Update Python packages

.gitattributes

@@ -1,10 +1,8 @@
-* text=auto eol=lf
+* text=auto
 
 # to move existing files into LFS:
 # git add --renormalize .
 *.onnx filter=lfs diff=lfs merge=lfs -text
-openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl filter=lfs diff=lfs merge=lfs -text
-openpilot/sunnypilot/modeld_v2/models/*.pkl filter=lfs diff=lfs merge=lfs -text
 *.svg filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
 *.gif filter=lfs diff=lfs merge=lfs -text

.github/workflows/build-default-models.yaml

@@ -46,6 +46,7 @@ jobs:
 
           if [ "${{ inputs.target }}" = "big" ]; then
             NAME=$(python3 -c "from openpilot.sunnypilot.models.model_name import DEFAULT_BIG_MODEL; print(DEFAULT_BIG_MODEL)")
+            ONNX_PATH="openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
             HF_DEFAULTS_PATH="models/defaults/big"
           elif [ "${{ inputs.target }}" = "dm" ]; then
             ONNX_PATH="openpilot/selfdrive/modeld/models/dmonitoring_model.onnx"
@@ -56,9 +57,8 @@ jobs:
             ONNX_PATH="openpilot/selfdrive/modeld/models/driving_supercombo.onnx"
             HF_DEFAULTS_PATH="models/defaults/small"
           fi
-          ONNX_REF=""
-          [ -n "$ONNX_PATH" ] && ONNX_REF=$(git log -1 --format='%H' -- "$ONNX_PATH")
 
+          ONNX_REF=$(git log -1 --format='%H' -- "$ONNX_PATH")
           TINYGRAD_REF=$(python3 openpilot/sunnypilot/models/tinygrad_ref.py)
           if [ -z "$TINYGRAD_REF" ]; then
             echo "::error::Failed to resolve tinygrad ref"
@@ -78,8 +78,6 @@ jobs:
     if: ${{ inputs.target == 'small' }}
     runs-on: [self-hosted, tici]
     env:
-      MODELS_DIR: openpilot/selfdrive/modeld/models
-      COMPILER: tinygrad_repo/examples/openpilot
       SMALL_ONNX: openpilot/selfdrive/modeld/models/driving_supercombo.onnx
       SMALL_PKL: openpilot/selfdrive/modeld/models/driving_tinygrad.pkl
     steps:
@@ -87,10 +85,9 @@ jobs:
         with:
           submodules: recursive
 
-      - name: Pull small ONNX via LFS
+      - name: Pull ONNX via LFS
         run: git lfs pull -I "${{ env.SMALL_ONNX }}"
 
-
       - name: Set environment variables
         run: |
           source /etc/profile
@@ -106,35 +103,24 @@ jobs:
           source ${UV_PROJECT_ENVIRONMENT}/bin/activate
           PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable
 
-      - name: Compile small model from ONNX
+      - name: Compile small model with stock compiler
         run: |
           source ${UV_PROJECT_ENVIRONMENT}/bin/activate
           export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
-          TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
-          env ${TG_FLAGS} python3 ${{ github.workspace }}/${{ env.COMPILER }}/compile_onnx.py \
-            ${{ github.workspace }}/${{ env.SMALL_ONNX }} \
-            ${{ github.workspace }}/${{ env.SMALL_PKL }} \
-            --device-input "*" --out-of-band --benchmark-runs 1
 
-      - name: Compile driving warps (stock)
-        run: |
-          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
-          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
-          MODEL_W=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(s[0])")
-          MODEL_H=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(s[1])")
+          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}')")
+          FRAME_SKIP=$(python3 -c "from openpilot.selfdrive.modeld.constants import ModelConstants as MC; print(MC.MODEL_RUN_FREQ // MC.MODEL_CONTEXT_FREQ)")
+
           TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
-          for CAM in "1928,1208" "1344,760"; do
-            CAM_W=$(echo $CAM | cut -d, -f1)
-            CAM_H=$(echo $CAM | cut -d, -f2)
-            STRIDE_Y_UV=$(python3 -c 'import sys; from openpilot.system.camerad.cameras.nv12_info import get_nv12_info; s,y,u,_=get_nv12_info(int(sys.argv[1]),int(sys.argv[2])); print(f"{s},{y},{u},{s*(y+u)}")' $CAM_W $CAM_H)
-            OUTPUT="${{ github.workspace }}/${{ env.MODELS_DIR }}/driving_warp_${CAM_W}x${CAM_H}_tinygrad.pkl"
-            env ${TG_FLAGS} python3 ${{ github.workspace }}/${{ env.COMPILER }}/compile_warp.py \
-              --frame ${CAM_W},${CAM_H},${STRIDE_Y_UV} \
-              --warp-to ${MODEL_W}x${MODEL_H} \
-              --layout yuv420 \
-              --frames 2 \
-              --output "${OUTPUT}"
-          done
+
+          env ${TG_FLAGS} python3 \
+            ${{ github.workspace }}/openpilot/selfdrive/modeld/compile_modeld.py \
+            --onnx ${{ github.workspace }}/${{ env.SMALL_ONNX }} \
+            --model-size $MODEL_SIZE \
+            --camera-resolutions $CAMERA_RES \
+            --frame-skip $FRAME_SKIP \
+            --output ${{ github.workspace }}/${{ env.SMALL_PKL }}
 
       - name: Chunk small pkl
         run: |
@@ -195,16 +181,15 @@ jobs:
     if: ${{ inputs.target == 'big' }}
     runs-on: [self-hosted, chestnut]
     env:
-      MODELS_DIR: openpilot/selfdrive/modeld/models
-      COMPILER: tinygrad_repo/examples/openpilot
+      BIG_ONNX: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
       BIG_PKL: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
     steps:
       - uses: actions/checkout@v4
         with:
           submodules: recursive
 
-      - name: Pull big PKL via LFS
-        run: git lfs pull -I "${{ env.BIG_PKL }}"
+      - name: Pull big ONNX via LFS
+        run: git lfs pull -I "${{ env.BIG_ONNX }}"
 
       - name: Set environment variables
         run: |
@@ -237,25 +222,24 @@ jobs:
               raise RuntimeError('Chestnut PCIe link not ready after 10 attempts')
           "
 
-      - name: Compile driving warps (chestnut)
+      - name: Compile big model with stock compiler
         run: |
           source ${UV_PROJECT_ENVIRONMENT}/bin/activate
           export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
-          MODEL_W=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(s[0])")
-          MODEL_H=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(s[1])")
-          TG_FLAGS="DEBUG=1 DEV=USB+AMD:LLVM FRAME_DEV=CPU FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_MIN_GLOBALS=32"
-          for CAM in "1928,1208" "1344,760"; do
-            CAM_W=$(echo $CAM | cut -d, -f1)
-            CAM_H=$(echo $CAM | cut -d, -f2)
-            STRIDE_Y_UV=$(python3 -c 'import sys; from openpilot.system.camerad.cameras.nv12_info import get_nv12_info; s,y,u,_=get_nv12_info(int(sys.argv[1]),int(sys.argv[2])); print(f"{s},{y},{u},{s*(y+u)}")' $CAM_W $CAM_H)
-            OUTPUT="${{ github.workspace }}/${{ env.MODELS_DIR }}/big_driving_warp_${CAM_W}x${CAM_H}_tinygrad.pkl"
-            env ${TG_FLAGS} python3 ${{ github.workspace }}/${{ env.COMPILER }}/compile_warp.py \
-              --frame ${CAM_W},${CAM_H},${STRIDE_Y_UV} \
-              --warp-to ${MODEL_W}x${MODEL_H} \
-              --layout yuv420 \
-              --frames 2 \
-              --output "${OUTPUT}"
-          done
+
+          MODEL_SIZE=$(python3 -c "from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
+          CAMERA_RES=$(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')")
+          FRAME_SKIP=$(python3 -c "from openpilot.selfdrive.modeld.constants import ModelConstants as MC; print(MC.MODEL_RUN_FREQ // MC.MODEL_CONTEXT_FREQ)")
+
+          TG_FLAGS="DEBUG=2 DEV=USB+AMD:LLVM WARP_DEV=QCOM FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2"
+
+          env ${TG_FLAGS} python3 \
+            ${{ github.workspace }}/openpilot/selfdrive/modeld/compile_modeld.py \
+            --onnx ${{ github.workspace }}/${{ env.BIG_ONNX }} \
+            --model-size $MODEL_SIZE \
+            --camera-resolutions $CAMERA_RES \
+            --frame-skip $FRAME_SKIP \
+            --output ${{ github.workspace }}/${{ env.BIG_PKL }}
 
       - name: Chunk big pkl
         run: |
@@ -285,14 +269,11 @@ jobs:
           cp "$MODELS_DIR/${PKL_BASE}".chunk* "$OUTPUT_DIR/"
           cp "$MODELS_DIR/${PKL_BASE}.chunkmanifest" "$OUTPUT_DIR/"
 
-          PKL_LFS_SHA256=$(sha256sum "$MODELS_DIR/${PKL_BASE}" | cut -d' ' -f1)
-
           python3 "${{ github.workspace }}/release/ci/model_generator.py" \
             --model-dir "$MODELS_DIR" \
             --output-dir "$OUTPUT_DIR" \
             --custom-name "$MODEL_NAME" \
-            --upstream-branch "${{ needs.resolve.outputs.onnx_ref }}" \
-            --onnx-sha256 "$PKL_LFS_SHA256"
+            --upstream-branch "${{ needs.resolve.outputs.onnx_ref }}"
 
           echo "model-${MODEL_NAME}-${{ github.run_number }}" > "$OUTPUT_DIR/artifact_name.txt"
 
@@ -330,8 +311,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Pull LFS for resolved ONNX files
-        if: ${{ needs.resolve.outputs.onnx_path != '' }}
+      - name: Pull ONNX via LFS
         run: git lfs pull -I "${{ needs.resolve.outputs.onnx_path }}"
 
       - name: Install huggingface_hub
@@ -371,6 +351,8 @@ jobs:
             --hf-defaults-path "${{ needs.resolve.outputs.hf_defaults_path }}" \
             --artifact-name "$ARTIFACT_NAME" \
             --model-dir output \
+            --onnx-path "${{ needs.resolve.outputs.onnx_path }}" \
+            --onnx-ref "${{ needs.resolve.outputs.onnx_ref }}" \
             --model-name "${{ needs.resolve.outputs.model_name }}" \
             --tinygrad-ref "${{ needs.resolve.outputs.tinygrad_ref }}" \
             --run-number "${{ github.run_number }}"
@@ -508,27 +490,15 @@ jobs:
           export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
 
           TG_FLAGS="DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1"
-          COMPILER="${{ github.workspace }}/tinygrad_repo/examples/openpilot"
-          MODELS_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld/models"
+          MODEL_DIR="${{ github.workspace }}/openpilot/selfdrive/modeld"
           DM_SIZE=$(python3 -c "from openpilot.common.transformations.model import DM_INPUT_SIZE as s; print(f'{s[0]}x{s[1]}')")
 
           for res in $(python3 -c "from openpilot.common.transformations.camera import _ar_ox_fisheye as a, _os_fisheye as o; print(f'{a.width}x{a.height} {o.width}x{o.height}')"); do
-            CAM_W=$(echo $res | cut -d x -f1)
-            CAM_H=$(echo $res | cut -d x -f2)
-            STRIDE_INFO=$(python3 -c "
-          import sys
-          from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
-          s,y,u,sz = get_nv12_info(int(sys.argv[1]), int(sys.argv[2]))
-          print(f'{s},{y},{u},{sz}')
-          " $CAM_W $CAM_H)
-            WARP_PKL="${MODELS_DIR}/dm_warp_${res}_tinygrad.pkl"
-            taskset -c 7 env ${TG_FLAGS} python3 "${COMPILER}/compile_warp.py" \
-              --frame ${CAM_W},${CAM_H},${STRIDE_INFO} \
+            WARP_PKL="${MODEL_DIR}/models/dm_warp_${res}_tinygrad.pkl"
+            taskset -c 7 env ${TG_FLAGS} python3 ${MODEL_DIR}/compile_dm_warp.py \
+              --camera-resolution ${res} \
               --warp-to ${DM_SIZE} \
-              --layout luma \
-              --border-fill 16 \
-              --transform-device NPY \
-              --output "${WARP_PKL}"
+              --output ${WARP_PKL}
           done
 
       - name: Prepare DM output

.github/workflows/compile-warp-kernels.yaml

@@ -1,73 +0,0 @@
-name: Compile warp for sunnypilot modeld
-
-on:
-  workflow_dispatch:
-  schedule:
-    - cron: '0 0 * * 0'
-
-jobs:
-  compile_warps:
-    runs-on: [self-hosted, chestnut]
-    steps:
-      - uses: actions/checkout@v4
-        with:
-          submodules: recursive
-
-      - name: Set environment variables
-        run: |
-          source /etc/profile
-          export UV_PROJECT_ENVIRONMENT=${HOME}/venv
-          export UV_PYTHON_PREFERENCE=managed
-          export UV_PYTHON_INSTALL_DIR=${HOME}/uv/python
-          export VIRTUAL_ENV=$UV_PROJECT_ENVIRONMENT
-          uv sync --frozen
-          printenv >> $GITHUB_ENV
-
-      - name: Disable powersave
-        run: |
-          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
-          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --disable
-
-      - name: Compile Warp Kernels
-        run: |
-          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
-          export PYTHONPATH="${PYTHONPATH}:${{ github.workspace }}/tinygrad_repo:${{ github.workspace }}"
-          mkdir -p openpilot/sunnypilot/modeld_v2/models/
-          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}')")
-          echo "Model size: $MODEL_SIZE"
-          echo "Camera resolutions: $CAMERA_RES"
-
-          for res in $CAMERA_RES; do
-            NV12_INFO=$(python3 -c "from openpilot.system.camerad.cameras.nv12_info import get_nv12_info; w, h = map(int, '${res}'.split('x')); print(','.join(map(str, get_nv12_info(w, h))))")
-
-            WARP_PKL="openpilot/sunnypilot/modeld_v2/models/driving_warp_${res}_tinygrad.pkl"
-            echo "Compiling $WARP_PKL on QCOM"
-            taskset -c 7 env DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1 python3 tinygrad_repo/examples/openpilot/compile_warp.py \
-              --frame ${res/x/,},${NV12_INFO} --warp-to ${MODEL_SIZE} --layout yuv420 --frames 2 --output "${WARP_PKL}"
-
-            BIG_WARP_PKL="openpilot/sunnypilot/modeld_v2/models/big_driving_warp_${res}_tinygrad.pkl"
-            echo "Compiling $BIG_WARP_PKL on AMD"
-            taskset -c 7 env DEBUG=1 DEV=USB+AMD:LLVM FRAME_DEV=CPU FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_MIN_GLOBALS=32 python3 tinygrad_repo/examples/openpilot/compile_warp.py \
-              --frame ${res/x/,},${NV12_INFO} --warp-to ${MODEL_SIZE} --layout yuv420 --frames 2 --output "${BIG_WARP_PKL}"
-          done
-
-      - name: Re-enable powersave
-        if: always()
-        run: |
-          source ${UV_PROJECT_ENVIRONMENT}/bin/activate
-          PYTHONPATH=$PYTHONPATH:${{ github.workspace }}/ ${{ github.workspace }}/scripts/manage-powersave.py --enable
-
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@9153d834b60caba6d51c9b9510b087acf9f33f83
-        with:
-          author: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "[bot] Update warp pkl for modeld_v2"
-          title: "[bot] Update modeld_v2 Warp"
-          branch: "auto/compile-warp-kernels"
-          base: "master"
-          delete-branch: true
-          labels: bot
-          add-paths: |
-            openpilot/sunnypilot/modeld_v2/models/*.pkl

.github/workflows/docs.yaml

@@ -18,25 +18,24 @@ concurrency:
 env:
   GIT_CONFIG_COUNT: 1
   GIT_CONFIG_KEY_0: lfs.fetchexclude
-  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
+  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
 
 jobs:
   docs:
     name: build docs
-    runs-on: ${{
-      (github.repository == 'commaai/openpilot') &&
-      ((github.event_name != 'pull_request') ||
-      (github.event.pull_request.head.repo.full_name == 'commaai/openpilot'))
-      && fromJSON('["namespace-profile-amd64-8x16"]')
-      || fromJSON('["ubuntu-24.04"]') }}
+    runs-on: ubuntu-24.04
     steps:
     - uses: commaai/timeout@v1
+
     - uses: actions/checkout@v7
-    - run: ./tools/op.sh setup
+      with:
+        submodules: true
 
     # Build
     - name: Build docs
-      run: ./tools/op.sh docs --build
+      run: |
+        git lfs pull
+        python docs/serve.py --build
 
     # Push to docs.comma.ai
     - uses: actions/checkout@v7

.github/workflows/release.yaml

@@ -7,7 +7,7 @@ on:
 env:
   GIT_CONFIG_COUNT: 1
   GIT_CONFIG_KEY_0: lfs.fetchexclude
-  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
+  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
 
 jobs:
   build___nightly:
@@ -34,7 +34,3 @@ jobs:
     - run: ./tools/op.sh setup
     - name: Push __nightly
       run: BRANCH=__nightly tools/release/build_stripped.sh
-    - name: Push chestnut nightly
-      run: |
-        git lfs pull --exclude=''
-        INCLUDE_BIG_MODEL=1 BRANCH=__nightly-chestnut tools/release/build_stripped.sh

.github/workflows/repo-maintenance.yaml

@@ -11,7 +11,7 @@ env:
   PYTHONPATH: ${{ github.workspace }}
   GIT_CONFIG_COUNT: 1
   GIT_CONFIG_KEY_0: lfs.fetchexclude
-  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
+  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
 
 jobs:
   package_updates:

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

@@ -103,23 +103,21 @@ jobs:
       - run: |
           cd ${{ github.workspace }}/openpilot/openpilot
           if [ "${{ inputs.target_hardware }}" != "chestnut" ]; then
-            git lfs pull -X "**/selfdrive/modeld/models/big_*.onnx,**/selfdrive/modeld/models/dmonitoring_*.onnx,**/selfdrive/modeld/models/big_*.pkl,**/selfdrive/modeld/models/dmonitoring_*.pkl"
-            rm -f selfdrive/modeld/models/big_*.onnx selfdrive/modeld/models/dmonitoring_*.onnx selfdrive/modeld/models/big_*.pkl selfdrive/modeld/models/dmonitoring_*.pkl
+            git lfs pull -X "**/selfdrive/modeld/models/big_*.onnx,**/selfdrive/modeld/models/dmonitoring_*.onnx"
+            rm -f selfdrive/modeld/models/big_*.onnx selfdrive/modeld/models/dmonitoring_*.onnx
           else
-            git lfs pull -I "**/selfdrive/modeld/models/big_*.onnx,**/selfdrive/modeld/models/big_*.pkl" -X ""
-            find selfdrive/modeld/models -type f \( -name "*.onnx" -o -name "*.pkl" \) ! -name "big_*.onnx" ! -name "big_*.pkl" -delete
+            git lfs pull -I "**/selfdrive/modeld/models/big_*.onnx" -X ""
+            find selfdrive/modeld/models -name "*.onnx" ! -name "big_*.onnx" -delete
           fi
-          if grep -lIF "version https://git-lfs.github.com/spec/v1" selfdrive/modeld/models/*.onnx selfdrive/modeld/models/*.pkl 2>/dev/null; then
-            echo "::error::the ONNX or PKL files above are still LFS pointers, not real models"
+          if grep -lIF "version https://git-lfs.github.com/spec/v1" selfdrive/modeld/models/*.onnx; then
+            echo "::error::the ONNX files above are still LFS pointers, not real models"
             exit 1
           fi
       - name: 'Upload Artifact'
         uses: actions/upload-artifact@v4
         with:
           name: models-${{ env.REF }}${{ inputs.artifact_suffix  }}
-          path: |
-            ${{ github.workspace }}/openpilot/openpilot/selfdrive/modeld/models/*.onnx
-            ${{ github.workspace }}/openpilot/openpilot/selfdrive/modeld/models/*.pkl
+          path: ${{ github.workspace }}/openpilot/openpilot/selfdrive/modeld/models/*.onnx
           if-no-files-found: error
 
   build_model:
@@ -198,75 +196,64 @@ jobs:
             OUTPUT_PKL="${{ env.MODELS_DIR }}/driving_tinygrad.pkl"
           fi
 
-          NATIVE_PKL=$(find "${{ env.MODELS_DIR }}" -maxdepth 1 -name "*.pkl" -print -quit)
+          # Generate metadata for all ONNX files
+          find "${{ env.MODELS_DIR }}" -maxdepth 1 -name '*.onnx' | while IFS= read -r onnx_file; do
+            echo "Generating metadata: $onnx_file"
+            env ${TG_FLAGS_QCOM} python3 "${{ env.MODELS_DIR }}/../get_model_metadata.py" "$onnx_file" || true
+          done
 
-          if [ -n "$NATIVE_PKL" ]; then
-            echo "Found native precompiled pkl: $NATIVE_PKL"
-            if [ "$NATIVE_PKL" != "$OUTPUT_PKL" ]; then
-              mv "$NATIVE_PKL" "$OUTPUT_PKL"
+          # Detect model type and build compile args
+          VISION_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/driving_vision.onnx" "${{ env.MODELS_DIR }}/big_driving_vision.onnx"; do
+            [ -f "$f" ] && VISION_ONNX="$f" && break
+          done
+
+          POLICY_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/driving_policy.onnx" "${{ env.MODELS_DIR }}/big_driving_policy.onnx"; do
+            [ -f "$f" ] && POLICY_ONNX="$f" && break
+          done
+
+          OFF_POLICY_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/driving_off_policy.onnx" "${{ env.MODELS_DIR }}/big_driving_off_policy.onnx"; do
+            [ -f "$f" ] && OFF_POLICY_ONNX="$f" && break
+          done
+
+          ON_POLICY_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/driving_on_policy.onnx" "${{ env.MODELS_DIR }}/big_driving_on_policy.onnx"; do
+            [ -f "$f" ] && ON_POLICY_ONNX="$f" && break
+          done
+
+          SUPERCOMBO_ONNX=""
+          for f in "${{ env.MODELS_DIR }}/supercombo.onnx" "${{ env.MODELS_DIR }}/driving_supercombo.onnx" "${{ env.MODELS_DIR }}/big_supercombo.onnx" "${{ env.MODELS_DIR }}/big_driving_supercombo.onnx"; do
+            [ -f "$f" ] && SUPERCOMBO_ONNX="$f" && break
+          done
+
+          MODEL_TYPE="" ONNX_ARGS="" OUTPUT_NAME=""
+          if [ -f "$VISION_ONNX" ]; then
+            ONNX_ARGS="--vision-onnx $VISION_ONNX"
+            if [ -f "$ON_POLICY_ONNX" ] && [ -f "$OFF_POLICY_ONNX" ]; then
+              MODEL_TYPE=vision_multi_policy
+              ONNX_ARGS="$ONNX_ARGS --off-policy-onnx $OFF_POLICY_ONNX --on-policy-onnx $ON_POLICY_ONNX"
+            elif [ -f "$OFF_POLICY_ONNX" ] && [ -f "$POLICY_ONNX" ]; then
+              MODEL_TYPE=vision_multi_policy
+              ONNX_ARGS="$ONNX_ARGS --policy-onnx $POLICY_ONNX --off-policy-onnx $OFF_POLICY_ONNX"
+            elif [ -f "$POLICY_ONNX" ]; then
+              MODEL_TYPE=vision_policy
+              ONNX_ARGS="$ONNX_ARGS --policy-onnx $POLICY_ONNX"
             fi
-            echo "Chunking pkl"
-            python3 -c "from openpilot.common.file_chunker import chunk_file, get_chunk_targets; import os; p='$OUTPUT_PKL'; chunk_file(p, get_chunk_targets(p, os.path.getsize(p)))"
-          else
-            # Generate metadata for all ONNX files
-            find "${{ env.MODELS_DIR }}" -maxdepth 1 -name '*.onnx' | while IFS= read -r onnx_file; do
-              echo "Generating metadata: $onnx_file"
-              env ${TG_FLAGS_QCOM} python3 "${{ env.MODELS_DIR }}/../get_model_metadata.py" "$onnx_file" || true
-            done
+          elif [ -f "$SUPERCOMBO_ONNX" ]; then
+            MODEL_TYPE=supercombo
+            ONNX_ARGS="--supercombo-onnx $SUPERCOMBO_ONNX"
+          fi
 
-            # Detect model type and build compile args
-            VISION_ONNX=""
-            for f in "${{ env.MODELS_DIR }}/driving_vision.onnx" "${{ env.MODELS_DIR }}/big_driving_vision.onnx"; do
-              [ -f "$f" ] && VISION_ONNX="$f" && break
-            done
-
-            POLICY_ONNX=""
-            for f in "${{ env.MODELS_DIR }}/driving_policy.onnx" "${{ env.MODELS_DIR }}/big_driving_policy.onnx"; do
-              [ -f "$f" ] && POLICY_ONNX="$f" && break
-            done
-
-            OFF_POLICY_ONNX=""
-            for f in "${{ env.MODELS_DIR }}/driving_off_policy.onnx" "${{ env.MODELS_DIR }}/big_driving_off_policy.onnx"; do
-              [ -f "$f" ] && OFF_POLICY_ONNX="$f" && break
-            done
-
-            ON_POLICY_ONNX=""
-            for f in "${{ env.MODELS_DIR }}/driving_on_policy.onnx" "${{ env.MODELS_DIR }}/big_driving_on_policy.onnx"; do
-              [ -f "$f" ] && ON_POLICY_ONNX="$f" && break
-            done
-
-            SUPERCOMBO_ONNX=""
-            for f in "${{ env.MODELS_DIR }}/supercombo.onnx" "${{ env.MODELS_DIR }}/driving_supercombo.onnx" "${{ env.MODELS_DIR }}/big_supercombo.onnx" "${{ env.MODELS_DIR }}/big_driving_supercombo.onnx"; do
-              [ -f "$f" ] && SUPERCOMBO_ONNX="$f" && break
-            done
-
-            MODEL_TYPE="" ONNX_ARGS="" OUTPUT_NAME=""
-            if [ -f "$VISION_ONNX" ]; then
-              ONNX_ARGS="--vision-onnx $VISION_ONNX"
-              if [ -f "$ON_POLICY_ONNX" ] && [ -f "$OFF_POLICY_ONNX" ]; then
-                MODEL_TYPE=vision_multi_policy
-                ONNX_ARGS="$ONNX_ARGS --off-policy-onnx $OFF_POLICY_ONNX --on-policy-onnx $ON_POLICY_ONNX"
-              elif [ -f "$OFF_POLICY_ONNX" ] && [ -f "$POLICY_ONNX" ]; then
-                MODEL_TYPE=vision_multi_policy
-                ONNX_ARGS="$ONNX_ARGS --policy-onnx $POLICY_ONNX --off-policy-onnx $OFF_POLICY_ONNX"
-              elif [ -f "$POLICY_ONNX" ]; then
-                MODEL_TYPE=vision_policy
-                ONNX_ARGS="$ONNX_ARGS --policy-onnx $POLICY_ONNX"
-              fi
-            elif [ -f "$SUPERCOMBO_ONNX" ]; then
-              MODEL_TYPE=supercombo
-              ONNX_ARGS="--supercombo-onnx $SUPERCOMBO_ONNX"
-            fi
-
-            if [ -n "$MODEL_TYPE" ]; then
-              echo "Detected: $MODEL_TYPE -> $OUTPUT_PKL"
-              env ${TG_FLAGS} python3 "$COMPILE_MODELD" \
-                --model-type $MODEL_TYPE \
-                --model-size $MODEL_SIZE \
-                --camera-resolutions $CAMERA_RES \
-                $ONNX_ARGS \
-                --output "$OUTPUT_PKL"
-            fi
+          if [ -n "$MODEL_TYPE" ]; then
+            echo "Detected: $MODEL_TYPE -> $OUTPUT_PKL"
+            env ${TG_FLAGS} python3 "$COMPILE_MODELD" \
+              --model-type $MODEL_TYPE \
+              --model-size $MODEL_SIZE \
+              --camera-resolutions $CAMERA_RES \
+              $ONNX_ARGS \
+              --output "$OUTPUT_PKL"
           fi
 
       - name: Prepare Output

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

@@ -226,16 +226,16 @@ jobs:
       HF_REPO: sunnypilot/sunnypilot_models_v1
       HF_DEFAULTS_PATH: models/defaults/big
     steps:
-      - name: Resolve tinygrad ref via API
+      - name: Resolve ONNX hash and tinygrad ref via API
         id: resolve
         run: |
           REF="${{ github.head_ref || github.ref_name }}"
 
-          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl?ref=${REF}" --jq '.sha')
-          [ -n "$BLOB_SHA" ] || { echo "::error::Failed to resolve big_driving_tinygrad.pkl blob SHA"; exit 1; }
+          BLOB_SHA=$(gh api "repos/${GH_REPO}/contents/openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx?ref=${REF}" --jq '.sha')
           ONNX_HASH=$(gh api "repos/${GH_REPO}/git/blobs/${BLOB_SHA}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
-          echo "big PKL hash: $ONNX_HASH"
-          [ -n "$ONNX_HASH" ] || { echo "::error::Failed to extract big PKL hash"; exit 1; }
+          echo "ONNX hash: $ONNX_HASH"
+          [ -n "$ONNX_HASH" ] || { echo "::error::Failed to extract ONNX hash"; exit 1; }
+          echo "onnx_sha256=$ONNX_HASH" >> $GITHUB_OUTPUT
 
           TINYGRAD_REF=$(gh api "repos/${GH_REPO}/contents/tinygrad_repo?ref=${REF}" --jq '.sha')
           echo "tinygrad ref: $TINYGRAD_REF"
@@ -251,7 +251,7 @@ jobs:
           }
 
           if check_defaults; then
-            echo "HF defaults match repo tinygrad ref"
+            echo "HF defaults match repo ONNX hash and tinygrad ref"
             exit 0
           fi
 
@@ -309,7 +309,7 @@ jobs:
       HF_REPO: sunnypilot/sunnypilot_models_v1
       HF_DEFAULTS_PATH: models/defaults/small
     steps:
-      - name: Resolve tinygrad ref via API
+      - name: Resolve ONNX hash and tinygrad ref via API
         id: resolve
         run: |
           REF="${{ github.head_ref || github.ref_name }}"
@@ -318,6 +318,7 @@ jobs:
           DRIVING_HASH=$(gh api "repos/${GH_REPO}/git/blobs/${BLOB_SHA}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
           echo "Driving ONNX hash: $DRIVING_HASH"
           [ -n "$DRIVING_HASH" ] || { echo "::error::Failed to extract driving ONNX hash"; exit 1; }
+          echo "driving_onnx_sha256=$DRIVING_HASH" >> $GITHUB_OUTPUT
 
           TINYGRAD_REF=$(gh api "repos/${GH_REPO}/contents/tinygrad_repo?ref=${REF}" --jq '.sha')
           echo "tinygrad ref: $TINYGRAD_REF"
@@ -333,7 +334,7 @@ jobs:
           }
 
           if check_defaults; then
-            echo "HF defaults match repo tinygrad ref"
+            echo "HF defaults match repo ONNX hash and tinygrad ref"
             exit 0
           fi
 
@@ -391,7 +392,7 @@ jobs:
       HF_REPO: sunnypilot/sunnypilot_models_v1
       HF_DEFAULTS_PATH: models/defaults/dm
     steps:
-      - name: Resolve tinygrad ref via API
+      - name: Resolve ONNX hash and tinygrad ref via API
         id: resolve
         run: |
           REF="${{ github.head_ref || github.ref_name }}"
@@ -400,6 +401,7 @@ jobs:
           DM_HASH=$(gh api "repos/${GH_REPO}/git/blobs/${BLOB_SHA}" --jq '.content' | base64 -d | grep '^oid sha256:' | cut -d: -f2)
           echo "DM ONNX hash: $DM_HASH"
           [ -n "$DM_HASH" ] || { echo "::error::Failed to extract DM ONNX hash"; exit 1; }
+          echo "dm_onnx_sha256=$DM_HASH" >> $GITHUB_OUTPUT
 
           TINYGRAD_REF=$(gh api "repos/${GH_REPO}/contents/tinygrad_repo?ref=${REF}" --jq '.sha')
           echo "tinygrad ref: $TINYGRAD_REF"
@@ -415,7 +417,7 @@ jobs:
           }
 
           if check_defaults; then
-            echo "HF defaults match DM tinygrad ref"
+            echo "HF defaults match DM ONNX hash and tinygrad ref"
             exit 0
           fi
 

.github/workflows/test_models.yml

@@ -48,8 +48,6 @@ jobs:
           submodules: true
 
       - name: Download Model Chunks in Parallel
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
           mkdir -p /tmp/model_chunks
           echo '${{ toJson(matrix.artifact.chunks) }}' > chunks.json
@@ -65,15 +63,12 @@ jobs:
           with open(manifest_path, "w") as f:
             f.write(str(len(chunks)))
           base_dir = os.environ["BASE_DIR"]
-          hf_token = os.environ.get("HF_TOKEN", "")
           with open("/tmp/curl_config.txt", "w") as f:
             for c in chunks:
               fn = c["file_name"]
               f.write(f"url = \"{base_dir}/{fn}\"\noutput = \"/tmp/model_chunks/{fn}\"\n")
-              if hf_token:
-                f.write(f"header = \"Authorization: Bearer {hf_token}\"\n")
           '
-          curl -Z --parallel-immediate --parallel-max 12 --retry 3 --retry-all-errors -s -S -f -L -K /tmp/curl_config.txt
+          curl -Z --parallel-immediate --parallel-max 16 -s -S -f -L -K /tmp/curl_config.txt
 
       - name: Run Model Compatibility Test
         env:

.github/workflows/tests.yaml

@@ -22,7 +22,7 @@ env:
   PYTHONPATH: ${{ github.workspace }}
   GIT_CONFIG_COUNT: 1
   GIT_CONFIG_KEY_0: lfs.fetchexclude
-  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
+  GIT_CONFIG_VALUE_0: openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx
 
 jobs:
   build_release:

.gitignore

@@ -50,8 +50,6 @@ st[0-9A-Za-z][0-9A-Za-z][0-9A-Za-z][0-9A-Za-z][0-9A-Za-z][0-9A-Za-z]
 *.stats
 *.pkl
 *.pkl*
-!openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl
-!openpilot/sunnypilot/modeld_v2/models/*.pkl
 config.json
 compile_commands.json
 compare_runtime*.html

.gitmodules

@@ -19,4 +19,3 @@
 [submodule "sunnypilot/neural_network_data"]
 	path = openpilot/sunnypilot/neural_network_data
   url = https://github.com/sunnypilot/neural-network-data.git
-

.lfsconfig

@@ -1,5 +1,4 @@
 [lfs]
 	url = https://gitlab.com/sunnypilot/public/sunnypilot-new-lfs.git/info/lfs
 	pushurl = ssh://git@gitlab.com/sunnypilot/public/sunnypilot-new-lfs.git
-
 	locksverify = false

Jenkinsfile

@@ -12,17 +12,16 @@ def retryWithDelay(int maxRetries, int delay, Closure body) {
 def device(String ip, String step_label, String cmd) {
   withCredentials([file(credentialsId: 'id_rsa', variable: 'key_file')]) {
     def ssh_cmd = """
-ssh -o ControlMaster=no -o ControlPath=none -o ConnectTimeout=5 -o ServerAliveInterval=5 -o ServerAliveCountMax=12 -o BatchMode=yes -o StrictHostKeyChecking=no -i ${key_file} 'comma@${ip}' exec setpriv --pdeathsig HUP /usr/bin/bash <<'END'
+ssh -o ControlMaster=auto -o ControlPath=/tmp/ssh_control_%C -o ControlPersist=yes -o ConnectTimeout=5 -o ServerAliveInterval=5 -o ServerAliveCountMax=2 -o BatchMode=yes -o StrictHostKeyChecking=no -i ${key_file} 'comma@${ip}' exec /usr/bin/bash <<'END'
 
 set -e
 
 export TERM=xterm-256color
 
-trap 'kill 0' HUP # stop this process group on SSH disconnect
+shopt -s huponexit # kill all child processes when the shell exits
 
 export CI=1
 export PYTHONWARNINGS=error
-export PYTHONFAULTHANDLER=1
 export COMMA_CACHE=/data/tmp/comma_download_cache
 #export LOGPRINT=debug # this has gotten too spammy...
 export TEST_DIR=${env.TEST_DIR}
@@ -70,8 +69,7 @@ export LD_LIBRARY_PATH="\$(python -c 'import ffmpeg; print(ffmpeg.LIB_DIR)'):/us
 ln -snf ${env.TEST_DIR} /data/pythonpath
 
 cd ${env.TEST_DIR} || true
-time ( ${cmd} ) &
-wait \$!
+time ${cmd}
 END"""
 
     sh script: ssh_cmd, label: step_label
@@ -171,7 +169,7 @@ node {
   env.GIT_BRANCH = checkout(scm).GIT_BRANCH
   env.GIT_COMMIT = checkout(scm).GIT_COMMIT
 
-  def excludeBranches = ['__nightly', '__nightly-chestnut', 'devel', 'devel-staging',
+  def excludeBranches = ['__nightly', 'devel', 'devel-staging',
                          'release-tizi', 'release-tizi-staging', 'release-mici', 'release-mici-staging', 'testing-closet*', 'hotfix-*']
   def excludeRegex = excludeBranches.join('|').replaceAll('\\*', '.*')
 
