpilot.sunnypilot.modeld_v2.compile_modeld import make_supercombo_input_queues
       frame_skip = derive_frame_skip({}, model_metadata['input_shapes'])
-      self.input_queues, self.numpy_inputs = make_supercombo_input_queues(model_metadata['input_shapes'], frame_skip, device=self.DEV)
+      self.input_queues, self.numpy_inputs = make_supercombo_input_queues(model_metadata['input_shapes'],
+                                                                          frame_skip, device=self.QUEUE_DEV, use_packed=use_packed)
     else:
       vision_metadata = metadata['vision']
       policy_keys = [k for k in metadata if k != 'vision']
@@ -139,11 +155,12 @@ class ModelState(ModelStateBase):
       policy_input_shapes = first_policy_metadata['input_shapes']
       self._vision_input_names = [k for k in vision_input_shapes if 'img' in k]
       frame_skip = derive_frame_skip(vision_input_shapes, policy_input_shapes)
-      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_input_shapes, policy_input_shapes, frame_skip, device=self.DEV)
+      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_input_shapes, policy_input_shapes,
+                                                                     frame_skip, device=self.QUEUE_DEV, use_packed=use_packed)
 
-    from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
-    from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
-    self.parser = SplitParser() if self._combined_model_type != 'supercombo' else CombinedParser()
+    self._desire_key = next(key for key in self.numpy_inputs if key.startswith('desire'))
+    self._road_key = next(key for key in self._vision_input_names if 'big' not in key)
+    self._wide_key = next(key for key in self._vision_input_names if 'big' in key)
 
     is_20hz = bundle.is20hz if bundle else self._combined_model_type in ('split', 'multi_policy')
     if is_20hz:
@@ -153,20 +170,24 @@ class ModelState(ModelStateBase):
       from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
       self.constants = ModelConstants()
 
+    if self._combined_model_type != 'supercombo':
+      from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
+      self.parser = SplitParser()
+    else:
+      from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
+      self.parser = CombinedParser()
+
     self.prev_desire = np.zeros(self.constants.DESIRE_LEN, dtype=np.float32)
     self.full_frames: dict = {}
     self._blob_cache: dict = {}
     nv12_info = get_nv12_info(cam_w, cam_h)
     self.frame_buf_params = dict.fromkeys(self._vision_input_names, nv12_info)
 
-    self._run_policy = jits[(cam_w, cam_h)]['run_policy']
-    self._warp_enqueue = jits[(cam_w, cam_h)]['warp_enqueue']
-    road_name = next(k for k in self._vision_input_names if 'big' not in k)
-    yuv_size = self.frame_buf_params[road_name][3]
+    yuv_size = self.frame_buf_params[self._road_key][3]
     self._warp_enqueue(
       **self.input_queues,
-      frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.DEV).contiguous().realize(),
-      big_frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.DEV).contiguous().realize())
+      frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize(),
+      big_frame=Tensor(np.zeros(yuv_size, dtype=np.uint8), device=self.WARP_DEV).contiguous().realize())
 
 
   @property
@@ -179,30 +200,28 @@ class ModelState(ModelStateBase):
 
   @property
   def desire_key(self) -> str:
-    return next(k for k in self.numpy_inputs if k.startswith('desire'))
+    return self._desire_key
 
   def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
                 inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
-    from tinygrad.tensor import Tensor
-
     for key in bufs.keys():
       ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
       yuv_size = self.frame_buf_params[key][3]
       cache_key = (key, ptr)
       if cache_key not in self._blob_cache:
-        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.DEV)
+        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
       self.full_frames[key] = self._blob_cache[cache_key]
 
     desire_key = self.desire_key
     inputs[desire_key][0] = 0
     self.numpy_inputs[desire_key][:] = np.where(inputs[desire_key] - self.prev_desire > .99, inputs[desire_key], 0)
     self.prev_desire[:] = inputs[desire_key]
-    for key in ('traffic_convention', 'lateral_control_params'):
+    for key in ('traffic_convention', 'lateral_control_params', 'action_t'):
       if key in self.numpy_inputs and key in inputs:
         self.numpy_inputs[key][:] = inputs[key]
 
-    road_key = next(n for n in bufs if 'big' not in n)
-    wide_key = next(n for n in bufs if 'big' in n)
+    road_key = self._road_key
+    wide_key = self._wide_key
     self.numpy_inputs['tfm'][:, :] = transforms[road_key].reshape(3, 3)
     self.numpy_inputs['big_tfm'][:, :] = transforms[wide_key].reshape(3, 3)
 
@@ -216,17 +235,26 @@ class ModelState(ModelStateBase):
       model_output = raw_outputs.numpy().flatten()
       sliced = {k: model_output[np.newaxis, v] for k, v in self.vision_output_slices.items()}
       outputs = self.parser.parse_outputs(sliced)
+      if 'prev_feat' in self.numpy_inputs:
+        self.numpy_inputs['prev_feat'][:] = model_output[self.vision_output_slices['hidden_state']]
     else:
       vision_output = raw_outputs[0].numpy().flatten()
       vision_sliced = {k: vision_output[np.newaxis, v] for k, v in self.vision_output_slices.items()}
       outputs = self.parser.parse_vision_outputs(vision_sliced)
 
+      if 'prev_feat' in self.numpy_inputs and 'hidden_state' in self.vision_output_slices:
+        self.numpy_inputs['prev_feat'][:] = vision_output[self.vision_output_slices['hidden_state']]
+
       for i, policy_slices in enumerate(self._policy_slices_list):
         policy_output = raw_outputs[i + 1].numpy().flatten()
         policy_sliced = {k: policy_output[np.newaxis, v] for k, v in policy_slices.items()}
         parsed = self.parser.parse_policy_outputs(policy_sliced)
-        if 'off' in self._policy_keys[i] and self._has_on_policy:
+        if ('off' in self._policy_keys[i]
+          and self._has_on_policy
+          and any('plan' in self._policy_slices_list[j] for j, k in enumerate(self._policy_keys) if 'on' in k.lower())):
+
           parsed.pop('plan', None)
+
         outputs.update(parsed)
 
       if 'planplus' in outputs and 'plan' in outputs:
@@ -237,17 +265,30 @@ class ModelState(ModelStateBase):
       buf[0, :-1] = buf[0, 1:]
       buf[0, -1, :] = outputs['desired_curvature'][0, :] if not self.mlsim else 0
 
+    # TODO-SP: This is a hack to prevent GPU corruption by calculating in CPU space, it can be removed on next recompile
+    if 'prev_feat' not in self.numpy_inputs and 'feat_q' in self.input_queues:
+      feat_val = self.input_queues['feat_q'].numpy()
+      self.input_queues['feat_q'].assign(feat_val).realize()
+
     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) -> log.ModelDataV2.Action:
-    plan = model_output['plan'][0]
-    desired_accel, should_stop = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
-                                                     action_t=long_action_t)
+    if 'action' not in model_output:
+      plan = model_output['plan'][0]
+      desired_accel, should_stop = get_accel_from_plan(plan[:, Plan.VELOCITY][:, 0], plan[:, Plan.ACCELERATION][:, 0], self.constants.T_IDXS,
+                                                       action_t=long_action_t)
+
+      curvature_plan = (plan + (self.PLANPLUS_CONTROL - 1.0) * model_output['planplus'][0]
+                        if 'planplus' in model_output and self.PLANPLUS_CONTROL != 1.0 else plan)
+      desired_curvature = get_curvature_from_output(model_output, curvature_plan, v_ego, lat_action_t, self.mlsim)
+    else:
+      desired_accel = model_output['action'][0, 1]
+      desired_curvature = model_output['action'][0, 0] / (max(1.0, v_ego))**2
+      should_stop = (v_ego < 0.3 and desired_accel < 0.1)
+
     desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, self.LONG_SMOOTH_SECONDS)
 
-    curvature_plan = plan + (self.PLANPLUS_CONTROL - 1.0) * model_output['planplus'][0] if 'planplus' in model_output and self.PLANPLUS_CONTROL != 1.0 else plan
-    desired_curvature = get_curvature_from_output(model_output, curvature_plan, v_ego, lat_action_t, self.mlsim)
     if self.generation is not None and self.generation >= 10: # smooth curvature for post FOF models
       if v_ego > self.MIN_LAT_CONTROL_SPEED:
         desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, self.LAT_SMOOTH_SECONDS)
@@ -400,6 +441,12 @@ def main(demo=False):
 
     bufs = {name: buf_extra if 'big' in name else buf_main for name in model.vision_input_names}
     transforms = {name: model_transform_extra if 'big' in name else model_transform_main for name in model.vision_input_names}
+
+    frame_delay = DT_MDL # compensate for time passed since the frame was captured: current_time - timestamp_eof is 50ms on average
+    action_delay = DT_MDL / 2 # middle of the interval between model output (current state) and next frame (expected state)
+    lat_action_t = lat_delay + frame_delay + action_delay
+    long_action_t = long_delay + frame_delay + action_delay
+
     inputs:dict[str, np.ndarray] = {
       model.desire_key: vec_desire,
       'traffic_convention': traffic_convention,
@@ -408,6 +455,9 @@ def main(demo=False):
     if 'lateral_control_params' in model.numpy_inputs:
       inputs['lateral_control_params'] = np.array([v_ego, lat_delay], dtype=np.float32)
 
+    if 'action_t' in model.numpy_inputs:
+      inputs['action_t'] = np.array([lat_action_t, long_action_t], dtype=np.float32)
+
     mt1 = time.perf_counter()
     model_output = model.run(bufs, transforms, inputs, prepare_only)
     mt2 = time.perf_counter()
@@ -419,7 +469,7 @@ def main(demo=False):
       posenet_send = messaging.new_message('cameraOdometry')
       mdv2sp_send = messaging.new_message('modelDataV2SP')
 
-      action = model.get_action_from_model(model_output, prev_action, lat_delay + DT_MDL, long_delay + DT_MDL, v_ego)
+      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,

openpilot/sunnypilot/modeld_v2/parse_model_outputs.py

@@ -1,13 +1,16 @@
 import numpy as np
 from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
 
+
 def safe_exp(x, out=None):
   # -11 is around 10**14, more causes float16 overflow
   return np.exp(np.clip(x, -np.inf, 11), out=out)
 
+
 def sigmoid(x):
   return 1. / (1. + safe_exp(-x))
 
+
 def softmax(x, axis=-1):
   x -= np.max(x, axis=axis, keepdims=True)
   if x.dtype == np.float32 or x.dtype == np.float64:
@@ -17,6 +20,19 @@ def softmax(x, axis=-1):
   x /= np.sum(x, axis=axis, keepdims=True)
   return x
 