@@ -203,12 +201,6 @@ node {
       )
     }
 
-    if (env.BRANCH_NAME == '__nightly-chestnut') {
-      deviceStage("build nightly-chestnut", "mici-chestnut-ci", [], [
-        step("build nightly-chestnut", "SCONSFLAGS=-j4 INCLUDE_BIG_MODEL=1 PANDA_DEBUG_BUILD=1 RELEASE_BRANCH=nightly-chestnut $SOURCE_DIR/tools/release/build_release.sh TestChestnutOnroad"),
-      ])
-    }
-
     if (!env.BRANCH_NAME.matches(excludeRegex)) {
     parallel (
       'onroad tests': {
@@ -260,14 +252,6 @@ node {
           step("test amp", "./openpilot/common/hardware/comma/tests/test_amplifier.py"),
         ])
       },
-      'chestnut': {
-        deviceStage("chestnut", "mici-chestnut-ci", ["UNSAFE=1", "CHESTNUT=1"], [
-          step("build", "./openpilot/selfdrive/test/chestnut.sh"),
-          step("model replay", "openpilot/selfdrive/test/process_replay/model_replay.py --chestnut"),
-          step("onroad tests", "./openpilot/selfdrive/test/test_onroad.py TestChestnutOnroad", [timeout: 120]),
-          step("test power draw", "./openpilot/selfdrive/test/test_power_draw.py"),
-        ])
-      },
 
     )
     }

SConstruct

@@ -87,6 +87,7 @@ acados_include_dirs = [
 # vendored in commaai/dependencies.
 allowed_system_libs = {
   "EGL", "GLESv2", "GL",
+  "Qt5Charts", "Qt5Core", "Qt5Gui", "Qt5Widgets",
   "dl", "drm", "gbm", "m", "pthread",
 }
 
@@ -345,8 +346,6 @@ AddPostAction(BUILD_TARGETS or [Dir('.')], prune_cache_dir)
 def check_build_product_size(target, source, env):
   limit = 50 * 1024 * 1024  # GitHub max size
   for t in target:
-    if str(t).endswith('.pkl'):  # chunked during release packaging
-      continue
     if hasattr(t, 'isfile') and t.isfile() and (size := os.path.getsize(t.abspath)) > limit:
       raise SCons.Errors.UserError(f"{t} is {size / (1024 * 1024):.1f} MiB, exceeding the {limit / (1024 * 1024):.1f} MiB limit")
 if not GetOption('extras'):

docs/CARS.md

@@ -34,7 +34,7 @@ A supported vehicle is one that just works when you install a comma device. All
 |Chrysler|Pacifica Hybrid 2019-25|Adaptive Cruise Control (ACC)|Stock|0 mph|39 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 FCA connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Chrysler Pacifica Hybrid 2019-25">Buy Here</a></sub></details>|||
 |comma|body|All|openpilot|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|None|<a href="https://youtu.be/VT-i3yRsX2s?t=2736" target="_blank"><img height="18px" src="assets/icon-youtube.svg" /></a>||
 |CUPRA[<sup>12</sup>](#footnotes)|Ateca 2018-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=CUPRA Ateca 2018-23">Buy Here</a></sub></details>|||
-|CUPRA|Born 2021-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot[<sup>16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW MEB connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=CUPRA Born 2021-23">Buy Here</a></sub></details>|||
+|CUPRA[<sup>12</sup>](#footnotes)|Born 2021-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot[<sup>16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=CUPRA Born 2021-23">Buy Here</a></sub></details>|||
 |Dodge|Durango 2020-21|Adaptive Cruise Control (ACC)|Stock|0 mph|39 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 FCA connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Dodge Durango 2020-21">Buy Here</a></sub></details>|||
 |Ford|Bronco Sport 2021-24|Co-Pilot360 Assist+|openpilot|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Ford Q3 connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Ford Bronco Sport 2021-24">Buy Here</a></sub></details>|||
 |Ford|Escape 2020-22|Co-Pilot360 Assist+|openpilot|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Ford Q3 connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Ford Escape 2020-22">Buy Here</a></sub></details>|||
@@ -99,7 +99,7 @@ A supported vehicle is one that just works when you install a comma device. All
 |Honda|Fit 2018-20|Honda Sensing|openpilot|26 mph|12 mph|[![star](assets/icon-star-empty.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Nidec connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda Fit 2018-20">Buy Here</a></sub></details>|||
 |Honda|Freed 2020|Honda Sensing|openpilot|26 mph|12 mph|[![star](assets/icon-star-empty.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Nidec connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda Freed 2020">Buy Here</a></sub></details>|||
 |Honda|HR-V 2019-22|Honda Sensing|openpilot|26 mph|12 mph|[![star](assets/icon-star-empty.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Nidec connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda HR-V 2019-22">Buy Here</a></sub></details>|||
-|Honda|HR-V 2023-27|All|openpilot available[<sup>1,5</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Bosch B connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda HR-V 2023-27">Buy Here</a></sub></details>|||
+|Honda|HR-V 2023-25|All|openpilot available[<sup>1,5</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Bosch B connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda HR-V 2023-25">Buy Here</a></sub></details>|||
 |Honda|Insight 2019-22|All|openpilot available[<sup>1,5</sup>](#footnotes)|0 mph|3 mph|[![star](assets/icon-star-empty.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Bosch A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda Insight 2019-22">Buy Here</a></sub></details>|||
 |Honda|Inspire 2018|All|openpilot available[<sup>1,5</sup>](#footnotes)|0 mph|3 mph|[![star](assets/icon-star-empty.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Bosch A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda Inspire 2018">Buy Here</a></sub></details>|||
 |Honda|N-Box 2018|All|openpilot available[<sup>1,5</sup>](#footnotes)|0 mph|11 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 Honda Bosch A connector<br>- 1 OBD-C cable (2 ft)<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Honda N-Box 2018">Buy Here</a></sub></details>|||
@@ -268,8 +268,8 @@ A supported vehicle is one that just works when you install a comma device. All
 |Škoda[<sup>12</sup>](#footnotes)|Superb 2015-22[<sup>15</sup>](#footnotes)|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Škoda Superb 2015-22">Buy Here</a></sub></details>|||
 |Tesla[<sup>10</sup>](#footnotes)|Model 3 (with HW3) 2019-23[<sup>9</sup>](#footnotes)|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Tesla A connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Tesla Model 3 (with HW3) 2019-23">Buy Here</a></sub></details>|||
 |Tesla[<sup>10</sup>](#footnotes)|Model 3 (with HW4) 2024-25[<sup>9</sup>](#footnotes)|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Tesla B connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Tesla Model 3 (with HW4) 2024-25">Buy Here</a></sub></details>|||
-|Tesla[<sup>10</sup>](#footnotes)|Model Y (with HW3) 2020-24[<sup>9</sup>](#footnotes)|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Tesla A connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Tesla Model Y (with HW3) 2020-24">Buy Here</a></sub></details>|||
-|Tesla[<sup>10</sup>](#footnotes)|Model Y (with HW4) 2023-25[<sup>9</sup>](#footnotes)|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Tesla B connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Tesla Model Y (with HW4) 2023-25">Buy Here</a></sub></details>|||
+|Tesla[<sup>10</sup>](#footnotes)|Model Y (with HW3) 2020-23[<sup>9</sup>](#footnotes)|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Tesla A connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Tesla Model Y (with HW3) 2020-23">Buy Here</a></sub></details>|||
+|Tesla[<sup>10</sup>](#footnotes)|Model Y (with HW4) 2024-25[<sup>9</sup>](#footnotes)|All|openpilot available[<sup>1</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Tesla B connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Tesla Model Y (with HW4) 2024-25">Buy Here</a></sub></details>|||
 |Toyota|Alphard 2019-20|All|openpilot|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Toyota A connector<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Toyota Alphard 2019-20">Buy Here</a></sub></details>|||
 |Toyota|Alphard Hybrid 2021|All|openpilot|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Toyota A connector<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Toyota Alphard Hybrid 2021">Buy Here</a></sub></details>|||
 |Toyota|Avalon 2016|Toyota Safety Sense P|Stock|19 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-empty.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 Toyota A connector<br>- 1 comma four<br>- 1 comma power v3<br>- 1 harness box<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Toyota Avalon 2016">Buy Here</a></sub></details>|||
@@ -335,8 +335,8 @@ A supported vehicle is one that just works when you install a comma device. All
 |Volkswagen[<sup>12</sup>](#footnotes)|Golf R 2015-19|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen Golf R 2015-19">Buy Here</a></sub></details>|||
 |Volkswagen[<sup>12</sup>](#footnotes)|Golf SportsVan 2015-20|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen Golf SportsVan 2015-20">Buy Here</a></sub></details>|||
 |Volkswagen[<sup>12</sup>](#footnotes)|Grand California 2019-24|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|31 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen Grand California 2019-24">Buy Here</a></sub></details>|<a href="https://youtu.be/4100gLeabmo" target="_blank"><img height="18px" src="assets/icon-youtube.svg" /></a>||
-|Volkswagen|ID.4 2021-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot[<sup>16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW MEB connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen ID.4 2021-23">Buy Here</a></sub></details>|||
-|Volkswagen|ID.4 2024-25|Adaptive Cruise Control (ACC) & Lane Assist|openpilot[<sup>16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW MEB connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen ID.4 2024-25">Buy Here</a></sub></details>|||
+|Volkswagen[<sup>12</sup>](#footnotes)|ID.4 2021-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot[<sup>16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen ID.4 2021-23">Buy Here</a></sub></details>|||
+|Volkswagen[<sup>12</sup>](#footnotes)|ID.4 2024-25|Adaptive Cruise Control (ACC) & Lane Assist|openpilot[<sup>16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen ID.4 2024-25">Buy Here</a></sub></details>|||
 |Volkswagen[<sup>12</sup>](#footnotes)|Jetta 2019-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen Jetta 2019-23">Buy Here</a></sub></details>|||
 |Volkswagen[<sup>12</sup>](#footnotes)|Jetta GLI 2021-23|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen Jetta GLI 2021-23">Buy Here</a></sub></details>|||
 |Volkswagen|Passat 2015-22[<sup>14</sup>](#footnotes)|Adaptive Cruise Control (ACC) & Lane Assist|openpilot available[<sup>1,16</sup>](#footnotes)|0 mph|0 mph|[![star](assets/icon-star-full.svg)](##)|[![star](assets/icon-star-full.svg)](##)|<details><summary>Parts</summary><sub>- 1 OBD-C cable (2 ft)<br>- 1 VW J533 connector<br>- 1 comma four<br>- 1 harness box<br>- 1 long OBD-C cable (9.5 ft)<br>- 1 mount<br><a href="https://comma.ai/shop/comma-3x?harness=Volkswagen Passat 2015-22">Buy Here</a></sub></details>|||
@@ -363,7 +363,7 @@ A supported vehicle is one that just works when you install a comma device. All
 <sup>6</sup>See more setup details for <a href="https://github.com/commaai/openpilot/wiki/nissan" target="_blank">Nissan</a>. <br />
 <sup>7</sup>In the non-US market, openpilot requires the car to come equipped with EyeSight with Lane Keep Assistance. <br />
 <sup>8</sup>Enabling longitudinal control (alpha) will disable all EyeSight functionality, including AEB, LDW, and RAB. <br />
-<sup>9</sup>Model years 2023 and 2024 can have either hardware type, depending on build date and factory. To check which hardware type your vehicle has, look for <b>Autopilot computer</b> under <b>Software -> Additional Vehicle Information</b> on your vehicle's touchscreen. See <a href="https://www.notateslaapp.com/news/2173/how-to-check-if-your-tesla-has-hardware-4-ai4-or-hardware-3">this page</a> for more information. <br />
+<sup>9</sup>Some 2023 model years have HW4. To check which hardware type your vehicle has, look for <b>Autopilot computer</b> under <b>Software -> Additional Vehicle Information</b> on your vehicle's touchscreen. See <a href="https://www.notateslaapp.com/news/2173/how-to-check-if-your-tesla-has-hardware-4-ai4-or-hardware-3">this page</a> for more information. <br />
 <sup>10</sup>See more setup details for <a href="https://github.com/commaai/openpilot/wiki/tesla" target="_blank">Tesla</a>. <br />
 <sup>11</sup>openpilot operates above 28mph for Camry 4CYL L, 4CYL LE and 4CYL SE which don't have Full-Speed Range Dynamic Radar Cruise Control. <br />
 <sup>12</sup>The J533 harness plugs in at the CAN gateway under the dashboard, just above the steering column. More information can be found at <a href="https://docs.howtocomma.com/docs/j533-harness-install" target="_blank">this guide</a>. <br />

docs/README.md

@@ -5,10 +5,10 @@ The site is updated on pushes to master by this [workflow](../.github/workflows/
 
 **1. Build the site**
 ``` bash
-op docs --build
+python docs/serve.py --build
 ```
 
 **2. Run the site locally** (rebuilds on change)
 ``` bash
-op docs
+python docs/serve.py
 ```

launch_chffrplus.sh

@@ -18,21 +18,21 @@ function agnos_init {
   sudo chmod 660 /dev/adsprpc-smd /dev/ion /dev/kgsl-3d0
 
   # Check if AGNOS update is required
-  if [ "$(< /VERSION)" != "$AGNOS_VERSION" ]; then
+  if [ $(< /VERSION) != "$AGNOS_VERSION" ]; then
     AGNOS_PY="$DIR/openpilot/common/hardware/comma/agnos.py"
     MANIFEST="$DIR/openpilot/system/hardware/comma/agnos.json"
-    if "$AGNOS_PY" --verify "$MANIFEST"; then
+    if $AGNOS_PY --verify $MANIFEST; then
       sudo reboot
     fi
     while true; do
-      "$DIR/openpilot/common/hardware/comma/updater" "$AGNOS_PY" "$MANIFEST"
+      $DIR/openpilot/common/hardware/comma/updater $AGNOS_PY $MANIFEST
     done
   fi
 }
 
 function launch {
   # Remove orphaned git lock if it exists on boot
-  [ -f "$DIR/.git/index.lock" ] && rm -f "$DIR/.git/index.lock"
+  [ -f "$DIR/.git/index.lock" ] && rm -f $DIR/.git/index.lock
 
   # Check to see if there's a valid overlay-based update available. Conditions
   # are as follows:
@@ -44,7 +44,7 @@ function launch {
   #    that completed successfully and synced to disk.
 
   if [ -f "${DIR}/.overlay_init" ]; then
-    find "${DIR}/.git" -newer "${DIR}/.overlay_init" | grep -q '.' 2> /dev/null
+    find ${DIR}/.git -newer ${DIR}/.overlay_init | grep -q '.' 2> /dev/null
     if [ $? -eq 0 ]; then
       echo "${DIR} has been modified, skipping overlay update installation"
     else
@@ -53,9 +53,9 @@ function launch {
           echo "Valid overlay update found, installing"
           LAUNCHER_LOCATION="${BASH_SOURCE[0]}"
 
-          mv "$DIR" /data/safe_staging/old_openpilot
-          mv "${STAGING_ROOT}/finalized" "$DIR"
-          cd "$DIR"
+          mv $DIR /data/safe_staging/old_openpilot
+          mv "${STAGING_ROOT}/finalized" $DIR
+          cd $DIR
 
           echo "Restarting launch script ${LAUNCHER_LOCATION}"
           unset AGNOS_VERSION
@@ -69,7 +69,7 @@ function launch {
   fi
 
   # handle pythonpath
-  ln -sfn "$(pwd)" /data/pythonpath
+  ln -sfn $(pwd) /data/pythonpath
   export PYTHONPATH="$PWD"
 
   # submodule package symlinks for PYTHONPATH imports on device.
@@ -90,7 +90,7 @@ function launch {
 
   # start manager
   cd openpilot/system/manager
-  if [ ! -f "$DIR/prebuilt" ]; then
+  if [ ! -f $DIR/prebuilt ]; then
     ./build.py
   fi
   ./manager.py

openpilot/cereal/log.capnp

@@ -2594,7 +2594,6 @@ struct Event {
     clocks @35 :Clocks;
     deviceState @6 :DeviceState;
     chestnutState @152 :ChestnutState;
-    chestnutGpuState @153 :ChestnutState;
     logMessage @18 :Text;
     errorLogMessage @85 :Text;
 

openpilot/cereal/services.py

@@ -25,8 +25,7 @@ _services: dict[str, tuple] = {
   "accelerometer": (True, 104., 104),
   "temperatureSensor": (True, 2., 200),
   "deviceState": (True, 2., 1),
-  "chestnutState": (True, 10., 1),
-  "chestnutGpuState": (False, 10.),
+  "chestnutState": (True, 10., 10),
   "touch": (True, 20., 1),
   "can": (True, 100., 2053, QueueSize.BIG),  # decimation gives ~3 msgs in a full segment
   "controlsState": (True, 100., 10, QueueSize.MEDIUM),

openpilot/common/esim/base.py

@@ -21,13 +21,6 @@ class Profile:
   def is_comma(self) -> bool:
     return self.provider == 'Webbing' and self.iccid.startswith('8985235')
 
-  @property
-  def display_name(self) -> str:
-    if self.is_comma:
-      return "comma prime"
-    name = self.nickname or self.provider or "<unnamed>"
-    return f"{name} (...{self.iccid[-4:]})"
-
 
 class LPABase(ABC):
   @abstractmethod

openpilot/common/esim/lpa.py

@@ -613,7 +613,7 @@ def parse_lpa_activation_code(activation_code: str) -> tuple[str, str]:
   if not activation_code.startswith("LPA:"):
     raise ValueError("Invalid activation code format")
   parts = activation_code[4:].split("$")
-  if len(parts) != 3 or not all(parts):
+  if len(parts) != 3:
     raise ValueError("Invalid activation code format")
   return parts[1], parts[2]
 

openpilot/common/file_chunker.py

@@ -24,7 +24,6 @@ def chunk_file(path, targets):
   manifest_path, *chunk_paths = targets
   actual_num_chunks = max(1, math.ceil(os.path.getsize(path) / CHUNK_SIZE))
   assert len(chunk_paths) >= actual_num_chunks, f"Allowed {len(chunk_paths)} chunks but needs at least {actual_num_chunks}, for path {path}"
-  Path(manifest_path).unlink(missing_ok=True)
   with open(path, 'rb') as f:
     for chunk_path in chunk_paths:
       with open(chunk_path, 'wb') as out:
@@ -32,6 +31,14 @@ def chunk_file(path, targets):
   Path(manifest_path).write_text(str(len(chunk_paths)))
   os.remove(path)
 
+def get_existing_chunks(path):
+  if os.path.isfile(path):
+    return [path]
+  if os.path.isfile(manifest := get_manifest_path(path)):
+    num_chunks = int(Path(manifest).read_text().strip())
+    return _chunk_paths(path, num_chunks)
+  raise FileNotFoundError(path)
+
 class ChunkStream(io.RawIOBase):
   def __init__(self, paths):
     self._paths = iter(paths)
@@ -59,11 +66,11 @@ class ChunkStream(io.RawIOBase):
 
 def open_file_chunked(path):
   manifest_path = get_manifest_path(path)
-  if os.path.isfile(path):
-    paths = [path]
-  elif os.path.isfile(manifest_path):
+  if os.path.isfile(manifest_path):
     num_chunks = int(Path(manifest_path).read_text().strip())
     paths = [get_chunk_name(path, i, num_chunks) for i in range(num_chunks)]
+  elif os.path.isfile(path):
+    paths = [path]
   else:
     raise FileNotFoundError(path)
   return io.BufferedReader(ChunkStream(paths))

openpilot/common/hardware/base.py

@@ -145,9 +145,6 @@ class HardwareBase(ABC):
   def get_modem_temperatures(self):
     return []
 
-  def get_modem_state(self) -> dict:
-    return {}
-
   def initialize_hardware(self):
     pass
 

openpilot/common/hardware/comma/power_monitor.py

@@ -9,25 +9,22 @@ from openpilot.common.realtime import Ratekeeper
 from openpilot.common.filter_simple import FirstOrderFilter
 
 
-def read_power(panda=None):
-  if panda is not None and panda.get_type() == panda.HW_TYPE_CUATRO:
-    health = panda.health()
-    return health['voltage'] * health['current'] / 1e6
+def read_power():
   with open("/sys/bus/i2c/devices/0-0040/hwmon/hwmon1/power1_input") as f:
     return int(f.read()) / 1e6
 
-def sample_power(seconds=5, panda=None) -> list[float]:
+def sample_power(seconds=5) -> list[float]:
   rate = 123
   rk = Ratekeeper(rate, print_delay_threshold=None)
 
   pwrs = []
   for _ in range(rate*seconds):
-    pwrs.append(read_power(panda))
+    pwrs.append(read_power())
     rk.keep_time()
   return pwrs
 
-def get_power(seconds=5, panda=None):
-  pwrs = sample_power(seconds, panda)
+def get_power(seconds=5):
+  pwrs = sample_power(seconds)
   return np.mean(pwrs)
 
 def wait_for_power(min_pwr, max_pwr, min_secs_in_range, timeout):

openpilot/common/hardware/comma/updater

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6a7adb302d378dda7b1788a841b89e4f905c872550a70a76650dcde977b4ece0
-size 24709209
+oid sha256:3a94ab8395f20d20a9d5a2a2bacca0694f072df8421cf13adca6250d28065bdc
+size 24709205

openpilot/common/params_keys.h

@@ -28,7 +28,6 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"ControlsReady", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, BOOL}},
     {"CurrentBootlog", {PERSISTENT, STRING}},
     {"CurrentRoute", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, STRING}},
-    {"DisableDriverCameraIR", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, BOOL}},
     {"DisableLogging", {CLEAR_ON_MANAGER_START | CLEAR_ON_ONROAD_TRANSITION, BOOL}},
     {"DisablePowerDown", {PERSISTENT | BACKUP, BOOL}},
     {"DisableUpdates", {PERSISTENT | BACKUP, BOOL, "0"}},
@@ -139,6 +138,7 @@ inline static std::unordered_map<std::string, ParamKeyAttributes> keys = {
     {"UptimeOnroad", {PERSISTENT, FLOAT, "0.0"}},
     {"ChestnutActive", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
     {"ChestnutLoading", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
+    {"ChestnutModelError", {CLEAR_ON_MANAGER_START | CLEAR_ON_OFFROAD_TRANSITION | CLEAR_ON_IGNITION_ON, BOOL}},
     {"Version", {PERSISTENT, STRING}},
 
     // --- sunnypilot params --- //

openpilot/common/qrcode.py

@@ -1,47 +1,16 @@
-"""QR code encoding, decoding, and UI textures."""
-
-import functools
-import itertools
+"""Small QR encoder for the UI's byte-mode, error-correction-level-L codes."""
 
 import numpy as np
 import pyray as rl
 
 
-# (ec codewords per block, block count) for levels L, M, Q, H, versions 1-40
-_EC = [
-  ((7, 1), (10, 1), (13, 1), (17, 1)), ((10, 1), (16, 1), (22, 1), (28, 1)), ((15, 1), (26, 1), (18, 2), (22, 2)),
-  ((20, 1), (18, 2), (26, 2), (16, 4)), ((26, 1), (24, 2), (18, 4), (22, 4)), ((18, 2), (16, 4), (24, 4), (28, 4)),
-  ((20, 2), (18, 4), (18, 6), (26, 5)), ((24, 2), (22, 4), (22, 6), (26, 6)), ((30, 2), (22, 5), (20, 8), (24, 8)),
-  ((18, 4), (26, 5), (24, 8), (28, 8)), ((20, 4), (30, 5), (28, 8), (24, 11)), ((24, 4), (22, 8), (26, 10), (28, 11)),
-  ((26, 4), (22, 9), (24, 12), (22, 16)), ((30, 4), (24, 9), (20, 16), (24, 16)), ((22, 6), (24, 10), (30, 12), (24, 18)),
-  ((24, 6), (28, 10), (24, 17), (30, 16)), ((28, 6), (28, 11), (28, 16), (28, 19)), ((30, 6), (26, 13), (28, 18), (28, 21)),
-  ((28, 7), (26, 14), (26, 21), (26, 25)), ((28, 8), (26, 16), (30, 20), (28, 25)), ((28, 8), (26, 17), (28, 23), (30, 25)),
-  ((28, 9), (28, 17), (30, 23), (24, 34)), ((30, 9), (28, 18), (30, 25), (30, 30)), ((30, 10), (28, 20), (30, 27), (30, 32)),
-  ((26, 12), (28, 21), (30, 29), (30, 35)), ((28, 12), (28, 23), (28, 34), (30, 37)), ((30, 12), (28, 25), (30, 34), (30, 40)),
-  ((30, 13), (28, 26), (30, 35), (30, 42)), ((30, 14), (28, 28), (30, 38), (30, 45)), ((30, 15), (28, 29), (30, 40), (30, 48)),
-  ((30, 16), (28, 31), (30, 43), (30, 51)), ((30, 17), (28, 33), (30, 45), (30, 54)), ((30, 18), (28, 35), (30, 48), (30, 57)),
-  ((30, 19), (28, 37), (30, 51), (30, 60)), ((30, 19), (28, 38), (30, 53), (30, 63)), ((30, 20), (28, 40), (30, 56), (30, 66)),
-  ((30, 21), (28, 43), (30, 59), (30, 70)), ((30, 22), (28, 45), (30, 62), (30, 74)), ((30, 24), (28, 47), (30, 65), (30, 77)),
-  ((30, 25), (28, 49), (30, 68), (30, 81)),
-]
+# Indexes are QR versions. These are the only two Reed-Solomon parameters needed
+# for error-correction level L.
+_ECC_LEN = (0, 7, 10, 15, 20, 26, 18, 20, 24, 30, 18, 20, 24, 26, 30, 22, 24, 28, 30, 28, 28)
+_NUM_BLOCKS = (0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 8)
 
-# GF(256) with the QR polynomial x^8 + x^4 + x^3 + x^2 + 1: powers of alpha and their logs
-_EXP = [1]
-for _ in range(254):
-  _EXP.append(_EXP[-1] << 1 ^ (0x11D if _EXP[-1] & 0x80 else 0))
-_LOG = {v: i for i, v in enumerate(_EXP)}
-
-
-def _bch_format(data: int) -> int:
-  v = data << 10
-  for shift in range(14, 9, -1):
-    if v >> shift & 1:
-      v ^= 0x537 << (shift - 10)
-  return (data << 10 | v) ^ 0x5412
-
-
-# 15-bit format info indexed by (level bits << 3 | mask). Level bits: L=01, M=00, Q=11, H=10.
-_FORMATS = [_bch_format(d) for d in range(32)]
+# 15 format-info bits for level L (01) with mask 0: ((0x08 << 10) | bch_remainder) ^ 0x5412
+_FORMAT_BITS = 0b111011111000100
 
 
 def _raw_modules(version: int) -> int:
@@ -52,24 +21,8 @@ def _raw_modules(version: int) -> int:
   return result - (36 if version >= 7 else 0)
 
 
-def _block_lengths(version: int, level: int) -> list[int]:
-  """Data codewords per Reed-Solomon block. The last blocks may be one longer."""
-  ec, nblocks = _EC[version - 1][level]
-  total = _raw_modules(version) // 8 - ec * nblocks
-  return [total // nblocks + (i >= nblocks - total % nblocks) for i in range(nblocks)]
-
-
-def _interleaved(version: int, level: int) -> list[tuple[int, int]]:
-  """(block, index within block) of each transmitted codeword: data column-major, then ECC column-major."""
-  ec, nblocks = _EC[version - 1][level]
-  lens = _block_lengths(version, level)
-  data = [(b, i) for i in range(max(lens)) for b in range(nblocks) if i < lens[b]]
-  ecc = [(b, lens[b] + i) for i in range(ec) for b in range(nblocks)]
-  return data + ecc
-
-
 def _capacity(version: int) -> int:
-  return sum(_block_lengths(version, 0))
+  return _raw_modules(version) // 8 - _ECC_LEN[version] * _NUM_BLOCKS[version]
 
 
 def _append_bits(bits: list[int], value: int, length: int) -> None:
@@ -96,18 +49,37 @@ def _data_codewords(data: bytes, version: int) -> bytes:
 def _codewords(data: bytes, version: int) -> bytes:
   """Split data codewords into Reed-Solomon blocks and interleave data + ECC."""
   data = _data_codewords(data, version)
-  divisor = _divisor(_EC[version - 1][0][0])
-  blocks = []
+  num_blocks = _NUM_BLOCKS[version]
+  ecc_len = _ECC_LEN[version]
+  raw_codewords = _raw_modules(version) // 8
+  short_len = raw_codewords // num_blocks
+  num_short = num_blocks - raw_codewords % num_blocks
+  divisor = _divisor(ecc_len)
+  blocks: list[tuple[bytes, bytes]] = []
   offset = 0
-  for length in _block_lengths(version, 0):
+  for i in range(num_blocks):
+    length = short_len - ecc_len + (0 if i < num_short else 1)
     block = data[offset:offset + length]
-    blocks.append(block + _remainder(block, divisor))
+    blocks.append((block, _remainder(block, divisor)))
     offset += length
-  return bytes(blocks[b][i] for b, i in _interleaved(version, 0))
+  result = bytearray()
+  for i in range(short_len - ecc_len + 1):
+    for block, _ in blocks:
+      result.extend(block[i:i + 1])
+  for i in range(ecc_len):
+    for _, ecc in blocks:
+      result.append(ecc[i])
+  return bytes(result)
 
 
 def _multiply(x: int, y: int) -> int:
-  return _EXP[(_LOG[x] + _LOG[y]) % 255] if x and y else 0
+  result = 0
+  for _ in range(8):
+    result = (result << 1) ^ (0x11D if result & 0x80 else 0)
+    if y & 0x80:
+      result ^= x
+    y <<= 1
+  return result
 
 
 def _divisor(degree: int) -> bytes:
@@ -136,7 +108,7 @@ def _alignment_positions(version: int) -> list[int]:
   if version == 1:
     return []
   count = version // 7 + 2
-  step = (version * 8 + count * 3 + 5) // (count * 4 - 4) * 2
+  step = ((version * 4 + count * 2 + 1) // (count * 2 - 2)) * 2
   return [6] + [version * 4 + 10 - step * i for i in range(count - 1)][::-1]
 
 
@@ -199,7 +171,7 @@ class _Qr:
 
   def _format(self) -> None:
     for i in range(15):
-      bit = ((_FORMATS[1 << 3 | 0] >> i) & 1) != 0  # level L, mask 0
+      bit = ((_FORMAT_BITS >> i) & 1) != 0
       y_pos = i if i < 6 else i + 1 if i < 8 else self.size - 15 + i
       self._set_function(8, y_pos, bit)
       x_pos = self.size - 1 - i if i < 8 else 15 - i if i < 9 else 14 - i
@@ -244,496 +216,3 @@ def make_texture(data: str, inverted: bool = False) -> rl.Texture:
   rl_image.mipmaps = 1
   rl_image.format = rl.PixelFormat.PIXELFORMAT_UNCOMPRESSED_R8G8B8A8
   return rl.load_texture_from_image(rl_image)
-
-
-# ---- Symbol structure for decoding ----
-
-
-class QRError(Exception):
-  pass
-
-
-_LEVELS = (1, 0, 3, 2)  # format info level bits -> column in _EC
-
-_MASKS = [
-  lambda i, j: (i + j) % 2 == 0,
-  lambda i, j: i % 2 == 0,
-  lambda i, j: j % 3 == 0,
-  lambda i, j: (i + j) % 3 == 0,
-  lambda i, j: (i // 2 + j // 3) % 2 == 0,
-  lambda i, j: (i * j) % 2 + (i * j) % 3 == 0,
-  lambda i, j: ((i * j) % 2 + (i * j) % 3) % 2 == 0,
-  lambda i, j: ((i + j) % 2 + (i * j) % 3) % 2 == 0,
-]
-
-_ALIGNMENT = np.ones((5, 5), dtype=bool)
-_ALIGNMENT[1:4, 1:4] = False
-_ALIGNMENT[2, 2] = True
-
-
-def _gf_inv(a: int) -> int:
-  return _EXP[-_LOG[a] % 255]
-
-
-@functools.lru_cache
-def _data_coords(version: int) -> tuple[np.ndarray, np.ndarray]:
-  """(rows, cols) of the data and error correction modules in placement order: two-column zigzag from the right."""
-  dim = version * 4 + 17
-  func = np.zeros((dim, dim), dtype=bool)  # finder, timing, alignment, format, and version modules
-  func[:9, :9] = func[:9, dim - 8:] = func[dim - 8:, :9] = True
-  func[6, :] = func[:, 6] = True
-  positions = _alignment_positions(version)
-  for r, c in itertools.product(positions, positions):
-    if (r, c) not in ((6, 6), (6, dim - 7), (dim - 7, 6)):
-      func[r - 2:r + 3, c - 2:c + 3] = True
-  if version >= 7:
-    func[:6, dim - 11:dim - 8] = func[dim - 11:dim - 8, :6] = True
-  ys = np.arange(dim)
-  rows, cols = [], []
-  # the vertical timing column is skipped, so the pairs left of it start at odd columns
-  for i, right in enumerate(col if col > 6 else col - 1 for col in range(dim - 1, 0, -2)):
-    r = np.repeat(ys[::-1] if i % 2 == 0 else ys, 2)
-    c = np.tile((right, right - 1), dim)
-    keep = ~func[r, c]
-    rows.append(r[keep])
-    cols.append(c[keep])
-  return np.concatenate(rows), np.concatenate(cols)
-
-
-# ---- Matrix decoding ----
-
-
-def _poly_eval(p: list[int], x: int) -> int:
-  # p is highest degree first
-  y = 0
-  for c in p:
-    y = _multiply(y, x) ^ c
-  return y
-
-
-_EXP_TABLE = np.array(_EXP)
-_LOG_TABLE = np.array([_LOG.get(v, 0) for v in range(256)])
-
-
-def _syndromes(msg: list[int], nsym: int) -> list[int]:
-  """syn[i] = msg(alpha^i), msg highest degree first."""
-  m = np.array(msg)
-  exponents = np.arange(nsym)[:, None] * (len(msg) - 1 - np.arange(len(msg)))
-  return np.bitwise_xor.reduce(_EXP_TABLE[(_LOG_TABLE[m] + exponents) % 255] * (m != 0), axis=1).tolist()
-
-
-def _rs_correct(msg: list[int], nsym: int) -> list[int]:
-  """Corrects up to nsym // 2 errors in a Reed-Solomon codeword, in place."""
-  n = len(msg)
-  syn = _syndromes(msg, nsym)
-  if not any(syn):
-    return msg
-
-  # Berlekamp-Massey, sigma is lowest degree first
-  sigma, prev, L, m, b = [1], [1], 0, 1, 1
-  for r in range(nsym):
-    d = syn[r]
-    for i in range(1, L + 1):
-      d ^= _multiply(sigma[i], syn[r - i])
-    if d == 0:
-      m += 1
-      continue
-    coef = _multiply(d, _gf_inv(b))
-    shifted = [0] * m + prev
-    saved = sigma[:]
-    sigma = sigma + [0] * max(0, len(shifted) - len(sigma))
-    for i, c in enumerate(shifted):
-      sigma[i] ^= _multiply(coef, c)
-    if 2 * L <= r:
-      L, prev, b, m = r + 1 - L, saved, d, 1
-    else:
-      m += 1
-  sigma = sigma[:L + 1]
-  if 2 * L > nsym:
-    raise QRError("too many errors")
-
-  # Chien search: codeword position p has locator alpha^(n-1-p)
-  positions = [p for p in range(n) if _poly_eval(sigma[::-1], _EXP[(p - n + 1) % 255]) == 0]
-  if len(positions) != L:
-    raise QRError("error locator mismatch")
-
-  # solve syn[i] = sum_k e_k * X_k^i for the magnitudes e_k
-  xlog = [(n - 1 - p) % 255 for p in positions]
-  A = [[_EXP[(xlog[k] * i) % 255] for k in range(L)] + [syn[i]] for i in range(L)]
-  for col in range(L):
-    piv = next((r for r in range(col, L) if A[r][col]), None)
-    if piv is None:
-      raise QRError("singular")
-    A[col], A[piv] = A[piv], A[col]
-    inv = _gf_inv(A[col][col])
-    A[col] = [_multiply(inv, v) for v in A[col]]
-    for r in range(L):
-      if r != col and A[r][col]:
-        f = A[r][col]
-        A[r] = [a ^ _multiply(f, c) for a, c in zip(A[r], A[col], strict=True)]
-  for k, p in enumerate(positions):
-    msg[p] ^= A[k][L]
-
-  if any(_syndromes(msg, nsym)):
-    raise QRError("uncorrectable")
-  return msg
-
-
-def _read_format(m: np.ndarray) -> int:
-  """Returns the closest format info (level bits << 3 | mask) from either copy."""
-  dim = m.shape[0]
-  copies = ([(8, i) for i in range(6)] + [(8, 7), (8, 8), (7, 8)] + [(5 - i, 8) for i in range(6)],
-            [(dim - 1 - i, 8) for i in range(7)] + [(8, dim - 8 + i) for i in range(8)])  # (row, col), msb first
-  candidates = []
-  for coords in copies:
-    bits = int("".join(str(int(m[r, c])) for r, c in coords), 2)
-    candidates += [((bits ^ f).bit_count(), i) for i, f in enumerate(_FORMATS)]
-  distance, fmt = min(candidates)
-  if distance > 3:
-    raise QRError("bad format info")
-  return fmt
-
-
-_ALNUM = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ $%*+-./:"
-
-_ECI_ENCODINGS = {
-  0: "cp437", 2: "cp437", 1: "iso8859-1", 3: "iso8859-1",
-  **{i + 2: f"iso8859-{i}" for i in range(2, 17) if i != 12},
-  20: "shift_jis", 21: "cp1250", 22: "cp1251", 23: "cp1252", 24: "cp1256",
-  25: "utf-16-be", 26: "utf-8", 27: "ascii", 170: "ascii", 28: "big5", 29: "gb18030", 30: "euc_kr",
-}
-
-
-class _Bits:
-  def __init__(self, data: list[int]):
-    self._value = int.from_bytes(bytes(data), "big")
-    self.remaining = len(data) * 8
-
-  def read(self, n: int) -> int:
-    if n > self.remaining:
-      raise QRError("bitstream underflow")
-    self.remaining -= n
-    return self._value >> self.remaining & (1 << n) - 1
-
-  def read_below(self, n: int, limit: int) -> int:
-    v = self.read(n)
-    if v >= limit:
-      raise QRError("value out of range")
-    return v
-
-
-def _parse_data(data: list[int], version: int) -> str:
-  bits = _Bits(data)
-  out: list[str] = []
-  encoding = None
-  band = 0 if version <= 9 else 1 if version <= 26 else 2
-  while bits.remaining >= 4:
-    mode = bits.read(4)
-    if mode == 0:
-      break
-    if mode == 7:  # ECI character set assignment
-      first = bits.read(8)
-      extra = 0 if first < 0x80 else 8 if first < 0xC0 else 16 if first < 0xE0 else -1  # 1, 2, or 3 byte assignment
-      if extra < 0:
-        raise QRError("bad ECI assignment")
-      assignment = (first & 0x7F >> extra // 8) << extra | bits.read(extra)
-      encoding = _ECI_ENCODINGS.get(assignment)
-      if encoding is None:
-        raise QRError(f"unsupported ECI assignment {assignment}")
-    elif mode == 1:
-      n = bits.read((10, 12, 14)[band])
-      while n > 0:
-        k = min(n, 3)  # 3 digits in 10 bits, the last 2 or 1 in 7 or 4
-        out.append(f"{bits.read_below((4, 7, 10)[k - 1], 10 ** k):0{k}d}")
-        n -= k
-    elif mode == 2:
-      n = bits.read((9, 11, 13)[band])
-      while n > 0:
-        k = min(n, 2)  # 2 characters in 11 bits, a last one in 6
-        v = bits.read_below((6, 11)[k - 1], 45 ** k)
-        out.append(_ALNUM[v // 45] * (k - 1) + _ALNUM[v % 45])
-        n -= k
-    elif mode == 4:
-      n = bits.read((8, 16, 16)[band])
-      segment = bytes(bits.read(8) for _ in range(n))
-      try:
-        out.append(segment.decode(encoding or "utf-8"))
-      except UnicodeDecodeError as e:
-        if encoding is not None:
-          raise QRError("invalid ECI byte segment") from e
-        out.append(segment.decode("latin-1"))
-    elif mode == 8:
-      n = bits.read((8, 10, 12)[band])
-      for _ in range(n):
-        v = bits.read(13)
-        c = (v // 0xC0) << 8 | v % 0xC0
-        c += 0x8140 if c < 0x1F00 else 0xC140
-        try:
-          out.append(c.to_bytes(2, "big").decode("shift_jis"))
-        except UnicodeDecodeError as e:
-          raise QRError("invalid Kanji character") from e
-    else:
-      raise QRError(f"unsupported mode {mode}")
-  return "".join(out)
-
-
-def decode_matrix(m: np.ndarray) -> str:
-  """Decodes a square boolean module matrix (True = dark) without a quiet zone."""
-  dim = m.shape[0]
-  if m.shape != (dim, dim) or dim % 4 != 1 or not 21 <= dim <= 177:
-    raise QRError("bad matrix size")
-  version = (dim - 17) // 4
-
-  fmt = _read_format(m)
-  level = _LEVELS[fmt >> 3]
-  rows, cols = _data_coords(version)
-  bits = m[rows, cols] ^ _MASKS[fmt & 7](rows, cols)
-  codewords = np.packbits(bits[:len(bits) // 8 * 8]).tolist()
-
-  ec, _ = _EC[version - 1][level]
-  lens = _block_lengths(version, level)
-  blocks = [[0] * (n + ec) for n in lens]
-  for (b, i), codeword in zip(_interleaved(version, level), codewords, strict=True):
-    blocks[b][i] = codeword
-
-  data: list[int] = []
-  for block, n in zip(blocks, lens, strict=True):
-    data += _rs_correct(block, ec)[:n]
-  return _parse_data(data, version)
-
-
-# ---- Image decoding ----
-
-
-def _box_sums(a: np.ndarray, radii: tuple[int, ...]) -> list[np.ndarray]:
-  """Sums over (2r + 1)^2 neighborhoods of the last two axes, edge padded, from one integral image."""
-  P = max(radii)
-  lead = [(0, 0)] * (a.ndim - 2)
-  cs = np.pad(np.cumsum(np.cumsum(np.pad(a, lead + [(P, P), (P, P)], mode="edge"), -2), -1), lead + [(1, 0), (1, 0)])
-  H, W = a.shape[-2:]
-  out = []
-  for r in radii:
-    lo, hi = P - r, P + r + 1
-    out.append(cs[..., hi:hi + H, hi:hi + W] - cs[..., lo:lo + H, hi:hi + W] - cs[..., hi:hi + H, lo:lo + W] + cs[..., lo:lo + H, lo:lo + W])
-  return out
-
-
-def _binarize(gray: np.ndarray) -> np.ndarray:
-  """Adaptive threshold: each pixel against the mean of the surrounding tiles that have contrast."""
-  h, w = gray.shape
-  if h < 21 or w < 21:
-    raise QRError("image too small")
-  B = max(8, min(h, w) // 128 * 2)
-  H, W = -(-h // B), -(-w // B)
-  padded = np.pad(gray, ((0, H * B - h), (0, W * B - w)), mode="edge")
-  # block statistics from a subsample are plenty
-  sub = np.ascontiguousarray(padded[::2, ::2].reshape(H, B // 2, W, B // 2).transpose(0, 2, 1, 3)).reshape(H, W, -1)
-  blocks = sub.sum(axis=2, dtype=np.uint32) / sub.shape[2]
-  known = sub.max(axis=2) - sub.min(axis=2) >= 32
-  # Flat tiles cannot estimate their own threshold: use the tiles with contrast nearby, then
-  # further out, then the global midrange. A flat tile is then all dark or all light.
-  est = np.full((H, W), (blocks.min() + blocks.max()) / 2)
-  filled = np.zeros((H, W), dtype=bool)
-  for total, count in _box_sums(np.stack((known * blocks, known.astype(float))), (2, 6)):
-    fill = ~filled & (count > 0)
-    est[fill] = total[fill] / count[fill]
-    filled |= fill
-  thr = np.where(known, np.minimum(est, 254) + 1, np.where(blocks <= est, 255, 0)).astype(np.uint8)
-  return (padded.reshape(H, B, W, B) < thr[:, None, :, None]).reshape(H * B, W * B)[:h, :w]
-
-
-class _Runs:
-  """Run-length table of a padded, flattened binary image with a per-pixel run index."""
-
-  def __init__(self, padded: np.ndarray):
-    self.flat = padded.ravel()
-    self.lines, self.stride = padded.shape
-    change = self.flat[1:] != self.flat[:-1]
-    self.starts = np.concatenate(([0], np.flatnonzero(change) + 1))
-    self.lengths = np.diff(np.append(self.starts, self.flat.size)).astype(np.int32)
-
-  def run_at(self, line: np.ndarray, pos: np.ndarray) -> np.ndarray:
-    """Index of the run containing the pixel at `pos` along `line`."""
-    return np.searchsorted(self.starts, line * self.stride + pos + 1, side="right") - 1
-
-  @staticmethod
-  def _match(lengths: list[np.ndarray], ratios: tuple[int, ...]) -> tuple[np.ndarray, np.ndarray]:
-    """Checks windows of runs against the ratios, given the length of each run. Returns (ok, module size)."""
-    S = sum(ratios)
-    total = sum(lengths[1:], start=lengths[0])
-    ok = total >= 2 * S  # modules need to be at least 2 px
-    for L, r in zip(lengths, ratios, strict=True):
-      ok &= np.abs(2 * S * L - 2 * r * total) <= r * total  # integer form of |L - r * total / S| <= r * total / (2 * S)
-    return ok, total / S
-
-  def scan(self, ratios: tuple[int, ...]) -> np.ndarray:
-    """Returns the indices of all dark runs starting a window of runs matching the ratios."""
-    n = len(ratios)
-    N = len(self.lengths) - n + 1
-    if N <= 0:
-      return np.zeros(0, dtype=int)
-    ok, _ = self._match([self.lengths[k:N + k] for k in range(n)], ratios)
-    ok &= self.flat[self.starts[:N]]
-    first = np.flatnonzero(ok)
-    return first[self.starts[first] // self.stride == self.starts[first + n - 1] // self.stride]
-
-  def check(self, first: np.ndarray, ratios: tuple[int, ...]) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Checks the run windows starting at run index `first`. Returns (ok, center position along the line, module size)."""
-    n, half = len(ratios), len(ratios) // 2
-    ok = (first >= 0) & (first + n <= len(self.starts))
-    idx = np.clip(first[:, None] + np.arange(n), 0, len(self.starts) - 1)
-    matched, module = self._match([self.lengths[idx[:, k]] for k in range(n)], ratios)
-    ok &= matched & self.flat[self.starts[idx[:, 0]]]
-    ok &= self.starts[idx[:, 0]] // self.stride == self.starts[idx[:, -1]] // self.stride
-    center = self.starts[idx[:, half]] % self.stride - 1 + self.lengths[idx[:, half]] / 2
-    return ok, center, module
-
-
-def _find_patterns(binary: np.ndarray, ratios: tuple[int, ...]) -> list[tuple[float, float, float]]:
-  """Finds dark/light run patterns with the given module ratios. Returns (x, y, module size)."""
-  half = len(ratios) // 2
-  step = 2  # the center rows of a 2 px finder pattern still get scanned twice
-  rows_t = _Runs(np.pad(binary[::step], ((0, 0), (1, 1))))
-  first = rows_t.scan(ratios)
-  if len(first) == 0:
-    return []
-  _, cx, hmod = rows_t.check(first, ratios)
-  row = rows_t.starts[first] // rows_t.stride * step
-
-  xi = cx.astype(int)
-  xs, col = np.unique(xi, return_inverse=True)
-  cols_t = _Runs(np.pad(binary[:, xs].T, ((0, 0), (1, 1))))
-  ok, cy, vmod = cols_t.check(cols_t.run_at(col, row) - half, ratios)
-  ok &= (0.5 <= vmod / hmod) & (vmod / hmod <= 2)
-  line = np.clip(np.rint(cy / step), 0, rows_t.lines - 1).astype(int)
-  ok2, cx2, hmod2 = rows_t.check(rows_t.run_at(line, xi) - half, ratios)
-  ok &= ok2 & (0.5 <= hmod2 / vmod) & (hmod2 / vmod <= 2)
-
-  found: list[list[float]] = []  # [x, y, module, count]
-  for x, y, module in zip(cx2[ok], cy[ok], (hmod2[ok] + vmod[ok]) / 2, strict=True):
-    for f in found:
-      if abs(f[0] - x) <= f[2] and abs(f[1] - y) <= f[2] and 0.5 <= f[2] / module <= 2:
-        c = f[3]
-        f[0], f[1], f[2], f[3] = (f[0] * c + x) / (c + 1), (f[1] * c + y) / (c + 1), (f[2] * c + module) / (c + 1), c + 1
-        break
-    else:
-      found.append([x, y, module, 1])
-  found.sort(key=lambda f: -f[3])
-  return [(f[0], f[1], f[2]) for f in found if f[3] >= 2]
-
-
-def _pick_finders(patterns: list[tuple[float, float, float]]) -> tuple[np.ndarray, np.ndarray, np.ndarray, float]:
-  """Returns (top-left, top-right, bottom-left) centers and the module size of the most square-looking triple."""
-  best = None
-  for a, b, c in itertools.combinations(patterns[:10], 3):
-    mods = sorted((a[2], b[2], c[2]))
-    if mods[2] / mods[0] > 1.5:
-      continue
-    pts = [np.array(p[:2]) for p in (a, b, c)]
-    d = [np.linalg.norm(pts[(i + 1) % 3] - pts[(i + 2) % 3]) for i in range(3)]
-    tl = int(np.argmax(d))  # opposite the hypotenuse
-    p1, p2 = pts[(tl + 1) % 3], pts[(tl + 2) % 3]
-    v1, v2 = p1 - pts[tl], p2 - pts[tl]
-    n1, n2 = np.linalg.norm(v1), np.linalg.norm(v2)
-    if n1 == 0 or n2 == 0:
-      continue
-    cos = abs(np.dot(v1, v2)) / (n1 * n2)
-    if cos > 0.35 or not 0.6 <= n1 / n2 <= 1.6:
-      continue
-    score = cos + abs(np.log(n1 / n2)) + np.log(mods[2] / mods[0])
-    if best is not None and score >= best[0]:
-      continue
-    if v1[0] * v2[1] - v1[1] * v2[0] < 0:
-      p1, p2 = p2, p1
-    best = (score, pts[tl], p1, p2, float(sum(mods) / 3))
-  if best is None:
-    raise QRError("no finder patterns")
-  return best[1:]
-
-
-def _perspective(src: np.ndarray, dst: np.ndarray) -> np.ndarray:
-  """Homography mapping the four src points onto the four dst points."""
-  A = [row for (x, y), (u, v) in zip(src, dst, strict=True)
-       for row in ([x, y, 1, 0, 0, 0, -u * x, -u * y], [0, 0, 0, x, y, 1, -v * x, -v * y])]
-  try:
-    h = np.linalg.solve(np.array(A, dtype=float), np.asarray(dst, dtype=float).ravel())
-  except np.linalg.LinAlgError as e:
-    raise QRError("degenerate geometry") from e
-  return np.append(h, 1).reshape(3, 3)
-
-
-def _transform(H: np.ndarray, pts: np.ndarray) -> np.ndarray:
-  p = np.column_stack((pts, np.ones(len(pts)))) @ H.T
-  return p[:, :2] / p[:, 2:3]
-
-
-def _match_alignment(binary: np.ndarray, est: np.ndarray, offs: np.ndarray, r: int, module: float) -> np.ndarray | None:
-  h, w = binary.shape
-  dy = np.arange(max(0, int(est[1]) - r), min(h, int(est[1]) + r)) - est[1]
-  dx = np.arange(max(0, int(est[0]) - r), min(w, int(est[0]) + r)) - est[0]
-  if len(dy) == 0 or len(dx) == 0:
-    return None
-  y = np.rint(est[1] + dy[:, None, None] + offs[None, None, :, 1]).astype(int)
-  x = np.rint(est[0] + dx[None, :, None] + offs[None, None, :, 0]).astype(int)
-  valid = ((y >= 0) & (y < h) & (x >= 0) & (x < w)).all(axis=2)
-  samples = binary[np.clip(y, 0, h - 1), np.clip(x, 0, w - 1)]
-  score = np.where(valid, (samples == _ALIGNMENT.ravel()).sum(axis=2), 0)
-  if score.max() < 23:
-    return None
-  hits = np.argwhere(score == score.max())
-  centers = np.column_stack((est[0] + dx[hits[:, 1]], est[1] + dy[hits[:, 0]]))
-  closest = centers[np.argmin(np.linalg.norm(centers - est, axis=1))]
-  return centers[np.linalg.norm(centers - closest, axis=1) <= module / 2].mean(axis=0)
-
-
-def _locate_alignment(binary: np.ndarray, H: np.ndarray, center: float, module: float) -> np.ndarray | None:
-  """Template matches the 5x5 alignment pattern around its position estimated from H."""
-  grid = np.mgrid[-2:3, -2:3].reshape(2, -1).T[:, ::-1] + center  # (25, 2) module coords (x, y)
-  pts = _transform(H, grid)
-  # the affine estimate can be off in both position and local scale under perspective
-  for radius in (2, 4, 8, 16):
-    for scale in (1.0, 0.8, 1.25, 0.65, 1.5):
-      found = _match_alignment(binary, pts[12], (pts - pts[12]) * scale, int(module * radius), module)
-      if found is not None:
-        return found
-  return None
-
-
-def _sample(binary: np.ndarray, tl: np.ndarray, tr: np.ndarray, bl: np.ndarray, module: float, dim: int, use_alignment: bool) -> np.ndarray:
-  src = np.array([(3.5, 3.5), (dim - 3.5, 3.5), (3.5, dim - 3.5), (dim - 3.5, dim - 3.5)])
-  dst = np.array([tl, tr, bl, tr + bl - tl])
-  H = _perspective(src, dst)
-  if use_alignment and dim > 21:
-    align = _locate_alignment(binary, H, dim - 6.5, module)
-    if align is not None:
-      src[3], dst[3] = (dim - 6.5, dim - 6.5), align
-      H = _perspective(src, dst)
-
-  rows, cols = np.mgrid[0:dim, 0:dim]
-  pts = _transform(H, np.column_stack((cols.ravel() + 0.5, rows.ravel() + 0.5)))
-  xy = np.rint(pts).astype(int)
-  h, w = binary.shape
-  if (xy < 0).any() or (xy[:, 0] >= w).any() or (xy[:, 1] >= h).any():
-    raise QRError("code extends outside image")
-  return binary[xy[:, 1], xy[:, 0]].reshape(dim, dim)
-
-
-def decode(gray: np.ndarray) -> str | None:
-  """Decodes the QR code in a 2D uint8 grayscale image. Modules need to be at least 2 px.
-  Returns None if nothing could be decoded."""
-  try:
-    binary = _binarize(gray)
-    tl, tr, bl, module = _pick_finders(_find_patterns(binary, (1, 1, 3, 1, 1)))
-  except QRError:
-    return None
-
-  d = (np.linalg.norm(tr - tl) + np.linalg.norm(bl - tl)) / 2
-  dim = int(round((d / module + 7 - 17) / 4)) * 4 + 17
-  dims = [cand for cand in (dim, dim - 4, dim + 4) if 21 <= cand <= 177]
-  for cand, use_alignment, transpose in itertools.product(dims, (True, False), (False, True)):
-    try:
-      m = _sample(binary, tl, tr, bl, module, cand, use_alignment)
-      return decode_matrix(m.T if transpose else m)
-    except QRError:
-      pass
-  return None

openpilot/common/tests/test_qrcode.py

@@ -1,178 +0,0 @@
-import hashlib
-import math
-from pathlib import Path
-
-import numpy as np
-
-from openpilot.common import qrcode as qr
-from openpilot.common.test import OpenpilotTestCase
-
-LPA = "LPA:1$rsp.truphone.com$QRF-BETTERROAMING-PMRDGIR2EARDEIT5"
-
-
-# Matrices generated with python-qrcode 8.2, covering all versions and EC levels.
-# Packed fixtures keep the decoder tests independent of our encoder.
-FIXTURES = {}
-for path in Path(__file__).with_name("fixtures").glob("qrcode_*.npz"):
-  with np.load(path) as fixtures:
-    FIXTURES.update({key: fixtures[key] for key in fixtures.files})
-
-
-def fixture(key: str) -> np.ndarray:
-  bits = np.unpackbits(FIXTURES[key])
-  size = math.isqrt(len(bits))
-  return bits[:size * size].reshape(size, size).astype(bool)
-
-
-def render(matrix: np.ndarray, box: int = 6, border: int = 4) -> np.ndarray:
-  img = np.repeat(np.repeat(np.pad(matrix, border), box, axis=0), box, axis=1)
-  return np.where(img, 0, 255).astype(np.uint8)
-
-
-def make(data: str, version: int | None = None, level: int = 0, box: int = 6, border: int = 4):
-  matrix = fixture(hashlib.sha256(f"{version}:{level}:{data}".encode()).hexdigest())
-  return matrix, render(matrix, box, border)
-
-
-def warp(img: np.ndarray, H: np.ndarray) -> np.ndarray:
-  """Bilinear resampling through the output -> input homography H, white outside the image."""
-  h, w = img.shape
-  rows, cols = np.mgrid[0:h, 0:w]
-  pts = qr._transform(H, np.column_stack((cols.ravel() + 0.5, rows.ravel() + 0.5))) - 0.5
-  x0, y0 = np.floor(pts[:, 0]).astype(int), np.floor(pts[:, 1]).astype(int)
-  fx, fy = pts[:, 0] - x0, pts[:, 1] - y0
-  padded = np.pad(img.astype(float), 1, constant_values=255)
-
-  def at(y, x):
-    return padded[np.clip(y + 1, 0, h + 1), np.clip(x + 1, 0, w + 1)]
-
-  out = at(y0, x0) * (1 - fx) * (1 - fy) + at(y0, x0 + 1) * fx * (1 - fy) + at(y0 + 1, x0) * (1 - fx) * fy + at(y0 + 1, x0 + 1) * fx * fy
-  return np.clip(out, 0, 255).astype(np.uint8).reshape(h, w)
-
-
-def rotate(img: np.ndarray, angle: float) -> np.ndarray:
-  h, w = img.shape
-  t = np.radians(angle)
-  R = np.array([[np.cos(t), -np.sin(t)], [np.sin(t), np.cos(t)]])
-  center = np.array([w / 2, h / 2])
-  corners = np.array([(0, 0), (w, 0), (w, h), (0, h)], dtype=float)
-  return warp(img, qr._perspective((corners - center) @ R.T + center, corners))
-
-
-class TestQRCode(OpenpilotTestCase):
-  def test_alignment_positions(self):
-    assert qr._alignment_positions(7) == [6, 22, 38]
-    assert qr._alignment_positions(32) == [6, 34, 60, 86, 112, 138]
-    assert qr._alignment_positions(40) == [6, 30, 58, 86, 114, 142, 170]
-
-  def test_all_versions(self):
-    for version in range(1, 41):
-      for level in range(4):
-        with self.subTest(version=version, level=level):
-          data = "".join(chr(ord("a") + i % 26) for i in range(version))
-          matrix, img = make(data, version, level, box=3)
-          assert qr.decode_matrix(matrix) == data
-          assert qr.decode(img) == data
-
-  def test_modes(self):
-    for data in ["0123456789012345", "HELLO WORLD $1.50", LPA, "こんにちは", "ünïcødé", "mixed 123 ABC xyz"]:
-      with self.subTest(data=data):
-        matrix, img = make(data)
-        assert qr.decode_matrix(matrix) == data
-        assert qr.decode(img) == data
-
-  def test_error_correction(self):
-    matrix, _ = make(LPA, level=2)
-    rng = np.random.default_rng(0)
-    flipped = matrix.copy()
-    for r, c in rng.integers(9, matrix.shape[0] - 9, size=(40, 2)):
-      flipped[r, c] ^= True
-    assert qr.decode_matrix(flipped) == LPA
-
-  def test_large_modules(self):
-    for data in ["0123456789012345", "HELLO WORLD $1.50", LPA, "mixed 123 ABC xyz"]:
-      for box in [16, 20, 24, 32]:
-        for dark, light in [(0, 255), (60, 200), (140, 250)]:
-          with self.subTest(data=data, box=box, dark=dark):
-            _, img = make(data, box=box)
-            img = np.where(img == 0, dark, light).astype(np.uint8)
-            assert qr.decode(img) == data
-
-  def test_image_edges(self):
-    # a code touching the image edge must not lose the rows and columns left over from tiling
-    matrix, _ = make(LPA)
-    for size in (200, 203):
-      with self.subTest(size=size):
-        img = np.full((size, size), 255, dtype=np.uint8)
-        code = render(matrix, box=5, border=0)
-        img[size - code.shape[0]:, size - code.shape[1]:] = code
-        assert qr.decode(img) == LPA
-
-  def test_eci(self):
-    # qrcode_eci.npz: packed Segno 1.6.6 matrices, generated with mode='byte',
-    # eci=True, micro=False and the named encoding. Mixed also includes numeric,
-    # alphanumeric, and Kanji segments after changing the byte encoding twice.
-    cases = {
-      "iso8859-5": "Привет", "utf-16-be": "héllo", "utf-8": "こんにちは",
-      "shift_jis": "日本語", "cp1251": "Привет", "iso8859-1": "héllo",
-      "mixed": "hélloПривет日本語123ABC漢字",
-    }
-    for encoding, expected in cases.items():
-      with self.subTest(encoding=encoding):
-        matrix = fixture(encoding)
-        assert qr.decode_matrix(matrix) == expected
-        assert qr.decode(render(matrix)) == expected
-
-  def test_parse_data(self):
-    def parse(stream: str) -> str:
-      stream += '0' * (-len(stream) % 8)
-      return qr._parse_data([int(stream[i:i + 8], 2) for i in range(0, len(stream), 8)], 1)
-
-    def eci(assignment: str, payload: bytes = b'A') -> str:
-      return parse('0111' + assignment + '0100' + f'{len(payload):08b}' + ''.join(f'{b:08b}' for b in payload) + '0000')
-
-    # ASCII assignment 170 uses the two-byte ECI representation.
-    assert eci('1000000010101010') == 'A'
-    for assignment in ['00001110', '1000001111100111', '110000010000000000000000', '11100000']:
-      with self.subTest(assignment=assignment), self.assertRaises(qr.QRError):
-        eci(assignment)
-    with self.assertRaises(qr.QRError):
-      eci('00011010', b'\xff')  # Invalid UTF-8 must not fall back to Latin-1.
-
-    # out-of-range numeric, alphanumeric, and Kanji values are format errors, not crashes
-    for stream in ['0001' + '0000000011' + '1111111111', '0001' + '0000000010' + '1111111',
-                   '0010' + '000000010' + '11111111111', '0010' + '000000001' + '111111',
-                   '1000' + '00000001' + '0000000111111']:
-      with self.subTest(stream=stream), self.assertRaises(qr.QRError):
-        parse(stream)
-
-  def test_rotation(self):
-    for angle in [0, 90, 180, 270, 25, 110]:
-      with self.subTest(angle=angle):
-        _, img = make(LPA, box=8, border=12)
-        assert qr.decode(rotate(img, angle)) == LPA
-
-  def test_mirrored(self):
-    _, img = make(LPA)
-    assert qr.decode(img[:, ::-1]) == LPA
-
-  def test_perspective_and_noise(self):
-    _, img = make(LPA, box=10, border=8)
-    h, w = img.shape
-    corners = np.array([(40, 60), (w - 20, 30), (w - 60, h - 40), (30, h - 90)])
-    arr = warp(img, qr._perspective(corners, np.array([(0, 0), (w, 0), (w, h), (0, h)]))).astype(float)
-    rng = np.random.default_rng(1)
-    arr = arr * 0.6 + 60 + rng.normal(0, 12, arr.shape)  # low contrast + noise
-    # uneven lighting
-    arr += np.linspace(-40, 40, w)[None, :]
-    assert qr.decode(np.clip(arr, 0, 255).astype(np.uint8)) == LPA
-
-  def test_no_code(self):
-    rng = np.random.default_rng(2)
-    assert qr.decode(rng.integers(0, 256, size=(240, 320), dtype=np.uint8)) is None
-    assert qr.decode(np.full((240, 320), 200, dtype=np.uint8)) is None
-
-  def test_encoder_roundtrip(self):
-    for version in range(1, 21):
-      with self.subTest(version=version):
-        assert qr.decode_matrix(np.array(qr._Qr(version, b"hello").modules)) == "hello"

openpilot/selfdrive/assets/icons_mici/chestnut_orange.png

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bf97a6738b294ac0aed9b2d075916cee0b7d3215bcd23381760900ede6a92748
-size 13256
+oid sha256:845c40ff0d37612e8f2f482a36845744b5ae91ce2fcfc8117990d7d278b59820
+size 13079

openpilot/selfdrive/assets/prep-svg.sh

@@ -23,8 +23,8 @@ done
 
 # sudo apt install inkscape
 
-for svg in $(find "$DIR" -type f | grep svg$); do
-  bunx svgo "$svg" --multipass --pretty --indent 2
+for svg in $(find $DIR -type f | grep svg$); do
+  bunx svgo $svg --multipass --pretty --indent 2
 
   # convert to PNG
   png="${svg%.svg}.png"

openpilot/selfdrive/car/card.py

@@ -186,6 +186,9 @@ class Car:
     # card is driven by can recv, expected at 100Hz
     self.rk = Ratekeeper(100, print_delay_threshold=None)
 
+    # log fingerprint in sentry
+    sunnypilot_interfaces.log_fingerprint(self.CP)
+
   def state_update(self) -> tuple[car.CarState, custom.CarStateSP, structs.RadarDataT | None]:
     """carState update loop, driven by can"""
 

openpilot/selfdrive/locationd/locationd.py

@@ -278,12 +278,12 @@ def main():
   estimator = LocationEstimator(DEBUG)
 
   filter_initialized = False
-  critical_services = ["accelerometer", "gyroscope", "cameraOdometry"]
+  critcal_services = ["accelerometer", "gyroscope", "cameraOdometry"]
   observation_input_invalid = defaultdict(int)
 
-  input_invalid_limit = {s: round(INPUT_INVALID_LIMIT * (SERVICE_LIST[s].frequency / 20.)) for s in critical_services}
-  input_invalid_threshold = {s: input_invalid_limit[s] - 0.5 for s in critical_services}
-  input_invalid_decay = {s: calculate_invalid_input_decay(input_invalid_limit[s], INPUT_INVALID_RECOVERY, SERVICE_LIST[s].frequency) for s in critical_services}
+  input_invalid_limit = {s: round(INPUT_INVALID_LIMIT * (SERVICE_LIST[s].frequency / 20.)) for s in critcal_services}
+  input_invalid_threshold = {s: input_invalid_limit[s] - 0.5 for s in critcal_services}
+  input_invalid_decay = {s: calculate_invalid_input_decay(input_invalid_limit[s], INPUT_INVALID_RECOVERY, SERVICE_LIST[s].frequency) for s in critcal_services}
 
   initial_pose_data = params.get("LocationFilterInitialState")
   if initial_pose_data is not None:
@@ -313,7 +313,7 @@ def main():
         if valid:
           t = log_mono_time * 1e-9
           res = estimator.handle_log(t, which, msg)
-          if which not in critical_services:
+          if which not in critcal_services:
             continue
 
           if res == HandleLogResult.TIMING_INVALID:
@@ -328,7 +328,7 @@ def main():
       filter_initialized = sm.all_checks() and sensor_all_checks(acc_msgs, gyro_msgs, sensor_valid, sensor_recv_time, sensor_alive, SIMULATION)
 
     if sm.updated["cameraOdometry"]:
-      critical_service_inputs_valid = all(observation_input_invalid[s] < input_invalid_threshold[s] for s in critical_services)
+      critical_service_inputs_valid = all(observation_input_invalid[s] < input_invalid_threshold[s] for s in critcal_services)
       inputs_valid = sm.all_valid() and critical_service_inputs_valid
       sensors_valid = sensor_all_checks(acc_msgs, gyro_msgs, sensor_valid, sensor_recv_time, sensor_alive, SIMULATION)
 

openpilot/selfdrive/modeld/SConscript

@@ -1,90 +1,142 @@
 import glob
+import json
 import os
 import time
 from SCons.Script import Action, Value
+from openpilot.common.file_chunker import chunk_file, get_chunk_targets, get_existing_chunks
 from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
 from openpilot.common.transformations.model import MEDMODEL_INPUT_SIZE, DM_INPUT_SIZE
-from openpilot.selfdrive.modeld.helpers import chestnut_present
-from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.selfdrive.modeld.constants import ModelConstants
+from openpilot.selfdrive.modeld.helpers import TG_INPUT_DEVICES_PATH, chestnut_present, modeld_pkl_path
 
 
 Import('env', 'arch')
+chunker_file = File("#openpilot/common/file_chunker.py")
 lenv = env.Clone()
-lenv.PrependENVPath('PYTHONPATH', Dir('#tinygrad_repo').abspath)
 
 tinygrad_root = env.Dir("#").abspath
 tinygrad_files = ["#"+x for x in glob.glob(env.Dir("#tinygrad_repo").relpath + "/**", recursive=True, root_dir=tinygrad_root)
                   if 'pycache' not in x and os.path.isfile(os.path.join(tinygrad_root, x))]
 
-camera_configs = [(c.width, c.height) for c in (_ar_ox_fisheye, _os_fisheye)]
+def estimate_pickle_max_size(onnx_size):
+  # QCOM programs for models with spatial recurrent features can approach 2x
+  # the ONNX size. Overestimating only adds an empty trailing chunk.
+  return 2.0 * onnx_size + 10 * 1024 * 1024
 
 if arch == 'comma_arm64':
-  tg_flags = 'DEV=QCOM IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1'
+  from openpilot.common.hardware import HARDWARE
+  camera = _os_fisheye if HARDWARE.get_device_type() == "mici" else _ar_ox_fisheye
+  camera_configs = [(camera.width, camera.height)]
+  tg_backend = 'QCOM'
+  tg_flags = f'DEV={tg_backend} IMAGE=1 FLOAT16=1 NOLOCALS=1 JIT_BATCH_SIZE=0 OPENPILOT_HACKS=1'
 else:
-  # JIT=2 disables graph batching, which produces incorrect outputs after buffers change.
-  tg_flags = 'DEV=METAL JIT=2' if arch == 'Darwin' else 'DEV=CPU:LLVM'
+  camera_configs = [(c.width, c.height) for c in (_ar_ox_fisheye, _os_fisheye)]
+  tg_backend = 'CPU'
+  tg_flags = f'DEV=CPU' if arch == 'Darwin' else 'DEV=CPU:LLVM'
+
+tg_devices = { # which device to put jit inputs to at runtime
+  'openpilot.selfdrive.modeld.dmonitoringmodeld': {
+    'default': {'DEV': tg_backend}
+  },
+}
 
 CHESTNUT = chestnut_present()
 if CHESTNUT:
-  chestnut_tg_flags = 'DEBUG=1 DEV=USB+AMD:LLVM FRAME_DEV=CPU FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_MIN_GLOBALS=32'
+  chestnut_tg_flags = 'DEBUG=1 DEV=USB+AMD:LLVM FRAME_DEV=CPU FLOAT16=1 JIT_BATCH_SIZE=0 GMMU=0 TC_OPT=2 TC_OCCUPANCY_OPT=1'
   # the USB+AMD GPU takes an exclusive flock; serialize all targets that touch it
   chestnut_lock = File("models/.chestnut.lock").abspath
 
+def write_tg_devices(target, source, env):
+  with open(str(target[0]), "w") as f:
+    json.dump(tg_devices, f)
+    f.write("\n")
+
+tg_devices_node = lenv.Command(
+  str(TG_INPUT_DEVICES_PATH),
+  [Value(tg_devices)],
+  write_tg_devices,
+)
+
 # tinygrad calls brew which needs a $HOME in the env
 mac_brew_string = f'HOME={os.path.expanduser("~")}' if arch == 'Darwin' else ''
 
-warp_deps = [File("#openpilot/system/camerad/cameras/nv12_info.py")]
-compiler = Dir('#tinygrad_repo/examples/openpilot').abspath
-# CPU 7 is isolated with isolcpus on AGNOS, so explicitly pin the compiler to it.
-taskset = 'taskset -c 7 ' if arch == 'comma_arm64' else ''
-
-def chestnut_action(command):
-  def do_compile(target, source, env):
-    from openpilot.system.hardware.chestnut.flash import link_up
-    # chestnut can enumerate before its PCIe link is up due to varying 12V power behavior across cars
-    for _ in range(10):
-      if link_up():
-        break
-      time.sleep(1)
-    else:
-      print("Chestnut not ready, skipping warp build")
-      return
-    return env.Execute(command)
-  return Action(do_compile, "  [CHESTNUT] $TARGET")
-
-def compile_model(onnx_path, pkl_path):
-  onnx_path, target_pkl_path = File(onnx_path).abspath, File(pkl_path).abspath
-  cmd = (f'{tg_flags} {mac_brew_string} {taskset}python3 "{compiler}/compile_onnx.py" '
-         f'"{onnx_path}" "{target_pkl_path}" --device-input "*" --out-of-band --benchmark-runs 1')
-  lenv.Command(
-    target_pkl_path,
-    tinygrad_files + [onnx_path, Value(cmd)],
-    Action(cmd, "  [ONNX] $TARGET"),
-  )
-
-compile_model('models/dmonitoring_model.onnx', 'models/dmonitoring_model_tinygrad.pkl')
-compile_model('models/driving_supercombo.onnx', 'models/driving_tinygrad.pkl')
-
+modeld_dir = Dir("#openpilot/selfdrive/modeld").abspath
+compile_modeld_script = [
+  File(f"{modeld_dir}/compile_modeld.py"),
+  File(f"{modeld_dir}/get_model_metadata.py"),
+  File("#openpilot/system/camerad/cameras/nv12_info.py"),
+  File("#openpilot/common/hardware/hw.py"),
+]
 model_w, model_h = MEDMODEL_INPUT_SIZE
-for chestnut in [False, True] if CHESTNUT else [False]:
-  file_prefix, cmd_flags = ('big_', chestnut_tg_flags) if chestnut else ('', tg_flags)
-  for cam_w, cam_h in camera_configs:
-    warp_pkl_path = File(f"models/{file_prefix}driving_warp_{cam_w}x{cam_h}_tinygrad.pkl").abspath
-    stride, y_height, uv_height, _ = get_nv12_info(cam_w, cam_h)
-    cmd = (f'{cmd_flags} {mac_brew_string} {taskset}python3 "{compiler}/compile_warp.py" '
-           f'--frame {cam_w},{cam_h},{stride},{y_height},{uv_height},{stride * (y_height + uv_height)} '
-           f'--warp-to {model_w}x{model_h} --layout yuv420 --frames 2 '
-           f'--output {warp_pkl_path}')
-    action = chestnut_action(cmd) if chestnut else cmd
-    node = lenv.Command(warp_pkl_path, tinygrad_files + warp_deps + [Value(cmd)], action)
+frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
+
+if not os.getenv('SKIP_TINYGRAD_COMPILE'):
+  for chestnut in [False, True] if CHESTNUT else [False]:
+    target_pkl_path = File(modeld_pkl_path(chestnut)).abspath
+    file_prefix, cmd_flags = ('big_', chestnut_tg_flags) if chestnut else ('', tg_flags)
+    driving_onnx_deps = get_existing_chunks(File(f"models/{file_prefix}driving_supercombo.onnx").abspath)
+    camera_res_args = ' '.join(f'{cw}x{ch}' for cw, ch in camera_configs)
+    # CPU 7 is isolated with isolcpus on AGNOS, so explicitly pin the compiler to it.
+    taskset = 'taskset -c 7 ' if arch == 'comma_arm64' else ''
+    cmd = (f'{cmd_flags} {mac_brew_string} {taskset}python3 {modeld_dir}/compile_modeld.py '
+          f'--model-size {model_w}x{model_h} '
+          f'--camera-resolutions {camera_res_args} '
+          f'--onnx {File(f"models/{file_prefix}driving_supercombo.onnx").abspath} '
+          f'--output {target_pkl_path} --frame-skip {frame_skip}')
+    onnx_sizes_sum = sum(os.path.getsize(f) for f in driving_onnx_deps)
+    chunk_targets = get_chunk_targets(target_pkl_path, estimate_pickle_max_size(onnx_sizes_sum))
+    def do_compile(target, source, env, command=cmd, pkl=target_pkl_path, chunks=chunk_targets):
+      from openpilot.system.hardware.chestnut.flash import link_up
+      # chestnut can enumerate before its PCIe link is up due to varying 12V power behavior across cars
+      for _ in range(10):
+        if link_up():
+          break
+        time.sleep(1)
+      else:
+        print("Chestnut not ready, skipping big model build")
+        return
+      if ret := env.Execute(command):
+        return ret
+      chunk_file(pkl, chunks)
+    def do_chunk(target, source, env, pkl=target_pkl_path, chunks=chunk_targets):
+      chunk_file(pkl, chunks)
+    actions = Action(do_compile, "  [CHESTNUT] $TARGET") if chestnut else [cmd, Action(do_chunk, "  [CHUNK] $TARGET")]
+    node = lenv.Command(
+      chunk_targets,
+      tinygrad_files + compile_modeld_script + driving_onnx_deps + [Value(camera_res_args), Value(chunk_targets), chunker_file],
+      actions,
+    )
     if chestnut:
       lenv.SideEffect(chestnut_lock, node)
 