+
+def _infer_mhp(slice_size: int, prod_out_shape: int, max_in_n: int = 16, max_out_n: int = 6) -> tuple[int, int]:
+  for out_n in range(max_out_n + 1):
+    per = 2 * prod_out_shape + out_n
+    if per <= 0:
+      continue
+    if slice_size % per == 0:
+      in_n = slice_size // per
+      if 1 <= in_n <= max_in_n:
+        return in_n, out_n
+  return 1, 0  # single hypothesis, no weights — matches a non-MDN output
+
+
 class Parser:
   def __init__(self, ignore_missing=False):
     self.ignore_missing = ignore_missing
@@ -40,17 +56,22 @@ class Parser:
     raw = outs[name]
     outs[name] = sigmoid(raw)
 
-  def parse_mdn(self, name, outs, in_N=0, out_N=1, out_shape=None):
+  def parse_mdn(self, name, outs, out_shape, in_N=0, out_N=0):
     if self.check_missing(outs, name):
       return
     raw = outs[name]
-    raw = raw.reshape((raw.shape[0], max(in_N, 1), -1))
+
+    if in_N == 0 and out_N == 0:
+      prod = int(np.prod(out_shape))
+      in_N, out_N = _infer_mhp(raw.shape[1], prod)
+
+    raw = raw.reshape((raw.shape[0], in_N, -1))
 
     n_values = (raw.shape[2] - out_N)//2
     pred_mu = raw[:,:,:n_values]
     pred_std = safe_exp(raw[:,:,n_values: 2*n_values])
 
-    if in_N > 1:
+    if in_N > 1 and out_N > 0:
       weights = np.zeros((raw.shape[0], in_N, out_N), dtype=raw.dtype)
       for i in range(out_N):
         weights[:,:,i - out_N] = softmax(raw[:,:,i - out_N], axis=-1)
@@ -61,7 +82,6 @@ class Parser:
           weights[fidx] = weights[fidx][idxs]
           pred_mu[fidx] = pred_mu[fidx][idxs]
           pred_std[fidx] = pred_std[fidx][idxs]
-      assert out_shape is not None
       full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
       outs[name + '_weights'] = weights
       outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
@@ -74,37 +94,43 @@ class Parser:
           idxs = np.argsort(weights[fidx,:,hidx])[::-1]
           pred_mu_final[fidx, hidx] = pred_mu[fidx, idxs[0]]
           pred_std_final[fidx, hidx] = pred_std[fidx, idxs[0]]
+    elif in_N > 1 and out_N == 0:
+      # MHP without weights: keep every hypothesis intact, surface them as
+      # ``*_hypotheses`` and propagate the full multi-hypothesis tensor.
+      full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
+      outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
+      outs[name + '_stds_hypotheses'] = pred_std.reshape(full_shape)
+      pred_mu_final = pred_mu
+      pred_std_final = pred_std
     else:
       pred_mu_final = pred_mu
       pred_std_final = pred_std
 
-    if out_N > 1:
-      assert out_shape is not None
-      final_shape = tuple([raw.shape[0], out_N] + list(out_shape))
+    if out_N > 1 or (in_N > 1 and out_N == 0):
+      n_selections = out_N if out_N > 1 else in_N
+      final_shape = tuple([raw.shape[0], n_selections] + list(out_shape))
     else:
-      assert out_shape is not None
       final_shape = tuple([raw.shape[0],] + list(out_shape))
     outs[name] = pred_mu_final.reshape(final_shape)
     outs[name + '_stds'] = pred_std_final.reshape(final_shape)
 
   def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
-    self.parse_mdn('plan', outs, in_N=ModelConstants.PLAN_MHP_N, out_N=ModelConstants.PLAN_MHP_SELECTION,
-                   out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_WIDTH))
-    self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
-    self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
+    # supercombo (4955 / 102) and newer variants (e.g. 990 / 144).
+    self.parse_mdn('plan',   outs, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
+    self.parse_mdn('lane_lines', outs, out_shape=(ModelConstants.NUM_LANE_LINES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('road_edges', outs, out_shape=(ModelConstants.NUM_ROAD_EDGES, ModelConstants.IDX_N, ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('pose',         outs, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('road_transform', outs, out_shape=(ModelConstants.POSE_WIDTH,))
     if 'sim_pose' in outs:
-      self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
-    self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
-    self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
-                   out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
+      self.parse_mdn('sim_pose', outs, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('wide_from_device_euler', outs, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
+    self.parse_mdn('lead', outs, out_shape=(ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH))
     if 'lat_planner_solution' in outs:
-      self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N,ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
+      self.parse_mdn('lat_planner_solution', outs, out_shape=(ModelConstants.IDX_N, ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
     if 'desired_curvature' in outs:
-      self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
+      self.parse_mdn('desired_curvature', outs, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
     for k in ['lead_prob', 'lane_lines_prob', 'meta']:
       self.parse_binary_crossentropy(k, outs)
     self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
-    self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
+    self.parse_categorical_crossentropy('desire_pred',  outs, out_shape=(ModelConstants.DESIRE_PRED_LEN, ModelConstants.DESIRE_PRED_WIDTH))
     return outs

openpilot/sunnypilot/modeld_v2/parse_model_outputs_split.py

@@ -123,7 +123,7 @@ class Parser:
       self.parse_categorical_crossentropy('desire_state', outs, out_shape=(SplitModelConstants.DESIRE_PRED_WIDTH,))
     if 'lane_lines' in outs:
       self.parse_mdn('lane_lines', outs, in_N=0, out_N=0,
-                    out_shape=(SplitModelConstants.NUM_LANE_LINES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
+                     out_shape=(SplitModelConstants.NUM_LANE_LINES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
     if 'lane_lines_prob' in outs:
       self.parse_binary_crossentropy('lane_lines_prob', outs)
     if 'lead_prob' in outs:
@@ -134,9 +134,11 @@ class Parser:
       self.parse_binary_crossentropy('meta', outs)
     if 'road_edges' in outs:
       self.parse_mdn('road_edges', outs, in_N=0, out_N=0,
-                    out_shape=(SplitModelConstants.NUM_ROAD_EDGES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
+                     out_shape=(SplitModelConstants.NUM_ROAD_EDGES,SplitModelConstants.IDX_N,SplitModelConstants.LANE_LINES_WIDTH))
     if 'sim_pose' in outs:
       self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))
+    if 'action' in outs:
+      self.parse_mdn('action', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.ACTION_WIDTH,))
 
   def parse_vision_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
     self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(SplitModelConstants.POSE_WIDTH,))

openpilot/sunnypilot/modeld_v2/tests/test_combined_pkl_loader.py

@@ -67,22 +67,12 @@ class TestStockEquivalence:
     state = model_state_factory(ARCHETYPES['vision_policy_split'])
 
     frame_skip = derive_frame_skip(SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES)
-    # action_t is a deep-model prerequisite the SP loader doesn't provide yet; see skip_keys below
     stock_shapes = {**SPLIT_VISION_INPUT_SHAPES, **SPLIT_POLICY_INPUT_SHAPES, 'action_t': (1, 2)}
     stock_queues, stock_npy = make_input_queues(stock_shapes, frame_skip, device='NPY')
 
-    # TODO-SP: remove action_t skip once SP adds prerequisite for deep models (action_t input queue)
-    # prev_feat is a stock QCOM corruption workaround handled inside the SP loader's JIT path
-    skip_keys = {'action_t', 'prev_feat'}
-    # stock packs the per-key policy inputs into packed_npy_inputs; the npy views carry the individual keys
-    stock_queue_keys = set(stock_queues.keys())
-    if 'packed_npy_inputs' in stock_queue_keys:
-      stock_queue_keys.remove('packed_npy_inputs')
-      stock_queue_keys |= set(stock_npy.keys())
-    assert set(state.input_queues.keys()) == stock_queue_keys - skip_keys, \
-      f"Queue keys differ: v2={set(state.input_queues.keys())}, stock={stock_queue_keys}"
-    assert set(state.numpy_inputs.keys()) == set(stock_npy.keys()) - skip_keys, \
-      f"Npy keys differ: v2={set(state.numpy_inputs.keys())}, stock={set(stock_npy.keys())}"
+    assert set(state.input_queues.keys()) == set(stock_queues.keys())
+    assert {'desire', 'traffic_convention'} <= set(state.numpy_inputs.keys())
+    assert set(state.numpy_inputs.keys()) == set(stock_npy.keys()) - {'action_t', 'prev_feat'}
 
   def test_split_queue_keys_work_with_desire_key(self, model_state_factory):
     from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues

openpilot/sunnypilot/modeld_v2/tests/test_warp.py

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

openpilot/sunnypilot/modeld_v2/warp.py

@@ -1,171 +0,0 @@
-import pickle
-import time
-import numpy as np
-from pathlib import Path
-from tinygrad.tensor import Tensor
-from tinygrad.engine.jit import TinyJit
-from tinygrad.device import Device
-
-from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE
-from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
-from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, make_frame_prepare as _make_frame_prepare
-
-CAMERA_CONFIGS = [
-  (_ar_ox_fisheye.width, _ar_ox_fisheye.height),
-  (_os_fisheye.width, _os_fisheye.height),
-]
-
-
-def make_frame_prepare(cam_w, cam_h, model_w, model_h):
-  nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
-  return _make_frame_prepare(nv12, model_w, model_h)
-
-
-def warp_pkl_path(w, h):
-  from openpilot.selfdrive.modeld.helpers import MODELS_DIR
-  return MODELS_DIR / f'warp_{w}x{h}_tinygrad.pkl'
-
-
-def make_update_img_input(frame_prepare, model_w, model_h):
-  def update_img_input_tinygrad(tensor, frame, M_inv):
-    M_inv = M_inv.to(Device.DEFAULT)
-    new_img = frame_prepare(frame, M_inv)
-    tensor.assign(tensor[6:].cat(new_img, dim=0).contiguous())
-    return Tensor.cat(tensor[:6], tensor[-6:], dim=0).contiguous().reshape(1, 12, model_h//2, model_w//2)
-  return update_img_input_tinygrad
-
-
-def make_update_both_imgs(frame_prepare, model_w, model_h):
-  update_img = make_update_img_input(frame_prepare, model_w, model_h)
-  def update_both_imgs_tinygrad(calib_img_buffer, new_img, M_inv,
-                                calib_big_img_buffer, new_big_img, M_inv_big):
-    calib_img_pair = update_img(calib_img_buffer, new_img, M_inv)
-    calib_big_img_pair = update_img(calib_big_img_buffer, new_big_img, M_inv_big)
-    return calib_img_pair, calib_big_img_pair
-  return update_both_imgs_tinygrad
-
-MODELS_DIR = Path(__file__).parent / 'models'
-MODEL_W, MODEL_H = MEDMODEL_INPUT_SIZE
-UPSTREAM_BUFFER_LENGTH = 5
-
-
-def v2_warp_pkl_path(cam_w, cam_h, buffer_length):
-  return MODELS_DIR / f'warp_{cam_w}x{cam_h}_b{buffer_length}_tinygrad.pkl'
-
-
-def compile_v2_warp(cam_w, cam_h, buffer_length):
-  _, _, _, yuv_size = get_nv12_info(cam_w, cam_h)
-  img_buffer_shape = (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
-
-  print(f"Compiling v2 warp for {cam_w}x{cam_h} buffer_length={buffer_length}...")
-
-  frame_prepare = make_frame_prepare(cam_w, cam_h, MODEL_W, MODEL_H)
-  update_both_imgs = make_update_both_imgs(frame_prepare, MODEL_W, MODEL_H)
-  update_img_jit = TinyJit(update_both_imgs, prune=True)
-
-  full_buffer = Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize()
-  big_full_buffer = Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize()
-  new_frame_np = np.random.default_rng(0).integers(0, 256, yuv_size, dtype=np.uint8)
-  new_big_frame_np = np.random.default_rng(1).integers(0, 256, yuv_size, dtype=np.uint8)
-  for i in range(10):
-    img_inputs = [full_buffer,
-                  Tensor.from_blob(new_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-                  Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    big_img_inputs = [big_full_buffer,
-                      Tensor.from_blob(new_big_frame_np.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-                      Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')]
-    inputs = img_inputs + big_img_inputs
-    Device.default.synchronize()
-
-    st = time.perf_counter()
-    _ = update_img_jit(*inputs)
-    mt = time.perf_counter()
-    Device.default.synchronize()
-    et = time.perf_counter()
-    print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
-
-  pkl_path = v2_warp_pkl_path(cam_w, cam_h, buffer_length)
-  with open(pkl_path, "wb") as f:
-    pickle.dump(update_img_jit, f)
-  print(f"  Saved to {pkl_path}")
-
-  jit = pickle.load(open(pkl_path, "rb"))
-  verify_frame = np.random.default_rng(0).integers(0, 256, yuv_size, dtype=np.uint8)
-  verify_big_frame = np.random.default_rng(1).integers(0, 256, yuv_size, dtype=np.uint8)
-  fresh_inputs = [
-    Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize(),
-    Tensor.from_blob(verify_frame.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-    Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY'),
-    Tensor.zeros(img_buffer_shape, dtype='uint8').contiguous().realize(),
-    Tensor.from_blob(verify_big_frame.ctypes.data, (yuv_size,), dtype='uint8').realize(),
-    Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY'),
-  ]
-  jit(*fresh_inputs)
-
-
-class Warp:
-  def __init__(self, buffer_length=2):
-    self.buffer_length = buffer_length
-    self.img_buffer_shape = (buffer_length * 6, MODEL_H // 2, MODEL_W // 2)
-
-    self.jit_cache = {}
-    self.full_buffers = {k: Tensor.zeros(self.img_buffer_shape, dtype='uint8').contiguous().realize() for k in ['img', 'big_img']}
-    self._blob_cache: dict[int, Tensor] = {}
-    self._nv12_cache: dict[tuple[int, int], int] = {}
-    self.transforms_np = {k: np.zeros((3, 3), dtype=np.float32) for k in ['img', 'big_img']}
-    self.transforms = {k: Tensor(v, device='NPY').realize() for k, v in self.transforms_np.items()}
-
-  def process(self, bufs, transforms):
-    if not bufs:
-      return {}
-    road = next(n for n in bufs if 'big' not in n)
-    wide = next(n for n in bufs if 'big' in n)
-    cam_w, cam_h = bufs[road].width, bufs[road].height
-    key = (cam_w, cam_h)
-
-    if key not in self.jit_cache:
-      v2_pkl = v2_warp_pkl_path(cam_w, cam_h, self.buffer_length)
-      if v2_pkl.exists():
-        with open(v2_pkl, 'rb') as f:
-          self.jit_cache[key] = pickle.load(f)
-      elif self.buffer_length == UPSTREAM_BUFFER_LENGTH:
-        upstream_pkl = warp_pkl_path(cam_w, cam_h)
-        if upstream_pkl.exists():
-          with open(upstream_pkl, 'rb') as f:
-            self.jit_cache[key] = pickle.load(f)
-      if key not in self.jit_cache:
-        frame_prepare = make_frame_prepare(cam_w, cam_h, MODEL_W, MODEL_H)
-        update_both_imgs = make_update_both_imgs(frame_prepare, MODEL_W, MODEL_H)
-        self.jit_cache[key] = TinyJit(update_both_imgs, prune=True)
-
-    if key not in self._nv12_cache:
-      self._nv12_cache[key] = get_nv12_info(cam_w, cam_h)[3]
-    yuv_size = self._nv12_cache[key]
-
-    road_ptr = bufs[road].data.ctypes.data
-    wide_ptr = bufs[wide].data.ctypes.data
-    if road_ptr not in self._blob_cache:
-      self._blob_cache[road_ptr] = Tensor.from_blob(road_ptr, (yuv_size,), dtype='uint8')
-    if wide_ptr not in self._blob_cache:
-      self._blob_cache[wide_ptr] = Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
-    road_blob = self._blob_cache[road_ptr]
-    wide_blob = self._blob_cache[wide_ptr] if wide_ptr != road_ptr else Tensor.from_blob(wide_ptr, (yuv_size,), dtype='uint8')
-    np.copyto(self.transforms_np['img'], transforms[road].reshape(3, 3))
-    np.copyto(self.transforms_np['big_img'], transforms[wide].reshape(3, 3))
-
-    Device.default.synchronize()
-    res = self.jit_cache[key](
-      self.full_buffers['img'], road_blob, self.transforms['img'],
-      self.full_buffers['big_img'], wide_blob, self.transforms['big_img'],
-    )
-    out_road = res[0].realize()
-    out_wide = res[1].realize()
-
-    return {road: out_road, wide: out_wide}
-
-
-if __name__ == "__main__":
-  for cam_w, cam_h in CAMERA_CONFIGS:
-    for bl in [2, 5]:
-      compile_v2_warp(cam_w, cam_h, bl)

openpilot/sunnypilot/models/fetcher.py

@@ -6,11 +6,12 @@ See the LICENSE.md file in the root directory for more details.
 """
 
 import time
-
+import os
 import requests
 from requests.exceptions import (SSLError, RequestException, HTTPError)
 from openpilot.common.params import Params
 from openpilot.common.swaglog import cloudlog
+from openpilot.common.hardware.hw import Paths
 from openpilot.sunnypilot.models.helpers import is_bundle_version_compatible
 
 from openpilot.cereal import custom
@@ -26,11 +27,35 @@ class ModelParser:
     download_uri.sha256 = download_uri_data.get("sha256")
     return download_uri
 
+  @staticmethod
+  def _parse_chunk(chunk_data) -> custom.ModelManagerSP.Chunk:
+    chunk = custom.ModelManagerSP.Chunk()
+    chunk.fileName = chunk_data.get("file_name")
+    chunk.sha256 = chunk_data.get("sha256")
+    return chunk
+
   @staticmethod
   def _parse_artifact(artifact_data) -> custom.ModelManagerSP.Artifact:
     artifact = custom.ModelManagerSP.Artifact()
     artifact.fileName = artifact_data.get("file_name")
     artifact.downloadUri = ModelParser._parse_download_uri(artifact_data.get("download_uri", {}))
+
+    if "chunks" in artifact_data:
+      artifact.chunks = [ModelParser._parse_chunk(chunk_data) for chunk_data in artifact_data["chunks"]]
+
+      try:
+        model_dir = Paths.model_root()
+        os.makedirs(model_dir, exist_ok=True)
+        manifest_path = os.path.join(model_dir, f"{artifact.fileName}.chunkmanifest")
+        num_chunks = str(len(artifact.chunks))
+
+        if not os.path.exists(manifest_path) or open(manifest_path).read().strip() != num_chunks:
+          with open(manifest_path, "w") as f:
+            f.write(num_chunks)
+          cloudlog.info(f"Wrote chunk manifest for {artifact.fileName}: {num_chunks} chunks")
+      except Exception as e:
+        cloudlog.warning(f"Failed to write chunk manifest for {artifact.fileName}: {e}")
+
     return artifact
 
   @staticmethod
@@ -39,8 +64,6 @@ class ModelParser:
 
     model.type = model_data.get("type")
     model.artifact = ModelParser._parse_artifact(model_data.get("artifact", {}))
-    if metadata := model_data.get("metadata"):
-      model.metadata = ModelParser._parse_artifact(metadata)
     return model
 
   @staticmethod
@@ -116,7 +139,7 @@ class ModelCache:
 
 class ModelFetcher:
   """Handles fetching and caching of model data from remote source"""
-  MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v17.json"
+  MODEL_URL = "https://raw.githubusercontent.com/sunnypilot/sunnypilot-models/refs/heads/gh-pages/docs/driving_models_v18.json"
 
   def __init__(self, params: Params):
     self.params = params
@@ -184,4 +207,5 @@ if __name__ == "__main__":
       # Print artifact details
       print(f"Artifact: {model.artifact.fileName}, Download URI: {model.artifact.downloadUri.uri}")
       # Print metadata details
-      print(f"Metadata: {model.metadata.fileName}, Download URI: {model.metadata.downloadUri.uri}")
+      if model.artifact.chunks:
+        print(f"Contains {len(model.artifact.chunks)} chunks.")

openpilot/sunnypilot/models/helpers.py

@@ -18,7 +18,7 @@ from openpilot.sunnypilot.models.constants import Meta, MetaSimPose, MetaTombRai
 from openpilot.common.hardware.hw import Paths
 
 # SET ME TO THE EXACT JSON VERSION WE SET IN SUNNYPILOT_MODELS REPO
-REQUIRED_JSON_VERSION = 15
+REQUIRED_JSON_VERSION = 16
 
 CUSTOM_MODEL_PATH = Paths.model_root()
 METADATA_PATH = Path(__file__).parent / '../models/supercombo_metadata.pkl'
@@ -56,12 +56,20 @@ def is_bundle_version_compatible(bundle: dict) -> bool:
 
 def _bundle_artifacts(bundle: custom.ModelManagerSP.ModelBundle) -> list[tuple[str, str]]:
   artifacts = []
+  from openpilot.common.file_chunker import get_chunk_name
   for model in getattr(bundle, 'models', []) or []:
-    for artifact in (getattr(model, 'artifact', None), getattr(model, 'metadata', None)):
-      if artifact and getattr(artifact, 'fileName', None) and getattr(artifact, 'downloadUri', None):
-        sha256 = getattr(artifact.downloadUri, 'sha256', None)
-        if sha256:
-          artifacts.append((artifact.fileName, sha256))
+    for artifact in (getattr(model, 'artifact', None),):
+      if artifact and getattr(artifact, 'fileName', None):
+        if len(artifact.chunks) > 0:
+          for i, chunk in enumerate(artifact.chunks):
+            chunk_name = get_chunk_name(artifact.fileName, i, len(artifact.chunks))
+            if getattr(chunk, 'sha256', None):
+              artifacts.append((chunk_name, chunk.sha256))
+        else:
+          if getattr(artifact, 'downloadUri', None):
+            sha256 = getattr(artifact.downloadUri, 'sha256', None)
+            if sha256:
+              artifacts.append((artifact.fileName, sha256))
   return artifacts
 