+# get model metadata
+fn = File(f"models/dmonitoring_model").abspath
+script_files = [File(Dir("#openpilot/selfdrive/modeld").File("get_model_metadata.py").abspath)]
+cmd = f'{tg_flags} {mac_brew_string} python3 {Dir("#openpilot/selfdrive/modeld").abspath}/get_model_metadata.py {fn}.onnx'
+lenv.Command(fn + "_metadata.pkl", [fn + ".onnx"] + tinygrad_files + script_files + [tg_devices_node], cmd)
+
 dm_w, dm_h = DM_INPUT_SIZE
+compile_dm_warp_script = [File(f"{modeld_dir}/compile_dm_warp.py")]
 for cam_w, cam_h in camera_configs:
   dm_pkl_path = File(f"models/dm_warp_{cam_w}x{cam_h}_tinygrad.pkl").abspath
-  stride, y_height, uv_height, frame_size = get_nv12_info(cam_w, cam_h)
-  cmd = (f'{tg_flags} {mac_brew_string} python3 "{compiler}/compile_warp.py" '
-         f'--frame {cam_w},{cam_h},{stride},{y_height},{uv_height},{frame_size} --warp-to {dm_w}x{dm_h} '
-         f'--layout luma --border-fill 16 --transform-device NPY --output {dm_pkl_path}')
-  lenv.Command(dm_pkl_path, tinygrad_files + warp_deps + [Value(cmd)], cmd)
+  cmd = (f'{tg_flags} {mac_brew_string} python3 {modeld_dir}/compile_dm_warp.py '
+         f'--camera-resolution {cam_w}x{cam_h} --warp-to {dm_w}x{dm_h} '
+         f'--output {dm_pkl_path}')
+  lenv.Command(dm_pkl_path, tinygrad_files + compile_dm_warp_script + compile_modeld_script + [tg_devices_node], cmd)
+
+def tg_compile(flags, model_name):
+  pythonpath_string = 'PYTHONPATH="${PYTHONPATH}:' + env.Dir("#tinygrad_repo").abspath + '"'
+  fn = File(f"models/{model_name}").abspath
+  pkl = fn + "_tinygrad.pkl"
+  onnx_path = fn + ".onnx"
+  chunk_targets = get_chunk_targets(pkl, estimate_pickle_max_size(os.path.getsize(onnx_path)))
+  def do_chunk(target, source, env):
+    chunk_file(pkl, chunk_targets)
+  return lenv.Command(
+    chunk_targets,
+    [onnx_path] + tinygrad_files + [Value(chunk_targets), chunker_file, tg_devices_node],
+    [f'{pythonpath_string} {flags} python3 {Dir("#tinygrad_repo").abspath}/examples/openpilot/compile3.py {fn}.onnx {pkl}',
+     Action(do_chunk, "  [CHUNK] $TARGET")],
+  )
+
+tg_compile(tg_flags, 'dmonitoring_model')

openpilot/selfdrive/modeld/compile_dm_warp.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+import argparse
+import pickle
+import time
+
+from tinygrad.tensor import Tensor
+from tinygrad.device import Device
+from tinygrad.engine.jit import TinyJit
+
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, warp_perspective_tinygrad, _parse_size
+
+
+def make_warp_dm(nv12: NV12Frame, dm_w, dm_h):
+  cam_w, cam_h, stride, _, _, _ = nv12
+  stride_pad = stride - cam_w
+
+  def warp_dm(input_frame, M_inv):
+    M_inv = M_inv.to(Device.DEFAULT).realize()
+    return warp_perspective_tinygrad(input_frame[:cam_h*stride], M_inv,
+                                     (dm_w, dm_h), (cam_h, cam_w), stride_pad, border_fill_val=16).reshape(-1, dm_h * dm_w) # Y
+  return warp_dm
+
+
+def compile_dm_warp(nv12: NV12Frame, dm_w, dm_h, pkl_path):
+  print(f"Compiling DM warp for {nv12.width}x{nv12.height} -> {dm_w}x{dm_h}...")
+
+  warp_dm_jit = TinyJit(make_warp_dm(nv12, dm_w, dm_h), prune=True)
+
+  for i in range(10):
+    frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
+    M_inv = Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')
+    Device.default.synchronize()
+    st = time.perf_counter()
+    warp_dm_jit(frame, M_inv).realize()
+    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")
+
+  with open(pkl_path, "wb") as f:
+    pickle.dump(warp_dm_jit, f)
+  print(f"  Saved to {pkl_path}")
+
+
+if __name__ == "__main__":
+  p = argparse.ArgumentParser()
+  p.add_argument('--camera-resolution', type=_parse_size, required=True, help='camera resolution WxH')
+  p.add_argument('--warp-to', type=_parse_size, required=True, help='DM input WxH')
+  p.add_argument('--output', required=True)
+  args = p.parse_args()
+
+  cam_w, cam_h = args.camera_resolution
+  nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
+  dm_w, dm_h = args.warp_to
+  compile_dm_warp(nv12, dm_w, dm_h, args.output)

openpilot/selfdrive/modeld/compile_modeld.py

@@ -0,0 +1,332 @@
+#!/usr/bin/env python3
+import argparse
+import atexit
+import math
+import os
+import tempfile
+import time
+import shutil
+from functools import partial
+from collections import namedtuple
+
+import numpy as np
+
+from openpilot.selfdrive.modeld.helpers import dump_oob, load_oob
+
+def _patch_tinygrad_fetch_fw():
+  import hashlib
+  import pathlib
+  import zstandard
+  from tinygrad import helpers
+  _orig = 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(path, name, sha256)
+  helpers.fetch_fw = fetch_fw
+_patch_tinygrad_fetch_fw()
+
+
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import Context
+from tinygrad.device import Device
+from tinygrad.engine.jit import TinyJit
+
+
+NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
+MODELD_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
+
+
+def nv12_copy_size(stride: int, y_height: int, uv_height: int) -> int:
+  # Retain the padded Y and UV plane storage, but skip the trailing kernel/guard allocation.
+  return stride * (y_height + uv_height)
+
+
+def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad, border_fill_val=None):
+  w_dst, h_dst = dst_shape
+  h_src, w_src = src_shape
+
+  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
+  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
+
+  # inline 3x3 matmul as elementwise to avoid reduce op (enables fusion with gather)
+  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
+  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
+  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
+
+  src_x = src_x / src_w
+  src_y = src_y / src_w
+
+  x_round = Tensor.round(src_x)
+  y_round = Tensor.round(src_y)
+  x_nn_clipped = x_round.clip(0, w_src - 1).cast('int')
+  y_nn_clipped = y_round.clip(0, h_src - 1).cast('int')
+  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
+  sampled = src_flat[idx]
+
+  if border_fill_val is None:
+    return sampled
+
+  in_bounds = ((x_round >= 0) & (x_round <= w_src - 1) &
+               (y_round >= 0) & (y_round <= h_src - 1)).cast(sampled.dtype)
+  return sampled * in_bounds + Tensor(border_fill_val, dtype=sampled.dtype) * (1 - in_bounds)
+
+
+def frames_to_tensor(frames):
+  H = (frames.shape[0] * 2) // 3
+  W = frames.shape[1]
+  in_img1 = Tensor.cat(frames[0:H:2, 0::2],
+                       frames[1:H:2, 0::2],
+                       frames[0:H:2, 1::2],
+                       frames[1:H:2, 1::2],
+                       frames[H:H+H//4].reshape((H//2, W//2)),
+                       frames[H+H//4:H+H//2].reshape((H//2, W//2)), dim=0).reshape((6, H//2, W//2))
+  return in_img1
+
+
+def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
+  cam_w, cam_h, stride, y_height, uv_height, _ = nv12
+  uv_offset = stride * y_height
+  stride_pad = stride - cam_w
+
+  def frame_prepare_tinygrad(input_frame, M_inv):
+    # UV_SCALE @ M_inv @ UV_SCALE_INV simplifies to elementwise scaling
+    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]], device=Device.DEFAULT)
+    # deinterleave NV12 UV plane (UVUV... -> separate U, V)
+    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
+    with Context(SPLIT_REDUCEOP=0):
+      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
+                                    M_inv, (model_w, model_h),
+                                    (cam_h, cam_w), stride_pad).realize()
+      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
+    tensor = frames_to_tensor(yuv)
+    return tensor
+  return frame_prepare_tinygrad
+
+
+def get_policy_npy_shapes(input_shapes):
+  dp = input_shapes['desire_pulse']  # (1, 25, 8)
+  tc = input_shapes['traffic_convention']  # (1, 2)
+  at = input_shapes['action_t']  # (1, 2)
+  fb = input_shapes['features_buffer']  # (1, T-1, ...) e.g. (1, 24, 32, 512) with spatial features
+  feat_dim = math.prod(fb[2:])
+  # TODO prev_feat shouldn't exist and be handled inside the JIT, but corrupt on QCOM for now
+  shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], feat_dim)}
+  return shapes, [math.prod(s) for s in shapes.values()]
+
+
+def make_input_queues(input_shapes, frame_skip, device, frame_copy_size):
+  img = input_shapes['img']  # (1, 12, 128, 256)
+  fb = input_shapes['features_buffer']  # (1, T-1, ...), past features only; the model appends the current frame's feature
+  feat_dim = math.prod(fb[2:])
+  dp = input_shapes['desire_pulse']  # (1, 25, 8)
+  n_frames = img[1] // 6
+  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
+
+  policy_shapes, _ = get_policy_npy_shapes(input_shapes)
+  shapes = {'tfm': (3, 3), 'big_tfm': (3, 3)} | policy_shapes
+  sizes = [math.prod(s) for s in shapes.values()]
+  packed_npy_size = sum(sizes) * np.dtype(np.float32).itemsize
+  packed_input = np.zeros(packed_npy_size + 2 * frame_copy_size, dtype=np.uint8)
+  packed_npy_inputs = packed_input[:packed_npy_size].view(np.float32)
+  frames = packed_input[packed_npy_size:]
+  frame_views = {'img': frames[:frame_copy_size], 'big_img': frames[frame_copy_size:]}
+  # views into the packed inputs, to be refilled at runtime
+  npy = {k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)}
+  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], fb[0], feat_dim), 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(),
+    'packed_npy_inputs': Tensor(packed_input, device='NPY').realize(),
+  }
+  return input_queues, npy, frame_views
+
+
+def shift_and_sample(buf, new_val, sample_fn):
+  buf.assign(buf[1:].cat(new_val, dim=0).contiguous())
+  return sample_fn(buf)
+
+
+def sample_skip(buf, frame_skip):
+  return buf[::frame_skip].contiguous().flatten(0, 1).unsqueeze(0)
+
+
+def sample_desire(buf, frame_skip):
+  return buf.reshape(-1, frame_skip, *buf.shape[1:]).max(1).flatten(0, 1).unsqueeze(0)
+
+
+def make_warp(nv12, model_w, model_h):
+  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
+
+  def warp(tfm, big_tfm, frame, big_frame):
+    tfm = tfm.to(Device.DEFAULT)
+    big_tfm = big_tfm.to(Device.DEFAULT)
+    frame = frame.to(Device.DEFAULT)
+    big_frame = big_frame.to(Device.DEFAULT)
+    Tensor.realize(tfm, big_tfm, frame, big_frame)
+
+    warped_frame = frame_prepare(frame, tfm).unsqueeze(0)
+    warped_big_frame = frame_prepare(big_frame, big_tfm).unsqueeze(0)
+    return Tensor.cat(warped_frame, warped_big_frame)
+
+  return warp
+
+
+def make_run_policy(model_runner, model_metadata, frame_skip):
+  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
+  npy_shapes, npy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
+  model_input_dtypes = {name: spec.dtype for name, spec in model_runner.graph_inputs.items()}
+
+  def run_policy(warped, img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
+    packed_npy_inputs = packed_npy_inputs.to(Device.DEFAULT)
+    Tensor.realize(packed_npy_inputs, warped)
+
+    img = shift_and_sample(img_q, warped[0:1], sample_skip_fn)
+    big_img = shift_and_sample(big_img_q, warped[1:2], sample_skip_fn)
+
+    desire, traffic_convention, action_t, prev_feat = (t.reshape(s) for t, s in zip(packed_npy_inputs.split(npy_sizes), npy_shapes.values(), strict=True))
+    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
+    feat_buf = shift_and_sample(feat_q, prev_feat.reshape(1, 1, -1), sample_skip_fn)
+
+    inputs = {
+      'img': img,
+      'big_img': big_img,
+      'features_buffer': feat_buf.reshape(model_metadata['input_shapes']['features_buffer']),
+      'desire_pulse': desire_buf,
+      'traffic_convention': traffic_convention,
+      'action_t': action_t,
+    }
+    inputs = {name: value.cast(model_input_dtypes[name]) for name, value in inputs.items()}
+    out = next(iter(model_runner(inputs).values())).cast('float32')
+    return out,
+  return run_policy
+
+
+def make_run_model(warp, run_policy, model_metadata, frame_copy_size):
+  _, policy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
+  packed_npy_size = (18 + sum(policy_sizes)) * np.dtype(np.float32).itemsize
+
+  def run_model(img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
+    packed_input = packed_npy_inputs.to(Device.DEFAULT)
+    Tensor.realize(packed_input)
+    packed_npy_inputs = packed_input[:packed_npy_size].bitcast('float32')
+    frame = packed_input[packed_npy_size:packed_npy_size + frame_copy_size]
+    big_frame = packed_input[packed_npy_size + frame_copy_size:]
+    tfm, big_tfm, policy_inputs = packed_npy_inputs.split([9, 9, sum(policy_sizes)])
+    warped = warp(tfm.reshape(3, 3), big_tfm.reshape(3, 3), frame, big_frame)
+    return run_policy(warped, img_q, big_img_q, feat_q, desire_q, policy_inputs)
+  return run_model
+
+
+def compile_jit(jit, input_keys, make_queues, benchmark_runs):
+  if benchmark_runs < 1:
+    raise ValueError("benchmark_runs must be at least 1")
+
+  SEED = 42
+  def random_inputs_run(fn, seed, n_runs, test_val=None, test_buffers=None, expect_match=True):
+    input_queues, npy, frame_views = make_queues(Device.DEFAULT)
+    rng = np.random.default_rng(seed)
+
+    for i in range(n_runs):
+      for v in npy.values():
+        v[:] = rng.standard_normal(v.shape).astype(v.dtype)
+      for v in frame_views.values():
+        v[:] = rng.integers(0, 256, size=v.shape, dtype=np.uint8)
+      Device.default.synchronize()
+      st = time.perf_counter()
+      outs = fn(**{k: input_queues[k] for k in input_keys})
+      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 val, buffers
+
+  print('capture + replay')
+  test_val, test_buffers = random_inputs_run(jit, SEED, 3)
+  print(f'pickle round trip ({benchmark_runs} runs per seed)')
+  with tempfile.TemporaryFile(dir=".") as f:
+    dump_oob(jit, f)
+    f.seek(0)
+    loaded_jit = load_oob(f)
+  random_inputs_run(loaded_jit, SEED, benchmark_runs, test_val, test_buffers, expect_match=True)
+  random_inputs_run(loaded_jit, SEED+1, benchmark_runs, test_val, test_buffers, expect_match=False)
+  # Keep the original so per-resolution JITs share model weight buffers in the final pickle.
+  return jit
+
+
+def _parse_size(s):
+  w, h = s.lower().split('x')
+  return int(w), int(h)
+
+
+def read_file_chunked_to_disk(path):
+  from openpilot.common.file_chunker import open_file_chunked
+  tmp_path = f'{path}.unchunked'
+  with open(tmp_path, 'wb') as f, open_file_chunked(path) as src:
+    shutil.copyfileobj(src, f)
+  atexit.register(lambda: os.path.exists(tmp_path) and os.remove(tmp_path))
+  return tmp_path
+
+
+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
+  p = argparse.ArgumentParser()
+  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,
+                 help='camera resolutions WxH (one or more)')
+  p.add_argument('--onnx', required=True)
+  p.add_argument('--output', required=True)
+  p.add_argument('--frame-skip', type=int, required=True)
+  p.add_argument('--benchmark-runs', type=int, default=1,
+                 help='timed loaded-JIT runs for each correctness seed')
+  args = p.parse_args()
+
+  model_path = read_file_chunked_to_disk(args.onnx)
+  model_w, model_h = args.model_size
+
+  model_runner = OnnxRunner(model_path)
+  out = {
+    'metadata': make_metadata_dict(model_path),
+    'input_devices': {'model': Device.DEFAULT},
+    'run_model': {},
+  }
+
+  run_policy = make_run_policy(model_runner, out['metadata'], args.frame_skip)
+
+  for cam_w, cam_h in args.camera_resolutions:
+    nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
+    frame_copy_size = nv12_copy_size(nv12.stride, nv12.y_height, nv12.uv_height)
+    make_model_queues = partial(make_input_queues, out['metadata']['input_shapes'], args.frame_skip,
+                                frame_copy_size=frame_copy_size)
+    warp = make_warp(nv12, model_w, model_h)
+    run_model_jit = TinyJit(make_run_model(warp, run_policy, out['metadata'], frame_copy_size), prune=True)
+    out['run_model'][(cam_w,cam_h)] = compile_jit(run_model_jit, MODELD_INPUTS, make_model_queues,
+                                                  args.benchmark_runs)
+
+  with open(args.output, "wb") as f:
+    dump_oob(out, f)
+  print(f"Saved JITs to {args.output} ({os.path.getsize(args.output) / 1e6:.2f} MB)")

openpilot/selfdrive/modeld/dmonitoringmodeld.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 import os
-import base64
-from openpilot.selfdrive.modeld.helpers import MODELS_DIR, load_oob
+from openpilot.selfdrive.modeld.helpers import MODELS_DIR, get_tg_input_devices
 from tinygrad.tensor import Tensor
 import time
 import pickle
@@ -19,8 +18,10 @@ from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
 from openpilot.common.file_chunker import open_file_chunked
 from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid, safe_exp
 
+PROCESS_NAME = "openpilot.selfdrive.modeld.dmonitoringmodeld"
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
 MODEL_PKL_PATH = MODELS_DIR / 'dmonitoring_model_tinygrad.pkl'
+METADATA_PATH = MODELS_DIR / 'dmonitoring_model_metadata.pkl'
 
 
 class ModelState:
@@ -28,10 +29,11 @@ class ModelState:
   output: np.ndarray
 
   def __init__(self, cam_w: int, cam_h: int):
-    jits = load_oob(open_file_chunked(MODEL_PKL_PATH))
-    self.DEV = jits['input_specs']['input_img'][2]
-    self.input_shapes = jits['metadata']['input_shapes']
-    self.output_slices = pickle.loads(base64.b64decode(jits['metadata']['metadata']['output_slices']))
+    self.DEV = get_tg_input_devices(PROCESS_NAME, chestnut=False)['DEV']
+    with open(METADATA_PATH, 'rb') as f:
+      model_metadata = pickle.load(f)
+      self.input_shapes = model_metadata['input_shapes']
+      self.output_slices = model_metadata['output_slices']
 
     self.numpy_inputs = {
       'calib': np.zeros(self.input_shapes['calib'], dtype=np.float32),
@@ -40,19 +42,16 @@ class ModelState:
     self.warp_inputs_np = {'transform': np.zeros((3,3), dtype=np.float32)}
     self.warp_inputs = {k: Tensor(v, device='NPY') for k,v in self.warp_inputs_np.items()}
     self.frame_buf_params = get_nv12_info(cam_w, cam_h)
-    self.tensor_inputs = {k: Tensor(v, device=self.DEV).realize() for k,v in self.numpy_inputs.items()}
-    self.calib_host = Tensor(self.numpy_inputs['calib'], device='NPY')._buffer()
+    self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
     self._blob_cache : dict[int, Tensor] = {}
-    self.model_run = jits['run']
-    self.outputs = {name: Tensor(np.zeros(shape, dtype=dtype), device=device).realize() for name, (shape, dtype, device) in jits['output_specs'].items()}
+    self.model_run = pickle.load(open_file_chunked(str(MODEL_PKL_PATH)))
     with open(MODELS_DIR / f'dm_warp_{cam_w}x{cam_h}_tinygrad.pkl', "rb") as f:
-      self.image_warp = pickle.load(f)['run']
+      self.image_warp = pickle.load(f)
 
   def run(self, buf: VisionBuf, calib: np.ndarray, transform: np.ndarray) -> tuple[np.ndarray, float]:
     self.numpy_inputs['calib'][0,:] = calib
 
     t1 = time.perf_counter()
-    self.tensor_inputs['calib']._buffer().copy_from(self.calib_host)
 
     ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data
     # There is a ringbuffer of imgs, just cache tensors pointing to all of them
@@ -60,10 +59,9 @@ class ModelState:
       self._blob_cache[ptr] = Tensor.from_blob(ptr, (self.frame_buf_params[3],), dtype='uint8', device=self.DEV)
 
     self.warp_inputs_np['transform'][:] = transform[:]
-    self.tensor_inputs['input_img'] = self.image_warp(input_frame=self._blob_cache[ptr], M_inv=self.warp_inputs['transform'])
+    self.tensor_inputs['input_img'] = self.image_warp(self._blob_cache[ptr], self.warp_inputs['transform'])
 
-    self.model_run(output_buffers=self.outputs, **self.tensor_inputs)
-    output = self.outputs['outputs'].numpy().astype(np.float32).reshape(-1)
+    output = self.model_run(**self.tensor_inputs).numpy().flatten()
 
     t2 = time.perf_counter()
     return output, t2 - t1

openpilot/selfdrive/modeld/get_model_metadata.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+import sys
+import pathlib
+import codecs
+import pickle
+from typing import Any
+
+from tinygrad.nn.onnx import OnnxPBParser
+
+
+class MetadataOnnxPBParser(OnnxPBParser):
+  def _parse_ModelProto(self) -> dict:
+    obj: dict[str, Any] = {"graph": {"input": [], "output": []}, "metadata_props": []}
+    for fid, wire_type in self._parse_message(self.reader.len):
+      match fid:
+        case 7:
+          obj["graph"] = self._parse_GraphProto()
+        case 14:
+          obj["metadata_props"].append(self._parse_StringStringEntryProto())
+        case _:
+          self.reader.skip_field(wire_type)
+    return obj
+
+
+def get_name_and_shape(value_info: dict[str, Any]) -> tuple[str, tuple[int, ...]]:
+  shape = tuple(int(dim) if isinstance(dim, int) else 0 for dim in value_info["parsed_type"].shape)
+  name = value_info["name"]
+  return name, shape
+
+
+def get_metadata_value_by_name(model: dict[str, Any], name: str) -> str | Any:
+  for prop in model["metadata_props"]:
+    if prop["key"] == name:
+      return prop["value"]
+  return None
+
+
+def make_metadata_dict(model_path):
+  model = MetadataOnnxPBParser(model_path).parse()
+  output_slices = get_metadata_value_by_name(model, 'output_slices')
+  assert output_slices is not None, 'output_slices not found in metadata'
+  return {
+    '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"]),
+  }
+
+
+if __name__ == "__main__":
+  model_path = pathlib.Path(sys.argv[1])
+  metadata_path = model_path.parent / (model_path.stem + '_metadata.pkl')
+  with open(metadata_path, 'wb') as f:
+    pickle.dump(make_metadata_dict(model_path), f)
+  print(f'saved metadata to {metadata_path}')

openpilot/selfdrive/modeld/helpers.py

@@ -1,25 +1,49 @@
 import io
+import json
 import pickle
+import shutil
 import struct
+import tempfile
 from pathlib import Path
 
 from openpilot.common.file_chunker import get_manifest_path
 from openpilot.common.hardware.usb import CHESTNUT_USB_PRODUCT, USB_DEVICES_PATH, is_chestnut_usb_id
 
 MODELS_DIR = Path(__file__).resolve().parent / 'models'
+TG_INPUT_DEVICES_PATH = MODELS_DIR / 'tg_input_devices.json'
+CHESTNUT_POWERED_VOLTAGE = 5000
+CHESTNUT_PCIE_READY = 0x78
 
 
+def get_tg_input_devices(process_name: str, chestnut: bool):
+  with open(TG_INPUT_DEVICES_PATH) as f:
+    return json.load(f)[process_name]['default' if not chestnut else 'chestnut']
+
 def modeld_pkl_path(chestnut: bool):
   prefix = 'big_' if chestnut else ''
   return MODELS_DIR / f'{prefix}driving_tinygrad.pkl'
 
+def dump_oob(obj, f):
+  with tempfile.TemporaryFile(dir=".") as tmp:
+    def buffer_callback(pb: pickle.PickleBuffer):
+      m = pb.raw()
+      tmp.write(struct.pack('<q', m.nbytes))
+      tmp.write(m)
+      pb.release() # keep peak ram at ~1 buffer
+    stream = io.BytesIO()
+    pickle.Pickler(stream, protocol=5, buffer_callback=buffer_callback).dump(obj)
+    opcodes = stream.getvalue()
+    f.write(struct.pack('<q', len(opcodes)))
+    f.write(opcodes)
+    tmp.seek(0)
+    shutil.copyfileobj(tmp, f)
+
 def load_oob(f):
   opcodes = f.read(struct.unpack('<q', f.read(8))[0])
   def buffers():
     while (h := f.read(8)):
       pb = pickle.PickleBuffer(bytearray(struct.unpack('<q', h)[0]))
-      if f.readinto(pb) != pb.raw().nbytes:
-        raise EOFError("incomplete model buffer")
+      f.readinto(pb)
       yield pb
   return pickle.load(io.BytesIO(opcodes), buffers=buffers())
 
@@ -35,6 +59,8 @@ def chestnut_present() -> bool:
   return False
 
 def chestnut_compiled() -> bool:
-  path = modeld_pkl_path(chestnut=True)
-  return (path.is_file() or Path(get_manifest_path(path)).is_file()) and all(
-    (MODELS_DIR / f'big_driving_warp_{size}_tinygrad.pkl').is_file() for size in ('1344x760', '1928x1208'))
+  return Path(get_manifest_path(modeld_pkl_path(chestnut=True))).is_file()
+
+
+def chestnut_ready(state) -> bool:
+  return state.supplyVoltage >= CHESTNUT_POWERED_VOLTAGE and not state.supplyFault and state.pcieLtssm == CHESTNUT_PCIE_READY

openpilot/selfdrive/modeld/modeld.py

@@ -1,17 +1,12 @@
 #!/usr/bin/env python3
 from collections.abc import Callable
-import base64
 import ctypes
 from functools import cached_property
 import os
 os.environ['GMMU'] = '0' # for chestnut fast loading, noop for qcom
-from tinygrad.device import Buffer, Device
-from tinygrad.dtype import DType, dtypes
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import round_up
-from tinygrad.uop.ops import UOp
-import math
-import pickle
+from tinygrad.device import Device
+import usb1
+import struct
 import threading
 import time
 import numpy as np
@@ -33,10 +28,16 @@ from openpilot.common.transformations.model import get_warp_matrix
 from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
 from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan, should_stop, smooth_value, get_curvature_from_plan
 from openpilot.selfdrive.modeld.parse_model_outputs import Parser
+from openpilot.selfdrive.modeld.compile_modeld import make_input_queues, nv12_copy_size, MODELD_INPUTS
 from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_driving_model_data, fill_pose_msg, PublishState
 from openpilot.common.file_chunker import open_file_chunked
+from openpilot.common.hardware.usb import CHESTNUT_USB_IDS
 from openpilot.selfdrive.modeld.constants import ModelConstants, Plan
-from openpilot.selfdrive.modeld.helpers import MODELS_DIR, chestnut_present, chestnut_compiled, modeld_pkl_path, load_oob
+from openpilot.selfdrive.modeld.helpers import chestnut_present, chestnut_compiled, chestnut_ready, modeld_pkl_path, load_oob
+
+from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
+from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
+from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController
 
 SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
 
@@ -74,14 +75,45 @@ def get_action_from_model(model_output: dict[str, np.ndarray], prev_action: log.
                                 shouldStop=bool(stop))
 
 
-class ChestnutGpuState:
-  # GPU metrics require modeld's GPU context
+class ChestnutState:
+  # only modeld can access chestnut
   def __init__(self, pm: PubMaster, big: bool):
     self.pm = pm
     self.big = big
     self.valid = True
     self.sends = 0
     self.metrics = {}
+    self._asm_usb = None
+
+  def _close_asm_usb(self) -> None:
+    if self._asm_usb is not None:
+      self._asm_usb.close()
+      self._asm_usb = None
+
+  def _open_asm_usb(self):
+    context = usb1.USBContext()
+    for vendor_id, product_id in CHESTNUT_USB_IDS:
+      if (handle := context.openByVendorIDAndProductID(vendor_id, product_id, skip_on_error=True)) is not None:
+        return handle
+    context.close()
+
+  def _read_ina(self) -> tuple[int, int, bool]:
+    if "AMD" in Device._opened_devices and self._asm_usb is None:
+      try:
+        raw = Device["AMD"].iface.pci_dev.usb.usb.control_read(0xC0, 5)
+        return struct.unpack('<Hh?', bytes(raw))
+      except Exception:
+        pass
+    if self._asm_usb is None:
+      self._asm_usb = self._open_asm_usb()
+    if self._asm_usb is None:
+      raise usb1.USBErrorNoDevice
+    try:
+      raw = self._asm_usb.controlRead(0xC0, 0xC0, 0, 0, 5, timeout=100)
+    except usb1.USBError:
+      self._close_asm_usb()
+      raise
+    return struct.unpack('<Hh?', bytes(raw))
 
   @cached_property
   def power_limit(self) -> int:
@@ -89,8 +121,8 @@ class ChestnutGpuState:
     return smu._send_msg(smu.smu_mod.PPSMC_MSG_GetPptLimit, 0, read_back_arg=True, timeout=100)
 
   def send(self) -> None:
-    msg = messaging.new_message('chestnutGpuState')
-    state = msg.chestnutGpuState
+    msg = messaging.new_message('chestnutState')
+    state = msg.chestnutState
     self.sends += 1
     if self.big and "AMD" in Device._opened_devices and self.sends % 100 == 1:
       try:
@@ -116,8 +148,21 @@ class ChestnutGpuState:
       for k, v in self.metrics.items():
         setattr(state, k, v)
 
-    msg.valid = not self.big or (self.valid and bool(self.metrics))
-    self.pm.send('chestnutGpuState', msg)
+    asm_valid = False
+    try:
+      # ASM runs on USB-C power, these still read without a gpu
+      state.supplyVoltage, state.supplyCurrent, state.supplyFault = self._read_ina()
+      asm_valid = True
+    except Exception:
+      pass
+    if "AMD" in Device._opened_devices:
+      try:
+        state.pcieLtssm = Device["AMD"].iface.pci_dev.usb.read(0xB450, 1)[0]
+      except Exception:
+        pass
+
+    msg.valid = asm_valid and (not self.big or self.valid)
+    self.pm.send('chestnutState', msg)
 
 
 class FrameMeta:
@@ -130,64 +175,37 @@ class FrameMeta:
       self.frame_id, self.timestamp_sof, self.timestamp_eof = vipc.frame_id, vipc.timestamp_sof, vipc.timestamp_eof
 
 
-def input_view(buffer: Buffer, shape: tuple[int, ...], dtype: DType, offset: int) -> Tensor:
-  view = buffer.view(math.prod(shape), dtype, offset).ensure_allocated()
-  return Tensor(UOp.from_buffer(view)).reshape(shape)
-
-
-class ModelState:
+class ModelState(ModelStateBase):
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
 
   def __init__(self, cam_w: int, cam_h: int, chestnut: bool):
+    ModelStateBase.__init__(self)
     jits = load_oob(open_file_chunked(modeld_pkl_path(chestnut)))
-    self.model_device = jits['input_specs']['new_img'][2]
-    self.input_shapes = {name: (shape, np.dtype(dtype)) for name, (shape, dtype, _) in jits['input_specs'].items()}
-    self.state_pairs = {name: f'next_{name}' for name in self.input_shapes if f'next_{name}' in jits['metadata']['output_shapes']}
-    self.vision_input_names = ('img', 'big_img')
-    self.output_slices = pickle.loads(base64.b64decode(jits['metadata']['metadata']['output_slices']))
+    input_devices = jits['input_devices']
+    self.model_device = input_devices['model']
+    metadata = jits['metadata']
+    self.input_shapes = metadata['input_shapes']
+    self.vision_input_names = [k for k in self.input_shapes if 'img' in k]
+    self.output_slices = metadata['output_slices']
 
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
     self.chestnut = chestnut
 
-    stride, y_height, uv_height, _ = get_nv12_info(cam_w, cam_h)
-    self.frame_copy_size = stride * (y_height + uv_height)
-    self.pack_inputs()
-    with open(MODELS_DIR / f'{"big_" if chestnut else ""}driving_warp_{cam_w}x{cam_h}_tinygrad.pkl', 'rb') as f:
-      self.run_warp = pickle.load(f)['run']
-    self.run_model = jits['run']
-    self.outputs = {name: Tensor(np.zeros(shape, dtype=dtype), device=device).realize() for name, (shape, dtype, device) in jits['output_specs'].items()}
-    for name, next_name in self.state_pairs.items():
-      state = self.input_queues[name]
-      self.outputs[next_name] = input_view(state._buffer(), state.shape, state.dtype, 0)
+    self.frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
+    self.frame_copy_size = nv12_copy_size(*get_nv12_info(cam_w, cam_h)[:3])
+    self.input_queues, self.npy, self.frame_views = make_input_queues(
+      self.input_shapes, self.frame_skip, device=self.model_device, frame_copy_size=self.frame_copy_size)
     self.parser = Parser()
-
-  def pack_inputs(self) -> None:
-    # Pack host inputs into one upload to reduce USB transfer overhead for the eGPU.
-    self.input_queues = {name: Tensor(np.zeros(shape, dtype=dtype), device=self.model_device).realize()
-                         for name, (shape, dtype) in self.input_shapes.items() if name in self.state_pairs}
-    shapes = {'tfm': (2, 3, 3)} | {name: shape for name, (shape, _) in self.input_shapes.items()
-                                   if name not in self.state_pairs and name != 'new_img'}
-    npy_size = sum(round_up(math.prod(shape) * 4, 128) for shape in shapes.values())
-    self.packed_input = np.zeros(npy_size + 2 * self.frame_copy_size, dtype=np.uint8)
-    self.input_host = Tensor(self.packed_input, device='NPY')._buffer()
-    self.input_device = Tensor(self.packed_input, device=self.model_device)._buffer()
-    self.npy = {}
-    offset = 0
-    for name, shape in shapes.items():
-      self.npy[name] = np.ndarray(shape, dtype=np.float32, buffer=self.packed_input, offset=offset)
-      self.input_queues[name] = input_view(self.input_device, shape, dtypes.float32, offset)
-      offset += round_up(self.npy[name].nbytes, 128)
-    self.frames = self.packed_input[npy_size:].reshape(2, self.frame_copy_size)
-    self.warp_inputs = {'input_frame': input_view(self.input_device, self.frames.shape, dtypes.uint8, npy_size), 'M_inv': self.input_queues.pop('tfm')}
+    self.run_model = jits['run_model'][(cam_w,cam_h)]
 
   def slice_outputs(self, model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]:
-    return {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
+    parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
+    return parsed_model_outputs
 
   def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
           inputs: dict[str, np.ndarray], after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray]:
-    for i, key in enumerate(self.vision_input_names):
-      np.copyto(self.frames[i], np.frombuffer(bufs[key].data, dtype=np.uint8, count=self.frame_copy_size))
-      self.npy['tfm'][i] = transforms[key]
+    for key, buf in bufs.items():
+      np.copyto(self.frame_views[key], np.frombuffer(buf.data, dtype=np.uint8, count=self.frame_copy_size))
 
     # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
     inputs['desire_pulse'][0] = 0
@@ -195,16 +213,17 @@ class ModelState:
     self.prev_desire[:] = inputs['desire_pulse']
     self.npy['traffic_convention'][:] = inputs['traffic_convention']
     self.npy['action_t'][:] = inputs['action_t']
+    self.npy['tfm'][:,:] = transforms['img'][:,:]
+    self.npy['big_tfm'][:,:] = transforms['big_img'][:,:]
 
-    self.input_device.copy_from(self.input_host)
-    self.input_queues['new_img'] = self.run_warp(**self.warp_inputs)
-    self.run_model(output_buffers=self.outputs, **self.input_queues)
+    outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
     if after_enqueue is not None:
       after_enqueue()
-    model_output = self.outputs['outputs'].numpy()[0]
+    model_output = outs.numpy()[0]
     if self.chestnut and not np.all(np.isfinite(model_output)):
       raise RuntimeError("model output not finite")
     outputs_dict = self.parser.parse_outputs(self.slice_outputs(model_output, self.output_slices))
+    self.npy['prev_feat'][:] = model_output[self.output_slices['hidden_state']]
 
     if SEND_RAW_PRED:
       outputs_dict['raw_pred'] = model_output.copy()
@@ -215,21 +234,33 @@ class ModelState:
     eye = np.eye(3, dtype=np.float32)
     dims = {'desire_pulse': ModelConstants.DESIRE_LEN, 'traffic_convention': 2, 'action_t': 2}
     self.run(dummy_frames, dict.fromkeys(self.vision_input_names, eye), {k: np.zeros(v, dtype=np.float32) for k, v in dims.items()})
-    self.packed_input[:] = 0
-    for key in self.state_pairs:
-      self.input_queues[key].assign(0).realize()
+    self.input_queues, self.npy, self.frame_views = make_input_queues(
+      self.input_shapes, self.frame_skip, device=self.model_device, frame_copy_size=self.frame_copy_size)
     self.prev_desire[:] = 0
 
 
 def main(demo=False):
   cloudlog.warning("modeld init")
 
-  CHESTNUT = chestnut_present() and chestnut_compiled()
+  chestnut_available = chestnut_present() and chestnut_compiled()
+  CHESTNUT = False
+  if chestnut_available:
+    poller = messaging.Poller()
+    sock = messaging.sub_sock("chestnutState", poller=poller, conflate=True)
+    deadline = time.monotonic() + 4. / SERVICE_LIST['deviceState'].frequency
+    while not CHESTNUT and (remaining := deadline - time.monotonic()) > 0.:
+      if not poller.poll(round(remaining * 1000)):
+        break
+      msg = messaging.recv_one_or_none(sock)
+      CHESTNUT = msg is not None and msg.valid and chestnut_ready(msg.chestnutState)
   if CHESTNUT:
     os.environ['HCQDEV_WAIT_TIMEOUT_MS'] = '3000'
   params = Params()
   params.put_bool("ChestnutLoading", CHESTNUT)
-  params.remove("ChestnutActive")
+  if chestnut_available and not CHESTNUT:
+    params.put_bool("ChestnutActive", False)
+  else:
+    params.remove("ChestnutActive")
 
   config_realtime_process(7, 54)
 
@@ -273,22 +304,27 @@ def main(demo=False):
     loader.start()
     loader.join(BIG_MODEL_TIMEOUT)
     model = big_model
+    if model is None:
+      params.put_bool("ChestnutModelError", True)
     params.put_bool("ChestnutActive", model is not None)
+    if model is not None:
+      params.remove("ChestnutModelError")
 
   small_model = ModelState(vipc_client_main.width, vipc_client_main.height, False) if model is None or CHESTNUT else None
   if model is None:
     model = small_model
   params.put_bool("ChestnutLoading", False)
+  assert model is not None
   cloudlog.warning(f"models loaded in {time.monotonic() - st:.1f}s, modeld starting")
 
   # messaging
-  pub_socks = ["modelV2", "drivingModelData", "cameraOdometry"] + (["chestnutGpuState"] if CHESTNUT else [])
+  pub_socks = ["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"] + (["chestnutState"] if CHESTNUT else [])
   pm = PubMaster(pub_socks)
   sm = SubMaster(["deviceState", "carState", "narrowRoadCameraState", "extrinsicsCalibration", "driverMonitoringState", "carControl", "lateralDelay"])
 
   publish_state = PublishState()
   params = Params()
-  chestnut_state = ChestnutGpuState(pm, model.chestnut) if CHESTNUT else None
+  chestnut_state = ChestnutState(pm, model.chestnut) if CHESTNUT else None
 
   # setup filter to track dropped frames
   frame_dropped_filter = FirstOrderFilter(0., 10., 1. / ModelConstants.MODEL_RUN_FREQ)
@@ -315,6 +351,7 @@ def main(demo=False):
   prev_action = log.ModelDataV2.Action()
 
   DH = DesireHelper()
+  RELC = RoadEdgeLaneChangeController()
 
   while True:
     # Keep receiving frames until we are at least 1 frame ahead of previous extra frame
@@ -354,6 +391,7 @@ def main(demo=False):
     is_rhd = sm["driverMonitoringState"].isRHD
     frame_id = sm["narrowRoadCameraState"].frameId
     v_ego = max(sm["carState"].vEgo, 0.)
+    model.lat_delay = get_lat_delay(params, sm["lateralDelay"].lateralDelay)
     lat_delay = sm["lateralDelay"].lateralDelay + LAT_SMOOTH_SECONDS
     if sm.updated["extrinsicsCalibration"] and sm.seen['narrowRoadCameraState'] and sm.seen['deviceState']:
       device_from_calib_euler = np.array(sm["extrinsicsCalibration"].rpyCalib, dtype=np.float32)
@@ -397,14 +435,16 @@ def main(demo=False):
     mt1 = time.perf_counter()
     try:
       send_chestnut = (chestnut_state is not None and
-                       run_count % round(ModelConstants.MODEL_RUN_FREQ / SERVICE_LIST['chestnutGpuState'].frequency) == 0)
+                       run_count % round(ModelConstants.MODEL_RUN_FREQ / SERVICE_LIST['chestnutState'].frequency) == 0)
       model_output = model.run(bufs, transforms, inputs, chestnut_state.send if send_chestnut else None)
     except Exception:
       if not params.get_bool("ChestnutActive"):
         raise
       # fallback to small model
       cloudlog.exception("big model failed, fall back to small")
+      params.put_bool("ChestnutModelError", True)
       params.put_bool("ChestnutActive", False)
+      assert small_model is not None
       model = small_model
       if chestnut_state is not None:
         chestnut_state.big = False
@@ -429,15 +469,20 @@ def main(demo=False):
       l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
       r_lane_change_prob = desire_state[log.Desire.laneChangeRight]
       lane_change_prob = l_lane_change_prob + r_lane_change_prob
-      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob)
+      mdv2sp_send = messaging.new_message('modelDataV2SP')
+      left_edge, right_edge = RELC.update_and_fill(modelv2_send.modelV2, mdv2sp_send.modelDataV2SP, v_ego)
+      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob, left_edge, right_edge)
       modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
       modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
+      mdv2sp_send.valid = modelv2_send.valid
+      mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction
 
       fill_driving_model_data(drivingdata_send, modelv2_send)
       fill_pose_msg(posenet_send, model_output, meta_main.frame_id, vipc_dropped_frames, meta_main.timestamp_eof, extrinsics_calibration_seen)
       pm.send('modelV2', modelv2_send)
       pm.send('drivingModelData', drivingdata_send)
       pm.send('cameraOdometry', posenet_send)
+      pm.send('modelDataV2SP', mdv2sp_send)
     last_vipc_frame_id = meta_main.frame_id
 
 if __name__ == "__main__":

openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1791d5940b2c048d0639813426dd2cf1d6f2a6727ed51e17c8bcea8bbe754123
+size 765950064

openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:76cc0a9bc3af7a318889483dcbe126337f8d338f5abcbe664a8988c9b18b6639
-size 776634338

openpilot/selfdrive/modeld/models/driving_supercombo.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:65a08adc31d5c456219687d99b7bf5e44d61dae2d49ea67850e76105c7248cce
-size 60918562
+oid sha256:659727c4d4839adc4992a254409a54259a8756a743f2d567bf5fdc6579f8009b
+size 60881999

openpilot/selfdrive/pandad/__init__.py

@@ -1 +1,3 @@
-from openpilot.selfdrive.pandad.pandad_api_impl import can_list_to_can_capnp as can_list_to_can_capnp, can_capnp_to_list as can_capnp_to_list
+from openpilot.selfdrive.pandad.pandad_api_impl import can_list_to_can_capnp, can_capnp_to_list
+assert can_list_to_can_capnp
+assert can_capnp_to_list

openpilot/selfdrive/pandad/pandad.cc

@@ -342,8 +342,8 @@ void process_peripheral_state(Panda *panda, PubMaster *pm, bool no_fan_control,
       }
     }
 
-    // Disable IR on input timeout or when requested offroad.
-    if (nanos_since_boot() - last_cabin_camera_t > 1e9 || (!is_onroad && params.getBool("DisableDriverCameraIR"))) {
+    // Disable IR on input timeout
+    if (nanos_since_boot() - last_cabin_camera_t > 1e9) {
       ir_pwr = 0;
     }
 

openpilot/selfdrive/selfdrived/selfdrived.py

@@ -453,12 +453,11 @@ class SelfdriveD(CruiseHelper):
       self.logged_comm_issue = None
 
     if not self.CP.notCar and not big_model_settling:  # localization has nothing to work with during the load
-      # the defaults of a message that was never received are not a localizer failure
-      if self.sm.seen['deviceMotion'] and not self.sm['deviceMotion'].posenetOK:
+      if not self.sm['deviceMotion'].posenetOK:
         self.events.add(EventName.posenetInvalid)
-      if self.sm.seen['deviceMotion'] and not self.sm['deviceMotion'].inputsOK:
+      if not self.sm['deviceMotion'].inputsOK:
         self.events.add(EventName.locationdTemporaryError)
-      if (self.sm.seen['vehicleParameters'] and not self.sm['vehicleParameters'].valid and cal_status == log.ExtrinsicsCalibration.Status.calibrated and
+      if (not self.sm['vehicleParameters'].valid and cal_status == log.ExtrinsicsCalibration.Status.calibrated and
           not TESTING_CLOSET and (not SIMULATION or REPLAY)):
         self.events.add(EventName.paramsdTemporaryError)
 

openpilot/selfdrive/test/chestnut.sh

@@ -1,6 +0,0 @@
-#!/usr/bin/env bash
-set -e
-
-sudo python3 openpilot/system/hardware/chestnut/flash.py
-
-SCONSFLAGS="-j4" ./openpilot/system/manager/build.py

openpilot/selfdrive/test/process_replay/model_replay.py

@@ -20,13 +20,11 @@ from openpilot.tools.lib.framereader import FrameReader
 from openpilot.tools.lib.logreader import LogReader, save_log
 from openpilot.tools.lib.github_utils import GithubUtils
 
-TEST_ROUTE = "98395b7c5b27882e|0000002b--2686b5a2d0"
-SEGMENT = 1
+TEST_ROUTE = "8494c69d3c710e81|000001d4--2648a9a404"
+SEGMENT = 4
 START_FRAME = 0
 END_FRAME = 60
 
-CHESTNUT = "--chestnut" in sys.argv
-
 SEND_EXTRA_INPUTS = bool(int(os.getenv("SEND_EXTRA_INPUTS", "0")))
 
 DATA_TOKEN = os.getenv("CI_ARTIFACTS_TOKEN","")
@@ -41,7 +39,7 @@ EXEC_TIMINGS = [
 ]
 
 def get_log_fn(test_route, ref="master"):
-  return f"{test_route}_model_{'chestnut' if CHESTNUT else 'tici'}_{ref}.zst"
+  return f"{test_route}_model_tici_{ref}.zst"
 
 def plot(proposed, master, title, tmp):
   proposed = list(proposed)
@@ -172,8 +170,6 @@ def model_replay(lr, frs):
 
   msgs = modeld_msgs + dmonitoringmodeld_msgs
   chestnut = any(m.modelV2.big for m in modeld_msgs if m.which() == "modelV2")
-  if CHESTNUT:
-    assert chestnut and all(m.modelV2.big for m in modeld_msgs if m.which() == "modelV2"), "Chestnut replay must run the big model without fallback"
 
   header = ['model', 'max instant', 'max instant allowed', 'average', 'max average allowed', 'test result']
   rows = []
@@ -289,8 +285,7 @@ if __name__ == "__main__":
       diff_short, diff_long, failed = format_diff(results, log_paths, 'master')
 
       if "CI" in os.environ:
-        if not CHESTNUT:
-          comment_replay_report(log_msgs, cmp_log, log_msgs)
+        comment_replay_report(log_msgs, cmp_log, log_msgs)
         failed = False
         print(diff_long)
       print('-------------\n'*5)

openpilot/selfdrive/test/process_replay/process_replay.py

@@ -17,7 +17,7 @@ from openpilot.common.hardware.hw import Paths
 import openpilot.cereal.messaging as messaging
 from opendbc.car.structs import car
 from openpilot.cereal.services import SERVICE_LIST
-from msgq.visionipc import VisionIpcClient, VisionIpcServer, get_endpoint_name as vipc_get_endpoint_name
+from msgq.visionipc import VisionIpcServer, get_endpoint_name as vipc_get_endpoint_name
 from opendbc.car.can_definitions import CanData
 from opendbc.car.car_helpers import get_car, interfaces
 from openpilot.common.params import Params
@@ -210,7 +210,6 @@ class ProcessContainer:
         stride, y_height, _, yuv_size = get_nv12_info(frame_size[0], frame_size[1])
         vipc_server.create_buffers_with_sizes(meta.stream, 2, frame_size[0], frame_size[1], yuv_size, stride, stride * y_height)
     vipc_server.start_listener()
-    VisionIpcClient.available_streams("camerad", block=True)
 
     self.vipc_server = vipc_server
     self.cfg.vision_pubs = [meta.camera_state for meta in streams_metas if meta.camera_state in self.cfg.vision_pubs]

openpilot/selfdrive/test/scons_build_test.sh

@@ -3,7 +3,7 @@ set -e
 
 SCRIPT_DIR=$(dirname "$0")
 BASEDIR=$(realpath "$SCRIPT_DIR/../../../")
-cd "$BASEDIR"
+cd $BASEDIR
 
 # tests that our build system's dependencies are configured properly,
 # needs a machine with lots of cores
@@ -11,7 +11,7 @@ cd "$BASEDIR"
 # helpful commands:
 # scons -Q --tree=derived
 
-cd "$BASEDIR/opendbc_repo/"
+cd $BASEDIR/opendbc_repo/
 scons --clean
 scons --no-cache --random
 if ! scons -q; then

openpilot/selfdrive/test/setup_device_ci.sh

@@ -29,7 +29,7 @@ if [ -d /data/safe_staging/ ]; then
 fi
 
 CONTINUE_PATH="/data/continue.sh"
-tee "$CONTINUE_PATH" << EOF
+tee $CONTINUE_PATH << EOF
 #!/usr/bin/env bash
 
 sudo abctl --set_success
@@ -54,18 +54,13 @@ done
 
 sleep infinity
 EOF
-chmod +x "$CONTINUE_PATH"
+chmod +x $CONTINUE_PATH
 
 export GIT_LFS_SKIP_SMUDGE=1
 pull_lfs() {
-  if [ -n "${CHESTNUT:-}" ]
-  then
-    git lfs pull --exclude=''
-    return
-  fi
-
-  # Keep the precompiled big model as a pointer on devices without Chestnut.
-  LFS_EXCLUDE="openpilot/selfdrive/modeld/models/big_driving_tinygrad.pkl"
+  # The big driving model is not used on these devices yet. Keep its pointer in
+  # the worktree, but don't download or copy the 1.8 GB LFS object.
+  LFS_EXCLUDE="openpilot/selfdrive/modeld/models/big_driving_supercombo.onnx"
 
   git config --local lfs.fetchexclude "$LFS_EXCLUDE"
   git lfs pull --exclude="$LFS_EXCLUDE"
@@ -86,16 +81,16 @@ pull_lfs() {
 safe_checkout() {
   # completely clean TEST_DIR
 
-  cd "$SOURCE_DIR"
+  cd $SOURCE_DIR
 
   # cleanup orphaned locks
   find .git -type f -name "*.lock" -exec rm {} +
 
   git reset --hard
-  git fetch --no-tags --no-recurse-submodules -j4 --verbose --depth 1 origin "$GIT_COMMIT"
+  git fetch --no-tags --no-recurse-submodules -j4 --verbose --depth 1 origin $GIT_COMMIT
   find . -maxdepth 1 -not -path './.git' -not -name '.' -not -name '..' -exec rm -rf '{}' \;
-  git reset --hard "$GIT_COMMIT"
-  git checkout "$GIT_COMMIT"
+  git reset --hard $GIT_COMMIT
+  git checkout $GIT_COMMIT
   git clean -xdff
   git submodule sync
   git submodule foreach --recursive "git reset --hard && git clean -xdff"
@@ -105,22 +100,22 @@ safe_checkout() {
   pull_lfs
 
   echo "git checkout done, t=$SECONDS"
-  du -hs "$SOURCE_DIR" "$SOURCE_DIR/.git"
+  du -hs $SOURCE_DIR $SOURCE_DIR/.git
 
-  rsync -a --delete "$SOURCE_DIR" "$TEST_DIR"
+  rsync -a --delete $SOURCE_DIR $TEST_DIR
 }
 
 unsafe_checkout() {( set -e
   # checkout directly in test dir, leave old build products
 
-  cd "$TEST_DIR"
+  cd $TEST_DIR
 
   # cleanup orphaned locks
   find .git -type f -name "*.lock" -exec rm {} +
 
-  git fetch --no-tags --no-recurse-submodules -j8 --verbose --depth 1 origin "$GIT_COMMIT"
-  git checkout --force --no-recurse-submodules "$GIT_COMMIT"
-  git reset --hard "$GIT_COMMIT"
+  git fetch --no-tags --no-recurse-submodules -j8 --verbose --depth 1 origin $GIT_COMMIT
+  git checkout --force --no-recurse-submodules $GIT_COMMIT
+  git reset --hard $GIT_COMMIT
   git clean -dff
   git submodule sync
   git submodule foreach --recursive "git reset --hard && git clean -df"
@@ -134,7 +129,7 @@ export GIT_PACK_THREADS=8
 
 # set up environment
 if [ ! -d "$SOURCE_DIR" ]; then
-  git clone https://github.com/commaai/openpilot.git "$SOURCE_DIR"
+  git clone https://github.com/commaai/openpilot.git $SOURCE_DIR
 fi
 
 if [ ! -z "$UNSAFE" ]; then
@@ -151,7 +146,7 @@ else
 fi
 
 # submodule package symlinks for PYTHONPATH imports on device (same as launch_chffrplus.sh)
-cd "$TEST_DIR"
+cd $TEST_DIR
 ln -sfn msgq_repo/msgq msgq
 ln -sfn opendbc_repo/opendbc opendbc
 ln -sfn rednose_repo/rednose rednose

openpilot/selfdrive/test/test_onroad.py

@@ -20,12 +20,8 @@ from openpilot.common.basedir import BASEDIR
 from openpilot.common.timeout import Timeout
 from openpilot.common.params import Params
 from openpilot.selfdrive.selfdrived.events import EVENTS, ET
-from openpilot.selfdrive.test.helpers import set_params_enabled, release_only, processes_context, log_collector
-from openpilot.common.hardware import HARDWARE
+from openpilot.selfdrive.test.helpers import set_params_enabled, release_only
 from openpilot.common.hardware.hw import Paths
-from openpilot.common.mock import mock_messages
-from opendbc.car.car_helpers import get_demo_car_params
-from openpilot.selfdrive.modeld.helpers import chestnut_present, chestnut_compiled
 from openpilot.tools.lib.logreader import LogReader
 from openpilot.tools.lib.log_time_series import msgs_to_time_series
 
@@ -461,43 +457,5 @@ class TestOnroad(OpenpilotTestCase):
            f"Not engageable for whole segment:\n- selfdriveState.engageable: {Counter(eng)}\n- No entry events: {no_entries}"
 
 
-@unittest.skipUnless(HARDWARE.get_device_type() == "mici", "requires MICI")
-class TestChestnutOnroad(OpenpilotTestCase):
-  COMMA_HARDWARE_TEST = True
-
-  @mock_messages(['deviceMotion'])
-  def test_camera_models(self, subtests):
-    assert chestnut_present() and chestnut_compiled()
-    Params().put("CarParams", get_demo_car_params().to_bytes(), block=True)
-    services = ['narrowRoadCameraState', 'wideRoadCameraState', 'cabinCameraState', 'modelV2', 'driverStateV2']
-    sm = messaging.SubMaster(services)
-    pm = messaging.PubMaster(['deviceState'])
-    device_state = messaging.new_message('deviceState')
-    device_state.deviceState.deviceType = HARDWARE.get_device_type()
-    device_state_bytes = device_state.to_bytes()
-    with processes_context(['camerad', 'calibrationd', 'modeld', 'dmonitoringmodeld']):
-      with Timeout(60, "camera models didn't start"):
-        while not all(sm.seen.values()) or not sm.valid['modelV2']:
-          pm.send('deviceState', device_state_bytes)
-          sm.update(1000)
-      with log_collector(services) as (logs, _):
-        time.sleep(TEST_DURATION)
-
-    msgs = {s: [m for m in logs if m.which() == s] for s in services}
-    for service, messages in msgs.items():
-      with subtests.test(service=service):
-        expected = TEST_DURATION * SERVICE_LIST[service].frequency
-        assert np.isclose(len(messages), expected, rtol=0.05, atol=2), f"{service}: expected {expected}, got {len(messages)}"
-        assert all(m.valid for m in messages)
-        frame_ids = [getattr(m, service).frameId for m in messages]
-        assert np.all(np.diff(frame_ids) > 0), f"{service}: repeated or reordered frames"
-
-    camera_frames = {m.narrowRoadCameraState.frameId for m in msgs['narrowRoadCameraState']}
-    model_frames = {m.modelV2.frameId for m in msgs['modelV2']}
-    assert len(camera_frames & model_frames) >= TEST_DURATION * SERVICE_LIST['modelV2'].frequency * 0.9
-    assert all(m.modelV2.big for m in msgs['modelV2']), "Chestnut fell back to the small model"
-    assert all(np.isfinite(m.modelV2.position.x).all() for m in msgs['modelV2'])
-
-
 if __name__ == "__main__":
   unittest.main()

openpilot/selfdrive/test/test_power_draw.py

@@ -5,8 +5,6 @@ import time
 import unittest
 import numpy as np
 from dataclasses import dataclass
-from panda import Panda
-from openpilot.common.hardware import HARDWARE
 from openpilot.common.test import OpenpilotTestCase
 from openpilot.common.utils import tabulate
 
@@ -16,14 +14,11 @@ from opendbc.car.car_helpers import get_demo_car_params
 from openpilot.common.mock import mock_messages
 from openpilot.common.params import Params
 from openpilot.common.hardware.comma.power_monitor import get_power
-from openpilot.selfdrive.modeld.helpers import chestnut_present
 from openpilot.system.manager.process_config import managed_processes
 from openpilot.system.manager.manager import manager_cleanup
 
 SAMPLE_TIME = 2       # seconds to sample power
 MAX_WARMUP_TIME = 30  # seconds to wait for SAMPLE_TIME consecutive valid samples
-MICI = HARDWARE.get_device_type() == "mici"
-CHESTNUT = chestnut_present()
 
 @dataclass
 class Proc:
@@ -38,10 +33,9 @@ class Proc:
     return '+'.join(self.procs)
 
 
-# MICI readings exclude the separately powered Chestnut GPU.
 PROCS = [
-  Proc(['camerad'], 0.85 if MICI else 1.65, atol=0.4, msgs=['narrowRoadCameraState', 'wideRoadCameraState', 'cabinCameraState']),
-  Proc(['modeld'], 0.45 if MICI and CHESTNUT else 1.5, atol=0.2, msgs=['modelV2']),
+  Proc(['camerad'], 1.65, atol=0.4, msgs=['narrowRoadCameraState', 'wideRoadCameraState', 'cabinCameraState']),
+  Proc(['modeld'], 1.5, atol=0.2, msgs=['modelV2']),
   Proc(['dmonitoringmodeld'], 0.65, atol=0.35, msgs=['driverStateV2']),
   Proc(['encoderd'], 0.23, msgs=[]),
 ]
@@ -52,13 +46,6 @@ class TestPowerDraw(OpenpilotTestCase):
 
   def setup_method(self):
     Params().put("CarParams", get_demo_car_params().to_bytes(), block=True)
-    self.panda = None
-    if MICI:
-      HARDWARE.reset_internal_panda()
-      self.addCleanup(HARDWARE.reset_internal_panda)
-      Panda.wait_for_panda(None, 30)
-      self.panda = Panda(cli=False)
-      self.addCleanup(self.panda.close)
 
   def teardown_method(self):
     manager_cleanup()
@@ -91,7 +78,7 @@ class TestPowerDraw(OpenpilotTestCase):
     start_time = time.monotonic()
 
     while (time.monotonic() - start_time) < MAX_WARMUP_TIME:
-      power = get_power(1, self.panda)
+      power = get_power(1)
       iteration_msg_counts = {}
       for msg,sock in socks.items():
         iteration_msg_counts[msg] = len(messaging.drain_sock_raw(sock))
@@ -110,7 +97,7 @@ class TestPowerDraw(OpenpilotTestCase):
 
   @mock_messages(['deviceMotion'])
   def test_camera_procs(self, subtests):
-    baseline = get_power(panda=self.panda)
+    baseline = get_power()
 
     prev = baseline
     used = {}

openpilot/selfdrive/ui/lib/prime_state.py

@@ -101,10 +101,6 @@ class PrimeState:
     with self._lock:
       return bool(self.prime_type > PrimeType.NONE)
 
-  def is_full_prime(self) -> bool:
-    with self._lock:
-      return self.prime_type > PrimeType.NONE and self.prime_type != PrimeType.LITE
-
   def is_paired(self) -> bool:
     with self._lock:
       return self.prime_type > PrimeType.UNPAIRED

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

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import datetime
 import math
 import time
@@ -41,13 +39,16 @@ class AlertsPill(Widget):
     self.set_rect(rl.Rectangle(0, 0, 104, 52))
 
     self._pill_bg_txt = gui_app.texture("icons_mici/alerts_pill.png", 104, 52)
+    self._icon_red = gui_app.texture("icons_mici/offroad_alerts/red_warning.png", 36, 36)
+    self._icon_orange = gui_app.texture("icons_mici/offroad_alerts/orange_warning.png", 36, 36)
+    self._icon_green = gui_app.texture("icons_mici/offroad_alerts/green_wheel.png", 36, 36)
     self._alert_count_callback: Callable[[], int] | None = None
-    self._alert_icon_callback: Callable[[], rl.Texture | None] | None = None
+    self._max_severity_callback: Callable[[], int | None] | None = None
 
   def set_alert_count_callback(self, callback: Callable[[], int] | None,
-                               icon_callback: Callable[[], rl.Texture | None] | None = None):
+                               severity_callback: Callable[[], int | None] | None = None):
     self._alert_count_callback = callback
-    self._alert_icon_callback = icon_callback
+    self._max_severity_callback = severity_callback
 
   def _render(self, _):
     alert_count = self._alert_count_callback() if self._alert_count_callback else 0
@@ -55,12 +56,17 @@ class AlertsPill(Widget):
       pill_w, pill_h = self._pill_bg_txt.width, self._pill_bg_txt.height
       rl.draw_texture_ex(self._pill_bg_txt, rl.Vector2(self.rect.x, self.rect.y), 0.0, 1.0, rl.WHITE)
 
-      warning_txt = self._alert_icon_callback() if self._alert_icon_callback else None
-      if warning_txt is not None:
-        scale = 36 / max(warning_txt.width, warning_txt.height)
-        warn_x = self.rect.x + self.ICON_OFFSET
-        warn_y = self.rect.y + (pill_h - warning_txt.height * scale) / 2
-        rl.draw_texture_ex(warning_txt, rl.Vector2(warn_x, warn_y), 0.0, scale, rl.WHITE)
+      severity = self._max_severity_callback() if self._max_severity_callback else None
+      if severity == -1:
+        warning_txt = self._icon_green
+      elif severity is not None and severity > 0:
+        warning_txt = self._icon_red
+      else:
+        warning_txt = self._icon_orange
+
+      warn_x = self.rect.x + self.ICON_OFFSET
+      warn_y = self.rect.y + (pill_h - warning_txt.height) / 2
+      rl.draw_texture_ex(warning_txt, rl.Vector2(warn_x, warn_y), 0.0, 1.0, rl.WHITE)
 
       count_rect = rl.Rectangle(self.rect.x + self.COUNT_OFFSET, self.rect.y, pill_w - self.COUNT_OFFSET, pill_h)
       gui_label(count_rect, str(alert_count), font_size=36,
@@ -71,21 +77,21 @@ class AlertsPill(Widget):
 class NetworkIcon(Widget):
   def __init__(self):
     super().__init__()
-    self.set_rect(rl.Rectangle(0, 0, 60, 47))  # max size of all icons
+    self.set_rect(rl.Rectangle(0, 0, 54, 44))  # max size of all icons
     self._net_type = NetworkType.none
     self._net_strength = 0
 
-    self._wifi_slash_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_slash.png", 54, 47)
-    self._wifi_none_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_none.png", 54, 40)
-    self._wifi_low_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_low.png", 54, 40)
-    self._wifi_medium_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_medium.png", 54, 40)
-    self._wifi_full_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_full.png", 54, 40)
+    self._wifi_slash_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_slash.png", 50, 44)
+    self._wifi_none_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_none.png", 50, 37)
+    self._wifi_low_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_low.png", 50, 37)
+    self._wifi_medium_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_medium.png", 50, 37)
+    self._wifi_full_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_full.png", 50, 37)
 
-    self._cell_none_txt = gui_app.texture("icons_mici/settings/network/cell_strength_none.png", 60, 40)
-    self._cell_low_txt = gui_app.texture("icons_mici/settings/network/cell_strength_low.png", 60, 40)
-    self._cell_medium_txt = gui_app.texture("icons_mici/settings/network/cell_strength_medium.png", 60, 40)
-    self._cell_high_txt = gui_app.texture("icons_mici/settings/network/cell_strength_high.png", 60, 40)
-    self._cell_full_txt = gui_app.texture("icons_mici/settings/network/cell_strength_full.png", 60, 40)
+    self._cell_none_txt = gui_app.texture("icons_mici/settings/network/cell_strength_none.png", 54, 36)
+    self._cell_low_txt = gui_app.texture("icons_mici/settings/network/cell_strength_low.png", 54, 36)
+    self._cell_medium_txt = gui_app.texture("icons_mici/settings/network/cell_strength_medium.png", 54, 36)
+    self._cell_high_txt = gui_app.texture("icons_mici/settings/network/cell_strength_high.png", 54, 36)
+    self._cell_full_txt = gui_app.texture("icons_mici/settings/network/cell_strength_full.png", 54, 36)
 
   def _update_state(self):
     device_state = ui_state.sm['deviceState']
@@ -135,7 +141,7 @@ class MiciHomeLayout(Widget):
 
     self._experimental_icon = IconWidget("icons_mici/experimental_mode.png", (48, 48))
     self._usb_icon = IconWidget("icons_mici/usb.png", (62, 40))
-    self._chestnut_icon = IconWidget("icons_mici/chestnut_green.png", (54, 40))
+    self._chestnut_icon = IconWidget("icons_mici/chestnut_green.png", (68, 40))
     self._chestnut_loading_icon = IconWidget("icons_mici/chestnut.png", (68, 40))
     self._chestnut_failed_icon = IconWidget("icons_mici/chestnut_orange.png", (68, 40))
     self._mic_icon = IconWidget("icons_mici/microphone.png", (32, 46))
@@ -181,11 +187,11 @@ class MiciHomeLayout(Widget):
 
   def set_callbacks(self, on_settings: Callable | None = None, on_alerts: Callable | None = None,
                     alert_count_callback: Callable[[], int] | None = None,
-                    alert_icon_callback: Callable[[], rl.Texture | None] | None = None):
+                    max_severity_callback: Callable[[], int | None] | None = None):
     self._on_settings_click = on_settings
     self._on_alerts_click = on_alerts
     self._alert_count_callback = alert_count_callback
-    self._alerts_pill.set_alert_count_callback(alert_count_callback, alert_icon_callback)
+    self._alerts_pill.set_alert_count_callback(alert_count_callback, max_severity_callback)
 
   def _handle_mouse_release(self, mouse_pos: MousePos):
     if not self._did_long_press:

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

@@ -77,7 +77,7 @@ class MiciMainLayout(Scroller):
       on_settings=lambda: gui_app.push_widget(self._settings_layout),
       on_alerts=lambda: self._scroll_to(self._alerts_layout),
       alert_count_callback=self._alerts_layout.active_alerts,
-      alert_icon_callback=self._alerts_layout.highest_severity_icon,
+      max_severity_callback=self._alerts_layout.max_severity,
     )
     for layout in (self._car_onroad_layout, self._body_onroad_layout):
       layout.set_click_callback(lambda: self._scroll_to(self._home_layout))

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

@@ -1,5 +1,3 @@
-from __future__ import annotations
-
 import pyray as rl
 import re
 import threading
@@ -31,7 +29,6 @@ class AlertData:
   text: str
   severity: int
   visible: bool = False
-  icon: str | None = None
 
 
 class AlertItem(Widget):
@@ -59,20 +56,10 @@ class AlertItem(Widget):
     self._bg_big = gui_app.texture("icons_mici/offroad_alerts/big_alert.png", self.ALERT_WIDTH, self.ALERT_HEIGHT_BIG)
     self._bg_big_pressed = gui_app.texture("icons_mici/offroad_alerts/big_alert_pressed.png", self.ALERT_WIDTH, self.ALERT_HEIGHT_BIG)
 
-    # Load alert icons
+    # Load warning icons
     self._icon_orange = gui_app.texture("icons_mici/offroad_alerts/orange_warning.png", self.ICON_SIZE, self.ICON_SIZE)
     self._icon_red = gui_app.texture("icons_mici/offroad_alerts/red_warning.png", self.ICON_SIZE, self.ICON_SIZE)
     self._icon_green = gui_app.texture("icons_mici/offroad_alerts/green_wheel.png", self.ICON_SIZE, self.ICON_SIZE)
-    self._custom_icon = gui_app.texture(alert_data.icon, self.ICON_SIZE, self.ICON_SIZE) if alert_data.icon else None
-
-    if self._custom_icon is not None:
-      self._icon = self._custom_icon
-    elif alert_data.severity == -1:
-      self._icon = self._icon_green
-    elif alert_data.severity > 0:
-      self._icon = self._icon_red
-    else:
-      self._icon = self._icon_orange
 
     self._title_label = UnifiedLabel(text="", font_size=32, font_weight=FontWeight.SEMI_BOLD, text_color=self.TEXT_COLOR,
                                      alignment=TextAlignment.LEFT,
@@ -88,10 +75,6 @@ class AlertItem(Widget):
 
     self._update_content()
 
-  @property
-  def icon(self) -> rl.Texture:
-    return self._icon
-
   def _split_text(self, text: str) -> tuple[str, str]:
     """Split text into title (first sentence) and body (remaining text)."""
     # Find the end of the first sentence (period, exclamation, or question mark followed by space or end)
@@ -193,9 +176,16 @@ class AlertItem(Widget):
       self._body_label.render(body_rect)
 
     # Draw warning icon on the right side
+    # Use green icon for update alerts (severity = -1), red for high severity, orange for low severity
+    if self.alert_data.severity == -1:
+      icon_texture = self._icon_green
+    elif self.alert_data.severity > 0:
+      icon_texture = self._icon_red
+    else:
+      icon_texture = self._icon_orange
     icon_x = self._rect.x + self.ALERT_WIDTH - self.ALERT_PADDING - self.ICON_SIZE
     icon_y = self._rect.y + self.ALERT_PADDING
-    rl.draw_texture_ex(self._icon, rl.Vector2(icon_x, icon_y), 0.0, 1.0, rl.WHITE)
+    rl.draw_texture_ex(icon_texture, rl.Vector2(icon_x, icon_y), 0.0, 1.0, rl.WHITE)
 
 
 class MiciOffroadAlerts(Scroller):
@@ -224,10 +214,8 @@ class MiciOffroadAlerts(Scroller):
   def active_alerts(self) -> int:
     return sum(alert.visible for alert in self.sorted_alerts)
 
-  def highest_severity_icon(self) -> rl.Texture | None:
-    item = max((item for item in self.alert_items if item.alert_data.visible),
-               key=lambda item: (item.alert_data.severity, bool(item.alert_data.icon)), default=None)
-    return item.icon if item is not None else None
+  def max_severity(self) -> int | None:
+    return max((alert.severity for alert in self.sorted_alerts if alert.visible), default=None)
 
   def scrolling(self):
     return self._scroller.scroll_panel.is_touch_valid()
@@ -247,7 +235,7 @@ class MiciOffroadAlerts(Scroller):
     # Add regular alerts sorted by severity
     for key, config in sorted(OFFROAD_ALERTS.items(), key=lambda x: x[1].get("severity", 0), reverse=True):
       severity = config.get("severity", 0)
-      alert_data = AlertData(key=key, text="", severity=severity, icon=config.get("icon"))
+      alert_data = AlertData(key=key, text="", severity=severity)
       self.sorted_alerts.append(alert_data)
 
       # Create alert item widget

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

@@ -23,6 +23,8 @@ class AlphaLongConfirmPage(NavScroller):
       GreyBigButton("", "WARNING: alpha longitudinal control may disable Automatic Emergency Braking (AEB)"),
       GreyBigButton("", "On this car, openpilot defaults to the stock system's built-in ACC."),
       GreyBigButton("", "Enabling this will switch to openpilot longitudinal control."),
+      GreyBigButton("", "Using Experimental mode is recommended with openpilot longitudinal control alpha."),
+      GreyBigButton("", "Changing this setting will restart openpilot if the car is powered on."),
       accept,
     ])
 