 
@@ -156,8 +164,7 @@ def _get_model():
 
 
 def load_metadata():
-  model = _get_model()
-  metadata_path = f"{CUSTOM_MODEL_PATH}/{model.metadata.fileName}" if model else METADATA_PATH
+  metadata_path = METADATA_PATH
 
   with open(metadata_path, 'rb') as f:
     return pickle.load(f)

openpilot/sunnypilot/models/manager.py

@@ -38,11 +38,11 @@ class ModelManagerSP:
     if not self.selected_bundle:
       return
     for model in self.selected_bundle.models:
-      for artifact in (model.artifact, model.metadata):
-        if artifact is not source_artifact and artifact.fileName == source_artifact.fileName:
-          artifact.downloadProgress.status = source_artifact.downloadProgress.status
-          artifact.downloadProgress.progress = source_artifact.downloadProgress.progress
-          artifact.downloadProgress.eta = source_artifact.downloadProgress.eta
+      artifact = model.artifact
+      if artifact is not source_artifact and artifact.fileName == source_artifact.fileName:
+        artifact.downloadProgress.status = source_artifact.downloadProgress.status
+        artifact.downloadProgress.progress = source_artifact.downloadProgress.progress
+        artifact.downloadProgress.eta = source_artifact.downloadProgress.eta
 
   def _calculate_eta(self, filename: str, progress: float) -> int:
     """Calculate ETA based on elapsed time and current progress"""
@@ -89,20 +89,16 @@ class ModelManagerSP:
         del self._download_start_times[model.fileName]
 
   async def _download_chunked(self, base_url: str, base_path: str, artifact) -> None:
-    from openpilot.common.file_chunker import get_manifest_path, get_chunk_name
-    manifest_url = get_manifest_path(base_url)
+    from openpilot.common.file_chunker import get_chunk_name, get_manifest_path
+
+    num_chunks = len(artifact.chunks)
+    if num_chunks == 0:
+      raise ValueError("No chunks defined in artifact")
+
     manifest_path = get_manifest_path(base_path)
-
-    async with aiohttp.ClientSession() as session:
-      async with session.get(manifest_url) as resp:
-        if resp.status == 404:
-          raise FileNotFoundError
-        resp.raise_for_status()
-        num_chunks = int((await resp.read()).strip())
-
     self._download_start_times[artifact.fileName] = time.monotonic()
 
-    for i in range(num_chunks):
+    for i, _ in enumerate(artifact.chunks):
       chunk_url = get_chunk_name(base_url, i, num_chunks)
       chunk_path = get_chunk_name(base_path, i, num_chunks)
       chunk_downloaded = 0
@@ -117,7 +113,7 @@ class ModelManagerSP:
               if self.params.get("ModelManager_DownloadIndex") is None:
                 raise Exception("Download cancelled")
               intra = chunk_downloaded / max(chunk_size, 1)
-              progress = min(99, (i + intra) / num_chunks * 100)
+              progress = min(99.0, ((i + intra) / num_chunks) * 100)
               artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.downloading
               artifact.downloadProgress.progress = progress
               artifact.downloadProgress.eta = self._calculate_eta(artifact.fileName, progress)
@@ -140,7 +136,22 @@ class ModelManagerSP:
     full_path = os.path.join(destination_path, filename)
 
     try:
-      if await verify_file(full_path, expected_hash):
+      is_cached = False
+      if len(artifact.chunks) > 0:
+        from openpilot.common.file_chunker import get_chunk_name
+        chunks_valid = True
+        for i, chunk in enumerate(artifact.chunks):
+          chunk_path = get_chunk_name(full_path, i, len(artifact.chunks))
+          if not await verify_file(chunk_path, chunk.sha256):
+            chunks_valid = False
+            break
+        if chunks_valid and len(artifact.chunks) > 0:
+          is_cached = True
+      else:
+        if await verify_file(full_path, expected_hash):
+          is_cached = True
+
+      if is_cached:
         artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
         artifact.downloadProgress.progress = 100
         artifact.downloadProgress.eta = 0
@@ -148,13 +159,17 @@ class ModelManagerSP:
         self._report_status()
         return
 
-      try:
+      if len(artifact.chunks) > 0:
         await self._download_chunked(url, full_path, artifact)
-      except (FileNotFoundError, aiohttp.ClientResponseError):
+        from openpilot.common.file_chunker import get_chunk_name
+        for i, chunk in enumerate(artifact.chunks):
+          chunk_path = get_chunk_name(full_path, i, len(artifact.chunks))
+          if not await verify_file(chunk_path, chunk.sha256):
+            raise ValueError(f"Hash validation failed for chunk {i+1} of {filename}")
+      else:
         await self._download_file(url, full_path, artifact)
-
-      if not await verify_file(full_path, expected_hash):
-        raise ValueError(f"Hash validation failed for {filename}")
+        if not await verify_file(full_path, expected_hash):
+          raise ValueError(f"Hash validation failed for {filename}")
 
       artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.downloaded
       artifact.downloadProgress.progress = 100
@@ -170,18 +185,15 @@ class ModelManagerSP:
       artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.failed
       artifact.downloadProgress.eta = 0
       self._sync_artifact_progress(artifact)
-      self.selected_bundle.status = custom.ModelManagerSP.DownloadStatus.failed
+      if self.selected_bundle:
+        self.selected_bundle.status = custom.ModelManagerSP.DownloadStatus.failed
       self._report_status()
       self._download_start_times.pop(artifact.fileName, None)
       raise
 
   async def _process_model(self, model, destination_path: str) -> None:
     """Processes a single model download including verification"""
-    model_artifact = model.artifact
-    metadata_artifact = model.metadata
-
-    await self._process_artifact(metadata_artifact, destination_path)
-    await self._process_artifact(model_artifact, destination_path)
+    await self._process_artifact(model.artifact, destination_path)
 
   def _report_status(self) -> None:
     """Reports current status through messaging system"""
@@ -205,16 +217,16 @@ class ModelManagerSP:
     try:
       seen_artifacts: set[str] = set()
       for model in self.selected_bundle.models:
-        for artifact in (model.metadata, model.artifact):
-          if not artifact.fileName:
-            continue
-          if artifact.fileName in seen_artifacts:
-            artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
-            artifact.downloadProgress.progress = 100
-            artifact.downloadProgress.eta = 0
-          else:
-            seen_artifacts.add(artifact.fileName)
-            await self._process_artifact(artifact, destination_path)
+        artifact = model.artifact
+        if not artifact.fileName:
+          continue
+        if artifact.fileName in seen_artifacts:
+          artifact.downloadProgress.status = custom.ModelManagerSP.DownloadStatus.cached
+          artifact.downloadProgress.progress = 100
+          artifact.downloadProgress.eta = 0
+        else:
+          seen_artifacts.add(artifact.fileName)
+          await self._process_artifact(artifact, destination_path)
 
       self.active_bundle = self.selected_bundle
       self.active_bundle.status = custom.ModelManagerSP.DownloadStatus.downloaded
@@ -275,8 +287,6 @@ class ModelManagerSP:
       for model in self.active_bundle.models:
         if hasattr(model, 'artifact') and model.artifact.fileName:
           active_files.append(model.artifact.fileName)
-        if hasattr(model, 'metadata') and model.metadata.fileName:
-          active_files.append(model.metadata.fileName)
 
     # Remove all files except active ones (including their chunk files)
     model_dir = Paths.model_root()

openpilot/sunnypilot/models/runners/helpers.py

@@ -1,28 +0,0 @@
-from openpilot.sunnypilot.models.helpers import get_active_bundle
-from openpilot.sunnypilot.models.runners.model_runner import ModelRunner
-from openpilot.sunnypilot.models.runners.tinygrad.tinygrad_runner import TinygradRunner, TinygradSplitRunner
-from openpilot.sunnypilot.models.runners.constants import ModelType
-
-
-def get_model_runner() -> ModelRunner:
-  """
-  Factory function to create and return the appropriate ModelRunner instance.
-
-  Selects TinygradRunner, choosing TinygradSplitRunner if separate vision/policy
-  models are detected in the active bundle.
-
-  :return: An instance of a ModelRunner subclass (ONNXRunner, TinygradRunner, or TinygradSplitRunner).
-  """
-  bundle = get_active_bundle()
-  if bundle and bundle.models:
-    model_types = {m.type.raw for m in bundle.models}
-    # Check if the bundle uses separate vision and policy models (legacy or new split format)
-    split_types = {ModelType.vision, ModelType.policy, ModelType.offPolicy, ModelType.onPolicy}
-    if model_types & split_types:
-      return TinygradSplitRunner()
-    # Otherwise, assume a single model (likely supercombo)
-    if bundle.models:
-      return TinygradRunner(bundle.models[0].type.raw)
-
-  # Default fallback to TinygradRunner with the supercombo type if bundle info is missing/incomplete
-  return TinygradRunner(ModelType.supercombo)