@@ -61,31 +63,28 @@ class DeveloperLayoutMici(NavScroller):
 
     txt_ssh = gui_app.texture("icons_mici/settings/developer/ssh.png", 56, 64)
     github_username = ui_state.params.get("GithubUsername") or ""
-    self._ssh_keys_btn = BigButton("SSH keys", "Not set" if not github_username else github_username, icon=txt_ssh,
-                                   description="Grant SSH access to all public keys in your GitHub settings. Only enter your own username.")
+    self._ssh_keys_btn = BigButton("SSH keys", "Not set" if not github_username else github_username, icon=txt_ssh)
     self._ssh_keys_btn.set_click_callback(ssh_keys_callback)
 
     # adb, ssh, ssh keys, debug mode, joystick debug mode, longitudinal maneuver mode, ip address
     # ******** Main Scroller ********
-    self._adb_toggle = BigCircleParamControl(gui_app.texture("icons_mici/adb_short.png", 82, 82), "AdbEnabled", icon_offset=(0, 12),
-                                             description="Use Android Debug Bridge (ADB) over USB or the network.", title="enable ADB")
-    self._ssh_toggle = BigCircleParamControl(gui_app.texture("icons_mici/ssh_short.png", 82, 82), "SshEnabled", icon_offset=(0, 12),
-                                             description="Access the device remotely using your SSH keys.", title="enable SSH")
-    self._joystick_toggle = BigToggle("joystick debug\nmode", initial_state=ui_state.params.get_bool("JoystickDebugMode"),
-                                      toggle_callback=self._on_joystick_debug_mode, description="Control the car with a joystick for debugging.")
-    self._long_maneuver_toggle = BigToggle("longitudinal maneuver mode", initial_state=ui_state.params.get_bool("LongitudinalManeuverMode"),
-                                           toggle_callback=self._on_long_maneuver_mode,
-                                           description="Run longitudinal maneuvers for testing gas and brake control.")
-    self._lat_maneuver_toggle = BigToggle("lateral maneuver mode", initial_state=ui_state.params.get_bool("LateralManeuverMode"),
-                                          toggle_callback=self._on_lat_maneuver_mode,
-                                          description="Run lateral maneuvers for testing steering control.")
-    self._alpha_long_toggle = BigToggle("alpha longitudinal", initial_state=ui_state.params.get_bool("AlphaLongitudinalEnabled"),
-                                        toggle_callback=self._on_alpha_long_enabled,
-                                        description="Use alpha openpilot longitudinal control instead of stock ACC. This may disable Automatic Emergency " +
-                                                    "Braking (AEB).")
+    self._adb_toggle = BigCircleParamControl(gui_app.texture("icons_mici/adb_short.png", 82, 82), "AdbEnabled", icon_offset=(0, 12))
+    self._ssh_toggle = BigCircleParamControl(gui_app.texture("icons_mici/ssh_short.png", 82, 82), "SshEnabled", icon_offset=(0, 12))
+    self._joystick_toggle = BigToggle("joystick debug mode",
+                                      initial_state=ui_state.params.get_bool("JoystickDebugMode"),
+                                      toggle_callback=self._on_joystick_debug_mode)
+    self._long_maneuver_toggle = BigToggle("longitudinal maneuver mode",
+                                           initial_state=ui_state.params.get_bool("LongitudinalManeuverMode"),
+                                           toggle_callback=self._on_long_maneuver_mode)
+    self._lat_maneuver_toggle = BigToggle("lateral maneuver mode",
+                                          initial_state=ui_state.params.get_bool("LateralManeuverMode"),
+                                          toggle_callback=self._on_lat_maneuver_mode)
+    self._alpha_long_toggle = BigToggle("alpha longitudinal",
+                                        initial_state=ui_state.params.get_bool("AlphaLongitudinalEnabled"),
+                                        toggle_callback=self._on_alpha_long_enabled)
     self._debug_mode_toggle = BigParamControl("ui debug mode", "ShowDebugInfo",
-                                              toggle_callback=lambda checked: (gui_app.set_show_touches(checked), gui_app.set_show_fps(checked)),
-                                              description="Show touch locations and the UI frame rate.")
+                                              toggle_callback=lambda checked: (gui_app.set_show_touches(checked),
+                                                                               gui_app.set_show_fps(checked)))
 
     self._scroller.add_widgets([
       self._adb_toggle,

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

@@ -1,7 +1,6 @@
 import os
 import pyray as rl
 from collections.abc import Callable
-from typing import Union
 
 from openpilot.common.basedir import BASEDIR
 from openpilot.common.params import Params
@@ -78,16 +77,15 @@ def _engaged_confirmation_click(callback: Callable, action_text: str, icon: rl.T
 
 class EngagedConfirmationCircleButton(BigCircleButton):
   def __init__(self, title: str, icon: rl.Texture, callback: Callable[[], None], exit_on_confirm: bool = True,
-               red: bool = False, icon_offset: tuple[int, int] = (0, 0), *, description: str = ""):
-    super().__init__(icon, red, icon_offset, description=description, title=title)
+               red: bool = False, icon_offset: tuple[int, int] = (0, 0)):
+    super().__init__(icon, red, icon_offset)
     self.set_click_callback(lambda: _engaged_confirmation_click(callback, title, icon, exit_on_confirm=exit_on_confirm, red=red))
 
 
 class EngagedConfirmationButton(BigButton):
   def __init__(self, text: str, action_text: str, icon: rl.Texture, callback: Callable[[], None],
-               exit_on_confirm: bool = True, red: bool = False, *, description: str = "",
-               description_icon: Union[rl.Texture, None] = None):
-    super().__init__(text, "", icon, description=description, description_icon=description_icon)
+               exit_on_confirm: bool = True, red: bool = False):
+    super().__init__(text, "", icon)
     self.set_click_callback(lambda: _engaged_confirmation_click(callback, action_text, icon, exit_on_confirm=exit_on_confirm, red=red))
 
 
@@ -179,9 +177,7 @@ class DeviceLayoutMici(NavScroller):
       params.put_bool("OnroadCycleRequested", True, block=True)
 
     reset_calibration_btn = EngagedConfirmationButton("reset calibration", "reset", gui_app.texture("icons_mici/settings/device/lkas.png", 122, 64),
-                                                      reset_calibration_callback,
-                                                      description="Mount the device within 4° left or right and 5° up or 9° down. openpilot calibrates " +
-                                                                  "continuously; resetting is rarely needed. Resetting clears learned calibration.")
+                                                      reset_calibration_callback)
 
     reboot_btn = EngagedConfirmationCircleButton("reboot", gui_app.texture("icons_mici/settings/device/reboot.png", 64, 70),
                                                  reboot_callback, exit_on_confirm=False)
@@ -193,8 +189,7 @@ class DeviceLayoutMici(NavScroller):
     regulatory_btn = BigButton("regulatory info", "", gui_app.texture("icons_mici/settings/device/info.png", 64, 64))
     regulatory_btn.set_click_callback(self._on_regulatory)
 
-    cabin_cam_btn = BigButton("driver\ncamera preview", "", gui_app.texture("icons_mici/settings/device/cameras.png", 64, 64),
-                              description="Preview the cabin camera to check driver monitoring visibility. The vehicle must be off.")
+    cabin_cam_btn = BigButton("driver\ncamera preview", "", gui_app.texture("icons_mici/settings/device/cameras.png", 64, 64))
     cabin_cam_btn.set_click_callback(lambda: gui_app.push_widget(CabinCameraDialog()))
     cabin_cam_btn.set_enabled(lambda: ui_state.is_offroad())
 

openpilot/selfdrive/ui/mici/layouts/settings/network/__init__.py

@@ -1,60 +1,13 @@
 import pyray as rl
 
-from openpilot.cereal import log
 from openpilot.selfdrive.ui.mici.layouts.settings.network.wifi_ui import WifiIcon
 from openpilot.selfdrive.ui.mici.widgets.button import BigButton
-from openpilot.common.hardware import HARDWARE
 from openpilot.system.ui.lib.application import gui_app
-from openpilot.system.ui.lib.cellular_manager import CellularManager
 from openpilot.system.ui.lib.wifi_manager import WifiManager, ConnectStatus, SecurityType, normalize_ssid
 
-NetworkStrength = log.DeviceState.NetworkStrength
-NetworkType = log.DeviceState.NetworkType
-
-
-class EsimNetworkButton(BigButton):
-  def __init__(self, cellular_manager: CellularManager, *, description: str = ""):
-    self._cellular_manager = cellular_manager
-    self._cell_icons = {
-      NetworkStrength.unknown: gui_app.texture("icons_mici/settings/network/cell_strength_none.png", 64, 47),
-      NetworkStrength.poor: gui_app.texture("icons_mici/settings/network/cell_strength_low.png", 64, 47),
-      NetworkStrength.moderate: gui_app.texture("icons_mici/settings/network/cell_strength_medium.png", 64, 47),
-      NetworkStrength.good: gui_app.texture("icons_mici/settings/network/cell_strength_high.png", 64, 47),
-      NetworkStrength.great: gui_app.texture("icons_mici/settings/network/cell_strength_full.png", 64, 47),
-    }
-    super().__init__("esim", "loading...", self._cell_icons[NetworkStrength.unknown], scroll=True, description=description)
-
-  def _update_state(self):
-    super()._update_state()
-    self.set_enabled(self._cellular_manager.is_euicc is not False)
-    text, value, icon = self._compute_state()
-    self.set_text(text)
-    self.set_value(value)
-    self.set_icon(icon)
-
-  def _compute_state(self):
-    cm = self._cellular_manager
-    none_icon = self._cell_icons[NetworkStrength.unknown]
-    ip = cm.modem_state.get("ip_address") or "connecting..."
-    if cm.is_euicc is False:
-      iccid = cm.modem_state.get("iccid") or ""
-      if not iccid:
-        return "sim", "no sim", none_icon
-      return f"sim (...{iccid[-4:]})", ip, self._cell_icon()
-
-    active = cm.active_profile
-    if active is None:
-      return "esim", "loading...", none_icon
-    return active.display_name, ip, self._cell_icon()
-
-  def _cell_icon(self):
-    # read directly from HARDWARE so it reflects modem state even when wifi is the active connection
-    strength = HARDWARE.get_network_strength(NetworkType.cell4G)
-    return self._cell_icons.get(strength, self._cell_icons[NetworkStrength.unknown])
-
 
 class WifiNetworkButton(BigButton):
-  def __init__(self, wifi_manager: WifiManager, *, description: str = ""):
+  def __init__(self, wifi_manager: WifiManager):
     self._wifi_manager = wifi_manager
     self._lock_txt = gui_app.texture("icons_mici/settings/network/new/lock.png", 28, 36)
     self._draw_lock = False
@@ -64,7 +17,7 @@ class WifiNetworkButton(BigButton):
     self._wifi_medium_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_medium.png", 64, 47)
     self._wifi_full_txt = gui_app.texture("icons_mici/settings/network/wifi_strength_full.png", 64, 47)
 
-    super().__init__("wi-fi", "not connected", self._wifi_slash_txt, scroll=True, description=description)
+    super().__init__("wi-fi", "not connected", self._wifi_slash_txt, scroll=True)
 
   def _update_state(self):
     super()._update_state()

openpilot/selfdrive/ui/mici/layouts/settings/network/esim_ui.py

@@ -1,412 +0,0 @@
-import threading
-
-import numpy as np
-import pyray as rl
-from collections.abc import Callable
-from openpilot.cereal import log
-from openpilot.cereal.visionipc import VisionStreamType
-
-from openpilot.common import qrcode
-from openpilot.common.swaglog import cloudlog
-from openpilot.selfdrive.ui.mici.onroad.cabin_camera_dialog import CabinCameraView
-from openpilot.selfdrive.ui.ui_state import ui_state
-from openpilot.system.ui.lib.multilang import tr
-from openpilot.system.ui.widgets.nav_widget import NavWidget
-
-from openpilot.selfdrive.ui.mici.widgets.button import BigButton, LABEL_COLOR
-from openpilot.selfdrive.ui.mici.widgets.dialog import BigDialog, BigInputDialog, BigConfirmationDialog
-from openpilot.common.esim.base import Profile
-from openpilot.common.esim.lpa import parse_lpa_activation_code
-from openpilot.system.ui.lib.application import DEFAULT_TEXT_COLOR, FontWeight, MousePos, TextAlignment, gui_app
-from openpilot.system.ui.lib.cellular_manager import CellularManager
-from openpilot.system.ui.widgets import Widget
-from openpilot.system.ui.widgets.label import UnifiedLabel, gui_label
-from openpilot.system.ui.widgets.scroller import NavRawScrollPanel, NavScroller
-
-
-class ProfileActionButton(Widget):
-  SIZE = 68
-  MARGIN = 10
-  HORIZONTAL_MARGIN = 4
-
-  def __init__(self, callback: Callable, delete: bool = False):
-    super().__init__()
-    self.set_click_callback(callback)
-    self._delete = delete
-    self._trash_txt = gui_app.texture("icons_mici/settings/network/new/trash.png", 25, 30) if delete else None
-
-    self._bg_txt = gui_app.texture("icons_mici/buttons/button_circle.png", self.SIZE, self.SIZE)
-    self._bg_pressed_txt = gui_app.texture("icons_mici/buttons/button_circle_pressed.png", self.SIZE, self.SIZE)
-    self.set_rect(rl.Rectangle(0, 0, self.SIZE + self.HORIZONTAL_MARGIN * 2, self.SIZE + self.MARGIN * 2))
-
-  def _render(self, _):
-    bg_txt = self._bg_pressed_txt if self.is_pressed else self._bg_txt
-    rl.draw_texture_ex(bg_txt, (self._rect.x + (self._rect.width - self._bg_txt.width) / 2,
-                                self._rect.y + (self._rect.height - self._bg_txt.height) / 2), 0, 1.0, rl.WHITE)
-    color = rl.Color(255, 105, 115, 255) if self._delete else DEFAULT_TEXT_COLOR
-    if not self.enabled:
-      color = rl.Color(color.r, color.g, color.b, 90)
-    if self._trash_txt:
-      rl.draw_texture_ex(self._trash_txt, (self._rect.x + (self._rect.width - self._trash_txt.width) / 2,
-                                          self._rect.y + (self._rect.height - self._trash_txt.height) / 2), 0, 1.0, color)
-    else:
-      gui_label(self._rect, "Aa", 30, color=color, alignment=TextAlignment.CENTER)
-
-
-class QRScannerDialog(NavWidget):
-  SCAN_INTERVAL_S = 0.25
-  INVALID_CODE_DURATION_S = 1.0
-
-  def __init__(self, on_qr_detected: Callable[[str], None]):
-    super().__init__()
-    self._on_qr_detected = on_qr_detected
-    self._camera_view = CabinCameraView("camerad", VisionStreamType.VISION_STREAM_CABIN)
-    self._detected = False
-    self._last_scan_time = 0.0
-    self._invalid_code_until = 0.0
-    self._scan_thread: threading.Thread | None = None
-    self._scan_result: str | None = None
-    self.set_rect(rl.Rectangle(0, 0, gui_app.width, gui_app.height))
-
-  def show_event(self):
-    super().show_event()
-    ui_state.params.put_bool("DisableDriverCameraIR", True)
-    ui_state.params.put_bool("IsDriverViewEnabled", True)
-
-  def hide_event(self):
-    super().hide_event()
-    ui_state.params.put_bool("IsDriverViewEnabled", False)
-    ui_state.params.put_bool("DisableDriverCameraIR", False)
-
-  def __del__(self):
-    self._camera_view.close()
-
-  def _update_state(self):
-    super()._update_state()
-
-    now = rl.get_time()
-    if self._detected or not self._camera_view.frame or now < self._invalid_code_until:
-      return
-
-    if self._scan_thread is not None:
-      if self._scan_thread.is_alive():
-        return
-      self._scan_thread = None
-      data = self._scan_result
-      if data is not None:
-        try:
-          parse_lpa_activation_code(data)
-        except ValueError:
-          self._invalid_code_until = now + self.INVALID_CODE_DURATION_S
-          self._last_scan_time = self._invalid_code_until
-        else:
-          self._detected = True
-          self.dismiss(lambda: self._on_qr_detected(data))
-        return
-
-    if now - self._last_scan_time < self.SCAN_INTERVAL_S:
-      return
-    self._last_scan_time = now
-
-    frame = self._camera_view.frame
-    y = np.frombuffer(frame.data, dtype=np.uint8, count=frame.height * frame.stride).reshape(frame.height, frame.stride)
-    gray = y[:, :frame.width].copy()  # the vision buffer is recycled under the scan thread
-    self._scan_thread = threading.Thread(target=self._scan, args=(gray,), daemon=True)
-    self._scan_thread.start()
-
-  def _scan(self, gray: np.ndarray):
-    self._scan_result = qrcode.decode(gray)
-
-  def _render(self, rect):
-    rl.begin_scissor_mode(int(rect.x), int(rect.y), int(rect.width), int(rect.height))
-    self._camera_view._render(rect)
-
-    if not self._camera_view.frame:
-      gui_label(rect, tr("camera starting"), font_size=54, font_weight=FontWeight.BOLD,
-                alignment=TextAlignment.CENTER)
-    else:
-      label_y = rect.y + rect.height * 3 / 4
-      label_rect = rl.Rectangle(rect.x, label_y + (rect.height - label_y) / 2 - 20, rect.width, 40)
-      text = "not an LPA code" if rl.get_time() < self._invalid_code_until else "hold QR code to camera"
-      gui_label(label_rect, text, font_size=32, font_weight=FontWeight.MEDIUM,
-                alignment=TextAlignment.CENTER,
-                color=rl.Color(255, 255, 255, int(255 * 0.9)))
-
-    rl.end_scissor_mode()
-
-
-class InstallingProfileDialog(BigDialog):
-  DOT_STEP = 0.6
-
-  def __init__(self):
-    super().__init__("installing profile", "please wait...")
-    self._show_time = 0.0
-
-  def show_event(self):
-    super().show_event()
-    self._nav_bar._alpha = 0.0
-    self._show_time = rl.get_time()
-
-  def _back_enabled(self) -> bool:
-    return False
-
-  def _render(self, _):
-    t = (rl.get_time() - self._show_time) % (self.DOT_STEP * 2)
-    dots = "." * min(int(t / (self.DOT_STEP / 4)), 3)
-    self._card.set_value(f"please wait{dots}")
-    super()._render(_)
-
-
-class EsimProfileButton(BigButton):
-  SUB_LABEL_DISABLED = rl.Color(255, 255, 255, int(255 * 0.585))
-  CHECK_ICON_COLOR = rl.Color(255, 255, 255, int(255 * 0.9 * 0.65))
-  LABEL_PADDING = 98
-  LABEL_WIDTH = 402 - 98 - 28
-  SUB_LABEL_WIDTH = 402 - BigButton.LABEL_HORIZONTAL_PADDING * 2
-
-  def __init__(self, profile: Profile, cellular_manager: CellularManager, profiles_enabled: Callable[[], bool]):
-    self._cellular_manager = cellular_manager
-    self._profiles_enabled = profiles_enabled
-    super().__init__(profile.display_name, scroll=True)
-
-    self._profile = profile
-
-    self._cell_full_txt = gui_app.texture("icons_mici/settings/network/cell_strength_full.png", 48, 36)
-    self._cell_none_txt = gui_app.texture("icons_mici/settings/network/cell_strength_none.png", 48, 36)
-    self._check_txt = gui_app.texture("icons_mici/setup/driver_monitoring/dm_check.png", 32, 32)
-    self._comma_txt = gui_app.texture("icons_mici/settings/comma_icon.png", 36, 36) if profile.is_comma else None
-
-    self._delete_btn = ProfileActionButton(self._on_delete, delete=True)
-    self._rename_btn = ProfileActionButton(self._on_rename) if not profile.is_comma else None
-    self._delete_btn.set_enabled(lambda: not self._locked and not self._cellular_manager.busy and self._show_delete_btn)
-    if self._rename_btn:
-      self._rename_btn.set_enabled(lambda: not self._locked and not self._cellular_manager.busy)
-    self.set_enabled(lambda: not self._profile.enabled and self._profiles_enabled() and not self._cellular_manager.busy)
-    self.update_profile(profile)
-
-  @property
-  def profile(self) -> Profile:
-    return self._profile
-
-  def update_profile(self, profile: Profile):
-    self._profile = profile
-    active = profile.enabled
-    self.set_text(profile.display_name)
-    self.set_value("active" if active else "switch")
-
-  def _update_state(self):
-    super()._update_state()
-    self._sub_label.set_color(DEFAULT_TEXT_COLOR if self.enabled else self.SUB_LABEL_DISABLED)
-    self._sub_label.set_font_weight(FontWeight.SEMI_BOLD if self.enabled else FontWeight.ROMAN)
-
-  @property
-  def _locked(self) -> bool:
-    return not self._profile.is_comma and not self._profiles_enabled()
-
-  @property
-  def _show_delete_btn(self) -> bool:
-    return not self._profile.enabled and not self._profile.is_comma
-
-  def _on_rename(self):
-    current = self._profile.nickname or ""
-    dlg = BigInputDialog("nickname", default_text=current, confirm_callback=self._on_nickname_entered,
-                         text_validator=lambda text: bool(text.strip()))
-    gui_app.push_widget(dlg)
-
-  def _on_delete(self):
-    icon = gui_app.texture("icons_mici/settings/network/new/trash.png", 54, 64)
-    gui_app.push_widget(BigConfirmationDialog("slide to delete", icon, self._delete_profile, red=True))
-
-  def _delete_profile(self):
-    if not self._locked and not self._cellular_manager.busy and self._show_delete_btn:
-      if ui_state.sm["deviceState"].networkType == log.DeviceState.NetworkType.none:
-        gui_app.push_widget(BigDialog("", tr("Ensure you're connected to the internet and try again.")))
-        return
-      self._cellular_manager.delete_profile(self._profile.iccid)
-
-  def _on_nickname_entered(self, nickname: str):
-    if not self._locked and not self._cellular_manager.busy:
-      self._cellular_manager.nickname_profile(self._profile.iccid, nickname.strip())
-
-  def _handle_mouse_release(self, mouse_pos: MousePos):
-    if self._show_delete_btn and rl.check_collision_point_rec(mouse_pos, self._delete_btn.rect):
-      return
-    if self._rename_btn is not None and rl.check_collision_point_rec(mouse_pos, self._rename_btn.rect):
-      return
-    super()._handle_mouse_release(mouse_pos)
-
-  def _get_label_font_size(self):
-    return 48
-
-  def _draw_content(self, btn_y: float):
-    self._label.set_color(self.SUB_LABEL_DISABLED if self._locked else LABEL_COLOR)
-    label_rect = rl.Rectangle(self._rect.x + self.LABEL_PADDING, btn_y + self.LABEL_VERTICAL_PADDING,
-                              self.LABEL_WIDTH, self._rect.height - self.LABEL_VERTICAL_PADDING * 2)
-    self._label.render(label_rect)
-
-    active = self._profile.enabled
-
-    if self.value:
-      sub_label_x = self._rect.x + self.LABEL_HORIZONTAL_PADDING
-      label_y = btn_y + self._rect.height - self.LABEL_VERTICAL_PADDING
-      action_w = self._rename_btn.rect.width if self._rename_btn is not None else 0
-      action_w += self._delete_btn.rect.width if self._show_delete_btn else 0
-      sub_label_w = self.SUB_LABEL_WIDTH - action_w
-      sub_label_height = self._sub_label.get_content_height(sub_label_w)
-
-      if active:
-        check_y = int(label_y - sub_label_height + (sub_label_height - self._check_txt.height) / 2)
-        rl.draw_texture_ex(self._check_txt, rl.Vector2(sub_label_x, check_y), 0.0, 1.0, self.CHECK_ICON_COLOR)
-        sub_label_x += self._check_txt.width + 14
-
-      sub_label_rect = rl.Rectangle(sub_label_x, label_y - sub_label_height, sub_label_w, sub_label_height)
-      self._sub_label.render(sub_label_rect)
-
-    if self._comma_txt:
-      rl.draw_texture_ex(self._comma_txt, (self._rect.x + 36, btn_y + 38), 0.0, 1.0, rl.WHITE)
-    else:
-      cell_icon = self._cell_full_txt if active else self._cell_none_txt
-      rl.draw_texture_ex(cell_icon, (self._rect.x + 30, btn_y + 38), 0.0, 1.0, rl.WHITE)
-
-    btn_x = self._rect.x + self._rect.width - (ProfileActionButton.MARGIN - ProfileActionButton.HORIZONTAL_MARGIN)
-    btn_bottom = btn_y + self._rect.height
-    if self._show_delete_btn:
-      btn_x -= self._delete_btn.rect.width
-      self._delete_btn.render(rl.Rectangle(
-        btn_x, btn_bottom - self._delete_btn.rect.height,
-        self._delete_btn.rect.width, self._delete_btn.rect.height,
-      ))
-    if self._rename_btn is not None:
-      btn_x -= self._rename_btn.rect.width
-      self._rename_btn.render(rl.Rectangle(
-        btn_x, btn_bottom - self._rename_btn.rect.height,
-        self._rename_btn.rect.width, self._rename_btn.rect.height,
-      ))
-
-  def set_touch_valid_callback(self, touch_callback: Callable[[], bool]) -> None:
-    def action_pressed() -> bool:
-      return self._delete_btn.is_pressed or (self._rename_btn is not None and self._rename_btn.is_pressed)
-    super().set_touch_valid_callback(lambda: touch_callback() and not action_pressed())
-    self._delete_btn.set_touch_valid_callback(touch_callback)
-    if self._rename_btn:
-      self._rename_btn.set_touch_valid_callback(touch_callback)
-
-
-class EsimErrorDialog(NavRawScrollPanel):
-  def __init__(self, error: str):
-    super().__init__()
-    self._title = UnifiedLabel("esim error", font_size=64, font_weight=FontWeight.BOLD)
-    self._error = UnifiedLabel(error, font_size=36, elide=False)
-
-  def _render(self, rect: rl.Rectangle):
-    width = int(rect.width - 80)
-    title_height = self._title.get_content_height(width)
-    error_height = self._error.get_content_height(width)
-    offset = self._scroll_panel.update(rect, title_height + error_height + 100)
-    y = rect.y + 40 + offset
-
-    rl.begin_scissor_mode(int(rect.x), int(rect.y), int(rect.width), int(rect.height))
-    self._title.render(rl.Rectangle(rect.x + 40, y, width, title_height))
-    self._error.render(rl.Rectangle(rect.x + 40, y + title_height + 20, width, error_height))
-    rl.end_scissor_mode()
-
-
-class EsimUI(NavScroller):
-  def __init__(self, cellular_manager: CellularManager, profiles_enabled: Callable[[], bool]):
-    super().__init__()
-
-    self._cellular_manager = cellular_manager
-    self._profiles_enabled = profiles_enabled
-
-    self._add_profile_btn = BigButton("add profile", "scan QR code")
-    self._add_profile_btn.set_click_callback(self._on_add_profile)
-    self._scroller.add_widget(self._add_profile_btn)
-    self._installing_dialog: InstallingProfileDialog | None = None
-
-    self._cellular_manager.on_profiles_updated = self._on_profiles_updated
-    self._cellular_manager.on_operation_error = self._on_error
-
-  def show_event(self):
-    super().show_event()
-    self._update_buttons(re_sort=True)
-    self._cellular_manager.refresh_profiles()
-
-  def _on_profiles_updated(self):
-    if self._installing_dialog:
-      existing = {btn.profile.iccid for btn in self._scroller.items if isinstance(btn, EsimProfileButton)}
-      added = [profile for profile in self._cellular_manager.profiles if profile.iccid not in existing]
-      # Start the normal tap-to-activate flow once the profile list is visible again.
-      self._installing_dialog.dismiss(lambda: self._on_profile_clicked(added[0]) if len(added) == 1 else None)
-      self._installing_dialog = None
-
-    self._update_buttons()
-
-  def _update_buttons(self, re_sort: bool = False):
-    existing = {btn.profile.iccid: btn for btn in self._scroller.items if isinstance(btn, EsimProfileButton)}
-    buttons = []
-    for profile in self._cellular_manager.profiles:
-      btn = existing.get(profile.iccid)
-      if btn is None:
-        btn = EsimProfileButton(profile, self._cellular_manager, self._profiles_enabled)
-        btn.set_click_callback(lambda btn=btn: self._on_profile_clicked(btn.profile))
-        self._scroller.add_widget(btn)
-      else:
-        btn.update_profile(profile)
-      buttons.append(btn)
-
-    if re_sort:
-      self._scroller.items[:] = sorted(buttons, key=lambda b: not b.profile.enabled)
-    else:
-      self._scroller.items[:] = [btn for btn in self._scroller.items if btn in buttons]
-
-    self._scroller.items.append(self._add_profile_btn)
-
-  def _move_profile_to_front(self, iccid: str | None, scroll: bool = False):
-    front_btn_idx = next((i for i, btn in enumerate(self._scroller.items)
-                          if isinstance(btn, EsimProfileButton) and btn.profile.iccid == iccid), None) if iccid else None
-
-    if front_btn_idx is not None and front_btn_idx > 0:
-      self._scroller.move_item(front_btn_idx, 0)
-
-      if scroll:
-        self._scroller.scroll_to(self._scroller.scroll_panel.get_offset(), smooth=True)
-
-  def _update_state(self):
-    super()._update_state()
-
-    self._add_profile_btn.set_enabled(not self._cellular_manager.busy and self._profiles_enabled())
-    active = self._cellular_manager.active_profile
-    self._move_profile_to_front(active.iccid if active else None)
-
-  def _on_add_profile(self):
-    if self._cellular_manager.busy or not self._profiles_enabled():
-      return
-    if ui_state.sm["deviceState"].networkType == log.DeviceState.NetworkType.none:
-      gui_app.push_widget(BigDialog("", tr("Ensure you're connected to the internet and try again.")))
-      return
-    gui_app.push_widget(QRScannerDialog(on_qr_detected=self._on_qr_scanned))
-
-  def _on_qr_scanned(self, lpa_code: str):
-    dlg = BigInputDialog("enter a nickname...", text_validator=lambda text: bool(text.strip()),
-                         confirm_callback=lambda nickname: self._download_profile(lpa_code, nickname))
-    gui_app.push_widget(dlg)
-
-  def _download_profile(self, lpa_code: str, nickname: str):
-    self._installing_dialog = InstallingProfileDialog()
-    gui_app.push_widget(self._installing_dialog)
-    self._cellular_manager.download_profile(lpa_code, nickname.strip())
-
-  def _on_error(self, error: str):
-    cloudlog.error("eSIM error: %s", error)
-    dlg = EsimErrorDialog(error)
-    if self._installing_dialog:
-      self._installing_dialog.dismiss(lambda: gui_app.push_widget(dlg))
-      self._installing_dialog = None
-    else:
-      gui_app.push_widget(dlg)
-
-  def _on_profile_clicked(self, profile: Profile):
-    if self._cellular_manager.busy or not self._profiles_enabled():
-      return
-    self._cellular_manager.switch_profile(profile.iccid)
-    self._move_profile_to_front(profile.iccid, scroll=True)

openpilot/selfdrive/ui/mici/layouts/settings/network/network_layout.py

@@ -1,13 +1,12 @@
-from openpilot.selfdrive.ui.mici.layouts.settings.network import EsimNetworkButton, WifiNetworkButton
-from openpilot.selfdrive.ui.mici.layouts.settings.network.esim_ui import EsimUI
+from openpilot.system.ui.widgets.scroller import NavScroller
+from openpilot.selfdrive.ui.mici.layouts.settings.network import WifiNetworkButton
 from openpilot.selfdrive.ui.mici.layouts.settings.network.wifi_ui import WifiUIMici
 from openpilot.selfdrive.ui.mici.widgets.button import BigButton, BigMultiToggle, BigParamControl, BigToggle
 from openpilot.selfdrive.ui.mici.widgets.dialog import BigInputDialog
 from openpilot.selfdrive.ui.ui_state import ui_state
+from openpilot.selfdrive.ui.lib.prime_state import PrimeType
 from openpilot.system.ui.lib.application import gui_app
-from openpilot.system.ui.lib.cellular_manager import CellularManager
 from openpilot.system.ui.lib.wifi_manager import WifiManager, Network, MeteredType
-from openpilot.system.ui.widgets.scroller import NavScroller
 
 
 class NetworkLayoutMici(NavScroller):
@@ -29,8 +28,7 @@ class NetworkLayoutMici(NavScroller):
       self._network_metered_btn.set_enabled(False)
       self._wifi_manager.set_tethering_active(checked)
 
-    self._tethering_toggle_btn = BigToggle("enable tethering", "", toggle_callback=tethering_toggle_callback,
-                                           description="Share the device’s internet connection through a Wi-Fi hotspot.")
+    self._tethering_toggle_btn = BigToggle("enable tethering", "", toggle_callback=tethering_toggle_callback)
 
     def tethering_password_callback(password: str):
       if password:
@@ -60,39 +58,26 @@ class NetworkLayoutMici(NavScroller):
 
     # TODO: signal for current network metered type when changing networks, this is wrong until you press it once
     # TODO: disable when not connected
-    self._network_metered_btn = BigMultiToggle("network usage", ["default", "metered", "unmetered"], select_callback=network_metered_callback,
-                                               description="Metered prevents large uploads on this Wi-Fi connection. Default uses the network’s detected " +
-                                                           "setting.")
+    self._network_metered_btn = BigMultiToggle("network usage", ["default", "metered", "unmetered"], select_callback=network_metered_callback)
     self._network_metered_btn.set_enabled(False)
 
     self._wifi_button = WifiNetworkButton(self._wifi_manager)
     self._wifi_button.set_click_callback(lambda: gui_app.push_widget(self._wifi_ui))
 
-    # ******** eSIM ********
-    self._cellular_manager = CellularManager()
-    self._esim_ui = EsimUI(
-      self._cellular_manager,
-      lambda: not ui_state.prime_state.is_full_prime(),
-    )
-    self._esim_button = EsimNetworkButton(self._cellular_manager)
-    self._esim_button.set_click_callback(lambda: gui_app.push_widget(self._esim_ui))
-
     # ******** Advanced settings ********
     # ******** Roaming toggle ********
-    self._roaming_btn = BigParamControl("enable roaming", "GsmRoaming", description="Allow cellular data roaming.")
+    self._roaming_btn = BigParamControl("enable roaming", "GsmRoaming")
 
     # ******** APN settings ********
-    self._apn_btn = BigButton("apn settings", "edit",
-                              description="Set the access point name required by your cellular carrier. Leave blank for automatic configuration.")
+    self._apn_btn = BigButton("apn settings", "edit")
     self._apn_btn.set_click_callback(self._edit_apn)
 
     # ******** Cellular metered toggle ********
-    self._cellular_metered_btn = BigParamControl("cellular metered", "GsmMetered", description="Prevent large uploads over the cellular connection.")
+    self._cellular_metered_btn = BigParamControl("cellular metered", "GsmMetered")
 
     # Main scroller ----------------------------------
     self._scroller.add_widgets([
       self._wifi_button,
-      self._esim_button,
       self._network_metered_btn,
       self._tethering_toggle_btn,
       self._tethering_password_btn,
@@ -106,7 +91,8 @@ class NetworkLayoutMici(NavScroller):
   def _update_state(self):
     super()._update_state()
 
-    show_cell_settings = not ui_state.prime_state.is_full_prime()
+    # If not using prime SIM, show GSM settings and enable IPv4 forwarding
+    show_cell_settings = ui_state.prime_state.get_type() in (PrimeType.NONE, PrimeType.LITE)
     self._wifi_manager.set_ipv4_forward(show_cell_settings)
     self._roaming_btn.set_visible(show_cell_settings)
     self._apn_btn.set_visible(show_cell_settings)
@@ -116,16 +102,14 @@ class NetworkLayoutMici(NavScroller):
     super().show_event()
     self._wifi_manager.set_active(True)
 
-    # Process wifi and esim callbacks while at any point in the nav stack
+    # Process wifi callbacks while at any point in the nav stack
     gui_app.add_nav_stack_tick(self._wifi_manager.process_callbacks)
-    gui_app.add_nav_stack_tick(self._cellular_manager.process_callbacks)
 
   def hide_event(self):
     super().hide_event()
     self._wifi_manager.set_active(False)
 
     gui_app.remove_nav_stack_tick(self._wifi_manager.process_callbacks)
-    gui_app.remove_nav_stack_tick(self._cellular_manager.process_callbacks)
 
   def _edit_apn(self):
     def update_apn(apn: str):

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

@@ -3,7 +3,7 @@ from openpilot.system.ui.widgets.scroller import NavScroller
 from openpilot.selfdrive.ui.mici.widgets.button import BigButton
 from openpilot.selfdrive.ui.mici.layouts.settings.toggles import TogglesLayoutMici
 from openpilot.selfdrive.ui.mici.layouts.settings.network.network_layout import NetworkLayoutMici
-from openpilot.selfdrive.ui.mici.layouts.settings.device import DeviceLayoutMici
+from openpilot.selfdrive.ui.mici.layouts.settings.device import DeviceLayoutMici, PairBigButton
 from openpilot.selfdrive.ui.mici.layouts.settings.developer import DeveloperLayoutMici
 from openpilot.selfdrive.ui.mici.layouts.settings.software import SoftwareLayoutMici
 from openpilot.selfdrive.ui.mici.layouts.settings.firehose import FirehoseLayout
@@ -49,6 +49,7 @@ class SettingsLayout(NavScroller):
       network_btn,
       device_btn,
       software_btn,
+      PairBigButton(),
       firehose_btn,
       developer_btn,
     ])

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

@@ -242,8 +242,7 @@ class BranchSelectPage(NavScroller):
 
 class TargetBranchButton(BigButton):
   def __init__(self, check_update_btn: CheckUpdateButton):
-    super().__init__("target branch", ui_state.params.get("UpdaterTargetBranch") or "",
-                     description="Select the software branch to download on the next update check.")
+    super().__init__("target branch", ui_state.params.get("UpdaterTargetBranch") or "")
     self._check_update_btn = check_update_btn
     self.set_click_callback(self._on_click)
     self.set_visible(not ui_state.params.get_bool("IsTestedBranch"))
@@ -277,9 +276,7 @@ class SoftwareLayoutMici(NavScroller):
 
     uninstall_openpilot_btn = EngagedConfirmationButton("uninstall sunnypilot", "uninstall",
                                                         gui_app.texture("icons_mici/settings/device/uninstall.png", 64, 64),
-                                                        uninstall_openpilot_callback, exit_on_confirm=False,
-                                                        description="Remove openpilot from this device.",
-                                                        description_icon=gui_app.texture("icons_mici/setup/factory_reset.png", 64, 64))
+                                                        uninstall_openpilot_callback, exit_on_confirm=False)
 
     check_update_btn = CheckUpdateButton()
     self._scroller.add_widgets([

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

@@ -41,34 +41,15 @@ class TogglesLayoutMici(NavScroller):
   def __init__(self):
     super().__init__()
 
-    self._personality_toggle = BigMultiParamToggle("driving personality", "LongitudinalPersonality", ["aggressive", "standard", "relaxed"],
-                                                   description="Standard is recommended.\n" +
-                                                               "Aggressive follows closer, with firmer gas and braking.\n" +
-                                                               "Relaxed leaves more space.\n" +
-                                                               "Use the steering wheel distance button on supported cars.")
-    self._experimental_btn = BigToggle("experimental mode", description_icon=gui_app.texture("icons_mici/experimental_mode.png", 64, 64),
-                                       initial_state=ui_state.params.get_bool("ExperimentalMode"), toggle_callback=self._on_experimental_mode,
-                                       description="Let the driving model control gas and brakes.\n" +
-                                                   "Includes stopping for red lights and stop signs.\n" +
-                                                   "Set speed is a maximum, not a target.\n" +
-                                                   "These are alpha features. Expect mistakes.\n" +
-                                                   "The path colors show acceleration and braking.")
+    self._personality_toggle = BigMultiParamToggle("driving personality", "LongitudinalPersonality", ["aggressive", "standard", "relaxed"])
+    self._experimental_btn = BigToggle("experimental mode", initial_state=ui_state.params.get_bool("ExperimentalMode"),
+                                       toggle_callback=self._on_experimental_mode)
     is_metric_toggle = BigParamControl("use metric units", "IsMetric")
-    ldw_toggle = BigParamControl("lane departure warnings", "IsLdwEnabled",
-                                 description="Warn when you drift across a detected lane line.\n" +
-                                             "Only above 31 mph (50 km/h), with no turn signal.")
-    always_on_dm_toggle = BigParamControl("always-on driver monitor", "AlwaysOnDM", description="Monitor the driver even when sunnypilot is not engaged.")
-    record_front = BigParamControl("record & upload cabin camera", "RecordFront",
-                                   description_icon=gui_app.texture("icons_mici/settings/device/cameras.png", 64, 64),
-                                   toggle_callback=restart_needed_callback, description="Upload cabin camera data to help improve driver monitoring.")
-    record_mic = BigParamControl("record & upload mic audio", "RecordAudio", description_icon=gui_app.texture("icons_mici/microphone.png", 64, 64),
-                                 toggle_callback=restart_needed_callback,
-                                 description="Record microphone audio while driving.\n" +
-                                             "Audio is included in dashcam videos in sunnylink.")
-    enable_openpilot = BigParamControl("enable sunnypilot", "OpenpilotEnabledToggle", toggle_callback=restart_needed_callback,
-                                       description="Enable to use sunnypilot driver assistance.\n" +
-                                                   "Disable to use your car's stock driver assistance.")
-
+    ldw_toggle = BigParamControl("lane departure warnings", "IsLdwEnabled")
+    always_on_dm_toggle = BigParamControl("always-on driver monitor", "AlwaysOnDM")
+    record_front = BigParamControl("record & upload cabin camera", "RecordFront", toggle_callback=restart_needed_callback)
+    record_mic = BigParamControl("record & upload mic audio", "RecordAudio", toggle_callback=restart_needed_callback)
+    enable_openpilot = BigParamControl("enable sunnypilot", "OpenpilotEnabledToggle", toggle_callback=restart_needed_callback)
 
     self._scroller.add_widgets([
       self._personality_toggle,

openpilot/selfdrive/ui/mici/widgets/button.py

@@ -29,41 +29,9 @@ class ScrollState(Enum):
   POST_SCROLL = 2
 
 
-class BaseButton(Widget):
-  def __init__(self, description: str, title: str, icon: Union[rl.Texture, None] = None):
+class BigCircleButton(Widget):
+  def __init__(self, icon: rl.Texture, red: bool = False, icon_offset: tuple[int, int] = (0, 0)):
     super().__init__()
-    self._shake_start: float | None = None
-    if description:
-      # Dialogs also use buttons; import lazily to avoid a circular import.
-      from openpilot.selfdrive.ui.mici.widgets.dialog import SettingDescriptionDialog
-      self.set_long_press_callback(lambda: gui_app.push_widget(SettingDescriptionDialog(title, description, icon)))
-    else:
-      self.set_long_press_callback(self.trigger_shake)
-
-  def trigger_shake(self):
-    self._shake_start = rl.get_time()
-
-  @property
-  def _shake_offset(self) -> float:
-    SHAKE_DURATION = 0.5
-    SHAKE_AMPLITUDE = 24.0
-    SHAKE_FREQUENCY = 32.0
-    if self._shake_start is None:
-      return 0.0
-    t = rl.get_time() - self._shake_start
-    if t > SHAKE_DURATION:
-      return 0.0
-    decay = 1.0 - t / SHAKE_DURATION
-    return decay * SHAKE_AMPLITUDE * math.sin(t * SHAKE_FREQUENCY)
-
-  def set_position(self, x: float, y: float) -> None:
-    super().set_position(x + self._shake_offset, y)
-
-class BigCircleButton(BaseButton):
-  def __init__(self, icon: rl.Texture, red: bool = False, icon_offset: tuple[int, int] = (0, 0),
-               *, description: str = "",
-               description_icon: Union[rl.Texture, None] = None, title: str = ""):
-    super().__init__(description, title, description_icon or icon)
     self._red = red
     self._icon_offset = icon_offset
 
@@ -105,9 +73,8 @@ class BigCircleButton(BaseButton):
 
 
 class BigCircleToggle(BigCircleButton):
-  def __init__(self, icon: rl.Texture, toggle_callback: Callable | None = None, icon_offset: tuple[int, int] = (0, 0),
-               *, description: str = "", description_icon: Union[rl.Texture, None] = None, title: str = ""):
-    super().__init__(icon, False, icon_offset=icon_offset, description=description, description_icon=description_icon, title=title)
+  def __init__(self, icon: rl.Texture, toggle_callback: Callable | None = None, icon_offset: tuple[int, int] = (0, 0)):
+    super().__init__(icon, False, icon_offset=icon_offset)
     self._toggle_callback = toggle_callback
 
     # State
@@ -136,16 +103,14 @@ class BigCircleToggle(BigCircleButton):
                        0, 1.0, rl.WHITE)
 
 
-class BigButton(BaseButton):
+class BigButton(Widget):
   LABEL_HORIZONTAL_PADDING = 40
   LABEL_VERTICAL_PADDING = 23  # visually matches 30 in figma
 
   """A lightweight stand-in for the Qt BigButton, drawn & updated each frame."""
 
-  def __init__(self, text: str, value: str = "", icon: Union[rl.Texture, None] = None, scroll: bool = False,
-               *, description: str = "",
-               description_icon: Union[rl.Texture, None] = None):
-    super().__init__(description, text, description_icon or icon or None)
+  def __init__(self, text: str, value: str = "", icon: Union[rl.Texture, None] = None, scroll: bool = False):
+    super().__init__()
     self.set_rect(rl.Rectangle(0, 0, 402, 180))
     self.text = text
     self.value = value
@@ -154,6 +119,7 @@ class BigButton(BaseButton):
 
     self._scale_filter = BounceFilter(1.0, 0.1, 1 / gui_app.target_fps)
     self._click_delay = 0.075
+    self._shake_start: float | None = None
     self._grow_animation_until: float | None = None
 
     self._rotate_icon_t: float | None = None
@@ -221,9 +187,28 @@ class BigButton(BaseButton):
   def get_text(self):
     return self.text
 
+  def trigger_shake(self):
+    self._shake_start = rl.get_time()
+
   def trigger_grow_animation(self, duration: float = 0.65):
     self._grow_animation_until = rl.get_time() + duration
 
+  @property
+  def _shake_offset(self) -> float:
+    SHAKE_DURATION = 0.5
+    SHAKE_AMPLITUDE = 24.0
+    SHAKE_FREQUENCY = 32.0
+    if self._shake_start is None:
+      return 0.0
+    t = rl.get_time() - self._shake_start
+    if t > SHAKE_DURATION:
+      return 0.0
+    decay = 1.0 - t / SHAKE_DURATION
+    return decay * SHAKE_AMPLITUDE * math.sin(t * SHAKE_FREQUENCY)
+
+  def set_position(self, x: float, y: float) -> None:
+    super().set_position(x + self._shake_offset, y)
+
   def _handle_background(self) -> tuple[rl.Texture, float, float, float]:
     if self._grow_animation_until is not None:
       if rl.get_time() >= self._grow_animation_until:
@@ -287,10 +272,8 @@ class BigButton(BaseButton):
 
 
 class BigToggle(BigButton):
-  def __init__(self, text: str, value: str = "", initial_state: bool = False, toggle_callback: Callable | None = None,
-               *, description: str = "",
-               description_icon: Union[rl.Texture, None] = None):
-    super().__init__(text, value, "", description=description, description_icon=description_icon)
+  def __init__(self, text: str, value: str = "", initial_state: bool = False, toggle_callback: Callable | None = None):
+    super().__init__(text, value, "")
     self._checked = initial_state
     self._toggle_callback = toggle_callback
 
@@ -325,8 +308,8 @@ class BigToggle(BigButton):
 
 class BigMultiToggle(BigToggle):
   def __init__(self, text: str, options: list[str], toggle_callback: Callable | None = None,
-               select_callback: Callable | None = None, *, description: str = "", description_icon: Union[rl.Texture, None] = None):
-    super().__init__(text, "", toggle_callback=toggle_callback, description=description, description_icon=description_icon)
+               select_callback: Callable | None = None):
+    super().__init__(text, "", toggle_callback=toggle_callback)
     assert len(options) > 0
     self._options = options
     self._select_callback = select_callback
@@ -391,9 +374,9 @@ class GreyBigButton(BigButton):
 
 class BigMultiParamToggle(BigMultiToggle):
   def __init__(self, text: str, param: str, options: list[str], toggle_callback: Callable | None = None,
-               select_callback: Callable | None = None, *, description: str = "", description_icon: Union[rl.Texture, None] = None):
+               select_callback: Callable | None = None):
     assert Params is not None
-    super().__init__(text, options, toggle_callback, select_callback, description=description, description_icon=description_icon)
+    super().__init__(text, options, toggle_callback, select_callback)
     self._param = param
 
     self._params = Params()
@@ -409,10 +392,9 @@ class BigMultiParamToggle(BigMultiToggle):
 
 
 class BigParamControl(BigToggle):
-  def __init__(self, text: str, param: str, toggle_callback: Callable | None = None, *, description: str = "",
-               description_icon: Union[rl.Texture, None] = None):
+  def __init__(self, text: str, param: str, toggle_callback: Callable | None = None):
     assert Params is not None
-    super().__init__(text, "", toggle_callback=toggle_callback, description=description, description_icon=description_icon)
+    super().__init__(text, "", toggle_callback=toggle_callback)
     self.param = param
     self.params = Params()
     self.set_checked(self.params.get_bool(self.param, False))
@@ -428,9 +410,9 @@ class BigParamControl(BigToggle):
 # TODO: param control base class
 class BigCircleParamControl(BigCircleToggle):
   def __init__(self, icon: rl.Texture, param: str, toggle_callback: Callable | None = None,
-               icon_offset: tuple[int, int] = (0, 0), *, description: str = "", description_icon: Union[rl.Texture, None] = None, title: str = ""):
+               icon_offset: tuple[int, int] = (0, 0)):
     assert Params is not None
-    super().__init__(icon, toggle_callback, icon_offset=icon_offset, description=description, description_icon=description_icon, title=title)
+    super().__init__(icon, toggle_callback, icon_offset=icon_offset)
     self._param = param
     self.params = Params()
     self.set_checked(self.params.get_bool(self._param, False))

openpilot/selfdrive/ui/mici/widgets/dialog.py

@@ -1,11 +1,9 @@
 import abc
 import math
-import re
 import pyray as rl
 from typing import Union
 from collections.abc import Callable
 from openpilot.system.ui.widgets.nav_widget import NavWidget
-from openpilot.system.ui.widgets.scroller import NavScroller
 from openpilot.system.ui.widgets.label import UnifiedLabel
 from openpilot.system.ui.widgets.mici_keyboard import MiciKeyboard
 from openpilot.system.ui.lib.text_measure import measure_text_cached
@@ -39,31 +37,6 @@ class BigDialog(BigDialogBase):
     ))
 
 
-class SettingDescriptionDialog(NavScroller):
-  def __init__(self, title: str, description: str, icon: Union[rl.Texture, None] = None):
-    super().__init__()
-    cards = [GreyBigButton(title, "scroll for details", icon or gui_app.texture("icons_mici/setup/green_info.png", 64, 64))]
-    # Explicit lines are authored cards; otherwise prefer sentence boundaries.
-    paragraphs = description.splitlines() if "\n" in description else re.split(r"(?<=[.!?])\s+", description.strip())
-    # Measure each card so longer text still fits with the actual font and padding.
-    for sentence in paragraphs:
-      card = GreyBigButton("", "")
-      words: list[str] = []
-      for word in sentence.split():
-        card.set_value(" ".join([*words, word]))
-        height = card._sub_label.get_content_height(card._subtitle_width_hint())
-        if words and height > card.rect.height - 2 * card.LABEL_VERTICAL_PADDING:
-          card.set_value(" ".join(words))
-          cards.append(card)
-          card = GreyBigButton("", "")
-          words = []
-        words.append(word)
-      if words:
-        card.set_value(" ".join(words))
-        cards.append(card)
-    self._scroller.add_widgets(cards)
-
-
 class BigConfirmationDialog(BigDialogBase):
   def __init__(self, title: str, icon: rl.Texture, confirm_callback: Callable[[], None],
                exit_on_confirm: bool = True, red: bool = False):
@@ -103,15 +76,14 @@ class BigInputDialog(BigDialogBase):
                default_text: str = "",
                minimum_length: int = 1,
                confirm_callback: Callable[[str], None] | None = None,
-               auto_return_to_letters: str = "",
-               text_validator: Callable[[str], bool] | None = None):
+               auto_return_to_letters: str = ""):
     super().__init__()
     self._hint_label = UnifiedLabel(hint, font_size=35, text_color=rl.Color(255, 255, 255, int(255 * 0.35)),
                                     font_weight=FontWeight.MEDIUM)
     self._keyboard = MiciKeyboard(auto_return_to_letters=auto_return_to_letters)
     self._keyboard.set_text(default_text)
     self._keyboard.set_enabled(lambda: self.enabled and not self.is_dismissing)  # for nav stack + NavWidget
-    self._text_valid = lambda text: len(text) >= minimum_length and (text_validator is None or text_validator(text))
+    self._minimum_length = minimum_length
 
     self._backspace_held_time: float | None = None
 
@@ -128,8 +100,7 @@ class BigInputDialog(BigDialogBase):
 
     def confirm_callback_wrapper():
       text = self._keyboard.text()
-      if self._text_valid(text):
-        self.dismiss((lambda: confirm_callback(text)) if confirm_callback else None)
+      self.dismiss((lambda: confirm_callback(text)) if confirm_callback else None)
     self._confirm_callback = confirm_callback_wrapper
 
   def _update_state(self):
@@ -214,7 +185,7 @@ class BigInputDialog(BigDialogBase):
                                                self._rect.width - (text_field_rect.x + text_field_rect.width), self._top_left_button_rect.height)
 
     # draw enter button
-    self._enter_img_alpha.update(255 if self._text_valid(text) else 0)
+    self._enter_img_alpha.update(255 if len(text) >= self._minimum_length else 0)
     color = rl.Color(255, 255, 255, int(self._enter_img_alpha.x))
     rl.draw_texture_ex(self._enter_img, rl.Vector2(self._rect.x + PADDING / 2, self._rect.y), 0.0, 1.0, color)
     color = rl.Color(255, 255, 255, 255 - int(self._enter_img_alpha.x))

openpilot/selfdrive/ui/sunnypilot/layouts/settings/vehicle/platform_selector.py

@@ -92,7 +92,7 @@ class PlatformSelector(Button):
 
   def _on_platform_selected(self, dialog, res):
     if res == DialogResult.CONFIRM and dialog.selection_ref:
-      offroad_msg = tr("This setting will take effect immediately.") if ui_state.is_offroad() else \
+      offroad_msg = tr("This setting will take effect immediately.") if ui_state.is_offroad else \
                     tr("This setting will take effect once the device enters offroad state.")
 
       callback = partial(self._confirm_platform, dialog.selection_ref)

openpilot/selfdrive/ui/translations/auto_translate.sh

@@ -4,7 +4,7 @@ set -euo pipefail
 DIR="$(cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd)"
 ROOT="$DIR/../../../"
 
-cd "$DIR"
+cd $DIR
 ./update_translations.py
 
 command -v codex >/dev/null || {

openpilot/sunnypilot/mapd/mapd_installer.py

@@ -20,6 +20,7 @@ from openpilot.common.hardware.hw import Paths
 from openpilot.common.spinner import Spinner
 from openpilot.common.version import is_prebuilt
 from openpilot.sunnypilot.mapd import MAPD_PATH, MAPD_BIN_DIR
+import openpilot.system.sentry as sentry
 
 VERSION = "v1.12.0"
 URL = f"https://github.com/pfeiferj/openpilot-mapd/releases/download/{VERSION}/mapd"
@@ -135,7 +136,9 @@ class MapdInstallManager:
                              f"Boot will continue in {5 - i}s...")
         time.sleep(1)
 
+      sentry.init(sentry.SentryProject.SELFDRIVE)
       traceback.print_exc()
+      sentry.capture_exception()
 
 
 if __name__ == "__main__":

openpilot/sunnypilot/modeld_v2/compile_modeld.py

@@ -12,7 +12,7 @@ import os
 import tempfile
 import time
 from functools import partial
-from openpilot.sunnypilot.modeld_v2.helpers import dump_oob, load_oob
+from openpilot.selfdrive.modeld.helpers import dump_oob, load_oob
 import numpy as np
 os.environ['GMMU'] = '0'
 
@@ -32,7 +32,7 @@ def _patch_tinygrad_fetch_fw():
   helpers.fetch_fw = fetch_fw
 _patch_tinygrad_fetch_fw()
 
-import openpilot.sunnypilot.modeld_v2.stock_dependencies as stock
+import openpilot.selfdrive.modeld.compile_modeld as stock
 from tinygrad import dtypes
 from tinygrad.device import Device
 from tinygrad.engine.jit import TinyJit

openpilot/sunnypilot/modeld_v2/helpers.py

@@ -55,11 +55,6 @@ def _dynamic_factory(real_class):
     return _enum_factory(real_class)
 
   def factory(*args, **kwargs):
-    if real_class.__name__ == 'Buffer':
-      # Tinygrad, before compile_modeld moved to tg, serialized lb_refcount at index 6. It has been removed.
-      if len(args) >= 7 and isinstance(args[6], int):
-        args = tuple(list(args[:6]) + list(args[7:]))
-
     try:
       return real_class(*args, **kwargs)
     except TypeError:
@@ -103,17 +98,3 @@ def load_oob(f):
       f.readinto(pb)
       yield pb
   return DynamicTinygradUnpickler(io.BytesIO(opcodes), buffers=buffers()).load()
-
-
-def dump_oob(obj, f):
-  buffers = []
-  def buffer_cb(buffer):
-    buffers.append(buffer)
-    return False
-
-  opcodes = pickle.dumps(obj, protocol=5, buffer_callback=buffer_cb)
-  f.write(struct.pack('<q', len(opcodes)))
-  f.write(opcodes)
-  for b in buffers:
-    f.write(struct.pack('<q', len(b.raw())))
-    f.write(b.raw())

openpilot/sunnypilot/modeld_v2/model_adapters.py

@@ -1,224 +0,0 @@
-"""
-Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
-
-This file is part of sunnypilot and is licensed under the MIT License.
-See the LICENSE.md file in the root directory for more details.
-"""
-import codecs
-import pickle
-import numpy as np
-from tinygrad.tensor import Tensor
-from tinygrad.dtype import DType, dtypes
-from tinygrad.device import Buffer
-from tinygrad.uop.ops import UOp
-from tinygrad.helpers import round_up
-import math
-from openpilot.common.basedir import BASEDIR
-from pathlib import Path
-from openpilot.sunnypilot.modeld_v2.helpers import DynamicTinygradUnpickler
-
-def input_view(buffer: Buffer, shape: tuple[int, ...], dtype: DType, offset: int) -> Tensor:
-  view = buffer.view(math.prod(shape), dtype, offset).ensure_allocated()
-  return Tensor(UOp.from_buffer(view)).reshape(shape)
-
-from openpilot.sunnypilot.modeld_v2.stock_dependencies import MODELD_INPUTS, make_input_queues as make_stock_input_queues
-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, make_supercombo_input_queues,
-                                                           WARP_INPUTS, POLICY_INPUTS, nv12_copy_size)
-
-
-class BaseModelAdapter:
-  def __init__(self, jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=False):
-    self.jits = jits
-    self.DEV = model_device
-    self.QUEUE_DEV = queue_device
-    self.WARP_DEV = warp_device
-    self.chestnut = chestnut
-    self.cam_w = cam_w
-    self.cam_h = cam_h
-    self._combined_model_type = 'supercombo'
-    self._policy_slices_list = []
-    self._has_on_policy = False
-    self.policy_output_slices = {}
-    self._policy_keys = []
-    self.full_frames = {}
-    self._blob_cache = {}
-    self.frame_buffers = {}
-    self.frame_views = {}
-    self.nv12_info = get_nv12_info(cam_w, cam_h)
-    self.is_native = False
-
-  def _init_common(self):
-    self._desire_key = next((key for key in getattr(self, 'numpy_inputs', {}) if key.startswith('desire')), 'desire')
-    self._road_key = next((key for key in getattr(self, '_vision_input_names', []) if 'big' not in key), 'img')
-    self._wide_key = next((key for key in getattr(self, '_vision_input_names', []) if 'big' in key), 'big_img')
-    self.frame_buf_params = dict.fromkeys(getattr(self, '_vision_input_names', ['img', 'big_img']), self.nv12_info)
-
-  def get_dummy_inputs(self):
-    dummy_size = getattr(self, 'frame_copy_size', self.frame_buf_params[self._road_key][3])
-    if getattr(self, 'is_run_model', True) is False:
-      dummy_size = self.frame_buf_params[self._road_key][3]
-
-    dummy_frames = {
-      k: np.zeros(self.frame_views[k].size, dtype=np.uint8) if self.is_native else np.zeros(dummy_size, dtype=np.uint8)
-      for k in self._vision_input_names
-    }
-    transforms = {k: np.eye(3, dtype=np.float32) for k in [self._road_key, self._wide_key] if k}
-    dummy_inputs = {k: np.zeros(v.shape, dtype=v.dtype) for k, v in self.numpy_inputs.items() if k not in ['tfm', 'big_tfm', 'prev_feat']}
-    return dummy_frames, transforms, dummy_inputs
-
-
-class LegacyModelAdapter(BaseModelAdapter):
-  def __init__(self, *args, **kwargs):
-    super().__init__(*args, **kwargs)
-
-    metadata = self.jits['metadata']
-    self.is_run_model = 'run_model' in self.jits
-    self.frame_copy_size = nv12_copy_size(*self.nv12_info[:3])
-
-    if self.is_run_model or 'model' in metadata:
-      model_metadata = metadata.get('model', metadata)
-      self.input_shapes = model_metadata['input_shapes']
-      self.vision_output_slices = model_metadata['output_slices']
-      self._vision_input_names = [key for key in self.input_shapes if 'img' in key]
-      self.frame_skip = derive_frame_skip({}, self.input_shapes)
-
-      if self.is_run_model:
-        self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
-          self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
-        self.frame_views, self.npy = self.frame_buffers, self.numpy_inputs
-        self.run_model, self.run_policy, self.warp = self.jits['run_model'][(self.cam_w, self.cam_h)], None, None
-      else:
-        self.input_queues, self.numpy_inputs = make_supercombo_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
-        self.run_model, self.run_policy, self.warp = None, self.jits['run_policy'], self.jits[(self.cam_w, self.cam_h)]
-    else:
-      self.run_model, self.run_policy, self.warp = None, self.jits['run_policy'], self.jits[(self.cam_w, self.cam_h)]
-      vision_metadata = metadata['vision']
-      policy_keys = [k for k in metadata if k not in ('vision', 'warp_dev')]
-      self._combined_model_type = 'split' if policy_keys == ['policy'] else 'multi_policy'
-      self.vision_output_slices = vision_metadata['output_slices']
-      self._policy_keys = policy_keys
-      self._policy_slices_list = [metadata[k]['output_slices'] for k in policy_keys]
-      self.policy_output_slices = self._policy_slices_list[0]
-      self._has_on_policy = any('on' in k.lower() for k in policy_keys)
-      self._vision_input_names = [key for key in vision_metadata['input_shapes'] if 'img' in key]
-      first_policy_meta = metadata[policy_keys[0]]
-      self.frame_skip = derive_frame_skip(vision_metadata['input_shapes'], first_policy_meta['input_shapes'])
-      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_metadata['input_shapes'],
-                                                                     first_policy_meta['input_shapes'],
-                                                                     self.frame_skip, device=self.QUEUE_DEV)
-
-    self._init_common()
-    if self.warp is not None:
-      self.full_frames = {k: Tensor(np.zeros(self.nv12_info[3], dtype=np.uint8), device=self.WARP_DEV).contiguous().realize() for k in self._vision_input_names}
-      self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
-
-  def copy_frames(self, bufs):
-    if getattr(self, 'is_run_model', True):
-      for key, buf in bufs.items():
-        data = buf.data if hasattr(buf, 'data') else buf
-        np.copyto(self.frame_buffers[key], np.frombuffer(data, dtype=np.uint8, count=self.frame_copy_size))
-    else:
-      for key, buf in bufs.items():
-        ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data
-        cache_key = (key, ptr)
-        if cache_key not in self._blob_cache:
-          self._blob_cache[cache_key] = Tensor.from_blob(ptr, (self.frame_buf_params[key][3],), dtype='uint8', device=self.WARP_DEV)
-        self.full_frames[key] = self._blob_cache[cache_key]
-
-  def reset_warmup_buffers(self):
-    if getattr(self, 'is_run_model', True):
-      self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
-        self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
-      self.frame_views = self.frame_buffers
-      self.npy = self.numpy_inputs
-    else:
-      for v in self.numpy_inputs.values():
-        v[:] = 0
-      self.full_frames.clear()
-      self._blob_cache.clear()
-
-  def run(self):
-    if self.run_model is not None:
-      outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
-      return outs
-    else:
-      assert self.warp is not None and self.run_policy is not None
-      warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
-      raw_outputs = self.run_policy(**{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped)
-      return raw_outputs
-
-
-class NativeTinygradAdapter(BaseModelAdapter):
-  def __init__(self, *args, **kwargs):
-    super().__init__(*args, **kwargs)
-    self.is_native = True
-    self.input_specs = self.jits['input_specs']
-    self.packed_specs = self.jits['packed_specs']
-
-    self.model_device = self.input_specs['new_img'][2]
-    self.input_shapes = {name: (shape, np.dtype(dtype)) for name, (shape, dtype, _) in self.input_specs.items()}
-    self.state_pairs = {name: f'next_{name}' for name in self.input_shapes if f'next_{name}' in self.jits['metadata']['output_shapes']}
-
-    stride, y_height, uv_height, _ = get_nv12_info(self.cam_w, self.cam_h)
-    self.frame_copy_size = stride * (y_height + uv_height)
-
-    self.input_shapes_orig = self.jits['metadata']['input_shapes']
-    self._vision_input_names = [k for k in self.input_shapes_orig if 'img' in k]
-    self.vision_output_slices = pickle.loads(codecs.decode(self.jits['metadata']['metadata']['output_slices'].encode(), 'base64'))
-
-    self.reset_warmup_buffers()
-    self._init_common()
-
-    warp_dir = Path(BASEDIR) / "openpilot/sunnypilot/modeld_v2/models"
-    with open(warp_dir / f'{"big_" if self.chestnut else ""}driving_warp_{self.cam_w}x{self.cam_h}_tinygrad.pkl', 'rb') as f:
-      self.run_warp = DynamicTinygradUnpickler(f).load()['run']
-    self.run_model = self.jits['run']
-
-    self.outputs = {name: Tensor(np.zeros(shape, dtype=dtype), device=device).realize() for name, (shape, dtype, device) in self.jits['output_specs'].items()}
-    for name, next_name in self.state_pairs.items():
-      state = self.input_queues[name]
-      self.outputs[next_name] = input_view(state._buffer(), state.shape, state.dtype, 0)
-
-  def copy_frames(self, bufs):
-    for i, key in enumerate(self._vision_input_names):
-      if key in bufs:
-        data = bufs[key].data if hasattr(bufs[key], 'data') else bufs[key]
-        np.copyto(self.frames[i], np.frombuffer(data, dtype=np.uint8, count=self.frame_copy_size))
-
-  def reset_warmup_buffers(self) -> None:
-    self.input_queues = {name: Tensor(np.zeros(shape, dtype=dtype), device=self.model_device).realize()
-                         for name, (shape, dtype) in self.input_shapes.items() if name in self.state_pairs}
-    shapes = {'tfm': (2, 3, 3)} | {name: shape for name, (shape, _) in self.input_shapes.items()
-                                   if name not in self.state_pairs and name != 'new_img'}
-    npy_size = sum(round_up(math.prod(shape) * 4, 128) for shape in shapes.values())
-    self.packed_input = np.zeros(npy_size + 2 * self.frame_copy_size, dtype=np.uint8)
-    self.input_host = Tensor(self.packed_input, device='NPY')._buffer()
-    self.input_device = Tensor(self.packed_input, device=self.model_device)._buffer()
-    self.numpy_inputs = {}
-    offset = 0
-    for name, shape in shapes.items():
-      self.numpy_inputs[name] = np.ndarray(shape, dtype=np.float32, buffer=self.packed_input, offset=offset)
-      self.input_queues[name] = input_view(self.input_device, shape, dtypes.float32, offset)
-      offset += round_up(self.numpy_inputs[name].nbytes, 128)
-    self.frames = self.packed_input[npy_size:].reshape(2, self.frame_copy_size)
-    self.warp_inputs = {'input_frame': input_view(self.input_device, self.frames.shape, dtypes.uint8, npy_size), 'M_inv': self.input_queues.pop('tfm')}
-
-    # Split tfm into tfm and big_tfm for modeld compatibility
-    if 'tfm' in self.numpy_inputs and self.numpy_inputs['tfm'].shape == (2, 3, 3):
-      real_tfm = self.numpy_inputs.pop('tfm')
-      self.numpy_inputs['tfm'] = real_tfm[0]
-      self.numpy_inputs['big_tfm'] = real_tfm[1]
-
-  def run(self):
-    self.input_device.copy_from(self.input_host)
-    self.input_queues['new_img'] = self.run_warp(**self.warp_inputs)
-    self.run_model(output_buffers=self.outputs, **self.input_queues)
-    return self.outputs['outputs']
-
-
-def get_model_adapter(jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=False):
-  if 'input_specs' in jits:
-    return NativeTinygradAdapter(jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=chestnut)
-  else:
-    return LegacyModelAdapter(jits, cam_w, cam_h, model_device, queue_device, warp_device, chestnut=chestnut)

openpilot/sunnypilot/modeld_v2/modeld.py

@@ -13,6 +13,7 @@ import numpy as np
 import threading
 import time
 from setproctitle import setproctitle
+from tinygrad.tensor import Tensor
 
 import openpilot.cereal.messaging as messaging
 from openpilot.common.hardware import COMMA_HARDWARE
@@ -31,19 +32,27 @@ from openpilot.common.filter_simple import FirstOrderFilter
 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
-from openpilot.selfdrive.modeld.modeld import ChestnutGpuState
+from openpilot.selfdrive.modeld.modeld import ChestnutState
 
+from openpilot.selfdrive.modeld.compile_modeld import (
+  MODELD_INPUTS,
+  make_input_queues as make_stock_input_queues,
+)
 from openpilot.sunnypilot.modeld_v2.fill_model_msg import fill_model_msg, fill_pose_msg, PublishState, get_curvature_from_output
 from openpilot.sunnypilot.modeld_v2.parse_model_outputs import Parser
 from openpilot.sunnypilot.modeld_v2.constants import ModelConstants, Plan
 from openpilot.sunnypilot.modeld_v2.meta_helper import load_meta_constants
 from openpilot.sunnypilot.modeld_v2.camera_offset_helper import CameraOffsetHelper
+from openpilot.sunnypilot.modeld_v2.compile_modeld import (derive_frame_skip, make_split_input_queues,
+                                                           make_supercombo_input_queues, nv12_copy_size,
+                                                           WARP_INPUTS, POLICY_INPUTS)
 from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
 from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
 from openpilot.sunnypilot.modeld_v2.helpers import load_oob
-from openpilot.sunnypilot.modeld_v2.model_adapters import get_model_adapter
 from openpilot.sunnypilot.models.helpers import get_active_bundle
 from openpilot.sunnypilot.selfdrive.controls.lib.relc import RoadEdgeLaneChangeController
 
@@ -114,19 +123,52 @@ class ModelState(ModelStateBase):
     self.WARP_DEV = metadata.get('warp_dev', 'QCOM') if COMMA_HARDWARE else 'CPU'
     self.DEV = ('AMD' if self.chestnut else 'QCOM') if COMMA_HARDWARE else 'CPU'
     self.QUEUE_DEV = self.DEV
-    self.adapter = get_model_adapter(jits, cam_w, cam_h, self.DEV, self.QUEUE_DEV, self.WARP_DEV, self.chestnut)
-    self.vision_output_slices = self.adapter.vision_output_slices
-    self.policy_output_slices = self.adapter.policy_output_slices
-    self._policy_slices_list = self.adapter._policy_slices_list
-    self._combined_model_type = self.adapter._combined_model_type
-    self._vision_input_names = self.adapter._vision_input_names
-    self.numpy_inputs = self.adapter.numpy_inputs
-    self._policy_keys = self.adapter._policy_keys
-    self._has_on_policy = self.adapter._has_on_policy
-    self._desire_key = self.adapter._desire_key
-    self._road_key = self.adapter._road_key
-    self._wide_key = self.adapter._wide_key
-    self.frame_buf_params = self.adapter.frame_buf_params
+    self.is_run_model = 'run_model' in jits
+
+    nv12_info = get_nv12_info(cam_w, cam_h)
+    self.frame_copy_size = nv12_copy_size(*nv12_info[:3])
+    self.full_frames: dict = {}
+    self._blob_cache: dict = {}
+    self.frame_buffers: dict = {}
+
+    if self.is_run_model or 'model' in metadata:
+      model_metadata = metadata.get('model', metadata)
+      self.input_shapes = model_metadata['input_shapes']
+      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 = [key for key in self.input_shapes if 'img' in key]
+      self.frame_skip = derive_frame_skip({}, self.input_shapes)
+      if self.is_run_model:
+        self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
+          self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
+        self.frame_views, self.npy = self.frame_buffers, self.numpy_inputs
+        self.run_model, self.run_policy, self.warp = jits['run_model'][(cam_w, cam_h)], None, None
+      else:
+        self.input_queues, self.numpy_inputs = make_supercombo_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
+        self.run_model, self.run_policy, self.warp = None, jits['run_policy'], jits[(cam_w, cam_h)]
+    else:
+      self.run_model, self.run_policy, self.warp = None, jits['run_policy'], jits[(cam_w, cam_h)]
+      vision_metadata = metadata['vision']
+      policy_keys = [k for k in metadata if k not in ('vision', 'warp_dev')]
+      self._combined_model_type = 'split' if policy_keys == ['policy'] else 'multi_policy'
+      self.vision_output_slices = vision_metadata['output_slices']
+      self._policy_keys = policy_keys
+      self._policy_slices_list = [metadata[k]['output_slices'] for k in policy_keys]
+      self.policy_output_slices = self._policy_slices_list[0]
+      self._has_on_policy = any('on' in k.lower() for k in policy_keys)
+      self._vision_input_names = [key for key in vision_metadata['input_shapes'] if 'img' in key]
+      first_policy_meta = metadata[policy_keys[0]]
+      frame_skip = derive_frame_skip(vision_metadata['input_shapes'], first_policy_meta['input_shapes'])
+      self.input_queues, self.numpy_inputs = make_split_input_queues(vision_metadata['input_shapes'],
+                                                                     first_policy_meta['input_shapes'],
+                                                                     frame_skip, device=self.QUEUE_DEV)
+
+    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)
+    self.frame_buf_params = dict.fromkeys(self._vision_input_names, nv12_info)
 
     is_20hz = bundle.is20hz if bundle else self._combined_model_type in ('split', 'multi_policy')
     if is_20hz:
@@ -138,10 +180,26 @@ class ModelState(ModelStateBase):
     self.parser = Parser()
     self.prev_desire = np.zeros(self.constants.DESIRE_LEN, dtype=np.float32)
 