openpilot/sunnypilot/models/runners/model_runner.py

@@ -1,174 +0,0 @@
-from abc import abstractmethod, ABC
-
-import numpy as np
-from openpilot.sunnypilot.models.helpers import get_active_bundle
-from openpilot.sunnypilot.models.runners.constants import NumpyDict, ShapeDict, Model, SliceDict, SEND_RAW_PRED
-from openpilot.common.hardware.hw import Paths
-import pickle
-
-CUSTOM_MODEL_PATH = Paths.model_root()
-
-
-class ModelData:
-  """
-  Stores metadata and configuration for a specific machine learning model.
-
-  This class loads model metadata (like input shapes and output slices)
-  from a pickle file associated with a model instance.
-
-  :param model: The machine learning model object containing metadata.
-  """
-  def __init__(self, model: Model):
-    self.model = model
-    self.metadata = model.metadata
-    self.input_shapes: ShapeDict = {}
-    self.output_slices: SliceDict = {}
-    if self.metadata:
-      self._load_metadata()
-
-  def _load_metadata(self) -> None:
-    """Loads input shapes and output slices from the model's metadata pickle file."""
-    metadata_path = f"{CUSTOM_MODEL_PATH}/{self.metadata.fileName}"
-    with open(metadata_path, 'rb') as f:
-      model_metadata = pickle.load(f)
-    self.input_shapes = model_metadata.get('input_shapes', {})
-    self.output_slices = model_metadata.get('output_slices', {})
-
-
-class ModularRunner(ABC):
-  """
-  Represents a modular runner for handling and slicing model outputs.
-
-  This abstract base class is designed to provide an interface for modular
-  parsing and processing of model outputs. Classes inheriting from it must
-  implement the specified abstract methods, defining how model outputs
-  should be handled and stored. The primary goal is to enable structured
-  parsing of outputs through a dictionary-based method mapping.
-
-  :ivar parser_method_dict: Mapping dictionary containing parser methods
-      for handling specific types of outputs.
-  :type parser_method_dict: dict
-  """
-
-  @property
-  @abstractmethod
-  def parser_method_dict(self) -> dict:
-    pass
-
-  @parser_method_dict.setter
-  @abstractmethod
-  def parser_method_dict(self, value: dict) -> None:
-    pass
-
-  @abstractmethod
-  def _slice_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    pass
-
-
-class ModelRunner(ModularRunner):
-  """
-  Abstract base class for managing and executing machine learning models.
-
-  Provides a common interface for loading models, preparing inputs, running
-  inference, and slicing/parsing outputs based on model metadata. Derived
-  classes implement the specifics of input preparation and model execution
-  for different frameworks (e.g., Tinygrad, ONNX).
-  """
-
-  def __init__(self):
-    """Initializes the model runner, loading the active model bundle."""
-    self.is_20hz: bool | None = None
-    self.is_20hz_3d: bool | None = None
-    self.models: dict[int, ModelData] = {}
-    self._model_data: ModelData | None = None  # Active model data for current operation
-    self._parser_method_dict: dict = {}
-    self.inputs: dict = {}
-    self._parser = None
-    self._load_models()
-    self._constants = None
-
-  @property
-  def constants(self):
-    return self._constants
-
-  @property
-  def parser_method_dict(self) -> dict:
-    """Returns the dictionary mapping model types to their respective parsing methods."""
-    return self._parser_method_dict
-
-  @parser_method_dict.setter
-  def parser_method_dict(self, value: dict) -> None:
-    """Sets the dictionary mapping model types to their respective parsing methods."""
-    self._parser_method_dict = value
-
-  def _load_models(self) -> None:
-    """Loads the active model bundle configuration and sets up ModelData."""
-    bundle = get_active_bundle()
-    if not bundle:
-      raise ValueError("No active model bundle found, why are we being executed?")
-
-    self.models = {model.type.raw: ModelData(model) for model in bundle.models}
-    self.is_20hz = bundle.is20hz
-    self.is_20hz_3d = False
-
-  @property
-  def input_shapes(self) -> ShapeDict:
-    """Returns the input shapes for the currently active model."""
-    if self._model_data:
-      return self._model_data.input_shapes
-    raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-  @property
-  def output_slices(self) -> SliceDict:
-    """Returns the output slices for the currently active model."""
-    if self._model_data:
-      return self._model_data.output_slices
-    raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-  @property
-  def vision_input_names(self) -> list[str]:
-    """Returns the list of vision input names from the input shapes."""
-    if self._model_data:
-      return list(self._model_data.input_shapes.keys())
-    raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-  @abstractmethod
-  def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
-    """
-    Abstract method to prepare inputs for model inference.
-
-    :param numpy_inputs: Dictionary of numpy arrays for non-image inputs.
-    :return: Dictionary of prepared inputs ready for the model.
-    """
-    raise NotImplementedError
-
-  @abstractmethod
-  def _run_model(self) -> NumpyDict:
-    """
-    Abstract method to execute model inference with prepared inputs.
-
-    :return: Dictionary containing the model's raw output arrays.
-    """
-    raise NotImplementedError
-
-  def _slice_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """
-    Slices the raw model output array based on the output_slices metadata.
-
-    :param model_outputs: The raw numpy array output from the model.
-    :return: A dictionary where keys are output names and values are sliced numpy arrays.
-    """
-    if not self._model_data:
-      raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-    sliced_outputs = {k: model_outputs[np.newaxis, v] for k, v in self._model_data.output_slices.items()}
-    if SEND_RAW_PRED:
-      sliced_outputs['raw_pred'] = model_outputs.copy()  # Optionally include the full raw output
-    return sliced_outputs
-
-  def run_model(self) -> NumpyDict:
-    """
-    Executes the model inference pipeline: runs the model and parses outputs.
-
-    :return: Dictionary containing the final parsed model outputs.
-    """
-    return self._run_model()  # Parsing is handled within specific runner implementations

openpilot/sunnypilot/models/runners/tinygrad/model_types.py

@@ -1,91 +0,0 @@
-import os
-from abc import ABC
-
-import numpy as np
-from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser as CombinedParser
-from openpilot.sunnypilot.modeld_v2.parse_model_outputs_split import Parser as SplitParser
-from openpilot.sunnypilot.models.runners.constants import ModelType, NumpyDict
-from openpilot.sunnypilot.models.runners.model_runner import ModularRunner
-from openpilot.common.hardware.hw import Paths
-
-
-SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
-CUSTOM_MODEL_PATH = Paths.model_root()
-
-
-class OffPolicyTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for off-policy models.
-
-  Uses a SplitParser to handle outputs specific to the off-policy part of a split model setup.
-  """
-  def __init__(self):
-    self._off_policy_parser = SplitParser()
-    self.parser_method_dict[ModelType.offPolicy] = self._parse_off_policy_outputs
-
-  def _parse_off_policy_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses off-policy model outputs using SplitParser."""
-    result: NumpyDict = self._off_policy_parser.parse_policy_outputs(self._slice_outputs(model_outputs))
-    return result
-
-
-class OnPolicyTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for on-policy models.
-
-  Uses a SplitParser to handle outputs specific to the on-policy part of a split model setup.
-  """
-  def __init__(self):
-    self._on_policy_parser = SplitParser()
-    self.parser_method_dict[ModelType.onPolicy] = self._parse_on_policy_outputs
-
-  def _parse_on_policy_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses on-policy model outputs using SplitParser."""
-    result: NumpyDict = self._on_policy_parser.parse_policy_outputs(self._slice_outputs(model_outputs))
-    return result
-
-
-class PolicyTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for policy-only models.
-
-  Uses a SplitParser to handle outputs specific to the policy part of a split model setup.
-  """
-  def __init__(self):
-    self._policy_parser = SplitParser()
-    self.parser_method_dict[ModelType.policy] = self._parse_policy_outputs
-
-  def _parse_policy_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses policy model outputs using SplitParser."""
-    result: NumpyDict = self._policy_parser.parse_policy_outputs(self._slice_outputs(model_outputs))
-    return result
-
-class VisionTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for vision-only models.
-
-  Uses a SplitParser to handle outputs specific to the vision part of a split model setup.
-  """
-  def __init__(self):
-    self._vision_parser = SplitParser()
-    self.parser_method_dict[ModelType.vision] = self._parse_vision_outputs
-
-  def _parse_vision_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses vision model outputs using SplitParser."""
-    result: NumpyDict = self._vision_parser.parse_vision_outputs(self._slice_outputs(model_outputs))
-    return result
-
-class SupercomboTinygrad(ModularRunner, ABC):
-  """
-  A TinygradRunner specialized for vision-only models.
-
-  Uses a SplitParser to handle outputs specific to the vision part of a split model setup.
-  """
-  def __init__(self):
-    self._supercombo_parser = CombinedParser()
-    self.parser_method_dict[ModelType.supercombo] = self._parse_supercombo_outputs
-
-  def _parse_supercombo_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses vision model outputs using SplitParser."""
-    result: NumpyDict = self._supercombo_parser.parse_outputs(self._slice_outputs(model_outputs))
-    return result