+    if self.warp is not None:
+      self.full_frames = {k: Tensor(np.zeros(nv12_info[3], dtype=np.uint8), device=self.WARP_DEV).contiguous().realize() for k in self._vision_input_names}
+      self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
+
   def warmup(self) -> None:
-    dummy_frames, transforms, dummy_inputs = self.adapter.get_dummy_inputs()
+    dummy_size = self.frame_copy_size if self.is_run_model else self.frame_buf_params[self._road_key][3]
+    dummy_frames = {k: np.zeros(dummy_size, dtype=np.uint8) for k in self._vision_input_names}
+    transforms = {k: np.eye(3, dtype=np.float32) for k in [self._road_key, self._wide_key] if k}
+    dummy_inputs = {k: np.zeros(v.shape, dtype=v.dtype) for k, v in self.numpy_inputs.items() if k not in ['tfm', 'big_tfm', 'prev_feat']}
     self.run(dummy_frames, transforms, dummy_inputs)
-    self.adapter.reset_warmup_buffers()
+    if self.is_run_model:
+      self.input_queues, self.numpy_inputs, self.frame_buffers = make_stock_input_queues(
+        self.input_shapes, self.frame_skip, device=self.DEV, frame_copy_size=self.frame_copy_size)
+      self.frame_views = self.frame_buffers
+      self.npy = self.numpy_inputs
+    else:
+      for v in self.numpy_inputs.values():
+        v[:] = 0
+      self.full_frames.clear()
+      self._blob_cache.clear()
     self.prev_desire[:] = 0
 
   @property
@@ -159,12 +217,21 @@ class ModelState(ModelStateBase):
   def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
           inputs: dict[str, np.ndarray],
           after_enqueue: Callable[[], None] | None = None) -> dict[str, np.ndarray] | None:
-    self.adapter.copy_frames(bufs)
+    if self.is_run_model:
+      for key, buf in bufs.items():
+        data = buf.data if hasattr(buf, 'data') else buf
+        np.copyto(self.frame_buffers[key], np.frombuffer(data, dtype=np.uint8, count=self.frame_copy_size))
+    else:
+      for key, buf in bufs.items():
+        ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data
+        cache_key = (key, ptr)
+        if cache_key not in self._blob_cache:
+          self._blob_cache[cache_key] = Tensor.from_blob(ptr, (self.frame_buf_params[key][3],), 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].flat if self.adapter.is_native else self.numpy_inputs[desire_key])[:] = \
-      np.where(inputs[desire_key] - self.prev_desire > .99, inputs[desire_key], 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', 'action_t'):
@@ -174,7 +241,13 @@ class ModelState(ModelStateBase):
     self.numpy_inputs['tfm'][:, :] = transforms[self._road_key].reshape(3, 3)
     self.numpy_inputs['big_tfm'][:, :] = transforms[self._wide_key].reshape(3, 3)
 
-    raw_outputs = self.adapter.run()
+    if self.run_model is not None:
+      outs, = self.run_model(**{k: self.input_queues[k] for k in MODELD_INPUTS})
+      raw_outputs = outs
+    else:
+      assert self.warp is not None and self.run_policy is not None
+      warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames[self._road_key], big_frame=self.full_frames[self._wide_key])
+      raw_outputs = self.run_policy(**{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped)
 
     if after_enqueue is not None:
       after_enqueue()
@@ -186,16 +259,14 @@ class ModelState(ModelStateBase):
       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 and 'hidden_state' in self.vision_output_slices:
-        (self.numpy_inputs['prev_feat'].flat if self.adapter.is_native else self.numpy_inputs['prev_feat'])[:] = \
-          model_output[self.vision_output_slices['hidden_state']]
+        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'].flat if self.adapter.is_native else self.numpy_inputs['prev_feat'])[:] = \
-          vision_output[self.vision_output_slices['hidden_state']]
+        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()
@@ -248,6 +319,7 @@ class ModelState(ModelStateBase):
 def main(demo=False):
   cloudlog.warning("modeld init")
 
+  sentry.set_tag("daemon", PROCESS_NAME)
   cloudlog.bind(daemon=PROCESS_NAME)
   setproctitle(PROCESS_NAME)
   config_realtime_process(7, 54)
@@ -301,7 +373,11 @@ def main(demo=False):
     loader.start()
     loader.join(BIG_MODEL_TIMEOUT)
     model = big_model
+    if model is None:
+      params.put_bool("ChestnutModelError", True)
     params.put_bool("ChestnutActive", model is not None)
+    if model is not None:
+      params.remove("ChestnutModelError")
 
   small_model = ModelState(cam_w=vipc_client_main.width, cam_h=vipc_client_main.height, chestnut=False) if model is None or CHESTNUT else None
   if model is None:
@@ -311,12 +387,12 @@ def main(demo=False):
   cloudlog.warning(f"models loaded in {time.monotonic() - st:.1f}s, modeld starting")
 
   # messaging
-  pub_socks = ["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"] + (["chestnutGpuState"] if CHESTNUT else [])
+  pub_socks = ["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"] + (["chestnutState"] if CHESTNUT else [])
   pm = PubMaster(pub_socks)
   sm = SubMaster(["deviceState", "carState", "narrowRoadCameraState", "extrinsicsCalibration", "driverMonitoringState", "carControl", "lateralDelay"])
 
   publish_state = PublishState()
-  chestnut_state = ChestnutGpuState(pm, model.chestnut) if CHESTNUT else None
+  chestnut_state = ChestnutState(pm, model.chestnut) if CHESTNUT else None
 
   # setup filter to track dropped frames
   frame_dropped_filter = FirstOrderFilter(0., 10., 1. / model.constants.MODEL_FREQ)
@@ -438,12 +514,13 @@ def main(demo=False):
     mt1 = time.perf_counter()
     try:
       send_chestnut = (chestnut_state is not None and
-                       run_count % round(model.constants.MODEL_FREQ / SERVICE_LIST['chestnutGpuState'].frequency) == 0)
+                       run_count % round(model.constants.MODEL_FREQ / SERVICE_LIST['chestnutState'].frequency) == 0)
       model_output = model.run(bufs, transforms, inputs, chestnut_state.send if send_chestnut else None)
     except Exception:
       if not params.get_bool("ChestnutActive"):
         raise
-      cloudlog.exception("big model failed, fall back to small")
+      cloudlog.exception("chestnut failed, falling back to small")
+      params.put_bool("ChestnutModelError", True)
       params.put_bool("ChestnutActive", False)
       assert small_model is not None
       model = small_model
@@ -497,5 +574,5 @@ if __name__ == "__main__":
   except KeyboardInterrupt:
     cloudlog.warning(f"child {PROCESS_NAME} got SIGINT")
   except Exception:
-    cloudlog.exception("modeld exception")
+    sentry.capture_exception()
     raise

openpilot/sunnypilot/modeld_v2/stock_dependencies.py

@@ -1,171 +0,0 @@
-"""
-Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
-
-This file is part of sunnypilot and is licensed under the MIT License.
-See the LICENSE.md file in the root directory for more details.
-"""
-import math
-import numpy as np
-from collections import namedtuple
-from tinygrad.tensor import Tensor
-from tinygrad.helpers import Context
-from tinygrad.device import Device
-
-"""
-Frozen in time compile_modeld dependencies to support all models prior to transition to tinygrad compilation.
-This file is not meant to be modified.
-"""
-NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
-MODELD_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
-
-
-def nv12_copy_size(stride: int, y_height: int, uv_height: int) -> int:
-  return stride * (y_height + uv_height)
-
-
-def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad, border_fill_val=None):
-  w_dst, h_dst = dst_shape
-  h_src, w_src = src_shape
-
-  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
-  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
-
-  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
-  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
-  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
-
-  src_x = src_x / src_w
-  src_y = src_y / src_w
-
-  x_round = Tensor.round(src_x)
-  y_round = Tensor.round(src_y)
-  x_nn_clipped = x_round.clip(0, w_src - 1).cast('int')
-  y_nn_clipped = y_round.clip(0, h_src - 1).cast('int')
-  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
-  sampled = src_flat[idx]
-
-  if border_fill_val is None:
-    return sampled
-
-  in_bounds = ((x_round >= 0) & (x_round <= w_src - 1) &
-               (y_round >= 0) & (y_round <= h_src - 1)).cast(sampled.dtype)
-  return sampled * in_bounds + Tensor(border_fill_val, dtype=sampled.dtype) * (1 - in_bounds)
-
-
-def frames_to_tensor(frames):
-  H = (frames.shape[0] * 2) // 3
-  W = frames.shape[1]
-  in_img1 = Tensor.cat(frames[0:H:2, 0::2],
-                       frames[1:H:2, 0::2],
-                       frames[0:H:2, 1::2],
-                       frames[1:H:2, 1::2],
-                       frames[H:H+H//4].reshape((H//2, W//2)),
-                       frames[H+H//4:H+H//2].reshape((H//2, W//2)), dim=0).reshape((6, H//2, W//2))
-  return in_img1
-
-
-def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
-  cam_w, cam_h, stride, y_height, uv_height, _ = nv12
-  uv_offset = stride * y_height
-  stride_pad = stride - cam_w
-
-  def frame_prepare_tinygrad(input_frame, M_inv):
-    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]], device=Device.DEFAULT)
-    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
-    with Context(SPLIT_REDUCEOP=0):
-      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
-                                    M_inv, (model_w, model_h),
-                                    (cam_h, cam_w), stride_pad).realize()
-      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
-                                    M_inv_uv, (model_w//2, model_h//2),
-                                    (cam_h//2, cam_w//2), 0).realize()
-      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
-                                    M_inv_uv, (model_w//2, model_h//2),
-                                    (cam_h//2, cam_w//2), 0).realize()
-    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
-    tensor = frames_to_tensor(yuv)
-    return tensor
-  return frame_prepare_tinygrad
-
-
-def _detect_desire_key(shapes: dict) -> str | None:
-  return next((key for key in shapes if key.startswith('desire')), None)
-
-
-def get_policy_npy_shapes(input_shapes):
-  dp = input_shapes['desire_pulse']
-  tc = input_shapes['traffic_convention']
-  at = input_shapes['action_t']
-  fb = input_shapes['features_buffer']
-  feat_dim = math.prod(fb[2:])
-  shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], feat_dim)}
-  return shapes, [math.prod(s) for s in shapes.values()]
-
-
-def make_input_queues(input_shapes, frame_skip, device, frame_copy_size):
-  img = input_shapes['img']
-  fb = input_shapes['features_buffer']
-  feat_dim = math.prod(fb[2:])
-  dp = input_shapes[_detect_desire_key(input_shapes)]
-  n_frames = img[1] // 6
-  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
-
-  policy_shapes, _ = get_policy_npy_shapes(input_shapes)
-  shapes = {'tfm': (3, 3), 'big_tfm': (3, 3)} | policy_shapes
-  sizes = [math.prod(s) for s in shapes.values()]
-  packed_npy_size = sum(sizes) * np.dtype(np.float32).itemsize
-  packed_input = np.zeros(packed_npy_size + 2 * frame_copy_size, dtype=np.uint8)
-  packed_npy_inputs = packed_input[:packed_npy_size].view(np.float32)
-  frames = packed_input[packed_npy_size:]
-  frame_views = {'img': frames[:frame_copy_size], 'big_img': frames[frame_copy_size:]}
-  npy = {k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)}
-  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], fb[0], feat_dim), 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(),
-    'packed_npy_inputs': Tensor(packed_input, device='NPY').realize(),
-  }
-  return input_queues, npy, frame_views
-
-
-def shift_and_sample(buf, new_val, sample_fn):
-  buf.assign(buf[1:].cat(new_val, dim=0).contiguous())
-  return sample_fn(buf)
-
-
-def sample_skip(buf, frame_skip):
-  return buf[::frame_skip].contiguous().flatten(0, 1).unsqueeze(0)
-
-
-def sample_desire(buf, frame_skip):
-  return buf.reshape(-1, frame_skip, *buf.shape[1:]).max(1).flatten(0, 1).unsqueeze(0)
-
-
-def make_warp(nv12, model_w, model_h):
-  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
-  def warp(tfm, big_tfm, frame, big_frame):
-    tfm = tfm.to(Device.DEFAULT)
-    big_tfm = big_tfm.to(Device.DEFAULT)
-    frame = frame.to(Device.DEFAULT)
-    big_frame = big_frame.to(Device.DEFAULT)
-    Tensor.realize(tfm, big_tfm, frame, big_frame)
-    warped_frame = frame_prepare(frame, tfm).unsqueeze(0)
-    warped_big_frame = frame_prepare(big_frame, big_tfm).unsqueeze(0)
-    return Tensor.cat(warped_frame, warped_big_frame)
-  return warp
-
-
-def make_run_model(warp, run_policy, model_metadata, frame_copy_size):
-  _, policy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
-  packed_npy_size = (18 + sum(policy_sizes)) * np.dtype(np.float32).itemsize
-  def run_model(img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
-    packed_input = packed_npy_inputs.to(Device.DEFAULT)
-    Tensor.realize(packed_input)
-    packed_npy_inputs = packed_input[:packed_npy_size].bitcast('float32')
-    frame = packed_input[packed_npy_size:packed_npy_size + frame_copy_size]
-    big_frame = packed_input[packed_npy_size + frame_copy_size:]
-    tfm, big_tfm, policy_inputs = packed_npy_inputs.split([9, 9, sum(policy_sizes)])
-    warped = warp(tfm.reshape(3, 3), big_tfm.reshape(3, 3), frame, big_frame)
-    return run_policy(warped, img_q, big_img_q, feat_q, desire_q, policy_inputs)
-  return run_model

openpilot/sunnypilot/modeld_v2/tests/helpers.py

@@ -7,8 +7,6 @@ See the LICENSE.md file in the root directory for more details.
 
 import pathlib
 import tempfile
-import codecs
-import pickle
 
 import openpilot.sunnypilot.models.helpers as helpers
 import openpilot.sunnypilot.modeld_v2.modeld as modeld_module
@@ -163,23 +161,6 @@ ARCHETYPES = {
 
 
 def make_pkl_data(archetype):
-  if archetype.expected_model_type == 'supercombo':
-    slices_b64 = codecs.encode(pickle.dumps(archetype.metadata_structure['model']['output_slices']), 'base64').decode()
-    return {
-      'metadata': {
-        'metadata': {'output_slices': slices_b64},
-        'model': {
-          'input_shapes': archetype.metadata_structure['model']['input_shapes'],
-          'output_slices': archetype.metadata_structure['model']['output_slices']
-        },
-        'input_shapes': archetype.metadata_structure['model']['input_shapes'],
-        'output_slices': archetype.metadata_structure['model']['output_slices'],
-        'output_shapes': {}
-      },
-      'run_policy': _noop_jit,
-      (CAM_W, CAM_H): _noop_jit
-    }
-
   return {
     'metadata': archetype.metadata_structure,
     'run_policy': _noop_jit,
@@ -188,7 +169,7 @@ def make_pkl_data(archetype):
 
 
 def write_pkl(tmp_path, archetype):
-  from openpilot.sunnypilot.modeld_v2.helpers import dump_oob
+  from openpilot.selfdrive.modeld.helpers import dump_oob
   pkl_path = tmp_path / 'driving_test_tinygrad.pkl'
   with open(pkl_path, 'wb') as f:
     dump_oob(make_pkl_data(archetype), f)

openpilot/sunnypilot/modeld_v2/tests/test_combined_pkl_loader.py

@@ -12,7 +12,7 @@ import openpilot.sunnypilot.modeld_v2.modeld as modeld_module
 from openpilot.sunnypilot.modeld_v2.modeld import _find_driving_pkl
 from openpilot.sunnypilot.modeld_v2.tests import helpers as tests_helpers
 from openpilot.sunnypilot.modeld_v2.tests.helpers import DummyModel, DummyBundle, ARCHETYPES, CAM_W, CAM_H, \
-  SPLIT_VISION_INPUT_SHAPES
+  SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES
 from openpilot.common.test import OpenpilotTestCase
 
 # resolved by name from this module when a test asks for them
@@ -66,6 +66,21 @@ class TestModelStateCombinedInit(OpenpilotTestCase):
 
 
 class TestStockEquivalence(OpenpilotTestCase):
+
+  def test_split_queue_keys_match_stock(self, model_state_factory):
+    from openpilot.selfdrive.modeld.compile_modeld import make_input_queues
+    from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip
+
+    state = model_state_factory(ARCHETYPES['vision_policy_split'])
+
+    frame_skip = derive_frame_skip(SPLIT_VISION_INPUT_SHAPES, SPLIT_POLICY_INPUT_SHAPES)
+    stock_shapes = {**SPLIT_VISION_INPUT_SHAPES, **SPLIT_POLICY_INPUT_SHAPES, 'action_t': (1, 2)}
+    stock_queues, stock_npy, _frame_views = make_input_queues(stock_shapes, frame_skip, device='NPY', frame_copy_size=49152)
+
+    # sunnypilot split pipeline has tfm/big_tfm as queues (stock has them in npy only)
+    assert set(stock_queues.keys()) <= set(state.input_queues.keys())
+    assert {'desire', 'traffic_convention'} <= set(state.numpy_inputs.keys())
+
   def test_split_queue_keys_work_with_desire_key(self, model_state_factory):
     from openpilot.sunnypilot.modeld_v2.compile_modeld import derive_frame_skip, make_split_input_queues
 
@@ -90,26 +105,20 @@ class TestStockEquivalence(OpenpilotTestCase):
 
   def test_unified_run_model(self, tmp_path, monkeypatch, patch_modeld):
     from openpilot.common.hardware import hw
-    from openpilot.sunnypilot.modeld_v2.helpers import dump_oob
+    from openpilot.selfdrive.modeld.helpers import dump_oob
     shapes = {'img': (1, 12, 128, 256), 'big_img': (1, 12, 128, 256), 'features_buffer': (1, 24, 32, 512),
               'desire_pulse': (1, 25, 8), 'traffic_convention': (1, 2), 'action_t': (1, 2)}
-    import codecs
-    import pickle
-    slices_b64 = codecs.encode(pickle.dumps({}), 'base64').decode()
-    pkl_data = {
-      'metadata': {'model': {'input_shapes': shapes, 'output_slices': {}}, 'metadata': {'output_slices': slices_b64},
-                   'input_shapes': shapes, 'output_slices': {}, 'output_shapes': {}},
-      'run_model': {(CAM_W, CAM_H): tests_helpers._noop_jit}
-    }
+    pkl_data = {'metadata': {'model': {'input_shapes': shapes, 'output_slices': {}}},
+                'run_model': {(CAM_W, CAM_H): tests_helpers._noop_jit}}
     with open(tmp_path / 'driving_test_tinygrad.pkl', 'wb') as f:
       dump_oob(pkl_data, f)
     bundle = DummyBundle(models=[DummyModel('supercombo', 'driving_test_tinygrad.pkl')])
     patch_modeld(bundle)
     monkeypatch.setattr(hw.Paths, 'model_root', staticmethod(lambda: str(tmp_path)))
     state = ModelState(cam_w=CAM_W, cam_h=CAM_H)
-    assert state.adapter.is_run_model and state.adapter.run_model is not None
-    assert state.adapter.run_policy is None and state.adapter.warp is None
-    assert 'img' in state.adapter.frame_views and 'big_img' in state.adapter.frame_views
+    assert state.is_run_model and state.run_model is not None
+    assert state.run_policy is None and state.warp is None
+    assert 'img' in state.frame_views and 'big_img' in state.frame_views
 
 
 ARCHETYPE_NAMES = list(ARCHETYPES.keys())
@@ -184,7 +193,7 @@ class TestInputQueueCreation(OpenpilotTestCase):
   def test_queues_not_empty(self, archetype_name, model_state_factory):
     arch = ARCHETYPES[archetype_name]
     state = model_state_factory(arch)
-    assert len(state.adapter.input_queues) > 0, f"{arch.name}: input_queues empty"
+    assert len(state.input_queues) > 0, f"{arch.name}: input_queues empty"
 
   @parameterized.expand(ARCHETYPE_NAMES, names=["archetype_name"])
   def test_npy_contains_transforms(self, archetype_name, model_state_factory):

openpilot/sunnypilot/modeld_v2/tests/test_compile_modeld.py

@@ -225,7 +225,6 @@ class Test4DFeaturesBuffer(OpenpilotTestCase):
 
 
 class TestStockCompileModeldEquivalence(OpenpilotTestCase):
-  @unittest.skip("upstream removed selfdrive/modeld/compile_modeld.py — no stock implementation to compare")
   def test_get_policy_npy_shapes_matches_stock(self):
     from openpilot.selfdrive.modeld.compile_modeld import get_policy_npy_shapes as stock_get_policy_npy_shapes
     from openpilot.sunnypilot.modeld_v2.compile_modeld import get_policy_npy_shapes as sunny_get_policy_npy_shapes
@@ -244,7 +243,6 @@ class TestStockCompileModeldEquivalence(OpenpilotTestCase):
     assert sunny_sizes == stock_sizes
     assert sunny_shapes['prev_feat'] == (1, 512)
 
-  @unittest.skip("upstream removed selfdrive/modeld/compile_modeld.py — no stock implementation to compare")
   def test_make_input_queues_full_stock_equivalence(self):
     from openpilot.selfdrive.modeld.compile_modeld import make_input_queues as stock_make_input_queues
     from openpilot.sunnypilot.modeld_v2.compile_modeld import make_supercombo_input_queues as sunny_make_supercombo_input_queues

openpilot/sunnypilot/modeld_v2/tests/test_fallback.py

@@ -11,7 +11,7 @@ import requests
 from openpilot.common.file_chunker import get_chunk_name
 from openpilot.common.hardware import hw
 from openpilot.common.test import OpenpilotTestCase
-from openpilot.sunnypilot.modeld_v2.helpers import dump_oob
+from openpilot.selfdrive.modeld.helpers import dump_oob
 import openpilot.sunnypilot.modeld_v2.modeld as modeld_module
 from openpilot.sunnypilot.modeld_v2.tests import helpers as tests_helpers
 from openpilot.sunnypilot.modeld_v2.tests.helpers import DummyModel, DummyBundle, CAM_W, CAM_H

openpilot/sunnypilot/models/manager.py

@@ -331,7 +331,8 @@ class ModelManagerSP:
         if get_selected_bundle(self.params, "chestnut") is not None and get_selected_bundle(self.params, "qcom") is None:
           if self.params.get("ModelManager_DownloadRef") is None:
             from openpilot.sunnypilot.models.model_name import DEFAULT_MODEL_REF
-            self.params.put("ModelManager_DownloadRef", DEFAULT_MODEL_REF)
+            if DEFAULT_MODEL_REF:
+              self.params.put("ModelManager_DownloadRef", DEFAULT_MODEL_REF)
 
         self._process_download_requests()
 

openpilot/sunnypilot/models/tests/model_hash

@@ -1 +1 @@
-afa673add3ffdff2607c4968912a0183c5883fd0159d56218a3c22a508a2ebd5
+c5be11d2fb1115be953c541f30c50f7c71a00bc4a0e128e19aa11b60689317fc

openpilot/sunnypilot/selfdrive/car/interfaces.py

@@ -13,9 +13,18 @@ from openpilot.common.swaglog import cloudlog
 from openpilot.sunnypilot.selfdrive.controls.lib.nnlc.helpers import get_nn_model_path
 from openpilot.sunnypilot.selfdrive.controls.lib.speed_limit.helpers import set_speed_limit_assist_availability
 
+import openpilot.system.sentry as sentry
+
 from openpilot.sunnypilot.sunnylink.statsd import STATSLOGSP
 
 
+def log_fingerprint(CP: structs.CarParams) -> None:
+  if CP.carFingerprint == "MOCK":
+    sentry.capture_fingerprint_mock()
+  else:
+    sentry.capture_fingerprint(CP.carFingerprint, CP.brand)
+
+
 def _enforce_torque_lateral_control(CP: structs.CarParams, params: Params | None = None, enabled: bool = False) -> bool:
   if params is None:
     params = Params()

openpilot/system/camerad/test/stress_restart.sh

@@ -4,6 +4,6 @@ while :; do
   ./camerad &
   pid="$!"
   sleep 2
-  kill -2 "$pid"
-  wait "$pid"
+  kill -2 $pid
+  wait $pid
 done

openpilot/system/hardware/chestnut/flash.py

@@ -63,9 +63,9 @@ def find_chestnut():
   found = []
   for d in glob.glob("/sys/bus/usb/devices/*"):
     try:
-      vid_pid = (Path(d, "idVendor").read_text().strip(), Path(d, "idProduct").read_text().strip())
+      vid_pid = (open(d + "/idVendor").read().strip(), open(d + "/idProduct").read().strip())
       if vid_pid in VID_PIDS + ROM_VID_PIDS:
-        found.append((d, vid_pid, Path(d, "product").read_text().strip()))
+        found.append((d, vid_pid, open(d + "/product").read().strip()))
     except OSError:
       pass
   if len(found) > 1:
@@ -101,7 +101,7 @@ def unbind_drivers(path):
 
 
 def open_device(path):
-  bus, dev = int(Path(path, "busnum").read_text()), int(Path(path, "devnum").read_text())
+  bus, dev = int(open(path + "/busnum").read()), int(open(path + "/devnum").read())
   return os.open(f"/dev/bus/usb/{bus:03d}/{dev:03d}", os.O_RDWR)
 
 

openpilot/system/hardware/chestnut/monitoring.py

@@ -1,53 +0,0 @@
-import struct
-
-import usb1
-
-import openpilot.cereal.messaging as messaging
-from openpilot.cereal.services import SERVICE_LIST
-from openpilot.common.hardware.usb import CHESTNUT_USB_PRODUCT, get_usb_state, is_chestnut_usb_id
-
-
-def read_chestnut_state(handle, gpu_state=None):
-  msg = messaging.new_message('chestnutState')
-  if gpu_state is not None:
-    msg.chestnutState = gpu_state
-  state = msg.chestnutState
-  try:
-    raw = handle.controlRead(0xC0, 0xC0, 0, 0, 5, timeout=100)
-    state.supplyVoltage, state.supplyCurrent, state.supplyFault = struct.unpack('<Hh?', bytes(raw))
-    raw = handle.controlRead(0xC0, 0xE4, 0xB450, 0, 1, timeout=100)
-    state.pcieLtssm, = struct.unpack('B', bytes(raw))
-    msg.valid = True
-  except (usb1.USBError, struct.error):
-    msg.valid = False
-  return msg
-
-
-def chestnut_state_thread(end_event):
-  pm = messaging.PubMaster(['chestnutState'])
-  sm = messaging.SubMaster(['chestnutGpuState'])
-  with usb1.USBContext() as context:
-    handle = None
-    try:
-      while not end_event.is_set():
-        if handle is None:
-          devices = [d for d in get_usb_state() if is_chestnut_usb_id(d['vendorId'], d['productId']) and
-                     d['product'] == CHESTNUT_USB_PRODUCT]
-          if len(devices) == 1:
-            try:
-              handle = context.openByVendorIDAndProductID(devices[0]['vendorId'], devices[0]['productId'], skip_on_error=True)
-            except usb1.USBError:
-              pass
-        if handle is not None:
-          sm.update(0)
-          gpu_valid = sm.alive['chestnutGpuState'] and sm.valid['chestnutGpuState']
-          msg = read_chestnut_state(handle, sm['chestnutGpuState'] if gpu_valid else None)
-          if not msg.valid:
-            handle.close()
-            handle = None
-          msg.valid &= not sm.seen['chestnutGpuState'] or gpu_valid
-          pm.send('chestnutState', msg)
-        end_event.wait(1 / SERVICE_LIST['chestnutState'].frequency if handle is not None else 1.)
-    finally:
-      if handle is not None:
-        handle.close()

openpilot/system/hardware/chestnut/status.py

@@ -5,7 +5,7 @@ from openpilot.common.version import get_build_metadata, CHESTNUT_BRANCHES
 from openpilot.selfdrive.modeld.helpers import MODELS_DIR, chestnut_compiled
 
 
-CHESTNUT_RELEASE_BRANCHES = ("release-chestnut", "release-chestnut-staging", "nightly-chestnut")
+CHESTNUT_RELEASE_BRANCHES = ("release-chestnut", "release-chestnut-staging")
 CHESTNUT_POWERED_VOLTAGE = 5000
 GPU_TEMP_LIMIT = 100.
 MEMORY_TEMP_LIMIT = 95.

openpilot/system/hardware/hardwared.py

@@ -27,7 +27,6 @@ from openpilot.common.swaglog import cloudlog
 from openpilot.sunnypilot.system.statsd import statlog
 from openpilot.system.hardware.power_monitoring import PowerMonitoring
 from openpilot.system.hardware.fan_controller import FanController
-from openpilot.system.hardware.chestnut.monitoring import chestnut_state_thread
 from openpilot.system.hardware.chestnut.status import ChestnutStatus
 from openpilot.common.version import terms_version, training_version, get_build_metadata, terms_version_sp
 
@@ -195,7 +194,7 @@ def hw_state_thread(end_event, hw_queue):
 
 
 def hardware_thread(end_event, hw_queue) -> None:
-  system_stats = LinuxSystemStats() if sys.platform == "linux" else None
+  system_stats = LinuxSystemStats()
   pm = messaging.PubMaster(['deviceState'])
   sm = messaging.SubMaster(["peripheralState", "gpsLocationExternal", "selfdriveState", "pandaStates", "chestnutState"], poll="pandaStates")
 
@@ -288,13 +287,10 @@ def hardware_thread(end_event, hw_queue) -> None:
     except queue.Empty:
       pass
 
-    memory_usage = system_stats.memory_usage_percent() if system_stats is not None else 0.
-    cpu_usage = system_stats.cpu_usage_percent() if system_stats is not None else []
-
     msg.deviceState.freeSpacePercent = get_available_percent(default=100.0)
-    msg.deviceState.memoryUsagePercent = int(round(memory_usage))
+    msg.deviceState.memoryUsagePercent = int(round(system_stats.memory_usage_percent()))
     msg.deviceState.gpuUsagePercent = int(round(HARDWARE.get_gpu_usage_percent()))
-    online_cpu_usage = [int(round(n)) for n in cpu_usage]
+    online_cpu_usage = [int(round(n)) for n in system_stats.cpu_usage_percent()]
     offline_cpu_usage = [0., ] * (len(msg.deviceState.cpuTempC) - len(online_cpu_usage))
     msg.deviceState.cpuUsagePercent = online_cpu_usage + offline_cpu_usage
 
@@ -531,7 +527,6 @@ def main():
 
   if COMMA_HARDWARE:
     threads.append(threading.Thread(target=touch_thread, args=(end_event,)))
-    threads.append(threading.Thread(target=chestnut_state_thread, args=(end_event,)))
 
   for t in threads:
     t.start()

openpilot/system/manager/manager.py

@@ -8,6 +8,7 @@ import traceback
 
 from openpilot.cereal import log
 import openpilot.cereal.messaging as messaging
+import openpilot.system.sentry as sentry
 from openpilot.common.utils import atomic_write
 from openpilot.common.params import Params, ParamKeyFlag
 from openpilot.common.text_window import TextWindow
@@ -94,6 +95,7 @@ def manager_init() -> None:
     os.environ['CLEAN'] = '1'
 
   # init logging
+  sentry.init(sentry.SentryProject.SELFDRIVE)
   cloudlog.bind_global(dongle_id=dongle_id,
                        version=build_metadata.openpilot.version,
                        origin=build_metadata.openpilot.git_normalized_origin,
@@ -202,7 +204,7 @@ def main() -> None:
     manager_thread()
   except Exception:
     traceback.print_exc()
-    cloudlog.exception("crash")
+    sentry.capture_exception()
   finally:
     manager_cleanup()
 

openpilot/system/manager/process.py

@@ -12,6 +12,7 @@ from setproctitle import setproctitle
 from openpilot.cereal import log
 from opendbc.car.structs import car
 import openpilot.cereal.messaging as messaging
+import openpilot.system.sentry as sentry
 from openpilot.common.basedir import BASEDIR
 from openpilot.common.params import Params
 from openpilot.common.swaglog import cloudlog
@@ -30,6 +31,7 @@ def launcher(proc: str, name: str) -> None:
 
     # add daemon name tag to logs
     cloudlog.bind(daemon=name)
+    sentry.set_tag("daemon", name)
 
     # exec the process
     mod.main()
@@ -38,7 +40,7 @@ def launcher(proc: str, name: str) -> None:
   except Exception:
     # can't install the crash handler because sys.excepthook doesn't play nice
     # with threads, so catch it here.
-    cloudlog.exception("crash")
+    sentry.capture_exception()
     raise
 
 

openpilot/system/sentry.py

@@ -0,0 +1,143 @@
+"""Install exception handler for process crash."""
+import os
+import traceback
+from datetime import datetime
+import sentry_sdk
+from enum import Enum
+from sentry_sdk.integrations.threading import ThreadingIntegration
+
+from openpilot.common.params import Params
+from openpilot.system.athena.registration import UNREGISTERED_DONGLE_ID
+from openpilot.common.hardware import HARDWARE
+from openpilot.common.hardware.hw import Paths
+from openpilot.common.swaglog import cloudlog
+from openpilot.common.version import get_build_metadata, get_version
+
+from openpilot.sunnypilot.sunnylink.api import UNREGISTERED_SUNNYLINK_DONGLE_ID
+
+CRASHES_DIR = Paths.crash_log_root()
+
+
+class SentryProject(Enum):
+  # python project
+  SELFDRIVE = "https://186a6736b7927e5ae9b92c869ba81b6b@o1138119.ingest.us.sentry.io/4508660076052480"
+  # native project
+  SELFDRIVE_NATIVE = SELFDRIVE
+
+
+def report_tombstone(fn: str, message: str, contents: str) -> None:
+  cloudlog.error({'tombstone': message})
+
+  with sentry_sdk.configure_scope() as scope:
+    set_user()
+    scope.set_extra("tombstone_fn", fn)
+    scope.set_extra("tombstone", contents)
+    sentry_sdk.capture_message(message=message)
+    sentry_sdk.flush()
+
+
+def capture_exception(*args, **kwargs) -> None:
+  cloudlog.error("crash", exc_info=kwargs.get('exc_info', 1))
+
+  try:
+    save_exception(traceback.format_exc())
+
+    set_user()
+    sentry_sdk.capture_exception(*args, **kwargs)
+    sentry_sdk.flush()  # https://github.com/getsentry/sentry-python/issues/291
+  except Exception:
+    cloudlog.exception("sentry exception")
+
+
+def save_exception(content: str) -> None:
+  try:
+    if not os.path.exists(CRASHES_DIR):
+      os.makedirs(CRASHES_DIR)
+
+    files = [
+      os.path.join(CRASHES_DIR, datetime.now().strftime("%Y-%m-%d--%H-%M-%S.log")),
+      os.path.join(CRASHES_DIR, "error.log")
+    ]
+
+    for fn in files:
+      with open(fn, 'w') as f:
+        if fn == "error.log":
+          lines = content.splitlines()[-3:]
+          f.write("\n".join(lines))
+        else:
+          f.write(content)
+
+    cloudlog.error(f"logged crash to {files}")
+  except Exception:
+    cloudlog.exception("error when attempting to save exception")
+
+
+def capture_fingerprint_mock() -> None:
+  try:
+    set_user()
+    message = "car doesn't match any fingerprints"
+    sentry_sdk.capture_message(message=message, level="error")
+    sentry_sdk.flush()
+  except Exception as e:
+    cloudlog.exception(f"sentry fingerprint MOCK exception: {e}")
+
+
+def capture_fingerprint(candidate: str, car_name: str) -> None:
+  try:
+    set_user()
+    sentry_sdk.set_tag("carFingerprint", candidate)
+    sentry_sdk.set_tag("carName", car_name)
+
+    message = f"Fingerprinted {candidate}"
+    sentry_sdk.capture_message(message=message, level="info")
+    sentry_sdk.flush()
+  except Exception as e:
+    cloudlog.exception(f"sentry fingerprint exception: {e}")
+
+
+def set_tag(key: str, value: str) -> None:
+  sentry_sdk.set_tag(key, value)
+
+
+def set_user() -> None:
+  dongle_id, git_username, _ = get_properties()
+  sentry_sdk.set_user({"id": dongle_id, "name": git_username})
+
+
+def get_properties() -> tuple[str, str, str]:
+  params = Params()
+  hardware_serial: str = params.get("HardwareSerial") or ""
+  git_username: str = params.get("GithubUsername") or ""
+  dongle_id: str = params.get("DongleId") or f"{UNREGISTERED_DONGLE_ID}-{hardware_serial}"
+  sunnylink_dongle_id: str = params.get("SunnylinkDongleId") or UNREGISTERED_SUNNYLINK_DONGLE_ID
+
+  return dongle_id, git_username, sunnylink_dongle_id
+
+
+def init(project: SentryProject) -> bool:
+  build_metadata = get_build_metadata()
+
+  env = build_metadata.channel_type
+  dongle_id, git_username, sunnylink_dongle_id = get_properties()
+
+  integrations = []
+  if project == SentryProject.SELFDRIVE:
+    integrations.append(ThreadingIntegration(propagate_hub=True))
+
+  sentry_sdk.init(project.value,
+                  default_integrations=False,
+                  release=get_version(),
+                  integrations=integrations,
+                  traces_sample_rate=1.0,
+                  max_value_length=8192,
+                  environment=env)
+
+  sentry_sdk.set_user({"id": dongle_id, "name": git_username})
+  sentry_sdk.set_tag("dirty", build_metadata.openpilot.is_dirty)
+  sentry_sdk.set_tag("origin", build_metadata.openpilot.git_origin)
+  sentry_sdk.set_tag("branch", build_metadata.channel)
+  sentry_sdk.set_tag("commit", build_metadata.openpilot.git_commit)
+  sentry_sdk.set_tag("device", HARDWARE.get_device_type())
+  sentry_sdk.set_tag("sunnylink_dongle_id", sunnylink_dongle_id)
+
+  return True