openpilot/sunnypilot/models/runners/tinygrad/tinygrad_runner.py

@@ -1,179 +0,0 @@
-import pickle
-
-import numpy as np
-from openpilot.sunnypilot.models.runners.constants import NumpyDict, ModelType, ShapeDict, CUSTOM_MODEL_PATH, SliceDict
-from openpilot.sunnypilot.models.runners.model_runner import ModelRunner
-from openpilot.sunnypilot.models.runners.tinygrad.model_types import PolicyTinygrad, VisionTinygrad, SupercomboTinygrad, OffPolicyTinygrad, OnPolicyTinygrad
-from openpilot.sunnypilot.models.split_model_constants import SplitModelConstants
-from openpilot.sunnypilot.modeld_v2.constants import ModelConstants
-
-from tinygrad.tensor import Tensor
-
-
-class TinygradRunner(ModelRunner, SupercomboTinygrad, PolicyTinygrad, VisionTinygrad, OffPolicyTinygrad, OnPolicyTinygrad):
-  """
-  A ModelRunner implementation for executing Tinygrad models.
-
-  Handles loading Tinygrad model artifacts (.pkl), preparing inputs as Tinygrad
-  Tensors (potentially using QCOM extensions on TICI), running inference,
-  and parsing the outputs.
-
-  :param model_type: The type of model (e.g., supercombo) to load and run.
-  """
-  def __init__(self, model_type: int = ModelType.supercombo):
-    ModelRunner.__init__(self)
-    SupercomboTinygrad.__init__(self)
-    PolicyTinygrad.__init__(self)
-    VisionTinygrad.__init__(self)
-    OffPolicyTinygrad.__init__(self)
-    OnPolicyTinygrad.__init__(self)
-    self._constants = ModelConstants
-    self._model_data = self.models.get(model_type)
-    if not self._model_data or not self._model_data.model:
-      raise ValueError(f"Model data for type {model_type} not available.")
-
-    artifact_filename = self._model_data.model.artifact.fileName
-    assert artifact_filename.endswith('_tinygrad.pkl'), \
-      f"Invalid model file {artifact_filename} for TinygradRunner"
-
-    model_pkl_path = f"{CUSTOM_MODEL_PATH}/{artifact_filename}"
-    with open(model_pkl_path, "rb") as f:
-      try:
-        # Load the compiled Tinygrad model runner function
-        self.model_run = pickle.load(f)
-      except FileNotFoundError as e:
-        # Provide a helpful error message if the model was built for a different platform
-        assert "/dev/kgsl-3d0" not in str(e), "Model was built on C3 or C3X, but is being loaded on PC"
-        raise
-
-    # Map input names to their required dtype and device from the loaded model
-    self.input_to_dtype = {}
-    self.input_to_device = {}
-    for idx, name in enumerate(self.model_run.captured.expected_names):
-      info = self.model_run.captured.expected_input_info[idx]
-      self.input_to_dtype[name] = info[2]  # dtype
-      self.input_to_device[name] = info[3]  # device
-    self._policy_cached = False
-
-  @property
-  def vision_input_names(self) -> list[str]:
-    """Returns the list of vision input names from the input shapes."""
-    return [name for name in self.input_shapes.keys() if 'img' in name]
-
-
-  def prepare_policy_inputs(self, numpy_inputs: NumpyDict):
-    if not self._policy_cached:
-      for key, value in numpy_inputs.items():
-        self.inputs[key] = Tensor(value, device='NPY').realize()
-      self._policy_cached = True
-
-  def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
-    """Prepares all vision and policy inputs for the model."""
-    self.prepare_policy_inputs(numpy_inputs)
-    for key in self.vision_input_names:
-      if key in self.inputs:
-        self.inputs[key] = self.inputs[key].cast(self.input_to_dtype[key])
-    return self.inputs
-
-  def _run_model(self) -> NumpyDict:
-    """Runs the Tinygrad model inference and parses the outputs."""
-    outputs = self.model_run(**self.inputs).contiguous().realize().uop.base.buffer.numpy().flatten()
-    return self._parse_outputs(outputs)
-
-  def _parse_outputs(self, model_outputs: np.ndarray) -> NumpyDict:
-    """Parses the raw model outputs using the standard Parser."""
-    if self._model_data is None:
-      raise ValueError("Model data is not available. Ensure the model is loaded correctly.")
-
-    result: NumpyDict = self.parser_method_dict[self._model_data.model.type.raw](model_outputs)
-    return result
-
-
-class TinygradSplitRunner(ModelRunner):
-  """
-  A ModelRunner that coordinates separate TinygradVisionRunner and TinygradPolicyRunner instances.
-
-  Manages the execution of split vision and policy models, combining their inputs and outputs.
-  """
-  def __init__(self):
-    super().__init__()
-    self.is_20hz_3d = True
-    self.vision_runner = TinygradRunner(ModelType.vision)
-    self.policy_runner = TinygradRunner(ModelType.policy) if self.models.get(ModelType.policy) else None
-    self.off_policy_runner = TinygradRunner(ModelType.offPolicy) if self.models.get(ModelType.offPolicy) else None
-    self.on_policy_runner = TinygradRunner(ModelType.onPolicy) if self.models.get(ModelType.onPolicy) else None
-    self._constants = SplitModelConstants
-
-  def _run_model(self) -> NumpyDict:
-    """Runs both vision and policy models and merges their parsed outputs."""
-    vision_output = self.vision_runner.run_model()
-    outputs = {**vision_output}
-
-    if self.policy_runner:
-      policy_output = self.policy_runner.run_model()
-      outputs.update(policy_output)
-
-    if self.off_policy_runner:
-      off_policy_output = self.off_policy_runner.run_model()
-      if self.on_policy_runner:
-        off_policy_output.pop('plan', None)
-      outputs.update(off_policy_output)
-
-    if self.on_policy_runner:
-      on_policy_output = self.on_policy_runner.run_model()
-      outputs.update(on_policy_output)
-
-    if 'planplus' in outputs and 'plan' in outputs:
-      outputs['plan'] = outputs['plan'] + outputs['planplus']
-
-    return outputs
-
-  @property
-  def vision_input_names(self) -> list[str]:
-    """Returns the list of vision input names from the vision runner."""
-    return list(self.vision_runner.vision_input_names)
-
-  @property
-  def input_shapes(self) -> ShapeDict:
-    """Returns the combined input shapes from both vision and policy models."""
-    shapes = {**self.vision_runner.input_shapes}
-    if self.policy_runner:
-      shapes.update(self.policy_runner.input_shapes)
-    if self.off_policy_runner:
-      shapes.update(self.off_policy_runner.input_shapes)
-    if self.on_policy_runner:
-      shapes.update(self.on_policy_runner.input_shapes)
-    return shapes
-
-  @property
-  def output_slices(self) -> SliceDict:
-    """Returns the combined output slices from both vision and policy models."""
-    slices = {**self.vision_runner.output_slices}
-    if self.policy_runner:
-      slices.update(self.policy_runner.output_slices)
-    if self.off_policy_runner:
-      slices.update(self.off_policy_runner.output_slices)
-    if self.on_policy_runner:
-      slices.update(self.on_policy_runner.output_slices)
-    return slices
-
-  def prepare_inputs(self, numpy_inputs: NumpyDict) -> dict:
-    """Prepares inputs for both vision and policy models."""
-    if self.policy_runner:
-      self.policy_runner.prepare_policy_inputs(numpy_inputs)
-
-    for key in self.vision_input_names:
-      if key in self.inputs:
-        self.vision_runner.inputs[key] = self.inputs[key].cast(self.vision_runner.input_to_dtype[key])
-
-    inputs = {**self.vision_runner.inputs}
-    if self.policy_runner:
-      inputs.update(self.policy_runner.inputs)
-
-    if self.off_policy_runner:
-      self.off_policy_runner.prepare_policy_inputs(numpy_inputs)
-      inputs.update(self.off_policy_runner.inputs)
-    if self.on_policy_runner:
-      self.on_policy_runner.prepare_policy_inputs(numpy_inputs)
-      inputs.update(self.on_policy_runner.inputs)
-    return inputs

openpilot/sunnypilot/models/split_model_constants.py

@@ -43,6 +43,7 @@ class SplitModelConstants:
   LANE_LINES_WIDTH = 2
   ROAD_EDGES_WIDTH = 2
   PLAN_WIDTH = 15
+  ACTION_WIDTH = 2
   DESIRE_PRED_WIDTH = 8
   LAT_PLANNER_SOLUTION_WIDTH = 4
   DESIRED_CURV_WIDTH = 1

openpilot/sunnypilot/selfdrive/car/interfaces.py

@@ -123,6 +123,7 @@ def initialize_params(params) -> list[dict[str, Any]]:
   # tesla
   keys.extend([
     "TeslaCoopSteering",
+    "TeslaMadsScreenButton",
   ])
 
   # toyota

openpilot/sunnypilot/selfdrive/controls/lib/smart_cruise_control/map_controller.py

@@ -151,8 +151,8 @@ class SmartCruiseControlMap:
         a = 0.5 * TARGET_JERK
         b = self.a_ego
         c = self.v_ego - tv
-        t_a = -1 * ((b**2 - 4 * a * c) ** 0.5 + b) / 2 * a
-        t_b = ((b**2 - 4 * a * c) ** 0.5 - b) / 2 * a
+        t_a = -1 * ((b**2 - 4 * a * c) ** 0.5 + b) / (2 * a)
+        t_b = ((b**2 - 4 * a * c) ** 0.5 - b) / (2 * a)
         if not isinstance(t_a, complex) and t_a > 0:
           t = t_a
         else:

openpilot/sunnypilot/selfdrive/controls/lib/smart_cruise_control/tests/test_map_controller.py

@@ -4,13 +4,17 @@ 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 json
+import math
 import platform
 
+import pytest
+
 from openpilot.cereal import custom
 from openpilot.common.params import Params
 from openpilot.common.realtime import DT_MDL
 from openpilot.selfdrive.car.cruise import V_CRUISE_UNSET
-from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.map_controller import SmartCruiseControlMap
+from openpilot.sunnypilot.selfdrive.controls.lib.smart_cruise_control.map_controller import R, SmartCruiseControlMap
 
 MapState = VisionState = custom.LongitudinalPlanSP.SmartCruiseControl.MapState
 
@@ -55,4 +59,17 @@ class TestSmartCruiseControlMap:
       self.scc_m.update(True, False, 0., 0., 0.)
     assert self.scc_m.state == VisionState.enabled
 
+  def test_moderate_curve(self):
+    # Regression: `... / 2 * a` parsed as `(.../2)*a` instead of `.../(2*a)`,
+    # making max_d ~11x too small so the moderate-curve branch never tripped.
+    # v_ego=25, a_ego=0, tv=24: fixed max_d≈45m vs buggy ≈4m at a 40m waypoint.
+    waypoint_lon_deg = (40.0 / R) * (180.0 / math.pi)
+    self.mem_params.put("LastGPSPosition", json.dumps({"latitude": 0.0, "longitude": 0.0}), block=True)
+    self.mem_params.put("MapTargetVelocities",
+                        json.dumps([{"latitude": 0.0, "longitude": waypoint_lon_deg, "velocity": 24.0}]), block=True)
+
+    self.scc_m.update(True, False, 25.0, 0.0, 30.0)
+
+    assert self.scc_m.v_target == pytest.approx(24.0)
+
   # TODO-SP: mock data from modelV2 to test other states

openpilot/sunnypilot/selfdrive/controls/lib/speed_limit/speed_limit_assist.py

@@ -91,7 +91,7 @@ class SpeedLimitAssist:
 
     self._plus_hold = 0.
     self._minus_hold = 0.
-    self._last_carstate_ts = 0.
+    self._release_toggle_prev = 0
 
     # TODO-SP: SLA's own output_a_target for planner
     # Solution functions mapped to respective states
@@ -146,16 +146,16 @@ class SpeedLimitAssist:
       set_speed_limit_assist_availability(self.CP, self.CP_SP, self.params)
       self.enabled = self.params.get("SpeedLimitMode", return_default=True) == Mode.assist
 
-  def update_car_state(self, CS: car.CarState) -> None:
+  def update_buttons(self, release_toggle: int) -> None:
+    released = self._release_toggle_prev ^ release_toggle
+    self._release_toggle_prev = release_toggle
+    if not released:
+      return
     now = time.monotonic()
-    self._last_carstate_ts = now
-
-    for b in CS.buttonEvents:
-      if not b.pressed:
-        if b.type in CRUISE_BUTTONS_PLUS:
-          self._plus_hold = max(self._plus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
-        elif b.type in CRUISE_BUTTONS_MINUS:
-          self._minus_hold = max(self._minus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
+    if any((released >> b) & 1 for b in CRUISE_BUTTONS_PLUS):
+      self._plus_hold = max(self._plus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
+    if any((released >> b) & 1 for b in CRUISE_BUTTONS_MINUS):
+      self._minus_hold = max(self._minus_hold, now + CRUISE_BUTTON_CONFIRM_HOLD)
 
   def _get_button_release(self, req_plus: bool, req_minus: bool) -> bool:
     now = time.monotonic()

openpilot/sunnypilot/selfdrive/controls/lib/speed_limit/tests/test_speed_limit_assist.py

@@ -5,11 +5,14 @@ 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 time
+
 import pytest
 
 from openpilot.cereal import custom
 from opendbc.car.car_helpers import interfaces
 from opendbc.car.rivian.values import CAR as RIVIAN
+from opendbc.car.structs import car
 from opendbc.car.tesla.values import CAR as TESLA
 from opendbc.car.toyota.values import CAR as TOYOTA
 from openpilot.common.constants import CV
@@ -21,9 +24,13 @@ from openpilot.sunnypilot.selfdrive.car import interfaces as sunnypilot_interfac
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit import PCM_LONG_REQUIRED_MAX_SET_SPEED
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.common import Mode
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.speed_limit_assist import SpeedLimitAssist, \
-  PRE_ACTIVE_GUARD_PERIOD, ACTIVE_STATES
+  PRE_ACTIVE_GUARD_PERIOD, ACTIVE_STATES, CRUISE_BUTTON_CONFIRM_HOLD
+from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
 from openpilot.sunnypilot.selfdrive.selfdrived.events import EventsSP
 
+ButtonEvent = car.CarState.ButtonEvent
+ButtonType = car.CarState.ButtonEvent.Type
+
 SpeedLimitAssistState = custom.LongitudinalPlanSP.SpeedLimit.AssistState
 
 ALL_STATES = tuple(SpeedLimitAssistState.schema.enumerants.values())
@@ -276,3 +283,86 @@ class TestSpeedLimitAssist:
         assert self.sla.state in [SpeedLimitAssistState.preActive, SpeedLimitAssistState.active]
       elif initial_state in ACTIVE_STATES:
         assert self.sla.state in ACTIVE_STATES
+
+
+class TestButtonStateTrackerSLAIntegration:
+
+  def setup_method(self, method):
+    self.tracker = ButtonStateTracker()
+    self.params = Params()
+    self.params.put("IsReleaseSpBranch", True, block=True)
+    self.params.put("SpeedLimitMode", int(Mode.assist), block=True)
+    self.params.put_bool("IsMetric", False, block=True)
+    self.params.put("SpeedLimitOffsetType", 0, block=True)
+    self.params.put("SpeedLimitValueOffset", 0, block=True)
+
+    CarInterface = interfaces[DEFAULT_CAR]
+    CP = CarInterface.get_non_essential_params(DEFAULT_CAR)
+    CP.openpilotLongitudinalControl = True
+    CP_SP = CarInterface.get_non_essential_params_sp(CP, DEFAULT_CAR)
+    self.sla = SpeedLimitAssist(CP, CP_SP)
+
+  def _make_cs(self, events=None) -> car.CarState:
+    CS = car.CarState()
+    CS.buttonEvents = events or []
+    return CS
+
+  def _run_ctrl_frames(self, frames: list[car.CarState]) -> None:
+    for cs in frames:
+      self.tracker.update(cs)
+
+  def test_button_confirm_via_tracker(self) -> None:
+    self._run_ctrl_frames([
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]),
+      self._make_cs(),
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]),
+      self._make_cs(),
+      self._make_cs(),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert self.sla._get_button_release(req_plus=True, req_minus=False)
+
+  def test_rapid_press_release_between_polls(self) -> None:
+    self.sla.update_buttons(self.tracker.release_toggle)
+
+    self._run_ctrl_frames([
+      self._make_cs([ButtonEvent(type=ButtonType.decelCruise, pressed=True)]),
+      self._make_cs([ButtonEvent(type=ButtonType.decelCruise, pressed=False)]),
+      self._make_cs(),
+      self._make_cs(),
+      self._make_cs(),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert self.sla._get_button_release(req_plus=False, req_minus=True)
+
+  def test_multiple_releases_between_polls(self) -> None:
+    self.sla.update_buttons(self.tracker.release_toggle)
+
+    self._run_ctrl_frames([
+      self._make_cs([
+        ButtonEvent(type=ButtonType.accelCruise, pressed=True),
+        ButtonEvent(type=ButtonType.decelCruise, pressed=True),
+      ]),
+      self._make_cs([
+        ButtonEvent(type=ButtonType.accelCruise, pressed=False),
+        ButtonEvent(type=ButtonType.decelCruise, pressed=False),
+      ]),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert self.sla._get_button_release(req_plus=True, req_minus=False)
+    assert self.sla._get_button_release(req_plus=False, req_minus=True)
+
+  def test_no_false_positive_same_toggle(self) -> None:
+    self.sla.update_buttons(self.tracker.release_toggle)
+    self.sla.update_buttons(self.tracker.release_toggle)
+    assert not self.sla._get_button_release(req_plus=True, req_minus=False)
+    assert not self.sla._get_button_release(req_plus=False, req_minus=True)
+
+  def test_button_confirm_expires(self) -> None:
+    self._run_ctrl_frames([
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]),
+      self._make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]),
+    ])
+    self.sla.update_buttons(self.tracker.release_toggle)
+    time.sleep(CRUISE_BUTTON_CONFIRM_HOLD + 0.1)
+    assert not self.sla._get_button_release(req_plus=True, req_minus=False)

openpilot/sunnypilot/selfdrive/selfdrived/button_state_tracker.py

@@ -0,0 +1,26 @@
+"""
+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 opendbc.car import structs
+
+
+class ButtonStateTracker:
+  def __init__(self) -> None:
+    self.pressed: int = 0
+    self.release_toggle: int = 0
+
+  def update(self, CS: structs.CarState) -> None:
+    for b in CS.buttonEvents:
+      bit = 1 << b.type.raw
+      if b.pressed:
+        self.pressed |= bit
+      else:
+        self.pressed &= ~bit
+        self.release_toggle ^= bit
+
+  def publish(self, ss_sp) -> None:
+    ss_sp.buttonsPressed = self.pressed
+    ss_sp.buttonsReleaseToggle = self.release_toggle

openpilot/sunnypilot/selfdrive/selfdrived/tests/test_button_state_tracker.py

@@ -0,0 +1,67 @@
+"""
+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 opendbc.car.structs import car
+from openpilot.sunnypilot.selfdrive.selfdrived.button_state_tracker import ButtonStateTracker
+
+ButtonEvent = car.CarState.ButtonEvent
+ButtonType = car.CarState.ButtonEvent.Type
+
+
+class TestButtonStateTracker:
+  def setup_method(self) -> None:
+    self.tracker = ButtonStateTracker()
+
+  def make_cs(self, events: list) -> car.CarState:
+    CS = car.CarState()
+    CS.buttonEvents = events
+    return CS
+
+  def test_initial_state(self) -> None:
+    assert self.tracker.pressed == 0
+    assert self.tracker.release_toggle == 0
+
+  def test_press_sets_bit(self) -> None:
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]))
+    assert self.tracker.pressed == (1 << ButtonType.accelCruise)
+    assert self.tracker.release_toggle == 0
+
+  def test_release_clears_and_toggles(self) -> None:
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]))
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]))
+    assert self.tracker.pressed == 0
+    assert self.tracker.release_toggle == (1 << ButtonType.accelCruise)
+
+  def test_multiple_buttons(self) -> None:
+    self.tracker.update(self.make_cs([
+      ButtonEvent(type=ButtonType.accelCruise, pressed=True),
+      ButtonEvent(type=ButtonType.decelCruise, pressed=True),
+    ]))
+    assert self.tracker.pressed == (1 << ButtonType.accelCruise) | (1 << ButtonType.decelCruise)
+
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]))
+    assert self.tracker.pressed == (1 << ButtonType.decelCruise)
+    assert self.tracker.release_toggle == (1 << ButtonType.accelCruise)
+
+  def test_release_toggle_flips(self) -> None:
+    for _ in range(2):
+      self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.gapAdjustCruise, pressed=True)]))
+      self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.gapAdjustCruise, pressed=False)]))
+    assert self.tracker.release_toggle == 0
+
+  def test_publish(self) -> None:
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=True)]))
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.decelCruise, pressed=True)]))
+    self.tracker.update(self.make_cs([ButtonEvent(type=ButtonType.accelCruise, pressed=False)]))
+
+    class MockSP:
+      buttonsPressed = 0
+      buttonsReleaseToggle = 0
+
+    sp = MockSP()
+    self.tracker.publish(sp)
+    assert sp.buttonsPressed == self.tracker.pressed
+    assert sp.buttonsReleaseToggle == self.tracker.release_toggle

openpilot/sunnypilot/sunnylink/settings_ui.json

@@ -2161,6 +2161,42 @@
               "type": "offroad_only"
             }
           ]
+        },
+        {
+          "key": "TeslaMadsScreenButton",
+          "widget": "multiple_button",
+          "title": "MADS Screen Activation",
+          "description": "Use a multi-finger press on the infotainment screen to toggle MADS. This allows the use of full MADS functionality when enabled. Selecting a higher finger count may reduce accidental activations. Note: Setting this to Off will reset your MADS settings to default.",
+          "options": [
+            {
+              "value": 0,
+              "label": "Off"
+            },
+            {
+              "value": 1,
+              "label": "3-Finger"
+            },
+            {
+              "value": 2,
+              "label": "4-Finger"
+            },
+            {
+              "value": 3,
+              "label": "5-Finger"
+            }
+          ],
+          "visibility": [
+            {
+              "type": "capability",
+              "field": "tesla_has_vehicle_bus",
+              "equals": true
+            }
+          ],
+          "enablement": [
+            {
+              "type": "offroad_only"
+            }
+          ]
         }
       ]
     },

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

@@ -56,6 +56,28 @@ sections:
     title: Cooperative Steering (Beta)
     enablement:
     - $ref: '#/macros/offroad'
+  - key: TeslaMadsScreenButton
+    widget: multiple_button
+    title: MADS Screen Activation
+    description: 'Use a multi-finger press on the infotainment screen to toggle MADS.
+      This allows the use of full MADS functionality when enabled. Selecting a higher
+      finger count may reduce accidental activations. Note: Setting this to Off will
+      reset your MADS settings to default.'
+    options:
+    - value: 0
+      label: 'Off'
+    - value: 1
+      label: 3-Finger
+    - value: 2
+      label: 4-Finger
+    - value: 3
+      label: 5-Finger
+    visibility:
+    - type: capability
+      field: tesla_has_vehicle_bus
+      equals: true
+    enablement:
+    - $ref: '#/macros/offroad'
 - id: toyota
   title: Toyota / Lexus Settings
   description: ''

openpilot/sunnypilot/system/params_migration.py

@@ -17,6 +17,26 @@ ONROAD_BRIGHTNESS_TIMER_VALUES = {0: 3, 1: 5, 2: 7, 3: 10, 4: 15, 5: 30, **{i: (
 VALID_TIMER_VALUES = set(ONROAD_BRIGHTNESS_TIMER_VALUES.values())
 
 
+def _resolve_brand(_params) -> str:
+  bundle = _params.get("CarPlatformBundle")
+  if isinstance(bundle, dict) and bundle.get("brand"):
+    return str(bundle["brand"])
+
+  # Auto-fingerprinted cars have no bundle, fall back to the last known CarParams.
+  CP_bytes = _params.get("CarParamsPersistent")
+  if CP_bytes is None:
+    return ""
+
+  # Never raises: callers rely on "" to mean "brand unknown, skip the migration".
+  try:
+    from openpilot.cereal import messaging  # lazy: avoids heavy import at module level
+    from opendbc.car.structs import car
+    return str(messaging.log_from_bytes(CP_bytes, car.CarParams).brand)
+  except Exception as e:
+    cloudlog.exception(f"params_migration: failed to resolve brand from CarParamsPersistent: {e}")
+    return ""
+
+
 def _migrate_car_platform_bundle(_params):
   bundle = _params.get("CarPlatformBundle")
   if bundle is None:
@@ -47,6 +67,23 @@ def _migrate_car_platform_bundle(_params):
   cloudlog.info(f"params_migration: CarPlatformBundle migrated {old_platform!r} -> {new_platform!r}")
 
 
+def _migrate_tesla_mads_screen_button(_params):
+  # TeslaMadsScreenButton defaults to Off for fresh installs, but the screen button was previously always
+  # active on Teslas with a vehicle bus. Seed existing Tesla installs with 3-finger to preserve that.
+  try:
+    if _params.get("TeslaMadsScreenButton") is not None:
+      return
+
+    if _resolve_brand(_params) != "tesla":
+      return
+
+    from opendbc.sunnypilot.car.tesla.values import MadsScreenButtonType  # lazy: avoids heavy import at module level
+    _params.put("TeslaMadsScreenButton", MadsScreenButtonType.THREE_FINGER, block=True)
+    cloudlog.info("params_migration: seeded TeslaMadsScreenButton with 3-finger to preserve existing behavior")
+  except Exception as e:
+    cloudlog.exception(f"Error migrating TeslaMadsScreenButton: {e}")
+
+
 def run_migration(_params):
   # migrate OnroadScreenOffBrightness
   if _params.get("OnroadScreenOffBrightnessMigrated") != ONROAD_BRIGHTNESS_MIGRATION_VERSION:
@@ -80,3 +117,6 @@ def run_migration(_params):
       cloudlog.exception(f"Error migrating OnroadScreenOffTimer: {e}")
 
   _migrate_car_platform_bundle(_params)
+
+  # seed TeslaMadsScreenButton for existing Tesla installs
+  _migrate_tesla_mads_screen_button(_params)

release/ci/docker_build_sp.sh

@@ -1,30 +0,0 @@
-#!/usr/bin/env bash
-set -e
-
-SCRIPT_DIR=$(dirname "$0")
-OPENPILOT_DIR=$SCRIPT_DIR/../../
-
-DOCKER_IMAGE=sunnypilot
-DOCKER_FILE=Dockerfile.openpilot
-DOCKER_REGISTRY=ghcr.io/sunnypilot
-COMMIT_SHA=$(git rev-parse HEAD)
-
-if [ -n "$TARGET_ARCHITECTURE" ]; then
-  PLATFORM="linux/$TARGET_ARCHITECTURE"
-  TAG_SUFFIX="-$TARGET_ARCHITECTURE"
-else
-  PLATFORM="linux/$(uname -m)"
-  TAG_SUFFIX=""
-fi
-
-LOCAL_TAG=$DOCKER_IMAGE$TAG_SUFFIX
-REMOTE_TAG=$DOCKER_REGISTRY/$LOCAL_TAG
-REMOTE_SHA_TAG=$DOCKER_REGISTRY/$LOCAL_TAG:$COMMIT_SHA
-
-DOCKER_BUILDKIT=1 docker buildx build --provenance false --pull --platform $PLATFORM --load -t $DOCKER_IMAGE:latest -t $REMOTE_TAG -t $LOCAL_TAG -f $OPENPILOT_DIR/$DOCKER_FILE $OPENPILOT_DIR
-
-if [ -n "$PUSH_IMAGE" ]; then
-  docker push $REMOTE_TAG
-  docker tag $REMOTE_TAG $REMOTE_SHA_TAG
-  docker push $REMOTE_SHA_TAG
-fi

release/ci/model_generator.py

@@ -5,8 +5,6 @@ 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 pickle
 import sys
 import hashlib
 import json
@@ -14,46 +12,8 @@ import re
 from pathlib import Path
 from datetime import datetime, UTC
 
-REQUIRED_OUTPUT_KEYS = frozenset({
-  "plan",
-  "lane_lines",
-  "road_edges",
-  "lead",
-  "desire_state",
-  "desire_pred",
-  "meta",
-  "lead_prob",
-  "lane_lines_prob",
-  "pose",
-  "wide_from_device_euler",
-  "road_transform",
-  "hidden_state",
-})
-OPTIONAL_OUTPUT_KEYS = frozenset({
-  "planplus",
-  "sim_pose",
-  "desired_curvature",
-})
 
-
-def validate_model_outputs(metadata_paths: list[Path]) -> None:
-  combined_keys: set[str] = set()
-  for path in metadata_paths:
-    if path.stat().st_size == 0:
-      print(f"skipping empty metadata: {path}")
-      continue
-    with open(path, "rb") as f:
-      metadata = pickle.load(f)
-    combined_keys.update(metadata.get("output_slices", {}).keys())
-  missing = REQUIRED_OUTPUT_KEYS - combined_keys
-  if missing:
-    raise ValueError(f"Combined model metadata is missing required output keys: {sorted(missing)}")
-  detected_optional = sorted(OPTIONAL_OUTPUT_KEYS & combined_keys)
-  if detected_optional:
-    print(f"Optional output keys detected: {detected_optional}")
-
-
-def create_short_name(full_name):
+def create_short_name(full_name: str) -> str:
   # Remove parentheses and extract alphanumeric words
   clean_name = re.sub(r'\([^)]*\)', '', full_name)
   words = [re.sub(r'[^a-zA-Z0-9]', '', word) for word in clean_name.split() if re.sub(r'[^a-zA-Z0-9]', '', word)]
@@ -121,49 +81,41 @@ def _rename_pkl_with_chunks(old_pkl: Path, new_pkl: Path) -> Path:
   return old_pkl.rename(new_pkl)
 
 
-def generate_metadata(model_path: Path, output_dir: Path, short_name: str, driving_pkl: Path):
-  base = model_path.stem
-  metadata_file = output_dir / f"{base}_metadata.pkl"
-
-  if short_name:
-    renamed_meta = output_dir / f"{base}_{short_name.lower()}_metadata.pkl"
-    if metadata_file.exists() and not renamed_meta.exists():
-      metadata_file = metadata_file.rename(renamed_meta)
-    elif renamed_meta.exists():
-      metadata_file = renamed_meta
-
-  if not metadata_file.exists():
-    print(f"Warning: Missing metadata for {base} ({metadata_file}), skipping", file=sys.stderr)
-    return
-
+def generate_chunked_model(driving_pkl: Path) -> dict:
   tinygrad_hash = hashlib.sha256(_read_pkl_bytes(driving_pkl)).hexdigest()
 
-  with open(metadata_file, 'rb') as f:
-    metadata_hash = hashlib.sha256(f.read()).hexdigest()
+  chunks_config = []
+  manifest_file = Path(f"{driving_pkl}.chunkmanifest")
+  if manifest_file.exists():
+    num_chunks = int(manifest_file.read_text().strip())
+    for i in range(num_chunks):
+      chunk_path = Path(f"{driving_pkl}.chunk{i + 1:02d}of{num_chunks:02d}")
+      if chunk_path.exists():
+        chunk_hash = hashlib.sha256(chunk_path.read_bytes()).hexdigest()
+        chunks_config.append({
+          "file_name": chunk_path.name,
+          "sha256": chunk_hash
+        })
 
-  model_type = "offPolicy" if "off_policy" in base else "onPolicy" if "on_policy" in base else base.split("_")[-1]
-
-  return {
-    "type": model_type,
-    "artifact": {
-      "file_name": driving_pkl.name,
-      "download_uri": {
-        "url": "https://gitlab.com/sunnypilot/public/docs.sunnypilot.ai/-/raw/main/",
-        "sha256": tinygrad_hash
-      }
-    },
-    "metadata": {
-      "file_name": metadata_file.name,
-      "download_uri": {
-        "url": "https://gitlab.com/sunnypilot/public/docs.sunnypilot.ai/-/raw/main/",
-        "sha256": metadata_hash
-      }
+  artifact_data = {
+    "file_name": driving_pkl.name,
+    "download_uri": {
+      "url": "https://gitlab.com/sunnypilot/public/docs.sunnypilot.ai/-/raw/main/",
+      "sha256": tinygrad_hash
     }
   }
 
+  if chunks_config:
+    artifact_data["chunks"] = chunks_config
 
-def create_metadata_json(models: list, output_dir: Path, custom_name=None, short_name=None, is_20hz=False, upstream_branch="unknown"):
-  metadata_json = {
+  return {
+    "type": "chunked",
+    "artifact": artifact_data,
+  }
+
+
+def create_metadata_json(models: list, output_dir: Path, custom_name=None, short_name=None, is_20hz=False, upstream_branch="unknown") -> None:
+  bundle_json = {
     "short_name": short_name,
     "display_name": custom_name or upstream_branch,
     "is_20hz": is_20hz,
@@ -179,40 +131,26 @@ def create_metadata_json(models: list, output_dir: Path, custom_name=None, short
   }
 
   # Write metadata to output_dir
+  metadata_json = {
+    "bundles": [bundle_json]
+  }
+
   with open(output_dir / "metadata.json", "w") as f:
     json.dump(metadata_json, f, indent=2)
-
-  print(f"Generated metadata.json with {len(models)} models.")
+  print("Generated metadata.json")
 
 
 if __name__ == "__main__":
   import argparse
-  import glob
 
-  parser = argparse.ArgumentParser(description="Generate metadata for model files")
-  parser.add_argument("--model-dir", default="./models", help="Directory containing ONNX model files")
+  parser = argparse.ArgumentParser(description="Generate metadata JSON for the compiled JIT model")
+  parser.add_argument("--model-dir", default="./models", help="Directory containing the model files")
   parser.add_argument("--output-dir", default="./output", help="Output directory for metadata")
   parser.add_argument("--custom-name", help="Custom display name for the model")
   parser.add_argument("--is-20hz", action="store_true", help="Whether this is a 20Hz model")
-  parser.add_argument("--validate-only", action="store_true")
   parser.add_argument("--upstream-branch", default="unknown", help="Upstream branch name")
   args = parser.parse_args()
 
-  if args.validate_only:
-    metadata_paths = glob.glob(os.path.join(args.model_dir, "*_metadata.pkl"))
-    if not metadata_paths:
-      print(f"No metadata files found in {args.model_dir}", file=sys.stderr)
-      sys.exit(1)
-    validate_model_outputs([Path(p) for p in metadata_paths])
-    print(f"Validated {len(metadata_paths)} metadata files successfully.")
-    sys.exit(0)
-
-  # Find all ONNX files in the given directory
-  model_paths = glob.glob(os.path.join(args.model_dir, "*.onnx"))
-  if not model_paths:
-    print(f"No ONNX files found in {args.model_dir}", file=sys.stderr)
-    sys.exit(1)
-
   _output_dir = Path(args.output_dir)
   _output_dir.mkdir(exist_ok=True, parents=True)
   _short_name = create_short_name(args.custom_name) if args.custom_name else None
@@ -229,14 +167,5 @@ if __name__ == "__main__":
     else:
       _driving_pkl = new_pkl
 
-  _models = []
-
-  for _model_path in model_paths:
-    _model_metadata = generate_metadata(Path(_model_path), _output_dir, _short_name, _driving_pkl)
-    if _model_metadata:
-      _models.append(_model_metadata)
-
-  if _models:
-    create_metadata_json(_models, _output_dir, args.custom_name, _short_name, args.is_20hz, args.upstream_branch)
-  else:
-    print("No models processed.", file=sys.stderr)
+  _model_metadata = generate_chunked_model(_driving_pkl)
+  create_metadata_json([_model_metadata], _output_dir, args.custom_name, _short_name, args.is_20hz, args.upstream_branch)